diff --git a/.bumpversion.toml b/.bumpversion.toml
index e32f518c9..e36bbe1f9 100644
--- a/.bumpversion.toml
+++ b/.bumpversion.toml
@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.40.0-beta.11"
+current_version = "0.41.0-beta.7"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.
diff --git a/.github/actions/setup-protoc/action.yml b/.github/actions/setup-protoc/action.yml
new file mode 100644
index 000000000..a99cf3320
--- /dev/null
+++ b/.github/actions/setup-protoc/action.yml
@@ -0,0 +1,80 @@
+name: Setup protoc
+description: Install the protoc version pinned for wheel builds, and expose it for builds that install protoc themselves
+
+inputs:
+  install:
+    description: "Install protoc on the runner. Set to false when the build runs in a container (e.g. manylinux) and installs protoc there."
+    required: false
+    default: "true"
+
+outputs:
+  version:
+    description: "The pinned protoc version, e.g. 36.2"
+    value: ${{ steps.pin.outputs.version }}
+  linux-x86_64-sha256:
+    description: "SHA-256 of the protoc linux-x86_64 zip"
+    value: ${{ steps.pin.outputs.linux-x86_64-sha256 }}
+  linux-aarch_64-sha256:
+    description: "SHA-256 of the protoc linux-aarch_64 zip"
+    value: ${{ steps.pin.outputs.linux-aarch_64-sha256 }}
+
+runs:
+  using: composite
+  steps:
+    # protobuf does not publish checksum files for protoc. These are the digests
+    # GitHub records for the release assets; when bumping, copy them from
+    # `gh api repos/protocolbuffers/protobuf/releases/tags/v<version>`.
+    - name: Set protoc pin
+      id: pin
+      shell: bash
+      run: |
+        {
+          echo "version=36.2"
+          echo "linux-x86_64-sha256=121f6c7afe1d4d0e3ea6aab9432038599250134cbf4474cb1167d2c7decd4278"
+          echo "linux-aarch_64-sha256=8b8f18bd2b30346efbc698dd5a73dd7c805f3ef8380f6dfc95c768f3f1852f6a"
+          echo "osx-universal_binary-sha256=83d0dc80672f53486f54299bca6177159f9cad1fd54d4d3143eeea53c4423e1b"
+          echo "win64-sha256=f0c128dc0d8492eceece83bb459a4c0e316764b929ffbf1aa416357fd644edd3"
+        } >> "$GITHUB_OUTPUT"
+    - name: Install protoc
+      if: ${{ inputs.install == 'true' }}
+      shell: bash
+      env:
+        PROTOC_VERSION: ${{ steps.pin.outputs.version }}
+        RUNNER_OS_NAME: ${{ runner.os }}
+        RUNNER_ARCH_NAME: ${{ runner.arch }}
+        LINUX_X86_64_SHA256: ${{ steps.pin.outputs.linux-x86_64-sha256 }}
+        LINUX_AARCH_64_SHA256: ${{ steps.pin.outputs.linux-aarch_64-sha256 }}
+        OSX_SHA256: ${{ steps.pin.outputs.osx-universal_binary-sha256 }}
+        WIN64_SHA256: ${{ steps.pin.outputs.win64-sha256 }}
+      run: |
+        set -euo pipefail
+        case "$RUNNER_OS_NAME/$RUNNER_ARCH_NAME" in
+          Linux/X64) platform="linux-x86_64"; sha256="$LINUX_X86_64_SHA256" ;;
+          Linux/ARM64) platform="linux-aarch_64"; sha256="$LINUX_AARCH_64_SHA256" ;;
+          macOS/*) platform="osx-universal_binary"; sha256="$OSX_SHA256" ;;
+          Windows/X64) platform="win64"; sha256="$WIN64_SHA256" ;;
+          *) echo "Unsupported runner: $RUNNER_OS_NAME/$RUNNER_ARCH_NAME" >&2; exit 1 ;;
+        esac
+        zip="$RUNNER_TEMP/protoc.zip"
+        dest="$RUNNER_TEMP/protoc"
+        curl -fsSL "https://github.com/protocolbuffers/protobuf/releases/download/v${PROTOC_VERSION}/protoc-${PROTOC_VERSION}-${platform}.zip" -o "$zip"
+        # Hash stdin rather than a path: Windows paths contain backslashes,
+        # which make sha256sum escape its output.
+        if [ "$RUNNER_OS_NAME" = "macOS" ]; then
+          actual=$(shasum -a 256 < "$zip" | cut -d' ' -f1)
+        else
+          actual=$(sha256sum < "$zip" | cut -d' ' -f1)
+        fi
+        if [ "$actual" != "$sha256" ]; then
+          echo "protoc checksum mismatch: expected $sha256, got $actual" >&2
+          exit 1
+        fi
+        if [ "$RUNNER_OS_NAME" = "Windows" ]; then
+          # Git Bash on Windows runners does not reliably ship unzip.
+          7z x -y -o"$dest" "$zip" > /dev/null
+        else
+          unzip -q -o "$zip" -d "$dest"
+        fi
+        rm "$zip"
+        echo "$dest/bin" >> "$GITHUB_PATH"
+        "$dest/bin/protoc" --version
diff --git a/.github/release_notes.json b/.github/release_notes.json
index a97860698..6593ef66a 100644
--- a/.github/release_notes.json
+++ b/.github/release_notes.json
@@ -6,6 +6,10 @@
             "title": "## 🏆 Highlights",
             "labels": ["highlight"]
         },
+        {
+            "title": "## 🚨 Critical Fixes",
+            "labels": ["critical-fix"]
+        },
         {
             "title": "## 🛠 Breaking Changes",
             "labels": ["breaking-change"]
@@ -38,4 +42,4 @@
             "labels": ["ci"]
         }
     ]
-}
\ No newline at end of file
+}
diff --git a/.github/workflows/build_linux_wheel/action.yml b/.github/workflows/build_linux_wheel/action.yml
index 8fa5b1f79..44ce82ee5 100644
--- a/.github/workflows/build_linux_wheel/action.yml
+++ b/.github/workflows/build_linux_wheel/action.yml
@@ -29,6 +29,11 @@ inputs:
 runs:
   using: "composite"
   steps:
+    - name: Get protoc version
+      id: protoc
+      uses: ./.github/actions/setup-protoc
+      with:
+        install: "false"
     - name: CONFIRM ARM BUILD
       shell: bash
       env:
@@ -54,13 +59,14 @@ runs:
         maturin-version: "1.12.4"
         command: build
         working-directory: python
-        docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/' -e PROTOC=/usr/local/bin/protoc ${{ inputs.rustflags != '' && format('-e RUSTFLAGS={0}', inputs.rustflags) || '' }}"
+        docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/' -e PROTOC=/usr/local/bin/protoc -e PROTOC_VERSION=${{ steps.protoc.outputs.version }} -e PROTOC_SHA256=${{ steps.protoc.outputs.linux-x86_64-sha256 }} ${{ inputs.rustflags != '' && format('-e RUSTFLAGS={0}', inputs.rustflags) || '' }}"
         target: x86_64-unknown-linux-gnu
         manylinux: ${{ inputs.manylinux }}
         args: ${{ inputs.args }}
         before-script-linux: |
           set -e
-          curl -fsSL https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-x86_64.zip -o /tmp/protoc.zip
+          curl -fsSL https://github.com/protocolbuffers/protobuf/releases/download/v${PROTOC_VERSION}/protoc-${PROTOC_VERSION}-linux-x86_64.zip -o /tmp/protoc.zip
+          echo "${PROTOC_SHA256}  /tmp/protoc.zip" | sha256sum -c -
           unzip /tmp/protoc.zip -d /usr/local
           rm /tmp/protoc.zip
           /usr/local/bin/protoc --version
@@ -71,14 +77,15 @@ runs:
         maturin-version: "1.12.4"
         command: build
         working-directory: python
-        docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/' -e PROTOC=/usr/local/bin/protoc ${{ inputs.rustflags != '' && format('-e RUSTFLAGS={0}', inputs.rustflags) || '' }}"
+        docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/' -e PROTOC=/usr/local/bin/protoc -e PROTOC_VERSION=${{ steps.protoc.outputs.version }} -e PROTOC_SHA256=${{ steps.protoc.outputs.linux-aarch_64-sha256 }} ${{ inputs.rustflags != '' && format('-e RUSTFLAGS={0}', inputs.rustflags) || '' }}"
         target: aarch64-unknown-linux-gnu
         manylinux: ${{ inputs.manylinux }}
         args: ${{ inputs.args }}
         before-script-linux: |
           set -e
           yum install -y clang
-          curl -fsSL https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-aarch_64.zip -o /tmp/protoc.zip
+          curl -fsSL https://github.com/protocolbuffers/protobuf/releases/download/v${PROTOC_VERSION}/protoc-${PROTOC_VERSION}-linux-aarch_64.zip -o /tmp/protoc.zip
+          echo "${PROTOC_SHA256}  /tmp/protoc.zip" | sha256sum -c -
           unzip /tmp/protoc.zip -d /usr/local
           rm /tmp/protoc.zip
           /usr/local/bin/protoc --version
diff --git a/.github/workflows/build_mac_wheel/action.yml b/.github/workflows/build_mac_wheel/action.yml
index 75d942c96..727cd190c 100644
--- a/.github/workflows/build_mac_wheel/action.yml
+++ b/.github/workflows/build_mac_wheel/action.yml
@@ -12,10 +12,7 @@ inputs:
 runs:
   using: "composite"
   steps:
-    - name: Install macos dependency
-      shell: bash
-      run: |
-        brew install protobuf
+    - uses: ./.github/actions/setup-protoc
     - name: Build wheel
       uses: PyO3/maturin-action@v1
       with:
diff --git a/.github/workflows/build_windows_wheel/action.yml b/.github/workflows/build_windows_wheel/action.yml
index 18f39818f..954ee7d35 100644
--- a/.github/workflows/build_windows_wheel/action.yml
+++ b/.github/workflows/build_windows_wheel/action.yml
@@ -12,15 +12,7 @@ inputs:
 runs:
   using: "composite"
   steps:
-    - name: Install Protoc v21.12
-      working-directory: C:\
-      run: |
-        New-Item -Path 'C:\protoc' -ItemType Directory
-        Set-Location C:\protoc
-        Invoke-WebRequest https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protoc-21.12-win64.zip -OutFile C:\protoc\protoc.zip
-        7z x protoc.zip
-        Add-Content $env:GITHUB_PATH "C:\protoc\bin"
-      shell: powershell
+    - uses: ./.github/actions/setup-protoc
     - name: Build wheel
       uses: PyO3/maturin-action@v1
       with:
diff --git a/.github/workflows/license-header-check.yml b/.github/workflows/license-header-check.yml
index 6a9bdabc3..8b8510b58 100644
--- a/.github/workflows/license-header-check.yml
+++ b/.github/workflows/license-header-check.yml
@@ -9,6 +9,7 @@ on:
       - python/**
       - nodejs/**
       - java/**
+      - docs/web-tests/**
       - .github/workflows/license-header-check.yml
 
 permissions:
diff --git a/.github/workflows/nodejs.yml b/.github/workflows/nodejs.yml
index 98acccf02..2e290166e 100644
--- a/.github/workflows/nodejs.yml
+++ b/.github/workflows/nodejs.yml
@@ -164,6 +164,71 @@ jobs:
         python ci/mock_openai.py &
         cd nodejs/examples
         node_modules/.bin/jest --testEnvironment jest-environment-node-single-context --verbose
+  arrow-compat:
+    # The main jobs resolve `apache-arrow` to the version pinned in
+    # devDependencies (the newest supported release). This job re-runs the
+    # suite with the library itself running on each older Arrow release the
+    # peer range still accepts, which is what a consumer on that version gets.
+    name: Linux (Arrow ${{ matrix.arrow-version }})
+    timeout-minutes: 30
+    strategy:
+      matrix:
+        arrow-version: [ "19.0.1", "20.0.0" ]
+    runs-on: "ubuntu-22.04"
+    defaults:
+      run:
+        shell: bash
+        working-directory: nodejs
+    steps:
+    - uses: actions/checkout@v6
+      with:
+        fetch-depth: 0
+        lfs: true
+    - uses: pnpm/action-setup@v6
+      with:
+        version: 11.1.1
+    - uses: actions/setup-node@v6
+      with:
+        node-version: 24
+        cache: 'pnpm'
+        cache-dependency-path: nodejs/pnpm-lock.yaml
+    - uses: Swatinem/rust-cache@v2
+      with:
+        # Same crate, profile and target as the `linux` job above, so reuse
+        # its cache: rust-cache keys on the job id by default, and this job
+        # would otherwise start cold on every run until it had saved its own.
+        shared-key: linux
+        save-if: ${{ github.ref == 'refs/heads/main' }}
+    - name: Install dependencies
+      run: |
+        sudo apt update
+        sudo apt install -y protobuf-compiler libssl-dev
+    - name: Build
+      run: |
+        pnpm install --frozen-lockfile
+        # No `--` separator: pnpm forwards it literally, which would
+        # make napi-rs treat `--profile ci` as a cargo passthrough arg.
+        pnpm build:debug --profile ci
+    - name: Resolve apache-arrow to ${{ matrix.arrow-version }}
+      run: |
+        # The older releases are already installed as the `apache-arrow-NN`
+        # test aliases. With pnpm's hoisted linker each alias is a complete,
+        # self-contained directory, so swap it in for the library's own
+        # `apache-arrow` instead of running a second install, which has been
+        # seen to hang after printing "Done" in CI. A copy, not a symlink: the
+        # cross-version tests rely on the alias being a separate module
+        # instance from the one the library loads.
+        major="$(echo '${{ matrix.arrow-version }}' | cut -d. -f1)"
+        mv node_modules/apache-arrow node_modules/apache-arrow.pinned
+        cp -R "node_modules/apache-arrow-${major}" node_modules/apache-arrow
+        # `require("apache-arrow/package.json")` is blocked by the package's exports map.
+        node -p "'apache-arrow resolved to ' + JSON.parse(require('fs').readFileSync('node_modules/apache-arrow/package.json')).version"
+        pnpm tsc
+    - name: Test
+      env:
+        # Newer @smithy/core uses dynamic ESM imports.
+        NODE_OPTIONS: "--experimental-vm-modules"
+      run: node_modules/.bin/jest --verbose
   macos:
     timeout-minutes: 30
     # macos-15 ships a newer linker; the older macos-14 linker fails to insert
diff --git a/.github/workflows/pypi-publish.yml b/.github/workflows/pypi-publish.yml
index 4f883aec1..d16203979 100644
--- a/.github/workflows/pypi-publish.yml
+++ b/.github/workflows/pypi-publish.yml
@@ -11,6 +11,7 @@ on:
       - .github/workflows/build_linux_wheel/action.yml
       - .github/workflows/build_mac_wheel/action.yml
       - .github/workflows/build_windows_wheel/action.yml
+      - .github/actions/setup-protoc/action.yml
       - Cargo.toml # Change in dependency frequently breaks builds
       - Cargo.lock
 
diff --git a/.github/workflows/python.yml b/.github/workflows/python.yml
index 3594bd826..3608c24ca 100644
--- a/.github/workflows/python.yml
+++ b/.github/workflows/python.yml
@@ -15,6 +15,7 @@ on:
       - .github/workflows/build_linux_wheel/**
       - .github/workflows/build_mac_wheel/**
       - .github/workflows/build_windows_wheel/**
+      - .github/actions/setup-protoc/**
       - .github/workflows/run_tests/**
 
 permissions:
diff --git a/.github/workflows/rust.yml b/.github/workflows/rust.yml
index 585abf5ee..cbfefc83d 100644
--- a/.github/workflows/rust.yml
+++ b/.github/workflows/rust.yml
@@ -285,7 +285,7 @@ jobs:
     runs-on: ubuntu-24.04
     strategy:
       matrix:
-        msrv: ["1.91.0"] # This should match up with rust-version in Cargo.toml
+        msrv: ["1.94.0"] # This should match up with rust-version in Cargo.toml
     env:
       # Need up-to-date compilers for kernels
       CC: clang-18
@@ -308,40 +308,9 @@ jobs:
           # unreadable outside their own branch anyway, since GitHub scopes
           # caches to the creating ref.
           save-if: ${{ github.ref == 'refs/heads/main' }}
-      - name: Downgrade  dependencies
-        # These packages have newer requirements for MSRV
-        run: |
-          cargo update -p aws-sdk-bedrockruntime --precise 1.77.0
-          cargo update -p aws-sdk-dynamodb --precise 1.68.0
-          cargo update -p aws-config --precise 1.6.0
-          cargo update -p aws-sdk-kms --precise 1.63.0
-          cargo update -p aws-sdk-s3 --precise 1.79.0
-          cargo update -p aws-sdk-sso --precise 1.62.0
-          cargo update -p aws-sdk-ssooidc --precise 1.63.0
-          cargo update -p aws-sdk-sts --precise 1.63.0
-          # aws-runtime/sigv4/credential-types/types and the aws-smithy-*
-          # crates bumped their MSRV to 1.91.1 in late 2026; pin to the last
-          # 1.91.0-compatible versions. The order matters — each downgrade
-          # only succeeds once everything that still pins it at a higher
-          # version has itself been downgraded.
-          cargo update -p aws-runtime --precise 1.5.12
-          cargo update -p aws-types --precise 1.3.9
-          cargo update -p aws-sigv4 --precise 1.3.5
-          cargo update -p aws-credential-types --precise 1.2.8
-          # aws-smithy-checksums must stay at or above 0.63.13: OpenDAL's S3
-          # service needs crc-fast ~1.9, and older releases pin it to ~1.3.
-          cargo update -p aws-smithy-checksums --precise 0.63.13
-          cargo update -p aws-smithy-runtime --precise 1.9.3
-          cargo update -p aws-smithy-http --precise 0.62.6
-          cargo update -p aws-smithy-eventstream --precise 0.60.14
-          cargo update -p aws-smithy-http-client --precise 1.1.3
-          cargo update -p aws-smithy-observability --precise 0.1.4
-          cargo update -p aws-smithy-query --precise 0.60.8
-          cargo update -p aws-smithy-runtime-api --precise 1.9.3
-          cargo update -p aws-smithy-async --precise 1.2.7
-          cargo update -p aws-smithy-types --precise 1.3.6
-          cargo update -p aws-smithy-xml --precise 0.60.11
-          cargo update -p home --precise 0.5.9
+      - name: Downgrade dependencies
+        # aws-smithy-types 1.6.1+ requires rustc 1.94.1
+        run: cargo update -p aws-smithy-types --precise 1.5.0
       - name: cargo +${{ matrix.msrv }} check
         env:
           RUSTUP_TOOLCHAIN: ${{ matrix.msrv }}
diff --git a/.typos.toml b/.typos.toml
index 7d1e4b14b..3083112e3 100644
--- a/.typos.toml
+++ b/.typos.toml
@@ -1,5 +1,15 @@
 [default]
-extend-ignore-re = ["(?Rm)^.*(#|//)\\s*spellchecker:disable-line$"]
+extend-ignore-re = [
+    "(?Rm)^.*(#|//)\\s*spellchecker:disable-line$",
+    # Conventional import aliases in the docs examples: `import fiftyone as fo`
+    # and `import torchvision.io as tio`.
+    "\\bas (fo|tio)\\b",
+    "\\btio\\.",
+    # The substring query in the n-gram full-text search example.
+    'search\("nce"',
+    # Garbled OCR output quoted verbatim in the VLM fine-tuning tutorial.
+    "permission nstallation",
+]
 
 [default.extend-words]
 # Azure Kubernetes Service, mentioned in rust/lancedb/src/remote/oauth.rs.
@@ -12,6 +22,10 @@ mmaped = "mmaped"
 # `WriteableBuffer` is the real name of a type from Python's `_typeshed` stubs,
 # used in python/python/lancedb/_blob.py.
 Writeable = "Writeable"
+# Superlinked's self-hosted inference engine (SIE), its `sie-lancedb` package,
+# and the `sie` embedding function it registers, in
+# docs/web/integrations/embedding/superlinked.mdx.
+SIE = "SIE"
 
 [files]
 extend-exclude = [
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 64337723b..0aabd8fec 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -71,7 +71,88 @@ are wrappers around the Rust library.
 * `nodejs`: Typescript package source code
 * `node`: **Deprecated** Typescript package source code
 * `java`: Java package source code
-* `docs`: Documentation source code
+* `docs/src`: SDK reference, built with mkdocs
+* `docs/web`: the open-source pages of [docs.lancedb.com](https://docs.lancedb.com)
+* `docs/web-tests`: the tests the documentation's code examples are extracted from
+
+## Documentation
+
+The open-source pages of [docs.lancedb.com](https://docs.lancedb.com) live in
+`docs/web`, so a change to behaviour and the change to the page describing it
+belong in the same pull request.
+
+Preview them locally — this serves `docs/web` on its own, with no other checkout:
+
+```bash
+npm i -g mint      # https://mintlify.com/docs/installation
+cd docs/web && mint dev
+```
+
+This repository holds a page at every path the site publishes, so the whole
+navigation resolves locally. Enterprise pages exist here as open-source pages --
+what the capability is, and what the embedded form does instead. A separate
+private repository supplies the fuller version of each, which replaces the page
+at the same path when the published site is assembled.
+
+Four things to know before editing:
+
+**Code examples are not written into the pages.** They live in real tests under
+`docs/web-tests/{py,ts,rs}`, are extracted into `docs/web/snippets/`, and are
+imported by the pages. Edit the test, then regenerate from the repository root:
+
+```bash
+uv run docs/web-tests/mdx_snippets_gen.py -s docs/web-tests/py -s docs/web-tests/ts -s docs/web-tests/rs -o docs/web/snippets
+```
+
+Review the diff before committing: a few modules currently regenerate with
+unrelated changes, so commit only the snippets for the examples you changed.
+
+Run the Python, TypeScript and Rust examples in `docs/web-tests/{py,ts,rs}`
+locally before submitting the change. These examples are not yet wired into
+pull-request CI; that integration remains follow-up work.
+
+**Every heading carries an explicit `{#anchor}`.** Those anchors are how the
+Enterprise pages attach their additions to the right section, so they are
+assigned once and never regenerated. Leave an existing anchor alone even when
+you reword the heading above it; only new headings need a new one.
+
+**A page can declare where it applies.** Its frontmatter says whether LanceDB
+OSS and LanceDB Enterprise support the feature: use only `available` or
+`unavailable`, and one sentence that explains its scope. Availability describes
+product support, not whether a particular deployment has enabled or configured
+the feature. A supported feature stays `available` when it requires setup, a
+supported SDK or server version, or has differences between offerings. State
+those requirements in the summary or the relevant section, including unsupported
+subfeatures. Do not use `varies`, infer support from an unknown status, or mark an
+absent capability available. Narrow a page to the capability it actually covers
+when necessary. For example:
+
+```yaml
+availability:
+  oss: unavailable
+  enterprise: available
+  summary: >-
+    Requests to a deployment carry an API key or an OAuth token. Embedded
+    LanceDB has no server to sign in to.
+```
+
+The assembled site renders this as a label above the page's content and as a
+sidebar tag when only one offering has the feature. Do not also open the page
+with a badge of its own, and keep a qualification that only one section needs
+in that section's text. A `mint dev` preview of `docs/web` alone renders
+neither the labels nor the tags.
+
+**A feature can be documented before its release.** List a draft page in
+`docs/web/.mintignore`, keep it out of the navigation in `docs.json`, and do not
+link to it from published pages. Wrap a draft section in an MDX comment,
+`{/* ... */}`, whose first line names the release, such as
+`Draft for the Feature X release.` Keep draft table rows and list items in a
+comment below the table or list, since a comment between rows or items breaks
+it. Drafts are hidden from readers but stay in the source, and availability does
+not change what is published. At release, remove the comments and the
+`.mintignore` entries, and restore the navigation and links. See
+[Writing docs before release](https://github.com/lancedb/docs#writing-docs-before-release)
+in the documentation repository's README for previews and release checks.
 
 ## Release process
 
diff --git a/Cargo.lock b/Cargo.lock
index 87bef3a25..88aabd4f5 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -141,7 +141,7 @@ version = "1.1.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "40c48f72fd53cd289104fc64099abca73db4166ad86ea0b4341abe65af83dadc"
 dependencies = [
- "windows-sys 0.60.2",
+ "windows-sys 0.61.2",
 ]
 
 [[package]]
@@ -152,7 +152,7 @@ checksum = "291e6a250ff86cd4a820112fb8898808a366d8f9f58ce16d1f538353ad55747d"
 dependencies = [
  "anstyle",
  "once_cell_polyfill",
- "windows-sys 0.60.2",
+ "windows-sys 0.61.2",
 ]
 
 [[package]]
@@ -181,9 +181,9 @@ dependencies = [
 
 [[package]]
 name = "arc-swap"
-version = "1.9.1"
+version = "1.9.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6a3a1fd6f75306b68087b831f025c712524bcb19aad54e557b1129cfa0a2b207"
+checksum = "c049c0be4daef0b145cb3555416b3b8ef5b7888a38aea1a3a155801fe7b0810b"
 dependencies = [
  "rustversion",
 ]
@@ -986,40 +986,36 @@ dependencies = [
 
 [[package]]
 name = "aws-smithy-http-client"
-version = "1.1.12"
+version = "1.1.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6a2f165a7feee6f263028b899d0a181987f4fa7179a6411a32a439fba7c5f769"
+checksum = "5c3ef8931ad1c98aa6a55b4256f847f3116090819844e0dd41ea682cac5dd2d3"
 dependencies = [
  "aws-smithy-async",
  "aws-smithy-runtime-api",
  "aws-smithy-types",
- "h2 0.3.27",
- "h2 0.4.16",
- "http 0.2.12",
+ "h2",
  "http 1.5.0",
- "http-body 0.4.6",
- "hyper 0.14.32",
- "hyper 1.9.0",
- "hyper-rustls 0.24.2",
- "hyper-rustls 0.27.9",
+ "hyper",
+ "hyper-rustls",
  "hyper-util",
  "pin-project-lite",
- "rustls 0.21.12",
- "rustls 0.23.45",
+ "rustls",
  "rustls-native-certs",
  "rustls-pki-types",
  "tokio",
- "tokio-rustls 0.26.4",
+ "tokio-rustls",
  "tower",
  "tracing",
 ]
 
 [[package]]
 name = "aws-smithy-json"
-version = "0.62.5"
+version = "0.62.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9648b0bb82a2eedd844052c6ad2a1a822d1f8e3adee5fbf668366717e428856a"
+checksum = "701a947f4797e52a911e114a898667c746c39feea467bbd1abd7b3721f702ffa"
 dependencies = [
+ "aws-smithy-runtime-api",
+ "aws-smithy-schema",
  "aws-smithy-types",
 ]
 
@@ -1070,9 +1066,9 @@ dependencies = [
 
 [[package]]
 name = "aws-smithy-runtime-api"
-version = "1.12.1"
+version = "1.12.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "dc117c179ecf39a62a0a3f49f600e9ac26a7ad7dd172177999f83933af776c32"
+checksum = "9db177daa6ba8afb9ee1aefcf548c907abcf52065e394ee11a92780057fe0e8c"
 dependencies = [
  "aws-smithy-async",
  "aws-smithy-runtime-api-macros",
@@ -1110,9 +1106,9 @@ dependencies = [
 
 [[package]]
 name = "aws-smithy-types"
-version = "1.6.3"
+version = "1.6.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8f94d16e797ec62cd999fc9d5942b48fa7050c3093ddadff48e4d7528d16fcb9"
+checksum = "0b791f3ac597193fe1d08b82366986eb1f5bc31f2ac6c194c0855276116c76cd"
 dependencies = [
  "base64-simd",
  "bytes",
@@ -1170,7 +1166,7 @@ dependencies = [
  "http 1.5.0",
  "http-body 1.1.0",
  "http-body-util",
- "hyper 1.9.0",
+ "hyper",
  "hyper-util",
  "itoa",
  "matchit 0.7.3",
@@ -1819,7 +1815,7 @@ version = "3.1.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "faf9468729b8cbcea668e36183cb69d317348c2e08e994829fb56ebfdfbaac34"
 dependencies = [
- "windows-sys 0.59.0",
+ "windows-sys 0.52.0",
 ]
 
 [[package]]
@@ -3315,7 +3311,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "39cab71617ae0d63f51a36d69f866391735b51691dbda63cf6f96d042b63efeb"
 dependencies = [
  "libc",
- "windows-sys 0.59.0",
+ "windows-sys 0.52.0",
 ]
 
 [[package]]
@@ -3588,8 +3584,8 @@ checksum = "42703706b716c37f96a77aea830392ad231f44c9e9a67872fa5548707e11b11c"
 
 [[package]]
 name = "fsst"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arrow-array",
  "rand 0.9.5",
@@ -4000,11 +3996,9 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "899def5c37c4fd7b2664648c28120ecec138e4d395b459e5ca34f9cce2dd77fd"
 dependencies = [
  "cfg-if 1.0.4",
- "js-sys",
  "libc",
  "r-efi 5.3.0",
  "wasip2",
- "wasm-bindgen",
 ]
 
 [[package]]
@@ -4110,25 +4104,6 @@ dependencies = [
  "subtle",
 ]
 
-[[package]]
-name = "h2"
-version = "0.3.27"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0beca50380b1fc32983fc1cb4587bfa4bb9e78fc259aad4a0032d2080309222d"
-dependencies = [
- "bytes",
- "fnv",
- "futures-core",
- "futures-sink",
- "futures-util",
- "http 0.2.12",
- "indexmap 2.14.0",
- "slab",
- "tokio",
- "tokio-util",
- "tracing",
-]
-
 [[package]]
 name = "h2"
 version = "0.4.16"
@@ -4430,30 +4405,6 @@ dependencies = [
  "typenum",
 ]
 
-[[package]]
-name = "hyper"
-version = "0.14.32"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "41dfc780fdec9373c01bae43289ea34c972e40ee3c9f6b3c8801a35f35586ce7"
-dependencies = [
- "bytes",
- "futures-channel",
- "futures-core",
- "futures-util",
- "h2 0.3.27",
- "http 0.2.12",
- "http-body 0.4.6",
- "httparse",
- "httpdate",
- "itoa",
- "pin-project-lite",
- "socket2 0.5.10",
- "tokio",
- "tower-service",
- "tracing",
- "want",
-]
-
 [[package]]
 name = "hyper"
 version = "1.9.0"
@@ -4464,7 +4415,7 @@ dependencies = [
  "bytes",
  "futures-channel",
  "futures-core",
- "h2 0.4.16",
+ "h2",
  "http 1.5.0",
  "http-body 1.1.0",
  "httparse",
@@ -4476,21 +4427,6 @@ dependencies = [
  "want",
 ]
 
-[[package]]
-name = "hyper-rustls"
-version = "0.24.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ec3efd23720e2049821a693cbc7e65ea87c72f1c58ff2f9522ff332b1491e590"
-dependencies = [
- "futures-util",
- "http 0.2.12",
- "hyper 0.14.32",
- "log",
- "rustls 0.21.12",
- "tokio",
- "tokio-rustls 0.24.1",
-]
-
 [[package]]
 name = "hyper-rustls"
 version = "0.27.9"
@@ -4498,12 +4434,12 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "33ca68d021ef39cf6463ab54c1d0f5daf03377b70561305bb89a8f83aab66e0f"
 dependencies = [
  "http 1.5.0",
- "hyper 1.9.0",
+ "hyper",
  "hyper-util",
- "rustls 0.23.45",
+ "rustls",
  "rustls-native-certs",
  "tokio",
- "tokio-rustls 0.26.4",
+ "tokio-rustls",
  "tower-service",
  "webpki-roots 1.0.7",
 ]
@@ -4514,7 +4450,7 @@ version = "0.5.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "2b90d566bffbce6a75bd8b09a05aa8c2cb1fabb6cb348f8840c9e4c90a0d83b0"
 dependencies = [
- "hyper 1.9.0",
+ "hyper",
  "hyper-util",
  "pin-project-lite",
  "tokio",
@@ -4533,12 +4469,12 @@ dependencies = [
  "futures-util",
  "http 1.5.0",
  "http-body 1.1.0",
- "hyper 1.9.0",
+ "hyper",
  "ipnet",
  "libc",
  "percent-encoding",
  "pin-project-lite",
- "socket2 0.5.10",
+ "socket2",
  "system-configuration",
  "tokio",
  "tower-service",
@@ -4880,7 +4816,7 @@ checksum = "3640c1c38b8e4e43584d8df18be5fc6b0aa314ce6ebf51b53313d4306cca8e46"
 dependencies = [
  "hermit-abi",
  "libc",
- "windows-sys 0.59.0",
+ "windows-sys 0.52.0",
 ]
 
 [[package]]
@@ -5095,26 +5031,6 @@ dependencies = [
  "wasm-bindgen",
 ]
 
-[[package]]
-name = "jsonb"
-version = "0.5.6"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "eb98fb29636087c40ad0d1274d9a30c0c1e83e03ae93f6e7e89247b37fcc6953"
-dependencies = [
- "byteorder",
- "ethnum",
- "fast-float2",
- "itoa",
- "jiff",
- "nom 8.0.0",
- "num-traits",
- "ordered-float 5.3.0",
- "rand 0.9.5",
- "serde",
- "serde_json",
- "zmij",
-]
-
 [[package]]
 name = "kanaria"
 version = "0.2.0"
@@ -5143,8 +5059,8 @@ checksum = "e037a2e1d8d5fdbd49b16a4ea09d5d6401c1f29eca5ff29d03d3824dba16256a"
 
 [[package]]
 name = "lance"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arc-swap",
  "arrow",
@@ -5193,7 +5109,7 @@ dependencies = [
  "lance-tokenizer",
  "log",
  "moka",
- "object_store 0.14.1",
+ "object_store 0.14.2",
  "permutation",
  "pin-project",
  "prost",
@@ -5217,8 +5133,8 @@ dependencies = [
 
 [[package]]
 name = "lance-arrow"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arrow-array",
  "arrow-buffer",
@@ -5232,7 +5148,7 @@ dependencies = [
  "futures",
  "getrandom 0.2.17",
  "half",
- "jsonb",
+ "lance-jsonb",
  "num-traits",
  "rand 0.9.5",
 ]
@@ -5240,7 +5156,7 @@ dependencies = [
 [[package]]
 name = "lance-arrow-scalar"
 version = "58.0.0"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arrow-array",
  "arrow-buffer",
@@ -5254,7 +5170,7 @@ dependencies = [
 [[package]]
 name = "lance-arrow-stats"
 version = "58.0.0"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arrow-array",
  "arrow-schema",
@@ -5263,8 +5179,8 @@ dependencies = [
 
 [[package]]
 name = "lance-bitpacking"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arrayref",
  "crunchy",
@@ -5274,8 +5190,8 @@ dependencies = [
 
 [[package]]
 name = "lance-core"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arrow-array",
  "arrow-buffer",
@@ -5294,7 +5210,7 @@ dependencies = [
  "log",
  "moka",
  "num_cpus",
- "object_store 0.14.1",
+ "object_store 0.14.2",
  "pin-project",
  "prost",
  "quick_cache",
@@ -5312,8 +5228,8 @@ dependencies = [
 
 [[package]]
 name = "lance-datafusion"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arrow",
  "arrow-array",
@@ -5330,10 +5246,10 @@ dependencies = [
  "datafusion-physical-expr",
  "futures",
  "half",
- "jsonb",
  "lance-arrow",
  "lance-core",
  "lance-geo",
+ "lance-jsonb",
  "log",
  "pin-project",
  "prost",
@@ -5344,8 +5260,8 @@ dependencies = [
 
 [[package]]
 name = "lance-datagen"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arrow",
  "arrow-array",
@@ -5362,8 +5278,8 @@ dependencies = [
 
 [[package]]
 name = "lance-derive"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -5372,8 +5288,8 @@ dependencies = [
 
 [[package]]
 name = "lance-encoding"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arrow-arith",
  "arrow-array",
@@ -5406,8 +5322,8 @@ dependencies = [
 
 [[package]]
 name = "lance-file"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arrow-arith",
  "arrow-array",
@@ -5428,7 +5344,7 @@ dependencies = [
  "lance-io",
  "log",
  "num-traits",
- "object_store 0.14.1",
+ "object_store 0.14.2",
  "prost",
  "prost-build",
  "prost-types",
@@ -5439,8 +5355,8 @@ dependencies = [
 
 [[package]]
 name = "lance-geo"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "datafusion",
  "geo-traits",
@@ -5454,8 +5370,8 @@ dependencies = [
 
 [[package]]
 name = "lance-index"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arc-swap",
  "arrow",
@@ -5482,10 +5398,10 @@ dependencies = [
  "geoarrow-array",
  "geoarrow-schema",
  "half",
+ "hashbrown 0.16.1",
  "hex",
  "itertools 0.14.0",
  "jieba-rs",
- "jsonb",
  "lance-arrow",
  "lance-arrow-stats",
  "lance-bitpacking",
@@ -5496,6 +5412,7 @@ dependencies = [
  "lance-geo",
  "lance-index-core",
  "lance-io",
+ "lance-jsonb",
  "lance-linalg",
  "lance-select",
  "lance-table",
@@ -5504,7 +5421,7 @@ dependencies = [
  "log",
  "ndarray",
  "num-traits",
- "object_store 0.14.1",
+ "object_store 0.14.2",
  "prost",
  "prost-build",
  "prost-types",
@@ -5525,8 +5442,8 @@ dependencies = [
 
 [[package]]
 name = "lance-index-core"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arrow-array",
  "arrow-schema",
@@ -5549,8 +5466,8 @@ dependencies = [
 
 [[package]]
 name = "lance-io"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arrow",
  "arrow-array",
@@ -5570,13 +5487,14 @@ dependencies = [
  "log",
  "metrics",
  "moka",
- "object_store 0.14.1",
+ "object_store 0.14.2",
  "object_store_opendal",
  "opendal",
  "opendal-service-goosefs",
  "path_abs",
  "pin-project",
  "prost",
+ "quick-xml 0.41.0",
  "rand 0.9.5",
  "reqsign-core",
  "reqsign-file-read-tokio",
@@ -5590,10 +5508,28 @@ dependencies = [
  "uuid",
 ]
 
+[[package]]
+name = "lance-jsonb"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
+dependencies = [
+ "byteorder",
+ "ethnum",
+ "fast-float2",
+ "itoa",
+ "jiff",
+ "nom 8.0.0",
+ "num-traits",
+ "ordered-float 5.3.0",
+ "serde",
+ "serde_json",
+ "zmij",
+]
+
 [[package]]
 name = "lance-linalg"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arrow-array",
  "arrow-schema",
@@ -5607,8 +5543,8 @@ dependencies = [
 
 [[package]]
 name = "lance-namespace"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arrow",
  "async-trait",
@@ -5622,9 +5558,10 @@ dependencies = [
 
 [[package]]
 name = "lance-namespace-impls"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
+ "arc-swap",
  "arrow",
  "arrow-ipc",
  "arrow-schema",
@@ -5645,7 +5582,7 @@ dependencies = [
  "lance-namespace",
  "lance-table",
  "log",
- "object_store 0.14.1",
+ "object_store 0.14.2",
  "quick-xml 0.40.1",
  "rand 0.9.5",
  "reqwest 0.12.28",
@@ -5662,9 +5599,9 @@ dependencies = [
 
 [[package]]
 name = "lance-namespace-reqwest-client"
-version = "0.12.0"
+version = "0.13.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d8d23e54b1634d5bbb434f8dd33dc3c05f6e58d876a9a27b3b4aef58ddbe11af"
+checksum = "828f291764feda98898452bea4ce3756b73e5d5c7c67b62edda27456b7cc72c1"
 dependencies = [
  "reqwest 0.12.28",
  "serde",
@@ -5676,8 +5613,8 @@ dependencies = [
 
 [[package]]
 name = "lance-select"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arrow-array",
  "arrow-buffer",
@@ -5691,8 +5628,8 @@ dependencies = [
 
 [[package]]
 name = "lance-table"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arrow",
  "arrow-array",
@@ -5713,7 +5650,7 @@ dependencies = [
  "lance-io",
  "lance-select",
  "log",
- "object_store 0.14.1",
+ "object_store 0.14.2",
  "prost",
  "prost-build",
  "prost-types",
@@ -5732,8 +5669,8 @@ dependencies = [
 
 [[package]]
 name = "lance-testing"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "arrow-array",
  "arrow-schema",
@@ -5746,8 +5683,8 @@ dependencies = [
 
 [[package]]
 name = "lance-tokenizer"
-version = "13.0.0-beta.15"
-source = "git+https://github.com/lance-format/lance.git?tag=v13.0.0-beta.15#db53dcf367088b32ae3a7461407d43593759ae3e"
+version = "14.0.0-beta.18"
+source = "git+https://github.com/lance-format/lance.git?tag=v14.0.0-beta.18#f80bf1d1e59f40d249bce2539b78e41d93dee9ec"
 dependencies = [
  "frostem",
  "icu_segmenter",
@@ -5760,7 +5697,7 @@ dependencies = [
 
 [[package]]
 name = "lancedb"
-version = "0.40.0-beta.11"
+version = "0.41.0-beta.7"
 dependencies = [
  "ahash",
  "anyhow",
@@ -5824,7 +5761,7 @@ dependencies = [
  "moka",
  "num-traits",
  "oauth2",
- "object_store 0.14.1",
+ "object_store 0.14.2",
  "pin-project",
  "polars",
  "polars-arrow",
@@ -5833,7 +5770,7 @@ dependencies = [
  "rand 0.9.5",
  "random_word",
  "regex",
- "reqwest 0.12.28",
+ "reqwest 0.13.4",
  "roaring",
  "rstest",
  "semver",
@@ -5843,6 +5780,7 @@ dependencies = [
  "serial_test",
  "sha2 0.10.9",
  "snafu 0.8.9",
+ "strum_macros 0.28.0",
  "tempfile",
  "test-log",
  "tokenizers",
@@ -5857,7 +5795,7 @@ dependencies = [
 
 [[package]]
 name = "lancedb-nodejs"
-version = "0.40.0-beta.11"
+version = "0.41.0-beta.7"
 dependencies = [
  "arrow-array",
  "arrow-buffer",
@@ -5882,7 +5820,7 @@ dependencies = [
 
 [[package]]
 name = "lancedb-python"
-version = "0.40.0-beta.11"
+version = "0.41.0-beta.7"
 dependencies = [
  "arc-swap",
  "arrow",
@@ -6473,9 +6411,9 @@ dependencies = [
 
 [[package]]
 name = "napi"
-version = "3.12.2"
+version = "3.12.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "58c5f4d5375213fdb7be2655e152386e82f026f9a5ba36a75556e11359aafe09"
+checksum = "f0c007d4a8ead952a81887661d41fd56b7e7a70d3d4d921f445fb37a8f6efa1e"
 dependencies = [
  "bitflags 2.11.1",
  "chrono",
@@ -6493,15 +6431,15 @@ dependencies = [
 
 [[package]]
 name = "napi-build"
-version = "2.4.2"
+version = "2.5.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "860e7c40864f95cfb83cde99f9ebadd88ef3d9bdccd7dd2cee0cc96a2dd4ffa7"
+checksum = "0941983559cd62bfa7f04646a16428967a847365adeef27711e4e3927046bbed"
 
 [[package]]
 name = "napi-derive"
-version = "3.6.5"
+version = "3.6.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "350057056a30368aa76c11a0d406b0aa61321710be2c36656ab4f6efe0b785a2"
+checksum = "6b1d232c24dc6754979b27efdf2047a5849efc7c92c1245de87e6bedbb9f5c3c"
 dependencies = [
  "convert_case",
  "ctor",
@@ -6513,9 +6451,9 @@ dependencies = [
 
 [[package]]
 name = "napi-derive-backend"
-version = "6.1.3"
+version = "6.1.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4c1c87a71568f3fe5c736b10878ff55064b250bb4ea4b48c8055713e07b9e463"
+checksum = "fd6856fe1686867da90ed0dc292d7c0c87a695eb7c578b913360f33619ee01e3"
 dependencies = [
  "convert_case",
  "proc-macro2",
@@ -6526,9 +6464,9 @@ dependencies = [
 
 [[package]]
 name = "napi-sys"
-version = "3.3.0"
+version = "3.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "85fbf1fa9f1babfe396d74bbbf52b3643770243e8f5b0b46715d4caf7f0dfc9a"
+checksum = "e22a4f25c16a5c5411d987cd6fbd48313522ae1789ade2d6dd3efdc6d40a0fc8"
 dependencies = [
  "libloading",
 ]
@@ -6881,13 +6819,12 @@ dependencies = [
 
 [[package]]
 name = "object_store"
-version = "0.14.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d354792e39fa5f0009e47623cf8b15b099bf9a652fa55c6f817fe28ac84fea50"
+version = "0.14.2"
+source = "git+https://github.com/apache/arrow-rs-object-store.git?rev=c10758c207c0103355f940f0c736fb8ecb157b04#c10758c207c0103355f940f0c736fb8ecb157b04"
 dependencies = [
  "async-trait",
  "aws-lc-rs",
- "base64 0.22.1",
+ "base64 0.23.1",
  "bytes",
  "chrono",
  "crc-fast",
@@ -6899,7 +6836,7 @@ dependencies = [
  "http-body-util",
  "httparse",
  "humantime",
- "hyper 1.9.0",
+ "hyper",
  "itertools 0.15.0",
  "md-5",
  "nix 0.31.3",
@@ -6933,7 +6870,7 @@ dependencies = [
  "bytes",
  "chrono",
  "futures",
- "object_store 0.14.1",
+ "object_store 0.14.2",
  "opendal",
  "pin-project",
  "tokio",
@@ -8139,7 +8076,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "343d3bd7056eda839b03204e68deff7d1b13aba7af2b2fd16890697274262ee7"
 dependencies = [
  "heck 0.4.1",
- "itertools 0.11.0",
+ "itertools 0.12.1",
  "log",
  "multimap",
  "petgraph",
@@ -8158,7 +8095,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b570b25f7617e43d59005d0990ccb79e950a423952cea19671b7a876da390adf"
 dependencies = [
  "anyhow",
- "itertools 0.11.0",
+ "itertools 0.12.1",
  "proc-macro2",
  "quote",
  "syn 2.0.117",
@@ -8379,8 +8316,8 @@ dependencies = [
  "quinn-proto",
  "quinn-udp",
  "rustc-hash",
- "rustls 0.23.45",
- "socket2 0.5.10",
+ "rustls",
+ "socket2",
  "thiserror 2.0.18",
  "tokio",
  "tracing",
@@ -8389,18 +8326,19 @@ dependencies = [
 
 [[package]]
 name = "quinn-proto"
-version = "0.11.14"
+version = "0.11.19"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "434b42fec591c96ef50e21e886936e66d3cc3f737104fdb9b737c40ffb94c098"
+checksum = "0e750cca55fe4f0439a15d0bb529da9651e79993e8e72c61a899a36d462befbe"
 dependencies = [
  "aws-lc-rs",
  "bytes",
- "getrandom 0.3.4",
+ "getrandom 0.4.2",
  "lru-slab",
- "rand 0.9.5",
+ "rand 0.10.1",
+ "rand_pcg",
  "ring",
  "rustc-hash",
- "rustls 0.23.45",
+ "rustls",
  "rustls-pki-types",
  "slab",
  "thiserror 2.0.18",
@@ -8418,9 +8356,9 @@ dependencies = [
  "cfg_aliases",
  "libc",
  "once_cell",
- "socket2 0.5.10",
+ "socket2",
  "tracing",
- "windows-sys 0.60.2",
+ "windows-sys 0.52.0",
 ]
 
 [[package]]
@@ -8565,6 +8503,15 @@ dependencies = [
  "rand 0.9.5",
 ]
 
+[[package]]
+name = "rand_pcg"
+version = "0.10.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "caa0f4137e1c0a72f4c651489402276c8e8e1cf081f3b0ba156d2cbeef09e86a"
+dependencies = [
+ "rand_core 0.10.1",
+]
+
 [[package]]
 name = "rand_xoshiro"
 version = "0.7.0"
@@ -8929,12 +8876,12 @@ dependencies = [
  "encoding_rs",
  "futures-core",
  "futures-util",
- "h2 0.4.16",
+ "h2",
  "http 1.5.0",
  "http-body 1.1.0",
  "http-body-util",
- "hyper 1.9.0",
- "hyper-rustls 0.27.9",
+ "hyper",
+ "hyper-rustls",
  "hyper-util",
  "js-sys",
  "log",
@@ -8943,7 +8890,7 @@ dependencies = [
  "percent-encoding",
  "pin-project-lite",
  "quinn",
- "rustls 0.23.45",
+ "rustls",
  "rustls-native-certs",
  "rustls-pki-types",
  "serde",
@@ -8951,7 +8898,7 @@ dependencies = [
  "serde_urlencoded",
  "sync_wrapper",
  "tokio",
- "tokio-rustls 0.26.4",
+ "tokio-rustls",
  "tokio-util",
  "tower",
  "tower-http 0.6.10",
@@ -8972,28 +8919,31 @@ checksum = "219c5811de6525e5416c7d5d53bb656d3afdbc6c5af816e0802bcfa42dbdc1c3"
 dependencies = [
  "base64 0.22.1",
  "bytes",
+ "encoding_rs",
  "futures-core",
  "futures-util",
- "h2 0.4.16",
+ "h2",
  "http 1.5.0",
  "http-body 1.1.0",
  "http-body-util",
- "hyper 1.9.0",
- "hyper-rustls 0.27.9",
+ "hyper",
+ "hyper-rustls",
  "hyper-util",
  "js-sys",
  "log",
+ "mime",
  "percent-encoding",
  "pin-project-lite",
  "quinn",
- "rustls 0.23.45",
+ "rustls",
  "rustls-pki-types",
  "rustls-platform-verifier",
  "serde",
  "serde_json",
+ "serde_urlencoded",
  "sync_wrapper",
  "tokio",
- "tokio-rustls 0.26.4",
+ "tokio-rustls",
  "tokio-util",
  "tower",
  "tower-http 0.6.10",
@@ -9218,19 +9168,7 @@ dependencies = [
  "errno",
  "libc",
  "linux-raw-sys",
- "windows-sys 0.59.0",
-]
-
-[[package]]
-name = "rustls"
-version = "0.21.12"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3f56a14d1f48b391359b22f731fd4bd7e43c97f3c50eee276f3aa09c94784d3e"
-dependencies = [
- "log",
- "ring",
- "rustls-webpki 0.101.7",
- "sct",
+ "windows-sys 0.52.0",
 ]
 
 [[package]]
@@ -9244,7 +9182,7 @@ dependencies = [
  "once_cell",
  "ring",
  "rustls-pki-types",
- "rustls-webpki 0.103.15",
+ "rustls-webpki",
  "subtle",
  "zeroize",
 ]
@@ -9282,14 +9220,14 @@ dependencies = [
  "jni",
  "log",
  "once_cell",
- "rustls 0.23.45",
+ "rustls",
  "rustls-native-certs",
  "rustls-platform-verifier-android",
- "rustls-webpki 0.103.15",
+ "rustls-webpki",
  "security-framework",
  "security-framework-sys",
  "webpki-root-certs",
- "windows-sys 0.59.0",
+ "windows-sys 0.52.0",
 ]
 
 [[package]]
@@ -9298,16 +9236,6 @@ version = "0.1.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "f87165f0995f63a9fbeea62b64d10b4d9d8e78ec6d7d51fb2125fda7bb36788f"
 
-[[package]]
-name = "rustls-webpki"
-version = "0.101.7"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8b6275d1ee7a1cd780b64aca7726599a1dbc893b1e64144529e55c3c2f745765"
-dependencies = [
- "ring",
- "untrusted 0.9.0",
-]
-
 [[package]]
 name = "rustls-webpki"
 version = "0.103.15"
@@ -9423,16 +9351,6 @@ dependencies = [
  "sha2 0.10.9",
 ]
 
-[[package]]
-name = "sct"
-version = "0.7.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "da046153aa2352493d6cb7da4b6e5c0c057d8a1d0a9aa8560baffdd945acd414"
-dependencies = [
- "ring",
- "untrusted 0.9.0",
-]
-
 [[package]]
 name = "sec1"
 version = "0.3.0"
@@ -9528,6 +9446,7 @@ version = "1.0.151"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "c841b55ecdae098c80dcae9cf767f6f8a0c2cdb3416bbef72181df4d0fe73f14"
 dependencies = [
+ "indexmap 2.14.0",
  "itoa",
  "memchr",
  "serde",
@@ -9580,9 +9499,9 @@ dependencies = [
 
 [[package]]
 name = "serde_with"
-version = "3.23.0"
+version = "3.24.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "935177bb8c0cd8ca1a4e6d1a2ac8988bea69cab4f9d3a31311e012ad27868ea4"
+checksum = "df9adc193c780ef8f159aee8b61e2d5801aaa555e6eb0947fe45530ec506296f"
 dependencies = [
  "base64 0.23.1",
  "bs58",
@@ -9601,9 +9520,9 @@ dependencies = [
 
 [[package]]
 name = "serde_with_macros"
-version = "3.23.0"
+version = "3.24.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1d607aa01a3cb0ad757d6fd216136910db3c97b102fe686585689615a02dbcdc"
+checksum = "3e17bbc68e28663bbbb90df47e058aa7eda4fb445b89fe70457bb94fbccf6e49"
 dependencies = [
  "darling 0.24.1",
  "proc-macro2",
@@ -9854,16 +9773,6 @@ dependencies = [
  "syn 2.0.117",
 ]
 
-[[package]]
-name = "socket2"
-version = "0.5.10"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e22376abed350d73dd1cd119b57ffccad95b4e585a7cda43e286245ce23c0678"
-dependencies = [
- "libc",
- "windows-sys 0.52.0",
-]
-
 [[package]]
 name = "socket2"
 version = "0.6.3"
@@ -9871,7 +9780,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "3a766e1110788c36f4fa1c2b71b387a7815aa65f88ce0229841826633d93723e"
 dependencies = [
  "libc",
- "windows-sys 0.60.2",
+ "windows-sys 0.61.2",
 ]
 
 [[package]]
@@ -9990,7 +9899,7 @@ dependencies = [
  "cfg-if 1.0.4",
  "libc",
  "psm",
- "windows-sys 0.60.2",
+ "windows-sys 0.61.2",
 ]
 
 [[package]]
@@ -10282,7 +10191,7 @@ dependencies = [
  "getrandom 0.4.2",
  "once_cell",
  "rustix",
- "windows-sys 0.59.0",
+ "windows-sys 0.52.0",
 ]
 
 [[package]]
@@ -10495,7 +10404,7 @@ dependencies = [
  "parking_lot",
  "pin-project-lite",
  "signal-hook-registry",
- "socket2 0.6.3",
+ "socket2",
  "tokio-macros",
  "windows-sys 0.61.2",
 ]
@@ -10522,23 +10431,13 @@ dependencies = [
  "tokio",
 ]
 
-[[package]]
-name = "tokio-rustls"
-version = "0.24.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c28327cf380ac148141087fbfb9de9d7bd4e84ab5d2c28fbc911d753de8a7081"
-dependencies = [
- "rustls 0.21.12",
- "tokio",
-]
-
 [[package]]
 name = "tokio-rustls"
 version = "0.26.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "1729aa945f29d91ba541258c8df89027d5792d85a8841fb65e8bf0f4ede4ef61"
 dependencies = [
- "rustls 0.23.45",
+ "rustls",
  "tokio",
 ]
 
@@ -10631,20 +10530,20 @@ dependencies = [
  "axum 0.8.9",
  "base64 0.22.1",
  "bytes",
- "h2 0.4.16",
+ "h2",
  "http 1.5.0",
  "http-body 1.1.0",
  "http-body-util",
- "hyper 1.9.0",
+ "hyper",
  "hyper-timeout",
  "hyper-util",
  "percent-encoding",
  "pin-project",
  "rustls-native-certs",
- "socket2 0.6.3",
+ "socket2",
  "sync_wrapper",
  "tokio",
- "tokio-rustls 0.26.4",
+ "tokio-rustls",
  "tokio-stream",
  "tower",
  "tower-layer",
@@ -10952,7 +10851,7 @@ dependencies = [
  "flate2",
  "log",
  "once_cell",
- "rustls 0.23.45",
+ "rustls",
  "rustls-pki-types",
  "serde",
  "serde_json",
@@ -11303,7 +11202,7 @@ version = "0.1.11"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "c2a7b1c03c876122aa43f3020e6c3c3ee5c05081c9a00739faf7503aeba10d22"
 dependencies = [
- "windows-sys 0.59.0",
+ "windows-sys 0.52.0",
 ]
 
 [[package]]
@@ -11773,7 +11672,7 @@ dependencies = [
  "crc32fast",
  "futures",
  "http 1.5.0",
- "hyper 1.9.0",
+ "hyper",
  "more-asserts",
  "rand 0.10.1",
  "redb",
@@ -11909,9 +11808,9 @@ checksum = "66fee0b777b0f5ac1c69bb06d361268faafa61cd4682ae064a171c16c433e9e4"
 
 [[package]]
 name = "xxhash-rust"
-version = "0.8.15"
+version = "0.8.19"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fdd20c5420375476fbd4394763288da7eb0cc0b8c11deed431a91562af7335d3"
+checksum = "550a2b930b62486a393c52d5c3b84bff264b28aa437ed64694d31e93b1757af7"
 
 [[package]]
 name = "yoke"
diff --git a/Cargo.toml b/Cargo.toml
index 1bf7ce86a..c6b9e84fe 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -10,23 +10,23 @@ repository = "https://github.com/lancedb/lancedb"
 description = "Serverless, low-latency vector database for AI applications"
 keywords = ["lancedb", "lance", "database", "vector", "search"]
 categories = ["database-implementations"]
-rust-version = "1.91.0"
+rust-version = "1.94.0"
 
 [workspace.dependencies]
-lance = { "version" = "=13.0.0-beta.15", default-features = false, "tag" = "v13.0.0-beta.15", "git" = "https://github.com/lance-format/lance.git" }
-lance-core = { "version" = "=13.0.0-beta.15", "tag" = "v13.0.0-beta.15", "git" = "https://github.com/lance-format/lance.git" }
-lance-datagen = { "version" = "=13.0.0-beta.15", "tag" = "v13.0.0-beta.15", "git" = "https://github.com/lance-format/lance.git" }
-lance-file = { "version" = "=13.0.0-beta.15", "tag" = "v13.0.0-beta.15", "git" = "https://github.com/lance-format/lance.git" }
-lance-io = { "version" = "=13.0.0-beta.15", default-features = false, "tag" = "v13.0.0-beta.15", "git" = "https://github.com/lance-format/lance.git" }
-lance-index = { "version" = "=13.0.0-beta.15", "tag" = "v13.0.0-beta.15", "git" = "https://github.com/lance-format/lance.git" }
-lance-linalg = { "version" = "=13.0.0-beta.15", "tag" = "v13.0.0-beta.15", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace = { "version" = "=13.0.0-beta.15", "tag" = "v13.0.0-beta.15", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace-impls = { "version" = "=13.0.0-beta.15", default-features = false, "tag" = "v13.0.0-beta.15", "git" = "https://github.com/lance-format/lance.git" }
-lance-table = { "version" = "=13.0.0-beta.15", "tag" = "v13.0.0-beta.15", "git" = "https://github.com/lance-format/lance.git" }
-lance-testing = { "version" = "=13.0.0-beta.15", "tag" = "v13.0.0-beta.15", "git" = "https://github.com/lance-format/lance.git" }
-lance-datafusion = { "version" = "=13.0.0-beta.15", "tag" = "v13.0.0-beta.15", "git" = "https://github.com/lance-format/lance.git" }
-lance-encoding = { "version" = "=13.0.0-beta.15", "tag" = "v13.0.0-beta.15", "git" = "https://github.com/lance-format/lance.git" }
-lance-arrow = { "version" = "=13.0.0-beta.15", "tag" = "v13.0.0-beta.15", "git" = "https://github.com/lance-format/lance.git" }
+lance = { "version" = "=14.0.0-beta.18", default-features = false, "tag" = "v14.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-core = { "version" = "=14.0.0-beta.18", "tag" = "v14.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-datagen = { "version" = "=14.0.0-beta.18", "tag" = "v14.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-file = { "version" = "=14.0.0-beta.18", "tag" = "v14.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-io = { "version" = "=14.0.0-beta.18", default-features = false, "tag" = "v14.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-index = { "version" = "=14.0.0-beta.18", "tag" = "v14.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-linalg = { "version" = "=14.0.0-beta.18", "tag" = "v14.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace = { "version" = "=14.0.0-beta.18", "tag" = "v14.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace-impls = { "version" = "=14.0.0-beta.18", default-features = false, "tag" = "v14.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-table = { "version" = "=14.0.0-beta.18", "tag" = "v14.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-testing = { "version" = "=14.0.0-beta.18", "tag" = "v14.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-datafusion = { "version" = "=14.0.0-beta.18", "tag" = "v14.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-encoding = { "version" = "=14.0.0-beta.18", "tag" = "v14.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
+lance-arrow = { "version" = "=14.0.0-beta.18", "tag" = "v14.0.0-beta.18", "git" = "https://github.com/lance-format/lance.git" }
 lancedb = { path = "rust/lancedb", default-features = false }
 ahash = "0.8"
 # Note that this one does not include pyarrow
@@ -72,13 +72,17 @@ oauth2 = { version = "5.0", default-features = false }
 regex = "1.10"
 semver = "1.0.25"
 serde = "1"
-serde_json = "1"
+serde_json = { version = "1", features = ["preserve_order"] }
 tempfile = "3.5.0"
 tokio = { version = "1.23", features = ["rt-multi-thread", "sync"] }
-tonic = { version = "0.14", features = ["tls-native-roots", "tls-ring"] }
+tonic = { version = "0.14", features = ["tls-native-roots", "tls-aws-lc"] }
 uuid = { version = "1.7.0", features = ["v4", "v7"] }
 chrono = { version = "0.4", default-features = false, features = ["clock"] }
 
+[patch.crates-io]
+# Lance v14.0.0-beta.18 requires multipart write modes from this upstream revision.
+object_store = { git = "https://github.com/apache/arrow-rs-object-store.git", rev = "c10758c207c0103355f940f0c736fb8ecb157b04" }
+
 [profile.ci]
 debug = "line-tables-only"
 inherits = "dev"
diff --git a/README.md b/README.md
index c8a70780d..7bf5d39fc 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,6 @@
-<a href="https://cloud.lancedb.com" target="_blank">
-  <img src="https://github.com/user-attachments/assets/92dad0a2-2a37-4ce1-b783-0d1b4f30a00c" alt="LanceDB Cloud Public Beta" width="100%" style="max-width: 100%;">
-</a>
 <div align="center">
 
+[![Reverie](docs/src/assets/reverie-banner.png)](https://www.reveriesummit.com/?utm_medium=social&utm_source=github&utm_campaign=reverie-2026)
 [![LanceDB](docs/src/assets/hero-header.png)](https://lancedb.com)
 [![Website](https://img.shields.io/badge/-Website-100000?style=for-the-badge&labelColor=645cfb&color=645cfb)](https://lancedb.com/)
 [![Blog](https://img.shields.io/badge/Blog-100000?style=for-the-badge&labelColor=645cfb&color=645cfb)](https://blog.lancedb.com/)
@@ -13,7 +11,7 @@
 
 <img src="docs/src/assets/lancedb.png" alt="LanceDB" width="50%">
 
-# **The Multimodal AI Lakehouse**
+# **The Multimodal Lakehouse for AI**
 
 [**How to Install** ](#how-to-install) ✦ [**Detailed Documentation**](https://docs.lancedb.com) ✦ [**Tutorials and Recipes**](https://github.com/lancedb/vectordb-recipes/tree/main) ✦  [**Contributors**](#contributors) 
 
diff --git a/deny.toml b/deny.toml
index 20510adc2..d5f42dbff 100644
--- a/deny.toml
+++ b/deny.toml
@@ -78,6 +78,27 @@ ignore = [
     { id = "RUSTSEC-2026-0098", reason = "only affects rustls-webpki 0.101 from legacy aws-smithy/rustls 0.21 chain" },
     { id = "RUSTSEC-2026-0099", reason = "only affects rustls-webpki 0.101 from legacy aws-smithy/rustls 0.21 chain" },
 
+    # rustls 0.23.45: four advisories fixed in 0.23.46 (Debug impls leaking
+    # TLS secrets, ECH rejection buffering, Brotli certificate decompression
+    # allocation, ServerHello key-exchange downgrade). 0.23.46 was published
+    # 2026-10-14 and the dependency age policy requires releases to be 48
+    # hours old, so the upgrade cannot land yet. Remove these entries and run
+    # `cargo update -p rustls` once the window has passed. Mirrors lance #9985.
+    # https://rustsec.org/advisories/RUSTSEC-2026-0346
+    # https://rustsec.org/advisories/RUSTSEC-2026-0347
+    # https://rustsec.org/advisories/RUSTSEC-2026-0348
+    # https://rustsec.org/advisories/RUSTSEC-2026-0349
+    { id = "RUSTSEC-2026-0346", reason = "fixed in rustls 0.23.46; blocked on the 48-hour release age policy" },
+    { id = "RUSTSEC-2026-0347", reason = "fixed in rustls 0.23.46; blocked on the 48-hour release age policy" },
+    { id = "RUSTSEC-2026-0348", reason = "fixed in rustls 0.23.46; blocked on the 48-hour release age policy" },
+    { id = "RUSTSEC-2026-0349", reason = "fixed in rustls 0.23.46; blocked on the 48-hour release age policy" },
+
+    # unsafe-libyaml: unmaintained. Transitive via lance-tokenizer → lindera
+    # → serde_yaml_ng. No maintained upgrade exists; clearing it requires
+    # lindera to move off serde_yaml_ng.
+    # https://rustsec.org/advisories/RUSTSEC-2024-0449
+    { id = "RUSTSEC-2024-0449", reason = "transitive via lindera (lance-tokenizer); no maintained upgrade available" },
+
     # rustls-webpki 0.101.7: reachable panic in CRL parsing. Same legacy
     # rustls 0.21 chain from aws-smithy-http-client as above. The 0.103 line
     # we actively use is upgraded to 0.103.13 which contains the fix.
@@ -197,4 +218,6 @@ allow-registry = ["https://github.com/rust-lang/crates.io-index"]
 # releases are cut to crates.io. Allow that specific host.
 allow-git = [
     "https://github.com/lance-format/lance",
+    # Pinned by Lance for the multipart write mode API (see [patch.crates-io] in Cargo.toml).
+    "https://github.com/apache/arrow-rs-object-store",
 ]
diff --git a/docs/README.md b/docs/README.md
index bce1ec668..c5a99943d 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -15,18 +15,21 @@ unreleased features.
 
 ### Preview the docs
 
+Run these commands from the LanceDB repository root. The subshell changes into
+`docs` to load `mkdocs.yml`, while leaving your shell in the repository root.
+
 ```shell
-cd docs
-mkdocs serve
+(cd docs && PYTHONPATH=. mkdocs serve)
 ```
 
 If you want to just generate the HTML files:
 
 ```shell
-PYTHONPATH=. mkdocs build -f docs/mkdocs.yml
+(cd docs && PYTHONPATH=. mkdocs build)
 ```
 
-If successful, you should see a `docs/site` directory that you can verify locally.
+If successful, you should see a `docs/site` directory in the repository root that
+you can verify locally.
 
 ## Adding examples
 
diff --git a/docs/src/assets/hero-header.png b/docs/src/assets/hero-header.png
index 588053722..aa474b06b 100644
Binary files a/docs/src/assets/hero-header.png and b/docs/src/assets/hero-header.png differ
diff --git a/docs/src/assets/reverie-banner.png b/docs/src/assets/reverie-banner.png
new file mode 100644
index 000000000..d7d49cb3b
Binary files /dev/null and b/docs/src/assets/reverie-banner.png differ
diff --git a/docs/src/java/java.md b/docs/src/java/java.md
index 5946e5862..d9207be9d 100644
--- a/docs/src/java/java.md
+++ b/docs/src/java/java.md
@@ -14,7 +14,7 @@ Add the following dependency to your `pom.xml`:
 <dependency>
     <groupId>com.lancedb</groupId>
     <artifactId>lancedb-core</artifactId>
-    <version>0.40.0-beta.11</version>
+    <version>0.41.0-beta.7</version>
 </dependency>
 ```
 
diff --git a/docs/src/js/classes/AutoQuery.md b/docs/src/js/classes/AutoQuery.md
index 1d7ea6952..1e9859215 100644
--- a/docs/src/js/classes/AutoQuery.md
+++ b/docs/src/js/classes/AutoQuery.md
@@ -350,7 +350,7 @@ input to this method would be:
 
 #### Parameters
 
-* **columns**: `string` \| `string`[] \| `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+* **columns**: `string` \| `string`[] \| `Map`&lt;`string`, `string`&gt; \| `Record`&lt;`string`, `string`&gt;
 
 #### Returns
 
@@ -417,25 +417,27 @@ Collect the results as an Arrow
 
 ArrowTable.
 
+Empty results retain the query's output schema.
+
 #### Inherited from
 
 `StandardQueryBase.toArrow`
 
 ***
 
-### useLsm()
+### useMemWal()
 
 ```ts
-useLsm(enable): this
+useMemWal(enable): this
 ```
 
 Control MemWAL read routing for this query.
 
 By default (unset), when the table carries a MemWAL write spec (see
-[Table#setLsmWriteSpec](Table.md#setlsmwritespec)), reads are routed through the LSM scanner so
+[Table#enableMemWal](Table.md#enablememwal)), reads are routed through the LSM scanner so
 they also return data written via the `mergeInsert` LSM path that has not yet
 been compacted into the base table (the active/frozen in-memory memtables and
-the flushed generations), deduplicated by primary key; a table without a spec
+the SSTables), deduplicated by primary key; a table without a spec
 reads the base table.
 
 #### Parameters
@@ -446,7 +448,7 @@ reads the base table.
     even when a spec is present.
     Note: the LSM scanner does not support every query shape (e.g. reranking,
     hybrid search, `orderBy`). On a MemWAL table those shapes error unless
-    `useLsm(false)` is set, because a base-only read would silently exclude
+    `useMemWal(false)` is set, because a base-only read would silently exclude
     un-compacted MemWAL data.
 
 #### Returns
@@ -455,7 +457,7 @@ reads the base table.
 
 #### Inherited from
 
-`StandardQueryBase.useLsm`
+`StandardQueryBase.useMemWal`
 
 ***
 
diff --git a/docs/src/js/classes/BlobFile.md b/docs/src/js/classes/BlobFile.md
index 84a596d1a..ee012be25 100644
--- a/docs/src/js/classes/BlobFile.md
+++ b/docs/src/js/classes/BlobFile.md
@@ -10,16 +10,52 @@ A lazy handle to blob bytes. Create one with [Table.fetchBlobFiles](Table.md#fet
 
 ## Methods
 
+### close()
+
+```ts
+close(): Promise<void>
+```
+
+Releases the handle. Reads after `close()` fail. Calling it again does
+nothing.
+
+#### Returns
+
+`Promise`&lt;`void`&gt;
+
+***
+
+### isClosed()
+
+```ts
+isClosed(): boolean
+```
+
+Returns true after [BlobFile.close](BlobFile.md#close).
+
+#### Returns
+
+`boolean`
+
+***
+
 ### read()
 
 ```ts
-read(): Promise<Buffer>
+read(maxBytes?, options?): Promise<Buffer>
 ```
 
-Reads from the cursor to the end and advances the cursor.
+Reads from the cursor and advances the cursor.
 
-A second call returns an empty buffer. [BlobFile.readRange](BlobFile.md#readrange) does
-not move the cursor.
+Reads to the end when `maxBytes` is omitted, or at most `maxBytes` bytes
+otherwise. Returns an empty buffer at the end of the blob.
+[BlobFile.readRange](BlobFile.md#readrange) does not move the cursor.
+
+#### Parameters
+
+* **maxBytes?**: `bigint`
+
+* **options?**: [`BlobReadOptions`](../type-aliases/BlobReadOptions.md)
 
 #### Returns
 
@@ -30,7 +66,10 @@ not move the cursor.
 ### readRange()
 
 ```ts
-readRange(start, end): Promise<Buffer>
+readRange(
+   start,
+   end,
+   options?): Promise<Buffer>
 ```
 
 Reads the half-open byte range `[start, end)`.
@@ -43,12 +82,55 @@ Fails when `end` is past the blob size. Does not move the cursor.
 
 * **end**: `bigint`
 
+* **options?**: [`BlobReadOptions`](../type-aliases/BlobReadOptions.md)
+
 #### Returns
 
 `Promise`&lt;`Buffer`&gt;
 
 ***
 
+### readRanges()
+
+```ts
+readRanges(ranges, options?): Promise<Buffer[]>
+```
+
+Reads several half-open byte ranges. Returns one buffer per range, in
+the order given.
+
+Fails when any `end` is past the blob size. Does not move the cursor.
+
+#### Parameters
+
+* **ranges**: [`BlobRange`](../type-aliases/BlobRange.md)[]
+
+* **options?**: [`BlobReadOptions`](../type-aliases/BlobReadOptions.md)
+
+#### Returns
+
+`Promise`&lt;`Buffer`[]&gt;
+
+***
+
+### seek()
+
+```ts
+seek(position): Promise<void>
+```
+
+Moves the cursor to `position`, in bytes from the start of the blob.
+
+#### Parameters
+
+* **position**: `bigint`
+
+#### Returns
+
+`Promise`&lt;`void`&gt;
+
+***
+
 ### size()
 
 ```ts
@@ -60,3 +142,17 @@ Returns the blob size in bytes.
 #### Returns
 
 `bigint`
+
+***
+
+### tell()
+
+```ts
+tell(): Promise<bigint>
+```
+
+Returns the cursor position in bytes.
+
+#### Returns
+
+`Promise`&lt;`bigint`&gt;
diff --git a/docs/src/js/classes/Catalog.md b/docs/src/js/classes/Catalog.md
index deac5e3ce..8351380e2 100644
--- a/docs/src/js/classes/Catalog.md
+++ b/docs/src/js/classes/Catalog.md
@@ -89,19 +89,30 @@ Drop an empty database. The server rejects nonempty databases.
 ### listDatabases()
 
 ```ts
-listDatabases(options): Promise<ListDatabasesResponse>
+listDatabases(options): DatabaseNames
 ```
 
-List one page of databases; pass pageToken from a response for the next page.
+Iterate lazily over all database names using `for await...of`.
+pageToken resumes from a saved token; omitted starts at the beginning.
+pageLimit limits each REST response, not the total; omitted uses the server default.
+Request errors are raised during iteration and terminate the iterator.
 
 #### Parameters
 
 * **options** = `{}`
 
-* **options.limit?**: `number`
+* **options.pageLimit?**: `number`
 
 * **options.pageToken?**: `string`
 
 #### Returns
 
-`Promise`&lt;[`ListDatabasesResponse`](../interfaces/ListDatabasesResponse.md)&gt;
+[`DatabaseNames`](DatabaseNames.md)
+
+#### Example
+
+```ts
+for await (const name of catalog.listDatabases({ pageLimit: 20 })) {
+  console.log(name);
+}
+```
diff --git a/docs/src/js/classes/Connection.md b/docs/src/js/classes/Connection.md
index f1aace87b..d5ab8c3d1 100644
--- a/docs/src/js/classes/Connection.md
+++ b/docs/src/js/classes/Connection.md
@@ -183,10 +183,6 @@ Define a materialized view named `name` over the table `source`.
 The view is populated before creation returns. Set `withNoData` to create
 only its definition and empty backing table. The view is a normal table:
 it can be queried, indexed and searched, and it appears in `tableNames`.
-The source table must have stable row ids (create it with
-the `newTableEnableStableRowIds` storage option); they keep the view's
-provenance valid across source compactions and cannot be enabled after
-a table exists.
 
 #### Parameters
 
@@ -615,30 +611,42 @@ Return true if the connection has not been closed
 ### listJobs()
 
 ```ts
-abstract listJobs(): Promise<JobInfo[]>
+abstract listJobs(options?): Listing<JobInfo>
 ```
 
-List server-side jobs across the database's tables.
+Iterate lazily over server-side jobs across the database's tables.
+Use `for await...of`. Options control page size and the starting token.
+See [Listing](Listing.md) for pagination state and error behavior.
+
+#### Parameters
+
+* **options?**: [`ListingOptions`](../interfaces/ListingOptions.md)
 
 #### Returns
 
-`Promise`&lt;[`JobInfo`](../interfaces/JobInfo.md)[]&gt;
+[`Listing`](Listing.md)&lt;[`JobInfo`](../interfaces/JobInfo.md)&gt;
 
 ***
 
 ### listMaterializedViews()
 
 ```ts
-abstract listMaterializedViews(): Promise<string[]>
+abstract listMaterializedViews(options?): Listing<string>
 ```
 
 The names of the materialized views in this database.
 
-Found by reading every table's schema, so this costs an open per table.
+Iterate lazily with `for await...of`. Local listings inspect table schemas
+one page at a time. Options control page size and the starting token.
+See [Listing](Listing.md) for pagination state and error behavior.
+
+#### Parameters
+
+* **options?**: [`ListingOptions`](../interfaces/ListingOptions.md)
 
 #### Returns
 
-`Promise`&lt;`string`[]&gt;
+[`Listing`](Listing.md)&lt;`string`&gt;
 
 ***
 
@@ -739,20 +747,24 @@ A page of table names and an
 ### listViews()
 
 ```ts
-abstract listViews(namespacePath?): Promise<string[]>
+abstract listViews(namespacePath?, options?): Listing<string>
 ```
 
 The names of the views in one namespace.
 
 Names only; a definition comes from [describeView](Connection.md#describeview).
+Iterate lazily with `for await...of`. See [Listing](Listing.md) for pagination
+state and error behavior; options control page size and the starting token.
 
 #### Parameters
 
 * **namespacePath?**: `string`[]
 
+* **options?**: [`ListingOptions`](../interfaces/ListingOptions.md)
+
 #### Returns
 
-`Promise`&lt;`string`[]&gt;
+[`Listing`](Listing.md)&lt;`string`&gt;
 
 ***
 
diff --git a/docs/src/js/classes/DatabaseNames.md b/docs/src/js/classes/DatabaseNames.md
new file mode 100644
index 000000000..6b78f93b8
--- /dev/null
+++ b/docs/src/js/classes/DatabaseNames.md
@@ -0,0 +1,79 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / DatabaseNames
+
+# Class: DatabaseNames
+
+A lazy async iterator of database names with pagination state.
+Returned by [Catalog.listDatabases](Catalog.md#listdatabases).
+
+## Implements
+
+- `AsyncIterableIterator`&lt;`string`&gt;
+
+## Methods
+
+### \[asyncIterator\]()
+
+```ts
+asyncIterator: AsyncIterableIterator<string>
+```
+
+#### Returns
+
+`AsyncIterableIterator`&lt;`string`&gt;
+
+#### Implementation of
+
+`AsyncIterableIterator.[asyncIterator]`
+
+***
+
+### next()
+
+```ts
+next(): Promise<IteratorResult<string, any>>
+```
+
+Fetch the next name, requesting another page only when needed.
+
+#### Returns
+
+`Promise`&lt;`IteratorResult`&lt;`string`, `any`&gt;&gt;
+
+#### Implementation of
+
+`AsyncIterableIterator.next`
+
+***
+
+### numPageResults()
+
+```ts
+numPageResults(): number
+```
+
+Number of names available without another REST request.
+
+#### Returns
+
+`number`
+
+***
+
+### pageToken()
+
+```ts
+pageToken(): undefined | string
+```
+
+Token for the next REST request. Initially this is the supplied starting token.
+Returns undefined after the final page is fetched, even if names remain cached.
+Drain the cache before saving a token to avoid skipping names when resuming.
+A failed request terminates iteration and retains its token for a new iterator.
+
+#### Returns
+
+`undefined` \| `string`
diff --git a/docs/src/js/classes/Listing.md b/docs/src/js/classes/Listing.md
new file mode 100644
index 000000000..1a94df2dc
--- /dev/null
+++ b/docs/src/js/classes/Listing.md
@@ -0,0 +1,91 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / Listing
+
+# Class: Listing&lt;T&gt;
+
+A lazy async iterator over database resources with pagination state.
+Requests occur during iteration; request errors terminate the iterator.
+
+## Example
+
+```ts
+for await (const name of connection.listViews([], { pageLimit: 20 })) {
+  console.log(name);
+}
+```
+
+## Type Parameters
+
+• **T**
+
+## Implements
+
+- `AsyncIterableIterator`&lt;`T`&gt;
+
+## Methods
+
+### \[asyncIterator\]()
+
+```ts
+asyncIterator: AsyncIterableIterator<T>
+```
+
+#### Returns
+
+`AsyncIterableIterator`&lt;`T`&gt;
+
+#### Implementation of
+
+`AsyncIterableIterator.[asyncIterator]`
+
+***
+
+### next()
+
+```ts
+next(): Promise<IteratorResult<T, any>>
+```
+
+Fetch the next item, requesting another page only when needed.
+
+#### Returns
+
+`Promise`&lt;`IteratorResult`&lt;`T`, `any`&gt;&gt;
+
+#### Implementation of
+
+`AsyncIterableIterator.next`
+
+***
+
+### numPageResults()
+
+```ts
+numPageResults(): number
+```
+
+Number of cached items available without another request.
+
+#### Returns
+
+`number`
+
+***
+
+### pageToken()
+
+```ts
+pageToken(): undefined | string
+```
+
+Token for the next request. Initially this is the supplied starting token.
+Returns undefined after fetching the final page, even with cached items.
+Drain the cache before saving a token to avoid skipping items on resumption.
+Failed requests terminate iteration and retain their token for a new iterator.
+
+#### Returns
+
+`undefined` \| `string`
diff --git a/docs/src/js/classes/MaterializedView.md b/docs/src/js/classes/MaterializedView.md
index 58eaa3c13..0e2413328 100644
--- a/docs/src/js/classes/MaterializedView.md
+++ b/docs/src/js/classes/MaterializedView.md
@@ -65,21 +65,14 @@ refresh(options?): Promise<RefreshMaterializedViewResult>
 
 Recompute the view from its source.
 
-The refresh is incremental when the source's changes can be reconciled
-into the view -- rows added, changed or removed since the last one --
-and otherwise rebuilds. `full` forces a rebuild; `sourceVersion`
-refreshes to that source version instead of the latest.
-
-Concurrent refreshes of one view do not duplicate its rows. Two that
-plan the same source rows conflict on commit, and the loser throws
-rather than writing them a second time.
+Every refresh rebuilds the complete view. For local views,
+`sourceVersion` refreshes to that source version instead of the latest;
+remote SQL refreshes do not support source-version pinning.
 
 #### Parameters
 
 * **options?**
 
-* **options.full?**: `boolean`
-
 * **options.sourceVersion?**: `number`
 
 #### Returns
diff --git a/docs/src/js/classes/MergeInsertBuilder.md b/docs/src/js/classes/MergeInsertBuilder.md
index 81349a0bc..cf40837d3 100644
--- a/docs/src/js/classes/MergeInsertBuilder.md
+++ b/docs/src/js/classes/MergeInsertBuilder.md
@@ -76,15 +76,15 @@ the query optimizer chooses a suboptimal path.
 
 ***
 
-### useLsm()
+### useMemWal()
 
 ```ts
-useLsm(enable): MergeInsertBuilder
+useMemWal(enable): MergeInsertBuilder
 ```
 
 Control MemWAL routing for this merge.
 
-By default (unset), a `mergeInsert` on a table with an LSM write spec is
+By default (unset), a `mergeInsert` on a table with a MemWAL write spec is
 routed through Lance's MemWAL shard writer, and a table without one uses the
 standard path.
 
@@ -92,7 +92,7 @@ standard path.
 
 * **enable**: `boolean`
     `true` forces MemWAL routing and errors if the table has no
-    LSM write spec. `false` forces the standard write path even when a spec is set.
+    MemWAL write spec. `false` forces the standard write path even when a spec is set.
 
 #### Returns
 
@@ -108,12 +108,12 @@ validateSingleShard(validateSingleShard): MergeInsertBuilder
 
 Controls how an LSM merge checks that its input targets a single shard.
 
-When a table has an LSM write spec, every row in a `mergeInsert` call must
+When a table has a MemWAL write spec, every row in a `mergeInsert` call must
 route to the same shard. When `true` (the default), every row is inspected
 to verify this. When `false`, only the first row is inspected and the
 shard it routes to is used for the whole input — a faster path for callers
 that have already pre-sharded their input. Has no effect on tables without
-an LSM write spec.
+a MemWAL write spec.
 
 #### Parameters
 
diff --git a/docs/src/js/classes/Query.md b/docs/src/js/classes/Query.md
index 6ebaebd75..9d83404f4 100644
--- a/docs/src/js/classes/Query.md
+++ b/docs/src/js/classes/Query.md
@@ -424,7 +424,7 @@ input to this method would be:
 
 #### Parameters
 
-* **columns**: `string` \| `string`[] \| `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+* **columns**: `string` \| `string`[] \| `Map`&lt;`string`, `string`&gt; \| `Record`&lt;`string`, `string`&gt;
 
 #### Returns
 
@@ -491,25 +491,27 @@ Collect the results as an Arrow
 
 ArrowTable.
 
+Empty results retain the query's output schema.
+
 #### Inherited from
 
 `StandardQueryBase.toArrow`
 
 ***
 
-### useLsm()
+### useMemWal()
 
 ```ts
-useLsm(enable): this
+useMemWal(enable): this
 ```
 
 Control MemWAL read routing for this query.
 
 By default (unset), when the table carries a MemWAL write spec (see
-[Table#setLsmWriteSpec](Table.md#setlsmwritespec)), reads are routed through the LSM scanner so
+[Table#enableMemWal](Table.md#enablememwal)), reads are routed through the LSM scanner so
 they also return data written via the `mergeInsert` LSM path that has not yet
 been compacted into the base table (the active/frozen in-memory memtables and
-the flushed generations), deduplicated by primary key; a table without a spec
+the SSTables), deduplicated by primary key; a table without a spec
 reads the base table.
 
 #### Parameters
@@ -520,7 +522,7 @@ reads the base table.
     even when a spec is present.
     Note: the LSM scanner does not support every query shape (e.g. reranking,
     hybrid search, `orderBy`). On a MemWAL table those shapes error unless
-    `useLsm(false)` is set, because a base-only read would silently exclude
+    `useMemWal(false)` is set, because a base-only read would silently exclude
     un-compacted MemWAL data.
 
 #### Returns
@@ -529,7 +531,7 @@ reads the base table.
 
 #### Inherited from
 
-`StandardQueryBase.useLsm`
+`StandardQueryBase.useMemWal`
 
 ***
 
diff --git a/docs/src/js/classes/QueryBase.md b/docs/src/js/classes/QueryBase.md
index 35c071525..0ef4fa286 100644
--- a/docs/src/js/classes/QueryBase.md
+++ b/docs/src/js/classes/QueryBase.md
@@ -193,7 +193,7 @@ input to this method would be:
 
 #### Parameters
 
-* **columns**: `string` \| `string`[] \| `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+* **columns**: `string` \| `string`[] \| `Map`&lt;`string`, `string`&gt; \| `Record`&lt;`string`, `string`&gt;
 
 #### Returns
 
@@ -252,6 +252,8 @@ Collect the results as an Arrow
 
 ArrowTable.
 
+Empty results retain the query's output schema.
+
 ***
 
 ### withRowId()
diff --git a/docs/src/js/classes/Table.md b/docs/src/js/classes/Table.md
index 17e2d4f34..3eaf9c9ca 100644
--- a/docs/src/js/classes/Table.md
+++ b/docs/src/js/classes/Table.md
@@ -143,7 +143,8 @@ containing the new version number of the table after altering the columns.
 abstract blobColumns(): Promise<string[]>
 ```
 
-Blob v2 columns, including nested dotted paths.
+Blob v2 columns addressable by row ID, including struct-nested dotted paths.
+Blobs inside lists are omitted because a row ID does not identify an element.
 
 #### Returns
 
@@ -227,17 +228,17 @@ version of the table.
 
 ***
 
-### checkpointLsm()
+### checkpointMemWal()
 
 ```ts
-abstract checkpointLsm(): Promise<void>
+abstract checkpointMemWal(): Promise<void>
 ```
 
-Converge this table's LSM write path into its base table.
+Converge this table's MemWAL write path into its base table.
 
-Seals once, then triggers compaction and polls until the L0 that existed
+Freezes once, then triggers compaction and polls until the SSTables that existed
 at the start is gone. The target set is fixed at the start, so
-generations created *during* the checkpoint are ignored — that is what
+SSTables created *during* the checkpoint are ignored — that is what
 lets it terminate under write load, and what makes it best-effort: it
 converges the fresh tier as of some instant. Idempotent, abandonable at
 any point, and safe to run on a cadence.
@@ -253,9 +254,9 @@ is merging. The caller owns the deadline.
 #### Example
 
 ```ts
-const before = await table.getLsmStats();
-await table.checkpointLsm();
-const after = await table.getLsmStats();
+const before = await table.getMemWalStats();
+await table.checkpointMemWal();
+const after = await table.getMemWalStats();
 ```
 
 ***
@@ -278,15 +279,15 @@ Any attempt to use the table after it is closed will result in an error.
 
 ***
 
-### closeLsmWriters()
+### closeMemWalWriters()
 
 ```ts
-abstract closeLsmWriters(): Promise<void>
+abstract closeMemWalWriters(): Promise<void>
 ```
 
 Drain and close any cached MemWAL shard writers held for this table.
 
-When an [LsmWriteSpec](../interfaces/LsmWriteSpec.md) is installed, `mergeInsert` opens MemWAL
+When an [MemWalWriteSpec](../interfaces/MemWalWriteSpec.md) is installed, `mergeInsert` opens MemWAL
 shard writers and caches them for reuse across calls. This closes them,
 flushing pending data; writers reopen lazily on the next `mergeInsert`.
 It is a no-op when no writers are cached.
@@ -297,17 +298,17 @@ It is a no-op when no writers are cached.
 
 ***
 
-### compactLsm()
+### compactMemWal()
 
 ```ts
-abstract compactLsm(): Promise<void>
+abstract compactMemWal(): Promise<void>
 ```
 
-Trigger a background L0 → base compaction pass per bucket.
+Trigger a background SSTable compaction pass per table shard.
 
 Returns once the passes are *dispatched*, not once they finish — watch
-[Table#getLsmStats](Table.md#getlsmstats) for progress, or use
-[Table#checkpointLsm](Table.md#checkpointlsm) to wait for convergence.
+[Table#getMemWalStats](Table.md#getmemwalstats) for progress, or use
+[Table#checkpointMemWal](Table.md#checkpointmemwal) to wait for convergence.
 
 #### Returns
 
@@ -448,6 +449,23 @@ containing the new version number of the table
 
 ***
 
+### disableMemWal()
+
+```ts
+abstract disableMemWal(): Promise<void>
+```
+
+Remove the [MemWalWriteSpec](../interfaces/MemWalWriteSpec.md) from this table, reverting to the standard
+`mergeInsert` write path.
+
+Errors if no spec is currently set.
+
+#### Returns
+
+`Promise`&lt;`void`&gt;
+
+***
+
 ### display()
 
 ```ts
@@ -513,10 +531,61 @@ Drop an index from the table.
 
 ***
 
+### enableMemWal()
+
+```ts
+abstract enableMemWal(spec): Promise<void>
+```
+
+Install an [MemWalWriteSpec](../interfaces/MemWalWriteSpec.md) on this table, selecting Lance's MemWAL
+LSM-style write path for future `mergeInsert` calls.
+
+`MemWalWriteSpec` chooses one of three sharding strategies via `specType`:
+
+- `"bucket"` — hash-bucket writes by the single-column unenforced primary
+  key (`column` and `numBuckets` required).
+- `"identity"` — shard by the raw value of a scalar `column`.
+- `"unsharded"` — route every write to a single shard.
+
+All variants require the table to have an unenforced primary key
+([Table#setUnenforcedPrimaryKey](Table.md#setunenforcedprimarykey)); bucket sharding additionally
+requires it to be the single column being bucketed.
+
+Omitting `maintainedIndexes` maintains every index the table has,
+including ones created later, and skips a kind the MemWAL cannot maintain.
+Naming them pins an exact set, and a still-building index is rejected
+rather than quietly omitted.
+
+#### Parameters
+
+* **spec**: [`MemWalWriteSpec`](../interfaces/MemWalWriteSpec.md)
+    The sharding spec to install.
+
+#### Returns
+
+`Promise`&lt;`void`&gt;
+
+#### Example
+
+```ts
+await table.setUnenforcedPrimaryKey("id");
+await table.enableMemWal({
+  specType: "bucket",
+  column: "id",
+  numBuckets: 16,
+  maintainedIndexes: ["id_idx"],
+});
+```
+
+***
+
 ### fetchBlobFiles()
 
 ```ts
-abstract fetchBlobFiles(column, rowIds): Promise<(null | BlobFile)[]>
+abstract fetchBlobFiles(
+   column,
+   rowIds,
+   options?): Promise<(null | BlobFile)[]>
 ```
 
 Opens lazy blob handles for `column` at the given row IDs using the
@@ -524,6 +593,8 @@ table's current checkout.
 
 Preserves input order, duplicates, and nulls. Use this for large payloads.
 See [Table.fetchBlobs](Table.md#fetchblobs) for row-ID validity across versions.
+One missing or deleted row ID rejects the entire batch, including on remote
+tables; no partial handles are returned.
 
 #### Parameters
 
@@ -531,6 +602,8 @@ See [Table.fetchBlobs](Table.md#fetchblobs) for row-ID validity across versions.
 
 * **rowIds**: readonly (`number` \| `bigint`)[]
 
+* **options?**: [`BlobReadOptions`](../type-aliases/BlobReadOptions.md)
+
 #### Returns
 
 `Promise`&lt;(`null` \| [`BlobFile`](BlobFile.md))[]&gt;
@@ -540,7 +613,10 @@ See [Table.fetchBlobs](Table.md#fetchblobs) for row-ID validity across versions.
 ### fetchBlobs()
 
 ```ts
-abstract fetchBlobs(column, rowIds): Promise<(null | Buffer)[]>
+abstract fetchBlobs(
+   column,
+   rowIds,
+   options?): Promise<(null | Buffer)[]>
 ```
 
 Bytes for `column` at row IDs from [Query.withRowId](Query.md#withrowid).
@@ -548,6 +624,10 @@ Bytes for `column` at row IDs from [Query.withRowId](Query.md#withrowid).
 Reads the table's current checkout. IDs from another version can fail after
 compaction unless stable row ids are enabled. Results keep input order and
 duplicates. Null blobs are `null`. Empty blobs are empty buffers.
+One missing or deleted row ID rejects the entire batch, including on remote
+tables; no partial results are returned.
+Blobs inside lists cannot be fetched by row ID.
+
 Remote servers limit each request to 1024 row IDs and 64 MiB of blob bytes.
 The client splits requests automatically and reads an individual larger
 blob through the Range route. This method still materializes all bytes in
@@ -559,21 +639,23 @@ memory; use [Table.fetchBlobFiles](Table.md#fetchblobfiles) for large values.
 
 * **rowIds**: readonly (`number` \| `bigint`)[]
 
+* **options?**: [`BlobReadOptions`](../type-aliases/BlobReadOptions.md)
+
 #### Returns
 
 `Promise`&lt;(`null` \| `Buffer`)[]&gt;
 
 ***
 
-### flushLsm()
+### flushMemWal()
 
 ```ts
-abstract flushLsm(): Promise<void>
+abstract flushMemWal(): Promise<void>
 ```
 
-Seal every bucket's active memtable into a new L0 generation.
+Freeze every table shard's active memtable into a new SSTable.
 
-Returns once the seal is committed. Sealing an empty memtable is a no-op,
+Returns once the freeze is committed. Freezing an empty memtable is a no-op,
 so this is safe to call repeatedly.
 
 #### Returns
@@ -617,49 +699,48 @@ const { records, truncated } = await table.functionErrors({ column: "embedding"
 
 ***
 
-### getLsmStats()
+### getMemWalSpec()
 
 ```ts
-abstract getLsmStats(includeGenerationRows?): Promise<undefined | LsmStats>
+abstract getMemWalSpec(): Promise<undefined | MemWalWriteSpec>
 ```
 
-Read live per-bucket LSM state.
+Read the [MemWalWriteSpec](../interfaces/MemWalWriteSpec.md) currently installed on this table.
 
-Answers "how far behind is my fresh tier", "which bucket is hot", and
+Resolves to `undefined` when the MemWAL write path is not enabled (no
+spec has been set, or it was removed with [Table#disableMemWal](Table.md#disablememwal)).
+The spec is the one installed, including its maintained-index selection:
+an absent `maintainedIndexes` for every index the table has, an empty
+array for none.
+
+#### Returns
+
+`Promise`&lt;`undefined` \| [`MemWalWriteSpec`](../interfaces/MemWalWriteSpec.md)&gt;
+
+***
+
+### getMemWalStats()
+
+```ts
+abstract getMemWalStats(includeSstableRows?): Promise<undefined | MemWalStats>
+```
+
+Read live per-table-shard MemWAL state.
+
+Answers "how far behind is my fresh tier", "which table shard is hot", and
 "why is my fresh-tier vector search brute-force". Mutates no table state.
 
-Resolves to `undefined` only when the LSM write path is not enabled.
+Resolves to `undefined` only when the MemWAL write path is not enabled.
 
 #### Parameters
 
-* **includeGenerationRows?**: `boolean`
-    Also count rows per L0 generation.
+* **includeSstableRows?**: `boolean`
+    Also count rows per SSTable.
     Off by default because each count opens an uncached Lance dataset.
 
 #### Returns
 
-`Promise`&lt;`undefined` \| [`LsmStats`](../interfaces/LsmStats.md)&gt;
-
-***
-
-### getLsmWriteSpec()
-
-```ts
-abstract getLsmWriteSpec(): Promise<undefined | LsmWriteSpec>
-```
-
-Read the [LsmWriteSpec](../interfaces/LsmWriteSpec.md) currently installed on this table.
-
-Resolves to `undefined` when the MemWAL LSM write path is not enabled (no
-spec has been set, or it was removed with [Table#unsetLsmWriteSpec](Table.md#unsetlsmwritespec)).
-The returned spec mirrors what was passed to
-[Table#setLsmWriteSpec](Table.md#setlsmwritespec), except that `maintainedIndexes` always
-reports the concrete list resolved when the spec was set — `undefined`
-never round-trips.
-
-#### Returns
-
-`Promise`&lt;`undefined` \| [`LsmWriteSpec`](../interfaces/LsmWriteSpec.md)&gt;
+`Promise`&lt;`undefined` \| [`MemWalStats`](../interfaces/MemWalStats.md)&gt;
 
 ***
 
@@ -1077,54 +1158,6 @@ of the given query
 
 ***
 
-### setLsmWriteSpec()
-
-```ts
-abstract setLsmWriteSpec(spec): Promise<void>
-```
-
-Install an [LsmWriteSpec](../interfaces/LsmWriteSpec.md) on this table, selecting Lance's MemWAL
-LSM-style write path for future `mergeInsert` calls.
-
-`LsmWriteSpec` chooses one of three sharding strategies via `specType`:
-
-- `"bucket"` — hash-bucket writes by the single-column unenforced primary
-  key (`column` and `numBuckets` required).
-- `"identity"` — shard by the raw value of a scalar `column`.
-- `"unsharded"` — route every write to a single shard.
-
-All variants require the table to have an unenforced primary key
-([Table#setUnenforcedPrimaryKey](Table.md#setunenforcedprimarykey)); bucket sharding additionally
-requires it to be the single column being bucketed.
-
-Omitting `maintainedIndexes` maintains every index on the table, resolved
-here, failing if one cannot be maintained — name them to install anyway.
-Naming them pins an exact set, and a still-building index is rejected
-rather than quietly omitted.
-
-#### Parameters
-
-* **spec**: [`LsmWriteSpec`](../interfaces/LsmWriteSpec.md)
-    The sharding spec to install.
-
-#### Returns
-
-`Promise`&lt;`void`&gt;
-
-#### Example
-
-```ts
-await table.setUnenforcedPrimaryKey("id");
-await table.setLsmWriteSpec({
-  specType: "bucket",
-  column: "id",
-  numBuckets: 16,
-  maintainedIndexes: ["id_idx"],
-});
-```
-
-***
-
 ### setUnenforcedPrimaryKey()
 
 ```ts
@@ -1279,23 +1312,6 @@ same tokenizer model files must also exist locally.
 
 ***
 
-### unsetLsmWriteSpec()
-
-```ts
-abstract unsetLsmWriteSpec(): Promise<void>
-```
-
-Remove the [LsmWriteSpec](../interfaces/LsmWriteSpec.md) from this table, reverting to the standard
-`mergeInsert` write path.
-
-Errors if no spec is currently set.
-
-#### Returns
-
-`Promise`&lt;`void`&gt;
-
-***
-
 ### update()
 
 #### update(opts)
@@ -1371,7 +1387,7 @@ repeatedly calling this method.
 
 ##### Parameters
 
-* **updates**: `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+* **updates**: `Map`&lt;`string`, `string`&gt; \| `Record`&lt;`string`, `string`&gt;
     the
     columns to update
 
diff --git a/docs/src/js/classes/TakeQuery.md b/docs/src/js/classes/TakeQuery.md
index 6ae2f9c8c..9e8d03149 100644
--- a/docs/src/js/classes/TakeQuery.md
+++ b/docs/src/js/classes/TakeQuery.md
@@ -200,7 +200,7 @@ input to this method would be:
 
 #### Parameters
 
-* **columns**: `string` \| `string`[] \| `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+* **columns**: `string` \| `string`[] \| `Map`&lt;`string`, `string`&gt; \| `Record`&lt;`string`, `string`&gt;
 
 #### Returns
 
@@ -267,16 +267,18 @@ Collect the results as an Arrow
 
 ArrowTable.
 
+Empty results retain the query's output schema.
+
 #### Inherited from
 
 [`QueryBase`](QueryBase.md).[`toArrow`](QueryBase.md#toarrow)
 
 ***
 
-### useLsm()
+### useMemWal()
 
 ```ts
-useLsm(enable): this
+useMemWal(enable): this
 ```
 
 Control MemWAL read routing for this take query.
diff --git a/docs/src/js/classes/VectorQuery.md b/docs/src/js/classes/VectorQuery.md
index f9412c76d..e329cb254 100644
--- a/docs/src/js/classes/VectorQuery.md
+++ b/docs/src/js/classes/VectorQuery.md
@@ -447,6 +447,8 @@ nprobes(nprobes): VectorQuery
 
 Set the number of partitions to search (probe)
 
+The number of probes must be greater than 0.
+
 This argument is only used when the vector column has an IVF PQ index.
 If there is no index then this value is ignored.
 
@@ -458,17 +460,16 @@ exhaustiely searched to find matches.  This parameter controls how many
 partitions should be searched.
 
 Increasing this value will increase the recall of your query but will
-also increase the latency of your query.  The default value is 20.  This
-default is good for many cases but the best value to use will depend on
-your data and the recall that you need to achieve.
+also increase the latency of your query. If this method is not called,
+Lance's adaptive probe defaults are used.
 
 For best results we recommend tuning this parameter with a benchmark against
 your actual data to find the smallest possible value that will still give
 you the desired recall.
 
-For more fine grained control over behavior when you have a very narrow filter
-you can use `minimumNprobes` and `maximumNprobes`.  This method sets both
-the minimum and maximum to the same value.
+The value is retained as `nprobes` through client and server request
+construction. Lance sets both probe bounds to this value. Explicit minimum
+or maximum settings can override their respective bounds.
 
 #### Parameters
 
@@ -645,6 +646,38 @@ rerank(reranker): VectorQuery
 
 ***
 
+### searchEffort()
+
+```ts
+searchEffort(searchEffort): VectorQuery
+```
+
+Set the initial IVF search effort to a finite value in [0, 1].
+
+The default, null, uses Auto (internally 0.5). Zero starts at the caller
+minimum; one searches all partitions within the caller maximum. Later
+probing may continue when too few candidates are found.
+Any explicit effort, including 0.5, overrides nprobes in either call order.
+Explicit minimum and maximum bounds still apply. This is not a recall
+guarantee and does not change refinement or HNSW ef. The MemWAL LSM
+scanner does not yet support this option.
+
+#### Parameters
+
+* **searchEffort**: `null` \| `number`
+
+#### Returns
+
+[`VectorQuery`](VectorQuery.md)
+
+#### Example
+
+```ts
+table.vectorSearch([0.1, 0.2]).searchEffort(0.75).limit(10);
+```
+
+***
+
 ### select()
 
 ```ts
@@ -673,7 +706,7 @@ input to this method would be:
 
 #### Parameters
 
-* **columns**: `string` \| `string`[] \| `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+* **columns**: `string` \| `string`[] \| `Map`&lt;`string`, `string`&gt; \| `Record`&lt;`string`, `string`&gt;
 
 #### Returns
 
@@ -740,25 +773,27 @@ Collect the results as an Arrow
 
 ArrowTable.
 
+Empty results retain the query's output schema.
+
 #### Inherited from
 
 `StandardQueryBase.toArrow`
 
 ***
 
-### useLsm()
+### useMemWal()
 
 ```ts
-useLsm(enable): this
+useMemWal(enable): this
 ```
 
 Control MemWAL read routing for this query.
 
 By default (unset), when the table carries a MemWAL write spec (see
-[Table#setLsmWriteSpec](Table.md#setlsmwritespec)), reads are routed through the LSM scanner so
+[Table#enableMemWal](Table.md#enablememwal)), reads are routed through the LSM scanner so
 they also return data written via the `mergeInsert` LSM path that has not yet
 been compacted into the base table (the active/frozen in-memory memtables and
-the flushed generations), deduplicated by primary key; a table without a spec
+the SSTables), deduplicated by primary key; a table without a spec
 reads the base table.
 
 #### Parameters
@@ -769,7 +804,7 @@ reads the base table.
     even when a spec is present.
     Note: the LSM scanner does not support every query shape (e.g. reranking,
     hybrid search, `orderBy`). On a MemWAL table those shapes error unless
-    `useLsm(false)` is set, because a base-only read would silently exclude
+    `useMemWal(false)` is set, because a base-only read would silently exclude
     un-compacted MemWAL data.
 
 #### Returns
@@ -778,7 +813,7 @@ reads the base table.
 
 #### Inherited from
 
-`StandardQueryBase.useLsm`
+`StandardQueryBase.useMemWal`
 
 ***
 
diff --git a/docs/src/js/functions/blob.md b/docs/src/js/functions/blob.md
index 20a734cd4..1a65b5133 100644
--- a/docs/src/js/functions/blob.md
+++ b/docs/src/js/functions/blob.md
@@ -14,6 +14,8 @@ Declares a `lance.blob.v2` column.
 
 Query results are descriptors, not payload bytes. Use [Table.fetchBlobs](../classes/Table.md#fetchblobs)
 or [Table.fetchBlobFiles](../classes/Table.md#fetchblobfiles) to read bytes.
+Blobs inside lists can be written and queried as descriptors, but cannot
+currently be fetched because a row ID does not identify a list element.
 
 ## Parameters
 
diff --git a/docs/src/js/functions/connectCatalog.md b/docs/src/js/functions/connectCatalog.md
index 2bdb3182f..ca51c0ed4 100644
--- a/docs/src/js/functions/connectCatalog.md
+++ b/docs/src/js/functions/connectCatalog.md
@@ -28,5 +28,7 @@ headers; opened database connections inherit authentication and client options.
 ```ts
 const catalog = await connectCatalog("https://my-server.example", { apiKey: "secret" });
 const db = await catalog.createDatabase("analytics", { existOk: true });
-const page = await catalog.listDatabases({ limit: 20 });
+for await (const name of catalog.listDatabases({ pageLimit: 20 })) {
+  console.log(name);
+}
 ```
diff --git a/docs/src/js/globals.md b/docs/src/js/globals.md
index 2f49578b1..739b9033a 100644
--- a/docs/src/js/globals.md
+++ b/docs/src/js/globals.md
@@ -27,9 +27,11 @@
 - [Branches](classes/Branches.md)
 - [Catalog](classes/Catalog.md)
 - [Connection](classes/Connection.md)
+- [DatabaseNames](classes/DatabaseNames.md)
 - [HeaderProvider](classes/HeaderProvider.md)
 - [Index](classes/Index.md)
 - [Job](classes/Job.md)
+- [Listing](classes/Listing.md)
 - [MakeArrowTableOptions](classes/MakeArrowTableOptions.md)
 - [MatchQuery](classes/MatchQuery.md)
 - [MaterializedView](classes/MaterializedView.md)
@@ -64,7 +66,6 @@
 - [BranchDiff](interfaces/BranchDiff.md)
 - [BranchIndexSummary](interfaces/BranchIndexSummary.md)
 - [BranchRowCountSummary](interfaces/BranchRowCountSummary.md)
-- [BucketStats](interfaces/BucketStats.md)
 - [CatalogOptions](interfaces/CatalogOptions.md)
 - [CherryPickError](interfaces/CherryPickError.md)
 - [CherryPickPreview](interfaces/CherryPickPreview.md)
@@ -96,7 +97,6 @@
 - [FunctionErrorRecord](interfaces/FunctionErrorRecord.md)
 - [FunctionErrors](interfaces/FunctionErrors.md)
 - [FunctionErrorsOptions](interfaces/FunctionErrorsOptions.md)
-- [GenerationStats](interfaces/GenerationStats.md)
 - [HnswPqOptions](interfaces/HnswPqOptions.md)
 - [HnswSqOptions](interfaces/HnswSqOptions.md)
 - [IndexConfig](interfaces/IndexConfig.md)
@@ -108,14 +108,14 @@
 - [JobEventsOptions](interfaces/JobEventsOptions.md)
 - [JobFailureInfo](interfaces/JobFailureInfo.md)
 - [JobInfo](interfaces/JobInfo.md)
-- [ListDatabasesResponse](interfaces/ListDatabasesResponse.md)
 - [ListNamespacesOptions](interfaces/ListNamespacesOptions.md)
 - [ListNamespacesResponse](interfaces/ListNamespacesResponse.md)
 - [ListTablesOptions](interfaces/ListTablesOptions.md)
 - [ListTablesResponse](interfaces/ListTablesResponse.md)
-- [LsmStats](interfaces/LsmStats.md)
-- [LsmWriteSpec](interfaces/LsmWriteSpec.md)
+- [ListingOptions](interfaces/ListingOptions.md)
 - [MaterializedViewDefinition](interfaces/MaterializedViewDefinition.md)
+- [MemWalStats](interfaces/MemWalStats.md)
+- [MemWalWriteSpec](interfaces/MemWalWriteSpec.md)
 - [MemtableStats](interfaces/MemtableStats.md)
 - [MergeResult](interfaces/MergeResult.md)
 - [NativeOAuthConfig](interfaces/NativeOAuthConfig.md)
@@ -138,7 +138,9 @@
 - [SplitHashOptions](interfaces/SplitHashOptions.md)
 - [SplitRandomOptions](interfaces/SplitRandomOptions.md)
 - [SplitSequentialOptions](interfaces/SplitSequentialOptions.md)
+- [SsTableStats](interfaces/SsTableStats.md)
 - [TableNamesOptions](interfaces/TableNamesOptions.md)
+- [TableShardStats](interfaces/TableShardStats.md)
 - [TableStatistics](interfaces/TableStatistics.md)
 - [TimeoutConfig](interfaces/TimeoutConfig.md)
 - [TlsConfig](interfaces/TlsConfig.md)
@@ -157,9 +159,15 @@
 
 - [AnalyzePlanDistributedMetrics](type-aliases/AnalyzePlanDistributedMetrics.md)
 - [BaseTokenizer](type-aliases/BaseTokenizer.md)
+- [BlobData](type-aliases/BlobData.md)
+- [BlobInput](type-aliases/BlobInput.md)
 - [BlobOptions](type-aliases/BlobOptions.md)
+- [BlobRange](type-aliases/BlobRange.md)
+- [BlobReadOptions](type-aliases/BlobReadOptions.md)
+- [BlobUri](type-aliases/BlobUri.md)
 - [Data](type-aliases/Data.md)
 - [DataLike](type-aliases/DataLike.md)
+- [DataTypeLike](type-aliases/DataTypeLike.md)
 - [FieldLike](type-aliases/FieldLike.md)
 - [IntoSql](type-aliases/IntoSql.md)
 - [IntoVector](type-aliases/IntoVector.md)
diff --git a/docs/src/js/interfaces/CherryPickResult.md b/docs/src/js/interfaces/CherryPickResult.md
index c837ab143..fdec8eedd 100644
--- a/docs/src/js/interfaces/CherryPickResult.md
+++ b/docs/src/js/interfaces/CherryPickResult.md
@@ -38,9 +38,9 @@ preview: CherryPickPreview;
 
 ```ts
 status:
-  | "failed"
-  | "unknown"
   | "ready"
+  | "failed"
   | "notImplemented"
-  | "cherryPicked";
+  | "cherryPicked"
+  | "unknown";
 ```
diff --git a/docs/src/js/interfaces/ClientConfig.md b/docs/src/js/interfaces/ClientConfig.md
index 2b4984b8d..d5ce32117 100644
--- a/docs/src/js/interfaces/ClientConfig.md
+++ b/docs/src/js/interfaces/ClientConfig.md
@@ -8,6 +8,18 @@
 
 ## Properties
 
+### blobRequestConcurrency?
+
+```ts
+optional blobRequestConcurrency: number;
+```
+
+Maximum number of concurrent blob HTTP requests across all tables and
+blob handles on this connection. Defaults to 8. Can also be set with
+`LANCE_CLIENT_BLOB_REQUEST_CONCURRENCY`. Must be greater than zero.
+
+***
+
 ### extraHeaders?
 
 ```ts
diff --git a/docs/src/js/interfaces/DropNamespaceOptions.md b/docs/src/js/interfaces/DropNamespaceOptions.md
index 1863bb0cd..6e5f01d34 100644
--- a/docs/src/js/interfaces/DropNamespaceOptions.md
+++ b/docs/src/js/interfaces/DropNamespaceOptions.md
@@ -21,7 +21,7 @@ Refuse to drop if non-empty (restrict) or drop recursively (cascade).
 ### mode?
 
 ```ts
-optional mode: "fail" | "skip";
+optional mode: "skip" | "fail";
 ```
 
 Whether to skip if the namespace doesn't exist, or fail.
diff --git a/docs/src/js/interfaces/FtsOptions.md b/docs/src/js/interfaces/FtsOptions.md
index 1d3c9ff82..654fa138f 100644
--- a/docs/src/js/interfaces/FtsOptions.md
+++ b/docs/src/js/interfaces/FtsOptions.md
@@ -37,6 +37,8 @@ The following tokenizers are available:
 
 "raw" - Raw tokenizer. This tokenizer does not split the text into tokens and indexes the entire text as a single token.
 
+"code" - Code tokenizer. This tokenizer keeps snake_case and camelCase identifiers as single tokens.
+
 "icu" - ICU dictionary-based word segmentation.
 
 "icu/split" - ICU segmentation with simple-style delimiter splitting.
diff --git a/docs/src/js/interfaces/GenerationStats.md b/docs/src/js/interfaces/GenerationStats.md
deleted file mode 100644
index 19dd2afda..000000000
--- a/docs/src/js/interfaces/GenerationStats.md
+++ /dev/null
@@ -1,40 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / GenerationStats
-
-# Interface: GenerationStats
-
-One flushed L0 generation.
-
-## Properties
-
-### bytes
-
-```ts
-bytes: number;
-```
-
-On-disk size of the generation.
-
-***
-
-### generation
-
-```ts
-generation: number;
-```
-
-The generation number. Increases as memtables are sealed into L0.
-
-***
-
-### rows?
-
-```ts
-optional rows: number;
-```
-
-Present only when `includeGenerationRows` was requested. Off by default
-because each count opens an uncached Lance dataset.
diff --git a/docs/src/js/interfaces/ListDatabasesResponse.md b/docs/src/js/interfaces/ListDatabasesResponse.md
deleted file mode 100644
index a1a247746..000000000
--- a/docs/src/js/interfaces/ListDatabasesResponse.md
+++ /dev/null
@@ -1,23 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / ListDatabasesResponse
-
-# Interface: ListDatabasesResponse
-
-## Properties
-
-### databases
-
-```ts
-databases: string[];
-```
-
-***
-
-### pageToken?
-
-```ts
-optional pageToken: string;
-```
diff --git a/docs/src/js/interfaces/ListTablesOptions.md b/docs/src/js/interfaces/ListTablesOptions.md
index 52ace47e5..b8dd8b5ef 100644
--- a/docs/src/js/interfaces/ListTablesOptions.md
+++ b/docs/src/js/interfaces/ListTablesOptions.md
@@ -15,6 +15,7 @@ optional limit: number;
 ```
 
 An upper bound on how many tables to return.
+Defaults to 100. Zero returns an empty page without a continuation token.
 
 A page may hold fewer than this and still not be the last one, so keep
 going while the response carries a page token rather than while pages are
diff --git a/docs/src/js/interfaces/ListingOptions.md b/docs/src/js/interfaces/ListingOptions.md
new file mode 100644
index 000000000..2070f8e3a
--- /dev/null
+++ b/docs/src/js/interfaces/ListingOptions.md
@@ -0,0 +1,29 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / ListingOptions
+
+# Interface: ListingOptions
+
+Pagination controls for lazy resource listings.
+
+## Properties
+
+### pageLimit?
+
+```ts
+optional pageLimit: number;
+```
+
+Maximum results per request, not a limit on the whole listing.
+
+***
+
+### pageToken?
+
+```ts
+optional pageToken: string;
+```
+
+Starting continuation token; omitted starts at the beginning.
diff --git a/docs/src/js/interfaces/LsmStats.md b/docs/src/js/interfaces/LsmStats.md
deleted file mode 100644
index 76a2f50db..000000000
--- a/docs/src/js/interfaces/LsmStats.md
+++ /dev/null
@@ -1,22 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / LsmStats
-
-# Interface: LsmStats
-
-Live per-bucket LSM state, as returned by `Table#getLsmStats`.
-
-Nothing here is derived: sums and differences (total L0 bytes, WAL lag) are
-the caller's to compute.
-
-## Properties
-
-### buckets
-
-```ts
-buckets: BucketStats[];
-```
-
-One entry per bucket backing this table.
diff --git a/docs/src/js/interfaces/MemWalStats.md b/docs/src/js/interfaces/MemWalStats.md
new file mode 100644
index 000000000..4221867fc
--- /dev/null
+++ b/docs/src/js/interfaces/MemWalStats.md
@@ -0,0 +1,22 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / MemWalStats
+
+# Interface: MemWalStats
+
+Live per-table-shard MemWAL state, as returned by `Table#getMemWalStats`.
+
+Nothing here is derived: sums and differences (total SSTable bytes, WAL lag) are
+the caller's to compute.
+
+## Properties
+
+### tableShards
+
+```ts
+tableShards: TableShardStats[];
+```
+
+One entry per table shard backing this table.
diff --git a/docs/src/js/interfaces/LsmWriteSpec.md b/docs/src/js/interfaces/MemWalWriteSpec.md
similarity index 81%
rename from docs/src/js/interfaces/LsmWriteSpec.md
rename to docs/src/js/interfaces/MemWalWriteSpec.md
index f2ae91186..50b74da41 100644
--- a/docs/src/js/interfaces/LsmWriteSpec.md
+++ b/docs/src/js/interfaces/MemWalWriteSpec.md
@@ -2,9 +2,9 @@
 
 ***
 
-[@lancedb/lancedb](../globals.md) / LsmWriteSpec
+[@lancedb/lancedb](../globals.md) / MemWalWriteSpec
 
-# Interface: LsmWriteSpec
+# Interface: MemWalWriteSpec
 
 Specification selecting Lance's MemWAL LSM-style write path for
 `mergeInsert`.
@@ -34,9 +34,8 @@ Bucket and identity variants: the sharding column.
 optional maintainedIndexes: string[];
 ```
 
-Indexes the MemWAL keeps up to date. Omit to maintain every supported
-index, resolved on install — a snapshot, so indexes created later are not
-maintained. Pass `[]` for none.
+Indexes the MemWAL keeps up to date. Omit for every index the table has,
+including ones created later. Pass `[]` for none.
 
 ***
 
diff --git a/docs/src/js/interfaces/MemtableStats.md b/docs/src/js/interfaces/MemtableStats.md
index fdc1e4467..63b8eace8 100644
--- a/docs/src/js/interfaces/MemtableStats.md
+++ b/docs/src/js/interfaces/MemtableStats.md
@@ -36,7 +36,7 @@ Estimated in-memory size.
 generation: number;
 ```
 
-The generation this memtable will become once sealed.
+The generation this memtable will become once frozen.
 
 ***
 
diff --git a/docs/src/js/interfaces/RefreshMaterializedViewResult.md b/docs/src/js/interfaces/RefreshMaterializedViewResult.md
index cb7100cd8..1c8b8bc21 100644
--- a/docs/src/js/interfaces/RefreshMaterializedViewResult.md
+++ b/docs/src/js/interfaces/RefreshMaterializedViewResult.md
@@ -14,7 +14,7 @@
 mode: string;
 ```
 
-How the view was brought up to date: "rebuild", "incremental" or "no_op".
+How the view was brought up to date: "rebuild".
 
 ***
 
diff --git a/docs/src/js/interfaces/SsTableStats.md b/docs/src/js/interfaces/SsTableStats.md
new file mode 100644
index 000000000..7b01a46f2
--- /dev/null
+++ b/docs/src/js/interfaces/SsTableStats.md
@@ -0,0 +1,40 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / SsTableStats
+
+# Interface: SsTableStats
+
+One SSTable.
+
+## Properties
+
+### bytes
+
+```ts
+bytes: number;
+```
+
+On-disk size of the SSTable.
+
+***
+
+### generation
+
+```ts
+generation: number;
+```
+
+The generation number. Increases as memtables are frozen into SSTables.
+
+***
+
+### rows?
+
+```ts
+optional rows: number;
+```
+
+Present only when `includeSstableRows` was requested. Off by default
+because each count opens an uncached Lance dataset.
diff --git a/docs/src/js/interfaces/TableNamesOptions.md b/docs/src/js/interfaces/TableNamesOptions.md
index 9254e9fa7..123aa6c9f 100644
--- a/docs/src/js/interfaces/TableNamesOptions.md
+++ b/docs/src/js/interfaces/TableNamesOptions.md
@@ -19,7 +19,7 @@ instead.
 optional limit: number;
 ```
 
-An optional limit to the number of results to return.
+The maximum number of names to return. Omitted returns all names; zero returns none.
 
 ***
 
diff --git a/docs/src/js/interfaces/BucketStats.md b/docs/src/js/interfaces/TableShardStats.md
similarity index 71%
rename from docs/src/js/interfaces/BucketStats.md
rename to docs/src/js/interfaces/TableShardStats.md
index 3f5095672..64be5e863 100644
--- a/docs/src/js/interfaces/BucketStats.md
+++ b/docs/src/js/interfaces/TableShardStats.md
@@ -2,12 +2,12 @@
 
 ***
 
-[@lancedb/lancedb](../globals.md) / BucketStats
+[@lancedb/lancedb](../globals.md) / TableShardStats
 
-# Interface: BucketStats
+# Interface: TableShardStats
 
-Live state of one bucket. A table is N buckets on one node; flattening to a
-single number hides the one hot bucket that is usually why someone opened
+Live state of one table shard. A table is N table shards on one node; flattening to a
+single number hides the one hot table shard that is usually why someone opened
 this endpoint.
 
 ## Properties
@@ -18,7 +18,7 @@ this endpoint.
 compacting: boolean;
 ```
 
-Whether a pass owns this bucket's compaction latch right now. Says *a*
+Whether a pass owns this table shard's compaction latch right now. Says *a*
 driver is running, not *whose*, and the latch is held from dispatch —
 including while the pass queues for a pod-wide compactor permit. Read it
 as "do not pile on", never as "mine is progressing".
@@ -35,16 +35,6 @@ The generation the active memtable will become.
 
 ***
 
-### generations
-
-```ts
-generations: GenerationStats[];
-```
-
-Flushed L0 generations not yet merged into the base table.
-
-***
-
 ### manifestVersion
 
 ```ts
@@ -61,7 +51,7 @@ Version of the shard manifest these numbers were read from.
 optional memtables: MemtableStats[];
 ```
 
-Oldest first, active last. Absent for a `"Sealed"` bucket, whose
+Oldest first, active last. Absent for a `"Sealed"` table shard, whose
 in-memory state is torn down.
 
 ***
@@ -82,7 +72,17 @@ WAL position replay resumes from.
 shardId: string;
 ```
 
-The shard this bucket writes.
+The id of this table shard.
+
+***
+
+### sstables
+
+```ts
+sstables: SsTableStats[];
+```
+
+SSTables not yet merged into the base table.
 
 ***
 
diff --git a/docs/src/js/interfaces/TableStatistics.md b/docs/src/js/interfaces/TableStatistics.md
index e2e8ef34d..4061a9f15 100644
--- a/docs/src/js/interfaces/TableStatistics.md
+++ b/docs/src/js/interfaces/TableStatistics.md
@@ -18,6 +18,23 @@ Statistics on table fragments
 
 ***
 
+### numDeletedRows?
+
+```ts
+optional numDeletedRows: number;
+```
+
+The number of rows marked as deleted across all fragments of the table
+
+These rows are not included in `numRows`, but still occupy space on disk
+until the table is compacted, so a large value here indicates that the
+table should be optimized. Fragments in which every row was deleted are
+dropped outright, so their rows are not counted here.
+
+Absent (`undefined`) when the backend does not report deletion counts.
+
+***
+
 ### numIndices
 
 ```ts
diff --git a/docs/src/js/namespaces/embedding/classes/EmbeddingFunction.md b/docs/src/js/namespaces/embedding/classes/EmbeddingFunction.md
index 574a0d71f..b6291e0be 100644
--- a/docs/src/js/namespaces/embedding/classes/EmbeddingFunction.md
+++ b/docs/src/js/namespaces/embedding/classes/EmbeddingFunction.md
@@ -52,7 +52,7 @@ new EmbeddingFunction<T, M>(): EmbeddingFunction<T, M>
 ### computeQueryEmbeddings()
 
 ```ts
-computeQueryEmbeddings(data): Promise<number[] | Uint8Array | Float32Array | Float64Array>
+computeQueryEmbeddings(data): Promise<Float32Array | Float64Array | Uint8Array | number[]>
 ```
 
 Compute the embeddings for a single query
@@ -63,7 +63,7 @@ Compute the embeddings for a single query
 
 #### Returns
 
-`Promise`&lt;`number`[] \| `Uint8Array` \| `Float32Array` \| `Float64Array`&gt;
+`Promise`&lt;`Float32Array` \| `Float64Array` \| `Uint8Array` \| `number`[]&gt;
 
 ***
 
diff --git a/docs/src/js/namespaces/embedding/classes/TextEmbeddingFunction.md b/docs/src/js/namespaces/embedding/classes/TextEmbeddingFunction.md
index 444c4c3f0..0a0cc619d 100644
--- a/docs/src/js/namespaces/embedding/classes/TextEmbeddingFunction.md
+++ b/docs/src/js/namespaces/embedding/classes/TextEmbeddingFunction.md
@@ -37,7 +37,7 @@ new TextEmbeddingFunction<M>(): TextEmbeddingFunction<M>
 ### computeQueryEmbeddings()
 
 ```ts
-computeQueryEmbeddings(data): Promise<number[] | Uint8Array | Float32Array | Float64Array>
+computeQueryEmbeddings(data): Promise<Float32Array | Float64Array | Uint8Array | number[]>
 ```
 
 Compute the embeddings for a single query
@@ -48,7 +48,7 @@ Compute the embeddings for a single query
 
 #### Returns
 
-`Promise`&lt;`number`[] \| `Uint8Array` \| `Float32Array` \| `Float64Array`&gt;
+`Promise`&lt;`Float32Array` \| `Float64Array` \| `Uint8Array` \| `number`[]&gt;
 
 #### Overrides
 
diff --git a/docs/src/js/type-aliases/BaseTokenizer.md b/docs/src/js/type-aliases/BaseTokenizer.md
index 75b377f27..5fd1b55d4 100644
--- a/docs/src/js/type-aliases/BaseTokenizer.md
+++ b/docs/src/js/type-aliases/BaseTokenizer.md
@@ -12,6 +12,7 @@ type BaseTokenizer:
   | "whitespace"
   | "raw"
   | "ngram"
+  | "code"
   | "icu"
   | "icu/split"
   | `jieba/${string}`
diff --git a/docs/src/js/type-aliases/BlobData.md b/docs/src/js/type-aliases/BlobData.md
new file mode 100644
index 000000000..2af0a3659
--- /dev/null
+++ b/docs/src/js/type-aliases/BlobData.md
@@ -0,0 +1,16 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / BlobData
+
+# Type Alias: BlobData
+
+```ts
+type BlobData: Buffer | Uint8Array | ArrayBuffer | Blob;
+```
+
+Bytes accepted for a blob value. `ArrayBuffer` is wrapped without copying.
+`Blob` and `File` are read with `arrayBuffer()`, which only the async write
+paths ([Connection.createTable](../classes/Connection.md#createtable), [Table.add](../classes/Table.md#add),
+[Table.mergeInsert](../classes/Table.md#mergeinsert)) can do.
diff --git a/docs/src/js/type-aliases/BlobInput.md b/docs/src/js/type-aliases/BlobInput.md
new file mode 100644
index 000000000..d1738a760
--- /dev/null
+++ b/docs/src/js/type-aliases/BlobInput.md
@@ -0,0 +1,36 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / BlobInput
+
+# Type Alias: BlobInput
+
+```ts
+type BlobInput:
+  | BlobData
+  | BlobUri
+  | object
+  | object
+  | null
+  | undefined;
+```
+
+A value accepted for a `lance.blob.v2` column (see [blob](../functions/blob.md)).
+
+Either inline bytes, a URI pointing at external bytes, or a struct that sets
+exactly one of `data` or `uri`. `null` and `undefined` write a null blob.
+
+## Example
+
+```ts
+import { pathToFileURL } from "node:url";
+import type { BlobInput } from "@lancedb/lancedb";
+
+const rows: { id: bigint; image: BlobInput }[] = [
+  { id: 1n, image: await (await fetch(url)).arrayBuffer() },
+  { id: 2n, image: pathToFileURL("/data/cat.png") },
+  { id: 3n, image: { data: new Blob(["hello"]) } },
+];
+await table.add(rows);
+```
diff --git a/docs/src/js/type-aliases/BlobRange.md b/docs/src/js/type-aliases/BlobRange.md
new file mode 100644
index 000000000..85cd48242
--- /dev/null
+++ b/docs/src/js/type-aliases/BlobRange.md
@@ -0,0 +1,27 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / BlobRange
+
+# Type Alias: BlobRange
+
+```ts
+type BlobRange: object;
+```
+
+A half-open byte range `[start, end)` for [BlobFile.readRanges](../classes/BlobFile.md#readranges).
+
+## Type declaration
+
+### end
+
+```ts
+end: bigint;
+```
+
+### start
+
+```ts
+start: bigint;
+```
diff --git a/docs/src/js/type-aliases/BlobReadOptions.md b/docs/src/js/type-aliases/BlobReadOptions.md
new file mode 100644
index 000000000..17264b0e4
--- /dev/null
+++ b/docs/src/js/type-aliases/BlobReadOptions.md
@@ -0,0 +1,24 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / BlobReadOptions
+
+# Type Alias: BlobReadOptions
+
+```ts
+type BlobReadOptions: object;
+```
+
+Options for blob reads.
+
+## Type declaration
+
+### signal?
+
+```ts
+optional signal: AbortSignal;
+```
+
+Cancels the read. The call rejects with `signal.reason`, and the native
+read stops, including in-flight requests to a remote table.
diff --git a/docs/src/js/type-aliases/BlobUri.md b/docs/src/js/type-aliases/BlobUri.md
new file mode 100644
index 000000000..a90b395fc
--- /dev/null
+++ b/docs/src/js/type-aliases/BlobUri.md
@@ -0,0 +1,13 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / BlobUri
+
+# Type Alias: BlobUri
+
+```ts
+type BlobUri: string | URL;
+```
+
+A URI accepted for a blob value. A `URL` is stored as its `href`.
diff --git a/docs/src/js/type-aliases/DataTypeLike.md b/docs/src/js/type-aliases/DataTypeLike.md
new file mode 100644
index 000000000..1f0ee6b81
--- /dev/null
+++ b/docs/src/js/type-aliases/DataTypeLike.md
@@ -0,0 +1,18 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / DataTypeLike
+
+# Type Alias: DataTypeLike
+
+```ts
+type DataTypeLike: DataType | object;
+```
+
+A `DataType` from any copy or version of apache-arrow.
+
+Arrow 21 brands its classes with `unique symbol` properties, so a type
+object from a second copy of the library no longer satisfies the `DataType`
+type of this one even though it is structurally identical. Inputs that only
+need to be sanitized accept this looser shape instead.
diff --git a/docs/src/python/python.md b/docs/src/python/python.md
index 73e6caf8b..ae913c514 100644
--- a/docs/src/python/python.md
+++ b/docs/src/python/python.md
@@ -26,6 +26,14 @@ is also an [asynchronous API client](#connections-asynchronous).
 
 ::: lancedb.db.DBConnection
 
+Resource listings (`list_secrets`, `list_views`, `list_jobs`, `list_functions`, and
+`list_materialized_views`) return lazy iterators. Use `list(db.list_views())` to
+collect synchronous results, or `[name async for name in db.list_views()]` with
+an asynchronous connection. `page_limit` controls each request, and `page_token`
+resumes a listing. Drain the iterator's cached results before saving its token.
+
+::: lancedb.listing.Listing
+
 ::: lancedb.Session
 
 ## Catalogs (Synchronous)
@@ -37,7 +45,171 @@ are ordinary connections. Dropping a database requires it to be empty.
 
 ::: lancedb.catalog.Catalog
 
-::: lancedb.catalog.ListDatabasesResponse
+::: lancedb.catalog.DatabaseNames
+
+
+## Authorization
+
+Some catalogs support the authorization API. This is an API that can be used to
+bind access control lists (ACLs) to individual catalog objects.
+
+Each ACL contains a subject, an object, and privilege.
+
+### Subjects
+
+There are three kinds of subjects: principals, groups, and roles.
+
+A principal uniquely identifies each user accessing the system. Each LanceDB
+Enterprise API key corresponds to a unique principal, and each OpenID user has
+a unique principal.
+
+All principals have an immutable ID which never changes, and is safe to include
+in log files. For example, the API key
+`sk_AAAQEAYEAUDAOCAJBIFQYDIOB4IBCEQTCQKRMFYYDENBWHA5DYPQ====` maps to the
+principal ID `pk_aaaqeayeaudaocajbifqydiob4`. APIs such as `list_acls` will
+return the principal ID, never the API key.
+
+OpenID principals also typically have a human-readable name associated with
+them. For example, a principal might be named `bob@example.com` and might have
+a principal ID of `8a004400-9443-4fab-9997-b9372a7ca8fd`. Whenever
+possible, APIs will return the principal name for convenience. However,
+principal names are mutable and can be changed by the identity provider.
+
+Here is an example of getting information about an API key:
+```python
+from lancedb.authz import Subject
+
+catalog = lancedb.connect_catalog("https://catalog.example.com", api_key=api_key)
+principal = catalog.authz.get_principal(Subject.principal_api_key(api_key))
+print(f"Found principal {principal.principal_id}")
+```
+
+OpenID principals can be members of groups. Like principals, groups also have
+IDs and human-readable names. It is not possible to modify group membership via
+this API; it is managed by the OpenID identity provider.
+
+Here is an example of getting information about a group based on its name:
+```python
+from lancedb.authz import Subject
+
+catalog = lancedb.connect_catalog("https://catalog.example.com", api_key=api_key)
+group = catalog.authz.get_group(Subject.group_name("Engineering"))
+print(f"Found group {group.group_id} with name {group.group_name}")
+```
+
+Roles can contain either principals or groups. Unlike groups, they are managed
+by the catalog, not the identity provider. Roles can also contain API key
+principals.
+
+Here is an example of managing the `reader` role:
+```python
+from lancedb.authz import Subject
+
+catalog = lancedb.connect_catalog("https://catalog.example.com", api_key=api_key)
+catalog.authz.add_role_binding(subject=Subject.principal_name("Alice"), role="reader")
+```
+It may take a few milliseconds for changes to role bindings to become visible
+to all clients.
+
+More subject types may be added in the future.
+
+### Objects
+
+An object is any resource in the system that we can attach authorization rules to.
+The Python builders and Rust client share resource validation and wire encoding.
+Methods also accept literal strings such as `table:tenant/db:public$events`;
+malformed names for known resource types raise `ValueError` locally before a
+request is sent. Unknown resource types are preserved for compatibility with
+newer servers.
+
+Object types include:
+
+The System object, a singleton which represents the entire system. You can
+refer to it as `Object.system()`.
+
+Database objects which represents specific databases. For example,
+`Object.database("analytics")` represents the "analytics" database.
+
+Table objects which represent specific tables. For example,
+`Object.table(database="analytics", name="events")` represents the "events"
+table inside the "analytics" database. Tables are in the default namespace (which
+is named "public") unless a specific namespace is given.
+
+Similarly, View objects represent specific views. For example,
+`Object.view(database="analytics", name="recent_events")` represents the
+"recent_events" view inside the "analytics" database. Views are in the default
+namespace (which is named "public") unless a specific namespace is given.
+
+Secret objects identify named credentials, for example,
+`Object.secret(database="analytics", name="api-key")`. Function objects identify
+registered functions, for example,
+`Object.function(database="analytics", name="caption")`. Both default to the
+`public` namespace. Pass `namespace_path=["platform", "ml"]` to select a nested
+namespace. These objects identify resources for ACL operations; a secret object
+contains the credential's name, not its value.
+
+More object types may be added in the future.
+
+### Access control lists
+
+An access control list attaches a specific set of privileges to a specific
+(subject, object) pair. So, for example, we might have an access control list
+that gives `bob@example.com` the ability to read the foo table, that looks like
+this:
+
+```python
+from lancedb.authz import Object, Subject, Privilege
+
+catalog = lancedb.connect_catalog("https://catalog.example.com", api_key=api_key)
+catalog.authz.add_acl(
+    object=Object.table(database="analytics", name="foo"),
+    subject=Subject.principal_name("bob@example.com"),
+    privilege=Privilege.SELECT,
+)
+```
+
+Reading a table generally requires USAGE on its database and namespace, plus
+SELECT on the table. Each object has at most one owner.
+
+OWNERSHIP is a special privilege which grants all the other privileges. By
+default, the principal who created an object owns it. Only one subject can own
+an object; granting OWNERSHIP to a new subject removes it from the previous
+owner. OWNERSHIP cannot be deleted.
+
+To delegate secret or function creation, grant `CREATE_SECRET` or
+`CREATE_FUNCTION` on the containing namespace. This preserves namespace ownership:
+
+```python
+for privilege in [Privilege.CREATE_SECRET, Privilege.CREATE_FUNCTION]:
+    catalog.authz.add_acl(
+        object=Object.namespace(database="analytics", namespace_path=["public"]),
+        subject=Subject.role("developer"),
+        privilege=privilege,
+    )
+```
+
+Both privileges also accept their string names in synchronous and asynchronous
+ACL methods.
+
+By default, the `list_acls` function lists all the access control entries in
+the system. By adding a function parameter, the output is filtered to just ACLs
+matching that parameter. For example, this code lists all the ACLs for the
+"reader" role:
+
+```python
+from lancedb.authz import Subject
+
+catalog = lancedb.connect_catalog("https://catalog.example.com", api_key=api_key)
+for entry in catalog.authz.list_acls(subject=Subject.role("reader")):
+    print(entry)
+```
+
+Or, when using the async API:
+```python
+catalog = await lancedb.connect_catalog_async(endpoint, api_key=api_key)
+async for entry in catalog.authz.list_acls(subject=Subject.role("reader")):
+    print(entry)
+```
 
 ## Remote SQL
 
@@ -77,6 +249,15 @@ print(query.describe().status)
 for batch in query.reader():
     print(batch.num_rows)
 
+# Values bind to $1 / $name placeholders and travel as Arrow, so a float32
+# stays a float32 and a vector stays a compact fixed-size list:
+import numpy as np
+
+reader = db.execute_query(
+    "SELECT id FROM docs ORDER BY distance(vector, $vector) LIMIT $k",
+    parameters={"vector": np.random.rand(768).astype(np.float32), "k": 10},
+)
+
 # The async connection exposes the same lifecycle without blocking:
 # async_db = await lancedb.connect_async(
 #     "db://analytics",
@@ -118,7 +299,7 @@ listing a storage directory.
 
 ::: lancedb.table.Branches
 
-::: lancedb.LsmWriteSpec
+::: lancedb.MemWalWriteSpec
 
 ## Functions and Jobs
 
@@ -194,39 +375,14 @@ listing a storage directory.
 
 ::: lancedb.sql.QueryDescription
 
+::: lancedb.sql.QueryParameters
+
 ## Materialized Views (Synchronous)
 
 ::: lancedb.materialized_view.MaterializedView
 
 ::: lancedb.materialized_view.MaterializedViewDefinition
 
-::: lancedb.vector_dedup
-
-::: lancedb.materialized_view.VectorDedupSource
-
-For an existing indexed source, declare and materialize a dedup result without
-writing SQL. Creation captures the source version once and waits for the complete
-result; remote connections use the service's MV jobs.
-
-```python
-from lancedb import vector_dedup
-
-view = db.create_materialized_view(
-    "images_clean",
-    vector_dedup("images", column="phash", distance_threshold=4),
-)
-cleaned = view.table
-```
-
-To submit without waiting, call `db.create_materialized_view_async(...)` with the
-same source expression and wait on the returned job. To declare only, pass
-`with_no_data=True`, then use `view.refresh_async().wait()` when ready. Pass
-`dataset_version=...` to `vector_dedup` for an explicit snapshot. The default
-policy retains direct representatives (A-B-C without A-C keeps A and C), preserves
-the source, and materializes its original columns. Only within-segment/partition
-pairs are covered, using the index's distance representation.
-
-
 ## Views
 
 ::: lancedb.view.ViewDescription
@@ -351,6 +507,12 @@ pylance, LanceDB uses a matching `lance.blob.v2` extension type so blob columns
 still work. Queries return descriptors. Call
 [`fetch_blob_files`][lancedb.table.Table.fetch_blob_files] for lazy reads or
 [`fetch_blobs`][lancedb.table.Table.fetch_blobs] for eager bytes.
+One missing or deleted row ID rejects the entire blob-read batch; the APIs do
+not return partial results.
+
+Blobs inside lists can be written and queried as descriptors. They are omitted
+from `blob_columns()` because row ids do not identify individual list elements,
+so the fetch APIs cannot read their bytes yet.
 
 ::: lancedb.blob
 
@@ -368,6 +530,8 @@ still work. Queries return descriptors. Call
 
 ## Exceptions
 
+::: lancedb.remote.errors.HttpError
+
 ::: lancedb.exceptions.MissingValueError
 
 ::: lancedb.exceptions.MissingColumnError
@@ -442,6 +606,8 @@ latency on your workload before opting in.
 
 ::: lancedb.catalog.AsyncCatalog
 
+::: lancedb.catalog.AsyncDatabaseNames
+
 Connections represent a connection to a LanceDb database and
 can be used to create, list, or open tables.
 
@@ -449,6 +615,8 @@ can be used to create, list, or open tables.
 
 ::: lancedb.db.AsyncConnection
 
+::: lancedb.listing.AsyncListing
+
 ## Namespaces (Asynchronous)
 
 ::: lancedb.connect_namespace_async
diff --git a/docs/web-tests/camelot.json b/docs/web-tests/camelot.json
new file mode 100644
index 000000000..e41bd459c
--- /dev/null
+++ b/docs/web-tests/camelot.json
@@ -0,0 +1,66 @@
+[
+  {
+    "id": 1,
+    "name": "King Arthur",
+    "role": "King of Camelot",
+    "description": "The legendary ruler of Camelot, wielder of Excalibur, and leader of the Knights of the Round Table.",
+    "vector": [0.72, -0.28, 0.60, 0.86],
+    "stats": { "strength": 2, "courage": 5, "magic": 1, "wisdom": 4 }
+  },
+  {
+    "id": 2,
+    "name": "Merlin",
+    "role": "Wizard and Advisor",
+    "description": "A powerful wizard and prophet who mentors Arthur and shapes the destiny of Camelot through magic and foresight.",
+    "vector": [0.05, 0.88, 0.62, 0.85],
+    "stats": { "strength": 2, "courage": 4, "magic": 5, "wisdom": 5 }
+  },
+  {
+    "id": 3,
+    "name": "Queen Guinevere",
+    "role": "Queen of Camelot",
+    "description": "Arthur's queen, admired for her grace and diplomacy, whose romances and loyalties influence Camelot's fate.",
+    "vector": [0.22, -0.22, 0.42, 0.82],
+    "stats": { "strength": 1, "courage": 3, "magic": 1, "wisdom": 4 }
+  },
+  {
+    "id": 4,
+    "name": "Sir Lancelot",
+    "role": "Knight of the Round Table",
+    "description": "Arthur's most skilled knight, famed for unmatched combat prowess and his tragic love for Queen Guinevere.",
+    "vector": [0.86, -0.35, 0.38, 0.55],
+    "stats": { "strength": 5, "courage": 5, "magic": 1, "wisdom": 3 }
+  },
+  {
+    "id": 5,
+    "name": "Sir Gawain",
+    "role": "Knight of the Round Table",
+    "description": "A noble and honorable knight known for his courtesy and his encounter with the Green Knight.",
+    "vector": [0.82, -0.32, 0.52, 0.60],
+    "stats": { "strength": 4, "courage": 5, "magic": 1, "wisdom": 4 }
+  },
+  {
+    "id": 6,
+    "name": "Sir Galahad",
+    "role": "Knight of the Round Table",
+    "description": "The purest and most virtuous knight, chosen to achieve the Holy Grail due to his unwavering spiritual purity.",
+    "vector": [0.80, -0.20, 0.70, 0.78],
+    "stats": { "strength": 4, "courage": 5, "magic": 2, "wisdom": 5 }
+  },
+  {
+    "id": 7,
+    "name": "Sir Percival",
+    "role": "Knight of the Round Table",
+    "description": "A loyal and innocent knight whose bravery and sincerity make him one of the key seekers of the Holy Grail.",
+    "vector": [0.78, -0.36, 0.48, 0.52],
+    "stats": { "strength": 4, "courage": 4, "magic": 1, "wisdom": 3 }
+  },
+  {
+    "id": 8,
+    "name": "Mordred",
+    "role": "Traitor Knight",
+    "description": "Arthur's treacherous son or nephew who ultimately rebels against him, leading to Camelot's downfall.",
+    "vector": [0.68, -0.30, -0.65, 0.20],
+    "stats": { "strength": 4, "courage": 2, "magic": 1, "wisdom": 2 }
+  }
+]
diff --git a/docs/web-tests/mdx_snippets_gen.py b/docs/web-tests/mdx_snippets_gen.py
new file mode 100644
index 000000000..1c2930fc0
--- /dev/null
+++ b/docs/web-tests/mdx_snippets_gen.py
@@ -0,0 +1,362 @@
+"""
+Generate MDX-ready snippet modules that export raw string constants.
+
+The generator groups snippets by the originating test file name rather than by
+language. For a source file like `tests/py/test_basic_usage.py`, the snippets are
+written under `docs/snippets/basic.mdx`. Each exported constant is prefixed with
+a language identifier (Py|Ts|Rs) followed by the snippet name converted to
+TitleCase so that docs authors can selectively assemble `<CodeGroup>` blocks.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import re
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, Iterable, Iterator, List, Mapping, MutableMapping, Tuple
+
+# Supported languages and relevant metadata.
+LANG_ORDER = ("py", "ts", "rs")
+LANG_PREFIX = {"py": "Py", "ts": "Ts", "rs": "Rs"}
+LANG_EXTENSIONS = {
+    ".py": "py",
+    ".ts": "ts",
+    ".tsx": "ts",
+    ".rs": "rs",
+}
+LANG_MARKERS = {"py": ["#"], "ts": ["//"], "rs": ["//"]}
+# Match each export block; we rely on the blank line we emit after each export to
+# avoid accidentally truncating on semicolons inside the JSON string literal.
+EXPORT_RE = re.compile(r"export const (\w+)\s*=\s*(.+?);\s*\n(?:\s*\n|$)", re.DOTALL)
+
+DEFAULT_SOURCE_DIRS = (
+    Path("tests") / "py",
+    Path("tests") / "ts",
+    Path("tests") / "rs",
+)
+
+
+@dataclass(frozen=True)
+class SnippetRecord:
+    lang: str
+    snippet_name: str
+    export_name: str
+    text: str
+    source_rel: str
+
+
+def build_markers(
+    comment_markers: Iterable[str],
+) -> Tuple[re.Pattern[str], re.Pattern[str]]:
+    escaped = "|".join(re.escape(c) for c in comment_markers)
+    start_re = re.compile(rf"^\s*(?:{escaped})\s*--8<--\s*\[start:([^\]]+)\]")
+    end_re = re.compile(rf"^\s*(?:{escaped})\s*--8<--\s*\[end:([^\]]+)\]")
+    return start_re, end_re
+
+
+def parse_snippets(
+    lines: List[str], start_re: re.Pattern[str], end_re: re.Pattern[str]
+) -> Dict[str, List[str]]:
+    snippets: Dict[str, List[str]] = {}
+    stack: List[Tuple[str, int]] = []
+
+    for idx, line in enumerate(lines):
+        m_start = start_re.match(line)
+        if m_start:
+            name = m_start.group(1).strip()
+            stack.append((name, idx))
+            continue
+
+        m_end = end_re.match(line)
+        if m_end:
+            name = m_end.group(1).strip()
+            if not stack:
+                raise ValueError(
+                    f"End marker for '{name}' found at line {idx + 1} without matching start"
+                )
+            start_name, start_idx = stack.pop()
+            if start_name != name:
+                raise ValueError(
+                    f"Mismatched markers: start '{start_name}' at line {start_idx + 1}, "
+                    f"end '{name}' at line {idx + 1}"
+                )
+            snippets[name] = lines[start_idx + 1 : idx]
+            continue
+
+    if stack:
+        open_names = ", ".join(f"{n}@{i + 1}" for n, i in stack)
+        raise ValueError(f"Unclosed snippet markers for: {open_names}")
+
+    return snippets
+
+
+def dedent(lines: List[str]) -> List[str]:
+    min_indent = None
+    for ln in lines:
+        if ln.strip() == "":
+            continue
+        expanded = ln.expandtabs(4)
+        indent = len(expanded) - len(expanded.lstrip(" "))
+        if min_indent is None or indent < min_indent:
+            min_indent = indent
+
+    if not lines:
+        return []
+    if min_indent in (None, 0):
+        return [ln.rstrip("\n") for ln in lines]
+
+    dedented: List[str] = []
+    for ln in lines:
+        expanded = ln.expandtabs(4)
+        if expanded.strip() == "":
+            dedented.append(expanded.rstrip("\n"))
+        else:
+            dedented.append(expanded[min_indent:].rstrip("\n"))
+    return dedented
+
+
+def ensure_dir(path: Path) -> None:
+    path.mkdir(parents=True, exist_ok=True)
+
+
+def detect_language(file_path: Path) -> str:
+    lang = LANG_EXTENSIONS.get(file_path.suffix.lower())
+    if not lang:
+        raise ValueError(f"Unsupported file extension for {file_path}")
+    return lang
+
+
+def markers_for_lang(lang: str) -> Tuple[re.Pattern[str], re.Pattern[str]]:
+    markers = LANG_MARKERS.get(lang)
+    if not markers:
+        raise ValueError(f"Unsupported language for markers: {lang}")
+    return build_markers(markers)
+
+
+def normalize_target_name(file_path: Path) -> str:
+    stem = file_path.stem
+    if stem.startswith("test_"):
+        stem = stem[len("test_") :]
+    if stem.endswith("_test"):
+        stem = stem[: -len("_test")]
+    if stem.endswith(".test"):
+        stem = stem[: -len(".test")]
+    if stem.endswith(".spec"):
+        stem = stem[: -len(".spec")]
+    if stem == "":
+        stem = file_path.stem
+    return stem.replace(" ", "_")
+
+
+def to_title_case(name: str) -> str:
+    parts = re.split(r"[^0-9a-zA-Z]+", name)
+    filtered = [p for p in parts if p]
+    return "".join(p[:1].upper() + p[1:].lower() for p in filtered) or "Snippet"
+
+
+def format_export_name(lang: str, snippet_name: str) -> str:
+    prefix = LANG_PREFIX[lang]
+    return f"{prefix}{to_title_case(snippet_name)}"
+
+
+def snippet_text(lines: List[str]) -> str:
+    if not lines:
+        return ""
+    joined = "\n".join(lines).rstrip("\n")
+    return (joined + "\n") if joined else ""
+
+
+def to_js_literal(text: str) -> str:
+    return json.dumps(text, ensure_ascii=False)
+
+
+def split_export_prefix(export_name: str) -> Tuple[str | None, str]:
+    for lang, prefix in LANG_PREFIX.items():
+        if export_name.startswith(prefix):
+            suffix = export_name[len(prefix) :]
+            return lang, suffix or export_name
+    return None, export_name
+
+
+def parse_existing_module(path: Path) -> Mapping[str, Mapping[str, SnippetRecord]]:
+    try:
+        text = path.read_text(encoding="utf-8")
+    except FileNotFoundError:
+        return {}
+
+    existing: Dict[str, Dict[str, SnippetRecord]] = {}
+    for match in EXPORT_RE.finditer(text):
+        export_name, literal = match.group(1), match.group(2).strip()
+        lang, snippet_suffix = split_export_prefix(export_name)
+        if not lang:
+            continue
+        try:
+            decoded = json.loads(literal)
+        except Exception:
+            decoded = literal.strip().strip(";")
+
+        record = SnippetRecord(
+            lang=lang,
+            snippet_name=snippet_suffix or export_name,
+            export_name=export_name,
+            text=decoded,
+            source_rel="(existing module)",
+        )
+        existing.setdefault(lang, {})[record.snippet_name] = record
+
+    return existing
+
+
+def iter_source_files(source_dirs: Iterable[Path]) -> Iterator[Path]:
+    for root in source_dirs:
+        for path in root.rglob("*"):
+            if path.is_file() and path.suffix.lower() in LANG_EXTENSIONS:
+                yield path
+
+
+def write_if_changed(path: Path, content: str) -> bool:
+    if path.exists():
+        try:
+            current = path.read_text(encoding="utf-8")
+            if current == content:
+                return False
+        except Exception:
+            pass
+    ensure_dir(path.parent)
+    path.write_text(content, encoding="utf-8")
+    return True
+
+
+def collect_snippets(
+    source_dirs: Iterable[Path],
+) -> Mapping[str, Mapping[str, Mapping[str, SnippetRecord]]]:
+    result: Dict[str, Dict[str, Dict[str, SnippetRecord]]] = {}
+    for file_path in iter_source_files(source_dirs):
+        lang = detect_language(file_path)
+        start_re, end_re = markers_for_lang(lang)
+        try:
+            text = file_path.read_text(encoding="utf-8")
+        except Exception as exc:  # pragma: no cover
+            raise RuntimeError(f"Failed to read {file_path}: {exc}") from exc
+        lines = text.splitlines(keepends=True)
+        snippets = parse_snippets(lines, start_re, end_re)
+        if not snippets:
+            continue
+
+        target = normalize_target_name(file_path)
+        lang_map = result.setdefault(target, {}).setdefault(lang, {})
+        for snippet_name, content_lines in snippets.items():
+            if snippet_name in lang_map:
+                raise ValueError(
+                    f"Duplicate snippet '{snippet_name}' in {file_path} and {lang_map[snippet_name].source_rel}"
+                )
+            dedented = dedent(content_lines)
+            export_name = format_export_name(lang, snippet_name)
+            source_rel = os.path.relpath(file_path, Path.cwd())
+            lang_map[snippet_name] = SnippetRecord(
+                lang=lang,
+                snippet_name=snippet_name,
+                export_name=export_name,
+                text=snippet_text(dedented),
+                source_rel=source_rel,
+            )
+    return result
+
+
+def render_module(
+    target: str, lang_map: Mapping[str, Mapping[str, SnippetRecord]]
+) -> str:
+    parts: List[str] = []
+    parts.append(
+        "{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}\n\n"
+    )
+    for lang in LANG_ORDER:
+        snippets = lang_map.get(lang)
+        if not snippets:
+            continue
+        for snippet_name in sorted(snippets.keys()):
+            record = snippets[snippet_name]
+            literal = to_js_literal(record.text)
+            parts.append(f"export const {record.export_name} = {literal};\n\n")
+    return "".join(parts)
+
+
+def generate_modules(
+    snippets_by_target: Mapping[str, Mapping[str, Mapping[str, SnippetRecord]]],
+    output_root: Path,
+) -> None:
+    modules_written = 0
+    total_exports = 0
+
+    for target in sorted(snippets_by_target.keys()):
+        module_path = output_root / f"{target}.mdx"
+        lang_map = snippets_by_target[target]
+        existing_langs = parse_existing_module(module_path)
+        for lang, snippets in existing_langs.items():
+            if lang in lang_map:
+                continue
+            print(module_path, " -- ", lang)
+            merged_lang = lang_map.setdefault(lang, {})
+            existing_export_names = {rec.export_name for rec in merged_lang.values()}
+            for snippet_name, record in snippets.items():
+                # If an export with the same name already exists (e.g., regenerated),
+                # prefer the newly generated snippet and skip the old one to avoid duplicates.
+                if record.export_name in existing_export_names:
+                    continue
+                merged_lang.setdefault(snippet_name, record)
+
+        total_exports += sum(len(snippets) for snippets in lang_map.values())
+        module_content = render_module(target, lang_map)
+        if write_if_changed(module_path, module_content):
+            modules_written += 1
+    print(
+        f"\nGenerated {total_exports} snippets\n---"
+    )
+
+
+def resolve_source_dirs(args_dirs: List[str] | None) -> List[Path]:
+    if args_dirs:
+        dirs = [Path(p) for p in args_dirs]
+    else:
+        dirs = list(DEFAULT_SOURCE_DIRS)
+
+    missing = [str(p) for p in dirs if not p.exists()]
+    if missing:
+        raise FileNotFoundError(f"Source directories not found: {', '.join(missing)}")
+    return dirs
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description="Generate MDX snippet modules grouped by test filename."
+    )
+    parser.add_argument(
+        "-s",
+        "--source-dir",
+        action="append",
+        help="Path(s) to language test directories (default: tests/py, ts, rs)",
+    )
+    parser.add_argument(
+        "-o",
+        "--output-dir",
+        default=str(Path("docs") / "snippets"),
+        help="Directory where snippet modules are written (default: docs/snippets)",
+    )
+    args = parser.parse_args()
+
+    source_dirs = resolve_source_dirs(args.source_dir)
+    snippets = collect_snippets(source_dirs)
+    if not snippets:
+        print("No snippets found.")
+        return 0
+
+    output_root = Path(args.output_dir)
+    generate_modules(snippets, output_root)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/docs/web-tests/py/conftest.py b/docs/web-tests/py/conftest.py
new file mode 100644
index 000000000..ebd2c811d
--- /dev/null
+++ b/docs/web-tests/py/conftest.py
@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import shutil
+from pathlib import Path
+
+import lancedb
+import pytest
+import pytest_asyncio
+from lancedb.db import AsyncConnection, DBConnection
+
+
+class DatabasePathFactory:
+    """Create per-test database directories and ensure they get removed."""
+
+    def __init__(self, base_dir: Path) -> None:
+        self._base_dir = base_dir
+        self._created: list[Path] = []
+
+    def __call__(self, name: str) -> Path:
+        safe_name = name.replace("/", "_")
+        path = self._base_dir / safe_name
+        if path.exists():
+            shutil.rmtree(path, ignore_errors=True)
+        path.mkdir(parents=True, exist_ok=True)
+        self._created.append(path)
+        return path
+
+    def cleanup(self) -> None:
+        for path in reversed(self._created):
+            shutil.rmtree(path, ignore_errors=True)
+        self._created.clear()
+
+
+@pytest.fixture
+def db_path_factory(tmp_path: Path):
+    factory = DatabasePathFactory(tmp_path)
+    yield factory
+    factory.cleanup()
+
+
+@pytest.fixture
+def mem_db() -> DBConnection:
+    return lancedb.connect("memory://")
+
+
+@pytest.fixture
+def tmp_db(db_path_factory) -> DBConnection:
+    """Create a temporary database connection for testing."""
+    db_path = db_path_factory("tmp_db")
+    return lancedb.connect(str(db_path))
+
+
+@pytest_asyncio.fixture
+async def mem_db_async() -> AsyncConnection:
+    return await lancedb.connect_async("memory://")
diff --git a/docs/web-tests/py/provider_examples.py b/docs/web-tests/py/provider_examples.py
new file mode 100644
index 000000000..2ba24fe96
--- /dev/null
+++ b/docs/web-tests/py/provider_examples.py
@@ -0,0 +1,355 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+"""Reader examples with explicit placeholders.
+
+These functions are documentation source. test_provider_examples.py runs the
+reranker examples offline, with stand-ins for the model and the provider, to
+check that each one writes only to its own new directory. Corresponding opt-in
+integration tests remain in test_integrations.py and retain real credential
+loading. Replace placeholders before running these examples.
+"""
+
+
+def embedding_ibm_usage():
+    # --8<-- [start:embedding_ibm_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import EmbeddingFunctionRegistry
+    from lancedb.pydantic import LanceModel, Vector
+
+    registry = EmbeddingFunctionRegistry.get_instance()
+    registry.set_var("watsonx-api-key", "your-api-key")
+    watsonx_embed = registry.get("watsonx").create(
+        name="ibm/slate-125m-english-rtrvr",
+        api_key="$var:watsonx-api-key",
+        project_id="your-project-id",
+    )
+
+    class TextModel(LanceModel):
+        text: str = watsonx_embed.SourceField()
+        vector: Vector(watsonx_embed.ndims()) = watsonx_embed.VectorField()
+
+    data = [
+        {"text": "hello world"},
+        {"text": "goodbye world"},
+    ]
+
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "watsonx-demo"))
+    tbl = db.create_table("watsonx_test", schema=TextModel, mode="overwrite")
+    tbl.add(data)
+
+    rs = tbl.search("hello").limit(1).to_pandas()
+    print(rs.head())
+    # --8<-- [end:embedding_ibm_usage]
+
+
+def embedding_jina_text():
+    # --8<-- [start:embedding_jina_text]
+    import os
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import EmbeddingFunctionRegistry
+    from lancedb.pydantic import LanceModel, Vector
+
+    os.environ["JINA_API_KEY"] = "your-api-key"
+
+    jina_embed = (
+        EmbeddingFunctionRegistry.get_instance()
+        .get("jina")
+        .create(name="jina-embeddings-v2-base-en")
+    )
+
+    class TextModel(LanceModel):
+        text: str = jina_embed.SourceField()
+        vector: Vector(jina_embed.ndims()) = jina_embed.VectorField()
+
+    data = [{"text": "hello world"}, {"text": "goodbye world"}]
+
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "jina-text"))
+    tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+
+    tbl.add(data)
+    # --8<-- [end:embedding_jina_text]
+
+
+def embedding_jina_multimodal():
+    # --8<-- [start:embedding_jina_multimodal]
+    import os
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    import pandas as pd
+    import requests
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+
+    os.environ["JINA_API_KEY"] = "your-api-key"
+
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "jina-images"))
+    func = get_registry().get("jina").create()
+
+    class Images(LanceModel):
+        label: str
+        image_uri: str = func.SourceField()
+        image_bytes: bytes = func.SourceField()
+        vector: Vector(func.ndims()) = func.VectorField()
+        vec_from_bytes: Vector(func.ndims()) = func.VectorField()
+
+    table = db.create_table("images", schema=Images)
+    labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
+    uris = [
+        "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
+        "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
+        "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
+        "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
+        "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
+        "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
+    ]
+    image_bytes = [requests.get(uri).content for uri in uris]
+    table.add(
+        pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": image_bytes})
+    )
+    # --8<-- [end:embedding_jina_multimodal]
+
+
+def reranking_cohere_usage():
+    # --8<-- [start:reranking_cohere_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.rerankers import CohereReranker
+
+    embedder = get_registry().get("sentence-transformers").create()
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "cohere-rerank"))
+
+    class Schema(LanceModel):
+        text: str = embedder.SourceField()
+        vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+    data = [
+        {"text": "hello world"},
+        {"text": "goodbye world"},
+    ]
+    tbl = db.create_table("test", schema=Schema)
+    tbl.add(data)
+    reranker = CohereReranker(api_key="your-api-key")
+
+    # Run vector search with a reranker
+    vector_results = tbl.search("hello").rerank(reranker=reranker).to_list()
+    print(vector_results)
+
+    # Build the full-text index before either FTS or hybrid search.
+    tbl.create_fts_index("text", replace=True)
+
+    # Run FTS search with a reranker
+    fts_results = (
+        tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
+    )
+    print(fts_results)
+
+    # Run hybrid search with a reranker
+    hybrid_results = (
+        tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+    )
+    print(hybrid_results)
+    # --8<-- [end:reranking_cohere_usage]
+
+
+def reranking_jina_usage():
+    # --8<-- [start:reranking_jina_usage]
+    import os
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.rerankers import JinaReranker
+
+    os.environ["JINA_API_KEY"] = "your-api-key"
+    embedder = get_registry().get("jina").create()
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "jina-rerank"))
+
+    class Schema(LanceModel):
+        text: str = embedder.SourceField()
+        vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+    data = [
+        {"text": "hello world"},
+        {"text": "goodbye world"},
+    ]
+    tbl = db.create_table("test", schema=Schema)
+    tbl.add(data)
+    reranker = JinaReranker(api_key="your-api-key")
+
+    # Run vector search with a reranker
+    vector_results = tbl.search("hello").rerank(reranker=reranker).to_list()
+    print(vector_results)
+
+    # Build the full-text index before either FTS or hybrid search.
+    tbl.create_fts_index("text", replace=True)
+
+    # Run FTS search with a reranker
+    fts_results = (
+        tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
+    )
+    print(fts_results)
+
+    # Run hybrid search with a reranker
+    hybrid_results = (
+        tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+    )
+    print(hybrid_results)
+    # --8<-- [end:reranking_jina_usage]
+
+
+def reranking_watsonx_usage():
+    # --8<-- [start:reranking_watsonx_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.rerankers import WatsonxReranker
+
+    embedder = get_registry().get("sentence-transformers").create()
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "watsonx-rerank"))
+
+    class Schema(LanceModel):
+        text: str = embedder.SourceField()
+        vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+    data = [
+        {"text": "hello world"},
+        {"text": "goodbye world"},
+    ]
+    tbl = db.create_table("test", schema=Schema)
+    tbl.add(data)
+
+    # Supply your API key and project ID (or use space_id instead of project_id).
+    reranker = WatsonxReranker(
+        api_key="your-api-key",
+        project_id="your-project-id",
+    )
+
+    # Run vector search with a reranker
+    vector_results = tbl.search("hello").rerank(reranker=reranker).to_list()
+    print(vector_results)
+
+    # Build the full-text index before either FTS or hybrid search.
+    tbl.create_fts_index("text", replace=True)
+
+    # Run FTS search with a reranker
+    fts_results = (
+        tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
+    )
+    print(fts_results)
+
+    # Run hybrid search with a reranker
+    hybrid_results = (
+        tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+    )
+    print(hybrid_results)
+    # --8<-- [end:reranking_watsonx_usage]
+
+
+def reranking_openai_usage():
+    # --8<-- [start:reranking_openai_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.rerankers import OpenaiReranker
+
+    embedder = get_registry().get("sentence-transformers").create()
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "openai-rerank"))
+
+    class Schema(LanceModel):
+        text: str = embedder.SourceField()
+        vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+    data = [
+        {"text": "hello world"},
+        {"text": "goodbye world"},
+    ]
+    tbl = db.create_table("test", schema=Schema)
+    tbl.add(data)
+    reranker = OpenaiReranker(api_key="your-api-key")
+
+    # Run vector search with a reranker
+    vector_results = tbl.search("hello").rerank(reranker=reranker).to_list()
+    print(vector_results)
+
+    # Build the full-text index before either FTS or hybrid search.
+    tbl.create_fts_index("text", replace=True)
+
+    # Run FTS search with a reranker
+    fts_results = (
+        tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
+    )
+    print(fts_results)
+
+    # Run hybrid search with a reranker
+    hybrid_results = (
+        tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+    )
+    print(hybrid_results)
+    # --8<-- [end:reranking_openai_usage]
+
+
+def reranking_voyageai_usage():
+    # --8<-- [start:reranking_voyageai_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.rerankers import VoyageAIReranker
+
+    embedder = get_registry().get("sentence-transformers").create()
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "voyageai-rerank"))
+
+    class Schema(LanceModel):
+        text: str = embedder.SourceField()
+        vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+    data = [
+        {"text": "hello world"},
+        {"text": "goodbye world"},
+    ]
+    tbl = db.create_table("test", schema=Schema)
+    tbl.add(data)
+    reranker = VoyageAIReranker(api_key="your-api-key", model_name="rerank-2")
+
+    # Run vector search with a reranker
+    vector_results = tbl.search("hello").rerank(reranker=reranker).to_list()
+    print(vector_results)
+
+    # Build the full-text index before either FTS or hybrid search.
+    tbl.create_fts_index("text", replace=True)
+
+    # Run FTS search with a reranker
+    fts_results = (
+        tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
+    )
+    print(fts_results)
+
+    # Run hybrid search with a reranker
+    hybrid_results = (
+        tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+    )
+    print(hybrid_results)
+    # --8<-- [end:reranking_voyageai_usage]
diff --git a/docs/web-tests/py/test_basic_usage.py b/docs/web-tests/py/test_basic_usage.py
new file mode 100644
index 000000000..260ebb6f9
--- /dev/null
+++ b/docs/web-tests/py/test_basic_usage.py
@@ -0,0 +1,205 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+# --8<-- [start:basic_imports]
+import json
+
+import lancedb
+import pandas as pd
+import polars as pl
+import pyarrow as pa
+# --8<-- [end:basic_imports]
+import pytest
+
+data_path = "tests/camelot.json"
+
+def test_basic_usage(db_path_factory):
+    uri = "./basic_usage_db"
+    uri = db_path_factory("basic_usage_db")
+    db = lancedb.connect(uri)
+
+    # --8<-- [start:data_load]
+    with open(data_path, "r") as f:
+        data = json.load(f)
+    # --8<-- [end:data_load]
+
+    # --8<-- [start:basic_create_table]
+    table = db.create_table("camelot", data=data, mode="overwrite")
+    # --8<-- [end:basic_create_table]
+    assert len(table) == 8
+
+    # --8<-- [start:basic_open_table]
+    table = db.open_table("camelot")
+    # --8<-- [end:basic_open_table]
+
+    # --8<-- [start:basic_create_table_pandas]
+    pandas_df = pd.DataFrame(data)
+    table_pd = db.create_table("camelot_pd", data=pandas_df, mode="overwrite")
+    # --8<-- [end:basic_create_table_pandas]
+    assert len(table_pd) == 8
+    db.drop_table("camelot_pd")
+
+    # --8<-- [start:basic_create_table_polars]
+    polars_df = pl.DataFrame(data)
+    table_pl = db.create_table("camelot_pl", data=polars_df, mode="overwrite")
+    # --8<-- [end:basic_create_table_polars]
+    assert len(table_pl) == 8
+    db.drop_table("camelot_pl")
+
+    # --8<-- [start:basic_create_empty_table]
+    schema = pa.schema(
+        [
+            pa.field("id", pa.uint16()),
+            pa.field("name", pa.string()),
+            pa.field("role", pa.string()),
+            pa.field("description", pa.string()),
+            pa.field("vector", pa.list_(pa.float32(), 4)),
+            pa.field(
+                "stats",
+                pa.struct(
+                    [
+                        pa.field("strength", pa.int8()),
+                        pa.field("courage", pa.int8()),
+                        pa.field("magic", pa.int8()),
+                        pa.field("wisdom", pa.int8()),
+                    ]
+                ),
+            ),
+        ]
+    )
+    db.create_table("camelot_pa", schema=schema, mode="overwrite")
+    # --8<-- [end:basic_create_empty_table]
+    assert "camelot_pa" in db.list_tables().tables
+    db.drop_table("camelot_pa")
+
+    # --8<-- [start:basic_add_data]
+    magical_characters = [
+        {
+            "id": 9,
+            "name": "Morgan le Fay",
+            "role": "Sorceress",
+            "description": "A powerful enchantress, Arthur's half-sister, and a complex figure who oscillates between aiding and opposing Camelot.",
+            "vector": [0.10, 0.84, 0.25, 0.70],
+            "stats": { "strength": 2, "courage": 3, "magic": 5, "wisdom": 4 }
+        },
+        {
+            "id": 10,
+            "name": "The Lady of the Lake",
+            "role": "Mystical Guardian",
+            "description": "A mysterious supernatural figure associated with Avalon, known for giving Arthur the sword Excalibur.",
+            "vector": [0.00, 0.90, 0.58, 0.88],
+            "stats": { "strength": 2, "courage": 3, "magic": 5, "wisdom": 5 }
+        }
+    ]
+    table.add(magical_characters)
+    # --8<-- [end:basic_add_data]
+    assert len(table) == 10
+
+    # --8<-- [start:basic_vector_search]
+    query_vector = [0.03, 0.85, 0.61, 0.90]
+    result = table.search(query_vector).limit(5).to_polars()
+    print(result)
+    # --8<-- [end:basic_vector_search]
+
+    # --8<-- [start:basic_add_columns]
+    table.add_columns(
+        {
+            "power": "cast(((stats.strength + stats.courage + stats.magic + stats.wisdom) / 4.0) as float)"
+        }
+    )
+    # --8<-- [end:basic_add_columns]
+    assert "power" in table.schema.names
+
+    # Run examples to illustrate search
+    # --8<-- [start:basic_vector_search_q1]
+    # Who are the characters similar to  "wizard"?
+    query_vector_1 = [0.03, 0.85, 0.61, 0.90]
+    r1 = (
+        table.search(query_vector_1)
+        .limit(5)
+        .select(["name", "role", "description"])
+        .to_polars()
+    )
+    print(r1)
+    # --8<-- [end:basic_vector_search_q1]
+
+    # --8<-- [start:basic_vector_search_q2]
+    # Who are the characters with high magic stats?
+    query_vector_2 = [0.03, 0.85, 0.61, 0.90]
+    r2 = (
+        table.search(query_vector_2)
+        .where("stats.magic > 3")
+        .select(["name", "role", "description"])
+        .limit(5)
+        .to_polars()
+    )
+    print(r2)
+    # --8<-- [end:basic_vector_search_q2]
+
+    # --8<-- [start:basic_vector_search_q3]
+    # Who are the strongest characters?
+    r3 = (
+        table.search()
+        .where("stats.strength > 3")
+        .select(["name", "role", "description"])
+        .limit(5)
+        .to_polars()
+    )
+    print(r3)
+    # --8<-- [end:basic_vector_search_q3]
+
+    # --8<-- [start:basic_vector_search_q4]
+    # Who are the strongest characters?
+    r4 = (
+        table.search()
+        .select(["name", "role", "description", "power"])
+        .to_polars()
+    )
+    print(r4)
+    # --8<-- [end:basic_vector_search_q4]
+
+    # --8<-- [start:basic_sort_polars]
+    # Sort Polars DataFrame by power in descending order
+    print(r4.sort("power", descending=True).limit(5))
+    # --8<-- [end:basic_sort_polars]
+    sorted_power = r4.sort("power", descending=True)["power"].to_list()
+    assert sorted_power == sorted(sorted_power, reverse=True)
+    assert sorted_power[0] == 4.0
+
+    # --8<-- [start:basic_drop_columns]
+    table.drop_columns(["power"])
+    # --8<-- [end:basic_drop_columns]
+    # --8<-- [start:basic_delete_rows]
+    table.delete('role = "Traitor Knight"')
+    # --8<-- [end:basic_delete_rows]
+    assert len(table) == 9
+    # --8<-- [start:basic_drop_table]
+    db.drop_table("camelot")
+    # --8<-- [end:basic_drop_table]
+    assert "camelot" not in db.list_tables().tables
+
+
+@pytest.mark.asyncio
+async def test_basic_usage_async_api(db_path_factory):
+    uri = db_path_factory("basic_usage_async_db")
+    with open(data_path, "r") as f:
+        data = json.load(f)
+
+    # --8<-- [start:basic_async_api]
+    import lancedb
+
+    async_db = await lancedb.connect_async(uri)
+    async_table = await async_db.create_table(
+        "camelot_async",
+        data=data,
+        mode="overwrite",
+    )
+
+    query_vector = [0.03, 0.85, 0.61, 0.90]
+    async_results = await (
+        await async_table.search(query_vector)
+    ).limit(5).select(["name", "role", "description"]).to_polars()
+    print(async_results)
+    # --8<-- [end:basic_async_api]
+
+    assert async_results.height >= 1
diff --git a/docs/web-tests/py/test_build_with_ai_agents.py b/docs/web-tests/py/test_build_with_ai_agents.py
new file mode 100644
index 000000000..1ab7c9cdc
--- /dev/null
+++ b/docs/web-tests/py/test_build_with_ai_agents.py
@@ -0,0 +1,109 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+# --8<-- [start:camelot_schema]
+from lancedb.pydantic import LanceModel
+from pydantic import ConfigDict
+
+
+class Stats(LanceModel):
+    model_config = ConfigDict(strict=True, extra="forbid")
+
+    strength: int
+    courage: int
+    magic: int
+    wisdom: int
+
+
+class Character(LanceModel):
+    model_config = ConfigDict(strict=True, extra="forbid")
+
+    id: int
+    name: str
+    role: str
+    description: str
+    stats: Stats
+    image_filename: str
+    image: bytes
+# --8<-- [end:camelot_schema]
+
+
+# --8<-- [start:camelot_batches]
+import json
+from collections.abc import Iterator
+from pathlib import Path
+
+
+def validated_batches(
+    input_path: Path, batch_size: int
+) -> Iterator[list[dict]]:
+    raw_records = json.loads(input_path.read_text())
+    asset_root = input_path.parent.parent
+    batch: list[dict] = []
+
+    for raw in raw_records:
+        payload = dict(raw)
+        image_path = asset_root / payload.pop("img")
+        payload["image_filename"] = image_path.name
+        payload["image"] = image_path.read_bytes()
+
+        character = Character.model_validate(payload)
+        batch.append(character.model_dump(mode="python"))
+
+        if len(batch) == batch_size:
+            yield batch
+            batch = []
+
+    if batch:
+        yield batch
+# --8<-- [end:camelot_batches]
+
+
+# --8<-- [start:camelot_oss_ingestion]
+import lancedb
+
+
+def ingest_oss(
+    input_path: Path,
+    uri: str = "data/camelot.lancedb",
+    table_name: str = "camelot_multimodal",
+    batch_size: int = 64,
+):
+    db = lancedb.connect(uri)
+    if table_name in db.list_tables():
+        raise ValueError(
+            f"Table {table_name!r} already exists. Choose a fresh table name."
+        )
+
+    table = db.create_table(table_name, schema=Character)
+    for batch in validated_batches(input_path, batch_size):
+        table.add(batch)
+
+    table.optimize()
+    return table
+
+
+if __name__ == "__main__":
+    ingest_oss(Path("data/camelot.json"))
+# --8<-- [end:camelot_oss_ingestion]
+
+
+def test_camelot_oss_ingestion(tmp_path):
+    source = Path(
+        "docs/web/static/assets/tutorials/build-with-ai-agents/camelot/data/camelot.json"
+    )
+    table = ingest_oss(
+        source,
+        uri=str(tmp_path / "camelot.lancedb"),
+        table_name="camelot",
+    )
+
+    rows = (
+        table.search()
+        .select(["id", "name", "image", "image_filename"])
+        .limit(8)
+        .to_list()
+    )
+    assert len(rows) == 8
+    assert all(row["image"] for row in rows)
+    assert all(row["image_filename"].endswith(".jpg") for row in rows)
diff --git a/docs/web-tests/py/test_connection.py b/docs/web-tests/py/test_connection.py
new file mode 100644
index 000000000..f5c1f2b22
--- /dev/null
+++ b/docs/web-tests/py/test_connection.py
@@ -0,0 +1,210 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+from pathlib import Path
+import shutil
+
+import pytest
+
+
+def test_connection():
+    # --8<-- [start:connect]
+    import lancedb
+
+    uri = "ex_lancedb"
+    db = lancedb.connect(uri)
+    # --8<-- [end:connect]
+    assert db is not None
+    shutil.rmtree(uri, ignore_errors=True)
+    assert not Path(uri).exists()
+
+
+async def connect_async_example():
+    # --8<-- [start:connect_async]
+    import lancedb
+
+    uri = "ex_lancedb"
+    async_db = await lancedb.connect_async(uri)
+    # --8<-- [end:connect_async]
+
+    return async_db
+
+
+def connect_enterprise_quickstart_config():
+    # --8<-- [start:connect_enterprise_quickstart]
+    import os
+
+    import lancedb
+
+    db = lancedb.connect(
+        uri=os.environ["LANCEDB_URI"],  # db://your-database
+        api_key=os.environ["LANCEDB_API_KEY"],
+        region=os.environ.get("LANCEDB_REGION", "us-east-1"),
+        host_override=os.environ["LANCEDB_HOST_OVERRIDE"],  # https://your-endpoint
+    )
+    # --8<-- [end:connect_enterprise_quickstart]
+    return db
+
+
+def test_connect_enterprise_quickstart(monkeypatch):
+    import lancedb
+
+    captured = {}
+
+    def fake_connect(**kwargs):
+        captured.update(kwargs)
+        return object()
+
+    monkeypatch.setattr(lancedb, "connect", fake_connect)
+    monkeypatch.setenv("LANCEDB_URI", "db://your-database")
+    monkeypatch.setenv("LANCEDB_API_KEY", "your-api-key")
+    monkeypatch.delenv("LANCEDB_REGION", raising=False)
+    monkeypatch.setenv("LANCEDB_HOST_OVERRIDE", "https://your-endpoint")
+
+    assert connect_enterprise_quickstart_config() is not None
+    assert captured == {
+        "uri": "db://your-database",
+        "api_key": "your-api-key",
+        "region": "us-east-1",
+        "host_override": "https://your-endpoint",
+    }
+
+
+def test_connect_enterprise_quickstart_requires_an_endpoint(monkeypatch):
+    monkeypatch.setenv("LANCEDB_URI", "db://your-database")
+    monkeypatch.setenv("LANCEDB_API_KEY", "your-api-key")
+    monkeypatch.delenv("LANCEDB_HOST_OVERRIDE", raising=False)
+
+    with pytest.raises(KeyError, match="LANCEDB_HOST_OVERRIDE"):
+        connect_enterprise_quickstart_config()
+
+
+def connect_object_storage_config():
+    # --8<-- [start:connect_object_storage]
+    import lancedb
+
+    uri = "s3://your-bucket/path"
+    # You can also use "gs://your-bucket/path" or "az://your-container/path".
+    db = lancedb.connect(uri)
+    # --8<-- [end:connect_object_storage]
+
+    return db
+
+
+def namespace_table_ops_example():
+    # --8<-- [start:namespace_table_ops]
+    import lancedb
+
+    db = lancedb.connect_namespace("dir", {"root": "./local_lancedb"})
+
+    # Create namespace tree: prod/search
+    db.create_namespace(["prod"], mode="exist_ok")
+    db.create_namespace(["prod", "search"], mode="exist_ok")
+    db.create_namespace(["prod", "recommendations"], mode="exist_ok")
+
+    db.create_table(
+        "user",
+        data=[{"id": 1, "vector": [0.1, 0.2], "name": "alice"}],
+        namespace_path=["prod", "search"],
+        mode="create",  # use "overwrite" only if you want to replace existing table
+    )
+
+    db.create_table(
+        "user",
+        data=[{"id": 2, "vector": [0.3, 0.4], "name": "bob"}],
+        namespace_path=["prod", "recommendations"],
+        mode="create",  # use "overwrite" only if you want to replace existing table
+    )
+
+    # Verify: listings return a response with the names and a page token
+    print(db.list_namespaces().namespaces)  # ['prod']
+    print(db.list_namespaces(namespace_path=["prod"]).namespaces)
+    # ['recommendations', 'search']
+    print(db.list_tables(namespace_path=["prod", "search"]).tables)  # ['user']
+    print(db.list_tables(namespace_path=["prod", "recommendations"]).tables)  # ['user']
+    # --8<-- [end:namespace_table_ops]
+
+
+def test_namespace_table_ops(tmp_path, monkeypatch, capsys):
+    monkeypatch.chdir(tmp_path)
+    namespace_table_ops_example()
+    assert capsys.readouterr().out.splitlines() == [
+        "['prod']",
+        "['recommendations', 'search']",
+        "['user']",
+        "['user']",
+    ]
+
+
+def namespace_admin_ops_example():
+    # --8<-- [start:namespace_admin_ops]
+    import lancedb
+
+    db = lancedb.connect_namespace("dir", {"root": "./local_lancedb"})
+    namespace = ["prod", "search"]
+
+    db.create_namespace(["prod"])
+    db.create_namespace(["prod", "search"])
+
+    child_namespaces = db.list_namespaces(namespace_path=["prod"]).namespaces
+    print(f"Child namespaces under ['prod']: {child_namespaces}")
+    # Child namespaces under ['prod']: ['search']
+
+    metadata = db.describe_namespace(["prod", "search"])
+    print(f"Metadata for namespace {namespace}: {metadata}")
+    # Metadata for namespace ['prod', 'search']: properties=None
+
+    db.drop_namespace(["prod", "search"], mode="skip")
+    db.drop_namespace(["prod"], mode="skip")
+    # --8<-- [end:namespace_admin_ops]
+    return db, child_namespaces, metadata
+
+
+def test_namespace_admin_ops(tmp_path, monkeypatch, capsys):
+    monkeypatch.chdir(tmp_path)
+    db, child_namespaces, metadata = namespace_admin_ops_example()
+    assert child_namespaces == ["search"]
+    assert capsys.readouterr().out.splitlines()[0] == (
+        "Child namespaces under ['prod']: ['search']"
+    )
+    assert db.list_namespaces().namespaces == []
+
+async def connect_object_storage_config_async():
+    # --8<-- [start:connect_object_storage_async]
+    import lancedb
+
+    uri = "s3://your-bucket/path"
+    # You can also use "gs://your-bucket/path" or "az://your-container/path".
+    async_db = await lancedb.connect_async(uri)
+    # --8<-- [end:connect_object_storage_async]
+
+    return async_db
+
+
+def test_connect_enterprise_placeholders(monkeypatch):
+    import lancedb
+
+    captured = {}
+
+    def fake_connect(**kwargs):
+        captured.update(kwargs)
+        return object()
+
+    monkeypatch.setattr(lancedb, "connect", fake_connect)
+    # --8<-- [start:connect_enterprise_placeholders]
+    import lancedb
+
+    db = lancedb.connect(
+        uri="db://your-database",
+        api_key="your-api-key",
+        region="us-east-1",
+        host_override="https://your-endpoint",
+    )
+    # --8<-- [end:connect_enterprise_placeholders]
+    assert db is not None
+    assert captured == {
+        "uri": "db://your-database",
+        "api_key": "your-api-key",
+        "region": "us-east-1",
+        "host_override": "https://your-endpoint",
+    }
diff --git a/docs/web-tests/py/test_curation_sampling.py b/docs/web-tests/py/test_curation_sampling.py
new file mode 100644
index 000000000..d50b8a43f
--- /dev/null
+++ b/docs/web-tests/py/test_curation_sampling.py
@@ -0,0 +1,27 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+
+def test_sample_dataset():
+    # --8<-- [start:sample_dataset]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+    from lancedb.permutation import Permutation, permutation_builder
+
+    with TemporaryDirectory(prefix="lancedb-sampling-") as uri:
+        db = lancedb.connect(uri)
+        table = db.create_table("records", [{"id": i} for i in range(10)])
+        splits = (
+            permutation_builder(table).split_random(counts=[3, 7], seed=42).execute()
+        )
+        sample = Permutation.from_tables(table, splits, split=0).with_format("arrow")
+        selected_ids = []
+        for batch in sample.iter(batch_size=3):
+            selected_ids.extend(batch.column("id").to_pylist())
+        print(f"Selected {len(selected_ids)} rows: {selected_ids}")
+    # --8<-- [end:sample_dataset]
+
+    assert len(selected_ids) == 3
+    assert len(set(selected_ids)) == 3
+    assert all(0 <= value < 10 for value in selected_ids)
diff --git a/docs/web-tests/py/test_doc_function_examples.py b/docs/web-tests/py/test_doc_function_examples.py
new file mode 100644
index 000000000..fa72e66ef
--- /dev/null
+++ b/docs/web-tests/py/test_doc_function_examples.py
@@ -0,0 +1,34 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+from lancedb.index import FTS
+
+
+def test_python_boolean_query_operators(tmp_db):
+    table = tmp_db.create_table(
+        "boolean_docs",
+        [
+            {"id": 1, "text": "puppy"},
+            {"id": 2, "text": "merrily"},
+            {"id": 3, "text": "puppy merrily"},
+            {"id": 4, "text": "other"},
+        ],
+    )
+    table.create_index("text", config=FTS())
+
+    # --8<-- [start:fts_boolean_queries]
+    from lancedb.query import MatchQuery
+
+    intersection = MatchQuery("puppy", "text") & MatchQuery("merrily", "text")
+    union = MatchQuery("puppy", "text") | MatchQuery("merrily", "text")
+    # --8<-- [end:fts_boolean_queries]
+
+    def ids(query):
+        return set(table.search(query, query_type="fts").to_arrow()["id"].to_pylist())
+
+    assert ids(intersection) == {3}
+    assert ids(union) == {1, 2, 3}
+    a = MatchQuery("puppy", "text")
+    b = MatchQuery("merrily", "text")
+    assert ids(a and b) == {2, 3}
+    assert ids(a or b) == {1, 3}
diff --git a/docs/web-tests/py/test_doc_storage_examples.py b/docs/web-tests/py/test_doc_storage_examples.py
new file mode 100644
index 000000000..13bedb731
--- /dev/null
+++ b/docs/web-tests/py/test_doc_storage_examples.py
@@ -0,0 +1,84 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import lancedb
+import numpy as np
+import pyarrow as pa
+import pytest
+
+
+def test_rebuild_vector_index(tmp_db):
+    rng = np.random.default_rng(7)
+    table = tmp_db.create_table(
+        "rebuild_index",
+        [{"id": i, "vector": v.tolist()} for i, v in enumerate(rng.random((512, 4)))],
+    )
+    table.create_index(
+        "vector", config=lancedb.index.IvfFlat(distance_type="l2", num_partitions=4)
+    )
+    original_index = table.list_indices()[0]
+    table.add(
+        [
+            {"id": 512 + i, "vector": v.tolist()}
+            for i, v in enumerate(rng.random((64, 4)) + 5)
+        ]
+    )
+    assert table.index_stats("vector_idx").num_unindexed_rows == 64
+
+    # --8<-- [start:storage_rebuild_vector_index]
+    from lancedb.index import IvfFlat
+
+    table.create_index(
+        "vector",
+        config=IvfFlat(distance_type="l2", num_partitions=4),
+        replace=True,
+    )
+    # --8<-- [end:storage_rebuild_vector_index]
+
+    indices = table.list_indices()
+    assert len(indices) == 1
+    assert indices[0].name == original_index.name
+    assert indices[0].index_uuid != original_index.index_uuid
+    stats = table.index_stats("vector_idx")
+    assert stats.num_indexed_rows == 576
+    assert stats.num_unindexed_rows == 0
+
+
+def test_local_shallow_clone_pins_source_version(tmp_path):
+    db = lancedb.connect(str(tmp_path))
+    source = db.create_table("source", [{"id": 1}])
+    clone = db.clone_table("snapshot", source_uri=str(tmp_path / "source.lance"))
+    source.add([{"id": 2}])
+    assert source.count_rows() == 2
+    assert clone.to_arrow().to_pylist() == [{"id": 1}]
+
+
+def _reader():
+    return pa.RecordBatchReader.from_batches(
+        pa.schema([("id", pa.int64())]),
+        [pa.record_batch([[1, 2]], names=["id"]), pa.record_batch([[3]], names=["id"])],
+    )
+
+
+@pytest.mark.parametrize("parallelism", [1, 2])
+def test_streaming_write_parallelism(tmp_db, parallelism):
+    table = tmp_db.create_table("streaming", [{"id": 0}])
+    progress = []
+    table.add(_reader(), write_parallelism=parallelism, progress=progress.append)
+    assert sorted(table.to_arrow()["id"].to_pylist()) == [0, 1, 2, 3]
+    assert progress[-1]["done"]
+    assert progress[-1]["output_rows"] == 3
+    assert {p["total_tasks"] for p in progress} == {parallelism}
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("parallelism", [1, 2])
+async def test_async_streaming_write_parallelism(mem_db_async, parallelism):
+    table = await mem_db_async.create_table("streaming", [{"id": 0}])
+    progress = []
+    await table.add(_reader(), write_parallelism=parallelism, progress=progress.append)
+    data = await table.to_arrow()
+    assert sorted(data["id"].to_pylist()) == [0, 1, 2, 3]
+    assert progress[-1]["done"]
+    assert progress[-1]["output_rows"] == 3
+    assert {p["total_tasks"] for p in progress} == {parallelism}
diff --git a/docs/web-tests/py/test_embedding.py b/docs/web-tests/py/test_embedding.py
new file mode 100644
index 000000000..490be268e
--- /dev/null
+++ b/docs/web-tests/py/test_embedding.py
@@ -0,0 +1,141 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import lancedb
+
+# --8<-- [start:imports]
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+
+# --8<-- [end:imports]
+import pytest
+
+
+@pytest.mark.skip(reason="OpenAI is not available in the test environment")
+def test_create_embedding_function():
+    # --8<-- [start:create_embedding_function]
+    func = get_registry().get("openai").create(
+        name="text-embedding-3-small",
+        max_retries=7,
+    )
+    # --8<-- [end:create_embedding_function]
+    assert func is not None
+
+
+@pytest.mark.skip(reason="OpenAI is not available in the test environment")
+def test_embeddings_openai():
+    # --8<-- [start:openai_embeddings]
+    db = lancedb.connect("/tmp/db")
+    func = get_registry().get("openai").create(name="text-embedding-ada-002")
+
+    class Words(LanceModel):
+        text: str = func.SourceField()
+        vector: Vector(func.ndims()) = func.VectorField()
+
+    table = db.create_table("words", schema=Words, mode="overwrite")
+    table.add([{"text": "hello world"}, {"text": "goodbye world"}])
+
+    query = "greetings"
+    actual = table.search(query).limit(1).to_pydantic(Words)[0]
+    print(actual.text)
+    # --8<-- [end:openai_embeddings]
+
+
+@pytest.mark.skip(reason="OpenAI is not available in the test environment")
+@pytest.mark.asyncio
+async def test_embeddings_openai_async():
+    uri = "memory://"
+    # --8<-- [start:async_openai_embeddings]
+    db = await lancedb.connect_async(uri)
+    func = get_registry().get("openai").create(name="text-embedding-ada-002")
+
+    class Words(LanceModel):
+        text: str = func.SourceField()
+        vector: Vector(func.ndims()) = func.VectorField()
+
+    table = await db.create_table("words", schema=Words, mode="overwrite")
+    await table.add([{"text": "hello world"}, {"text": "goodbye world"}])
+
+    query = "greetings"
+    actual = await (await table.search(query)).limit(1).to_pydantic(Words)[0]
+    print(actual.text)
+    # --8<-- [end:async_openai_embeddings]
+
+
+@pytest.mark.skip(reason="OpenAI is not available in the test environment")
+def test_embeddings_manual_query():
+    # --8<-- [start:manual_query_embeddings]
+    db = lancedb.connect("/tmp/db")
+    func = get_registry().get("openai").create(name="text-embedding-ada-002")
+
+    class Words(LanceModel):
+        text: str = func.SourceField()
+        vector: Vector(func.ndims()) = func.VectorField()
+
+    table = db.create_table("words", schema=Words, mode="overwrite")
+    table.add([{"text": "hello world"}, {"text": "goodbye world"}])
+
+    query_vector = func.generate_embeddings(["greetings"])[0]
+    # --8<-- [start:manual_query_search]
+    # query_vector is assumed to already be generated by your embedding function
+    actual = table.search(query_vector).limit(1).to_pydantic(Words)[0]
+    print(actual.text)
+    # --8<-- [end:manual_query_search]
+    # --8<-- [end:manual_query_embeddings]
+
+
+def test_custom_embedding_function():
+    # --8<-- [start:embedding_function]
+    from functools import cached_property
+
+    from lancedb.embeddings import TextEmbeddingFunction, register
+
+    class MyEmbeddingModel:
+        def __init__(self, model_name: str):
+            self.model_name = model_name
+
+        def encode(self, texts: list[str]) -> list[list[float]]:
+            return [[1.0, 2.0, 3.0] for _ in texts]
+
+    @register("my-embedder")
+    class MyTextEmbedder(TextEmbeddingFunction):
+        model_name: str = "my-model"
+
+        def generate_embeddings(self, texts: list[str]) -> list[list[float]]:
+            # Your embedding logic here
+            return self._model.encode(texts)
+
+        def ndims(self) -> int:
+            # Return the dimensionality of the embeddings
+            return len(self.generate_embeddings(["test"])[0])
+
+        @cached_property
+        def _model(self) -> MyEmbeddingModel:
+            # Initialize your model once
+            return MyEmbeddingModel(self.model_name)
+    # --8<-- [end:embedding_function]
+
+
+def test_embeddings_secret():
+    # --8<-- [start:register_secret]
+    registry = get_registry()
+    registry.set_var("api_key", "sk-...")
+
+    func = registry.get("openai").create(api_key="$var:api_key")
+    # --8<-- [end:register_secret]
+
+    try:
+        import torch
+    except ImportError:
+        pytest.skip("torch not installed")
+
+    # --8<-- [start:register_device]
+    import torch
+
+    registry = get_registry()
+    if torch.cuda.is_available():
+        registry.set_var("device", "cuda")
+
+    func = registry.get("huggingface").create(device="$var:device:cpu")
+    # --8<-- [end:register_device]
+    assert func.device == "cuda" if torch.cuda.is_available() else "cpu"
diff --git a/docs/web-tests/py/test_function.py b/docs/web-tests/py/test_function.py
new file mode 100644
index 000000000..f66a50aec
--- /dev/null
+++ b/docs/web-tests/py/test_function.py
@@ -0,0 +1,335 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+"""Examples for docs/web/management/function.mdx.
+
+Registering a Function and filling its column need a LanceDB Enterprise
+deployment, so `test_function_enterprise` runs only when
+LANCEDB_DOCS_TEST_DEPLOYMENT=1 asks for one, with the connection variables the
+Enterprise examples already use: LANCEDB_URI, LANCEDB_API_KEY and
+LANCEDB_HOST_OVERRIDE, plus LANCEDB_REGION if the deployment needs one. The
+walkthrough calls its table `function_demo` and its Function `double`; the test
+creates both under names unique to its run, never replaces an existing object,
+and drops only what it created.
+
+The other tests run locally and check the client side of the same code: what
+`@udf` sends for registration, how a registered version binds a column, where a
+local connection stops, and when the Enterprise test connects. They do not run
+a Function remotely.
+"""
+
+import json
+import os
+import sys
+import uuid
+from pathlib import Path
+
+import lancedb
+import pyarrow as pa
+import pytest
+from lancedb import col
+from lancedb.functions import FunctionApplication, FunctionVersion
+
+# --8<-- [start:function_define]
+from lancedb import udf
+
+
+@udf
+def double(value: float) -> float:
+    return value * 2
+
+
+# --8<-- [end:function_define]
+
+# The definition the walkthrough registers. The Enterprise test registers a copy
+# of it under a name that only its run uses.
+WALKTHROUGH_DOUBLE = double
+
+
+def describe(value: float):
+    return {"half": value / 2, "note": None}
+
+
+# A Function version as the service returns it after registration: the SDK's
+# shared golden reply, with the name and signature of the definition above.
+SERVICE_REPLY = (
+    Path(__file__).parents[3]
+    / "rust"
+    / "lancedb"
+    / "tests"
+    / "fixtures"
+    / "first_class_functions"
+    / "v1"
+    / "remote_function_job.json"
+)
+
+
+def registered_version(definition) -> FunctionVersion:
+    request = definition.registration_request
+    reply = json.loads(SERVICE_REPLY.read_text())["result"]
+    reply["name"] = request.name
+    reply["signature"] = json.loads(request.signature.to_canonical_json())
+    return FunctionVersion.from_json(json.dumps(reply))
+
+
+DEPLOYMENT = ("LANCEDB_URI", "LANCEDB_HOST_OVERRIDE", "LANCEDB_API_KEY")
+# The connection settings alone never send this test to a deployment.
+OPT_IN = "LANCEDB_DOCS_TEST_DEPLOYMENT"
+
+
+def require_deployment():
+    if os.environ.get(OPT_IN) != "1":
+        pytest.skip(f"set {OPT_IN}=1 and {', '.join(DEPLOYMENT)} to use a deployment")
+    missing = [name for name in DEPLOYMENT if not os.environ.get(name)]
+    if missing:
+        pytest.fail(f"{OPT_IN}=1 needs {', '.join(missing)}")
+
+
+def test_function_runs_locally_as_python():
+    # --8<-- [start:function_local_call]
+    print(double(1.5))
+    # 3.0
+    # --8<-- [end:function_local_call]
+    assert double(1.5) == 3.0
+
+
+def test_function_registration_request():
+    request = double.registration_request
+    assert request.name == "double"
+    assert [
+        (parameter.name, parameter.arrow_type, parameter.nullable)
+        for parameter in request.signature.inputs
+    ] == [("value", "float64", False)]
+    output = request.signature.output
+    assert (output.kind, output.arrow_type, output.nullable) == (
+        "scalar",
+        "float64",
+        False,
+    )
+    version = request.runtime.python_version
+    assert version == f"{sys.version_info.major}.{sys.version_info.minor}"
+    assert request.runtime.environment.packages == ()
+
+
+def test_function_output_nullability():
+    inputs = pa.schema([pa.field("value", pa.float64(), nullable=False)])
+
+    # A named-struct output may declare nullable fields.
+    fields = [
+        pa.field("half", pa.float64(), nullable=False),
+        pa.field("note", pa.string(), nullable=True),
+    ]
+    struct = udf(input_schema=inputs, output_schema=pa.schema(fields))(describe)
+    output = struct.registration_request.signature.output
+    assert output.kind == "named_struct"
+    assert [(field.name, field.nullable) for field in output.fields] == [
+        ("half", False),
+        ("note", True),
+    ]
+
+    # A scalar output, and a struct declared as one nullable field, may not be.
+    for nullable in (
+        pa.field("result", pa.float64(), nullable=True),
+        pa.field("result", pa.struct(fields), nullable=True),
+    ):
+        with pytest.raises(ValueError, match="non-nullable"):
+            udf(input_schema=inputs, output_schema=nullable)(describe)
+
+
+def test_function_registration_is_remote(tmp_path):
+    db = lancedb.connect(tmp_path)
+    with pytest.raises(NotImplementedError):
+        db.create_function(double)
+
+
+def test_function_version_binds_a_column(tmp_path):
+    version = registered_version(double)
+
+    application = version(value=col("value"))
+
+    assert isinstance(application, FunctionApplication)
+    assert application.function.name == "double"
+    assert application.function.version == version.version
+    assert [
+        (binding.parameter, binding.kind, binding.value["path"])
+        for binding in application.inputs
+    ] == [("value", "column", "value")]
+    with pytest.raises(TypeError, match="direct col"):
+        version(value=1.5)
+    with pytest.raises(TypeError, match="missing"):
+        version(x=col("value"))
+
+    # The binding is accepted by add_columns, which a local table refuses:
+    # a Function column needs LanceDB Cloud or Enterprise.
+    table = lancedb.connect(tmp_path).create_table("t", [{"value": 1.0}])
+    with pytest.raises(NotImplementedError, match="Cloud and Enterprise"):
+        table.add_columns({"doubled": application})
+
+
+def test_function_enterprise(monkeypatch):
+    require_deployment()
+
+    # --8<-- [start:function_connect]
+    import os
+
+    import lancedb
+
+    db = lancedb.connect(
+        uri=os.environ["LANCEDB_URI"],  # db://your-database
+        api_key=os.environ["LANCEDB_API_KEY"],
+        region=os.environ.get("LANCEDB_REGION", "us-east-1"),
+        host_override=os.environ["LANCEDB_HOST_OVERRIDE"],
+    )
+    # --8<-- [end:function_connect]
+
+    # In a shared database `function_demo` or `double` may already exist, so
+    # this run creates both under names only it uses, and drops only those.
+    run = uuid.uuid4().hex[:12]
+    table_name = f"function_demo_{run}"
+    double = udf(name=f"double_{run}")(WALKTHROUGH_DOUBLE.__wrapped__)
+    create_table = db.create_table
+
+    def create_run_table(name, *args, **kwargs):
+        assert name == "function_demo"
+        return create_table(table_name, *args, **kwargs)
+
+    monkeypatch.setattr(db, "create_table", create_run_table)
+
+    table = None
+    version = None
+    try:
+        # --8<-- [start:function_create_table]
+        import pyarrow as pa
+
+        schema = pa.schema([pa.field("value", pa.float64(), nullable=False)])
+        table = db.create_table(
+            "function_demo",
+            [{"value": 1.0}, {"value": 2.0}, {"value": 3.0}],
+            schema=schema,
+        )
+        # --8<-- [end:function_create_table]
+
+        # --8<-- [start:function_register]
+        job = db.create_function_async(double)
+        version = job.wait()
+        # --8<-- [end:function_register]
+        assert version.name == f"double_{run}"
+
+        # --8<-- [start:function_apply]
+        from lancedb import col
+
+        table.add_columns({"doubled": version(value=col("value"))})
+        # --8<-- [end:function_apply]
+
+        # --8<-- [start:function_refresh]
+        refresh = table.refresh_column_async("doubled")
+        result = refresh.wait()
+        print(result.rows_assigned)
+        # 3
+        # --8<-- [end:function_refresh]
+        assert result.rows_assigned == 3
+        assert result.rows_failed == 0
+
+        # --8<-- [start:function_inspect]
+        rows = table.search().select(["value", "doubled"]).to_list()
+        print(sorted((row["value"], row["doubled"]) for row in rows))
+        # [(1.0, 2.0), (2.0, 4.0), (3.0, 6.0)]
+        # --8<-- [end:function_inspect]
+        assert sorted((row["value"], row["doubled"]) for row in rows) == [
+            (1.0, 2.0),
+            (2.0, 4.0),
+            (3.0, 6.0),
+        ]
+    finally:
+        if table is not None:
+            db.drop_table(table_name)
+        if version is not None:
+            db.drop_function(version.name, version=version.version)
+
+
+class ReachedConnection(Exception):
+    pass
+
+
+SETTINGS = {
+    "LANCEDB_URI": "db://docs-test",
+    "LANCEDB_HOST_OVERRIDE": "https://deployment.invalid",
+    "LANCEDB_API_KEY": "docs-test-key",
+}
+
+
+@pytest.fixture
+def connect_attempts(monkeypatch):
+    """`lancedb.connect`, replaced so that it records each call and stops."""
+    attempts = []
+
+    def connect(**kwargs):
+        attempts.append(kwargs)
+        raise ReachedConnection
+
+    monkeypatch.setattr(lancedb, "connect", connect)
+    for name in (OPT_IN, *DEPLOYMENT, "LANCEDB_REGION"):
+        monkeypatch.delenv(name, raising=False)
+    return attempts
+
+
+def enterprise_outcome(monkeypatch):
+    try:
+        test_function_enterprise(monkeypatch)
+    except (pytest.skip.Exception, pytest.fail.Exception, ReachedConnection) as outcome:
+        return outcome
+
+
+@pytest.mark.parametrize(
+    "opt_in",
+    [None, "", "0", "true", "TRUE", "yes", "on", " 1", "1 ", "01", "1.0", "11"],
+)
+def test_function_enterprise_needs_the_exact_opt_in(
+    monkeypatch, connect_attempts, opt_in
+):
+    for name, value in SETTINGS.items():
+        monkeypatch.setenv(name, value)
+    if opt_in is not None:
+        monkeypatch.setenv(OPT_IN, opt_in)
+    outcome = enterprise_outcome(monkeypatch)
+    assert isinstance(outcome, pytest.skip.Exception), outcome
+    assert str(outcome).startswith(f"set {OPT_IN}=1 ")
+    assert connect_attempts == []
+
+
+@pytest.mark.parametrize("blank", ["unset", "empty"])
+@pytest.mark.parametrize(
+    "missing", [*([name] for name in DEPLOYMENT), list(DEPLOYMENT)]
+)
+def test_function_enterprise_fails_when_asked_without_settings(
+    monkeypatch, connect_attempts, missing, blank
+):
+    monkeypatch.setenv(OPT_IN, "1")
+    for name, value in SETTINGS.items():
+        if name not in missing:
+            monkeypatch.setenv(name, value)
+        elif blank == "empty":
+            monkeypatch.setenv(name, "")
+    outcome = enterprise_outcome(monkeypatch)
+    assert isinstance(outcome, pytest.fail.Exception), outcome
+    assert str(outcome) == f"{OPT_IN}=1 needs {', '.join(missing)}"
+    assert connect_attempts == []
+
+
+@pytest.mark.parametrize("region", [None, "eu-west-1"])
+def test_function_enterprise_connects_when_asked(monkeypatch, connect_attempts, region):
+    monkeypatch.setenv(OPT_IN, "1")
+    for name, value in SETTINGS.items():
+        monkeypatch.setenv(name, value)
+    if region:
+        monkeypatch.setenv("LANCEDB_REGION", region)
+    outcome = enterprise_outcome(monkeypatch)
+    assert isinstance(outcome, ReachedConnection), outcome
+    assert connect_attempts == [
+        {
+            "uri": "db://docs-test",
+            "api_key": "docs-test-key",
+            "region": region or "us-east-1",
+            "host_override": "https://deployment.invalid",
+        }
+    ]
diff --git a/docs/web-tests/py/test_indexing.py b/docs/web-tests/py/test_indexing.py
new file mode 100644
index 000000000..80010343c
--- /dev/null
+++ b/docs/web-tests/py/test_indexing.py
@@ -0,0 +1,757 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import random
+import string
+import uuid
+
+import numpy as np
+import pyarrow as pa
+import pytest
+
+
+def _make_vector_rows(count: int, dim: int, column: str = "vector"):
+    rows = [
+        {column: np.random.random(dim).astype(np.float32).tolist(), "id": i}
+        for i in range(count)
+    ]
+    assert len(rows) == count
+    assert all(len(row[column]) == dim for row in rows)
+    return rows
+
+
+def test_vector_index_configure_ivf(tmp_db):
+    table = tmp_db.create_table(
+        "vector_index_configure_ivf",
+        _make_vector_rows(512, 4),
+        mode="overwrite",
+    )
+
+    # --8<-- [start:vector_index_configure_ivf]
+    table.create_index(metric="l2", num_partitions=16, num_sub_vectors=4)
+    # --8<-- [end:vector_index_configure_ivf]
+
+    assert table.list_indices()
+
+
+def test_vector_index_setup(tmp_db):
+    tmp_db.create_table(
+        "vector-index-tbl",
+        _make_vector_rows(8, 4),
+        mode="overwrite",
+    )
+
+    db = tmp_db
+    # --8<-- [start:vector_index_setup]
+    table_name = "vector-index-tbl"
+    table = db.open_table(table_name)
+    # --8<-- [end:vector_index_setup]
+
+    assert table.name == table_name
+
+
+def test_vector_index_build_ivf(tmp_db):
+    table = tmp_db.create_table(
+        "vector-index-build-ivf",
+        _make_vector_rows(512, 4, column="keywords_embeddings"),
+        mode="overwrite",
+    )
+    db = tmp_db
+    # --8<-- [start:vector_index_build_ivf]
+    table_name = "vector-index-build-ivf"
+    table = db.open_table(table_name)
+    table.create_index(
+        metric="cosine",
+        vector_column_name="keywords_embeddings",
+    )
+    # --8<-- [end:vector_index_build_ivf]
+
+    assert table.list_indices()
+
+
+def test_vector_index_nested_field(tmp_db):
+    dim = 2
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int32()),
+            pa.field(
+                "image",
+                pa.struct([pa.field("embedding", pa.list_(pa.float32(), dim))]),
+            ),
+        ]
+    )
+    data = [
+        {
+            "id": i,
+            "image": {"embedding": np.random.random(dim).astype(np.float32).tolist()},
+        }
+        for i in range(512)
+    ]
+    table = tmp_db.create_table(
+        "vector_index_nested_field", data=data, schema=schema, mode="overwrite"
+    )
+
+    # --8<-- [start:vector_index_nested_field]
+    # The vector column `embedding` is nested inside the `image` struct.
+    # Pass its full dotted path as `vector_column_name`; the same path is used
+    # at query time and is what `list_indices()` reports under `columns`.
+    table.create_index(
+        vector_column_name="image.embedding",
+        num_partitions=1,
+        num_sub_vectors=1,
+        name="image_embedding_idx",
+    )
+
+    results = (
+        table.search([0.0, 1.0], vector_column_name="image.embedding")
+        .limit(1)
+        .to_list()
+    )
+    # --8<-- [end:vector_index_nested_field]
+
+    assert table.index_stats("image_embedding_idx")
+    assert len(results) == 1
+
+
+@pytest.mark.asyncio
+async def test_vector_index_async_config(tmp_path, monkeypatch):
+    monkeypatch.chdir(tmp_path)
+
+    # --8<-- [start:vector_index_async_config]
+    import lancedb
+    import numpy as np
+    from lancedb.index import IvfPq
+
+    async def main():
+        data = [
+            {"id": i, "vector": np.random.random(8).astype(np.float32).tolist()}
+            for i in range(512)
+        ]
+
+        db = await lancedb.connect_async("ex_lancedb")
+        table = await db.create_table(
+            "vector_index_async", data=data, mode="overwrite"
+        )
+
+        await table.create_index(
+            "vector",
+            config=IvfPq(
+                distance_type="cosine",
+                num_partitions=16,
+                num_sub_vectors=4,
+            ),
+        )
+        return await table.list_indices()
+    # --8<-- [end:vector_index_async_config]
+
+    assert await main()
+
+
+def test_vector_index_query_ivf(tmp_db):
+    dim = 1536
+    data = [
+        {"id": i, "keywords_embeddings": np.random.random(dim).tolist()}
+        for i in range(512)
+    ]
+    table = tmp_db.create_table("vector_index_query_ivf", data, mode="overwrite")
+    table.create_index(
+        metric="cosine",
+        vector_column_name="keywords_embeddings",
+    )
+
+    # --8<-- [start:vector_index_query_ivf]
+    tbl = table
+    tbl.search(np.random.random((1536))).limit(2).nprobes(20).refine_factor(
+        10
+    ).to_pandas()
+    # --8<-- [end:vector_index_query_ivf]
+
+    df = (
+        table.search(np.random.random((1536)))
+        .limit(2)
+        .nprobes(20)
+        .refine_factor(10)
+        .to_pandas()
+    )
+    assert len(df) == 2
+
+
+def test_vector_index_nprobes(tmp_db):
+    dim = 128
+    data = [
+        {"id": i, "keywords_embeddings": np.random.random(dim).tolist()}
+        for i in range(512)
+    ]
+    table = tmp_db.create_table("vector_index_nprobes", data, mode="overwrite")
+    table.create_index(
+        metric="cosine",
+        vector_column_name="keywords_embeddings",
+    )
+
+    # --8<-- [start:vector_index_nprobes]
+    # Always scan 10 partitions; scan up to 50 only if the initial pass
+    # returns fewer than `limit` results (common with narrow filters).
+    (
+        table.search(np.random.random(128))
+        .minimum_nprobes(10)
+        .maximum_nprobes(50)
+        .where("id > 100")
+        .limit(5)
+        .to_pandas()
+    )
+    # --8<-- [end:vector_index_nprobes]
+
+
+def test_vector_index_distance_range(tmp_db):
+    dim = 128
+    data = [
+        {"id": i, "keywords_embeddings": np.random.random(dim).tolist()}
+        for i in range(256)
+    ]
+    table = tmp_db.create_table("vector_index_distance_range", data, mode="overwrite")
+    table.create_index(
+        metric="cosine",
+        vector_column_name="keywords_embeddings",
+    )
+
+    # --8<-- [start:vector_index_distance_range]
+    # Only return results whose distance falls within [0.0, 0.5).
+    # Useful for near-duplicate detection or thresholded similarity search.
+    (
+        table.search(np.random.random(128))
+        .distance_range(lower_bound=0.0, upper_bound=0.5)
+        .limit(10)
+        .to_pandas()
+    )
+    # --8<-- [end:vector_index_distance_range]
+
+
+def test_vector_index_bypass_recall(tmp_db):
+    dim = 128
+    data = [
+        {"id": i, "keywords_embeddings": np.random.random(dim).tolist()}
+        for i in range(256)
+    ]
+    table = tmp_db.create_table("vector_index_bypass_recall", data, mode="overwrite")
+    table.create_index(
+        metric="cosine",
+        vector_column_name="keywords_embeddings",
+    )
+
+    # --8<-- [start:vector_index_bypass_recall]
+    query = np.random.random(128)
+    k = 10
+
+    # Ground truth: flat (exhaustive) scan, ignoring the ANN index.
+    truth = set(table.search(query).bypass_vector_index().limit(k).to_pandas()["id"])
+
+    # ANN results with the current nprobes setting.
+    ann = set(table.search(query).nprobes(20).limit(k).to_pandas()["id"])
+
+    recall_at_k = len(truth & ann) / k
+    # --8<-- [end:vector_index_bypass_recall]
+    assert 0.0 <= recall_at_k <= 1.0
+
+
+def test_vector_index_custom_name(tmp_db):
+    table = tmp_db.create_table(
+        "vector_index_custom_name",
+        _make_vector_rows(512, 8, column="keywords_embeddings"),
+        mode="overwrite",
+    )
+
+    # --8<-- [start:vector_index_custom_name]
+    # Override the default `{column}_idx` convention by passing `name=...`.
+    table.create_index(
+        metric="cosine",
+        vector_column_name="keywords_embeddings",
+        name="my_custom_index",
+    )
+    table.wait_for_index(["my_custom_index"])
+    print(table.index_stats("my_custom_index"))
+    # --8<-- [end:vector_index_custom_name]
+
+    assert table.index_stats("my_custom_index")
+
+
+def test_vector_index_hnsw(tmp_db):
+    table = tmp_db.create_table(
+        "vector_index_hnsw",
+        _make_vector_rows(64, 16),
+        mode="overwrite",
+    )
+
+    # --8<-- [start:vector_index_build_hnsw]
+    table.create_index(index_type="IVF_HNSW_SQ")
+    # --8<-- [end:vector_index_build_hnsw]
+
+    # --8<-- [start:vector_index_query_hnsw]
+    tbl = table
+    tbl.search(np.random.random((16))).limit(2).to_pandas()
+    # --8<-- [end:vector_index_query_hnsw]
+
+    df = table.search(np.random.random((16))).limit(2).to_pandas()
+    assert len(df) == 2
+
+
+def test_vector_index_binary(tmp_db):
+    table_name = "hamming-index-tbl"
+    ndim = 256
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int64()),
+            pa.field("vector", pa.list_(pa.uint8(), ndim // 8)),
+        ]
+    )
+
+    # --8<-- [start:vector_index_binary_schema]
+    table = tmp_db.create_table(table_name, schema=schema, mode="overwrite")
+    # --8<-- [end:vector_index_binary_schema]
+
+    data = []
+    for i in range(64):
+        vector = np.random.randint(0, 2, size=ndim)
+        vector = np.packbits(vector)
+        data.append({"id": i, "vector": vector})
+
+    # --8<-- [start:vector_index_binary_add_data]
+    table.add(data)
+    # --8<-- [end:vector_index_binary_add_data]
+
+    # --8<-- [start:vector_index_binary_build_index]
+    table.create_index(
+        metric="hamming",
+        vector_column_name="vector",
+        index_type="IVF_FLAT",
+    )
+    # --8<-- [end:vector_index_binary_build_index]
+
+    # --8<-- [start:vector_index_binary_search]
+    query = np.random.randint(0, 2, size=ndim)
+    query = np.packbits(query)
+    df = table.search(query).metric("hamming").limit(10).to_pandas()
+    df.vector = df.vector.apply(np.unpackbits)
+    # --8<-- [end:vector_index_binary_search]
+
+    assert not df.empty
+
+
+def test_quantization_custom_params(tmp_db):
+    table = tmp_db.create_table(
+        "quantization-custom-params",
+        _make_vector_rows(256, 64),
+        mode="overwrite",
+    )
+
+    # --8<-- [start:quantization_custom_params]
+    table.create_index(
+        index_type="IVF_RQ",
+        num_bits=2,
+        max_iterations=100,
+        sample_rate=512,
+    )
+    # --8<-- [end:quantization_custom_params]
+
+    assert table.list_indices()
+
+
+def test_vector_index_check_status(tmp_db):
+    table = tmp_db.create_table(
+        "vector_index_check_status",
+        _make_vector_rows(512, 8, column="keywords_embeddings"),
+        mode="overwrite",
+    )
+    table.create_index(
+        metric="cosine",
+        vector_column_name="keywords_embeddings",
+    )
+
+    # --8<-- [start:vector_index_check_status]
+    index_name = "keywords_embeddings_idx"
+    table.wait_for_index([index_name])
+    print(table.index_stats(index_name))
+    # --8<-- [end:vector_index_check_status]
+
+    assert table.index_stats(index_name)
+
+
+def test_scalar_index_build(tmp_db):
+    table = tmp_db.create_table(
+        "scalar_index_build",
+        [
+            {"book_id": 1, "publisher": "A", "vector": [0.1, 0.2]},
+            {"book_id": 2, "publisher": "B", "vector": [0.2, 0.3]},
+        ],
+        mode="overwrite",
+    )
+    db = tmp_db
+    # --8<-- [start:scalar_index_build]
+    tbl = db.open_table("scalar_index_build")
+    tbl.create_scalar_index("book_id")
+    tbl.create_scalar_index("publisher", index_type="BITMAP")
+    # --8<-- [end:scalar_index_build]
+
+    assert tbl.list_indices()
+
+
+def test_scalar_index_wait(tmp_db):
+    table = tmp_db.create_table(
+        "scalar_index_wait",
+        [{"label": "fiction"}],
+        mode="overwrite",
+    )
+    table.create_scalar_index("label")
+
+    # --8<-- [start:scalar_index_wait]
+    index_name = "label_idx"
+    table.wait_for_index([index_name])
+    # --8<-- [end:scalar_index_wait]
+
+    assert table.list_indices()
+
+
+def test_scalar_index_optimize(tmp_db):
+    table = tmp_db.create_table(
+        "scalar_index_optimize",
+        [{"vector": [7.0, 8.0], "book_id": 3}],
+        mode="overwrite",
+    )
+
+    # --8<-- [start:scalar_index_optimize]
+    table.add([{"vector": [7, 8], "book_id": 4}])
+    table.optimize()
+    # --8<-- [end:scalar_index_optimize]
+
+    result = table.search().where("book_id = 4").limit(10).to_pandas()
+    assert len(result) == 1
+
+
+def test_scalar_index_filter(tmp_db):
+    table = tmp_db.create_table(
+        "books",
+        [
+            {"vector": [1.1, 1.2], "book_id": 1},
+            {"vector": [2.1, 2.2], "book_id": 2},
+        ],
+        mode="overwrite",
+    )
+    db = tmp_db
+    # --8<-- [start:scalar_index_filter]
+    table = db.open_table("books")
+    result = table.search().where("book_id = 2").limit(10).to_pandas()
+    # --8<-- [end:scalar_index_filter]
+
+    assert len(result) == 1
+
+
+def test_scalar_index_prefilter(tmp_db):
+    table = tmp_db.create_table(
+        "book_with_embeddings",
+        [
+            {"vector": [1.2, 1.3], "book_id": 1},
+            {"vector": [4.2, 4.3], "book_id": 2},
+        ],
+        mode="overwrite",
+    )
+    db = tmp_db
+    # --8<-- [start:scalar_index_prefilter]
+    table = db.open_table("book_with_embeddings")
+    table.search([1.2] * 2).where("book_id != 3").limit(10).to_pandas()
+    # --8<-- [end:scalar_index_prefilter]
+
+    result = table.search([1.2] * 2).where("book_id != 3").limit(10).to_pandas()
+    assert len(result) == 2
+
+
+def test_scalar_index_uuid(tmp_db):
+    # --8<-- [start:scalar_index_uuid_type]
+    import pyarrow as pa
+    # --8<-- [end:scalar_index_uuid_type]
+
+    # --8<-- [start:scalar_index_uuid_data]
+    def generate_random_names():
+        base_names = ["Alice", "Bob", "Carla", "David", "Eve", "Frank", "Grace"]
+        letter = random.choice(string.ascii_uppercase)
+        return f"{random.choice(base_names)} {letter}."
+
+    def generate_uuids(num_items):
+        return [uuid.uuid4().bytes for _ in range(num_items)]
+
+    # Generate some UUIDs and random names
+    n = 7
+    uuids = generate_uuids(n)
+    names = [generate_random_names() for _ in range(n)]
+    # --8<-- [end:scalar_index_uuid_data]
+
+    db = tmp_db
+    # --8<-- [start:scalar_index_uuid_table]
+    table_name = "index-on-uuid"
+
+    uuid_array = pa.array(uuids, pa.uuid())
+    name_array = pa.array(names, pa.string())
+    schema = pa.schema(
+        [
+            pa.field("id", pa.uuid()),
+            pa.field("name", pa.string()),
+        ]
+    )
+    data_table = pa.Table.from_arrays([uuid_array, name_array], schema=schema)
+    table = db.create_table(table_name, data=data_table, mode="overwrite")
+    # --8<-- [end:scalar_index_uuid_table]
+
+    # --8<-- [start:scalar_index_uuid_wait]
+    index_name = "id_idx"
+    table.create_scalar_index("id")
+    table.wait_for_index([index_name])
+    # --8<-- [end:scalar_index_uuid_wait]
+
+    # --8<-- [start:scalar_index_uuid_upsert]
+    new_users = [
+        {"id": uuid.uuid4().bytes, "name": "Hannah D."},
+        {"id": uuid.uuid4().bytes, "name": "Ian B."},
+    ]
+    # Insert or update using the UUID index
+    table.merge_insert(
+        "id"
+    ).when_matched_update_all().when_not_matched_insert_all().execute(new_users)
+    # --8<-- [end:scalar_index_uuid_upsert]
+
+    assert table.list_indices()
+    result = table.search().limit(100).to_pandas()
+    assert len(result) == n + len(new_users)
+
+
+@pytest.mark.asyncio
+async def test_scalar_index_nested_fields(mem_db_async):
+    db = mem_db_async
+
+    # --8<-- [start:scalar_index_nested_fields]
+    import pyarrow as pa
+    from lancedb.index import BTree
+
+    metadata_type = pa.struct(
+        [
+            pa.field("user_id", pa.int32()),
+            pa.field("user.id", pa.int32()),
+        ]
+    )
+    data = pa.Table.from_arrays(
+        [
+            pa.array([1, 2, 3], type=pa.int32()),
+            pa.array(
+                [
+                    {"user_id": 10, "user.id": 100},
+                    {"user_id": 20, "user.id": 200},
+                    {"user_id": 30, "user.id": 300},
+                ],
+                type=metadata_type,
+            ),
+        ],
+        names=["user_id", "metadata"],
+    )
+    table = await db.create_table("nested_scalar_index", data)
+
+    # Index a nested struct field.
+    await table.create_index(
+        "metadata.user_id", config=BTree(), name="nested_user_id_idx"
+    )
+
+    # Escape literal dots inside a segment with backticks.
+    await table.create_index(
+        "metadata.`user.id`", config=BTree(), name="escaped_user_id_idx"
+    )
+
+    # `columns` is returned as the canonical path you passed in.
+    for index in await table.list_indices():
+        print(index.name, index.columns)
+    # nested_user_id_idx  ['metadata.user_id']
+    # escaped_user_id_idx ['metadata.`user.id`']
+    # --8<-- [end:scalar_index_nested_fields]
+
+    index_columns = {index.name: index.columns for index in await table.list_indices()}
+    assert index_columns["nested_user_id_idx"] == ["metadata.user_id"]
+    assert index_columns["escaped_user_id_idx"] == ["metadata.`user.id`"]
+
+
+def test_fts_index_create(tmp_db):
+    table = tmp_db.create_table(
+        "fts-index-create",
+        [{"text": "hello world", "vector": [0.1, 0.2]}],
+        mode="overwrite",
+    )
+
+    db = tmp_db
+    # --8<-- [start:fts_index_create]
+    table_name = "fts-index-create"
+    table = db.open_table(table_name)
+    table.create_fts_index("text")
+    # --8<-- [end:fts_index_create]
+
+    assert table.list_indices()
+
+
+def test_fts_index_wait(tmp_db):
+    table = tmp_db.create_table(
+        "fts-index-wait",
+        [{"text": "full text search"}],
+        mode="overwrite",
+    )
+    
+    db = tmp_db
+    # --8<-- [start:fts_index_wait]
+    table_name = "fts-index-wait"
+
+    table = db.open_table(table_name)
+    table.create_fts_index("text")
+
+    index_name = "text_idx"
+    table.wait_for_index([index_name])
+    # --8<-- [end:fts_index_wait]
+
+    assert table.list_indices()
+
+
+def test_fts_index_nested_field(tmp_db):
+    nested_schema = pa.struct([
+        pa.field("text", pa.string()),
+        pa.field("count", pa.int32()),
+    ])
+    schema = pa.schema([
+        pa.field("id", pa.int64()),
+        pa.field("payload", nested_schema),
+    ])
+    tmp_db.create_table(
+        "fts-index-nested",
+        pa.table(
+            {
+                "id": pa.array([1, 2], pa.int64()),
+                "payload": pa.array(
+                    [
+                        {"text": "Frodo was a happy puppy", "count": 1},
+                        {"text": "puppy runs through the meadow", "count": 2},
+                    ],
+                    type=nested_schema,
+                ),
+            },
+            schema=schema,
+        ),
+        mode="overwrite",
+    )
+
+    db = tmp_db
+    # --8<-- [start:fts_index_nested]
+    from lancedb.query import MatchQuery, PhraseQuery
+
+    table = db.open_table("fts-index-nested")
+
+    # Index a text leaf inside a struct column using a dotted path.
+    table.create_fts_index("payload.text", with_position=True)
+
+    # The same dotted path works in MatchQuery and PhraseQuery.
+    matches = (
+        table.search(MatchQuery("puppy", "payload.text")).limit(5).to_list()
+    )
+    phrases = (
+        table.search(PhraseQuery("puppy runs", "payload.text"))
+        .limit(5)
+        .to_list()
+    )
+    # --8<-- [end:fts_index_nested]
+
+    assert len(matches) > 0
+    assert all("puppy" in row["payload"]["text"] for row in matches)
+    assert len(phrases) > 0
+    assert all("puppy runs" in row["payload"]["text"] for row in phrases)
+
+
+@pytest.mark.asyncio
+async def test_fts_index_async(tmp_path, monkeypatch):
+    monkeypatch.chdir(tmp_path)
+
+    # --8<-- [start:fts_index_async]
+    import asyncio
+
+    import lancedb
+    import polars as pl
+    from lancedb.index import FTS
+
+    data = pl.DataFrame(
+        {
+            "id": [1, 2],
+            "text": [
+                "His first language is spanish",
+                "Her first language is english",
+            ],
+        }
+    )
+
+    async def main(data: pl.DataFrame):
+        uri = "ex_lancedb"
+        db = await lancedb.connect_async(uri)
+        tbl = await db.create_table("my_text", data=data, mode="overwrite")
+
+        await tbl.create_index("text", config=FTS(language="English"))
+
+        response = await tbl.search("spanish", query_type="fts")
+        result = await response.limit(1).to_polars()
+        print(result)
+        return result
+
+    if __name__ == "__main__":
+        asyncio.run(main(data))
+    # --8<-- [end:fts_index_async]
+
+    result = await main(data)
+    assert result.height == 1
+
+
+def test_gpu_index_snippets(tmp_db, monkeypatch):
+    table = tmp_db.create_table(
+        "gpu_index",
+        _make_vector_rows(32, 8),
+        mode="overwrite",
+    )
+
+    calls = []
+
+    def fake_create_index(*args, **kwargs):
+        calls.append(kwargs)
+        return None
+
+    monkeypatch.setattr(table, "create_index", fake_create_index)
+
+    # --8<-- [start:gpu_index_cuda]
+    table.create_index(
+        num_partitions=256,
+        num_sub_vectors=96,
+        accelerator="cuda",
+    )
+    # --8<-- [end:gpu_index_cuda]
+
+    # --8<-- [start:gpu_index_mps]
+    table.create_index(
+        num_partitions=256,
+        num_sub_vectors=96,
+        accelerator="mps",
+    )
+    # --8<-- [end:gpu_index_mps]
+
+    assert calls[0]["accelerator"] == "cuda"
+    assert calls[1]["accelerator"] == "mps"
+
+
+def test_reindexing_incremental(tmp_db):
+    table = tmp_db.create_table(
+        "reindexing_incremental",
+        [{"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"}],
+        mode="overwrite",
+    )
+    db = tmp_db
+    # --8<-- [start:reindexing_incremental]
+    table = db.open_table("reindexing_incremental")
+    table.add([{"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"}])
+    table.optimize()
+    # --8<-- [end:reindexing_incremental]
+
+    result = table.search().limit(10).to_pandas()
+    assert len(result) == 2
diff --git a/docs/web-tests/py/test_ingestion_examples.py b/docs/web-tests/py/test_ingestion_examples.py
new file mode 100644
index 000000000..0dedc1458
--- /dev/null
+++ b/docs/web-tests/py/test_ingestion_examples.py
@@ -0,0 +1,739 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+
+def test_create_table_from_dicts():
+    # --8<-- [start:create_table_from_dicts]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        data = [
+            {"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7},
+            {"vector": [0.2, 1.8], "lat": 40.1, "long": -74.1},
+        ]
+        db.create_table("test_table", data, mode="overwrite")
+        tbl = db["test_table"]
+        tbl.head()
+
+        print("Rows:", tbl.count_rows())
+        # --8<-- [end:create_table_from_dicts]
+        assert tbl.count_rows() == 2
+
+
+def test_create_table_conflict_handling():
+    # --8<-- [start:create_table_conflict_handling]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        data = [{"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7}]
+        db.create_table("conflict_table", data)
+        # Idempotent open: reuse the existing table if it exists.
+        # The provided data is ignored; the schema is validated against the
+        # existing table and a mismatch raises an error.
+        tbl = db.create_table("conflict_table", data, exist_ok=True)
+
+        # Overwrite: replace the contents with the provided data.
+        tbl = db.create_table("conflict_table", data, mode="overwrite")
+        print("Rows:", tbl.count_rows())
+    # --8<-- [end:create_table_conflict_handling]
+
+
+def test_create_table_custom_schema():
+    # --8<-- [start:create_table_custom_schema]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        import pyarrow as pa
+
+        custom_schema = pa.schema(
+            [
+                pa.field("vector", pa.list_(pa.float32(), 4)),
+                pa.field("lat", pa.float32()),
+                pa.field("long", pa.float32()),
+            ]
+        )
+
+        data = [
+            {"vector": [1.1, 1.2, 1.3, 1.4], "lat": 45.5, "long": -122.7},
+            {"vector": [0.2, 1.8, 0.4, 3.6], "lat": 40.1, "long": -74.1},
+        ]
+        tbl = db.create_table(
+            "my_table_custom_schema", data, schema=custom_schema, mode="overwrite"
+        )
+
+        print("Rows:", tbl.count_rows())
+        # --8<-- [end:create_table_custom_schema]
+        assert tbl.count_rows() == 2
+
+
+def test_create_table_from_pandas():
+    # --8<-- [start:create_table_from_pandas]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        import pandas as pd
+
+        data = pd.DataFrame(
+            {
+                "vector": [[1.1, 1.2, 1.3, 1.4], [0.2, 1.8, 0.4, 3.6]],
+                "lat": [45.5, 40.1],
+                "long": [-122.7, -74.1],
+            }
+        )
+        db.create_table("my_table_pandas", data, mode="overwrite")
+        db["my_table_pandas"].head()
+    # --8<-- [end:create_table_from_pandas]
+
+
+def test_create_table_from_polars():
+    # --8<-- [start:create_table_from_polars]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        import polars as pl
+
+        data = pl.DataFrame(
+            {
+                "vector": [[3.1, 4.1], [5.9, 26.5]],
+                "item": ["foo", "bar"],
+                "price": [10.0, 20.0],
+            }
+        )
+        tbl = db.create_table("my_table_pl", data, mode="overwrite")
+
+        print("Rows:", tbl.count_rows())
+        # --8<-- [end:create_table_from_polars]
+        assert tbl.count_rows() == 2
+
+
+def test_create_table_from_arrow():
+    # --8<-- [start:create_table_from_arrow]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        import numpy as np
+        import pyarrow as pa
+
+        dim = 16
+        total = 2
+        schema = pa.schema(
+            [
+                pa.field("vector", pa.list_(pa.float16(), dim)),
+                pa.field("text", pa.string()),
+            ]
+        )
+        data = pa.Table.from_arrays(
+            [
+                pa.array(
+                    [np.random.randn(dim).astype(np.float16) for _ in range(total)],
+                    pa.list_(pa.float16(), dim),
+                ),
+                pa.array(["foo", "bar"]),
+            ],
+            ["vector", "text"],
+        )
+        tbl = db.create_table("f16_tbl", data, schema=schema, mode="overwrite")
+
+        print("Rows:", tbl.count_rows())
+        # --8<-- [end:create_table_from_arrow]
+        assert tbl.count_rows() == 2
+
+
+def test_create_table_from_pydantic():
+    # --8<-- [start:create_table_from_pydantic]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        from lancedb.pydantic import LanceModel, Vector
+
+        class Content(LanceModel):
+            movie_id: int
+            vector: Vector(128)
+            genres: str
+            title: str
+            imdb_id: int
+
+            @property
+            def imdb_url(self) -> str:
+                return f"https://www.imdb.com/title/tt{self.imdb_id}"
+
+        tbl = db.create_table("movielens_small", schema=Content, mode="overwrite")
+
+        print("Rows:", tbl.count_rows())
+        # --8<-- [end:create_table_from_pydantic]
+        assert tbl.count_rows() == 0
+
+
+def test_create_table_nested_schema():
+    # --8<-- [start:create_table_nested_schema]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        from lancedb.pydantic import LanceModel, Vector
+        from pydantic import BaseModel
+
+        class Document(BaseModel):
+            content: str
+            source: str
+
+        class NestedSchema(LanceModel):
+            id: str
+            vector: Vector(1536)
+            document: Document
+
+        tbl = db.create_table("nested_table", schema=NestedSchema, mode="overwrite")
+
+        print("Rows:", tbl.count_rows())
+        # --8<-- [end:create_table_nested_schema]
+        assert tbl.count_rows() == 0
+
+
+def test_create_empty_table():
+    # --8<-- [start:create_empty_table]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        import pyarrow as pa
+
+        schema = pa.schema(
+            [
+                pa.field("vector", pa.list_(pa.float32(), 2)),
+                pa.field("item", pa.string()),
+                pa.field("price", pa.float32()),
+            ]
+        )
+        tbl = db.create_table("test_empty_table", schema=schema, mode="overwrite")
+
+        print("Rows:", tbl.count_rows())
+        # --8<-- [end:create_empty_table]
+        assert tbl.count_rows() == 0
+
+
+def test_create_empty_table_pydantic():
+    # --8<-- [start:create_empty_table_pydantic]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        from lancedb.pydantic import LanceModel, Vector
+
+        class Item(LanceModel):
+            vector: Vector(2)
+            item: str
+            price: float
+
+        tbl = db.create_table(
+            "test_empty_table_new", schema=Item.to_arrow_schema(), mode="overwrite"
+        )
+
+        print("Rows:", tbl.count_rows())
+        # --8<-- [end:create_empty_table_pydantic]
+        assert tbl.count_rows() == 0
+
+
+def test_open_existing_table():
+    # --8<-- [start:open_existing_table]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        # Create a table first
+        data = [{"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7}]
+        db.create_table("test_table", data, mode="overwrite")
+
+        # List table names
+        print(db.list_tables().tables)
+
+        # Open existing table
+        tbl = db.open_table("test_table")
+
+        print("Rows:", tbl.count_rows())
+        # --8<-- [end:open_existing_table]
+        assert tbl.count_rows() == 1
+
+
+def test_drop_table():
+    # --8<-- [start:drop_table]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        # Create a table first
+        data = [{"vector": [1.1, 1.2], "lat": 45.5}]
+        db.create_table("my_table", data, mode="overwrite")
+
+        # Drop the table
+        db.drop_table("my_table")
+    # --8<-- [end:drop_table]
+
+
+def test_add_from_dataset():
+    # --8<-- [start:add_from_dataset]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        from pathlib import Path
+
+        import pyarrow as pa
+        import pyarrow.parquet as pq
+
+        data_path = str(Path(uri) / "input.parquet")
+        pq.write_table(pa.table({"id": [1, 2], "name": ["Alice", "Bob"]}), data_path)
+        import pyarrow.dataset as ds
+
+        dataset = ds.dataset(data_path, format="parquet")
+        table = db.create_table("my_table", schema=dataset.schema, mode="overwrite")
+        table.add(dataset)
+
+        print("Rows:", table.count_rows())
+        # --8<-- [end:add_from_dataset]
+        assert table.count_rows() == 2
+
+
+def test_create_table_from_iterator():
+    # --8<-- [start:create_table_from_iterator]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        import pyarrow as pa
+
+        schema = pa.schema(
+            [
+                pa.field("vector", pa.list_(pa.float32(), 4)),
+                pa.field("item", pa.utf8()),
+                pa.field("price", pa.float32()),
+            ]
+        )
+
+        def make_batches():
+            for i in range(5):
+                yield pa.RecordBatch.from_arrays(
+                    [
+                        pa.array(
+                            [[3.1, 4.1, 5.1, 6.1], [5.9, 26.5, 4.7, 32.8]],
+                            pa.list_(pa.float32(), 4),
+                        ),
+                        pa.array(["foo", "bar"]),
+                        pa.array([10.0, 20.0]),
+                    ],
+                    ["vector", "item", "price"],
+                )
+
+        table = db.create_table("batched_table", schema=schema)
+        table.add(make_batches())
+
+        print("Rows:", table.count_rows())
+        # --8<-- [end:create_table_from_iterator]
+        assert table.count_rows() == 10
+
+
+def test_update_operation():
+    # --8<-- [start:update_operation]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        import pyarrow as pa
+
+        table = db.create_table(
+            "users_example",
+            data=pa.table(
+                {
+                    "id": [1, 2],
+                    "name": ["Alice", "Bob"],
+                    "login_count": [10, 20],
+                }
+            ),
+            mode="overwrite",
+        )
+        table.update(where="id = 2", values={"name": "Bobby"})
+
+        print("Rows:", table.count_rows())
+        # --8<-- [end:update_operation]
+        assert table.count_rows() == 2
+        assert table.search().where("id = 2").to_list()[0]["name"] == "Bobby"
+
+
+def test_update_using_sql():
+    # --8<-- [start:update_using_sql]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        import pyarrow as pa
+
+        table = db.create_table(
+            "users_example",
+            data=pa.table(
+                {
+                    "id": [1, 2],
+                    "name": ["Alice", "Bob"],
+                    "login_count": [10, 20],
+                }
+            ),
+            mode="overwrite",
+        )
+        table.update(where="id = 2", values_sql={"login_count": "login_count + 1"})
+
+        print("Rows:", table.count_rows())
+        # --8<-- [end:update_using_sql]
+        assert table.count_rows() == 2
+        assert table.search().where("id = 2").to_list()[0]["login_count"] == 21
+
+
+def test_merge_matched_update_only():
+    # --8<-- [start:merge_matched_update_only]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        import pyarrow as pa
+
+        table = db.create_table(
+            "users_example",
+            data=pa.table(
+                {
+                    "id": [1, 2],
+                    "name": ["Alice", "Bob"],
+                    "login_count": [10, 20],
+                }
+            ),
+            mode="overwrite",
+        )
+
+        incoming_users = pa.table(
+            {
+                "id": [2, 3],
+                "name": ["Bobby", "Charlie"],
+                "login_count": [21, 5],
+            }
+        )
+
+        (table.merge_insert("id").when_matched_update_all().execute(incoming_users))
+
+        print("Rows:", table.count_rows())
+        # --8<-- [end:merge_matched_update_only]
+        assert table.count_rows() == 2
+
+
+def test_insert_if_not_exists():
+    # --8<-- [start:insert_if_not_exists]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        import pyarrow as pa
+
+        table = db.create_table(
+            "users_example",
+            data=pa.table(
+                {
+                    "id": [1, 2],
+                    "name": ["Alice", "Bob"],
+                    "login_count": [10, 20],
+                }
+            ),
+            mode="overwrite",
+        )
+
+        incoming_users = pa.table(
+            {
+                "id": [2, 3],
+                "name": ["Bobby", "Charlie"],
+                "login_count": [21, 5],
+            }
+        )
+
+        (table.merge_insert("id").when_not_matched_insert_all().execute(incoming_users))
+
+        print("Rows:", table.count_rows())
+        # --8<-- [end:insert_if_not_exists]
+        assert table.count_rows() == 3
+
+
+def test_merge_update_insert():
+    # --8<-- [start:merge_update_insert]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        import pyarrow as pa
+
+        table = db.create_table(
+            "users_example",
+            data=pa.table(
+                {
+                    "id": [1, 2],
+                    "name": ["Alice", "Bob"],
+                    "login_count": [10, 20],
+                }
+            ),
+            mode="overwrite",
+        )
+
+        incoming_users = pa.table(
+            {
+                "id": [2, 3],
+                "name": ["Bobby", "Charlie"],
+                "login_count": [21, 5],
+            }
+        )
+
+        (
+            table.merge_insert("id")
+            .when_matched_update_all()
+            .when_not_matched_insert_all()
+            .execute(incoming_users)
+        )
+
+        print("Rows:", table.count_rows())
+        # --8<-- [end:merge_update_insert]
+        assert table.count_rows() == 3
+        assert table.search().where("id = 2").to_list()[0]["login_count"] == 21
+
+
+def test_merge_delete_missing_by_source():
+    # --8<-- [start:merge_delete_missing_by_source]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        import pyarrow as pa
+
+        table = db.create_table(
+            "users_example",
+            data=pa.table(
+                {
+                    "id": [1, 2, 3],
+                    "name": ["Alice", "Bob", "Charlie"],
+                    "login_count": [10, 20, 5],
+                }
+            ),
+            mode="overwrite",
+        )
+
+        incoming_users = pa.table(
+            {
+                "id": [2, 3],
+                "name": ["Bobby", "Charlie"],
+                "login_count": [21, 5],
+            }
+        )
+
+        (
+            table.merge_insert("id")
+            .when_matched_update_all()
+            .when_not_matched_insert_all()
+            .when_not_matched_by_source_delete()
+            .execute(incoming_users)
+        )
+
+        print("Rows:", table.count_rows())
+        # --8<-- [end:merge_delete_missing_by_source]
+        assert table.count_rows() == 2
+        assert table.count_rows("id = 1") == 0
+
+
+def test_merge_partial_columns():
+    # --8<-- [start:merge_partial_columns]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        import pyarrow as pa
+
+        table = db.create_table(
+            "users_example",
+            data=pa.table(
+                {
+                    "id": [1, 2],
+                    "name": ["Alice", "Bob"],
+                    "login_count": [10, 20],
+                }
+            ),
+            mode="overwrite",
+        )
+
+        incoming_users = pa.table(
+            {
+                "id": [2, 3],
+                "name": ["Bobby", "Charlie"],
+            }
+        )
+
+        (
+            table.merge_insert("id")
+            .when_matched_update_all()
+            .when_not_matched_insert_all()
+            .execute(incoming_users)
+        )
+
+        print("Rows:", table.count_rows())
+        # --8<-- [end:merge_partial_columns]
+        assert table.count_rows() == 3
+        assert table.search().where("id = 3").to_list()[0]["login_count"] is None
+
+
+def test_write_workflow():
+    # --8<-- [start:write_workflow]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        table = db.create_table(
+            "users",
+            [
+                {"id": 1, "name": "Alice", "login_count": 10},
+                {"id": 2, "name": "Bob", "login_count": 20},
+            ],
+        )
+        table.add([{"id": 3, "name": "Charlie", "login_count": 5}])
+        table.update(where="id = 2", values={"name": "Bobby"})
+        table.update(where="id = 2", values_sql={"login_count": "login_count + 1"})
+        (
+            table.merge_insert("id")
+            .when_matched_update_all()
+            .when_not_matched_insert_all()
+            .execute(
+                [
+                    {"id": 3, "name": "Charlie", "login_count": 6},
+                    {"id": 4, "name": "Dana", "login_count": 1},
+                ]
+            )
+        )
+        print("Rows selected for deletion:", table.search().where("id = 4").to_list())
+        table.delete("id = 4")
+        rows = sorted(table.search().to_list(), key=lambda row: row["id"])
+        print(rows)
+    # --8<-- [end:write_workflow]
+
+    assert rows == [
+        {"id": 1, "name": "Alice", "login_count": 10},
+        {"id": 2, "name": "Bobby", "login_count": 21},
+        {"id": 3, "name": "Charlie", "login_count": 6},
+    ]
+
+
+def test_append_rows():
+    # --8<-- [start:append_rows]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        table = db.create_table("users", [{"id": 1, "name": "Alice"}])
+        table.add([{"id": 2, "name": "Bob"}, {"id": 3, "name": "Charlie"}])
+        rows = sorted(table.search().to_list(), key=lambda row: row["id"])
+        print(rows)
+    # --8<-- [end:append_rows]
+
+    assert [row["id"] for row in rows] == [1, 2, 3]
+
+
+def test_delete_rows():
+    # --8<-- [start:delete_rows]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        table = db.create_table(
+            "users",
+            [
+                {"id": 1, "name": "Alice"},
+                {"id": 2, "name": "Bob"},
+                {"id": 3, "name": "Charlie"},
+            ],
+        )
+        predicate = "id = 3"
+        print("Selected:", table.search().where(predicate).to_list())
+        table.delete(predicate)
+        rows = sorted(table.search().to_list(), key=lambda row: row["id"])
+        print("Remaining:", rows)
+    # --8<-- [end:delete_rows]
+
+    assert rows == [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]
+
+
+def test_add_progress():
+    # --8<-- [start:add_progress]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    def report_progress(progress):
+        print(f"Rows written: {progress['output_rows']}")
+
+    with TemporaryDirectory(prefix="lancedb-ingestion-") as uri:
+        db = lancedb.connect(uri)
+        table = db.create_table("users", [{"id": 1, "name": "Alice"}])
+        table.add(
+            [{"id": 2, "name": "Bob"}, {"id": 3, "name": "Charlie"}],
+            progress=report_progress,
+        )
+        rows = sorted(table.search().to_list(), key=lambda row: row["id"])
+        print("After insertion:", rows)
+    # --8<-- [end:add_progress]
+
+    assert rows == [
+        {"id": 1, "name": "Alice"},
+        {"id": 2, "name": "Bob"},
+        {"id": 3, "name": "Charlie"},
+    ]
diff --git a/docs/web-tests/py/test_integrations.py b/docs/web-tests/py/test_integrations.py
new file mode 100644
index 000000000..577847504
--- /dev/null
+++ b/docs/web-tests/py/test_integrations.py
@@ -0,0 +1,1794 @@
+import os
+
+import pytest
+
+
+def require_env(var_name: str) -> str:
+    """Skip the test unless the required environment variable is provided."""
+    value = os.environ.get(var_name)
+    if not value:
+        pytest.skip(f"Set {var_name} to run this integration snippet")
+    return value
+
+
+def require_flag(flag_name: str) -> None:
+    """Skip unless a generic feature flag is set to a truthy value."""
+    value = os.environ.get(flag_name, "").lower()
+    if value not in {"1", "true", "yes", "on"}:
+        pytest.skip(f"Enable {flag_name} to run this integration snippet")
+
+
+def test_embedding_openai_basic() -> None:
+    require_env("OPENAI_API_KEY")
+
+    # --8<-- [start:embedding_openai_basic]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+
+    db_path = Path(tempfile.mkdtemp()) / "openai-embeddings"
+    db = lancedb.connect(str(db_path))
+    func = get_registry().get("openai").create(name="text-embedding-ada-002")
+
+    class Words(LanceModel):
+        text: str = func.SourceField()
+        vector: Vector(func.ndims()) = func.VectorField()
+
+    table = db.create_table("words", schema=Words, mode="overwrite")
+    table.add(
+        [
+            {"text": "hello world"},
+            {"text": "goodbye world"},
+        ]
+    )
+
+    query = "greetings"
+    actual = table.search(query).limit(1).to_pydantic(Words)[0]
+    print(actual.text)
+    # --8<-- [end:embedding_openai_basic]
+
+
+def test_embedding_aws_usage() -> None:
+    require_flag("RUN_AWS_BEDROCK_SNIPPETS")
+
+    # --8<-- [start:embedding_aws_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    import pandas as pd
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+
+    model = get_registry().get("bedrock-text").create()
+
+    class TextModel(LanceModel):
+        text: str = model.SourceField()
+        vector: Vector(model.ndims()) = model.VectorField()
+
+    df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "bedrock-demo"))
+    tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+
+    tbl.add(df)
+    rs = tbl.search("hello").limit(1).to_pandas()
+    print(rs.head())
+    # --8<-- [end:embedding_aws_usage]
+
+
+def test_embedding_cohere_usage() -> None:
+    require_env("COHERE_API_KEY")
+
+    # --8<-- [start:embedding_cohere_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import EmbeddingFunctionRegistry
+    from lancedb.pydantic import LanceModel, Vector
+
+    cohere = (
+        EmbeddingFunctionRegistry.get_instance()
+        .get("cohere")
+        .create(name="embed-multilingual-v2.0")
+    )
+
+    class TextModel(LanceModel):
+        text: str = cohere.SourceField()
+        vector: Vector(cohere.ndims()) = cohere.VectorField()
+
+    data = [{"text": "hello world"}, {"text": "goodbye world"}]
+
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "cohere-demo"))
+    tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+    tbl.add(data)
+    # --8<-- [end:embedding_cohere_usage]
+
+
+def test_embedding_gemini_usage() -> None:
+    require_flag("RUN_GEMINI_SNIPPETS")
+
+    # --8<-- [start:embedding_gemini_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    import pandas as pd
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+
+    model = get_registry().get("gemini-text").create()
+
+    class TextModel(LanceModel):
+        text: str = model.SourceField()
+        vector: Vector(model.ndims()) = model.VectorField()
+
+    df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "gemini-demo"))
+    tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+
+    tbl.add(df)
+    rs = tbl.search("hello").limit(1).to_pandas()
+    print(rs.head())
+    # --8<-- [end:embedding_gemini_usage]
+
+
+def test_embedding_huggingface_usage() -> None:
+    require_flag("RUN_HUGGINGFACE_SNIPPETS")
+
+    # --8<-- [start:embedding_huggingface_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    import pandas as pd
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "huggingface-demo"))
+    model = get_registry().get("huggingface").create(name="facebook/bart-base")
+
+    class Words(LanceModel):
+        text: str = model.SourceField()
+        vector: Vector(model.ndims()) = model.VectorField()
+
+    df = pd.DataFrame({"text": ["hi hello sayonara", "goodbye world"]})
+    table = db.create_table("greets", schema=Words)
+    table.add(df)
+    query = "old greeting"
+    actual = table.search(query).limit(1).to_pydantic(Words)[0]
+    print(actual.text)
+    # --8<-- [end:embedding_huggingface_usage]
+
+
+def test_frameworks_lerobot_lancedb_usage() -> None:
+    require_flag("RUN_LEROBOT_LANCEDB_SNIPPETS")
+
+    # --8<-- [start:frameworks_lerobot_lancedb_image_dataset]
+    from lerobot_lancedb import LeRobotLanceDataset
+
+    dataset = LeRobotLanceDataset(
+        repo_id="your-org/your-lerobot-lance-images",
+        delta_timestamps={
+            "observation.images.front": [-0.2, -0.1, 0.0],
+        },
+        return_uint8=True,
+    )
+
+    sample = dataset[0]
+    print(sample["observation.state"].shape)
+    print(sample["action"].shape)
+    # --8<-- [end:frameworks_lerobot_lancedb_image_dataset]
+
+    # --8<-- [start:frameworks_lerobot_lancedb_video_dataset]
+    from lerobot_lancedb import LeRobotLanceVideoDataset
+
+    video_dataset = LeRobotLanceVideoDataset(
+        repo_id="lance-format/lerobot-pusht-lance",
+        delta_timestamps={
+            "observation.images.image": [-0.2, -0.1, 0.0],
+        },
+        return_uint8=True,
+    )
+
+    video_sample = video_dataset[0]
+    print(video_sample["observation.images.image"].shape)
+    # --8<-- [end:frameworks_lerobot_lancedb_video_dataset]
+
+    # --8<-- [start:frameworks_lerobot_open_lance_tables]
+    import lancedb
+
+    db = lancedb.connect("hf://datasets/lance-format/lerobot-pusht-lance/data")
+    frames = db.open_table("frames")
+    episodes = db.open_table("episodes")
+    videos = db.open_table("videos")
+
+    print(len(frames), len(episodes), len(videos))
+    print(frames.schema)
+    # --8<-- [end:frameworks_lerobot_open_lance_tables]
+
+    # --8<-- [start:frameworks_lerobot_filter_frames]
+    frame_rows = (
+        frames.search()
+        .where("episode_index = 0 AND frame_index < 10", prefilter=True)
+        .select(["episode_index", "frame_index", "timestamp", "action"])
+        .limit(10)
+        .to_list()
+    )
+
+    for row in frame_rows:
+        print(row["episode_index"], row["frame_index"], row["timestamp"])
+    # --8<-- [end:frameworks_lerobot_filter_frames]
+
+
+def test_frameworks_stable_worldmodel_usage() -> None:
+    require_flag("RUN_STABLE_WORLDMODEL_SNIPPETS")
+
+    # --8<-- [start:frameworks_stable_worldmodel_collect_lance]
+    import stable_worldmodel as swm
+
+    world = swm.World("swm/PushT-v1", num_envs=8)
+    world.set_policy(your_expert_policy)
+    world.collect("data/pusht_demo.lance", episodes=100, seed=0)
+    # --8<-- [end:frameworks_stable_worldmodel_collect_lance]
+
+    # --8<-- [start:frameworks_stable_worldmodel_load_lance]
+    dataset = swm.data.load_dataset("data/pusht_demo.lance", num_steps=16)
+
+    batch = dataset[0]
+    print(batch.keys())
+    # --8<-- [end:frameworks_stable_worldmodel_load_lance]
+
+    # --8<-- [start:frameworks_stable_worldmodel_convert]
+    swm.data.convert(
+        "data/pusht_demo.lance",
+        "data/pusht_video",
+        dest_format="video",
+        fps=30,
+    )
+    # --8<-- [end:frameworks_stable_worldmodel_convert]
+
+    # --8<-- [start:frameworks_stable_worldmodel_evaluate]
+    from stable_worldmodel.policy import PlanConfig, WorldModelPolicy
+    from stable_worldmodel.solver import CEMSolver
+
+    solver = CEMSolver(model=world_model, num_samples=300)
+    policy = WorldModelPolicy(solver=solver, config=PlanConfig(horizon=10))
+
+    world.set_policy(policy)
+    results = world.evaluate(episodes=50)
+    print(f"Success Rate: {results['success_rate']:.1f}%")
+    # --8<-- [end:frameworks_stable_worldmodel_evaluate]
+
+
+def test_embedding_ibm_usage() -> None:
+    require_flag("RUN_IBM_WATSONX_SNIPPETS")
+
+    # --8<-- [start:embedding_ibm_usage]
+    import os
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import EmbeddingFunctionRegistry
+    from lancedb.pydantic import LanceModel, Vector
+
+    watsonx_embed = (
+        EmbeddingFunctionRegistry.get_instance()
+        .get("watsonx")
+        .create(
+            name="ibm/slate-125m-english-rtrvr",
+            api_key=os.environ.get("WATSONX_API_KEY"),
+            project_id=os.environ.get("WATSONX_PROJECT_ID"),
+        )
+    )
+
+    class TextModel(LanceModel):
+        text: str = watsonx_embed.SourceField()
+        vector: Vector(watsonx_embed.ndims()) = watsonx_embed.VectorField()
+
+    data = [
+        {"text": "hello world"},
+        {"text": "goodbye world"},
+    ]
+
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "watsonx-demo"))
+    tbl = db.create_table("watsonx_test", schema=TextModel, mode="overwrite")
+    tbl.add(data)
+
+    rs = tbl.search("hello").limit(1).to_pandas()
+    print(rs.head())
+    # --8<-- [end:embedding_ibm_usage]
+
+
+def test_embedding_imagebind_examples() -> None:
+    require_flag("RUN_IMAGEBIND_SNIPPETS")
+    pytest.importorskip("imagebind")
+
+    # --8<-- [start:embedding_imagebind_setup]
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+
+    db = lancedb.connect("/tmp/imagebind-db")
+    func = get_registry().get("imagebind").create()
+
+    class ImageBindModel(LanceModel):
+        text: str
+        image_uri: str = func.SourceField()
+        audio_path: str
+        vector: Vector(func.ndims()) = func.VectorField()
+
+    text_list = ["A dog.", "A car", "A bird"]
+    image_paths = [
+        "./assets/dog_image.jpg",
+        "./assets/car_image.jpg",
+        "./assets/bird_image.jpg",
+    ]
+    audio_paths = [
+        "./assets/dog_audio.wav",
+        "./assets/car_audio.wav",
+        "./assets/bird_audio.wav",
+    ]
+
+    inputs = [
+        {"text": a, "audio_path": b, "image_uri": c}
+        for a, b, c in zip(text_list, audio_paths, image_paths)
+    ]
+
+    table = db.create_table("img_bind", schema=ImageBindModel)
+    table.add(inputs)
+    # --8<-- [end:embedding_imagebind_setup]
+
+    # --8<-- [start:embedding_imagebind_image_search]
+    query_image = "./assets/dog_image2.jpg"
+    actual = table.search(query_image).limit(1).to_pydantic(ImageBindModel)[0]
+    print(actual.text == "dog")
+    # --8<-- [end:embedding_imagebind_image_search]
+
+    # --8<-- [start:embedding_imagebind_audio_search]
+    query_audio = "./assets/car_audio2.wav"
+    actual = table.search(query_audio).limit(1).to_pydantic(ImageBindModel)[0]
+    print(actual.text == "car")
+    # --8<-- [end:embedding_imagebind_audio_search]
+
+    # --8<-- [start:embedding_imagebind_text_search]
+    query = "an animal which flies and tweets"
+    actual = table.search(query).limit(1).to_pydantic(ImageBindModel)[0]
+    print(actual.text == "bird")
+    # --8<-- [end:embedding_imagebind_text_search]
+
+
+def test_embedding_colpali_examples() -> None:
+    require_flag("RUN_COLPALI_SNIPPETS")
+    pytest.importorskip("colpali_engine")
+
+    # --8<-- [start:embedding_colpali_setup]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    import pandas as pd
+    import requests
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, MultiVector
+
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "colpali-demo"))
+    func = get_registry().get("colpali").create()
+
+    class Images(LanceModel):
+        label: str
+        image_uri: str = func.SourceField()
+        image_bytes: bytes = func.SourceField()
+        vector: MultiVector(func.ndims()) = func.VectorField()
+        vec_from_bytes: MultiVector(func.ndims()) = func.VectorField()
+
+    table = db.create_table("images", schema=Images)
+    labels = ["cat", "dog", "horse"]
+    uris = [
+        "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
+        "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
+        "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
+    ]
+    image_bytes = [requests.get(uri).content for uri in uris]
+    table.add(
+        pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": image_bytes})
+    )
+    # --8<-- [end:embedding_colpali_setup]
+
+    # --8<-- [start:embedding_colpali_text_search]
+    actual = (
+        table.search("a furry pet", vector_column_name="vector")
+        .limit(1)
+        .to_pydantic(Images)[0]
+    )
+    print(actual.label)
+    # --8<-- [end:embedding_colpali_text_search]
+
+
+def test_embedding_instructor_usage() -> None:
+    require_flag("RUN_INSTRUCTOR_SNIPPETS")
+
+    # --8<-- [start:embedding_instructor_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+
+    instructor = (
+        get_registry()
+        .get("instructor")
+        .create(
+            source_instruction="represent the document for retrieval",
+            query_instruction="represent the document for retrieving the most similar documents",
+        )
+    )
+
+    class Schema(LanceModel):
+        vector: Vector(instructor.ndims()) = instructor.VectorField()
+        text: str = instructor.SourceField()
+
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "instructor-demo"))
+    tbl = db.create_table("test", schema=Schema, mode="overwrite")
+
+    texts = [
+        {
+            "text": "Capitalism has been dominant in the Western world since the end of feudalism."
+        },
+        {
+            "text": "The disparate impact theory is especially controversial under the Fair Housing Act."
+        },
+        {
+            "text": "Disparate impact in United States labor law refers to practices in employment."
+        },
+    ]
+
+    tbl.add(texts)
+    # --8<-- [end:embedding_instructor_usage]
+
+
+def test_embedding_jina_text() -> None:
+    require_env("JINA_API_KEY")
+
+    # --8<-- [start:embedding_jina_text]
+    import os
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import EmbeddingFunctionRegistry
+    from lancedb.pydantic import LanceModel, Vector
+
+    os.environ["JINA_API_KEY"] = os.environ["JINA_API_KEY"]
+
+    jina_embed = (
+        EmbeddingFunctionRegistry.get_instance()
+        .get("jina")
+        .create(name="jina-embeddings-v2-base-en")
+    )
+
+    class TextModel(LanceModel):
+        text: str = jina_embed.SourceField()
+        vector: Vector(jina_embed.ndims()) = jina_embed.VectorField()
+
+    data = [{"text": "hello world"}, {"text": "goodbye world"}]
+
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "jina-text"))
+    tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+
+    tbl.add(data)
+    # --8<-- [end:embedding_jina_text]
+
+
+def test_embedding_jina_multimodal() -> None:
+    require_flag("RUN_JINA_MULTIMODAL_SNIPPETS")
+
+    # --8<-- [start:embedding_jina_multimodal]
+    import os
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    import pandas as pd
+    import requests
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+
+    os.environ["JINA_API_KEY"] = os.environ.get("JINA_API_KEY", "jina_*")
+
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "jina-images"))
+    func = get_registry().get("jina").create()
+
+    class Images(LanceModel):
+        label: str
+        image_uri: str = func.SourceField()
+        image_bytes: bytes = func.SourceField()
+        vector: Vector(func.ndims()) = func.VectorField()
+        vec_from_bytes: Vector(func.ndims()) = func.VectorField()
+
+    table = db.create_table("images", schema=Images)
+    labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
+    uris = [
+        "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
+        "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
+        "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
+        "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
+        "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
+        "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
+    ]
+    image_bytes = [requests.get(uri).content for uri in uris]
+    table.add(
+        pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": image_bytes})
+    )
+    # --8<-- [end:embedding_jina_multimodal]
+
+
+def test_embedding_ollama_usage() -> None:
+    require_flag("RUN_OLLAMA_SNIPPETS")
+
+    # --8<-- [start:embedding_ollama_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "ollama-demo"))
+    func = get_registry().get("ollama").create(name="nomic-embed-text")
+
+    class Words(LanceModel):
+        text: str = func.SourceField()
+        vector: Vector(func.ndims()) = func.VectorField()
+
+    table = db.create_table("words", schema=Words, mode="overwrite")
+    table.add(
+        [
+            {"text": "hello world"},
+            {"text": "goodbye world"},
+        ]
+    )
+
+    query = "greetings"
+    actual = table.search(query).limit(1).to_pydantic(Words)[0]
+    print(actual.text)
+    # --8<-- [end:embedding_ollama_usage]
+
+
+def test_embedding_openclip_examples() -> None:
+    require_flag("RUN_OPENCLIP_SNIPPETS")
+
+    # --8<-- [start:embedding_openclip_setup]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    import pandas as pd
+    import requests
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "openclip-demo"))
+    func = get_registry().get("open-clip").create()
+
+    class Images(LanceModel):
+        label: str
+        image_uri: str = func.SourceField()
+        image_bytes: bytes = func.SourceField()
+        vector: Vector(func.ndims()) = func.VectorField()
+        vec_from_bytes: Vector(func.ndims()) = func.VectorField()
+
+    table = db.create_table("images", schema=Images)
+    labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
+    uris = [
+        "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
+        "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
+        "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
+        "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
+        "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
+        "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
+    ]
+    image_bytes = [requests.get(uri).content for uri in uris]
+    table.add(
+        pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": image_bytes})
+    )
+    # --8<-- [end:embedding_openclip_setup]
+
+    # --8<-- [start:embedding_openclip_text_search]
+    actual = table.search("man's best friend").limit(1).to_pydantic(Images)[0]
+    print(actual.label)
+
+    frombytes = (
+        table.search("man's best friend", vector_column_name="vec_from_bytes")
+        .limit(1)
+        .to_pydantic(Images)[0]
+    )
+    print(frombytes.label)
+    # --8<-- [end:embedding_openclip_text_search]
+
+    # --8<-- [start:embedding_openclip_image_search]
+    import io
+
+    from PIL import Image
+
+    query_image_uri = "http://farm1.staticflickr.com/200/467715466_ed4a31801f_z.jpg"
+    image_bytes = requests.get(query_image_uri).content
+    query_image = Image.open(io.BytesIO(image_bytes))
+    actual = table.search(query_image).limit(1).to_pydantic(Images)[0]
+    print(actual.label == "dog")
+
+    other = (
+        table.search(query_image, vector_column_name="vec_from_bytes")
+        .limit(1)
+        .to_pydantic(Images)[0]
+    )
+    print(other.label)
+    # --8<-- [end:embedding_openclip_image_search]
+
+
+def test_embedding_sentence_transformers_baai() -> None:
+    require_flag("RUN_SENTENCE_TRANSFORMERS_SNIPPETS")
+
+    # --8<-- [start:embedding_sentence_transformers_baai]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "sentence-transformers"))
+    model = (
+        get_registry()
+        .get("sentence-transformers")
+        .create(name="BAAI/bge-small-en-v1.5", device="cpu")
+    )
+
+    class Words(LanceModel):
+        text: str = model.SourceField()
+        vector: Vector(model.ndims()) = model.VectorField()
+
+    table = db.create_table("words", schema=Words)
+    table.add(
+        [
+            {"text": "hello world"},
+            {"text": "goodbye world"},
+        ]
+    )
+
+    query = "greetings"
+    actual = table.search(query).limit(1).to_pydantic(Words)[0]
+    print(actual.text)
+    # --8<-- [end:embedding_sentence_transformers_baai]
+
+
+def test_embedding_voyageai_usage() -> None:
+    require_env("VOYAGE_API_KEY")
+
+    # --8<-- [start:embedding_voyageai_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import EmbeddingFunctionRegistry
+    from lancedb.pydantic import LanceModel, Vector
+
+    voyageai = (
+        EmbeddingFunctionRegistry.get_instance().get("voyageai").create(name="voyage-3")
+    )
+
+    class TextModel(LanceModel):
+        text: str = voyageai.SourceField()
+        vector: Vector(voyageai.ndims()) = voyageai.VectorField()
+
+    data = [{"text": "hello world"}, {"text": "goodbye world"}]
+
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "voyageai-demo"))
+    tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+
+    tbl.add(data)
+    # --8<-- [end:embedding_voyageai_usage]
+
+
+def test_embedding_voyageai_multimodal() -> None:
+    require_env("VOYAGE_API_KEY")
+
+    # --8<-- [start:embedding_voyageai_multimodal]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import EmbeddingFunctionRegistry
+    from lancedb.pydantic import LanceModel, Vector
+
+    # Create multimodal embedding function with custom dimension
+    voyageai = (
+        EmbeddingFunctionRegistry.get_instance()
+        .get("voyageai")
+        .create(name="voyage-multimodal-3.5", output_dimension=512)
+    )
+
+    class ImageModel(LanceModel):
+        image_uri: str = voyageai.SourceField()
+        vector: Vector(voyageai.ndims()) = voyageai.VectorField()
+
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "voyageai-multimodal"))
+    tbl = db.create_table("images", schema=ImageModel, mode="overwrite")
+
+    # Add images using URLs
+    tbl.add(
+        [
+            {"image_uri": "https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/300px-PNG_transparency_demonstration_1.png"},
+        ]
+    )
+
+    # Search with text query
+    results = tbl.search("dice").limit(1).to_list()
+    print(results)
+    # --8<-- [end:embedding_voyageai_multimodal]
+
+
+# Reranking integrations
+
+
+def test_reranking_answerdotai_usage() -> None:
+    require_flag("RUN_RERANKER_SNIPPETS")
+
+    # --8<-- [start:reranking_answerdotai_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.rerankers import AnswerdotaiRerankers
+
+    embedder = get_registry().get("sentence-transformers").create()
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "answerdotai-rerank"))
+
+    class Schema(LanceModel):
+        text: str = embedder.SourceField()
+        vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+    data = [
+        {"text": "hello world"},
+        {"text": "goodbye world"},
+    ]
+    tbl = db.create_table("test", schema=Schema)
+    tbl.add(data)
+    reranker = AnswerdotaiRerankers()
+
+    # Run vector search with a reranker
+    result = tbl.search("hello").rerank(reranker=reranker).to_list()
+
+    # Build the full-text index before either FTS or hybrid search.
+    tbl.create_fts_index("text", replace=True)
+
+    # Run FTS search with a reranker
+    result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
+
+    # Run hybrid search with a reranker
+    result = (
+        tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+    )
+    # --8<-- [end:reranking_answerdotai_usage]
+
+
+def test_reranking_cohere_usage() -> None:
+    require_flag("RUN_RERANKER_SNIPPETS")
+    os.environ["COHERE_API_KEY"] = require_env("COHERE_API_KEY")
+
+    # --8<-- [start:reranking_cohere_usage]
+    import os
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.rerankers import CohereReranker
+
+    embedder = get_registry().get("sentence-transformers").create()
+    db = lancedb.connect("~/.lancedb")
+
+    class Schema(LanceModel):
+        text: str = embedder.SourceField()
+        vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+    data = [
+        {"text": "hello world"},
+        {"text": "goodbye world"},
+    ]
+    tbl = db.create_table("test", schema=Schema, mode="overwrite")
+    tbl.add(data)
+    reranker = CohereReranker(api_key=os.environ["COHERE_API_KEY"])
+
+    # Run vector search with a reranker
+    result = tbl.search("hello").rerank(reranker=reranker).to_list()
+
+    # Run FTS search with a reranker
+    result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
+
+    # Run hybrid search with a reranker
+    tbl.create_fts_index("text", replace=True)
+    result = (
+        tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+    )
+    # --8<-- [end:reranking_cohere_usage]
+
+
+def test_reranking_colbert_usage() -> None:
+    require_flag("RUN_RERANKER_SNIPPETS")
+
+    # --8<-- [start:reranking_colbert_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.rerankers import ColbertReranker
+
+    embedder = get_registry().get("sentence-transformers").create()
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "colbert-rerank"))
+
+    class Schema(LanceModel):
+        text: str = embedder.SourceField()
+        vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+    data = [
+        {"text": "hello world"},
+        {"text": "goodbye world"},
+    ]
+    tbl = db.create_table("test", schema=Schema)
+    tbl.add(data)
+    reranker = ColbertReranker()
+
+    # Run vector search with a reranker
+    result = tbl.search("hello").rerank(reranker=reranker).to_list()
+
+    # Build the full-text index before either FTS or hybrid search.
+    tbl.create_fts_index("text", replace=True)
+
+    # Run FTS search with a reranker
+    result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
+
+    # Run hybrid search with a reranker
+    result = (
+        tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+    )
+    # --8<-- [end:reranking_colbert_usage]
+
+
+def test_reranking_cross_encoder_usage() -> None:
+    require_flag("RUN_RERANKER_SNIPPETS")
+
+    # --8<-- [start:reranking_cross_encoder_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.rerankers import CrossEncoderReranker
+
+    embedder = get_registry().get("sentence-transformers").create()
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "cross-encoder-rerank"))
+
+    class Schema(LanceModel):
+        text: str = embedder.SourceField()
+        vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+    data = [
+        {"text": "hello world"},
+        {"text": "goodbye world"},
+    ]
+    tbl = db.create_table("test", schema=Schema)
+    tbl.add(data)
+    reranker = CrossEncoderReranker()
+
+    # Run vector search with a reranker
+    result = tbl.search("hello").rerank(reranker=reranker).to_list()
+
+    # Build the full-text index before either FTS or hybrid search.
+    tbl.create_fts_index("text", replace=True)
+
+    # Run FTS search with a reranker
+    result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
+
+    # Run hybrid search with a reranker
+    result = (
+        tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+    )
+    # --8<-- [end:reranking_cross_encoder_usage]
+
+
+def test_reranking_jina_usage() -> None:
+    require_flag("RUN_RERANKER_SNIPPETS")
+    os.environ["JINA_API_KEY"] = require_env("JINA_API_KEY")
+
+    # --8<-- [start:reranking_jina_usage]
+    import os
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.rerankers import JinaReranker
+
+    embedder = get_registry().get("jina").create()
+    db = lancedb.connect("~/.lancedb")
+
+    class Schema(LanceModel):
+        text: str = embedder.SourceField()
+        vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+    data = [
+        {"text": "hello world"},
+        {"text": "goodbye world"},
+    ]
+    tbl = db.create_table("test", schema=Schema, mode="overwrite")
+    tbl.add(data)
+    reranker = JinaReranker(api_key=os.environ["JINA_API_KEY"])
+
+    # Run vector search with a reranker
+    result = tbl.search("hello").rerank(reranker=reranker).to_list()
+
+    # Run FTS search with a reranker
+    result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
+
+    # Run hybrid search with a reranker
+    tbl.create_fts_index("text", replace=True)
+    result = (
+        tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+    )
+    # --8<-- [end:reranking_jina_usage]
+
+
+def test_reranking_linear_combination_usage() -> None:
+    require_flag("RUN_RERANKER_SNIPPETS")
+
+    # --8<-- [start:reranking_linear_combination_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.rerankers import LinearCombinationReranker
+
+    embedder = get_registry().get("sentence-transformers").create()
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "linear-combination-rerank"))
+
+    class Schema(LanceModel):
+        text: str = embedder.SourceField()
+        vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+    data = [
+        {"text": "hello world"},
+        {"text": "goodbye world"},
+    ]
+    tbl = db.create_table("test", schema=Schema)
+    tbl.add(data)
+    reranker = LinearCombinationReranker()
+
+    # Run hybrid search with a reranker
+    tbl.create_fts_index("text", replace=True)
+    result = (
+        tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+    )
+    # --8<-- [end:reranking_linear_combination_usage]
+
+
+def test_reranking_openai_usage() -> None:
+    require_flag("RUN_RERANKER_SNIPPETS")
+    os.environ["OPENAI_API_KEY"] = require_env("OPENAI_API_KEY")
+
+    # --8<-- [start:reranking_openai_usage]
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.rerankers import OpenaiReranker
+
+    embedder = get_registry().get("sentence-transformers").create()
+    db = lancedb.connect("~/.lancedb")
+
+    class Schema(LanceModel):
+        text: str = embedder.SourceField()
+        vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+    data = [
+        {"text": "hello world"},
+        {"text": "goodbye world"},
+    ]
+    tbl = db.create_table("test", schema=Schema, mode="overwrite")
+    tbl.add(data)
+    reranker = OpenaiReranker()
+
+    # Run vector search with a reranker
+    result = tbl.search("hello").rerank(reranker=reranker).to_list()
+
+    # Run FTS search with a reranker
+    result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
+
+    # Run hybrid search with a reranker
+    tbl.create_fts_index("text", replace=True)
+    result = (
+        tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+    )
+    # --8<-- [end:reranking_openai_usage]
+
+
+def test_reranking_rrf_usage() -> None:
+    require_flag("RUN_RERANKER_SNIPPETS")
+
+    # --8<-- [start:reranking_rrf_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.rerankers import RRFReranker
+
+    embedder = get_registry().get("sentence-transformers").create()
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "rrf-rerank"))
+
+    class Schema(LanceModel):
+        text: str = embedder.SourceField()
+        vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+    data = [
+        {"text": "hello world"},
+        {"text": "goodbye world"},
+    ]
+    tbl = db.create_table("test", schema=Schema)
+    tbl.add(data)
+    reranker = RRFReranker()
+
+    # Run hybrid search with a reranker
+    tbl.create_fts_index("text", replace=True)
+    result = (
+        tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+    )
+    # --8<-- [end:reranking_rrf_usage]
+
+
+def test_reranking_mrr_usage() -> None:
+    require_flag("RUN_RERANKER_SNIPPETS")
+
+    # --8<-- [start:reranking_mrr_usage]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.rerankers import MRRReranker
+
+    embedder = get_registry().get("sentence-transformers").create()
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "mrr-rerank"))
+
+    class Schema(LanceModel):
+        text: str = embedder.SourceField()
+        vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+    data = [
+        {"text": "hello world"},
+        {"text": "goodbye world"},
+    ]
+    tbl = db.create_table("test", schema=Schema)
+    tbl.add(data)
+    reranker = MRRReranker(weight_vector=0.7, weight_fts=0.3)
+
+    # Run hybrid search with a reranker
+    tbl.create_fts_index("text", replace=True)
+    result = (
+        tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+    )
+
+    # Run multivector search across multiple vector columns
+    rs1 = tbl.search("hello").limit(10).with_row_id(True).to_arrow()
+    rs2 = tbl.search("greeting").limit(10).with_row_id(True).to_arrow()
+    combined = MRRReranker().rerank_multivector([rs1, rs2])
+    # --8<-- [end:reranking_mrr_usage]
+
+
+def test_reranking_voyageai_usage() -> None:
+    require_flag("RUN_RERANKER_SNIPPETS")
+    os.environ["VOYAGE_API_KEY"] = require_env("VOYAGE_API_KEY")
+
+    # --8<-- [start:reranking_voyageai_usage]
+    import os
+
+    import lancedb
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.rerankers import VoyageAIReranker
+
+    embedder = get_registry().get("sentence-transformers").create()
+    db = lancedb.connect("~/.lancedb")
+
+    class Schema(LanceModel):
+        text: str = embedder.SourceField()
+        vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+    data = [
+        {"text": "hello world"},
+        {"text": "goodbye world"},
+    ]
+    tbl = db.create_table("test", schema=Schema, mode="overwrite")
+    tbl.add(data)
+    reranker = VoyageAIReranker(model_name="rerank-2")
+
+    # Run vector search with a reranker
+    result = tbl.search("hello").rerank(reranker=reranker).to_list()
+
+    # Run FTS search with a reranker
+    result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
+
+    # Run hybrid search with a reranker
+    tbl.create_fts_index("text", replace=True)
+    result = (
+        tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+    )
+    # --8<-- [end:reranking_voyageai_usage]
+
+
+# Framework integrations
+
+
+def test_frameworks_langchain_examples() -> None:
+    require_flag("RUN_LANGCHAIN_SNIPPETS")
+    pytest.importorskip("langchain")
+    pytest.importorskip("langchain_openai")
+    pytest.importorskip("langchain_text_splitters")
+
+    # --8<-- [start:frameworks_langchain_quick_start]
+    import os
+
+    from langchain.document_loaders import TextLoader
+    from langchain.vectorstores import LanceDB
+    from langchain_openai import OpenAIEmbeddings
+    from langchain_text_splitters import CharacterTextSplitter
+
+    os.environ["OPENAI_API_KEY"] = "sk-..."
+
+    loader = TextLoader(
+        "../../modules/state_of_the_union.txt"
+    )  # Replace with your data path
+    documents = loader.load()
+
+    documents = CharacterTextSplitter().split_documents(documents)
+    embeddings = OpenAIEmbeddings()
+
+    docsearch = LanceDB.from_documents(documents, embeddings)
+    query = "What did the president say about Ketanji Brown Jackson"
+    docs = docsearch.similarity_search(query)
+    print(docs[0].page_content)
+    # --8<-- [end:frameworks_langchain_quick_start]
+
+    # --8<-- [start:frameworks_langchain_vector_store_config]
+    db_url = "db://lang_test"  # url of db you created
+    api_key = "xxxxx"  # your API key
+    region = "us-east-1-dev"  # your selected region
+
+    vector_store = LanceDB(
+        uri=db_url,
+        api_key=api_key,  # (dont include for local API)
+        region=region,  # (dont include for local API)
+        embedding=embeddings,
+        table_name="langchain_test",  # Optional
+    )
+    # --8<-- [end:frameworks_langchain_vector_store_config]
+
+    # --8<-- [start:frameworks_langchain_add_texts]
+    vector_store.add_texts(texts=["test_123"], metadatas=[{"source": "wiki"}])
+
+    # Additionally, to explore the table you can load it into a df or save it in a csv file:
+
+    tbl = vector_store.get_table()
+    print("tbl:", tbl)
+    pd_df = tbl.to_pandas()
+    pd_df.to_csv("docsearch.csv", index=False)
+
+    # you can also create a new vector store object using an older connection object:
+    vector_store = LanceDB(connection=tbl, embedding=embeddings)
+    # --8<-- [end:frameworks_langchain_add_texts]
+
+    # --8<-- [start:frameworks_langchain_create_index]
+    # for creating vector index
+    vector_store.create_index(vector_col="vector", metric="cosine")
+
+    # for creating scalar index(for non-vector columns)
+    vector_store.create_index(col_name="text")
+    # --8<-- [end:frameworks_langchain_create_index]
+
+    # --8<-- [start:frameworks_langchain_similarity_search]
+    docs = docsearch.similarity_search(query)
+    print(docs[0].page_content)
+    # --8<-- [end:frameworks_langchain_similarity_search]
+
+    # --8<-- [start:frameworks_langchain_similarity_search_by_vector]
+    docs = docsearch.similarity_search_by_vector(query)
+    print(docs[0].page_content)
+    # --8<-- [end:frameworks_langchain_similarity_search_by_vector]
+
+    # --8<-- [start:frameworks_langchain_similarity_search_with_scores]
+    docs = docsearch.similarity_search_with_relevance_scores(query)
+    print("relevance score - ", docs[0][1])
+    print("text- ", docs[0][0].page_content[:1000])
+    # --8<-- [end:frameworks_langchain_similarity_search_with_scores]
+
+    # --8<-- [start:frameworks_langchain_similarity_search_by_vector_with_scores]
+    query_embedding = embeddings.embed_query("text")
+    docs = docsearch.similarity_search_by_vector_with_relevance_scores(query_embedding)
+    print("relevance score - ", docs[0][1])
+    print("text- ", docs[0][0].page_content[:1000])
+    # --8<-- [end:frameworks_langchain_similarity_search_by_vector_with_scores]
+
+    # --8<-- [start:frameworks_langchain_max_marginal_relevance]
+    result = docsearch.max_marginal_relevance_search(query="text")
+    result_texts = [doc.page_content for doc in result]
+    print(result_texts)
+
+    # search by vector :
+    result = docsearch.max_marginal_relevance_search_by_vector(
+        embeddings.embed_query("text")
+    )
+    result_texts = [doc.page_content for doc in result]
+    print(result_texts)
+    # --8<-- [end:frameworks_langchain_max_marginal_relevance]
+
+    # --8<-- [start:frameworks_langchain_add_images]
+    image_uris = ["./assets/image-1.png", "./assets/image-2.png"]
+    vector_store.add_images(uris=image_uris)
+    # here image_uris are local fs paths to the images.
+    # --8<-- [end:frameworks_langchain_add_images]
+
+
+def test_frameworks_llamaindex_examples() -> None:
+    require_flag("RUN_LLAMAINDEX_SNIPPETS")
+    pytest.importorskip("llama_index")
+    pytest.importorskip("llama_index.vector_stores.lancedb")
+
+    # --8<-- [start:frameworks_llamaindex_quick_start]
+    import logging
+    import sys
+    import textwrap
+
+    import openai
+
+    # Uncomment to see debug logs
+    # logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
+    # logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
+    from llama_index.core import (
+        Document,
+        SimpleDirectoryReader,
+        StorageContext,
+        VectorStoreIndex,
+    )
+    from llama_index.vector_stores.lancedb import LanceDBVectorStore
+
+    openai.api_key = "sk-..."
+
+    documents = SimpleDirectoryReader("./data/your-data-dir/").load_data()
+    print("Document ID:", documents[0].doc_id, "Document Hash:", documents[0].hash)
+
+    ## For LanceDB Enterprise :
+    # vector_store = LanceDBVectorStore(
+    #     uri="db://db_name", # your remote DB URI
+    #     api_key="sk_..", # lancedb enterprise api key
+    #     region="your-region" # the region you configured
+    #     host_override="https://your-host.com" # if you have a custom host, otherwise omit this
+    # )
+
+    vector_store = LanceDBVectorStore(
+        uri="./lancedb", mode="overwrite", query_type="vector"
+    )
+    storage_context = StorageContext.from_defaults(vector_store=vector_store)
+
+    index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
+    lance_filter = "metadata.file_name = 'paul_graham_essay.txt' "
+    retriever = index.as_retriever(vector_store_kwargs={"where": lance_filter})
+    response = retriever.retrieve("What did the author do growing up?")
+    # --8<-- [end:frameworks_llamaindex_quick_start]
+
+    # --8<-- [start:frameworks_llamaindex_filtering]
+    from llama_index.core.vector_stores import (
+        FilterCondition,
+        FilterOperator,
+        MetadataFilter,
+        MetadataFilters,
+    )
+
+    query_filters = MetadataFilters(
+        filters=[
+            MetadataFilter(
+                key="creation_date", operator=FilterOperator.EQ, value="2024-05-23"
+            ),
+            MetadataFilter(key="file_size", value=75040, operator=FilterOperator.GT),
+        ],
+        condition=FilterCondition.AND,
+    )
+    # --8<-- [end:frameworks_llamaindex_filtering]
+
+    # --8<-- [start:frameworks_llamaindex_hybrid_search]
+    from lancedb.rerankers import ColbertReranker
+
+    reranker = ColbertReranker()
+    vector_store._add_reranker(reranker)
+
+    query_engine = index.as_query_engine(
+        filters=query_filters,
+        vector_store_kwargs={
+            "query_type": "hybrid",
+        },
+    )
+
+    response = query_engine.query("How much did Viaweb charge per month?")
+    # --8<-- [end:frameworks_llamaindex_hybrid_search]
+
+    # --8<-- [start:frameworks_llamaindex_add_reranker]
+    from lancedb.rerankers import ColbertReranker
+
+    reranker = ColbertReranker()
+    vector_store._add_reranker(reranker)
+    # --8<-- [end:frameworks_llamaindex_add_reranker]
+
+
+def test_frameworks_pydantic_examples() -> None:
+    require_flag("RUN_PYDANTIC_SNIPPETS")
+    pytest.importorskip("pyarrow")
+
+    # --8<-- [start:frameworks_pydantic_imports]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    from lancedb.pydantic import LanceModel, Vector
+
+    # --8<-- [end:frameworks_pydantic_imports]
+    # --8<-- [start:frameworks_pydantic_base_model]
+    class LanceDocs(LanceModel):
+        text: str
+        vector: Vector(2)
+
+    # --8<-- [end:frameworks_pydantic_base_model]
+
+    # --8<-- [start:frameworks_pydantic_set_url]
+    db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "pydantic-docs"))
+    # --8<-- [end:frameworks_pydantic_set_url]
+
+    # --8<-- [start:frameworks_pydantic_vector_field]
+    import pyarrow as pa
+    import pydantic
+    from lancedb.pydantic import Vector, pydantic_to_schema
+
+    class MyModel(pydantic.BaseModel):
+        id: int
+        url: str
+        embeddings: Vector(768)
+
+    schema = pydantic_to_schema(MyModel)
+    assert schema == pa.schema(
+        [
+            pa.field("id", pa.int64(), False),
+            pa.field("url", pa.utf8(), False),
+            pa.field("embeddings", pa.list_(pa.float32(), 768)),
+        ]
+    )
+    # --8<-- [end:frameworks_pydantic_vector_field]
+
+    # --8<-- [start:frameworks_pydantic_type_conversion]
+    from typing import List, Optional
+
+    import pyarrow as pa
+    import pydantic
+    from lancedb.pydantic import Vector, pydantic_to_schema
+
+    class FooModel(pydantic.BaseModel):
+        id: int
+        s: str
+        vec: Vector(1536)  # fixed_size_list<item: float32>[1536]
+        li: List[int]
+
+    schema = pydantic_to_schema(FooModel)
+    assert schema == pa.schema(
+        [
+            pa.field("id", pa.int64(), False),
+            pa.field("s", pa.utf8(), False),
+            pa.field("vec", pa.list_(pa.float32(), 1536)),
+            pa.field("li", pa.list_(pa.int64()), False),
+        ]
+    )
+    # --8<-- [end:frameworks_pydantic_type_conversion]
+
+    # --8<-- [start:frameworks_pydantic_base_example]
+    table = db.create_table("docs", schema=LanceDocs, mode="overwrite")
+    table.add(
+        [
+            {"text": "hello world", "vector": [1.0, 0.0]},
+            {"text": "goodbye world", "vector": [0.0, 1.0]},
+        ]
+    )
+    results = table.search([1.0, 0.0]).limit(1).to_pydantic(LanceDocs)
+    print(results[0].text)
+    # hello world
+    # --8<-- [end:frameworks_pydantic_base_example]
+    assert isinstance(results[0], LanceDocs)
+    assert results[0].text == "hello world"
+
+
+# Platform integrations
+
+
+def test_platforms_dlt_examples() -> None:
+    require_flag("RUN_DLT_SNIPPETS")
+    pytest.importorskip("dlt")
+
+    # --8<-- [start:platforms_dlt_pipeline]
+    # Import necessary modules
+    import dlt
+    from rest_api import rest_api_source
+
+    # Configure the REST API source
+    movies_source = rest_api_source(
+        {
+            "client": {
+                "base_url": "https://www.omdbapi.com/",
+                "auth": {  # authentication strategy for the OMDb API
+                    "type": "api_key",
+                    "name": "apikey",
+                    "api_key": dlt.secrets[
+                        "sources.rest_api.api_token"
+                    ],  # read API credentials directly from secrets.toml
+                    "location": "query",
+                },
+                "paginator": {  # pagination strategy for the OMDb API
+                    "type": "page_number",
+                    "base_page": 1,
+                    "total_path": "totalResults",
+                    "maximum_page": 5,
+                },
+            },
+            "resources": [  # list of API endpoints to request
+                {
+                    "name": "movie_search",
+                    "endpoint": {
+                        "path": "/",
+                        "params": {
+                            "s": "godzilla",
+                            "type": "movie",
+                        },
+                    },
+                }
+            ],
+        }
+    )
+
+    if __name__ == "__main__":
+        # Create a pipeline object
+        pipeline = dlt.pipeline(
+            pipeline_name="movies_pipeline",
+            destination="lancedb",  # this tells dlt to load the data into LanceDB
+            dataset_name="movies_data_pipeline",
+        )
+
+        # Run the pipeline
+        load_info = pipeline.run(movies_source)
+
+        # pretty print the information on data that was loaded
+        print(load_info)
+    # --8<-- [end:platforms_dlt_pipeline]
+
+    # --8<-- [start:platforms_dlt_adapter_import]
+    from dlt.destinations.adapters import lancedb_adapter
+
+    # --8<-- [end:platforms_dlt_adapter_import]
+    # --8<-- [start:platforms_dlt_adapter_usage]
+    load_info = pipeline.run(
+        lancedb_adapter(
+            movies_source,
+            embed="Title",
+        )
+    )
+    # --8<-- [end:platforms_dlt_adapter_usage]
+
+
+def test_platforms_duckdb_examples() -> None:
+    require_flag("RUN_DUCKDB_SNIPPETS")
+    pytest.importorskip("duckdb")
+
+    # --8<-- [start:platforms_duckdb_create_table]
+    import lancedb
+
+    db = lancedb.connect("data/sample-lancedb")
+    data = [
+        {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+        {"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
+    ]
+    table = db.create_table("pd_table", data=data)
+    # --8<-- [end:platforms_duckdb_create_table]
+
+    # --8<-- [start:platforms_duckdb_query_table]
+    import duckdb
+
+    arrow_table = table.to_lance()
+
+    duckdb.query("SELECT * FROM arrow_table")
+    # --8<-- [end:platforms_duckdb_query_table]
+
+    # --8<-- [start:platforms_duckdb_mean_price]
+    duckdb.query("SELECT mean(price) FROM arrow_table")
+    # --8<-- [end:platforms_duckdb_mean_price]
+
+
+def test_frameworks_agno_openai_examples() -> None:
+    require_flag("RUN_AGNO_SNIPPETS")
+    pytest.importorskip("agno")
+    pytest.importorskip("youtube_transcript_api")
+
+    # --8<-- [start:frameworks_agno_setup]
+    import os
+    import re
+
+    from agno.agent import Agent
+    from agno.knowledge.embedder.openai import OpenAIEmbedder
+    from agno.knowledge.knowledge import Knowledge
+    from agno.models.openai import OpenAIResponses
+    from agno.vectordb.lancedb import LanceDb, SearchType
+    from youtube_transcript_api import YouTubeTranscriptApi
+
+    if "OPENAI_API_KEY" not in os.environ:
+        os.environ["OPENAI_API_KEY"] = "sk-..."
+
+    def extract_video_id(youtube_url: str) -> str:
+        match = re.search(r"(?<=v=)[\w-]+", youtube_url) or re.search(
+            r"(?<=be/)[\w-]+", youtube_url
+        )
+        if not match:
+            raise ValueError("Could not parse YouTube video ID from URL")
+        return match.group(0)
+
+    knowledge = Knowledge(
+        vector_db=LanceDb(
+            uri="./tmp/lancedb",
+            table_name="youtube_transcripts",
+            search_type=SearchType.hybrid,
+            embedder=OpenAIEmbedder(id="text-embedding-3-small"),
+        ),
+    )
+    # --8<-- [end:frameworks_agno_setup]
+
+    # --8<-- [start:frameworks_agno_ingest_youtube]
+    youtube_url = "https://www.youtube.com/watch?v=wl6mFyXoxos"
+    video_id = extract_video_id(youtube_url)
+    ytt = YouTubeTranscriptApi()
+    transcript_segments = ytt.fetch(video_id, languages=["en", "en-US"]).to_raw_data()
+    transcript_text = " ".join(segment["text"] for segment in transcript_segments)
+
+    knowledge.insert(
+        name=f"YouTube Transcript ({video_id})",
+        text_content=transcript_text,
+        metadata={"source": "youtube", "video_id": video_id, "video_url": youtube_url},
+    )
+    # --8<-- [end:frameworks_agno_ingest_youtube]
+
+    # --8<-- [start:frameworks_agno_agent]
+    agent = Agent(
+        model=OpenAIResponses(id="gpt-5-mini"),
+        knowledge=knowledge,
+        search_knowledge=True,
+        instructions="Search the transcript and answer only from retrieved context.",
+        markdown=True,
+    )
+    # --8<-- [end:frameworks_agno_agent]
+
+    # --8<-- [start:frameworks_agno_cli_chat]
+    agent.print_response(
+        "Summarize the loaded video transcript in 5 concise bullet points.",
+        stream=True,
+    )
+    while True:
+        question = input("You: ").strip()
+        if question.lower() in {"exit", "quit", "bye"}:
+            break
+        agent.print_response(question, stream=True)
+    # --8<-- [end:frameworks_agno_cli_chat]
+
+
+def test_platforms_voxel51_examples() -> None:
+    require_flag("RUN_VOXEL51_SNIPPETS")
+    pytest.importorskip("fiftyone")
+
+    # --8<-- [start:platforms_voxel51_load_dataset]
+    import fiftyone as fo
+    import fiftyone.brain as fob
+    import fiftyone.zoo as foz
+
+    # Step 1: Load your data into FiftyOne
+    dataset = foz.load_zoo_dataset("quickstart")
+    # --8<-- [end:platforms_voxel51_load_dataset]
+
+    # --8<-- [start:platforms_voxel51_compute_similarity]
+    # Steps 2 and 3: Compute embeddings and create a similarity index
+    lancedb_index = fob.compute_similarity(
+        dataset,
+        model="clip-vit-base32-torch",
+        brain_key="lancedb_index",
+        backend="lancedb",
+    )
+    # --8<-- [end:platforms_voxel51_compute_similarity]
+
+    # --8<-- [start:platforms_voxel51_sort_by_similarity]
+    # Step 4: Query your data
+    query = dataset.first().id  # query by sample ID
+    view = dataset.sort_by_similarity(
+        query,
+        brain_key="lancedb_index",
+        k=10,  # limit to 10 most similar samples
+    )
+    # --8<-- [end:platforms_voxel51_sort_by_similarity]
+
+    # --8<-- [start:platforms_voxel51_cleanup]
+    # Step 5 (optional): Cleanup
+
+    # Delete the LanceDB table
+    lancedb_index.cleanup()
+
+    # Delete run record from FiftyOne
+    dataset.delete_brain_run("lancedb_index")
+    # --8<-- [end:platforms_voxel51_cleanup]
+
+    if False:
+        # --8<-- [start:platforms_voxel51_backend_flag]
+        import fiftyone.brain as fob
+
+        # Re-run similarity creation using the LanceDB backend explicitly
+        fob.compute_similarity(
+            dataset,
+            model="clip-vit-base32-torch",
+            brain_key="lancedb_index",
+            backend="lancedb",
+        )
+        # --8<-- [end:platforms_voxel51_backend_flag]
+
+    # --8<-- [start:platforms_voxel51_brain_config]
+    import fiftyone.brain as fob
+
+    # Print your current brain config
+    print(fob.brain_config)
+    # --8<-- [end:platforms_voxel51_brain_config]
+
+    if False:
+        # --8<-- [start:platforms_voxel51_backend_params]
+        lancedb_index = fob.compute_similarity(
+            dataset,
+            model="clip-vit-base32-torch",
+            backend="lancedb",
+            brain_key="lancedb_index",
+            table_name="your-table",
+            metric="euclidean",
+            uri="/tmp/lancedb",
+        )
+        # --8<-- [end:platforms_voxel51_backend_params]
+
+
+def test_platforms_pandas_examples() -> None:
+    require_flag("RUN_PANDAS_SNIPPETS")
+    pytest.importorskip("pandas")
+
+    # --8<-- [start:platforms_pandas_imports]
+    import asyncio
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    import pandas as pd
+
+    # --8<-- [end:platforms_pandas_imports]
+    # --8<-- [start:platforms_pandas_create_table]
+    pandas_df = pd.DataFrame(
+        [
+            {"id": "1", "text": "dragon", "vector": [0.9, 0.1, 0.3]},
+            {"id": "2", "text": "griffin", "vector": [0.4, 0.5, 0.2]},
+            {"id": "3", "text": "phoenix", "vector": [0.7, 0.3, 0.6]},
+        ]
+    )
+    pandas_db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "pandas-demo"))
+    pandas_table = pandas_db.create_table("creatures", data=pandas_df, mode="overwrite")
+    # --8<-- [end:platforms_pandas_create_table]
+
+    # --8<-- [start:platforms_pandas_vector_search]
+    pandas_results = (
+        pandas_table.search([0.9, 0.1, 0.3])
+        .select(["text", "_distance"])
+        .limit(1)
+        .to_pandas()
+    )
+    print(pandas_results)
+    # --8<-- [end:platforms_pandas_vector_search]
+
+    # --8<-- [start:platforms_pandas_async_example]
+    async def run_pandas_async_example() -> pd.DataFrame:
+        async_db = await lancedb.connect_async(
+            str(Path(tempfile.mkdtemp()) / "pandas-async")
+        )
+        async_df = pd.DataFrame(
+            [
+                {"id": "10", "text": "sage", "vector": [0.6, 0.4, 0.8]},
+                {"id": "11", "text": "bard", "vector": [0.2, 0.7, 0.3]},
+            ]
+        )
+        async_table = await async_db.create_table(
+            "creatures_async", data=async_df, mode="overwrite"
+        )
+        # An async search returns its query builder from an await
+        query = await async_table.search([0.6, 0.4, 0.8])
+        async_results = await query.select(["text", "_distance"]).limit(1).to_pandas()
+        print(async_results)
+        return async_results
+
+    async_results = asyncio.run(run_pandas_async_example())
+    # --8<-- [end:platforms_pandas_async_example]
+    assert async_results["text"].tolist() == ["sage"]
+    assert async_results["_distance"].tolist() == [0.0]
+
+
+def test_platforms_polars_examples() -> None:
+    require_flag("RUN_POLARS_SNIPPETS")
+    pytest.importorskip("polars")
+
+    # --8<-- [start:platforms_polars_imports]
+    import tempfile
+    from pathlib import Path
+
+    import lancedb
+    import polars as pl
+    from lancedb.pydantic import LanceModel, Vector
+
+    # --8<-- [end:platforms_polars_imports]
+    # --8<-- [start:platforms_polars_create_table]
+    birds = pl.DataFrame(
+        {
+            "text": ["phoenix", "sparrow"],
+            "vector": [
+                [0.1, 0.2, 0.3],
+                [0.8, 0.6, 0.5],
+            ],
+        }
+    )
+    polars_db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "polars-demo"))
+    polars_table = polars_db.create_table(
+        "birds", data=birds.to_arrow(), mode="overwrite"
+    )
+    # --8<-- [end:platforms_polars_create_table]
+
+    # --8<-- [start:platforms_polars_vector_search]
+    polars_results = (
+        polars_table.search([0.1, 0.2, 0.3])
+        .select(["text", "_distance"])
+        .limit(1)
+        .to_polars()
+    )
+    print(polars_results)
+    # --8<-- [end:platforms_polars_vector_search]
+
+    # --8<-- [start:platforms_polars_lazyframe]
+    lazy_frame = polars_table.to_polars().lazy()
+    print(lazy_frame.select(["text"]).collect())
+    # --8<-- [end:platforms_polars_lazyframe]
+
+    # --8<-- [start:platforms_polars_pydantic]
+    class BirdModel(LanceModel):
+        text: str
+        vector: Vector(3)
+
+    schema_table = polars_db.create_table(
+        "birds_schema", schema=BirdModel, mode="overwrite"
+    )
+    schema_table.add(birds.to_dicts())
+    # --8<-- [end:platforms_polars_pydantic]
diff --git a/docs/web-tests/py/test_materialized_view.py b/docs/web-tests/py/test_materialized_view.py
new file mode 100644
index 000000000..414d6dc1e
--- /dev/null
+++ b/docs/web-tests/py/test_materialized_view.py
@@ -0,0 +1,79 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+"""Examples for docs/web/feature-engineering/materialized-views.mdx."""
+
+
+def test_materialized_view_create_and_refresh(tmp_path, monkeypatch):
+    monkeypatch.chdir(tmp_path)
+
+    # --8<-- [start:materialized_view_create]
+    import lancedb
+
+    db = lancedb.connect("./.lancedb")
+    table = db.create_table(
+        "people",
+        [{"name": "ada", "age": 36}, {"name": "kid", "age": 7}],
+        storage_options={"new_table_enable_stable_row_ids": "true"},
+    )
+
+    view = db.create_materialized_view(
+        "adults",
+        "people",
+        select=["name", ("shout", "upper(name)")],
+        where="age >= 18",
+    )
+    print(view.table.count_rows())
+    # 1
+    # --8<-- [end:materialized_view_create]
+    assert view.table.count_rows() == 1
+
+    # --8<-- [start:materialized_view_refresh]
+    table.add([{"name": "bea", "age": 41}])
+
+    view.refresh()
+    print(view.table.count_rows())
+    # 2
+    # --8<-- [end:materialized_view_refresh]
+    assert view.table.count_rows() == 2
+    assert sorted(row["shout"] for row in view.table.to_arrow().to_pylist()) == [
+        "ADA",
+        "BEA",
+    ]
+
+
+def test_materialized_view_with_no_data(tmp_path):
+    import lancedb
+
+    db = lancedb.connect(tmp_path)
+    db.create_table(
+        "people",
+        [{"name": "ada", "age": 36}, {"name": "kid", "age": 7}],
+        storage_options={"new_table_enable_stable_row_ids": "true"},
+    )
+    view = db.create_materialized_view(
+        "adults", "people", where="age >= 18", with_no_data=True
+    )
+    assert view.table.count_rows() == 0
+    assert view.refresh().rows_written == 1
+    assert view.table.count_rows() == 1
+
+
+def test_materialized_view_refresh_to_source_version(tmp_path):
+    import lancedb
+
+    db = lancedb.connect(tmp_path)
+    table = db.create_table(
+        "people",
+        [{"name": "ada", "age": 36}],
+        storage_options={"new_table_enable_stable_row_ids": "true"},
+    )
+    view = db.create_materialized_view("adults", "people", where="age >= 18")
+    first = table.version
+    table.add([{"name": "bea", "age": 41}])
+    view.refresh()
+    assert view.table.count_rows() == 2
+
+    result = view.refresh(source_version=first)
+    assert (result.source_version, result.rows_written) == (first, 1)
+    assert view.table.count_rows() == 1
diff --git a/docs/web-tests/py/test_multimodal.py b/docs/web-tests/py/test_multimodal.py
new file mode 100644
index 000000000..cb4a647c3
--- /dev/null
+++ b/docs/web-tests/py/test_multimodal.py
@@ -0,0 +1,232 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import pytest
+try:
+    import lancedb
+    import numpy as np
+    import pyarrow as pa
+    import io
+    from PIL import Image
+except ImportError:
+    pass
+
+# --8<-- [start:multimodal_imports]
+import lancedb
+import pyarrow as pa
+import pandas as pd
+import numpy as np
+import io
+from PIL import Image
+# --8<-- [end:multimodal_imports]
+
+def test_multimodal_ingestion(db_path_factory):
+    # Ensure dependencies are available
+    pytest.importorskip("PIL")
+    pytest.importorskip("lancedb")
+    pytest.importorskip("numpy")
+
+    # --8<-- [start:create_dummy_data]
+    # Create some dummy images
+    def create_dummy_image(color):
+        img = Image.new('RGB', (100, 100), color=color)
+        buf = io.BytesIO()
+        img.save(buf, format='PNG')
+        return buf.getvalue()
+
+    # Create dataset with metadata, vectors, and image blobs
+    data = [
+        {
+            "id": 1,
+            "filename": "red_square.png",
+            "vector": np.random.rand(128).astype(np.float32),
+            "image_blob": create_dummy_image('red'),
+            "label": "red"
+        },
+        {
+            "id": 2,
+            "filename": "blue_square.png",
+            "vector": np.random.rand(128).astype(np.float32),
+            "image_blob": create_dummy_image('blue'),
+            "label": "blue"
+        }
+    ]
+    # --8<-- [end:create_dummy_data]
+
+    # --8<-- [start:define_schema]
+    # Define schema explicitly to ensure image_blob is treated as binary
+    schema = pa.schema([
+        pa.field("id", pa.int32()),
+        pa.field("filename", pa.string()),
+        pa.field("vector", pa.list_(pa.float32(), 128)),
+        pa.field("image_blob", pa.binary()), # Important: Use pa.binary() for blobs
+        pa.field("label", pa.string())
+    ])
+    # --8<-- [end:define_schema]
+
+    db_uri = db_path_factory("multimodal_db")
+    db = lancedb.connect(db_uri)
+
+    # --8<-- [start:ingest_data]
+    tbl = db.create_table("images", data=data, schema=schema, mode="overwrite")
+    # --8<-- [end:ingest_data]
+   
+    assert len(tbl) == 2
+
+    # --8<-- [start:search_data]
+    # Search for similar images
+    query_vector = np.random.rand(128).astype(np.float32)
+    results = tbl.search(query_vector).limit(1).to_pandas()
+    # --8<-- [end:search_data]
+
+    # --8<-- [start:process_results]
+    # Convert back to PIL Image
+    for _, row in results.iterrows():
+        image_bytes = row['image_blob']
+        image = Image.open(io.BytesIO(image_bytes))
+        print(f"Retrieved image: {row['filename']}, Size: {image.size}")
+        # You can now use 'image' with other libraries or display it
+    # --8<-- [end:process_results]
+   
+    assert len(results) == 1
+
+def test_blob_api_definition(db_path_factory):
+    # --8<-- [start:blob_api_schema]
+    import pyarrow as pa
+
+    # Define schema with Blob API metadata for lazy loading
+    schema = pa.schema([
+        pa.field("id", pa.int64()),
+        pa.field(
+            "video", 
+            pa.large_binary(), 
+            metadata={"lance-encoding:blob": "true"} # Enable Blob API
+        ),
+    ])
+    # --8<-- [end:blob_api_schema]
+
+    # --8<-- [start:blob_api_ingest]
+    import lancedb
+
+    db = lancedb.connect(db_path_factory("blob_db"))
+    
+    # Create sample data
+    data = [
+        {"id": 1, "video": b"fake_video_bytes_1"},
+        {"id": 2, "video": b"fake_video_bytes_2"}
+    ]
+    
+    # Create the table
+    tbl = db.create_table("videos", data=data, schema=schema)
+    # --8<-- [end:blob_api_ingest]
+    assert len(tbl) == 2
+
+
+def test_blob_api_to_pandas(db_path_factory):
+    db = lancedb.connect(db_path_factory("blob_to_pandas_db"))
+    schema = pa.schema([
+        pa.field("id", pa.int64()),
+        pa.field(
+            "video",
+            pa.large_binary(),
+            metadata={"lance-encoding:blob": "true"},
+        ),
+    ])
+    tbl = db.create_table(
+        "videos",
+        data=[
+            {"id": 1, "video": b"fake_video_bytes_1"},
+            {"id": 2, "video": b"fake_video_bytes_2"},
+        ],
+        schema=schema,
+        mode="overwrite",
+    )
+
+    # --8<-- [start:blob_api_to_pandas]
+    # Default: blob columns come back lazily
+    df_lazy = tbl.to_pandas()
+
+    # Materialize blob bytes eagerly
+    df_bytes = tbl.to_pandas(blob_mode="bytes")
+
+    # Return descriptors instead of payloads
+    df_desc = tbl.to_pandas(blob_mode="descriptions")
+
+    # Forward extra kwargs to PyArrow's to_pandas
+    df_typed = tbl.to_pandas(split_blocks=True, self_destruct=True)
+    # --8<-- [end:blob_api_to_pandas]
+
+    assert len(df_lazy) == 2
+    assert isinstance(df_bytes["video"].iloc[0], bytes)
+    assert df_bytes["video"].tolist() == [
+        b"fake_video_bytes_1",
+        b"fake_video_bytes_2",
+    ]
+    assert len(df_desc) == 2
+    assert len(df_typed) == 2
+
+
+@pytest.mark.asyncio
+async def test_query_to_pandas_kwargs(db_path_factory):
+    schema = pa.schema([
+        pa.field("id", pa.int64()),
+        pa.field("vector", pa.list_(pa.float32(), 128)),
+        pa.field(
+            "video",
+            pa.large_binary(),
+            metadata={"lance-encoding:blob": "true"},
+        ),
+    ])
+    data = [
+        {
+            "id": i,
+            "vector": np.random.rand(128).astype(np.float32),
+            "video": f"fake_video_bytes_{i}".encode(),
+        }
+        for i in range(10)
+    ]
+
+    db = lancedb.connect(db_path_factory("query_to_pandas_db"))
+    tbl = db.create_table("search_demo", data=data, schema=schema, mode="overwrite")
+
+    async_db = await lancedb.connect_async(
+        str(db_path_factory("query_to_pandas_async_db"))
+    )
+    tbl_async = await async_db.create_table(
+        "search_demo", data=data, schema=schema, mode="overwrite"
+    )
+
+    query_vector = np.random.rand(128).astype(np.float32)
+
+    # --8<-- [start:query_to_pandas_kwargs]
+    # Plain scan query: blob_mode is supported end to end
+    df_lazy = (
+        tbl.search()
+        .where("id = 1")
+        .select(["id", "video"])
+        .to_pandas(blob_mode="lazy")
+    )
+
+    # Same call shape works on async query builders
+    df_bytes = await (
+        tbl_async.query()
+        .where("id = 1")
+        .select(["id", "video"])
+        .to_pandas(blob_mode="bytes")
+    )
+
+    # Vector / FTS / hybrid queries can't materialize blob columns,
+    # so omit them from the projection
+    df_vec = (
+        tbl.search(query_vector)
+        .limit(10)
+        .select(["id", "vector"])
+        .to_pandas(split_blocks=True, self_destruct=True)
+    )
+    # --8<-- [end:query_to_pandas_kwargs]
+
+    assert len(df_lazy) == 1
+    assert len(df_bytes) == 1
+    assert df_bytes["video"].iloc[0] == b"fake_video_bytes_1"
+    assert len(df_vec) == 10
+    assert "video" not in df_vec.columns
diff --git a/docs/web-tests/py/test_provider_examples.py b/docs/web-tests/py/test_provider_examples.py
new file mode 100644
index 000000000..e1836b6ff
--- /dev/null
+++ b/docs/web-tests/py/test_provider_examples.py
@@ -0,0 +1,160 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+"""The provider reranker examples write only to a directory they create.
+
+Each example runs offline. A stand-in embedding function answers for the model
+the example asks the registry for, a stand-in reranker replaces the provider
+client, and outbound connections are refused, so no model is downloaded and no
+provider is called. Sentinel tables in the owned home directory, where these
+examples used to write, and in the temporary directory must be left unchanged.
+"""
+
+import importlib.util
+import socket
+import tempfile
+from pathlib import Path
+from types import SimpleNamespace
+from typing import ClassVar
+
+import lancedb
+import lancedb.embeddings
+import lancedb.rerankers
+import pyarrow as pa
+import pytest
+from lancedb.embeddings import TextEmbeddingFunction, register
+from lancedb.rerankers import Reranker
+
+EXAMPLES = Path(__file__).with_name("provider_examples.py")
+RERANKERS = {
+    "reranking_cohere_usage": "CohereReranker",
+    "reranking_jina_usage": "JinaReranker",
+    "reranking_watsonx_usage": "WatsonxReranker",
+    "reranking_openai_usage": "OpenaiReranker",
+    "reranking_voyageai_usage": "VoyageAIReranker",
+}
+
+
+@register("docs-offline-text")
+class OfflineEmbedding(TextEmbeddingFunction):
+    """Deterministic four-dimensional vectors, computed locally."""
+
+    def ndims(self):
+        return 4
+
+    def generate_embeddings(self, texts):
+        return [[float(len(text)), 1.0, 0.0, 0.0] for text in texts]
+
+
+class OfflineReranker(Reranker):
+    """Scores every row 1.0 and records which searches it reranked."""
+
+    instances: ClassVar[list["OfflineReranker"]] = []
+
+    def __init__(self, **kwargs):
+        super().__init__()
+        self.kwargs = kwargs
+        self.searches = []
+        OfflineReranker.instances.append(self)
+
+    def _score(self, results, search):
+        self.searches.append(search)
+        drop = [c for c in ("_distance", "_score") if c in results.column_names]
+        results = results.drop_columns(drop)
+        scores = pa.array([1.0] * results.num_rows, pa.float32())
+        return results.append_column("_relevance_score", scores)
+
+    def rerank_vector(self, query, vector_results):
+        return self._score(vector_results, "vector")
+
+    def rerank_fts(self, query, fts_results):
+        return self._score(fts_results, "fts")
+
+    def rerank_hybrid(self, query, vector_results, fts_results):
+        merged = self.merge_results(vector_results, fts_results)
+        return self._score(merged, "hybrid")
+
+
+def snapshot(connect, uri):
+    db = connect(str(uri))
+    tables = {}
+    for name in db.list_tables().tables:
+        table = db.open_table(name)
+        tables[name] = (table.version, table.schema, table.to_arrow().to_pylist())
+    return tables
+
+
+def load_examples():
+    spec = importlib.util.spec_from_file_location("provider_examples", EXAMPLES)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+@pytest.fixture
+def owned(tmp_path, monkeypatch):
+    home, temp_root = tmp_path / "home", tmp_path / "tmp"
+    home.mkdir()
+    temp_root.mkdir()
+    monkeypatch.setenv("HOME", str(home))
+    monkeypatch.setattr(tempfile, "tempdir", str(temp_root))
+    monkeypatch.chdir(tmp_path)
+    # Anything the examples resolve from "~" must land in the owned home.
+    assert Path("~/.lancedb").expanduser().resolve().is_relative_to(home.resolve())
+
+    def refuse(*args, **kwargs):
+        raise OSError("network access is disabled in this test")
+
+    monkeypatch.setattr(socket.socket, "connect", refuse)
+    monkeypatch.setattr(socket, "create_connection", refuse)
+
+    real_connect = lancedb.connect
+    sentinels = [home / ".lancedb", temp_root / "existing"]
+    for uri in sentinels:
+        row = {"text": "keep me", "vector": [0.5, 0.5, 0.5, 0.5]}
+        real_connect(str(uri)).create_table("test", [row])
+    before = [snapshot(real_connect, uri) for uri in sentinels]
+
+    registry = lancedb.embeddings.get_registry()
+    monkeypatch.setattr(registry, "get", lambda name: OfflineEmbedding)
+    for name in RERANKERS.values():
+        monkeypatch.setattr(lancedb.rerankers, name, OfflineReranker)
+    monkeypatch.setenv("JINA_API_KEY", "replaced-by-the-example")
+    OfflineReranker.instances.clear()
+
+    connected = []
+
+    def recording_connect(uri, *args, **kwargs):
+        connected.append(Path(uri).expanduser().resolve())
+        return real_connect(uri, *args, **kwargs)
+
+    monkeypatch.setattr(lancedb, "connect", recording_connect)
+    return SimpleNamespace(
+        home=home,
+        temp_root=temp_root,
+        sentinels=sentinels,
+        before=before,
+        connect=real_connect,
+        connected=connected,
+    )
+
+
+@pytest.mark.parametrize("example", sorted(RERANKERS))
+def test_reranker_example_writes_only_to_its_own_directory(owned, example):
+    existing = set(owned.temp_root.iterdir())
+
+    getattr(load_examples(), example)()
+
+    after = [snapshot(owned.connect, uri) for uri in owned.sentinels]
+    assert after == owned.before
+    created = set(owned.temp_root.iterdir()) - existing
+    assert len(created) == 1
+    (own_directory,) = created
+    assert len(owned.connected) == 1
+    assert owned.connected[0].is_relative_to(own_directory.resolve())
+    assert owned.connect(str(owned.connected[0])).list_tables().tables == ["test"]
+
+    (reranker,) = OfflineReranker.instances
+    assert reranker.searches == ["vector", "fts", "hybrid"]
+    assert reranker.kwargs["api_key"] == "your-api-key"
+    assert reranker.kwargs.get("project_id", "your-project-id") == "your-project-id"
diff --git a/docs/web-tests/py/test_quickstart.py b/docs/web-tests/py/test_quickstart.py
new file mode 100644
index 000000000..779a268d4
--- /dev/null
+++ b/docs/web-tests/py/test_quickstart.py
@@ -0,0 +1,353 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import os
+import shutil
+import uuid
+from pathlib import Path
+
+import lancedb
+import pytest
+import pytest_asyncio
+
+# What the quickstart's download step leaves in an application's directory.
+IMAGE = (
+    Path(__file__).resolve().parents[2]
+    / "web/static/assets/images/quickstart/sir-lancelot.jpg"
+)
+DEPLOYMENT = ("LANCEDB_URI", "LANCEDB_HOST_OVERRIDE", "LANCEDB_API_KEY")
+# The connection settings alone never send these tests to a deployment.
+OPT_IN = "LANCEDB_DOCS_TEST_DEPLOYMENT"
+
+
+def deployment_settings():
+    if os.environ.get(OPT_IN) != "1":
+        pytest.skip(f"set {OPT_IN}=1 and {', '.join(DEPLOYMENT)} to use a deployment")
+    missing = [name for name in DEPLOYMENT if not os.environ.get(name)]
+    if missing:
+        pytest.fail(f"{OPT_IN}=1 needs {', '.join(missing)}")
+    return dict(
+        uri=os.environ["LANCEDB_URI"],
+        api_key=os.environ["LANCEDB_API_KEY"],
+        region=os.environ.get("LANCEDB_REGION", "us-east-1"),
+        host_override=os.environ["LANCEDB_HOST_OVERRIDE"],
+    )
+
+
+class OwnedTables:
+    """A deployment connection on which a test creates only tables of its own.
+
+    Each name gets a suffix unique to the test and is claimed only while no
+    table has it; the test drops what it claimed, whether or not it passed.
+    """
+
+    def __init__(self, db):
+        self.db = db
+        self.suffix = uuid.uuid4().hex[:12]
+        self.claimed = []
+
+    def claim(self, name, existing, mode):
+        owned = f"{name}_{self.suffix}"
+        if owned in self.claimed:
+            return owned, mode
+        if owned in existing:
+            raise RuntimeError(f"{owned} already exists and is not this test's")
+        self.claimed.append(owned)
+        return owned, "create"
+
+    def table_names(self):
+        names, token = [], None
+        while True:
+            page = self.db.list_tables(page_token=token)
+            names += page.tables
+            if not (token := page.page_token):
+                return names
+
+    def create_table(self, name, *args, mode="create", **kwargs):
+        owned, mode = self.claim(name, self.table_names(), mode)
+        return self.db.create_table(owned, *args, mode=mode, **kwargs)
+
+    def drop_claimed(self):
+        existing = self.table_names()
+        for name in self.claimed:
+            if name in existing:
+                self.db.drop_table(name)
+
+
+class AsyncOwnedTables(OwnedTables):
+    async def table_names(self):
+        names, token = [], None
+        while True:
+            page = await self.db.list_tables(page_token=token)
+            names += page.tables
+            if not (token := page.page_token):
+                return names
+
+    async def create_table(self, name, *args, mode="create", **kwargs):
+        owned, mode = self.claim(name, await self.table_names(), mode)
+        return await self.db.create_table(owned, *args, mode=mode, **kwargs)
+
+    async def drop_claimed(self):
+        existing = await self.table_names()
+        for name in self.claimed:
+            if name in existing:
+                await self.db.drop_table(name)
+
+
+@pytest.fixture
+def deployment():
+    owned = OwnedTables(lancedb.connect(**deployment_settings()))
+    yield owned
+    owned.drop_claimed()
+
+
+@pytest.fixture(params=["local", "enterprise"])
+def db(request, db_path_factory):
+    if request.param == "local":
+        yield lancedb.connect(db_path_factory("quickstart_db"))
+    else:
+        yield request.getfixturevalue("deployment")
+
+
+@pytest_asyncio.fixture(params=["local", "enterprise"])
+async def async_db(request, db_path_factory):
+    if request.param == "local":
+        yield await lancedb.connect_async(db_path_factory("quickstart_async_db"))
+        return
+    owned = AsyncOwnedTables(await lancedb.connect_async(**deployment_settings()))
+    yield owned
+    await owned.drop_claimed()
+
+
+def create_characters(db):
+    # --8<-- [start:quickstart_data]
+    data = [
+        {
+            "id": "1",
+            "name": "King Arthur",
+            "role": "King",
+            "description": "Leader of Camelot and wielder of Excalibur.",
+            "stats": {"strength": 4, "magic": 1, "leadership": 5, "wisdom": 4},
+            "vector": [0.7, 0.1, 0.9, 0.7],
+        },
+        {
+            "id": "2",
+            "name": "Merlin",
+            "role": "Wizard",
+            "description": "Advisor and prophet with deep magical knowledge.",
+            "stats": {"strength": 2, "magic": 5, "leadership": 4, "wisdom": 5},
+            "vector": [0.2, 0.9, 0.4, 0.9],
+        },
+        {
+            "id": "3",
+            "name": "Sir Lancelot",
+            "role": "Knight",
+            "description": "Legendary knight known for courage and combat skill.",
+            "stats": {"strength": 5, "magic": 1, "leadership": 3, "wisdom": 3},
+            "vector": [0.9, 0.1, 0.5, 0.4],
+        },
+    ]
+    # --8<-- [end:quickstart_data]
+
+    # --8<-- [start:quickstart_create_table]
+    table = db.create_table("characters", data=data, mode="overwrite")
+    # --8<-- [end:quickstart_create_table]
+    assert table.count_rows() == 3
+
+    # --8<-- [start:quickstart_add_data]
+    table.add(
+        [
+            {
+                "id": "4",
+                "name": "Morgana",
+                "role": "Sorceress",
+                "description": "Powerful sorceress of Avalon.",
+                "stats": {"strength": 2, "magic": 5, "leadership": 4, "wisdom": 4},
+                "vector": [0.3, 0.9, 0.6, 0.8],
+            }
+        ]
+    )
+    # --8<-- [end:quickstart_add_data]
+    assert table.count_rows() == 4
+    return table
+
+
+def test_quickstart(db):
+    table = create_characters(db)
+
+    # --8<-- [start:quickstart_vector_search_1]
+    # Search for examples similar to a "wise magical advisor"
+    query_vector = [0.2, 0.8, 0.4, 0.9]
+
+    # Ensure you run `pip install polars` beforehand
+    result = (
+        table.search(query_vector)
+        .select(["name", "role", "description", "_distance"])
+        .limit(2)
+        .to_polars()
+    )
+    print(result)
+    # --8<-- [end:quickstart_vector_search_1]
+    assert result["name"].to_list() == ["Merlin", "Morgana"]
+
+    # --8<-- [start:quickstart_output_pandas]
+    # Ensure you run `pip install pandas` beforehand
+    result = table.search(query_vector).limit(2).to_pandas()
+    print(result)
+    # --8<-- [end:quickstart_output_pandas]
+    assert result["name"].to_list() == ["Merlin", "Morgana"]
+
+    # --8<-- [start:quickstart_curate_with_metadata]
+    curated = (
+        table.search(query_vector)
+        .where("stats.magic >= 4")
+        .select(["name", "role", "description", "_distance"])
+        .limit(2)
+        .to_polars()
+    )
+    print(curated)
+    # --8<-- [end:quickstart_curate_with_metadata]
+    assert curated["name"].to_list() == ["Merlin", "Morgana"]
+
+    curated = table.search(query_vector).where("stats.magic < 4").limit(2).to_list()
+    assert [row["name"] for row in curated] == ["King Arthur", "Sir Lancelot"]
+
+
+@pytest.mark.asyncio
+async def test_quickstart_async_api(async_db):
+    data = [
+        {
+            "id": "2",
+            "name": "Merlin",
+            "role": "Wizard",
+            "description": "Advisor and prophet with deep magical knowledge.",
+            "stats": {"strength": 2, "magic": 5, "leadership": 4, "wisdom": 5},
+            "vector": [0.2, 0.9, 0.4, 0.9],
+        }
+    ]
+
+    # --8<-- [start:quickstart_create_table_async]
+    async_table = await async_db.create_table(
+        "characters",
+        data=data,
+        mode="overwrite",
+    )
+    # --8<-- [end:quickstart_create_table_async]
+
+    # --8<-- [start:quickstart_vector_search_1_async]
+    # Search for examples similar to a "wise magical advisor"
+    query_vector = [0.2, 0.8, 0.4, 0.9]
+
+    # Ensure you run `pip install polars` beforehand
+    async_result = await (
+        await async_table.search(query_vector)
+    ).select(["name", "role", "description", "_distance"]).limit(2).to_polars()
+    print(async_result)
+    # --8<-- [end:quickstart_vector_search_1_async]
+    assert async_result["name"].to_list() == ["Merlin"]
+
+
+def test_quickstart_derived_feature(db):
+    table = create_characters(db)
+
+    # --8<-- [start:quickstart_add_feature]
+    table.add_columns(
+        {
+            "power_score": "cast(((stats.strength + stats.magic + stats.leadership + stats.wisdom) / 4.0) as float)"
+        }
+    )
+    # --8<-- [end:quickstart_add_feature]
+
+    # --8<-- [start:quickstart_query_feature]
+    features = table.search().select(["name", "role", "power_score"]).to_polars()
+    print(features)
+    # --8<-- [end:quickstart_query_feature]
+    assert sorted(zip(features["name"], features["power_score"])) == [
+        ("King Arthur", 3.5),
+        ("Merlin", 4.0),
+        ("Morgana", 3.75),
+        ("Sir Lancelot", 3.0),
+    ]
+
+
+def description_lengths(table):
+    rows = table.search().select(["description", "description_length"]).to_list()
+    return {row["description"]: row["description_length"] for row in rows}
+
+
+def test_quickstart_computed_column(db_path_factory):
+    table = create_characters(lancedb.connect(db_path_factory("computed")))
+
+    # --8<-- [start:quickstart_computed_feature]
+    table.add_columns(computed={"description_length": "length(description)"})
+    table.refresh_column("description_length")
+    # --8<-- [end:quickstart_computed_feature]
+    lengths = description_lengths(table)
+    assert lengths == {text: len(text) for text in lengths}
+
+
+def test_quickstart_computed_column_on_a_deployment(deployment):
+    table = create_characters(deployment)
+
+    # --8<-- [start:quickstart_computed_feature_enterprise]
+    table.add_columns(computed={"description_length": "length(description)"})
+    job = table.refresh_column_async("description_length")
+    job.wait()
+    # --8<-- [end:quickstart_computed_feature_enterprise]
+    lengths = description_lengths(table)
+    assert len(lengths) == 4
+    assert lengths == {text: len(text) for text in lengths}
+    with pytest.raises(Exception):
+        table.refresh_column("description_length")
+
+
+def test_quickstart_multimodal(db, tmp_path, monkeypatch):
+    app = tmp_path / "app"
+    (app / "data").mkdir(parents=True)
+    shutil.copy(IMAGE, app / "data/sir-lancelot.jpg")
+    monkeypatch.chdir(app)
+
+    # --8<-- [start:quickstart_multimodal_bytes]
+    from pathlib import Path
+
+    image_bytes = Path("data/sir-lancelot.jpg").read_bytes()
+
+    multimodal_table = db.create_table(
+        "character_images",
+        data=[
+            {
+                "id": "lancelot",
+                "description": "Portrait of Sir Lancelot",
+                "image": image_bytes,
+                "vector": [0.9, 0.1, 0.5, 0.4],
+            }
+        ],
+        mode="overwrite",
+    )
+    # --8<-- [end:quickstart_multimodal_bytes]
+    assert multimodal_table.count_rows() == 1
+    stored = multimodal_table.search().select(["image"]).to_list()[0]["image"]
+    assert stored == IMAGE.read_bytes()
+
+
+def test_quickstart_training(db):
+    table = create_characters(db)
+
+    # --8<-- [start:quickstart_training]
+    import torch
+    from lancedb.permutation import Permutation
+
+    table.add_columns(
+        {
+            "magic": "cast(stats.magic as float)",
+            "strength": "cast(stats.strength as float)",
+        }
+    )
+    dataset = Permutation.identity(table).select_columns(["magic", "strength"])
+    dataloader = torch.utils.data.DataLoader(dataset, batch_size=1024, shuffle=True)
+    # --8<-- [end:quickstart_training]
+    batches = list(dataloader)
+    assert len(batches) == 1
+    assert set(batches[0]) == {"magic", "strength"}
+    assert all(isinstance(value, torch.Tensor) for value in batches[0].values())
+    assert sorted(batches[0]["magic"].tolist()) == [1.0, 1.0, 5.0, 5.0]
diff --git a/docs/web-tests/py/test_read_consistency.py b/docs/web-tests/py/test_read_consistency.py
new file mode 100644
index 000000000..886bb5676
--- /dev/null
+++ b/docs/web-tests/py/test_read_consistency.py
@@ -0,0 +1,35 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+
+def test_read_consistency():
+    # --8<-- [start:read_consistency]
+    from datetime import timedelta
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-consistency-") as uri:
+        writer = lancedb.connect(uri)
+        table = writer.create_table("documents", [{"id": 1, "text": "first"}])
+
+        default_reader = lancedb.connect(uri).open_table("documents")
+        latest_reader = lancedb.connect(
+            uri, read_consistency_interval=timedelta(0)
+        ).open_table("documents")
+
+        table.add([{"id": 2, "text": "second"}])
+
+        default_count = default_reader.count_rows()
+        latest_count = latest_reader.count_rows()
+        print(f"Default reader: {default_count} rows")  # 1
+        print(f"Zero-interval reader: {latest_count} rows")  # 2
+
+        default_reader.checkout_latest()
+        refreshed_count = default_reader.count_rows()
+        print(f"After explicit refresh: {refreshed_count} rows")  # 2
+    # --8<-- [end:read_consistency]
+
+    assert default_count == 1
+    assert latest_count == 2
+    assert refreshed_count == 2
diff --git a/docs/web-tests/py/test_reranker_examples.py b/docs/web-tests/py/test_reranker_examples.py
new file mode 100644
index 000000000..71505d423
--- /dev/null
+++ b/docs/web-tests/py/test_reranker_examples.py
@@ -0,0 +1,96 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+"""The published reranker examples in test_integrations.py write only to a directory they create.
+
+Each example runs as readers see it: its marked region is extracted the way the snippet
+generator extracts it, saved as a script and run offline. A stand-in embedding function answers for the
+sentence-transformers model, stand-in rerankers replace the model-backed AnswerDotAI, ColBERT
+and cross-encoder rerankers, and outbound connections are refused. The RRF, linear-combination
+and MRR rerankers need no model and run unchanged. Sentinel tables in the owned home directory,
+where these examples used to write, and in the temporary directory must be left unchanged.
+"""
+
+import importlib.util
+import runpy
+import sys
+from pathlib import Path
+
+import lancedb.rerankers
+import pytest
+import test_provider_examples as provider
+
+owned = provider.owned
+SOURCE = Path(__file__).with_name("test_integrations.py")
+GENERATOR = Path(__file__).parents[1] / "mdx_snippets_gen.py"
+MODEL_RERANKERS = {
+    "reranking_answerdotai_usage": "AnswerdotaiRerankers",
+    "reranking_colbert_usage": "ColbertReranker",
+    "reranking_cross_encoder_usage": "CrossEncoderReranker",
+}
+LOCAL_RERANKERS = [
+    "reranking_linear_combination_usage",
+    "reranking_mrr_usage",
+    "reranking_rrf_usage",
+]
+
+
+def load_generator():
+    if "mdx_snippets_gen" not in sys.modules:
+        spec = importlib.util.spec_from_file_location("mdx_snippets_gen", GENERATOR)
+        module = importlib.util.module_from_spec(spec)
+        sys.modules[spec.name] = module  # its dataclasses resolve annotations by module
+        spec.loader.exec_module(module)
+    return sys.modules["mdx_snippets_gen"]
+
+
+def reader_code(name):
+    """The example exactly as the snippet generator publishes it."""
+    generator = load_generator()
+    start, end = generator.markers_for_lang("py")
+    lines = SOURCE.read_text().splitlines(keepends=True)
+    snippet = generator.parse_snippets(lines, start, end)[name]
+    return generator.snippet_text(generator.dedent(snippet))
+
+
+def run_example(owned, tmp_path, name):
+    script = tmp_path / "scripts" / f"{name}.py"
+    script.parent.mkdir(exist_ok=True)
+    script.write_text(reader_code(name))
+    existing = set(owned.temp_root.iterdir())
+    namespace = runpy.run_path(str(script))
+
+    after = [provider.snapshot(owned.connect, uri) for uri in owned.sentinels]
+    assert after == owned.before
+    created = set(owned.temp_root.iterdir()) - existing
+    assert len(created) == 1
+    (own_directory,) = created
+    assert len(owned.connected) == 1
+    assert owned.connected[0].is_relative_to(own_directory.resolve())
+    assert owned.connect(str(owned.connected[0])).list_tables().tables == ["test"]
+    return namespace
+
+
+@pytest.mark.parametrize("name", sorted(MODEL_RERANKERS))
+def test_model_reranker_example_writes_only_to_its_own_directory(
+    owned, monkeypatch, tmp_path, name
+):
+    monkeypatch.setattr(
+        lancedb.rerankers, MODEL_RERANKERS[name], provider.OfflineReranker
+    )
+
+    namespace = run_example(owned, tmp_path, name)
+
+    (reranker,) = provider.OfflineReranker.instances
+    assert reranker.searches == ["vector", "fts", "hybrid"]
+    assert namespace["result"]
+
+
+@pytest.mark.parametrize("name", LOCAL_RERANKERS)
+def test_local_reranker_example_writes_only_to_its_own_directory(owned, tmp_path, name):
+    namespace = run_example(owned, tmp_path, name)
+
+    assert namespace["result"]
+    assert all("_relevance_score" in row for row in namespace["result"])
+    if name == "reranking_mrr_usage":
+        assert namespace["combined"].num_rows > 0
diff --git a/docs/web-tests/py/test_search.py b/docs/web-tests/py/test_search.py
new file mode 100644
index 000000000..4b7e8a9c7
--- /dev/null
+++ b/docs/web-tests/py/test_search.py
@@ -0,0 +1,730 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+# --8<-- [start:import-lancedb]
+import lancedb
+
+# --8<-- [end:import-lancedb]
+# --8<-- [start:import-numpy]
+from lancedb.query import BoostQuery, MatchQuery
+import numpy as np
+import pyarrow as pa
+
+# --8<-- [end:import-numpy]
+# --8<-- [start:import-datetime]
+from datetime import datetime
+
+# --8<-- [end:import-datetime]
+# --8<-- [start:import-lancedb-pydantic]
+from lancedb.pydantic import Vector, LanceModel
+
+# --8<-- [end:import-lancedb-pydantic]
+# --8<-- [start:import-pydantic-base-model]
+from pydantic import BaseModel
+
+# --8<-- [end:import-pydantic-base-model]
+# --8<-- [start:import-lancedb-fts]
+from lancedb.index import FTS
+
+# --8<-- [end:import-lancedb-fts]
+# --8<-- [start:import-os]
+import os
+
+# --8<-- [end:import-os]
+# --8<-- [start:import-embeddings]
+from lancedb.embeddings import get_registry
+
+# --8<-- [end:import-embeddings]
+import pytest
+
+
+# --8<-- [start:class-definition]
+class Metadata(BaseModel):
+    source: str
+    timestamp: datetime
+
+
+class Document(BaseModel):
+    content: str
+    meta: Metadata
+
+
+class LanceSchema(LanceModel):
+    id: str
+    vector: Vector(1536)
+    payload: Document
+
+
+# --8<-- [end:class-definition]
+
+
+def test_vector_search():
+    # --8<-- [start:exhaustive_search]
+    uri = "data/sample-lancedb"
+    db = lancedb.connect(uri)
+    data = [
+        {"vector": row, "item": f"item {i}"}
+        for i, row in enumerate(np.random.random((10_000, 1536)).astype("float32"))
+    ]
+    tbl = db.create_table("vector_search", data=data, mode="overwrite")
+    tbl.search(np.random.random((1536))).limit(10).to_list()
+    # --8<-- [end:exhaustive_search]
+    # --8<-- [start:exhaustive_search_cosine]
+    tbl.search(np.random.random((1536))).distance_type("cosine").limit(10).to_list()
+    # --8<-- [end:exhaustive_search_cosine]
+    # --8<-- [start:create_table_with_nested_schema]
+    # Let's add 100 sample rows to our dataset
+    data = [
+        LanceSchema(
+            id=f"id{i}",
+            vector=np.random.randn(1536),
+            payload=Document(
+                content=f"document{i}",
+                meta=Metadata(source=f"source{i % 10}", timestamp=datetime.now()),
+            ),
+        )
+        for i in range(100)
+    ]
+
+    # Synchronous client
+    tbl = db.create_table("documents", data=data, mode="overwrite")
+    # --8<-- [end:create_table_with_nested_schema]
+    # --8<-- [start:search_result_as_pyarrow]
+    tbl.search(np.random.randn(1536)).to_arrow()
+    # --8<-- [end:search_result_as_pyarrow]
+    # --8<-- [start:search_result_as_pandas]
+    tbl.search(np.random.randn(1536)).to_pandas()
+    # --8<-- [end:search_result_as_pandas]
+    # --8<-- [start:search_result_as_pandas_flatten_true]
+    tbl.search(np.random.randn(1536)).to_pandas(flatten=True)
+    # --8<-- [end:search_result_as_pandas_flatten_true]
+    # --8<-- [start:search_result_as_pandas_flatten_1]
+    tbl.search(np.random.randn(1536)).to_pandas(flatten=1)
+    # --8<-- [end:search_result_as_pandas_flatten_1]
+    # --8<-- [start:search_result_as_list]
+    tbl.search(np.random.randn(1536)).to_list()
+    # --8<-- [end:search_result_as_list]
+    # --8<-- [start:search_result_as_pydantic]
+    tbl.search(np.random.randn(1536)).to_pydantic(LanceSchema)
+    # --8<-- [end:search_result_as_pydantic]
+
+
+@pytest.mark.asyncio
+async def test_vector_search_async():
+    # --8<-- [start:exhaustive_search_async]
+    uri = "data/sample-lancedb"
+    async_db = await lancedb.connect_async(uri)
+    data = [
+        {"vector": row, "item": f"item {i}"}
+        for i, row in enumerate(np.random.random((10_000, 1536)).astype("float32"))
+    ]
+    async_tbl = await async_db.create_table(
+        "vector_search_async", data=data, mode="overwrite"
+    )
+    (await (await async_tbl.search(np.random.random((1536)))).limit(10).to_list())
+    # --8<-- [end:exhaustive_search_async]
+    # --8<-- [start:exhaustive_search_async_cosine]
+    (
+        await (await async_tbl.search(np.random.random((1536))))
+        .distance_type("cosine")
+        .limit(10)
+        .to_list()
+    )
+    # --8<-- [end:exhaustive_search_async_cosine]
+    # --8<-- [start:create_table_async_with_nested_schema]
+    # Let's add 100 sample rows to our dataset
+    data = [
+        LanceSchema(
+            id=f"id{i}",
+            vector=np.random.randn(1536),
+            payload=Document(
+                content=f"document{i}",
+                meta=Metadata(source=f"source{i % 10}", timestamp=datetime.now()),
+            ),
+        )
+        for i in range(100)
+    ]
+
+    async_tbl = await async_db.create_table(
+        "documents_async", data=data, mode="overwrite"
+    )
+    # --8<-- [end:create_table_async_with_nested_schema]
+    # --8<-- [start:search_result_async_as_pyarrow]
+    await (await async_tbl.search(np.random.randn(1536))).to_arrow()
+    # --8<-- [end:search_result_async_as_pyarrow]
+    # --8<-- [start:search_result_async_as_pandas]
+    await (await async_tbl.search(np.random.randn(1536))).to_pandas()
+    # --8<-- [end:search_result_async_as_pandas]
+    # --8<-- [start:search_result_async_as_list]
+    await (await async_tbl.search(np.random.randn(1536))).to_list()
+    # --8<-- [end:search_result_async_as_list]
+
+
+def test_fts_fuzzy_query():
+    uri = "data/fuzzy-example"
+    db = lancedb.connect(uri)
+
+    table = db.create_table(
+        "my_table_fts_fuzzy",
+        data=pa.table(
+            {
+                "text": [
+                    "fa",
+                    "fo",  # spellchecker:disable-line
+                    "fob",
+                    "focus",
+                    "foo",
+                    "food",
+                    "foul",
+                ]
+            }
+        ),
+        mode="overwrite",
+    )
+    table.create_fts_index("text", replace=True)
+
+    results = table.search(MatchQuery("foo", "text", fuzziness=1)).to_pandas()
+    assert len(results) == 4
+    assert set(results["text"].to_list()) == {
+        "foo",
+        "fo",  # 1 deletion # spellchecker:disable-line
+        "fob",  # 1 substitution
+        "food",  # 1 insertion
+    }
+
+
+def test_fts_boost_query():
+    uri = "data/boost-example"
+    db = lancedb.connect(uri)
+
+    table = db.create_table(
+        "my_table_fts_boost",
+        data=pa.table(
+            {
+                "title": [
+                    "The Hidden Gems of Travel",
+                    "Exploring Nature's Wonders",
+                    "Cultural Treasures Unveiled",
+                    "The Nightlife Chronicles",
+                    "Scenic Escapes and Challenges",
+                ],
+                "desc": [
+                    "A vibrant city with occasional traffic jams.",
+                    "Beautiful landscapes but overpriced tourist spots.",
+                    "Rich cultural heritage but humid summers.",
+                    "Bustling nightlife but noisy streets.",
+                    "Scenic views but limited public transport options.",
+                ],
+            }
+        ),
+        mode="overwrite",
+    )
+    table.create_fts_index("desc", replace=True)
+
+    results = table.search(
+        BoostQuery(
+            MatchQuery("beautiful, cultural, nightlife", "desc"),
+            MatchQuery("bad traffic jams, overpriced", "desc"),
+        ),
+    ).to_pandas()
+
+    # we will hit 3 results because the positive query has 3 hits
+    assert len(results) == 3
+    # the one containing "overpriced" will be negatively boosted,
+    # so it will be the last one
+    assert (
+        results["desc"].to_list()[2]
+        == "Beautiful landscapes but overpriced tourist spots."
+    )
+
+
+def test_fts_native():
+    # --8<-- [start:basic_fts]
+    uri = "data/sample-lancedb"
+    db = lancedb.connect(uri)
+
+    table = db.create_table(
+        "my_table_fts",
+        data=[
+            {"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"},
+            {"vector": [5.9, 26.5], "text": "There are several kittens playing"},
+        ],
+        mode="overwrite",
+    )
+
+    table.create_fts_index("text")
+    table.search("puppy").limit(10).select(["text"]).to_list()
+    # [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}]
+    # ...
+    # --8<-- [end:basic_fts]
+    # --8<-- [start:fts_config_stem]
+    table.create_fts_index("text", tokenizer_name="en_stem", replace=True)
+    # --8<-- [end:fts_config_stem]
+    # --8<-- [start:fts_config_folding]
+    table.create_fts_index(
+        "text",
+        language="French",
+        stem=True,
+        ascii_folding=True,
+        replace=True,
+    )
+    # --8<-- [end:fts_config_folding]
+    # --8<-- [start:fts_prefiltering]
+    table.search("puppy").limit(10).where("text='foo'", prefilter=True).to_list()
+    # --8<-- [end:fts_prefiltering]
+    # --8<-- [start:fts_postfiltering]
+    table.search("puppy").limit(10).where("text='foo'", prefilter=False).to_list()
+    # --8<-- [end:fts_postfiltering]
+    # --8<-- [start:fts_with_position]
+    table.create_fts_index("text", with_position=True, replace=True)
+    # --8<-- [end:fts_with_position]
+    # --8<-- [start:fts_incremental_index]
+    table.add([{"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"}])
+    table.optimize()
+    # --8<-- [end:fts_incremental_index]
+
+
+@pytest.mark.asyncio
+async def test_fts_native_async():
+    # --8<-- [start:basic_fts_async]
+    uri = "data/sample-lancedb"
+    async_db = await lancedb.connect_async(uri)
+
+    async_tbl = await async_db.create_table(
+        "my_table_fts_async",
+        data=[
+            {"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"},
+            {"vector": [5.9, 26.5], "text": "There are several kittens playing"},
+        ],
+        mode="overwrite",
+    )
+
+    # async API uses our native FTS algorithm
+    await async_tbl.create_index("text", config=FTS())
+    await (await async_tbl.search("puppy")).select(["text"]).limit(10).to_list()
+    # [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}]
+    # ...
+    # --8<-- [end:basic_fts_async]
+    # --8<-- [start:fts_config_stem_async]
+    await async_tbl.create_index(
+        "text", config=FTS(language="English", stem=True, remove_stop_words=True)
+    )
+    # --8<-- [end:fts_config_stem_async]
+    # --8<-- [start:fts_config_folding_async]
+    await async_tbl.create_index(
+        "text", config=FTS(language="French", stem=True, ascii_folding=True)
+    )
+    # --8<-- [end:fts_config_folding_async]
+    # --8<-- [start:fts_prefiltering_async]
+    await (await async_tbl.search("puppy")).limit(10).where("text='foo'").to_list()
+    # --8<-- [end:fts_prefiltering_async]
+    # --8<-- [start:fts_postfiltering_async]
+    await (
+        (await async_tbl.search("puppy"))
+        .limit(10)
+        .where("text='foo'")
+        .postfilter()
+        .to_list()
+    )
+    # --8<-- [end:fts_postfiltering_async]
+    # --8<-- [start:fts_with_position_async]
+    await async_tbl.create_index("text", config=FTS(with_position=True))
+    # --8<-- [end:fts_with_position_async]
+    # --8<-- [start:fts_incremental_index_async]
+    await async_tbl.add([{"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"}])
+    await async_tbl.optimize()
+    # --8<-- [end:fts_incremental_index_async]
+
+
+def _vectors(n, dim, seed=0, column="vector"):
+    rng = np.random.default_rng(seed)
+    return [
+        {column: rng.random(dim).astype("float32"), "id": i} for i in range(n)
+    ]
+
+
+def _build_indexed_table(db, name, dim=128, n=512):
+    tbl = db.create_table(name, _vectors(n, dim), mode="overwrite")
+    tbl.create_index(metric="cosine", num_partitions=4, num_sub_vectors=8)
+    return tbl
+
+
+def test_vs_distance_metric_and_brute_force(tmp_db):
+    tbl = tmp_db.create_table("vs_metric", _vectors(64, 1536), mode="overwrite")
+
+    # --8<-- [start:configure_distance_metric]
+    tbl.search(np.random.random((1536))).distance_type("cosine").limit(10).to_list()
+    # --8<-- [end:configure_distance_metric]
+
+    # --8<-- [start:brute_force_search]
+    tbl.search(np.random.random((1536))).limit(3).to_list()
+    # --8<-- [end:brute_force_search]
+
+
+def test_vs_select_vector_column(tmp_db):
+    db = tmp_db
+
+    # --8<-- [start:select_vector_column]
+    import pyarrow as pa
+
+    schema = pa.schema([
+        pa.field("id", pa.int32()),
+        pa.field(
+            "image",
+            pa.struct([pa.field("embedding", pa.list_(pa.float32(), 2))]),
+        ),
+    ])
+    table = db.create_table(
+        "nested",
+        data=[{"id": 0, "image": {"embedding": [0.0, 1.0]}}],
+        schema=schema,
+    )
+
+    # Inferred: the only vector leaf is `image.embedding`.
+    table.search([0.0, 1.0]).limit(1).to_list()
+
+    # Explicit: required when more than one vector column matches.
+    table.search([0.0, 1.0], vector_column_name="image.embedding").limit(1).to_list()
+    # --8<-- [end:select_vector_column]
+
+
+def test_vs_index_nested_column(tmp_db):
+    dim = 16
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int32()),
+            pa.field(
+                "image",
+                pa.struct([pa.field("embedding", pa.list_(pa.float32(), dim))]),
+            ),
+        ]
+    )
+    rng = np.random.default_rng(0)
+    data = [
+        {"id": i, "image": {"embedding": rng.random(dim).astype("float32").tolist()}}
+        for i in range(512)
+    ]
+    table = tmp_db.create_table(
+        "nested_index", data=data, schema=schema, mode="overwrite"
+    )
+
+    # --8<-- [start:index_nested_column]
+    table.create_index(vector_column_name="image.embedding")
+    # --8<-- [end:index_nested_column]
+
+    assert table.list_indices()
+
+
+def test_vs_indexed_queries(tmp_db):
+    table = _build_indexed_table(tmp_db, "vs_indexed", dim=128, n=512)
+    embedding = np.random.random(128)
+
+    # --8<-- [start:exact_vs_approximate_distances]
+    # Indexed ANN search without refinement (fast, approximate `_distance`)
+    fast_results = (
+        table.search(embedding)
+        .limit(10)
+        .to_pandas()
+    )
+
+    # Recompute distances on full vectors for reranked candidates
+    exact_distance_results = (
+        table.search(embedding)
+        .limit(10)
+        .refine_factor(1)
+        .to_pandas()
+    )
+
+    # Rerank a larger candidate set for better recall (higher latency)
+    higher_recall_results = (
+        table.search(embedding)
+        .limit(10)
+        .refine_factor(20)
+        .to_pandas()
+    )
+    # --8<-- [end:exact_vs_approximate_distances]
+
+    # --8<-- [start:bypass_vector_index]
+    table.search(embedding).bypass_vector_index().limit(5).to_pandas()
+    # --8<-- [end:bypass_vector_index]
+
+    assert len(fast_results) == 10
+    assert len(exact_distance_results) == 10
+    assert len(higher_recall_results) == 10
+
+
+def test_vs_fast_search(tmp_db, monkeypatch):
+    table = _build_indexed_table(tmp_db, "vs_fast", dim=128, n=512)
+    embedding = np.random.random(128)
+
+    # `fast_search` is an Enterprise/async query flag; strip it so the snippet
+    # runs against a local OSS table without changing what readers see.
+    real_search = table.search
+    monkeypatch.setattr(
+        table,
+        "search",
+        lambda *a, **k: real_search(
+            *a, **{key: val for key, val in k.items() if key != "fast_search"}
+        ),
+    )
+
+    # --8<-- [start:fast_search]
+    table.search(embedding, fast_search=True).limit(5).to_pandas()
+    # --8<-- [end:fast_search]
+
+
+def test_vs_distance_range(tmp_db):
+    tbl = tmp_db.create_table("vs_distance_range", _vectors(256, 256), mode="overwrite")
+
+    # --8<-- [start:search_distance_range]
+    query = np.random.random(256)
+
+    # Search for the vectors within the range of [0.1, 0.5)
+    tbl.search(query).distance_range(0.1, 0.5).to_arrow()
+
+    # Search for the vectors with the distance less than 0.5
+    tbl.search(query).distance_range(upper_bound=0.5).to_arrow()
+
+    # Search for the vectors with the distance greater or equal to 0.1
+    tbl.search(query).distance_range(lower_bound=0.1).to_arrow()
+    # --8<-- [end:search_distance_range]
+
+
+def test_vs_multivector_search(tmp_db):
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int64()),
+            pa.field("vector", pa.list_(pa.list_(pa.float32(), 256))),
+        ]
+    )
+    rng = np.random.default_rng(0)
+    data = [{"id": i, "vector": rng.random(size=(2, 256)).tolist()} for i in range(64)]
+    tbl = tmp_db.create_table(
+        "vs_multivector", data=data, schema=schema, mode="overwrite"
+    )
+
+    # --8<-- [start:multivector_search]
+    query_multi = np.random.random(size=(2, 256))
+    results_multi = tbl.search(query_multi).limit(5).to_pandas()
+    # --8<-- [end:multivector_search]
+
+    assert len(results_multi) <= 5
+
+
+def test_vs_binary_search(tmp_db):
+    db = tmp_db
+
+    # --8<-- [start:search_binary_vectors]
+    import numpy as np
+    import pyarrow as pa
+
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int64()),
+            # for dim=256, lance stores every 8 bits in a byte
+            # so the vector field should be a list of 256 / 8 = 32 bytes
+            pa.field("vector", pa.list_(pa.uint8(), 32)),
+        ]
+    )
+    tbl = db.create_table("my_binary_vectors", schema=schema)
+
+    data = []
+    for i in range(1024):
+        vector = np.random.randint(0, 2, size=256)
+        # pack the binary vector into bytes to save space
+        packed_vector = np.packbits(vector)
+        data.append(
+            {
+                "id": i,
+                "vector": packed_vector,
+            }
+        )
+    tbl.add(data)
+
+    query = np.random.randint(0, 2, size=256)
+    packed_query = np.packbits(query)
+    tbl.search(packed_query).distance_type("hamming").to_arrow()
+    # --8<-- [end:search_binary_vectors]
+
+
+def test_vs_enterprise_filtering(tmp_db, monkeypatch):
+    import sys
+    import types
+
+    dim = 384
+    rng = np.random.default_rng(0)
+    rows = [
+        {
+            "vector": rng.random(dim).astype("float32"),
+            "text": f"story {i}",
+            "keywords": f"kw{i}",
+            "label": i % 4,
+        }
+        for i in range(50)
+    ]
+    tmp_db.create_table("lancedb-enterprise-quickstart", data=rows, mode="overwrite")
+    db = tmp_db
+
+    # Mock the Hugging Face dataset loader to avoid network downloads.
+    class _FakeDataset:
+        def __init__(self, n):
+            self._emb = [rng.random(dim).astype("float32") for _ in range(n)]
+            self._kw = [f"kw{i}" for i in range(n)]
+
+        def __getitem__(self, key):
+            if isinstance(key, str):
+                return {"keywords_embeddings": self._emb, "keywords": self._kw}[key]
+            return {"keywords": self._kw[key], "keywords_embeddings": self._emb[key]}
+
+    fake_datasets = types.ModuleType("datasets")
+    fake_datasets.load_dataset = lambda *a, **k: _FakeDataset(10)
+    monkeypatch.setitem(sys.modules, "datasets", fake_datasets)
+
+    # --8<-- [start:vector_search_prefilter]
+    from datasets import load_dataset
+
+    # Load query vector from dataset
+    query_dataset = load_dataset("sunhaozhepy/ag_news_sbert_keywords_embeddings", split="test[5000:5001]")
+    print(f"Query keywords: {query_dataset[0]['keywords']}")
+    query_embed = query_dataset["keywords_embeddings"][0]
+
+    # Open table and perform search
+    table_name = "lancedb-enterprise-quickstart"
+    table = db.open_table(table_name)
+
+    # Vector search with filters (pre-filtering is the default)
+    search_results = (
+        table.search(query_embed)
+        .where("label > 2")
+        .select(["text", "keywords", "label"])
+        .limit(5)
+        .to_pandas()
+    )
+
+    print("Search results (with pre-filtering):")
+    print(search_results)
+    # --8<-- [end:vector_search_prefilter]
+
+    # --8<-- [start:vector_search_postfilter]
+    results_post_filtered = (
+        table.search(query_embed)
+        .where("label > 1", prefilter=False)
+        .select(["text", "keywords", "label"])
+        .limit(5)
+        .to_pandas()
+    )
+
+    print("Vector search results with post-filter:")
+    print(results_post_filtered)
+    # --8<-- [end:vector_search_postfilter]
+
+    # --8<-- [start:batch_search]
+    # Load a batch of query embeddings
+    query_dataset = load_dataset(
+        "sunhaozhepy/ag_news_sbert_keywords_embeddings", split="test[5000:5005]"
+    )
+    query_embeds = query_dataset["keywords_embeddings"]
+    batch_results = table.search(query_embeds).limit(5).to_pandas()
+    print(batch_results)
+    # --8<-- [end:batch_search]
+
+
+@pytest.mark.skip()
+def test_hybrid_search():
+    # --8<-- [start:import-openai]
+    import openai
+
+    # --8<-- [end:import-openai]
+    # --8<-- [start:openai-embeddings]
+    # Ingest embedding function in LanceDB table
+    # Configuring the environment variable OPENAI_API_KEY
+    if "OPENAI_API_KEY" not in os.environ:
+        # OR set the key here as a variable
+        openai.api_key = "sk-..."
+    embeddings = get_registry().get("openai").create()
+
+    # --8<-- [end:openai-embeddings]
+    # --8<-- [start:class-Documents]
+    class Documents(LanceModel):
+        vector: Vector(embeddings.ndims()) = embeddings.VectorField()
+        text: str = embeddings.SourceField()
+
+    # --8<-- [end:class-Documents]
+    # --8<-- [start:basic_hybrid_search]
+    data = [
+        {"text": "rebel spaceships striking from a hidden base"},
+        {"text": "have won their first victory against the evil Galactic Empire"},
+        {"text": "during the battle rebel spies managed to steal secret plans"},
+        {"text": "to the Empire's ultimate weapon the Death Star"},
+    ]
+    uri = "data/sample-lancedb"
+    db = lancedb.connect(uri)
+    table = db.create_table("documents", schema=Documents)
+    # ingest docs with auto-vectorization
+    table.add(data)
+    # Create a fts index before the hybrid search
+    table.create_fts_index("text")
+    # hybrid search with default re-ranker
+    table.search("flower moon", query_type="hybrid").to_pandas()
+    # --8<-- [end:basic_hybrid_search]
+    # --8<-- [start:hybrid_search_pass_vector_text]
+    vector_query = [0.1, 0.2, 0.3, 0.4, 0.5]
+    text_query = "flower moon"
+    (
+        table.search(query_type="hybrid")
+        .vector(vector_query)
+        .text(text_query)
+        .limit(5)
+        .to_pandas()
+    )
+    # --8<-- [end:hybrid_search_pass_vector_text]
+
+
+@pytest.mark.skip
+async def test_hybrid_search_async():
+    import openai
+
+    # --8<-- [start:openai-embeddings]
+    # Ingest embedding function in LanceDB table
+    # Configuring the environment variable OPENAI_API_KEY
+    if "OPENAI_API_KEY" not in os.environ:
+        # OR set the key here as a variable
+        openai.api_key = "sk-..."
+    embeddings = get_registry().get("openai").create()
+
+    # --8<-- [end:openai-embeddings]
+    # --8<-- [start:class-Documents]
+    class Documents(LanceModel):
+        vector: Vector(embeddings.ndims()) = embeddings.VectorField()
+        text: str = embeddings.SourceField()
+
+    # --8<-- [end:class-Documents]
+    # --8<-- [start:basic_hybrid_search_async]
+    uri = "data/sample-lancedb"
+    async_db = await lancedb.connect_async(uri)
+    data = [
+        {"text": "rebel spaceships striking from a hidden base"},
+        {"text": "have won their first victory against the evil Galactic Empire"},
+        {"text": "during the battle rebel spies managed to steal secret plans"},
+        {"text": "to the Empire's ultimate weapon the Death Star"},
+    ]
+    async_tbl = await async_db.create_table("documents_async", schema=Documents)
+    # ingest docs with auto-vectorization
+    await async_tbl.add(data)
+    # Create a fts index before the hybrid search
+    await async_tbl.create_index("text", config=FTS())
+    text_query = "flower moon"
+    # hybrid search with default re-ranker
+    await (await async_tbl.search("flower moon", query_type="hybrid")).to_pandas()
+    # --8<-- [end:basic_hybrid_search_async]
+    # --8<-- [start:hybrid_search_pass_vector_text_async]
+    vector_query = [0.1, 0.2, 0.3, 0.4, 0.5]
+    text_query = "flower moon"
+    await (
+        async_tbl.query()
+        .nearest_to(vector_query)
+        .nearest_to_text(text_query)
+        .limit(5)
+        .to_pandas()
+    )
+    # --8<-- [end:hybrid_search_pass_vector_text_async]
diff --git a/docs/web-tests/py/test_search_examples.py b/docs/web-tests/py/test_search_examples.py
new file mode 100644
index 000000000..241a0f771
--- /dev/null
+++ b/docs/web-tests/py/test_search_examples.py
@@ -0,0 +1,228 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+
+def test_vector_search():
+    # --8<-- [start:vector_search]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-search-") as uri:
+        db = lancedb.connect(uri)
+        table = db.create_table(
+            "documents",
+            [
+                {
+                    "id": 1,
+                    "text": "A puppy playing in the park",
+                    "category": "animals",
+                    "vector": [1.0, 0.0],
+                },
+                {
+                    "id": 2,
+                    "text": "A dog resting at home",
+                    "category": "animals",
+                    "vector": [0.9, 0.1],
+                },
+                {
+                    "id": 3,
+                    "text": "A guide to growing tomatoes",
+                    "category": "gardening",
+                    "vector": [0.0, 1.0],
+                },
+            ],
+        )
+        rows = (
+            table.search([1.0, 0.0])
+            .distance_type("l2")
+            .select(["id", "text", "_distance"])
+            .limit(2)
+            .to_list()
+        )
+        print(rows)
+    # --8<-- [end:vector_search]
+
+    assert [row["id"] for row in rows] == [1, 2]
+    assert rows[0]["_distance"] == 0.0
+
+
+def test_text_search():
+    # --8<-- [start:text_search]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+    from lancedb.index import FTS
+
+    with TemporaryDirectory(prefix="lancedb-search-") as uri:
+        db = lancedb.connect(uri)
+        table = db.create_table(
+            "documents",
+            [
+                {
+                    "id": 1,
+                    "text": "A puppy playing in the park",
+                    "category": "animals",
+                },
+                {
+                    "id": 2,
+                    "text": "A dog resting at home",
+                    "category": "animals",
+                },
+                {
+                    "id": 3,
+                    "text": "A guide to growing tomatoes",
+                    "category": "gardening",
+                },
+            ],
+        )
+        table.create_index("text", config=FTS())
+        rows = (
+            table.search("puppy", query_type="fts", fts_columns="text")
+            .select(["id", "text", "_score"])
+            .limit(2)
+            .to_list()
+        )
+        print(rows)
+    # --8<-- [end:text_search]
+
+    assert [row["id"] for row in rows] == [1]
+    assert rows[0]["_score"] > 0
+
+
+def test_hybrid_search():
+    # --8<-- [start:hybrid_search]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+    from lancedb.index import FTS
+
+    with TemporaryDirectory(prefix="lancedb-search-") as uri:
+        db = lancedb.connect(uri)
+        table = db.create_table(
+            "documents",
+            [
+                {
+                    "id": 1,
+                    "text": "A puppy playing in the park",
+                    "category": "animals",
+                    "vector": [1.0, 0.0],
+                },
+                {
+                    "id": 2,
+                    "text": "A dog resting at home",
+                    "category": "animals",
+                    "vector": [0.9, 0.1],
+                },
+                {
+                    "id": 3,
+                    "text": "A guide to growing tomatoes",
+                    "category": "gardening",
+                    "vector": [0.0, 1.0],
+                },
+            ],
+        )
+        from lancedb.rerankers import RRFReranker
+
+        table.create_index("text", config=FTS())
+        rows = (
+            table.search(
+                query_type="hybrid", vector_column_name="vector", fts_columns="text"
+            )
+            .vector([1.0, 0.0])
+            .text("puppy")
+            .rerank(RRFReranker())
+            .limit(2)
+            .to_list()
+        )
+        print(rows)
+    # --8<-- [end:hybrid_search]
+
+    assert [row["id"] for row in rows] == [1, 2]
+    assert rows[0]["_relevance_score"] > rows[1]["_relevance_score"]
+
+
+def test_filter_search():
+    # --8<-- [start:filter_search]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-search-") as uri:
+        db = lancedb.connect(uri)
+        table = db.create_table(
+            "documents",
+            [
+                {
+                    "id": 1,
+                    "text": "A puppy playing in the park",
+                    "category": "animals",
+                    "vector": [1.0, 0.0],
+                },
+                {
+                    "id": 2,
+                    "text": "A dog resting at home",
+                    "category": "animals",
+                    "vector": [0.9, 0.1],
+                },
+                {
+                    "id": 3,
+                    "text": "A guide to growing tomatoes",
+                    "category": "gardening",
+                    "vector": [0.0, 1.0],
+                },
+            ],
+        )
+        rows = (
+            table.search([0.0, 1.0])
+            .where("category = 'animals'", prefilter=True)
+            .select(["id", "text", "category", "_distance"])
+            .limit(2)
+            .to_list()
+        )
+        print(rows)
+    # --8<-- [end:filter_search]
+
+    assert {row["id"] for row in rows} == {1, 2}
+    assert all(row["category"] == "animals" for row in rows)
+
+
+def test_query_plan():
+    # --8<-- [start:query_plan]
+    from tempfile import TemporaryDirectory
+
+    import lancedb
+
+    with TemporaryDirectory(prefix="lancedb-search-") as uri:
+        db = lancedb.connect(uri)
+        table = db.create_table(
+            "documents",
+            [
+                {
+                    "id": 1,
+                    "text": "A puppy playing in the park",
+                    "category": "animals",
+                    "vector": [1.0, 0.0],
+                },
+                {
+                    "id": 2,
+                    "text": "A dog resting at home",
+                    "category": "animals",
+                    "vector": [0.9, 0.1],
+                },
+                {
+                    "id": 3,
+                    "text": "A guide to growing tomatoes",
+                    "category": "gardening",
+                    "vector": [0.0, 1.0],
+                },
+            ],
+        )
+        query = table.search([1.0, 0.0]).select(["id", "text", "_distance"]).limit(2)
+        print("Plan:", query.explain_plan())
+        print("Measured execution:", query.analyze_plan())
+        rows = query.to_list()
+        print(rows)
+    # --8<-- [end:query_plan]
+
+    assert [row["id"] for row in rows] == [1, 2]
diff --git a/docs/web-tests/py/test_storage.py b/docs/web-tests/py/test_storage.py
new file mode 100644
index 000000000..f08ab07cf
--- /dev/null
+++ b/docs/web-tests/py/test_storage.py
@@ -0,0 +1,162 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import lancedb
+import pytest
+
+
+class DummyTable:
+    def __init__(self, name: str, storage_options: dict | None = None):
+        self.name = name
+        self.storage_options = storage_options
+
+
+class DummyConnection:
+    def __init__(self, uri: str, options: dict):
+        self.uri = uri
+        self.options = options
+        self.created_tables: list[DummyTable] = []
+
+    def create_table(
+        self,
+        name: str,
+        data,
+        storage_options: dict | None = None,
+    ):
+        table = DummyTable(name, storage_options=storage_options)
+        self.created_tables.append(table)
+        return table
+
+
+@pytest.fixture
+def fake_connect(monkeypatch):
+    calls: list[DummyConnection] = []
+
+    def _fake_connect(uri: str, **kwargs):
+        conn = DummyConnection(uri, kwargs)
+        calls.append(conn)
+        return conn
+
+    monkeypatch.setattr(lancedb, "connect", _fake_connect)
+    return calls
+
+
+def test_storage_snippets(fake_connect):
+    # --8<-- [start:storage_connect_s3]
+    db = lancedb.connect("s3://bucket/path")
+    # --8<-- [end:storage_connect_s3]
+
+    # --8<-- [start:storage_connect_gcs]
+    db = lancedb.connect("gs://bucket/path")
+    # --8<-- [end:storage_connect_gcs]
+
+    # --8<-- [start:storage_connect_azure]
+    db = lancedb.connect("az://bucket/path")
+    # --8<-- [end:storage_connect_azure]
+
+    # --8<-- [start:storage_connect_timeout]
+    db = lancedb.connect(
+        "s3://bucket/path",
+        storage_options={"timeout": "60s"},
+    )
+    # --8<-- [end:storage_connect_timeout]
+
+    # --8<-- [start:storage_table_timeout]
+    table = db.create_table(
+        "table",
+        [{"a": 1, "b": 2}],
+        storage_options={"timeout": "60s"},
+    )
+    # --8<-- [end:storage_table_timeout]
+
+    # --8<-- [start:storage_s3_ddb]
+    db = lancedb.connect(
+        "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
+    )
+    # --8<-- [end:storage_s3_ddb]
+
+    # --8<-- [start:storage_s3_ddb_local]
+    db = lancedb.connect(
+        "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
+        storage_options={
+            "endpoint": "http://localhost:4566",
+            "dynamodb_endpoint": "http://localhost:4566",
+            "allow_http": "true",
+        },
+    )
+    # --8<-- [end:storage_s3_ddb_local]
+
+    # --8<-- [start:storage_s3_sse_kms]
+    db = lancedb.connect(
+        "s3://bucket/path",
+        storage_options={
+            "aws_server_side_encryption": "aws:kms",
+            "aws_sse_kms_key_id": "<your-kms-key-id-or-arn>",
+        },
+    )
+    # --8<-- [end:storage_s3_sse_kms]
+
+    # --8<-- [start:storage_azure_sas]
+    db = lancedb.connect(
+        "az://my-container/my-database",
+        storage_options={
+            "azure_storage_account_name": "some-account",
+            "azure_storage_sas_token": "<sas-token>",
+        },
+    )
+    # --8<-- [end:storage_azure_sas]
+
+    # --8<-- [start:storage_s3_minio]
+    db = lancedb.connect(
+        "s3://bucket/path",
+        storage_options={
+            "region": "us-east-1",
+            "endpoint": "http://minio:9000",
+        },
+    )
+    # --8<-- [end:storage_s3_minio]
+
+    # --8<-- [start:storage_s3_express]
+    db = lancedb.connect(
+        "s3://my-bucket--use1-az4--x-s3/path",
+        storage_options={
+            "region": "us-east-1",
+            "s3_express": "true",
+        },
+    )
+    # --8<-- [end:storage_s3_express]
+
+    # --8<-- [start:storage_gcs_service_account]
+    db = lancedb.connect(
+        "gs://my-bucket/my-database",
+        storage_options={
+            "service_account": "path/to/service-account.json",
+        },
+    )
+    # --8<-- [end:storage_gcs_service_account]
+
+    # --8<-- [start:storage_azure_account]
+    db = lancedb.connect(
+        "az://my-container/my-database",
+        storage_options={
+            "account_name": "some-account",
+            "account_key": "some-key",
+        },
+    )
+    # --8<-- [end:storage_azure_account]
+
+    # --8<-- [start:storage_tigris_connect]
+    db = lancedb.connect(
+        "s3://your-bucket/path",
+        storage_options={
+            "endpoint": "https://t3.storage.dev",
+            "region": "auto",
+        },
+    )
+    # --8<-- [end:storage_tigris_connect]
+
+    assert len(fake_connect) == 13
+    assert all(
+        conn.uri.startswith(("s3://", "gs://", "az://", "s3+ddb://"))
+        for conn in fake_connect
+    )
diff --git a/docs/web-tests/py/test_tables.py b/docs/web-tests/py/test_tables.py
new file mode 100644
index 000000000..d17344aec
--- /dev/null
+++ b/docs/web-tests/py/test_tables.py
@@ -0,0 +1,1536 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+# --8<-- [start:tables_imports]
+import lancedb
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+from numpy.random import randint, random
+
+# --8<-- [end:tables_imports]
+
+
+# ============================================================================
+# Table Creation Examples
+# ============================================================================
+
+
+def test_tables_basic_connect_snippet(monkeypatch):
+    calls = {}
+
+    class DummyDB:
+        pass
+
+    def fake_connect(uri):
+        calls["uri"] = uri
+        return DummyDB()
+
+    import lancedb as _lancedb
+
+    # Monkey patch is used only in this test.
+    # Maybe can just actually connect, and disconnect instead?
+    monkeypatch.setattr(_lancedb, "connect", fake_connect)
+
+    # --8<-- [start:tables_basic_connect]
+    import lancedb
+
+    uri = "data/sample-lancedb"
+    db = lancedb.connect(uri)
+    # --8<-- [end:tables_basic_connect]
+
+    assert calls["uri"] == "data/sample-lancedb"
+    assert isinstance(db, DummyDB)
+
+
+def test_update_connect_enterprise_snippet(monkeypatch):
+    calls = {}
+
+    class DummyDB:
+        pass
+
+    def fake_connect(**kwargs):
+        calls.update(kwargs)
+        return DummyDB()
+
+    import lancedb as _lancedb
+
+    monkeypatch.setattr(_lancedb, "connect", fake_connect)
+
+    # --8<-- [start:update_connect_enterprise]
+    import lancedb
+
+    db = lancedb.connect(
+        uri="db://your-project-slug",
+        api_key="your-api-key",
+        region="us-east-1",
+    )
+    # --8<-- [end:update_connect_enterprise]
+
+    assert calls["uri"] == "db://your-project-slug"
+    assert calls["api_key"] == "your-api-key"
+    assert calls["region"] == "us-east-1"
+    assert isinstance(db, DummyDB)
+
+
+def test_update_connect_local_snippet(monkeypatch):
+    calls = {}
+
+    class DummyDB:
+        pass
+
+    def fake_connect(uri):
+        calls["uri"] = uri
+        return DummyDB()
+
+    import lancedb as _lancedb
+
+    monkeypatch.setattr(_lancedb, "connect", fake_connect)
+
+    # --8<-- [start:update_connect_local]
+    import lancedb
+
+    db = lancedb.connect("./data")
+    # --8<-- [end:update_connect_local]
+
+    assert calls["uri"] == "./data"
+    assert isinstance(db, DummyDB)
+
+
+def test_table_creation_from_dicts(tmp_db):
+    # --8<-- [start:create_table_from_dicts]
+    data = [
+        {"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7},
+        {"vector": [0.2, 1.8], "lat": 40.1, "long": -74.1},
+    ]
+    db = tmp_db
+    db.create_table("test_table", data, mode="overwrite")
+    tbl = db["test_table"]
+    tbl.head()
+    # --8<-- [end:create_table_from_dicts]
+
+
+def test_create_table_conflict_handling(tmp_db):
+    db = tmp_db
+    data = [
+        {"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7},
+        {"vector": [0.2, 1.8], "lat": 40.1, "long": -74.1},
+    ]
+    db.create_table("conflict_table", data)
+
+    # --8<-- [start:create_table_conflict_handling]
+    # Idempotent open: reuse the existing table if it exists.
+    # The provided data is ignored; the schema is validated against the
+    # existing table and a mismatch raises an error.
+    tbl = db.create_table("conflict_table", data, exist_ok=True)
+
+    # Overwrite: drop the existing table and create a new one with the
+    # provided data. This permanently discards the old table's data.
+    tbl = db.create_table("conflict_table", data, mode="overwrite")
+    # --8<-- [end:create_table_conflict_handling]
+    assert tbl.count_rows() == 2
+
+
+def test_table_creation_from_pandas(tmp_db):
+    # --8<-- [start:create_table_from_pandas]
+    import pandas as pd
+
+    data = pd.DataFrame(
+        {
+            "vector": [[1.1, 1.2, 1.3, 1.4], [0.2, 1.8, 0.4, 3.6]],
+            "lat": [45.5, 40.1],
+            "long": [-122.7, -74.1],
+        }
+    )
+    db = tmp_db
+    db.create_table("my_table_pandas", data, mode="overwrite")
+    db["my_table_pandas"].head()
+    # --8<-- [end:create_table_from_pandas]
+
+
+def test_table_creation_with_custom_schema(tmp_db):
+    # --8<-- [start:create_table_custom_schema]
+    import pyarrow as pa
+
+    custom_schema = pa.schema(
+        [
+            pa.field("vector", pa.list_(pa.float32(), 4)),
+            pa.field("lat", pa.float32()),
+            pa.field("long", pa.float32()),
+        ]
+    )
+
+    data = [
+        {"vector": [1.1, 1.2, 1.3, 1.4], "lat": 45.5, "long": -122.7},
+        {"vector": [0.2, 1.8, 0.4, 3.6], "lat": 40.1, "long": -74.1},
+    ]
+    db = tmp_db
+    tbl = db.create_table(
+        "my_table_custom_schema", data, schema=custom_schema, mode="overwrite"
+    )
+    # --8<-- [end:create_table_custom_schema]
+
+
+def test_table_creation_from_polars(tmp_db):
+    # --8<-- [start:create_table_from_polars]
+    import polars as pl
+
+    data = pl.DataFrame(
+        {
+            "vector": [[3.1, 4.1], [5.9, 26.5]],
+            "item": ["foo", "bar"],
+            "price": [10.0, 20.0],
+        }
+    )
+    db = tmp_db
+    tbl = db.create_table("my_table_pl", data, mode="overwrite")
+    # --8<-- [end:create_table_from_polars]
+
+
+def test_table_creation_from_arrow(tmp_db):
+    # --8<-- [start:create_table_from_arrow]
+    import numpy as np
+    import pyarrow as pa
+
+    dim = 16
+    total = 2
+    schema = pa.schema(
+        [pa.field("vector", pa.list_(pa.float16(), dim)), pa.field("text", pa.string())]
+    )
+    data = pa.Table.from_arrays(
+        [
+            pa.array(
+                [np.random.randn(dim).astype(np.float16) for _ in range(total)],
+                pa.list_(pa.float16(), dim),
+            ),
+            pa.array(["foo", "bar"]),
+        ],
+        ["vector", "text"],
+    )
+    db = tmp_db
+    tbl = db.create_table("f16_tbl", data, schema=schema, mode="overwrite")
+    # --8<-- [end:create_table_from_arrow]
+
+
+def test_table_creation_from_pydantic(tmp_db):
+    # --8<-- [start:create_table_from_pydantic]
+    from lancedb.pydantic import LanceModel, Vector
+
+    class Content(LanceModel):
+        movie_id: int
+        vector: Vector(128)
+        genres: str
+        title: str
+        imdb_id: int
+
+        @property
+        def imdb_url(self) -> str:
+            return f"https://www.imdb.com/title/tt{self.imdb_id}"
+
+    db = tmp_db
+    tbl = db.create_table("movielens_small", schema=Content, mode="overwrite")
+    # --8<-- [end:create_table_from_pydantic]
+
+
+def test_table_creation_nested_schema(tmp_db):
+    # --8<-- [start:create_table_nested_schema]
+    from lancedb.pydantic import LanceModel, Vector
+
+    # --8<-- [start:tables_document_model]
+    from pydantic import BaseModel
+
+    class Document(BaseModel):
+        content: str
+        source: str
+
+    # --8<-- [end:tables_document_model]
+
+    class NestedSchema(LanceModel):
+        id: str
+        vector: Vector(1536)
+        document: Document
+
+    db = tmp_db
+    tbl = db.create_table("nested_table", schema=NestedSchema, mode="overwrite")
+    # --8<-- [end:create_table_nested_schema]
+
+
+def test_tables_tz_validator_snippet():
+    # --8<-- [start:tables_tz_validator]
+    from datetime import datetime
+    from zoneinfo import ZoneInfo
+
+    from lancedb.pydantic import LanceModel
+    from pydantic import Field, ValidationError, ValidationInfo, field_validator
+
+    tzname = "America/New_York"
+    tz = ZoneInfo(tzname)
+
+    class TestModel(LanceModel):
+        dt_with_tz: datetime = Field(json_schema_extra={"tz": tzname})
+
+        @field_validator("dt_with_tz")
+        @classmethod
+        def tz_must_match(cls, dt: datetime) -> datetime:
+            assert dt.tzinfo == tz
+            return dt
+
+    ok = TestModel(dt_with_tz=datetime.now(tz))
+
+    try:
+        TestModel(dt_with_tz=datetime.now(ZoneInfo("Asia/Shanghai")))
+        assert 0 == 1, "this should raise ValidationError"
+    except ValidationError:
+        print("A ValidationError was raised.")
+        pass
+    # --8<-- [end:tables_tz_validator]
+
+    assert ok is not None
+
+
+def test_add_from_dataset(tmp_db, tmp_path):
+    import pyarrow as pa
+    import pyarrow.dataset as ds
+    import pyarrow.parquet as pq
+
+    schema = pa.schema(
+        [
+            pa.field("vector", pa.list_(pa.float32(), 4)),
+            pa.field("item", pa.utf8()),
+            pa.field("price", pa.float32()),
+        ]
+    )
+    for i in range(3):
+        batch = pa.table(
+            {
+                "vector": [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]],
+                "item": [f"item{i * 2}", f"item{i * 2 + 1}"],
+                "price": [float(i * 2), float(i * 2 + 1)],
+            },
+            schema=schema,
+        )
+        pq.write_table(batch, tmp_path / f"part-{i}.parquet")
+
+    data_path = str(tmp_path)
+
+    # --8<-- [start:add_from_dataset]
+    import pyarrow.dataset as ds
+
+    dataset = ds.dataset(data_path, format="parquet")
+    db = tmp_db
+    table = db.create_table("my_table", schema=dataset.schema, mode="overwrite")
+    table.add(dataset)
+    # --8<-- [end:add_from_dataset]
+    assert table.count_rows() == 6
+
+
+def test_table_creation_from_iterator(tmp_db):
+    # --8<-- [start:create_table_from_iterator]
+    import pyarrow as pa
+
+    schema = pa.schema(
+        [
+            pa.field("vector", pa.list_(pa.float32(), 4)),
+            pa.field("item", pa.utf8()),
+            pa.field("price", pa.float32()),
+        ]
+    )
+
+    def make_batches():
+        for i in range(5):
+            yield pa.RecordBatch.from_arrays(
+                [
+                    pa.array(
+                        [[3.1, 4.1, 5.1, 6.1], [5.9, 26.5, 4.7, 32.8]],
+                        pa.list_(pa.float32(), 4),
+                    ),
+                    pa.array(["foo", "bar"]),
+                    pa.array([10.0, 20.0]),
+                ],
+                ["vector", "item", "price"],
+            )
+
+    db = tmp_db
+    db.create_table("batched_table", make_batches(), schema=schema, mode="overwrite")
+    # --8<-- [end:create_table_from_iterator]
+
+def test_open_existing_table(tmp_db):
+    # --8<-- [start:open_existing_table]
+    db = tmp_db
+    # Create a table first
+    data = [{"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7}]
+    db.create_table("test_table", data, mode="overwrite")
+
+    # List table names
+    print(db.list_tables().tables)
+
+    # Open existing table
+    tbl = db.open_table("test_table")
+    # --8<-- [end:open_existing_table]
+
+
+def test_create_empty_table(tmp_db):
+    # --8<-- [start:create_empty_table]
+    import pyarrow as pa
+
+    schema = pa.schema(
+        [
+            pa.field("vector", pa.list_(pa.float32(), 2)),
+            pa.field("item", pa.string()),
+            pa.field("price", pa.float32()),
+        ]
+    )
+    db = tmp_db
+    tbl = db.create_table("test_empty_table", schema=schema, mode="overwrite")
+    # --8<-- [end:create_empty_table]
+
+
+def test_create_empty_table_pydantic(tmp_db):
+    # --8<-- [start:create_empty_table_pydantic]
+    from lancedb.pydantic import LanceModel, Vector
+
+    class Item(LanceModel):
+        vector: Vector(2)
+        item: str
+        price: float
+
+    db = tmp_db
+    tbl = db.create_table(
+        "test_empty_table_new", schema=Item.to_arrow_schema(), mode="overwrite"
+    )
+    # --8<-- [end:create_empty_table_pydantic]
+
+
+def test_drop_table(tmp_db):
+    # --8<-- [start:drop_table]
+    db = tmp_db
+    # Create a table first
+    data = [{"vector": [1.1, 1.2], "lat": 45.5}]
+    db.create_table("my_table", data, mode="overwrite")
+
+    # Drop the table
+    db.drop_table("my_table")
+    # --8<-- [end:drop_table]
+
+
+# ============================================================================
+# Data Update Examples
+# ============================================================================
+
+
+def test_add_data_to_table(tmp_db):
+    db = tmp_db
+
+    # --8<-- [start:add_data_to_table]
+    import pyarrow as pa
+
+    # create an empty table with schema
+    data = [
+        {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+        {"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
+        {"vector": [10.2, 100.8], "item": "baz", "price": 30.0},
+        {"vector": [1.4, 9.5], "item": "fred", "price": 40.0},
+    ]
+
+    schema = pa.schema(
+        [
+            pa.field("vector", pa.list_(pa.float32(), 2)),
+            pa.field("item", pa.utf8()),
+            pa.field("price", pa.float32()),
+        ]
+    )
+
+    table_name = "basic_ingestion_example"
+    table = db.create_table(table_name, schema=schema, mode="overwrite")
+    # Add data
+    table.add(data)
+    # --8<-- [end:add_data_to_table]
+    assert table.count_rows() == len(data)
+
+
+def test_add_data_pydantic_model(tmp_db):
+    db = tmp_db
+
+    # --8<-- [start:add_data_pydantic_model]
+    from lancedb.pydantic import LanceModel, Vector
+
+    # Define a Pydantic model
+    class Content(LanceModel):
+        movie_id: int
+        vector: Vector(128)
+        genres: str
+        title: str
+        imdb_id: int
+
+        @property
+        def imdb_url(self) -> str:
+            return f"https://www.imdb.com/title/tt{self.imdb_id}"
+
+    # Create table with Pydantic model schema
+    table_name = "pydantic_example"
+    table = db.create_table(table_name, schema=Content, mode="overwrite")
+    # --8<-- [end:add_data_pydantic_model]
+    assert table.count_rows() == 0
+
+
+def test_add_data_nested_model(tmp_db):
+    db = tmp_db
+
+    # --8<-- [start:add_data_nested_model]
+    from lancedb.pydantic import LanceModel, Vector
+    from pydantic import BaseModel
+
+    class Document(BaseModel):
+        content: str
+        source: str
+
+    class NestedSchema(LanceModel):
+        id: str
+        vector: Vector(128)
+        document: Document
+
+    # Create table with nested schema
+    table_name = "nested_model_example"
+    table = db.create_table(table_name, schema=NestedSchema, mode="overwrite")
+    # --8<-- [end:add_data_nested_model]
+    assert table.count_rows() == 0
+
+
+def test_batch_data_insertion(tmp_db):
+    db = tmp_db
+
+    # --8<-- [start:batch_data_insertion]
+    import pyarrow as pa
+
+    def make_batches():
+        for i in range(5):  # Create 5 batches
+            yield pa.RecordBatch.from_arrays(
+                [
+                    pa.array([[3.1, 4.1], [5.9, 26.5]], pa.list_(pa.float32(), 2)),
+                    pa.array([f"item{i * 2 + 1}", f"item{i * 2 + 2}"]),
+                    pa.array([float((i * 2 + 1) * 10), float((i * 2 + 2) * 10)]),
+                ],
+                ["vector", "item", "price"],
+            )
+
+    schema = pa.schema(
+        [
+            pa.field("vector", pa.list_(pa.float32(), 2)),
+            pa.field("item", pa.utf8()),
+            pa.field("price", pa.float32()),
+        ]
+    )
+    # Create table with batches
+    table_name = "batch_ingestion_example"
+    table = db.create_table(table_name, make_batches(), schema=schema, mode="overwrite")
+    # --8<-- [end:batch_data_insertion]
+    assert table.count_rows() == 10
+
+
+def _create_users_example_table(db, table_name="users_example"):
+    return db.create_table(
+        table_name,
+        data=pa.table(
+            {
+                "id": [1, 2, 3],
+                "name": ["Alice", "Bob", "Charlie"],
+                "login_count": [10, 20, 5],
+            }
+        ),
+        mode="overwrite",
+    )
+
+
+def test_update_example_table_setup(tmp_db):
+    db = tmp_db
+
+    # --8<-- [start:update_example_table_setup]
+    import pyarrow as pa
+
+    table = db.create_table(
+        "users_example",
+        data=pa.table(
+            {
+                "id": [1, 2],
+                "name": ["Alice", "Bob"],
+                "login_count": [10, 20],
+            }
+        ),
+        mode="overwrite",
+    )
+    # --8<-- [end:update_example_table_setup]
+    assert table.count_rows() == 2
+
+
+def test_update_operation(tmp_db):
+    db = tmp_db
+
+    # --8<-- [start:update_operation]
+    import pyarrow as pa
+
+    table = db.create_table(
+        "users_example",
+        data=pa.table(
+            {
+                "id": [1, 2],
+                "name": ["Alice", "Bob"],
+                "login_count": [10, 20],
+            }
+        ),
+        mode="overwrite",
+    )
+    table.update(where="id = 2", values={"name": "Bobby"})
+    # --8<-- [end:update_operation]
+    rows = table.to_arrow().sort_by("id").to_pylist()
+    assert rows == [
+        {"id": 1, "name": "Alice", "login_count": 10},
+        {"id": 2, "name": "Bobby", "login_count": 20},
+    ]
+
+
+def test_update_using_sql(tmp_db):
+    db = tmp_db
+
+    # --8<-- [start:update_using_sql]
+    import pyarrow as pa
+
+    table = db.create_table(
+        "users_example",
+        data=pa.table(
+            {
+                "id": [1, 2],
+                "name": ["Alice", "Bob"],
+                "login_count": [10, 20],
+            }
+        ),
+        mode="overwrite",
+    )
+    table.update(where="id = 2", values_sql={"login_count": "login_count + 1"})
+    # --8<-- [end:update_using_sql]
+    rows = table.to_arrow().sort_by("id").to_pylist()
+    assert rows == [
+        {"id": 1, "name": "Alice", "login_count": 10},
+        {"id": 2, "name": "Bob", "login_count": 21},
+    ]
+
+
+def test_merge_matched_update_only(tmp_db):
+    db = tmp_db
+
+    # --8<-- [start:merge_matched_update_only]
+    import pyarrow as pa
+
+    table = db.create_table(
+        "users_example",
+        data=pa.table(
+            {
+                "id": [1, 2],
+                "name": ["Alice", "Bob"],
+                "login_count": [10, 20],
+            }
+        ),
+        mode="overwrite",
+    )
+
+    incoming_users = pa.table(
+        {
+            "id": [2, 3],
+            "name": ["Bobby", "Charlie"],
+            "login_count": [21, 5],
+        }
+    )
+
+    (table.merge_insert("id").when_matched_update_all().execute(incoming_users))
+    # --8<-- [end:merge_matched_update_only]
+    rows = table.to_arrow().sort_by("id").to_pylist()
+    assert rows == [
+        {"id": 1, "name": "Alice", "login_count": 10},
+        {"id": 2, "name": "Bobby", "login_count": 21},
+    ]
+
+
+def test_insert_if_not_exists(tmp_db):
+    db = tmp_db
+
+    # --8<-- [start:insert_if_not_exists]
+    import pyarrow as pa
+
+    table = db.create_table(
+        "users_example",
+        data=pa.table(
+            {
+                "id": [1, 2],
+                "name": ["Alice", "Bob"],
+                "login_count": [10, 20],
+            }
+        ),
+        mode="overwrite",
+    )
+
+    incoming_users = pa.table(
+        {
+            "id": [2, 3],
+            "name": ["Bobby", "Charlie"],
+            "login_count": [21, 5],
+        }
+    )
+
+    (table.merge_insert("id").when_not_matched_insert_all().execute(incoming_users))
+    # --8<-- [end:insert_if_not_exists]
+    rows = table.to_arrow().sort_by("id").to_pylist()
+    assert rows == [
+        {"id": 1, "name": "Alice", "login_count": 10},
+        {"id": 2, "name": "Bob", "login_count": 20},
+        {"id": 3, "name": "Charlie", "login_count": 5},
+    ]
+
+
+def test_merge_update_insert(tmp_db):
+    db = tmp_db
+
+    # --8<-- [start:merge_update_insert]
+    import pyarrow as pa
+
+    table = db.create_table(
+        "users_example",
+        data=pa.table(
+            {
+                "id": [1, 2],
+                "name": ["Alice", "Bob"],
+                "login_count": [10, 20],
+            }
+        ),
+        mode="overwrite",
+    )
+
+    incoming_users = pa.table(
+        {
+            "id": [2, 3],
+            "name": ["Bobby", "Charlie"],
+            "login_count": [21, 5],
+        }
+    )
+
+    (
+        table.merge_insert("id")
+        .when_matched_update_all()
+        .when_not_matched_insert_all()
+        .execute(incoming_users)
+    )
+    # --8<-- [end:merge_update_insert]
+    rows = table.to_arrow().sort_by("id").to_pylist()
+    assert rows == [
+        {"id": 1, "name": "Alice", "login_count": 10},
+        {"id": 2, "name": "Bobby", "login_count": 21},
+        {"id": 3, "name": "Charlie", "login_count": 5},
+    ]
+
+
+def test_merge_delete_missing_by_source(tmp_db):
+    db = tmp_db
+
+    # --8<-- [start:merge_delete_missing_by_source]
+    import pyarrow as pa
+
+    table = db.create_table(
+        "users_example",
+        data=pa.table(
+            {
+                "id": [1, 2, 3],
+                "name": ["Alice", "Bob", "Charlie"],
+                "login_count": [10, 20, 5],
+            }
+        ),
+        mode="overwrite",
+    )
+
+    incoming_users = pa.table(
+        {
+            "id": [2, 3],
+            "name": ["Bobby", "Charlie"],
+            "login_count": [21, 5],
+        }
+    )
+
+    (
+        table.merge_insert("id")
+        .when_matched_update_all()
+        .when_not_matched_insert_all()
+        .when_not_matched_by_source_delete()
+        .execute(incoming_users)
+    )
+    # --8<-- [end:merge_delete_missing_by_source]
+    rows = table.to_arrow().sort_by("id").to_pylist()
+    assert rows == [
+        {"id": 2, "name": "Bobby", "login_count": 21},
+        {"id": 3, "name": "Charlie", "login_count": 5},
+    ]
+
+
+def test_merge_partial_columns(tmp_db):
+    db = tmp_db
+
+    # --8<-- [start:merge_partial_columns]
+    import pyarrow as pa
+
+    table = db.create_table(
+        "users_example",
+        data=pa.table(
+            {
+                "id": [1, 2],
+                "name": ["Alice", "Bob"],
+                "login_count": [10, 20],
+            }
+        ),
+        mode="overwrite",
+    )
+
+    incoming_users = pa.table(
+        {
+            "id": [2, 3],
+            "name": ["Bobby", "Charlie"],
+        }
+    )
+
+    (
+        table.merge_insert("id")
+        .when_matched_update_all()
+        .when_not_matched_insert_all()
+        .execute(incoming_users)
+    )
+    # --8<-- [end:merge_partial_columns]
+    rows = table.to_arrow().sort_by("id").to_pylist()
+    assert rows == [
+        {"id": 1, "name": "Alice", "login_count": 10},
+        {"id": 2, "name": "Bobby", "login_count": 20},
+        {"id": 3, "name": "Charlie", "login_count": None},
+    ]
+
+
+def test_delete_operation(tmp_db):
+    db = tmp_db
+    table = _create_users_example_table(db)
+
+    # --8<-- [start:delete_operation]
+    # delete data
+    predicate = "id = 3"
+    table.delete(predicate)
+    # --8<-- [end:delete_operation]
+    assert table.count_rows() == 2
+
+
+def test_update_optimize_cleanup_snippet(tmp_db):
+    table = _create_users_example_table(tmp_db, table_name="users_cleanup_example")
+
+    # --8<-- [start:update_optimize_cleanup]
+    from datetime import timedelta
+
+    table.optimize(cleanup_older_than=timedelta(days=1))
+    # --8<-- [end:update_optimize_cleanup]
+
+
+# ============================================================================
+# Schema Evolution Examples
+# ============================================================================
+
+
+def _setup_schema_add_table(db):
+    # --8<-- [start:schema_add_setup]
+    import numpy as np
+
+    data = [
+        {
+            "id": 1,
+            "name": "Laptop",
+            "price": 1200.00,
+            "vector": np.random.random(128).tolist(),
+        },
+        {
+            "id": 2,
+            "name": "Smartphone",
+            "price": 800.00,
+            "vector": np.random.random(128).tolist(),
+        },
+        {
+            "id": 3,
+            "name": "Headphones",
+            "price": 150.00,
+            "vector": np.random.random(128).tolist(),
+        },
+    ]
+    table = db.create_table("schema_evolution_add_example", data, mode="overwrite")
+    # --8<-- [end:schema_add_setup]
+    return table
+
+
+def _setup_schema_alter_table(tmp_db, data=None):
+    # --8<-- [start:schema_alter_setup]
+    table_name = "schema_evolution_alter_example"
+    if data is None:
+        data = [
+            {
+                "id": 1,
+                "name": "Laptop",
+                "price": 1200,
+                "discount_price": 1080.0,
+                "vector": np.random.random(128).tolist(),
+            },
+            {
+                "id": 2,
+                "name": "Smartphone",
+                "price": 800,
+                "discount_price": 720.0,
+                "vector": np.random.random(128).tolist(),
+            },
+        ]
+    schema = pa.schema(
+        {
+            "id": pa.int64(),
+            "name": pa.string(),
+            "price": pa.int32(),
+            "discount_price": pa.float64(),
+            "vector": pa.list_(pa.float32(), 128),
+        }
+    )
+    table = tmp_db.create_table(table_name, data, schema=schema, mode="overwrite")
+    # --8<-- [end:schema_alter_setup]
+    return table
+
+
+def _setup_schema_drop_table(tmp_db, data=None):
+    # --8<-- [start:schema_drop_setup]
+    if data is None:
+        data = [
+            {
+                "id": 1,
+                "name": "Laptop",
+                "price": 1200.00,
+                "temp_col1": "X",
+                "temp_col2": 100,
+                "vector": np.random.random(128).tolist(),
+            },
+            {
+                "id": 2,
+                "name": "Smartphone",
+                "price": 800.00,
+                "temp_col1": "Y",
+                "temp_col2": 200,
+                "vector": np.random.random(128).tolist(),
+            },
+            {
+                "id": 3,
+                "name": "Headphones",
+                "price": 150.00,
+                "temp_col1": "Z",
+                "temp_col2": 300,
+                "vector": np.random.random(128).tolist(),
+            },
+        ]
+    table = tmp_db.create_table("schema_evolution_drop_example", data, mode="overwrite")
+    # --8<-- [end:schema_drop_setup]
+    return table
+
+
+def test_add_columns_calculated(tmp_db):
+    table = _setup_schema_add_table(tmp_db)
+
+    # --8<-- [start:add_columns_calculated]
+    # Add a discounted price column (10% discount)
+    table.add_columns({"discounted_price": "cast((price * 0.9) as float)"})
+    # --8<-- [end:add_columns_calculated]
+    assert "discounted_price" in table.schema.names
+
+
+def test_add_columns_default_values(tmp_db):
+    table = _setup_schema_add_table(tmp_db)
+
+    # --8<-- [start:add_columns_default_values]
+    # Add a stock status column with default value
+    table.add_columns({"in_stock": "cast(true as boolean)"})
+    # --8<-- [end:add_columns_default_values]
+    assert "in_stock" in table.schema.names
+
+
+def test_add_columns_nullable(tmp_db):
+    table = _setup_schema_add_table(tmp_db)
+
+    # --8<-- [start:add_columns_nullable]
+    # Add a nullable timestamp column
+    table.add_columns({"last_ordered": "cast(NULL as timestamp)"})
+    # --8<-- [end:add_columns_nullable]
+    assert "last_ordered" in table.schema.names
+
+
+def test_add_feature_columns_sql(tmp_db):
+    table = _setup_schema_add_table(tmp_db)
+
+    # --8<-- [start:add_feature_columns_sql]
+    table.add_columns(
+        {
+            "price_per_id": "cast(price / id as float)",
+            "price_log": "ln(price)",
+            "price_score": "cast(price / (price + 100.0) as float)",
+        }
+    )
+    # --8<-- [end:add_feature_columns_sql]
+    assert {"price_per_id", "price_log", "price_score"}.issubset(table.schema.names)
+
+
+def test_alter_columns_rename(tmp_db):
+    table = _setup_schema_alter_table(tmp_db)
+
+    # --8<-- [start:alter_columns_rename]
+    # Rename discount_price to sale_price
+    table.alter_columns({"path": "discount_price", "rename": "sale_price"})
+    # --8<-- [end:alter_columns_rename]
+    assert "sale_price" in table.schema.names
+    assert "discount_price" not in table.schema.names
+
+
+def test_alter_columns_data_type(tmp_db):
+    table = _setup_schema_alter_table(
+        tmp_db,
+        data=[
+            {
+                "id": 1,
+                "name": "Laptop",
+                "price": 1200,
+                "discount_price": 1080.0,
+                "vector": np.random.random(128).tolist(),
+            }
+        ],
+    )
+
+    # --8<-- [start:alter_columns_data_type]
+    # Change price from int32 to int64 for larger numbers
+    table.alter_columns({"path": "price", "data_type": pa.int64()})
+    # --8<-- [end:alter_columns_data_type]
+    assert table.schema.field("price").type == pa.int64()
+
+
+def test_alter_columns_nullable(tmp_db):
+    table = _setup_schema_alter_table(
+        tmp_db,
+        data=[
+            {
+                "id": 1,
+                "name": "Laptop",
+                "price": 1200,
+                "discount_price": 1080.0,
+                "vector": np.random.random(128).tolist(),
+            }
+        ],
+    )
+
+    # --8<-- [start:alter_columns_nullable]
+    # Make the name column nullable
+    table.alter_columns({"path": "name", "nullable": True})
+    # --8<-- [end:alter_columns_nullable]
+    assert table.schema.field("name").nullable is True
+
+
+def test_alter_columns_multiple(tmp_db):
+    table = _setup_schema_alter_table(
+        tmp_db,
+        data=[
+            {
+                "id": 1,
+                "name": "Laptop",
+                "price": 1200,
+                "discount_price": 1080.0,
+                "vector": np.random.random(128).tolist(),
+            }
+        ],
+    )
+    table.alter_columns({"path": "discount_price", "rename": "sale_price"})
+
+    # --8<-- [start:alter_columns_multiple]
+    # Rename, change type, and make nullable in one operation
+    table.alter_columns(
+        {
+            "path": "sale_price",
+            "rename": "final_price",
+            "data_type": pa.float64(),
+            "nullable": True,
+        }
+    )
+    # --8<-- [end:alter_columns_multiple]
+    assert "final_price" in table.schema.names
+    assert table.schema.field("final_price").nullable is True
+
+
+def test_alter_columns_with_expression(tmp_db):
+    # --8<-- [start:alter_columns_with_expression]
+    # For custom transforms, create a new column from a SQL expression.
+    expression_table = tmp_db.create_table(
+        "schema_evolution_expression_example",
+        [{"id": 1, "price_text": "$100"}],
+        mode="overwrite",
+    )
+
+    expression_table.add_columns(
+        {"price_numeric": "cast(replace(price_text, '$', '') as int)"}
+    )
+    expression_table.drop_columns(["price_text"])
+    expression_table.alter_columns({"path": "price_numeric", "rename": "price"})
+    # --8<-- [end:alter_columns_with_expression]
+    assert "price" in expression_table.schema.names
+
+
+def test_drop_columns_single(tmp_db):
+    table = _setup_schema_drop_table(tmp_db)
+
+    # --8<-- [start:drop_columns_single]
+    # Remove the first temporary column
+    table.drop_columns(["temp_col1"])
+    # --8<-- [end:drop_columns_single]
+    assert "temp_col1" not in table.schema.names
+
+
+def test_drop_columns_multiple(tmp_db):
+    table = _setup_schema_drop_table(
+        tmp_db,
+        data=[
+            {
+                "id": 1,
+                "name": "Laptop",
+                "price": 1200.00,
+                "temp_col1": "X",
+                "temp_col2": 100,
+                "vector": np.random.random(128).tolist(),
+            },
+        ],
+    )
+
+    # --8<-- [start:drop_columns_multiple]
+    # Remove the second temporary column
+    table.drop_columns(["temp_col2"])
+    # --8<-- [end:drop_columns_multiple]
+    assert "temp_col2" not in table.schema.names
+
+
+def test_alter_vector_column(tmp_db):
+    # --8<-- [start:alter_vector_column]
+    vector_dim = 768  # Your embedding dimension
+    table_name = "vector_alter_example"
+    db = tmp_db
+    data = [
+        {
+            "id": 1,
+            "embedding": np.random.random(vector_dim).tolist(),
+        },
+    ]
+    table = db.create_table(table_name, data, mode="overwrite")
+
+    table.alter_columns(
+        dict(path="embedding", data_type=pa.list_(pa.float32(), vector_dim))
+    )
+    # --8<-- [end:alter_vector_column]
+
+
+def test_schema_field_metadata(tmp_db):
+    table = tmp_db.create_table(
+        "schema_field_metadata_example",
+        pa.table({"id": [0, 1], "category": ["a", "b"]}),
+        mode="overwrite",
+    )
+
+    # --8<-- [start:schema_field_metadata_merge]
+    # Set two metadata keys on the `category` field.
+    res = table.update_field_metadata(
+        {"path": "category", "metadata": {"unit": "label", "pii": "false"}}
+    )
+    print(res.version)
+
+    # Merge: add a new key, delete one with None, keep the rest.
+    table.update_field_metadata(
+        {"path": "category", "metadata": {"source": "import", "pii": None}}
+    )
+
+    # Arrow stores field metadata as bytes.
+    assert table.schema.field("category").metadata == {
+        b"unit": b"label",
+        b"source": b"import",
+    }
+    # --8<-- [end:schema_field_metadata_merge]
+
+    # --8<-- [start:schema_field_metadata_replace]
+    table.update_field_metadata(
+        {
+            "path": "category",
+            "metadata": {"owner": "search-team"},
+            "replace": True,
+        }
+    )
+    # --8<-- [end:schema_field_metadata_replace]
+
+    assert table.schema.field("category").metadata == {b"owner": b"search-team"}
+
+
+# ============================================================================
+# Versioning Examples
+# ============================================================================
+
+
+def _setup_versioning_table(tmp_db, data=None, table_name="quotes_versioning_example"):
+    import pyarrow as pa
+
+    if data is None:
+        data = [
+            {"id": 1, "author": "Richard", "quote": "Wubba Lubba Dub Dub!"},
+            {"id": 2, "author": "Morty", "quote": "Rick, what's going on?"},
+            {
+                "id": 3,
+                "author": "Richard",
+                "quote": "I turned myself into a pickle, Morty!",
+            },
+        ]
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int64()),
+            pa.field("author", pa.string()),
+            pa.field("quote", pa.string()),
+        ]
+    )
+    return tmp_db.create_table(table_name, data, schema=schema, mode="overwrite")
+
+
+def test_versioning_basic_setup(tmp_db):
+    # --8<-- [start:versioning_basic_setup]
+    import pyarrow as pa
+
+    db = tmp_db
+
+    table_name = "quotes_versioning_example"
+    data = [
+        {"id": 1, "author": "Richard", "quote": "Wubba Lubba Dub Dub!"},
+        {"id": 2, "author": "Morty", "quote": "Rick, what's going on?"},
+        {
+            "id": 3,
+            "author": "Richard",
+            "quote": "I turned myself into a pickle, Morty!",
+        },
+    ]
+
+    # Define schema
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int64()),
+            pa.field("author", pa.string()),
+            pa.field("quote", pa.string()),
+        ]
+    )
+
+    table = db.create_table(table_name, data, schema=schema, mode="overwrite")
+    # --8<-- [end:versioning_basic_setup]
+    assert table.count_rows() == 3
+
+
+def test_versioning_check_initial_version(tmp_db):
+    table = _setup_versioning_table(tmp_db)
+
+    # --8<-- [start:versioning_check_initial_version]
+    versions = table.list_versions()
+    current_version = table.version
+    print(f"Number of versions after creation: {len(versions)}")
+    print(f"Current version: {current_version}")
+    # --8<-- [end:versioning_check_initial_version]
+    assert len(versions) == 1
+    assert current_version == versions[-1]["version"]
+
+
+def test_versioning_flow(tmp_db):
+    table = _setup_versioning_table(tmp_db)
+
+    # --8<-- [start:versioning_update_data]
+    table.update(where="author='Richard'", values={"author": "Richard Daniel Sanchez"})
+    rows_after_update = table.count_rows("author = 'Richard Daniel Sanchez'")
+    print(f"Rows updated to Richard Daniel Sanchez: {rows_after_update}")
+    # --8<-- [end:versioning_update_data]
+    assert rows_after_update == 2
+
+    # --8<-- [start:versioning_add_data]
+    more_data = [
+        {
+            "id": 4,
+            "author": "Richard Daniel Sanchez",
+            "quote": "That's the way the news goes!",
+        },
+        {"id": 5, "author": "Morty", "quote": "Aww geez, Rick!"},
+    ]
+    table.add(more_data)
+    # --8<-- [end:versioning_add_data]
+    assert table.count_rows() == 5
+
+    # --8<-- [start:versioning_check_versions_after_mod]
+    versions = table.list_versions()
+    version_count_after_mod = len(versions)
+    version_after_mod = table.version
+    print(f"Number of versions after modifications: {version_count_after_mod}")
+    print(f"Current version: {version_after_mod}")
+    # --8<-- [end:versioning_check_versions_after_mod]
+    assert version_count_after_mod >= 2
+    assert version_after_mod == versions[-1]["version"]
+
+    # --8<-- [start:versioning_list_all_versions]
+    versions = table.list_versions()
+    for v in versions:
+        print(f"Version {v['version']}, created at {v['timestamp']}")
+    # --8<-- [end:versioning_list_all_versions]
+    assert len(versions) >= 1
+
+    # --8<-- [start:versioning_rollback]
+    table.restore(version_after_mod)
+    versions = table.list_versions()
+    version_count_after_rollback = len(versions)
+    print(f"Total number of versions after rollback: {version_count_after_rollback}")
+    # --8<-- [end:versioning_rollback]
+    assert version_count_after_rollback == version_count_after_mod + 1
+    assert table.version == versions[-1]["version"]
+    assert table.count_rows() == 5
+
+    # --8<-- [start:versioning_checkout_latest]
+    table.checkout_latest()
+    # --8<-- [end:versioning_checkout_latest]
+    assert table.version == table.list_versions()[-1]["version"]
+
+    # --8<-- [start:versioning_delete_data]
+    table.delete("author = 'Morty'")
+    rows_after_deletion = table.count_rows()
+    print(f"Number of rows after deletion: {rows_after_deletion}")
+    # --8<-- [end:versioning_delete_data]
+    assert rows_after_deletion == 3
+
+
+def test_versioning_tags(tmp_db):
+    import pyarrow as pa
+
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int64()),
+            pa.field("author", pa.string()),
+            pa.field("quote", pa.string()),
+        ]
+    )
+    table = tmp_db.create_table(
+        "quotes_tags_example",
+        [{"id": 1, "author": "Richard", "quote": "Wubba Lubba Dub Dub!"}],
+        schema=schema,
+        mode="overwrite",
+    )  # v1
+    table.add([{"id": 2, "author": "Morty", "quote": "Aww geez, Rick!"}])  # v2
+    table.add([{"id": 3, "author": "Summer", "quote": "Whatever, Grandpa"}])  # v3
+
+    # --8<-- [start:versioning_tags]
+    # Create a tag pointing at a specific version
+    table.tags.create("baseline", 1)
+    table.tags.create("with-edits", table.version)
+
+    # List all tags on this table
+    print(table.tags.list())
+
+    # Look up the version a tag points at
+    print(table.tags.get_version("baseline"))
+
+    # Move an existing tag to a different version
+    table.tags.update("baseline", 2)
+
+    # Check out a version by tag name
+    table.checkout("baseline")
+    print(table.version)
+
+    # Delete a tag (does not delete the underlying version)
+    table.tags.delete("with-edits")
+
+    # Return to the latest version
+    table.checkout_latest()
+    # --8<-- [end:versioning_tags]
+    assert table.version == 3
+    assert "baseline" in table.tags.list()
+    assert "with-edits" not in table.tags.list()
+
+
+# ============================================================================
+# Branches Examples
+# ============================================================================
+
+
+def test_branches(tmp_db):
+    import numpy as np
+    import pyarrow as pa
+    from lancedb.index import FTS, IvfPq
+
+    db = tmp_db
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int64()),
+            pa.field("author", pa.string()),
+            pa.field("quote", pa.string()),
+        ]
+    )
+    table = db.create_table(
+        "quotes_branches_example",
+        [
+            {"id": 1, "author": "Lancelot", "quote": "My lance never fails."},
+            {"id": 2, "author": "Arthur", "quote": "Long live Camelot!"},
+            {"id": 3, "author": "Merlin", "quote": "Magic always has a price."},
+        ],
+        schema=schema,
+        mode="overwrite",
+    )
+
+    # --8<-- [start:branch_create]
+    # Fork an isolated, writable branch from main's latest version.
+    # `create` returns a table handle scoped to the new branch.
+    branch = table.branches.create("exp")
+    # --8<-- [end:branch_create]
+
+    # --8<-- [start:branch_write]
+    # Writes land on the branch handle only; main is left untouched.
+    branch.add([{"id": 4, "author": "Lancelot", "quote": "For the realm!"}])
+    print(branch.count_rows())  # 4 rows on the branch
+    print(table.count_rows())  # 3 rows; main is unaffected
+
+    # List every branch, each mapped to its metadata (including its fork point).
+    print(table.branches.list())
+    # --8<-- [end:branch_write]
+
+    # --8<-- [start:branch_reopen]
+    # Reopen an existing branch by name from the table handle...
+    checked_out = table.branches.checkout("exp")
+    # ...or open it directly from the database connection.
+    branch_handle = db.open_table("quotes_branches_example", branch="exp")
+    print(checked_out.count_rows(), branch_handle.count_rows())  # both 4
+    # --8<-- [end:branch_reopen]
+
+    # --8<-- [start:branch_delete]
+    # Delete the branch and its branch-local history. Data on main is safe.
+    table.branches.delete("exp")
+    # --8<-- [end:branch_delete]
+
+    assert table.count_rows() == 3
+    assert "exp" not in table.branches.list()
+
+    # Setup: a branch with row results that we want to apply to main.
+    candidate = table.branches.create("candidate")
+    candidate.update(where="id = 1", values={"quote": "Revised on the branch"})
+    candidate.add(
+        [{"id": 4, "author": "Galahad", "quote": "The grail awaits."}]
+    )
+
+    # --8<-- [start:branch_upsert_to_main]
+    # This is a row-level upsert, not a merge of branch histories.
+    # `merge_insert` updates matching rows and inserts new rows using a stable
+    # unique key. Filter the branch read if you only want to apply some results.
+    rows_to_apply = candidate.to_arrow()
+    (
+        table.merge_insert("id")
+        .when_matched_update_all()  # update rows that already exist on main
+        .when_not_matched_insert_all()  # insert rows that are new on the branch
+        .execute(rows_to_apply)
+    )
+    # --8<-- [end:branch_upsert_to_main]
+
+    main_rows = {
+        row["id"]: row["quote"] for row in table.to_arrow().to_pylist()
+    }
+    assert table.count_rows() == 4
+    assert main_rows[1] == "Revised on the branch"
+    assert 4 in main_rows
+    table.branches.delete("candidate")
+
+    # Setup: a larger table with a vector and a text column to index.
+    rng = np.random.default_rng(0)
+    products = db.create_table(
+        "products_branch_index",
+        [
+            {"id": i, "vector": rng.random(4).tolist(), "text": f"product number {i}"}
+            for i in range(512)
+        ],
+        mode="overwrite",
+    )
+
+    # --8<-- [start:branch_index]
+    # Build and validate indexes on a branch before using the configuration on
+    # main.
+    dev = products.branches.create("index-dev")
+
+    # A vector (ANN) index and a full-text search index, both branch-scoped.
+    dev.create_index(
+        "vector",
+        config=IvfPq(distance_type="cosine", num_partitions=1, num_sub_vectors=2),
+    )
+    dev.create_index("text", config=FTS())
+
+    # Both indexes live only on the branch; main still has none.
+    print([ix.name for ix in dev.list_indices()])  # branch: two indexes
+    print([ix.name for ix in products.list_indices()])  # main: [] (untouched)
+    # --8<-- [end:branch_index]
+
+    assert len(dev.list_indices()) == 2
+    assert len(products.list_indices()) == 0
+    products.branches.delete("index-dev")
+
+
+# ============================================================================
+# Consistency Examples
+# ============================================================================
+
+
+def test_consistency_strong(tmp_db):
+    # --8<-- [start:consistency_strong]
+    from datetime import timedelta
+
+    uri = str(tmp_db.uri)
+    writer_db = lancedb.connect(uri)
+    reader_db = lancedb.connect(uri, read_consistency_interval=timedelta(0))
+    writer_table = writer_db.create_table(
+        "consistency_strong_table", [{"id": 1}], mode="overwrite"
+    )
+    reader_table = reader_db.open_table("consistency_strong_table")
+    writer_table.add([{"id": 2}])
+    rows_after_write = reader_table.count_rows()
+    print(f"Rows visible with strong consistency: {rows_after_write}")
+    # --8<-- [end:consistency_strong]
+    assert rows_after_write == 2
+
+
+def test_consistency_eventual(tmp_db):
+    # --8<-- [start:consistency_eventual]
+    from datetime import timedelta
+
+    uri = str(tmp_db.uri)
+    writer_db = lancedb.connect(uri)
+    reader_db = lancedb.connect(uri, read_consistency_interval=timedelta(seconds=3600))
+    writer_table = writer_db.create_table(
+        "consistency_eventual_table", [{"id": 1}], mode="overwrite"
+    )
+    reader_table = reader_db.open_table("consistency_eventual_table")
+    writer_table.add([{"id": 2}])
+    rows_after_write = reader_table.count_rows()
+    print(f"Rows visible before eventual refresh interval: {rows_after_write}")
+    # --8<-- [end:consistency_eventual]
+    assert rows_after_write == 1
+
+
+def test_consistency_checkout_latest(tmp_db):
+    # --8<-- [start:consistency_checkout_latest]
+    uri = str(tmp_db.uri)
+    writer_db = lancedb.connect(uri)
+    reader_db = lancedb.connect(uri)
+    writer_table = writer_db.create_table(
+        "consistency_checkout_latest_table", [{"id": 1}], mode="overwrite"
+    )
+    reader_table = reader_db.open_table("consistency_checkout_latest_table")
+
+    writer_table.add([{"id": 2}])
+    rows_before_refresh = reader_table.count_rows()
+    print(f"Rows before checkout_latest: {rows_before_refresh}")
+
+    reader_table.checkout_latest()
+    rows_after_refresh = reader_table.count_rows()
+    print(f"Rows after checkout_latest: {rows_after_refresh}")
+    # --8<-- [end:consistency_checkout_latest]
+    assert rows_before_refresh == 1
+    assert rows_after_refresh == 2
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new file mode 100644
index 000000000..447bae7f6
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+dependencies = [
+ "anyhow",
+ "id-arena",
+ "indexmap 2.14.0",
+ "log",
+ "semver",
+ "serde",
+ "serde_derive",
+ "serde_json",
+ "unicode-xid",
+ "wasmparser",
+]
+
+[[package]]
+name = "writeable"
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+ "tap",
+]
+
+[[package]]
+name = "xxhash-rust"
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+ "stable_deref_trait",
+ "yoke-derive",
+ "zerofrom",
+]
+
+[[package]]
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+ "proc-macro2",
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+ "syn 2.0.117",
+ "synstructure",
+]
+
+[[package]]
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+]
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+ "proc-macro2",
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+ "syn 2.0.117",
+]
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+]
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+ "proc-macro2",
+ "quote",
+ "syn 2.0.117",
+ "synstructure",
+]
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+ "displaydoc",
+ "yoke",
+ "zerofrom",
+ "zerovec",
+]
+
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+source = "registry+https://github.com/rust-lang/crates.io-index"
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+ "serde",
+ "yoke",
+ "zerofrom",
+ "zerovec-derive",
+]
+
+[[package]]
+name = "zerovec-derive"
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+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 3.0.6",
+]
+
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+source = "registry+https://github.com/rust-lang/crates.io-index"
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+
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+dependencies = [
+ "zstd-safe",
+]
+
+[[package]]
+name = "zstd-safe"
+version = "7.2.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "8f49c4d5f0abb602a93fb8736af2a4f4dd9512e36f7f570d66e65ff867ed3b9d"
+dependencies = [
+ "zstd-sys",
+]
+
+[[package]]
+name = "zstd-sys"
+version = "2.0.16+zstd.1.5.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "91e19ebc2adc8f83e43039e79776e3fda8ca919132d68a1fed6a5faca2683748"
+dependencies = [
+ "cc",
+ "pkg-config",
+]
diff --git a/docs/web-tests/rs/Cargo.toml b/docs/web-tests/rs/Cargo.toml
new file mode 100644
index 000000000..8bc594c3c
--- /dev/null
+++ b/docs/web-tests/rs/Cargo.toml
@@ -0,0 +1,76 @@
+[package]
+name = "rs"
+version = "0.1.0"
+edition = "2024"
+
+[dependencies]
+arrow-array = "58.3.0"
+arrow-json = "58.3.0"
+arrow-schema = "58.3.0"
+futures = "0.3.31"
+futures-util = "0.3.31"
+lance-namespace = "8.0.0"
+lance-index = "=8.0.0"
+lancedb = { version = "0.31.0", features = ["polars", "remote"] }
+polars = ">=0.37,<0.40.0"
+polars-arrow = ">=0.37,<0.40.0"
+serde = { version = "1", features = ["derive"] }
+serde_json = "1.0.145"
+tempfile = "3.23.0"
+tokio = { version = "1", features = ["macros", "rt-multi-thread"] }
+
+[[example]]
+name = "basic_usage"
+path = "basic_usage.rs"
+
+[[example]]
+name = "connection"
+path = "connection.rs"
+
+[[example]]
+name = "quickstart"
+path = "quickstart.rs"
+
+[[example]]
+name = "tables"
+path = "tables.rs"
+
+[[example]]
+name = "multimodal"
+path = "multimodal.rs"
+
+[[example]]
+name = "search"
+path = "search.rs"
+
+[[example]]
+name = "ingestion_examples"
+path = "ingestion_examples.rs"
+
+[[example]]
+name = "ingestion_delete"
+path = "ingestion_delete.rs"
+
+[[example]]
+name = "ingestion_progress"
+path = "ingestion_progress.rs"
+
+[[example]]
+name = "search_vector_search"
+path = "search_vector_search.rs"
+
+[[example]]
+name = "search_text_search"
+path = "search_text_search.rs"
+
+[[example]]
+name = "search_hybrid_search"
+path = "search_hybrid_search.rs"
+
+[[example]]
+name = "search_filter_search"
+path = "search_filter_search.rs"
+
+[[example]]
+name = "search_query_plan"
+path = "search_query_plan.rs"
diff --git a/docs/web-tests/rs/basic_usage.rs b/docs/web-tests/rs/basic_usage.rs
new file mode 100644
index 000000000..0adcef474
--- /dev/null
+++ b/docs/web-tests/rs/basic_usage.rs
@@ -0,0 +1,346 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use std::fs;
+use std::path::PathBuf;
+use std::sync::Arc;
+
+use serde::Deserialize;
+
+type BatchIter = Box<dyn arrow_array::RecordBatchReader + Send>;
+
+// --8<-- [start:basic_imports]
+use arrow_array::types::Float32Type;
+use arrow_array::{
+    FixedSizeListArray, Int8Array, Int16Array, RecordBatch, RecordBatchIterator, StringArray,
+    StructArray,
+};
+use arrow_schema::{DataType, Field, FieldRef, Schema};
+use futures_util::TryStreamExt;
+use lancedb::database::CreateTableMode;
+use lancedb::query::{ExecutableQuery, QueryBase, Select};
+use lancedb::{connect, table::NewColumnTransform};
+// --8<-- [end:basic_imports]
+
+#[derive(Debug, Clone, Deserialize)]
+struct Stats {
+    strength: i8,
+    courage: i8,
+    magic: i8,
+    wisdom: i8,
+}
+
+#[derive(Debug, Clone, Deserialize)]
+struct Character {
+    id: i16,
+    name: String,
+    role: String,
+    description: String,
+    vector: [f32; 4],
+    stats: Stats,
+}
+
+fn camelot_schema() -> Arc<Schema> {
+    Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int16, false),
+        Field::new("name", DataType::Utf8, false),
+        Field::new("role", DataType::Utf8, false),
+        Field::new("description", DataType::Utf8, false),
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4),
+            false,
+        ),
+        Field::new(
+            "stats",
+            DataType::Struct(arrow_schema::Fields::from(vec![
+                Arc::new(Field::new("strength", DataType::Int8, false)),
+                Arc::new(Field::new("courage", DataType::Int8, false)),
+                Arc::new(Field::new("magic", DataType::Int8, false)),
+                Arc::new(Field::new("wisdom", DataType::Int8, false)),
+            ])),
+            false,
+        ),
+    ]))
+}
+
+fn characters_to_record_batch(schema: Arc<Schema>, characters: &[Character]) -> RecordBatch {
+    let ids = Int16Array::from_iter_values(characters.iter().map(|c| c.id));
+    let names = StringArray::from_iter_values(characters.iter().map(|c| c.name.as_str()));
+    let roles = StringArray::from_iter_values(characters.iter().map(|c| c.role.as_str()));
+    let descriptions =
+        StringArray::from_iter_values(characters.iter().map(|c| c.description.as_str()));
+
+    let vectors = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+        characters
+            .iter()
+            .map(|c| Some(c.vector.iter().copied().map(Some).collect::<Vec<_>>())),
+        4,
+    );
+
+    let strength = Int8Array::from_iter_values(characters.iter().map(|c| c.stats.strength));
+    let courage = Int8Array::from_iter_values(characters.iter().map(|c| c.stats.courage));
+    let magic = Int8Array::from_iter_values(characters.iter().map(|c| c.stats.magic));
+    let wisdom = Int8Array::from_iter_values(characters.iter().map(|c| c.stats.wisdom));
+
+    let stats_fields: Vec<FieldRef> = vec![
+        Arc::new(Field::new("strength", DataType::Int8, false)),
+        Arc::new(Field::new("courage", DataType::Int8, false)),
+        Arc::new(Field::new("magic", DataType::Int8, false)),
+        Arc::new(Field::new("wisdom", DataType::Int8, false)),
+    ];
+    let stats = StructArray::new(
+        stats_fields.into(),
+        vec![
+            Arc::new(strength),
+            Arc::new(courage),
+            Arc::new(magic),
+            Arc::new(wisdom),
+        ],
+        None,
+    );
+
+    RecordBatch::try_new(
+        schema,
+        vec![
+            Arc::new(ids),
+            Arc::new(names),
+            Arc::new(roles),
+            Arc::new(descriptions),
+            Arc::new(vectors),
+            Arc::new(stats),
+        ],
+    )
+    .unwrap()
+}
+
+fn characters_to_reader(schema: Arc<Schema>, characters: &[Character]) -> BatchIter {
+    let batch = characters_to_record_batch(schema.clone(), characters);
+    Box::new(RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema))
+}
+
+fn camelot_json_path() -> PathBuf {
+    PathBuf::from(env!("CARGO_MANIFEST_DIR"))
+        .join("..")
+        .join("camelot.json")
+}
+
+#[tokio::main]
+async fn main() {
+    let temp_dir = tempfile::tempdir().unwrap();
+    let uri = temp_dir.path().to_str().unwrap();
+    let db = connect(uri).execute().await.unwrap();
+
+    // --8<-- [start:data_load]
+    let data: Vec<Character> =
+        serde_json::from_str(&fs::read_to_string(camelot_json_path()).unwrap()).unwrap();
+    // --8<-- [end:data_load]
+
+    let schema = camelot_schema();
+
+    // --8<-- [start:basic_create_table]
+    let mut table = db
+        .create_table("camelot", characters_to_reader(schema.clone(), &data))
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:basic_create_table]
+    assert_eq!(table.count_rows(None).await.unwrap(), 8);
+
+    // --8<-- [start:basic_open_table]
+    table = db.open_table("camelot").execute().await.unwrap();
+    // --8<-- [end:basic_open_table]
+
+    // --8<-- [start:basic_create_empty_table]
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int16, false),
+        Field::new("name", DataType::Utf8, false),
+        Field::new("role", DataType::Utf8, false),
+        Field::new("description", DataType::Utf8, false),
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4),
+            false,
+        ),
+        Field::new(
+            "stats",
+            DataType::Struct(arrow_schema::Fields::from(vec![
+                Arc::new(Field::new("strength", DataType::Int8, false)),
+                Arc::new(Field::new("courage", DataType::Int8, false)),
+                Arc::new(Field::new("magic", DataType::Int8, false)),
+                Arc::new(Field::new("wisdom", DataType::Int8, false)),
+            ])),
+            false,
+        ),
+    ]));
+    db.create_empty_table("camelot_empty", schema)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:basic_create_empty_table]
+    db.drop_table("camelot_empty", &[]).await.unwrap();
+
+    // --8<-- [start:basic_add_data]
+    let magical_characters = vec![
+        Character {
+            id: 9,
+            name: "Morgan le Fay".to_string(),
+            role: "Sorceress".to_string(),
+            description: "A powerful enchantress, Arthur's half-sister, and a complex figure who oscillates between aiding and opposing Camelot.".to_string(),
+            vector: [0.10, 0.84, 0.25, 0.70],
+            stats: Stats {
+                strength: 2,
+                courage: 3,
+                magic: 5,
+                wisdom: 4,
+            },
+        },
+        Character {
+            id: 10,
+            name: "The Lady of the Lake".to_string(),
+            role: "Mystical Guardian".to_string(),
+            description: "A mysterious supernatural figure associated with Avalon, known for giving Arthur the sword Excalibur.".to_string(),
+            vector: [0.00, 0.90, 0.58, 0.88],
+            stats: Stats {
+                strength: 2,
+                courage: 3,
+                magic: 5,
+                wisdom: 5,
+            },
+        },
+    ];
+    table
+        .add(characters_to_reader(camelot_schema(), &magical_characters))
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:basic_add_data]
+
+    // --8<-- [start:basic_vector_search]
+    let query_vector = [0.03, 0.85, 0.61, 0.90];
+    let result = table
+        .query()
+        .nearest_to(&query_vector)
+        .unwrap()
+        .limit(5)
+        .execute()
+        .await
+        .unwrap()
+        .try_collect::<Vec<_>>()
+        .await
+        .unwrap();
+    println!("{result:?}");
+    // --8<-- [end:basic_vector_search]
+
+    // --8<-- [start:basic_add_columns]
+    table
+        .add_columns(
+            NewColumnTransform::SqlExpressions(vec![(
+                "power".to_string(),
+                "cast(((stats.strength + stats.courage + stats.magic + stats.wisdom) / 4.0) as float)"
+                    .to_string(),
+            )]),
+            None,
+        )
+        .await
+        .unwrap();
+    // --8<-- [end:basic_add_columns]
+
+    // --8<-- [start:basic_vector_search_q1]
+    // Who are the characters similar to  "wizard"?
+    let query_vector_1 = [0.03, 0.85, 0.61, 0.90];
+    let r1 = table
+        .query()
+        .nearest_to(&query_vector_1)
+        .unwrap()
+        .limit(5)
+        .select(Select::Columns(vec![
+            "name".to_string(),
+            "role".to_string(),
+            "description".to_string(),
+        ]))
+        .execute()
+        .await
+        .unwrap()
+        .try_collect::<Vec<_>>()
+        .await
+        .unwrap();
+    println!("{r1:?}");
+    // --8<-- [end:basic_vector_search_q1]
+
+    // --8<-- [start:basic_vector_search_q2]
+    // Who are the characters similar to "wizard" with high magic stats?
+    let query_vector_2 = [0.03, 0.85, 0.61, 0.90];
+    let r2 = table
+        .query()
+        .nearest_to(&query_vector_2)
+        .unwrap()
+        .only_if("stats.magic > 3")
+        .select(Select::Columns(vec![
+            "name".to_string(),
+            "role".to_string(),
+            "description".to_string(),
+        ]))
+        .limit(5)
+        .execute()
+        .await
+        .unwrap()
+        .try_collect::<Vec<_>>()
+        .await
+        .unwrap();
+    println!("{r2:?}");
+    // --8<-- [end:basic_vector_search_q2]
+
+    // --8<-- [start:basic_vector_search_q3]
+    // Who are the strongest characters?
+    let r3 = table
+        .query()
+        .only_if("stats.strength > 3")
+        .select(Select::Columns(vec![
+            "name".to_string(),
+            "role".to_string(),
+            "description".to_string(),
+        ]))
+        .limit(5)
+        .execute()
+        .await
+        .unwrap()
+        .try_collect::<Vec<_>>()
+        .await
+        .unwrap();
+    println!("{r3:?}");
+    // --8<-- [end:basic_vector_search_q3]
+
+    // --8<-- [start:basic_vector_search_q4]
+    // Who are the strongest characters?
+    let r4 = table
+        .query()
+        .select(Select::Columns(vec![
+            "name".to_string(),
+            "role".to_string(),
+            "description".to_string(),
+            "power".to_string(),
+        ]))
+        .execute()
+        .await
+        .unwrap()
+        .try_collect::<Vec<_>>()
+        .await
+        .unwrap();
+    println!("{r4:?}");
+    // --8<-- [end:basic_vector_search_q4]
+
+    // --8<-- [start:basic_drop_columns]
+    table.drop_columns(&["power"]).await.unwrap();
+    // --8<-- [end:basic_drop_columns]
+
+    // --8<-- [start:basic_delete_rows]
+    table.delete("role = 'Traitor Knight'").await.unwrap();
+    // --8<-- [end:basic_delete_rows]
+
+    // --8<-- [start:basic_drop_table]
+    db.drop_table("camelot", &[]).await.unwrap();
+    // --8<-- [end:basic_drop_table]
+}
diff --git a/docs/web-tests/rs/bedrock.rs b/docs/web-tests/rs/bedrock.rs
new file mode 100644
index 000000000..5cc7e0cbe
--- /dev/null
+++ b/docs/web-tests/rs/bedrock.rs
@@ -0,0 +1,92 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use std::{iter::once, sync::Arc};
+
+use arrow_array::{Float64Array, Int32Array, RecordBatch, RecordBatchIterator, StringArray};
+use arrow_schema::{DataType, Field, Schema};
+use aws_config::Region;
+use aws_sdk_bedrockruntime::Client;
+use futures::StreamExt;
+use lancedb::{
+    arrow::IntoArrow,
+    connect,
+    embeddings::{bedrock::BedrockEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction},
+    query::{ExecutableQuery, QueryBase},
+    Result,
+};
+
+#[tokio::main]
+async fn main() -> Result<()> {
+    let tempdir = tempfile::tempdir().unwrap();
+    let tempdir = tempdir.path().to_str().unwrap();
+
+    // create Bedrock embedding function
+    let region: String = "us-east-1".to_string();
+    let config = aws_config::defaults(aws_config::BehaviorVersion::latest())
+        .region(Region::new(region))
+        .load()
+        .await;
+
+    let embedding = Arc::new(BedrockEmbeddingFunction::new(
+        Client::new(&config), // AWS Region
+    ));
+
+    let db = connect(tempdir).execute().await?;
+    db.embedding_registry()
+        .register("bedrock", embedding.clone())?;
+
+    let table = db
+        .create_table("vectors", make_data())
+        .add_embedding(EmbeddingDefinition::new(
+            "text",
+            "bedrock",
+            Some("embeddings"),
+        ))?
+        .execute()
+        .await?;
+
+    // execute vector search
+    let query = Arc::new(StringArray::from_iter_values(once("something warm")));
+    let query_vector = embedding.compute_query_embeddings(query)?;
+    let mut results = table
+        .vector_search(query_vector)?
+        .limit(1)
+        .execute()
+        .await?;
+
+    let rb = results.next().await.unwrap()?;
+    let out = rb
+        .column_by_name("text")
+        .unwrap()
+        .as_any()
+        .downcast_ref::<StringArray>()
+        .unwrap();
+    let text = out.iter().next().unwrap().unwrap();
+    println!("Closest match: {}", text);
+    Ok(())
+}
+
+fn make_data() -> impl IntoArrow {
+    let schema = Schema::new(vec![
+        Field::new("id", DataType::Int32, true),
+        Field::new("text", DataType::Utf8, false),
+        Field::new("price", DataType::Float64, false),
+    ]);
+
+    let id = Int32Array::from(vec![1, 2, 3, 4]);
+    let text = StringArray::from_iter_values(vec![
+        "Black T-Shirt",
+        "Leather Jacket",
+        "Winter Parka",
+        "Hooded Sweatshirt",
+    ]);
+    let price = Float64Array::from(vec![10.0, 50.0, 100.0, 30.0]);
+    let schema = Arc::new(schema);
+    let rb = RecordBatch::try_new(
+        schema.clone(),
+        vec![Arc::new(id), Arc::new(text), Arc::new(price)],
+    )
+    .unwrap();
+    Box::new(RecordBatchIterator::new(vec![Ok(rb)], schema))
+}
diff --git a/docs/web-tests/rs/connection.rs b/docs/web-tests/rs/connection.rs
new file mode 100644
index 000000000..06fbf879f
--- /dev/null
+++ b/docs/web-tests/rs/connection.rs
@@ -0,0 +1,169 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use std::path::PathBuf;
+
+use lancedb::{Connection, connect};
+
+async fn connect_local() -> lancedb::Result<Connection> {
+    // --8<-- [start:connect]
+    let db = connect("ex_lancedb").execute().await?;
+    // --8<-- [end:connect]
+    Ok(db)
+}
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    let temp_dir = tempfile::tempdir()?;
+    std::env::set_current_dir(temp_dir.path())?;
+    let db = connect_local().await?;
+    db.create_empty_table(
+        "probe",
+        std::sync::Arc::new(arrow_schema::Schema::new(vec![arrow_schema::Field::new(
+            "id",
+            arrow_schema::DataType::Int64,
+            false,
+        )])),
+    )
+    .execute()
+    .await?;
+    let reopened = connect(temp_dir.path().join("ex_lancedb").to_str().unwrap())
+        .execute()
+        .await?;
+    assert_eq!(reopened.table_names().execute().await?, vec!["probe"]);
+
+    // Connects to a deployment only when asked, and only lists its tables.
+    if std::env::var("LANCEDB_DOCS_TEST_DEPLOYMENT").as_deref() == Ok("1") {
+        let db = connect_enterprise_quickstart().await?;
+        println!("{:?}", db.table_names().execute().await?);
+    }
+    Ok(())
+}
+
+async fn connect_enterprise_quickstart() -> Result<Connection, Box<dyn std::error::Error>> {
+    // --8<-- [start:connect_enterprise_quickstart]
+    let uri = std::env::var("LANCEDB_URI")?; // db://your-database
+    let api_key = std::env::var("LANCEDB_API_KEY")?;
+    let region = std::env::var("LANCEDB_REGION").unwrap_or_else(|_| "us-east-1".to_string());
+    let host_override = std::env::var("LANCEDB_HOST_OVERRIDE")?; // https://your-endpoint
+
+    let db = connect(&uri)
+        .api_key(&api_key)
+        .region(&region)
+        .host_override(&host_override)
+        .execute()
+        .await?;
+    // --8<-- [end:connect_enterprise_quickstart]
+    Ok(db)
+}
+
+// Compiled as documentation, never called by the example runner.
+#[allow(dead_code)]
+async fn connect_object_storage() -> lancedb::Result<Connection> {
+    // --8<-- [start:connect_object_storage]
+    let uri = "s3://your-bucket/path";
+    // You can also use "gs://your-bucket/path" or "az://your-container/path".
+    let db = connect(uri).execute().await?;
+    // --8<-- [end:connect_object_storage]
+    Ok(db)
+}
+
+async fn namespace_table_ops_example(uri: &str) -> lancedb::Result<()> {
+    // --8<-- [start:namespace_table_ops]
+    let conn = connect(uri).execute().await?;
+    let search_namespace = vec!["prod".to_string(), "search".to_string()];
+    let recommendations_namespace = vec!["prod".to_string(), "recommendations".to_string()];
+
+    let schema = std::sync::Arc::new(arrow_schema::Schema::new(vec![
+        arrow_schema::Field::new("id", arrow_schema::DataType::Int64, false),
+    ]));
+
+    conn.create_empty_table("user", schema.clone())
+        .namespace(search_namespace.clone())
+        .execute()
+        .await?;
+
+    conn.create_empty_table("user", schema)
+        .namespace(recommendations_namespace.clone())
+        .execute()
+        .await?;
+
+    let search_table_names = conn
+        .table_names()
+        .namespace(search_namespace)
+        .execute()
+        .await?;
+    let recommendation_table_names = conn
+        .table_names()
+        .namespace(recommendations_namespace)
+        .execute()
+        .await?;
+
+    println!("{search_table_names:?}"); // ["user"]
+    println!("{recommendation_table_names:?}"); // ["user"]
+    // --8<-- [end:namespace_table_ops]
+    Ok(())
+}
+
+async fn namespace_admin_ops_example() -> lancedb::Result<()> {
+    // --8<-- [start:namespace_admin_ops]
+    let mut properties = std::collections::HashMap::new();
+    properties.insert("root".to_string(), "./local_lancedb".to_string());
+    let db = lancedb::connect_namespace("dir", properties).execute().await?;
+    let namespace = vec!["prod".to_string(), "search".to_string()];
+
+    db.create_namespace(lance_namespace::models::CreateNamespaceRequest {
+        id: Some(vec!["prod".to_string()]),
+        ..Default::default()
+    })
+    .await?;
+    db.create_namespace(lance_namespace::models::CreateNamespaceRequest {
+        id: Some(namespace.clone()),
+        ..Default::default()
+    })
+    .await?;
+
+    let child_namespaces = db
+        .list_namespaces(lance_namespace::models::ListNamespacesRequest {
+            id: Some(vec!["prod".to_string()]),
+            ..Default::default()
+        })
+        .await?;
+    println!(
+        "Child namespaces under {:?}: {:?}",
+        namespace, child_namespaces
+    );
+    // Child namespaces under ["prod", "search"]: ["search"]
+
+    db.drop_namespace(lance_namespace::models::DropNamespaceRequest {
+        id: Some(namespace.clone()),
+        ..Default::default()
+    })
+    .await?;
+    db.drop_namespace(lance_namespace::models::DropNamespaceRequest {
+        id: Some(vec!["prod".to_string()]),
+        ..Default::default()
+    })
+    .await?;
+    // --8<-- [end:namespace_admin_ops]
+    Ok(())
+}
+
+#[allow(dead_code)]
+fn repo_root() -> PathBuf {
+    PathBuf::from(env!("CARGO_MANIFEST_DIR")).join("..")
+}
+
+// Compiled as documentation, never called by the example runner.
+#[allow(dead_code)]
+async fn connect_enterprise_placeholders() -> Result<Connection, Box<dyn std::error::Error>> {
+    // --8<-- [start:connect_enterprise_placeholders]
+    let db = connect("db://your-database")
+        .api_key("your-api-key")
+        .region("us-east-1")
+        .host_override("https://your-endpoint")
+        .execute()
+        .await?;
+    // --8<-- [end:connect_enterprise_placeholders]
+    Ok(db)
+}
diff --git a/docs/web-tests/rs/embedding.rs b/docs/web-tests/rs/embedding.rs
new file mode 100644
index 000000000..67f6bbc80
--- /dev/null
+++ b/docs/web-tests/rs/embedding.rs
@@ -0,0 +1,182 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+// --8<-- [start:openai_embeddings]
+use std::{iter::once, sync::Arc};
+
+use arrow_array::{record_batch, StringArray};
+use arrow_schema::{DataType, Field, Schema};
+use futures::StreamExt;
+use lancedb::{
+    connect,
+    embeddings::{openai::OpenAIEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction},
+    query::{ExecutableQuery, QueryBase},
+    Result,
+};
+
+#[tokio::main]
+async fn main() -> Result<()> {
+    let db = connect("./mydb").execute().await?;
+    let api_key = "your-openai-api-key".to_string();
+    let embedding = Arc::new(OpenAIEmbeddingFunction::new_with_model(
+        api_key,
+        "text-embedding-3-large",
+    )?);
+
+    db.embedding_registry().register("openai", embedding.clone())?;
+
+    let schema = Arc::new(Schema::new(vec![Field::new("text", DataType::Utf8, false)]));
+    let table = db
+        .create_empty_table("mytable", schema)
+        .add_embedding(EmbeddingDefinition::new("text", "openai", Some("vector")))?
+        .execute()
+        .await?;
+
+    table
+        .add(record_batch!(("text", Utf8, ["This is a test.", "Another example."]))?)
+        .execute()
+        .await?;
+
+    let query = Arc::new(StringArray::from_iter_values(once("test example")));
+    let query_vector = embedding.compute_query_embeddings(query)?;
+    let mut results = table.vector_search(query_vector)?.limit(5).execute().await?;
+
+    while let Some(batch) = results.next().await {
+        println!("{:?}", batch?);
+    }
+
+    Ok(())
+}
+// --8<-- [end:openai_embeddings]
+
+// --8<-- [start:create_embedding_function]
+use std::sync::Arc;
+
+use lancedb::embeddings::openai::OpenAIEmbeddingFunction;
+
+let api_key = "your-openai-api-key".to_string();
+let embedding = Arc::new(
+    OpenAIEmbeddingFunction::new_with_model(api_key, "text-embedding-3-small")
+        .expect("failed to create OpenAI embedding function"),
+);
+// --8<-- [end:create_embedding_function]
+
+// --8<-- [start:manual_query_embeddings]
+use std::{iter::once, sync::Arc};
+
+use arrow_array::{record_batch, StringArray};
+use arrow_schema::{DataType, Field, Schema};
+use futures::StreamExt;
+use lancedb::{
+    connect,
+    embeddings::{openai::OpenAIEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction},
+    query::{ExecutableQuery, QueryBase},
+    Result,
+};
+
+#[tokio::main]
+async fn main() -> Result<()> {
+    let db = connect("./mydb").execute().await?;
+    let api_key = "your-openai-api-key".to_string();
+    let embedding = Arc::new(OpenAIEmbeddingFunction::new_with_model(
+        api_key,
+        "text-embedding-3-large",
+    )?);
+    db.embedding_registry().register("openai", embedding.clone())?;
+
+    let schema = Arc::new(Schema::new(vec![Field::new("text", DataType::Utf8, false)]));
+    let table = db
+        .create_empty_table("mytable", schema)
+        .add_embedding(EmbeddingDefinition::new("text", "openai", Some("vector")))?
+        .execute()
+        .await?;
+
+    table
+        .add(record_batch!(("text", Utf8, ["This is a test.", "Another example."]))?)
+        .execute()
+        .await?;
+
+    // Manually generate embeddings for the query (Enterprise path)
+    let query = Arc::new(StringArray::from_iter_values(once("test example")));
+    let query_vector = embedding.compute_query_embeddings(query)?;
+    // --8<-- [start:manual_query_search]
+    // query_vector is assumed to already be generated by your embedding function
+    let mut results = table.vector_search(query_vector)?.limit(5).execute().await?;
+
+    while let Some(batch) = results.next().await {
+        println!("{:?}", batch?);
+    }
+    // --8<-- [end:manual_query_search]
+
+    Ok(())
+}
+// --8<-- [end:manual_query_embeddings]
+
+// --8<-- [start:embedding_function]
+use std::{borrow::Cow, sync::Arc};
+
+use arrow_array::{Array, FixedSizeListArray, Float32Array};
+use arrow_schema::{DataType, Field, Schema};
+use lancedb::{
+    connect,
+    embeddings::{EmbeddingDefinition, EmbeddingFunction},
+    Result,
+};
+
+#[derive(Debug, Clone)]
+struct MyTextEmbedder {
+    dim: usize,
+}
+
+impl EmbeddingFunction for MyTextEmbedder {
+    fn name(&self) -> &str {
+        "my-embedder"
+    }
+
+    fn source_type(&self) -> Result<Cow<'_, DataType>> {
+        Ok(Cow::Owned(DataType::Utf8))
+    }
+
+    fn dest_type(&self) -> Result<Cow<'_, DataType>> {
+        Ok(Cow::Owned(DataType::new_fixed_size_list(
+            DataType::Float32,
+            self.dim as i32,
+            true,
+        )))
+    }
+
+    fn compute_source_embeddings(&self, source: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
+        let values = Arc::new(Float32Array::from(vec![1.0f32; source.len() * self.dim]));
+        let field = Arc::new(Field::new("item", DataType::Float32, true));
+        Ok(Arc::new(FixedSizeListArray::new(
+            field,
+            self.dim as i32,
+            values,
+            None,
+        )))
+    }
+
+    fn compute_query_embeddings(&self, _input: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
+        unimplemented!()
+    }
+}
+
+#[tokio::main]
+async fn main() -> Result<()> {
+    let db = connect("./mydb").execute().await?;
+    db.embedding_registry()
+        .register("my-embedder", Arc::new(MyTextEmbedder { dim: 3 }))?;
+
+    let schema = Arc::new(Schema::new(vec![Field::new("text", DataType::Utf8, false)]));
+    db.create_empty_table("mytable", schema)
+        .add_embedding(EmbeddingDefinition::new(
+            "text",
+            "my-embedder",
+            Some("vector"),
+        ))?
+        .execute()
+        .await?;
+
+    Ok(())
+}
+// --8<-- [end:embedding_function]
diff --git a/docs/web-tests/rs/full_text_search.rs b/docs/web-tests/rs/full_text_search.rs
new file mode 100644
index 000000000..b79400a57
--- /dev/null
+++ b/docs/web-tests/rs/full_text_search.rs
@@ -0,0 +1,103 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use std::sync::Arc;
+
+use arrow_array::{Int32Array, RecordBatch, RecordBatchIterator, RecordBatchReader, StringArray};
+use arrow_schema::{DataType, Field, Schema};
+
+use futures::TryStreamExt;
+use lance_index::scalar::FullTextSearchQuery;
+use lancedb::connection::Connection;
+use lancedb::index::scalar::FtsIndexBuilder;
+use lancedb::index::Index;
+use lancedb::query::{ExecutableQuery, QueryBase};
+use lancedb::{connect, Result, Table};
+use rand::random;
+
+#[tokio::main]
+async fn main() -> Result<()> {
+    if std::path::Path::new("data").exists() {
+        std::fs::remove_dir_all("data").unwrap();
+    }
+    let uri = "data/sample-lancedb";
+    let db = connect(uri).execute().await?;
+    let tbl = create_table(&db).await?;
+
+    create_index(&tbl).await?;
+    search_index(&tbl).await?;
+    Ok(())
+}
+
+fn create_some_records() -> Result<Box<dyn RecordBatchReader + Send>> {
+    const TOTAL: usize = 1000;
+
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int32, false),
+        Field::new("doc", DataType::Utf8, true),
+    ]));
+
+    let words = random_word::all(random_word::Lang::En)
+        .iter()
+        .step_by(1024)
+        .take(500)
+        .copied()
+        .collect::<Vec<_>>();
+    let n_terms = 3;
+    let batches = RecordBatchIterator::new(
+        vec![RecordBatch::try_new(
+            schema.clone(),
+            vec![
+                Arc::new(Int32Array::from_iter_values(0..TOTAL as i32)),
+                Arc::new(StringArray::from_iter_values((0..TOTAL).map(|_| {
+                    (0..n_terms)
+                        .map(|_| words[random::<usize>() % words.len()])
+                        .collect::<Vec<_>>()
+                        .join(" ")
+                }))),
+            ],
+        )
+        .unwrap()]
+        .into_iter()
+        .map(Ok),
+        schema.clone(),
+    );
+    Ok(Box::new(batches))
+}
+
+async fn create_table(db: &Connection) -> Result<Table> {
+    let initial_data: Box<dyn RecordBatchReader + Send> = create_some_records()?;
+    let tbl = db.create_table("my_table", initial_data).execute().await?;
+    Ok(tbl)
+}
+
+async fn create_index(table: &Table) -> Result<()> {
+    table
+        .create_index(&["doc"], Index::FTS(FtsIndexBuilder::default()))
+        .execute()
+        .await?;
+    Ok(())
+}
+
+async fn search_index(table: &Table) -> Result<()> {
+    let words = random_word::all(random_word::Lang::En)
+        .iter()
+        .step_by(1024)
+        .take(500)
+        .copied()
+        .collect::<Vec<_>>();
+    let query = words[0].to_owned();
+    println!("Searching for: {}", query);
+
+    let mut results = table
+        .query()
+        .full_text_search(FullTextSearchQuery::new(words[0].to_owned()))
+        .select(lancedb::query::Select::Columns(vec!["doc".to_owned()]))
+        .limit(10)
+        .execute()
+        .await?;
+    while let Some(batch) = results.try_next().await? {
+        println!("{:?}", batch);
+    }
+    Ok(())
+}
diff --git a/docs/web-tests/rs/ingestion_delete.rs b/docs/web-tests/rs/ingestion_delete.rs
new file mode 100644
index 000000000..f23b04f2d
--- /dev/null
+++ b/docs/web-tests/rs/ingestion_delete.rs
@@ -0,0 +1,72 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+// --8<-- [start:example]
+use std::sync::Arc;
+
+use arrow_array::{Int64Array, RecordBatch, RecordBatchIterator, RecordBatchReader, StringArray};
+use arrow_schema::{DataType, Field, Schema};
+use futures::TryStreamExt;
+use lancedb::query::{ExecutableQuery, QueryBase};
+
+fn make_users_reader(rows: &[(i64, &str)]) -> Box<dyn RecordBatchReader + Send> {
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new("name", DataType::Utf8, false),
+    ]));
+    let batch = RecordBatch::try_new(
+        schema.clone(),
+        vec![
+            Arc::new(Int64Array::from_iter_values(rows.iter().map(|r| r.0))),
+            Arc::new(StringArray::from_iter_values(rows.iter().map(|r| r.1))),
+        ],
+    )
+    .unwrap();
+    Box::new(RecordBatchIterator::new(
+        vec![Ok(batch)].into_iter(),
+        schema,
+    ))
+}
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    let directory = tempfile::tempdir()?;
+    let db = lancedb::connect(directory.path().to_str().unwrap())
+        .execute()
+        .await?;
+    let table = db
+        .create_table(
+            "users",
+            make_users_reader(&[(1, "Alice"), (2, "Bob"), (3, "Charlie")]),
+        )
+        .execute()
+        .await?;
+    let predicate = "id = 3";
+    let selected: Vec<RecordBatch> = table
+        .query()
+        .only_if(predicate)
+        .execute()
+        .await?
+        .try_collect()
+        .await?;
+    println!("Selected: {selected:?}");
+    table.delete(predicate).await?;
+    let rows: Vec<RecordBatch> = table.query().execute().await?.try_collect().await?;
+    println!("Remaining: {rows:?}");
+    assert_eq!(table.count_rows(None).await?, 2);
+    assert_eq!(
+        table
+            .count_rows(Some("id = 1 AND name = 'Alice'".into()))
+            .await?,
+        1
+    );
+    assert_eq!(
+        table
+            .count_rows(Some("id = 2 AND name = 'Bob'".into()))
+            .await?,
+        1
+    );
+    assert_eq!(table.count_rows(Some("id = 3".into())).await?, 0);
+    Ok(())
+}
+// --8<-- [end:example]
diff --git a/docs/web-tests/rs/ingestion_examples.rs b/docs/web-tests/rs/ingestion_examples.rs
new file mode 100644
index 000000000..84748c1b9
--- /dev/null
+++ b/docs/web-tests/rs/ingestion_examples.rs
@@ -0,0 +1,97 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+// --8<-- [start:write_workflow]
+use std::sync::Arc;
+
+use arrow_array::{Int64Array, RecordBatch, RecordBatchIterator, RecordBatchReader, StringArray};
+use arrow_schema::{DataType, Field, Schema};
+use futures::TryStreamExt;
+use lancedb::query::{ExecutableQuery, QueryBase};
+
+fn make_users_reader(rows: &[(i64, &str, i64)]) -> Box<dyn RecordBatchReader + Send> {
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new("name", DataType::Utf8, false),
+        Field::new("login_count", DataType::Int64, false),
+    ]));
+    let batch = RecordBatch::try_new(
+        schema.clone(),
+        vec![
+            Arc::new(Int64Array::from_iter_values(rows.iter().map(|r| r.0))),
+            Arc::new(StringArray::from_iter_values(rows.iter().map(|r| r.1))),
+            Arc::new(Int64Array::from_iter_values(rows.iter().map(|r| r.2))),
+        ],
+    )
+    .unwrap();
+    Box::new(RecordBatchIterator::new(
+        vec![Ok(batch)].into_iter(),
+        schema,
+    ))
+}
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    let directory = tempfile::tempdir()?;
+    let db = lancedb::connect(directory.path().to_str().unwrap())
+        .execute()
+        .await?;
+    let table = db
+        .create_table(
+            "users",
+            make_users_reader(&[(1, "Alice", 10), (2, "Bob", 20)]),
+        )
+        .execute()
+        .await?;
+    table
+        .add(make_users_reader(&[(3, "Charlie", 5)]))
+        .execute()
+        .await?;
+    table
+        .update()
+        .only_if("id = 2")
+        .column("name", "'Bobby'")
+        .execute()
+        .await?;
+    table
+        .update()
+        .only_if("id = 2")
+        .column("login_count", "login_count + 1")
+        .execute()
+        .await?;
+    let mut merge = table.merge_insert(&["id"]);
+    merge
+        .when_matched_update_all(None)
+        .when_not_matched_insert_all();
+    merge
+        .execute(make_users_reader(&[(3, "Charlie", 6), (4, "Dana", 1)]))
+        .await?;
+    let selected: Vec<RecordBatch> = table
+        .query()
+        .only_if("id = 4")
+        .execute()
+        .await?
+        .try_collect()
+        .await?;
+    println!("Rows selected for deletion: {selected:?}");
+    table.delete("id = 4").await?;
+    let rows: Vec<RecordBatch> = table.query().execute().await?.try_collect().await?;
+    println!("Remaining rows: {rows:?}");
+    assert_eq!(table.count_rows(None).await?, 3);
+    assert_eq!(
+        table
+            .count_rows(Some(
+                "id = 2 AND name = 'Bobby' AND login_count = 21".into()
+            ))
+            .await?,
+        1
+    );
+    assert_eq!(
+        table
+            .count_rows(Some("id = 3 AND login_count = 6".into()))
+            .await?,
+        1
+    );
+    Ok(())
+}
+// --8<-- [end:write_workflow]
diff --git a/docs/web-tests/rs/ingestion_progress.rs b/docs/web-tests/rs/ingestion_progress.rs
new file mode 100644
index 000000000..afeb4c2ad
--- /dev/null
+++ b/docs/web-tests/rs/ingestion_progress.rs
@@ -0,0 +1,64 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+// --8<-- [start:example]
+use std::sync::Arc;
+
+use arrow_array::{Int64Array, RecordBatch, RecordBatchIterator, RecordBatchReader, StringArray};
+use arrow_schema::{DataType, Field, Schema};
+use futures::TryStreamExt;
+use lancedb::query::ExecutableQuery;
+
+fn make_users_reader(rows: &[(i64, &str)]) -> Box<dyn RecordBatchReader + Send> {
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new("name", DataType::Utf8, false),
+    ]));
+    let batch = RecordBatch::try_new(
+        schema.clone(),
+        vec![
+            Arc::new(Int64Array::from_iter_values(rows.iter().map(|r| r.0))),
+            Arc::new(StringArray::from_iter_values(rows.iter().map(|r| r.1))),
+        ],
+    )
+    .unwrap();
+    Box::new(RecordBatchIterator::new(
+        vec![Ok(batch)].into_iter(),
+        schema,
+    ))
+}
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    let directory = tempfile::tempdir()?;
+    let db = lancedb::connect(directory.path().to_str().unwrap())
+        .execute()
+        .await?;
+    let table = db
+        .create_table("users", make_users_reader(&[(1, "Alice")]))
+        .execute()
+        .await?;
+    table
+        .add(make_users_reader(&[(2, "Bob"), (3, "Charlie")]))
+        .progress(|progress| println!("Rows written: {}", progress.output_rows()))
+        .execute()
+        .await?;
+    let rows: Vec<RecordBatch> = table.query().execute().await?.try_collect().await?;
+    println!("After insertion: {rows:?}");
+    assert_eq!(table.count_rows(None).await?, 3);
+    assert_eq!(
+        table
+            .count_rows(Some("id = 1 AND name = 'Alice'".into()))
+            .await?,
+        1
+    );
+    assert_eq!(
+        table
+            .count_rows(Some("id = 2 AND name = 'Bob'".into()))
+            .await?,
+        1
+    );
+    assert_eq!(table.count_rows(Some("id = 3".into())).await?, 1);
+    Ok(())
+}
+// --8<-- [end:example]
diff --git a/docs/web-tests/rs/ivf_pq.rs b/docs/web-tests/rs/ivf_pq.rs
new file mode 100644
index 000000000..ccecc22f0
--- /dev/null
+++ b/docs/web-tests/rs/ivf_pq.rs
@@ -0,0 +1,154 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+//! This example demonstrates setting advanced parameters when building an IVF PQ index
+//!
+//! Snippets from this example are used in the documentation on ANN indices.
+
+use std::sync::Arc;
+
+use arrow_array::types::Float32Type;
+use arrow_array::{
+    FixedSizeListArray, Int32Array, RecordBatch, RecordBatchIterator, RecordBatchReader,
+};
+use arrow_schema::{DataType, Field, Schema};
+
+use futures::TryStreamExt;
+use lancedb::connection::Connection;
+use lancedb::index::vector::IvfPqIndexBuilder;
+use lancedb::index::Index;
+use lancedb::query::{ExecutableQuery, QueryBase};
+use lancedb::{connect, DistanceType, Result, Table};
+
+#[tokio::main]
+async fn main() -> Result<()> {
+    if std::path::Path::new("data").exists() {
+        std::fs::remove_dir_all("data").unwrap();
+    }
+    let uri = "data/sample-lancedb";
+    let db = connect(uri).execute().await?;
+    let tbl = create_table(&db).await?;
+
+    create_index(&tbl).await?;
+    search_index(&tbl).await?;
+    Ok(())
+}
+
+fn create_some_records() -> Result<Box<dyn RecordBatchReader + Send>> {
+    const TOTAL: usize = 1000;
+    const DIM: usize = 128;
+
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int32, false),
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(
+                Arc::new(Field::new("item", DataType::Float32, true)),
+                DIM as i32,
+            ),
+            true,
+        ),
+    ]));
+
+    // Create a RecordBatch stream.
+    let batches = RecordBatchIterator::new(
+        vec![RecordBatch::try_new(
+            schema.clone(),
+            vec![
+                Arc::new(Int32Array::from_iter_values(0..TOTAL as i32)),
+                Arc::new(
+                    FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                        (0..TOTAL).map(|_| Some(vec![Some(1.0); DIM])),
+                        DIM as i32,
+                    ),
+                ),
+            ],
+        )
+        .unwrap()]
+        .into_iter()
+        .map(Ok),
+        schema.clone(),
+    );
+    Ok(Box::new(batches))
+}
+
+async fn create_table(db: &Connection) -> Result<Table> {
+    let initial_data: Box<dyn RecordBatchReader + Send> = create_some_records()?;
+    let tbl = db
+        .create_table("my_table", initial_data)
+        .execute()
+        .await
+        .unwrap();
+    Ok(tbl)
+}
+
+async fn create_index(table: &Table) -> Result<()> {
+    // --8<-- [start:create_index]
+    // For this example, `table` is a lancedb::Table with a column named
+    // "vector" that is a vector column with dimension 128.
+
+    // By default, if the column "vector" appears to be a vector column,
+    // then an IVF_PQ index with reasonable defaults is created.
+    table
+        .create_index(&["vector"], Index::Auto)
+        .execute()
+        .await?;
+    // For advanced cases, it is also possible to specifically request an
+    // IVF_PQ index and provide custom parameters.
+    table
+        .create_index(
+            &["vector"],
+            Index::IvfPq(
+                // Here we specify advanced indexing parameters.  In this case
+                // we are creating an index that my have better recall than the
+                // default but is also larger and slower.
+                IvfPqIndexBuilder::default()
+                    // This overrides the default distance type of l2
+                    .distance_type(DistanceType::Cosine)
+                    // With 1000 rows this have been ~31 by default
+                    .num_partitions(50)
+                    // With dimension 128 this would have been 8 by default
+                    .num_sub_vectors(16),
+            ),
+        )
+        .execute()
+        .await?;
+    // --8<-- [end:create_index]
+    Ok(())
+}
+
+async fn search_index(table: &Table) -> Result<()> {
+    // --8<-- [start:search1]
+    let query_vector = [1.0; 128];
+    // By default the index will find the 10 closest results using default
+    // search parameters that give a reasonable tradeoff between accuracy
+    // and search latency
+    let mut results = table
+        .vector_search(&query_vector)?
+        // Note: you should always set the distance_type to match the value used
+        // to train the index
+        .distance_type(DistanceType::Cosine)
+        .execute()
+        .await?;
+    while let Some(batch) = results.try_next().await? {
+        println!("{:?}", batch);
+    }
+    // We can also provide custom search parameters.  Here we perform a
+    // slower but more accurate search
+    let mut results = table
+        .vector_search(&query_vector)?
+        .distance_type(DistanceType::Cosine)
+        // Override the default of 10 to get more rows
+        .limit(15)
+        // Override the default of 20 to search more partitions
+        .nprobes(30)
+        // Override the default of None to apply a refine step
+        .refine_factor(1)
+        .execute()
+        .await?;
+    while let Some(batch) = results.try_next().await? {
+        println!("{:?}", batch);
+    }
+    Ok(())
+    // --8<-- [end:search1]
+}
diff --git a/docs/web-tests/rs/multimodal.rs b/docs/web-tests/rs/multimodal.rs
new file mode 100644
index 000000000..8cae02eee
--- /dev/null
+++ b/docs/web-tests/rs/multimodal.rs
@@ -0,0 +1,178 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+// --8<-- [start:multimodal_imports]
+use std::collections::HashMap;
+use std::sync::Arc;
+
+use arrow_array::types::Float32Type;
+use arrow_array::{
+    BinaryArray, FixedSizeListArray, Int32Array, Int64Array, LargeBinaryArray, RecordBatch,
+    RecordBatchIterator, StringArray,
+};
+use arrow_schema::{DataType, Field, Schema};
+use futures_util::TryStreamExt;
+use lancedb::connect;
+use lancedb::database::CreateTableMode;
+use lancedb::query::{ExecutableQuery, QueryBase};
+// --8<-- [end:multimodal_imports]
+
+#[tokio::main]
+async fn main() {
+    let temp_dir = tempfile::tempdir().unwrap();
+    let db_uri = temp_dir.path().to_str().unwrap().to_string();
+    let db = connect(&db_uri).execute().await.unwrap();
+
+    // --8<-- [start:create_dummy_data]
+    let create_dummy_image = |color: u8| -> Vec<u8> {
+        let mut png_like = vec![137, 80, 78, 71, 13, 10, 26, 10];
+        png_like.push(color);
+        png_like
+    };
+
+    let data = vec![
+        (
+            1_i32,
+            "red_square.png",
+            vec![0.1_f32; 128],
+            create_dummy_image(1),
+            "red",
+        ),
+        (
+            2_i32,
+            "blue_square.png",
+            vec![0.2_f32; 128],
+            create_dummy_image(2),
+            "blue",
+        ),
+    ];
+    // --8<-- [end:create_dummy_data]
+
+    // --8<-- [start:define_schema]
+    let schema = Schema::new(vec![
+        Field::new("id", DataType::Int32, false),
+        Field::new("filename", DataType::Utf8, false),
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 128),
+            false,
+        ),
+        Field::new("image_blob", DataType::Binary, false),
+        Field::new("label", DataType::Utf8, false),
+    ]);
+    // --8<-- [end:define_schema]
+
+    // --8<-- [start:ingest_data]
+    let schema = Arc::new(schema);
+    let image_batch = RecordBatch::try_new(
+        schema.clone(),
+        vec![
+            Arc::new(Int32Array::from_iter_values(data.iter().map(|row| row.0))),
+            Arc::new(StringArray::from_iter_values(data.iter().map(|row| row.1))),
+            Arc::new(
+                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                    data.iter()
+                        .map(|row| Some(row.2.iter().copied().map(Some).collect::<Vec<_>>())),
+                    128,
+                ),
+            ),
+            Arc::new(BinaryArray::from_iter_values(
+                data.iter().map(|row| row.3.as_slice()),
+            )),
+            Arc::new(StringArray::from_iter_values(data.iter().map(|row| row.4))),
+        ],
+    )
+    .unwrap();
+    let image_reader: Box<dyn arrow_array::RecordBatchReader + Send> =
+        Box::new(RecordBatchIterator::new(vec![Ok(image_batch)].into_iter(), schema.clone()));
+    let table = db
+        .create_table("images", image_reader)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:ingest_data]
+    assert_eq!(table.count_rows(None).await.unwrap(), 2);
+
+    // --8<-- [start:search_data]
+    let query_vector = vec![0.1_f32; 128];
+    let results = table
+        .query()
+        .nearest_to(query_vector)
+        .unwrap()
+        .limit(1)
+        .execute()
+        .await
+        .unwrap()
+        .try_collect::<Vec<_>>()
+        .await
+        .unwrap();
+    // --8<-- [end:search_data]
+
+    // --8<-- [start:process_results]
+    for batch in &results {
+        let filenames = batch
+            .column_by_name("filename")
+            .unwrap()
+            .as_any()
+            .downcast_ref::<StringArray>()
+            .unwrap();
+        let images = batch
+            .column_by_name("image_blob")
+            .unwrap()
+            .as_any()
+            .downcast_ref::<BinaryArray>()
+            .unwrap();
+
+        for row in 0..batch.num_rows() {
+            let image_bytes = images.value(row);
+            println!(
+                "Retrieved image: {}, Byte length: {}",
+                filenames.value(row),
+                image_bytes.len()
+            );
+        }
+    }
+    // --8<-- [end:process_results]
+    let search_rows: usize = results.iter().map(|batch| batch.num_rows()).sum();
+    assert_eq!(search_rows, 1);
+
+    // --8<-- [start:blob_api_schema]
+    let blob_metadata = HashMap::from([(
+        "lance-encoding:blob".to_string(),
+        "true".to_string(),
+    )]);
+    let blob_schema = Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new("video", DataType::LargeBinary, true).with_metadata(blob_metadata),
+    ]);
+    // --8<-- [end:blob_api_schema]
+
+    // --8<-- [start:blob_api_ingest]
+    let blob_rows = vec![
+        (1_i64, b"fake_video_bytes_1".to_vec()),
+        (2_i64, b"fake_video_bytes_2".to_vec()),
+    ];
+
+    let blob_schema = Arc::new(blob_schema);
+    let blob_batch = RecordBatch::try_new(
+        blob_schema.clone(),
+        vec![
+            Arc::new(Int64Array::from_iter_values(blob_rows.iter().map(|row| row.0))),
+            Arc::new(LargeBinaryArray::from_iter_values(
+                blob_rows.iter().map(|row| row.1.as_slice()),
+            )),
+        ],
+    )
+    .unwrap();
+    let blob_reader: Box<dyn arrow_array::RecordBatchReader + Send> =
+        Box::new(RecordBatchIterator::new(vec![Ok(blob_batch)].into_iter(), blob_schema));
+    let blob_table = db
+        .create_table("videos", blob_reader)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:blob_api_ingest]
+    assert_eq!(blob_table.count_rows(None).await.unwrap(), 2);
+}
diff --git a/docs/web-tests/rs/openai.rs b/docs/web-tests/rs/openai.rs
new file mode 100644
index 000000000..73954f34a
--- /dev/null
+++ b/docs/web-tests/rs/openai.rs
@@ -0,0 +1,89 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+// --8<-- [start:imports]
+
+use std::{iter::once, sync::Arc};
+
+use arrow_array::{Float64Array, Int32Array, RecordBatch, RecordBatchIterator, StringArray};
+use arrow_schema::{DataType, Field, Schema};
+use futures::StreamExt;
+use lancedb::{
+    arrow::IntoArrow,
+    connect,
+    embeddings::{openai::OpenAIEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction},
+    query::{ExecutableQuery, QueryBase},
+    Result,
+};
+
+// --8<-- [end:imports]
+
+// --8<-- [start:openai_embeddings]
+#[tokio::main]
+async fn main() -> Result<()> {
+    let tempdir = tempfile::tempdir().unwrap();
+    let tempdir = tempdir.path().to_str().unwrap();
+    let api_key = std::env::var("OPENAI_API_KEY").expect("OPENAI_API_KEY is not set");
+    let embedding = Arc::new(OpenAIEmbeddingFunction::new_with_model(
+        api_key,
+        "text-embedding-3-large",
+    )?);
+
+    let db = connect(tempdir).execute().await?;
+    db.embedding_registry()
+        .register("openai", embedding.clone())?;
+
+    let table = db
+        .create_table("vectors", make_data())
+        .add_embedding(EmbeddingDefinition::new(
+            "text",
+            "openai",
+            Some("embeddings"),
+        ))?
+        .execute()
+        .await?;
+
+    let query = Arc::new(StringArray::from_iter_values(once("something warm")));
+    let query_vector = embedding.compute_query_embeddings(query)?;
+    let mut results = table
+        .vector_search(query_vector)?
+        .limit(1)
+        .execute()
+        .await?;
+
+    let rb = results.next().await.unwrap()?;
+    let out = rb
+        .column_by_name("text")
+        .unwrap()
+        .as_any()
+        .downcast_ref::<StringArray>()
+        .unwrap();
+    let text = out.iter().next().unwrap().unwrap();
+    println!("Closest match: {}", text);
+    Ok(())
+}
+// --8<-- [end:openai_embeddings]
+
+fn make_data() -> impl IntoArrow {
+    let schema = Schema::new(vec![
+        Field::new("id", DataType::Int32, true),
+        Field::new("text", DataType::Utf8, false),
+        Field::new("price", DataType::Float64, false),
+    ]);
+
+    let id = Int32Array::from(vec![1, 2, 3, 4]);
+    let text = StringArray::from_iter_values(vec![
+        "Black T-Shirt",
+        "Leather Jacket",
+        "Winter Parka",
+        "Hooded Sweatshirt",
+    ]);
+    let price = Float64Array::from(vec![10.0, 50.0, 100.0, 30.0]);
+    let schema = Arc::new(schema);
+    let rb = RecordBatch::try_new(
+        schema.clone(),
+        vec![Arc::new(id), Arc::new(text), Arc::new(price)],
+    )
+    .unwrap();
+    Box::new(RecordBatchIterator::new(vec![Ok(rb)], schema))
+}
diff --git a/docs/web-tests/rs/quickstart.rs b/docs/web-tests/rs/quickstart.rs
new file mode 100644
index 000000000..f6ea7e0f9
--- /dev/null
+++ b/docs/web-tests/rs/quickstart.rs
@@ -0,0 +1,369 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use lancedb::{connect, table::NewColumnTransform};
+
+// --8<-- [start:quickstart_define_struct]
+use std::sync::Arc;
+
+use lancedb::arrow::arrow_array::types::Float32Type;
+use lancedb::arrow::arrow_array::{
+    FixedSizeListArray, Int8Array, LargeStringArray, RecordBatch, RecordBatchIterator,
+    RecordBatchReader, StructArray,
+};
+use lancedb::arrow::arrow_schema::{DataType, Field, FieldRef, Fields, Schema};
+
+// Define structs representing the data schema
+#[derive(Debug, Clone)]
+struct Stats {
+    strength: i8,
+    magic: i8,
+    leadership: i8,
+    wisdom: i8,
+}
+
+#[derive(Debug, Clone)]
+struct Character {
+    id: String,
+    name: String,
+    role: String,
+    description: String,
+    stats: Stats,
+    vector: [f32; 4],
+}
+
+fn characters_schema() -> Arc<Schema> {
+    Arc::new(Schema::new(vec![
+        Field::new("id", DataType::LargeUtf8, false),
+        Field::new("name", DataType::LargeUtf8, false),
+        Field::new("role", DataType::LargeUtf8, false),
+        Field::new("description", DataType::LargeUtf8, false),
+        Field::new(
+            "stats",
+            DataType::Struct(Fields::from(vec![
+                Arc::new(Field::new("strength", DataType::Int8, false)),
+                Arc::new(Field::new("magic", DataType::Int8, false)),
+                Arc::new(Field::new("leadership", DataType::Int8, false)),
+                Arc::new(Field::new("wisdom", DataType::Int8, false)),
+            ])),
+            false,
+        ),
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4),
+            false,
+        ),
+    ]))
+}
+
+fn characters_to_reader(
+    schema: Arc<Schema>,
+    rows: &[Character],
+) -> Box<dyn RecordBatchReader + Send> {
+    let ids = LargeStringArray::from_iter_values(rows.iter().map(|row| row.id.as_str()));
+    let names = LargeStringArray::from_iter_values(rows.iter().map(|row| row.name.as_str()));
+    let roles = LargeStringArray::from_iter_values(rows.iter().map(|row| row.role.as_str()));
+    let descriptions =
+        LargeStringArray::from_iter_values(rows.iter().map(|row| row.description.as_str()));
+
+    let strength = Int8Array::from_iter_values(rows.iter().map(|row| row.stats.strength));
+    let magic = Int8Array::from_iter_values(rows.iter().map(|row| row.stats.magic));
+    let leadership = Int8Array::from_iter_values(rows.iter().map(|row| row.stats.leadership));
+    let wisdom = Int8Array::from_iter_values(rows.iter().map(|row| row.stats.wisdom));
+    let stats_fields: Vec<FieldRef> = vec![
+        Arc::new(Field::new("strength", DataType::Int8, false)),
+        Arc::new(Field::new("magic", DataType::Int8, false)),
+        Arc::new(Field::new("leadership", DataType::Int8, false)),
+        Arc::new(Field::new("wisdom", DataType::Int8, false)),
+    ];
+    let stats = StructArray::new(
+        stats_fields.into(),
+        vec![
+            Arc::new(strength),
+            Arc::new(magic),
+            Arc::new(leadership),
+            Arc::new(wisdom),
+        ],
+        None,
+    );
+
+    let vectors = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+        rows.iter()
+            .map(|row| Some(row.vector.iter().copied().map(Some).collect::<Vec<_>>())),
+        4,
+    );
+
+    let batch = RecordBatch::try_new(
+        schema.clone(),
+        vec![
+            Arc::new(ids),
+            Arc::new(names),
+            Arc::new(roles),
+            Arc::new(descriptions),
+            Arc::new(stats),
+            Arc::new(vectors),
+        ],
+    )
+    .unwrap();
+
+    Box::new(RecordBatchIterator::new(
+        vec![Ok(batch)].into_iter(),
+        schema,
+    ))
+}
+// --8<-- [end:quickstart_define_struct]
+
+#[tokio::main]
+async fn main() {
+    let temp_dir = tempfile::tempdir().unwrap();
+    let uri = temp_dir.path().to_str().unwrap();
+    let db = connect(uri).execute().await.unwrap();
+
+    // --8<-- [start:quickstart_data]
+    let data = vec![
+        Character {
+            id: "1".to_string(),
+            name: "King Arthur".to_string(),
+            role: "King".to_string(),
+            description: "Leader of Camelot and wielder of Excalibur.".to_string(),
+            stats: Stats {
+                strength: 4,
+                magic: 1,
+                leadership: 5,
+                wisdom: 4,
+            },
+            vector: [0.7, 0.1, 0.9, 0.7],
+        },
+        Character {
+            id: "2".to_string(),
+            name: "Merlin".to_string(),
+            role: "Wizard".to_string(),
+            description: "Advisor and prophet with deep magical knowledge.".to_string(),
+            stats: Stats {
+                strength: 2,
+                magic: 5,
+                leadership: 4,
+                wisdom: 5,
+            },
+            vector: [0.2, 0.9, 0.4, 0.9],
+        },
+        Character {
+            id: "3".to_string(),
+            name: "Sir Lancelot".to_string(),
+            role: "Knight".to_string(),
+            description: "Legendary knight known for courage and combat skill.".to_string(),
+            stats: Stats {
+                strength: 5,
+                magic: 1,
+                leadership: 3,
+                wisdom: 3,
+            },
+            vector: [0.9, 0.1, 0.5, 0.4],
+        },
+    ];
+    // --8<-- [end:quickstart_data]
+
+    // --8<-- [start:quickstart_create_table]
+    use lancedb::database::CreateTableMode;
+
+    let schema = characters_schema();
+    let table = db
+        .create_table("characters", characters_to_reader(schema.clone(), &data))
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:quickstart_create_table]
+    assert_eq!(table.count_rows(None).await.unwrap(), 3);
+
+    // --8<-- [start:quickstart_add_data]
+    let more_data = vec![Character {
+        id: "4".to_string(),
+        name: "Morgana".to_string(),
+        role: "Sorceress".to_string(),
+        description: "Powerful sorceress of Avalon.".to_string(),
+        stats: Stats {
+            strength: 2,
+            magic: 5,
+            leadership: 4,
+            wisdom: 4,
+        },
+        vector: [0.3, 0.9, 0.6, 0.8],
+    }];
+    table
+        .add(characters_to_reader(schema.clone(), &more_data))
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:quickstart_add_data]
+    assert_eq!(table.count_rows(None).await.unwrap(), 4);
+
+    // --8<-- [start:quickstart_vector_search_1]
+    use lancedb::arrow::IntoPolars;
+    use lancedb::query::{ExecutableQuery, QueryBase, Select};
+
+    // Search for examples similar to a "wise magical advisor"
+    let query_vector = [0.2, 0.8, 0.4, 0.9];
+
+    let result = table
+        .query()
+        .nearest_to(&query_vector)
+        .unwrap()
+        .select(Select::Columns(vec![
+            "name".to_string(),
+            "role".to_string(),
+            "description".to_string(),
+            "_distance".to_string(),
+        ]))
+        .limit(2)
+        .execute()
+        .await
+        .unwrap()
+        .into_polars()
+        .await
+        .unwrap();
+    println!("{result:?}");
+    // --8<-- [end:quickstart_vector_search_1]
+    let name_col = result.column("name").unwrap().str().unwrap();
+    assert_eq!(name_col.get(0).unwrap(), "Merlin");
+    assert_eq!(name_col.get(1).unwrap(), "Morgana");
+
+    // --8<-- [start:quickstart_curate_with_metadata]
+    let curated = table
+        .query()
+        .nearest_to(&query_vector)
+        .unwrap()
+        .only_if("stats.magic >= 4")
+        .select(Select::Columns(vec![
+            "name".to_string(),
+            "role".to_string(),
+            "description".to_string(),
+            "_distance".to_string(),
+        ]))
+        .limit(2)
+        .execute()
+        .await
+        .unwrap()
+        .into_polars()
+        .await
+        .unwrap();
+    println!("{curated:?}");
+    // --8<-- [end:quickstart_curate_with_metadata]
+    let curated_name_col = curated.column("name").unwrap().str().unwrap();
+    assert_eq!(curated_name_col.get(0).unwrap(), "Merlin");
+    assert_eq!(curated_name_col.get(1).unwrap(), "Morgana");
+
+    // --8<-- [start:quickstart_output_array]
+    let result = table
+        .query()
+        .nearest_to(&query_vector)
+        .unwrap()
+        .select(Select::Columns(vec![
+            "name".to_string(),
+            "role".to_string(),
+            "description".to_string(),
+            "_distance".to_string(),
+        ]))
+        .limit(2)
+        .execute()
+        .await
+        .unwrap()
+        .into_polars()
+        .await
+        .unwrap();
+    println!("{result:?}");
+    // --8<-- [end:quickstart_output_array]
+    let name_col = result.column("name").unwrap().str().unwrap();
+    assert_eq!(name_col.get(0).unwrap(), "Merlin");
+
+    // --8<-- [start:quickstart_add_feature]
+    table
+        .add_columns(
+            NewColumnTransform::SqlExpressions(vec![(
+                "power_score".to_string(),
+                "cast(((stats.strength + stats.magic + stats.leadership + stats.wisdom) / 4.0) as float)"
+                    .to_string(),
+            )]),
+            None,
+        )
+        .await
+        .unwrap();
+    // --8<-- [end:quickstart_add_feature]
+
+    // --8<-- [start:quickstart_query_feature]
+    let features = table
+        .query()
+        .select(Select::Columns(vec![
+            "name".to_string(),
+            "role".to_string(),
+            "power_score".to_string(),
+        ]))
+        .execute()
+        .await
+        .unwrap()
+        .into_polars()
+        .await
+        .unwrap();
+    println!("{features:?}");
+    // --8<-- [end:quickstart_query_feature]
+    assert_eq!(features.column("power_score").unwrap().len(), 4);
+
+    // What the quickstart's download step leaves in an application's directory.
+    let app = tempfile::tempdir().unwrap();
+    std::fs::create_dir(app.path().join("data")).unwrap();
+    std::fs::copy(
+        std::path::Path::new(env!("CARGO_MANIFEST_DIR"))
+            .join("../../web/static/assets/images/quickstart/sir-lancelot.jpg"),
+        app.path().join("data/sir-lancelot.jpg"),
+    )
+    .unwrap();
+    std::env::set_current_dir(app.path()).unwrap();
+
+    // --8<-- [start:quickstart_multimodal_bytes]
+    use lancedb::arrow::arrow_array::BinaryArray;
+
+    let image_bytes = std::fs::read("data/sir-lancelot.jpg").unwrap();
+
+    let image_schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::LargeUtf8, false),
+        Field::new("description", DataType::LargeUtf8, false),
+        Field::new("image", DataType::Binary, false),
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4),
+            false,
+        ),
+    ]));
+    let image_vectors = [[0.9_f32, 0.1, 0.5, 0.4]];
+    let image_batch = RecordBatch::try_new(
+        image_schema.clone(),
+        vec![
+            Arc::new(LargeStringArray::from_iter_values(["lancelot"])),
+            Arc::new(LargeStringArray::from_iter_values([
+                "Portrait of Sir Lancelot",
+            ])),
+            Arc::new(BinaryArray::from_iter_values([image_bytes.as_slice()])),
+            Arc::new(
+                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                    image_vectors
+                        .iter()
+                        .map(|vector| Some(vector.iter().copied().map(Some).collect::<Vec<_>>())),
+                    4,
+                ),
+            ),
+        ],
+    )
+    .unwrap();
+    let image_reader: Box<dyn RecordBatchReader + Send> = Box::new(RecordBatchIterator::new(
+        vec![Ok(image_batch)].into_iter(),
+        image_schema,
+    ));
+    let multimodal_table = db
+        .create_table("character_images", image_reader)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:quickstart_multimodal_bytes]
+    assert_eq!(multimodal_table.count_rows(None).await.unwrap(), 1);
+}
diff --git a/docs/web-tests/rs/search.rs b/docs/web-tests/rs/search.rs
new file mode 100644
index 000000000..bf510f08b
--- /dev/null
+++ b/docs/web-tests/rs/search.rs
@@ -0,0 +1,311 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+//! This example demonstrates vector search query options.
+//!
+//! Snippets from this example are used in the documentation on vector search.
+
+use std::sync::Arc;
+
+use arrow_array::types::Float32Type;
+use arrow_array::{
+    Array, FixedSizeListArray, Int32Array, Int64Array, RecordBatch, RecordBatchIterator,
+    RecordBatchReader, UInt8Array,
+};
+use arrow_schema::{DataType, Field, Schema};
+
+use futures::TryStreamExt;
+use lancedb::connection::Connection;
+use lancedb::index::vector::IvfFlatIndexBuilder;
+use lancedb::index::Index;
+use lancedb::query::{ExecutableQuery, QueryBase, Select};
+use lancedb::{connect, DistanceType, Result, Table};
+
+const DIM: usize = 128;
+
+#[tokio::main]
+async fn main() -> Result<()> {
+    if std::path::Path::new("data").exists() {
+        std::fs::remove_dir_all("data").unwrap();
+    }
+    let uri = "data/sample-lancedb";
+    let db = connect(uri).execute().await?;
+    let tbl = create_table(&db).await?;
+    tbl.create_index(&["vector"], Index::Auto).execute().await?;
+
+    configure_distance_metric(&tbl).await?;
+    exact_vs_approximate(&tbl).await?;
+    search_distance_range(&tbl).await?;
+    vector_search_prefilter(&tbl).await?;
+    vector_search_postfilter(&tbl).await?;
+    fast_search(&tbl).await?;
+    brute_force_search(&tbl).await?;
+    bypass_vector_index(&tbl).await?;
+    batch_search(&tbl).await?;
+    binary_search(&db).await?;
+    Ok(())
+}
+
+fn create_some_records() -> Result<Box<dyn RecordBatchReader + Send>> {
+    const TOTAL: usize = 1000;
+
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int32, false),
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(
+                Arc::new(Field::new("item", DataType::Float32, true)),
+                DIM as i32,
+            ),
+            true,
+        ),
+    ]));
+
+    let batches = RecordBatchIterator::new(
+        vec![RecordBatch::try_new(
+            schema.clone(),
+            vec![
+                Arc::new(Int32Array::from_iter_values(0..TOTAL as i32)),
+                Arc::new(
+                    FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                        (0..TOTAL).map(|_| Some(vec![Some(1.0); DIM])),
+                        DIM as i32,
+                    ),
+                ),
+            ],
+        )
+        .unwrap()]
+        .into_iter()
+        .map(Ok),
+        schema.clone(),
+    );
+    Ok(Box::new(batches))
+}
+
+async fn create_table(db: &Connection) -> Result<Table> {
+    let initial_data: Box<dyn RecordBatchReader + Send> = create_some_records()?;
+    let tbl = db
+        .create_table("my_vectors", initial_data)
+        .execute()
+        .await
+        .unwrap();
+    Ok(tbl)
+}
+
+async fn configure_distance_metric(table: &Table) -> Result<()> {
+    let query_vector = [1.0; DIM];
+    // --8<-- [start:configure_distance_metric]
+    // Use the same distance metric the index was trained with.
+    let mut results = table
+        .vector_search(&query_vector)?
+        .distance_type(DistanceType::Cosine)
+        .limit(10)
+        .execute()
+        .await?;
+    while let Some(batch) = results.try_next().await? {
+        println!("{:?}", batch);
+    }
+    // --8<-- [end:configure_distance_metric]
+    Ok(())
+}
+
+async fn exact_vs_approximate(table: &Table) -> Result<()> {
+    let query_vector = [1.0; DIM];
+    // --8<-- [start:exact_vs_approximate]
+    // Approximate ANN search (fast, distances may come from the index representation)
+    let mut fast_results = table.vector_search(&query_vector)?.limit(10).execute().await?;
+    while let Some(batch) = fast_results.try_next().await? {
+        println!("{:?}", batch);
+    }
+
+    // Rerank a larger candidate set on full vectors for better recall
+    let mut refined_results = table
+        .vector_search(&query_vector)?
+        .limit(10)
+        .refine_factor(20)
+        .execute()
+        .await?;
+    while let Some(batch) = refined_results.try_next().await? {
+        println!("{:?}", batch);
+    }
+    // --8<-- [end:exact_vs_approximate]
+    Ok(())
+}
+
+async fn search_distance_range(table: &Table) -> Result<()> {
+    let query_vector = [1.0; DIM];
+    // --8<-- [start:search_distance_range]
+    // Only return rows whose distance falls within [0.1, 0.5).
+    let mut results = table
+        .vector_search(&query_vector)?
+        .distance_range(Some(0.1), Some(0.5))
+        .execute()
+        .await?;
+    while let Some(batch) = results.try_next().await? {
+        println!("{:?}", batch);
+    }
+    // --8<-- [end:search_distance_range]
+    Ok(())
+}
+
+async fn vector_search_prefilter(table: &Table) -> Result<()> {
+    let query_vector = [1.0; DIM];
+    // --8<-- [start:vector_search_prefilter]
+    // Prefiltering is the default: the filter is applied before vector search.
+    let mut results = table
+        .vector_search(&query_vector)?
+        .only_if("id > 100")
+        .select(Select::columns(&["id"]))
+        .limit(5)
+        .execute()
+        .await?;
+    while let Some(batch) = results.try_next().await? {
+        println!("{:?}", batch);
+    }
+    // --8<-- [end:vector_search_prefilter]
+    Ok(())
+}
+
+async fn vector_search_postfilter(table: &Table) -> Result<()> {
+    let query_vector = [1.0; DIM];
+    // --8<-- [start:vector_search_postfilter]
+    // Apply the filter after vector search by calling postfilter().
+    let mut results = table
+        .vector_search(&query_vector)?
+        .only_if("id > 100")
+        .postfilter()
+        .select(Select::columns(&["id"]))
+        .limit(5)
+        .execute()
+        .await?;
+    while let Some(batch) = results.try_next().await? {
+        println!("{:?}", batch);
+    }
+    // --8<-- [end:vector_search_postfilter]
+    Ok(())
+}
+
+async fn fast_search(table: &Table) -> Result<()> {
+    let query_vector = [1.0; DIM];
+    // --8<-- [start:fast_search]
+    // Skip unindexed data for lower latency.
+    let mut results = table
+        .vector_search(&query_vector)?
+        .fast_search()
+        .limit(5)
+        .execute()
+        .await?;
+    while let Some(batch) = results.try_next().await? {
+        println!("{:?}", batch);
+    }
+    // --8<-- [end:fast_search]
+    Ok(())
+}
+
+async fn brute_force_search(table: &Table) -> Result<()> {
+    let query_vector = [1.0; DIM];
+    // --8<-- [start:brute_force_search]
+    // A plain vector search returns the top-k closest rows.
+    let mut results = table.vector_search(&query_vector)?.limit(3).execute().await?;
+    while let Some(batch) = results.try_next().await? {
+        println!("{:?}", batch);
+    }
+    // --8<-- [end:brute_force_search]
+    Ok(())
+}
+
+async fn bypass_vector_index(table: &Table) -> Result<()> {
+    let query_vector = [1.0; DIM];
+    // --8<-- [start:bypass_vector_index]
+    // Force an exhaustive (flat) scan for exact, ground-truth results.
+    let mut results = table
+        .vector_search(&query_vector)?
+        .bypass_vector_index()
+        .limit(5)
+        .execute()
+        .await?;
+    while let Some(batch) = results.try_next().await? {
+        println!("{:?}", batch);
+    }
+    // --8<-- [end:bypass_vector_index]
+    Ok(())
+}
+
+async fn batch_search(table: &Table) -> Result<()> {
+    let query_1 = [1.0; DIM];
+    let query_2 = [0.5; DIM];
+    // --8<-- [start:batch_search]
+    // Search multiple query vectors in one call. Each result row carries a
+    // `query_index` mapping it back to the query it matched.
+    let mut results = table
+        .vector_search(&query_1)?
+        .add_query_vector(&query_2)?
+        .limit(5)
+        .execute()
+        .await?;
+    while let Some(batch) = results.try_next().await? {
+        println!("{:?}", batch);
+    }
+    // --8<-- [end:batch_search]
+    Ok(())
+}
+
+async fn binary_search(db: &Connection) -> Result<()> {
+    // A 256-bit binary vector is stored as 256 / 8 = 32 packed uint8 bytes.
+    const NUM_BYTES: i32 = 32;
+    const TOTAL: usize = 1024;
+
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(
+                Arc::new(Field::new("item", DataType::UInt8, true)),
+                NUM_BYTES,
+            ),
+            true,
+        ),
+    ]));
+
+    let values =
+        UInt8Array::from_iter_values((0..TOTAL * NUM_BYTES as usize).map(|i| (i % 256) as u8));
+    let vectors = FixedSizeListArray::try_new(
+        Arc::new(Field::new("item", DataType::UInt8, true)),
+        NUM_BYTES,
+        Arc::new(values),
+        None,
+    )
+    .unwrap();
+    let batch = RecordBatch::try_new(
+        schema.clone(),
+        vec![
+            Arc::new(Int64Array::from_iter_values(0..TOTAL as i64)),
+            Arc::new(vectors),
+        ],
+    )
+    .unwrap();
+    let reader: Box<dyn RecordBatchReader + Send> =
+        Box::new(RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema.clone()));
+    let tbl = db.create_table("binary_vectors", reader).execute().await?;
+    tbl.create_index(
+        &["vector"],
+        Index::IvfFlat(IvfFlatIndexBuilder::default().distance_type(DistanceType::Hamming)),
+    )
+    .execute()
+    .await?;
+
+    // --8<-- [start:binary_search]
+    // Binary vectors use `hamming` distance over the packed uint8 bytes.
+    let query: Arc<dyn Array> = Arc::new(UInt8Array::from(vec![1u8; NUM_BYTES as usize]));
+    let mut results = tbl
+        .vector_search(query)?
+        .distance_type(DistanceType::Hamming)
+        .limit(10)
+        .execute()
+        .await?;
+    while let Some(batch) = results.try_next().await? {
+        println!("{:?}", batch);
+    }
+    // --8<-- [end:binary_search]
+    Ok(())
+}
diff --git a/docs/web-tests/rs/search_filter_search.rs b/docs/web-tests/rs/search_filter_search.rs
new file mode 100644
index 000000000..0417b5b15
--- /dev/null
+++ b/docs/web-tests/rs/search_filter_search.rs
@@ -0,0 +1,92 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+// --8<-- [start:example_body]
+use arrow_array::types::Float32Type;
+use arrow_array::{FixedSizeListArray, Int64Array, RecordBatch, RecordBatchIterator, StringArray};
+use arrow_schema::{DataType, Field, Schema};
+use futures::TryStreamExt;
+use lancedb::query::{ExecutableQuery, QueryBase, Select};
+use std::sync::Arc;
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    let directory = tempfile::tempdir()?;
+    let db = lancedb::connect(directory.path().to_str().unwrap())
+        .execute()
+        .await?;
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new("text", DataType::Utf8, false),
+        Field::new("category", DataType::Utf8, false),
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 2),
+            true,
+        ),
+    ]));
+    let batch = RecordBatch::try_new(
+        schema.clone(),
+        vec![
+            Arc::new(Int64Array::from(vec![1, 2, 3])),
+            Arc::new(StringArray::from(vec![
+                "A puppy playing in the park",
+                "A dog resting at home",
+                "A guide to growing tomatoes",
+            ])),
+            Arc::new(StringArray::from(vec!["animals", "animals", "gardening"])),
+            Arc::new(
+                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                    [[1.0, 0.0], [0.9, 0.1], [0.0, 1.0]]
+                        .into_iter()
+                        .map(|v| Some(v.into_iter().map(Some).collect::<Vec<_>>())),
+                    2,
+                ),
+            ),
+        ],
+    )?;
+    let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema);
+    let table = db
+        .create_table(
+            "documents",
+            Box::new(reader) as Box<dyn arrow_array::RecordBatchReader + Send>,
+        )
+        .execute()
+        .await?;
+    let query = table
+        .vector_search(&[0.0_f32, 1.0])?
+        .only_if("category = 'animals'")
+        .select(Select::columns(&["id", "text", "category", "_distance"]))
+        .limit(2);
+    let rows: Vec<RecordBatch> = query.execute().await?.try_collect().await?;
+    println!("{rows:?}");
+    // --8<-- [end:example_body]
+    let ids: Vec<i64> = rows
+        .iter()
+        .flat_map(|b| {
+            b.column_by_name("id")
+                .unwrap()
+                .as_any()
+                .downcast_ref::<Int64Array>()
+                .unwrap()
+                .values()
+                .to_vec()
+        })
+        .collect();
+    assert_eq!(
+        ids.into_iter().collect::<std::collections::BTreeSet<_>>(),
+        std::collections::BTreeSet::from([1, 2])
+    );
+    assert!(rows.iter().all(|b| {
+        b.column_by_name("category")
+            .unwrap()
+            .as_any()
+            .downcast_ref::<StringArray>()
+            .unwrap()
+            .iter()
+            .all(|v| v == Some("animals"))
+    }));
+    // --8<-- [start:example_tail]
+    Ok(())
+}
+// --8<-- [end:example_tail]
diff --git a/docs/web-tests/rs/search_hybrid_search.rs b/docs/web-tests/rs/search_hybrid_search.rs
new file mode 100644
index 000000000..6a57c259c
--- /dev/null
+++ b/docs/web-tests/rs/search_hybrid_search.rs
@@ -0,0 +1,99 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+// --8<-- [start:example_body]
+use arrow_array::types::Float32Type;
+use arrow_array::{FixedSizeListArray, Int64Array, RecordBatch, RecordBatchIterator, StringArray};
+use arrow_schema::{DataType, Field, Schema};
+use futures::TryStreamExt;
+use lance_index::scalar::FullTextSearchQuery;
+use lancedb::index::{Index, scalar::FtsIndexBuilder};
+use lancedb::query::{ExecutableQuery, QueryBase};
+use std::sync::Arc;
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    let directory = tempfile::tempdir()?;
+    let db = lancedb::connect(directory.path().to_str().unwrap())
+        .execute()
+        .await?;
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new("text", DataType::Utf8, false),
+        Field::new("category", DataType::Utf8, false),
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 2),
+            true,
+        ),
+    ]));
+    let batch = RecordBatch::try_new(
+        schema.clone(),
+        vec![
+            Arc::new(Int64Array::from(vec![1, 2, 3])),
+            Arc::new(StringArray::from(vec![
+                "A puppy playing in the park",
+                "A dog resting at home",
+                "A guide to growing tomatoes",
+            ])),
+            Arc::new(StringArray::from(vec!["animals", "animals", "gardening"])),
+            Arc::new(
+                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                    [[1.0, 0.0], [0.9, 0.1], [0.0, 1.0]]
+                        .into_iter()
+                        .map(|v| Some(v.into_iter().map(Some).collect::<Vec<_>>())),
+                    2,
+                ),
+            ),
+        ],
+    )?;
+    let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema);
+    let table = db
+        .create_table(
+            "documents",
+            Box::new(reader) as Box<dyn arrow_array::RecordBatchReader + Send>,
+        )
+        .execute()
+        .await?;
+    table
+        .create_index(&["text"], Index::FTS(FtsIndexBuilder::default()))
+        .execute()
+        .await?;
+    let query = table
+        .vector_search(&[1.0_f32, 0.0])?
+        .full_text_search(FullTextSearchQuery::new("puppy".into()).with_column("text".into())?)
+        .rerank(Arc::new(lancedb::rerankers::rrf::RRFReranker::default()))
+        .limit(2);
+    let rows: Vec<RecordBatch> = query.execute().await?.try_collect().await?;
+    println!("{rows:?}");
+    // --8<-- [end:example_body]
+    let ids: Vec<i64> = rows
+        .iter()
+        .flat_map(|b| {
+            b.column_by_name("id")
+                .unwrap()
+                .as_any()
+                .downcast_ref::<Int64Array>()
+                .unwrap()
+                .values()
+                .to_vec()
+        })
+        .collect();
+    assert_eq!(ids, vec![1, 2]);
+    let scores: Vec<f32> = rows
+        .iter()
+        .flat_map(|b| {
+            b.column_by_name("_relevance_score")
+                .unwrap()
+                .as_any()
+                .downcast_ref::<arrow_array::Float32Array>()
+                .unwrap()
+                .values()
+                .to_vec()
+        })
+        .collect();
+    assert!(scores[0] > scores[1]);
+    // --8<-- [start:example_tail]
+    Ok(())
+}
+// --8<-- [end:example_tail]
diff --git a/docs/web-tests/rs/search_query_plan.rs b/docs/web-tests/rs/search_query_plan.rs
new file mode 100644
index 000000000..97290fff1
--- /dev/null
+++ b/docs/web-tests/rs/search_query_plan.rs
@@ -0,0 +1,81 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+// --8<-- [start:example_body]
+use arrow_array::types::Float32Type;
+use arrow_array::{FixedSizeListArray, Int64Array, RecordBatch, RecordBatchIterator, StringArray};
+use arrow_schema::{DataType, Field, Schema};
+use futures::TryStreamExt;
+use lancedb::query::{ExecutableQuery, QueryBase, Select};
+use std::sync::Arc;
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    let directory = tempfile::tempdir()?;
+    let db = lancedb::connect(directory.path().to_str().unwrap())
+        .execute()
+        .await?;
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new("text", DataType::Utf8, false),
+        Field::new("category", DataType::Utf8, false),
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 2),
+            true,
+        ),
+    ]));
+    let batch = RecordBatch::try_new(
+        schema.clone(),
+        vec![
+            Arc::new(Int64Array::from(vec![1, 2, 3])),
+            Arc::new(StringArray::from(vec![
+                "A puppy playing in the park",
+                "A dog resting at home",
+                "A guide to growing tomatoes",
+            ])),
+            Arc::new(StringArray::from(vec!["animals", "animals", "gardening"])),
+            Arc::new(
+                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                    [[1.0, 0.0], [0.9, 0.1], [0.0, 1.0]]
+                        .into_iter()
+                        .map(|v| Some(v.into_iter().map(Some).collect::<Vec<_>>())),
+                    2,
+                ),
+            ),
+        ],
+    )?;
+    let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema);
+    let table = db
+        .create_table(
+            "documents",
+            Box::new(reader) as Box<dyn arrow_array::RecordBatchReader + Send>,
+        )
+        .execute()
+        .await?;
+    let query = table
+        .vector_search(&[1.0_f32, 0.0])?
+        .select(Select::columns(&["id", "text", "_distance"]))
+        .limit(2);
+    println!("Plan: {}", query.explain_plan(false).await?);
+    println!("Measured execution: {}", query.analyze_plan().await?);
+    let rows: Vec<RecordBatch> = query.execute().await?.try_collect().await?;
+    println!("{rows:?}");
+    // --8<-- [end:example_body]
+    let ids: Vec<i64> = rows
+        .iter()
+        .flat_map(|b| {
+            b.column_by_name("id")
+                .unwrap()
+                .as_any()
+                .downcast_ref::<Int64Array>()
+                .unwrap()
+                .values()
+                .to_vec()
+        })
+        .collect();
+    assert_eq!(ids, vec![1, 2]);
+    // --8<-- [start:example_tail]
+    Ok(())
+}
+// --8<-- [end:example_tail]
diff --git a/docs/web-tests/rs/search_text_search.rs b/docs/web-tests/rs/search_text_search.rs
new file mode 100644
index 000000000..ebb4b0292
--- /dev/null
+++ b/docs/web-tests/rs/search_text_search.rs
@@ -0,0 +1,85 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+// --8<-- [start:example_body]
+use arrow_array::{Int64Array, RecordBatch, RecordBatchIterator, StringArray};
+use arrow_schema::{DataType, Field, Schema};
+use futures::TryStreamExt;
+use lance_index::scalar::FullTextSearchQuery;
+use lancedb::index::{Index, scalar::FtsIndexBuilder};
+use lancedb::query::{ExecutableQuery, QueryBase, Select};
+use std::sync::Arc;
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    let directory = tempfile::tempdir()?;
+    let db = lancedb::connect(directory.path().to_str().unwrap())
+        .execute()
+        .await?;
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new("text", DataType::Utf8, false),
+        Field::new("category", DataType::Utf8, false),
+    ]));
+    let batch = RecordBatch::try_new(
+        schema.clone(),
+        vec![
+            Arc::new(Int64Array::from(vec![1, 2, 3])),
+            Arc::new(StringArray::from(vec![
+                "A puppy playing in the park",
+                "A dog resting at home",
+                "A guide to growing tomatoes",
+            ])),
+            Arc::new(StringArray::from(vec!["animals", "animals", "gardening"])),
+        ],
+    )?;
+    let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema);
+    let table = db
+        .create_table(
+            "documents",
+            Box::new(reader) as Box<dyn arrow_array::RecordBatchReader + Send>,
+        )
+        .execute()
+        .await?;
+    table
+        .create_index(&["text"], Index::FTS(FtsIndexBuilder::default()))
+        .execute()
+        .await?;
+    let query = table
+        .query()
+        .full_text_search(FullTextSearchQuery::new("puppy".into()).with_column("text".into())?)
+        .select(Select::columns(&["id", "text", "_score"]))
+        .limit(2);
+    let rows: Vec<RecordBatch> = query.execute().await?.try_collect().await?;
+    println!("{rows:?}");
+    // --8<-- [end:example_body]
+    let ids: Vec<i64> = rows
+        .iter()
+        .flat_map(|b| {
+            b.column_by_name("id")
+                .unwrap()
+                .as_any()
+                .downcast_ref::<Int64Array>()
+                .unwrap()
+                .values()
+                .to_vec()
+        })
+        .collect();
+    assert_eq!(ids, vec![1]);
+    let scores: Vec<f32> = rows
+        .iter()
+        .flat_map(|b| {
+            b.column_by_name("_score")
+                .unwrap()
+                .as_any()
+                .downcast_ref::<arrow_array::Float32Array>()
+                .unwrap()
+                .values()
+                .to_vec()
+        })
+        .collect();
+    assert!(scores[0] > 0.0);
+    // --8<-- [start:example_tail]
+    Ok(())
+}
+// --8<-- [end:example_tail]
diff --git a/docs/web-tests/rs/search_vector_search.rs b/docs/web-tests/rs/search_vector_search.rs
new file mode 100644
index 000000000..f2807ebef
--- /dev/null
+++ b/docs/web-tests/rs/search_vector_search.rs
@@ -0,0 +1,93 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+// --8<-- [start:example_body]
+use arrow_array::types::Float32Type;
+use arrow_array::{FixedSizeListArray, Int64Array, RecordBatch, RecordBatchIterator, StringArray};
+use arrow_schema::{DataType, Field, Schema};
+use futures::TryStreamExt;
+use lancedb::query::{ExecutableQuery, QueryBase, Select};
+use std::sync::Arc;
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    let directory = tempfile::tempdir()?;
+    let db = lancedb::connect(directory.path().to_str().unwrap())
+        .execute()
+        .await?;
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new("text", DataType::Utf8, false),
+        Field::new("category", DataType::Utf8, false),
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 2),
+            true,
+        ),
+    ]));
+    let batch = RecordBatch::try_new(
+        schema.clone(),
+        vec![
+            Arc::new(Int64Array::from(vec![1, 2, 3])),
+            Arc::new(StringArray::from(vec![
+                "A puppy playing in the park",
+                "A dog resting at home",
+                "A guide to growing tomatoes",
+            ])),
+            Arc::new(StringArray::from(vec!["animals", "animals", "gardening"])),
+            Arc::new(
+                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                    [[1.0, 0.0], [0.9, 0.1], [0.0, 1.0]]
+                        .into_iter()
+                        .map(|v| Some(v.into_iter().map(Some).collect::<Vec<_>>())),
+                    2,
+                ),
+            ),
+        ],
+    )?;
+    let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema);
+    let table = db
+        .create_table(
+            "documents",
+            Box::new(reader) as Box<dyn arrow_array::RecordBatchReader + Send>,
+        )
+        .execute()
+        .await?;
+    let query = table
+        .vector_search(&[1.0_f32, 0.0])?
+        .distance_type(lancedb::DistanceType::L2)
+        .select(Select::columns(&["id", "text", "_distance"]))
+        .limit(2);
+    let rows: Vec<RecordBatch> = query.execute().await?.try_collect().await?;
+    println!("{rows:?}");
+    // --8<-- [end:example_body]
+    let ids: Vec<i64> = rows
+        .iter()
+        .flat_map(|b| {
+            b.column_by_name("id")
+                .unwrap()
+                .as_any()
+                .downcast_ref::<Int64Array>()
+                .unwrap()
+                .values()
+                .to_vec()
+        })
+        .collect();
+    assert_eq!(ids, vec![1, 2]);
+    let scores: Vec<f32> = rows
+        .iter()
+        .flat_map(|b| {
+            b.column_by_name("_distance")
+                .unwrap()
+                .as_any()
+                .downcast_ref::<arrow_array::Float32Array>()
+                .unwrap()
+                .values()
+                .to_vec()
+        })
+        .collect();
+    assert_eq!(scores[0], 0.0);
+    // --8<-- [start:example_tail]
+    Ok(())
+}
+// --8<-- [end:example_tail]
diff --git a/docs/web-tests/rs/sentence_transformers.rs b/docs/web-tests/rs/sentence_transformers.rs
new file mode 100644
index 000000000..9250430d4
--- /dev/null
+++ b/docs/web-tests/rs/sentence_transformers.rs
@@ -0,0 +1,95 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use std::{iter::once, sync::Arc};
+
+use arrow_array::{RecordBatch, RecordBatchIterator, StringArray};
+use arrow_schema::{DataType, Field, Schema};
+use futures::StreamExt;
+use lancedb::{
+    arrow::IntoArrow,
+    connect,
+    embeddings::{
+        sentence_transformers::SentenceTransformersEmbeddings, EmbeddingDefinition,
+        EmbeddingFunction,
+    },
+    query::{ExecutableQuery, QueryBase},
+    Result,
+};
+
+#[tokio::main]
+async fn main() -> Result<()> {
+    let tempdir = tempfile::tempdir().unwrap();
+    let tempdir = tempdir.path().to_str().unwrap();
+    let embedding = SentenceTransformersEmbeddings::builder().build()?;
+    let embedding = Arc::new(embedding);
+    let db = connect(tempdir).execute().await?;
+    db.embedding_registry()
+        .register("sentence-transformers", embedding.clone())?;
+
+    let table = db
+        .create_table("vectors", make_data())
+        .add_embedding(EmbeddingDefinition::new(
+            "facts",
+            "sentence-transformers",
+            Some("embeddings"),
+        ))?
+        .execute()
+        .await?;
+
+    let query = Arc::new(StringArray::from_iter_values(once(
+        "How many bones are in the human body?",
+    )));
+    let query_vector = embedding.compute_query_embeddings(query)?;
+    let mut results = table
+        .vector_search(query_vector)?
+        .limit(1)
+        .execute()
+        .await?;
+
+    let rb = results.next().await.unwrap()?;
+    let out = rb
+        .column_by_name("facts")
+        .unwrap()
+        .as_any()
+        .downcast_ref::<StringArray>()
+        .unwrap();
+    let text = out.iter().next().unwrap().unwrap();
+    println!("Answer: {}", text);
+    Ok(())
+}
+
+fn make_data() -> impl IntoArrow {
+    let schema = Schema::new(vec![Field::new("facts", DataType::Utf8, false)]);
+
+    let facts = StringArray::from_iter_values(vec![
+        "Albert Einstein was a theoretical physicist.",
+        "The capital of France is Paris.",
+        "The Great Wall of China is one of the Seven Wonders of the World.",
+        "Python is a popular programming language.",
+        "Mount Everest is the highest mountain in the world.",
+        "Leonardo da Vinci painted the Mona Lisa.",
+        "Shakespeare wrote Hamlet.",
+        "The human body has 206 bones.",
+        "The speed of light is approximately 299,792 kilometers per second.",
+        "Water boils at 100 degrees Celsius.",
+        "The Earth orbits the Sun.",
+        "The Pyramids of Giza are located in Egypt.",
+        "Coffee is one of the most popular beverages in the world.",
+        "Tokyo is the capital city of Japan.",
+        "Photosynthesis is the process by which plants make their food.",
+        "The Pacific Ocean is the largest ocean on Earth.",
+        "Mozart was a prolific composer of classical music.",
+        "The Internet is a global network of computers.",
+        "Basketball is a sport played with a ball and a hoop.",
+        "The first computer virus was created in 1983.",
+        "Artificial neural networks are inspired by the human brain.",
+        "Deep learning is a subset of machine learning.",
+        "IBM's Watson won Jeopardy! in 2011.",
+        "The first computer programmer was Ada Lovelace.",
+        "The first chatbot was ELIZA, created in the 1960s.",
+    ]);
+    let schema = Arc::new(schema);
+    let rb = RecordBatch::try_new(schema.clone(), vec![Arc::new(facts)]).unwrap();
+    Box::new(RecordBatchIterator::new(vec![Ok(rb)], schema))
+}
diff --git a/docs/web-tests/rs/simple.rs b/docs/web-tests/rs/simple.rs
new file mode 100644
index 000000000..ac98957ae
--- /dev/null
+++ b/docs/web-tests/rs/simple.rs
@@ -0,0 +1,147 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+//! This example demonstrates basic usage of LanceDb.
+//!
+//! Snippets from this example are used in the quickstart documentation.
+
+use std::sync::Arc;
+
+use arrow_array::types::Float32Type;
+use arrow_array::{FixedSizeListArray, Int32Array, RecordBatch, RecordBatchIterator};
+use arrow_schema::{DataType, Field, Schema};
+use futures::TryStreamExt;
+
+use lancedb::arrow::IntoArrow;
+use lancedb::connection::Connection;
+use lancedb::index::Index;
+use lancedb::query::{ExecutableQuery, QueryBase};
+use lancedb::{connect, Result, Table as LanceDbTable};
+
+// --8<-- [start:connect]
+#[tokio::main]
+async fn main() -> Result<()> {
+    if std::path::Path::new("data").exists() {
+        std::fs::remove_dir_all("data").unwrap();
+    }
+    // --8<-- [start:connect_uri]
+    let uri = "data/sample-lancedb";
+    let db = connect(uri).execute().await?;
+    // --8<-- [end:connect_uri]
+
+    // --8<-- [start:list_names]
+    println!("{:?}", db.table_names().execute().await?);
+    // --8<-- [end:list_names]
+    let tbl = create_table(&db).await?;
+    create_index(&tbl).await?;
+    let batches = search(&tbl).await?;
+    println!("{:?}", batches);
+
+    create_empty_table(&db).await.unwrap();
+
+    // --8<-- [start:delete]
+    tbl.delete("id > 24").await.unwrap();
+    // --8<-- [end:delete]
+
+    // --8<-- [start:drop_table]
+    db.drop_table("my_table").await.unwrap();
+    // --8<-- [end:drop_table]
+    Ok(())
+}
+// --8<-- [end:connect]
+
+#[allow(dead_code)]
+async fn open_with_existing_tbl() -> Result<()> {
+    let uri = "data/sample-lancedb";
+    let db = connect(uri).execute().await?;
+    #[allow(unused_variables)]
+    // --8<-- [start:open_existing_tbl]
+    let table = db.open_table("my_table").execute().await.unwrap();
+    // --8<-- [end:open_existing_tbl]
+    Ok(())
+}
+
+fn create_some_records() -> Result<impl IntoArrow> {
+    const TOTAL: usize = 1000;
+    const DIM: usize = 128;
+
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int32, false),
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(
+                Arc::new(Field::new("item", DataType::Float32, true)),
+                DIM as i32,
+            ),
+            true,
+        ),
+    ]));
+
+    // Create a RecordBatch stream.
+    let batches = RecordBatchIterator::new(
+        vec![RecordBatch::try_new(
+            schema.clone(),
+            vec![
+                Arc::new(Int32Array::from_iter_values(0..TOTAL as i32)),
+                Arc::new(
+                    FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                        (0..TOTAL).map(|_| Some(vec![Some(1.0); DIM])),
+                        DIM as i32,
+                    ),
+                ),
+            ],
+        )
+        .unwrap()]
+        .into_iter()
+        .map(Ok),
+        schema.clone(),
+    );
+    Ok(Box::new(batches))
+}
+
+async fn create_table(db: &Connection) -> Result<LanceDbTable> {
+    // --8<-- [start:create_table]
+    let initial_data = create_some_records()?;
+    let tbl = db
+        .create_table("my_table", initial_data)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:create_table]
+
+    // --8<-- [start:add]
+    let new_data = create_some_records()?;
+    tbl.add(new_data).execute().await.unwrap();
+    // --8<-- [end:add]
+
+    Ok(tbl)
+}
+
+async fn create_empty_table(db: &Connection) -> Result<LanceDbTable> {
+    // --8<-- [start:create_empty_table]
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int32, false),
+        Field::new("item", DataType::Utf8, true),
+    ]));
+    db.create_empty_table("empty_table", schema).execute().await
+    // --8<-- [end:create_empty_table]
+}
+
+async fn create_index(table: &LanceDbTable) -> Result<()> {
+    // --8<-- [start:create_index]
+    table.create_index(&["vector"], Index::Auto).execute().await
+    // --8<-- [end:create_index]
+}
+
+async fn search(table: &LanceDbTable) -> Result<Vec<RecordBatch>> {
+    // --8<-- [start:search]
+    table
+        .query()
+        .limit(2)
+        .nearest_to(&[1.0; 128])?
+        .execute()
+        .await?
+        .try_collect::<Vec<_>>()
+        .await
+    // --8<-- [end:search]
+}
diff --git a/docs/web-tests/rs/tables.rs b/docs/web-tests/rs/tables.rs
new file mode 100644
index 000000000..0d568530d
--- /dev/null
+++ b/docs/web-tests/rs/tables.rs
@@ -0,0 +1,1516 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use std::sync::Arc;
+use std::time::Duration as StdDuration;
+
+use arrow_array::types::Float32Type;
+use arrow_array::{
+    Array, FixedSizeListArray, Float32Array, Float64Array, Int32Array, Int64Array, RecordBatch,
+    RecordBatchIterator, RecordBatchReader, StringArray,
+};
+use arrow_schema::{DataType, Field, Schema};
+use futures::TryStreamExt;
+use lancedb::connect;
+use lancedb::query::ExecutableQuery;
+use lancedb::database::CreateTableMode;
+use lancedb::table::{
+    ColumnAlteration, Duration, FieldMetadataUpdate, NewColumnTransform, OptimizeAction,
+};
+
+// --8<-- [start:update_make_users_reader]
+fn make_users_reader(
+    ids: Vec<i64>,
+    names: Vec<&str>,
+    login_counts: Option<Vec<i64>>,
+) -> Box<dyn RecordBatchReader + Send> {
+    let mut fields = vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new("name", DataType::Utf8, false),
+    ];
+    let mut columns: Vec<Arc<dyn Array>> =
+        vec![Arc::new(Int64Array::from(ids)), Arc::new(StringArray::from(names))];
+
+    if let Some(login_counts) = login_counts {
+        fields.push(Field::new("login_count", DataType::Int64, true));
+        columns.push(Arc::new(Int64Array::from(login_counts)));
+    }
+
+    let schema = Arc::new(Schema::new(fields));
+    let batch = RecordBatch::try_new(schema.clone(), columns).unwrap();
+    let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema);
+    Box::new(reader)
+}
+// --8<-- [end:update_make_users_reader]
+
+// --8<-- [start:versioning_make_quotes_reader]
+fn make_quotes_reader(rows: Vec<(i64, &str, &str)>) -> Box<dyn RecordBatchReader + Send> {
+    let ids: Vec<i64> = rows.iter().map(|(id, _, _)| *id).collect();
+    let authors: Vec<&str> = rows.iter().map(|(_, author, _)| *author).collect();
+    let quotes: Vec<&str> = rows.iter().map(|(_, _, quote)| *quote).collect();
+
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new("author", DataType::Utf8, false),
+        Field::new("quote", DataType::Utf8, false),
+    ]));
+
+    let batch = RecordBatch::try_new(
+        schema.clone(),
+        vec![
+            Arc::new(Int64Array::from(ids)),
+            Arc::new(StringArray::from(authors)),
+            Arc::new(StringArray::from(quotes)),
+        ],
+    )
+    .unwrap();
+    let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema);
+    Box::new(reader)
+}
+// --8<-- [end:versioning_make_quotes_reader]
+
+// Helper: a table with a `vector` and a `text` column, used to demonstrate
+// building indexes on a branch.
+fn make_products_reader(n: i32) -> Box<dyn RecordBatchReader + Send> {
+    const DIM: usize = 4;
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int32, false),
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(
+                Arc::new(Field::new("item", DataType::Float32, true)),
+                DIM as i32,
+            ),
+            true,
+        ),
+        Field::new("text", DataType::Utf8, false),
+    ]));
+
+    let ids = Int32Array::from_iter_values(0..n);
+    let vectors = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+        (0..n).map(|i| {
+            Some(
+                (0..DIM)
+                    .map(|d| Some((((i as usize * 31 + d * 7) % 97) as f32) / 97.0))
+                    .collect::<Vec<Option<f32>>>(),
+            )
+        }),
+        DIM as i32,
+    );
+    let texts = StringArray::from_iter_values((0..n).map(|i| format!("product number {i}")));
+
+    let batch = RecordBatch::try_new(
+        schema.clone(),
+        vec![Arc::new(ids), Arc::new(vectors), Arc::new(texts)],
+    )
+    .unwrap();
+    let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema);
+    Box::new(reader)
+}
+
+#[allow(dead_code)]
+async fn update_connect_enterprise_example() {
+    // --8<-- [start:update_connect_enterprise]
+    let uri = "db://your-project-slug";
+    let api_key = "your-api-key";
+    let region = "us-east-1";
+    // --8<-- [end:update_connect_enterprise]
+    let _ = (uri, api_key, region);
+}
+
+#[allow(dead_code)]
+async fn update_connect_local_example() {
+    // --8<-- [start:update_connect_local]
+    let db = connect("./data").execute().await.unwrap();
+    // --8<-- [end:update_connect_local]
+    let _ = db;
+}
+
+#[tokio::main]
+async fn main() {
+    let temp_dir = tempfile::tempdir().unwrap();
+    let db_uri = temp_dir.path().to_str().unwrap().to_string();
+    let db = connect(&db_uri).execute().await.unwrap();
+
+    // --8<-- [start:create_table_from_dicts]
+    struct Location {
+        vector: [f32; 2],
+        lat: f32,
+        long: f32,
+    }
+
+    let data = vec![
+        Location {
+            vector: [1.1, 1.2],
+            lat: 45.5,
+            long: -122.7,
+        },
+        Location {
+            vector: [0.2, 1.8],
+            lat: 40.1,
+            long: -74.1,
+        },
+    ];
+
+    let schema = Arc::new(Schema::new(vec![
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 2),
+            false,
+        ),
+        Field::new("lat", DataType::Float32, false),
+        Field::new("long", DataType::Float32, false),
+    ]));
+
+    let batch = RecordBatch::try_new(
+        schema.clone(),
+        vec![
+            Arc::new(
+                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                    data.iter()
+                        .map(|row| Some(row.vector.iter().copied().map(Some).collect::<Vec<_>>())),
+                    2,
+                ),
+            ),
+            Arc::new(Float32Array::from_iter_values(
+                data.iter().map(|row| row.lat),
+            )),
+            Arc::new(Float32Array::from_iter_values(
+                data.iter().map(|row| row.long),
+            )),
+        ],
+    )
+    .unwrap();
+    let reader: Box<dyn RecordBatchReader + Send> =
+        Box::new(RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema.clone()));
+    let table = db
+        .create_table("test_table", reader)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:create_table_from_dicts]
+    assert_eq!(table.count_rows(None).await.unwrap(), 2);
+
+    // Seed an existing table so the conflict-handling examples have something to act on.
+    let conflict_seed = RecordBatch::try_new(
+        schema.clone(),
+        vec![
+            Arc::new(
+                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                    data.iter()
+                        .map(|row| Some(row.vector.iter().copied().map(Some).collect::<Vec<_>>())),
+                    2,
+                ),
+            ),
+            Arc::new(Float32Array::from_iter_values(
+                data.iter().map(|row| row.lat),
+            )),
+            Arc::new(Float32Array::from_iter_values(
+                data.iter().map(|row| row.long),
+            )),
+        ],
+    )
+    .unwrap();
+    let conflict_seed_reader: Box<dyn RecordBatchReader + Send> =
+        Box::new(RecordBatchIterator::new(vec![Ok(conflict_seed)].into_iter(), schema.clone()));
+    db.create_table("conflict_table", conflict_seed_reader)
+        .execute()
+        .await
+        .unwrap();
+
+    // Build readers for the rows we want to ingest. Outside the snippet markers
+    // because the docs only need to highlight the `.mode(...)` differences.
+    let make_reader = || {
+        let batch = RecordBatch::try_new(
+            schema.clone(),
+            vec![
+                Arc::new(
+                    FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                        data.iter().map(|row| {
+                            Some(row.vector.iter().copied().map(Some).collect::<Vec<_>>())
+                        }),
+                        2,
+                    ),
+                ),
+                Arc::new(Float32Array::from_iter_values(
+                    data.iter().map(|row| row.lat),
+                )),
+                Arc::new(Float32Array::from_iter_values(
+                    data.iter().map(|row| row.long),
+                )),
+            ],
+        )
+        .unwrap();
+        let reader: Box<dyn RecordBatchReader + Send> =
+            Box::new(RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema.clone()));
+        reader
+    };
+    let exist_ok_reader = make_reader();
+    let overwrite_reader = make_reader();
+
+    // --8<-- [start:create_table_conflict_handling]
+    // Idempotent open: reuse the existing table if it exists.
+    // The provided data is ignored; the schema is validated against the
+    // existing table and a mismatch raises an error.
+    let _conflict_table = db
+        .create_table("conflict_table", exist_ok_reader)
+        .mode(CreateTableMode::exist_ok(|req| req))
+        .execute()
+        .await
+        .unwrap();
+
+    // Overwrite: drop the existing table and create a new one with the
+    // provided data. This permanently discards the old table's data.
+    let conflict_table = db
+        .create_table("conflict_table", overwrite_reader)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:create_table_conflict_handling]
+    assert_eq!(conflict_table.count_rows(None).await.unwrap(), 2);
+
+    // --8<-- [start:create_table_custom_schema]
+    let custom_schema = Arc::new(Schema::new(vec![
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4),
+            false,
+        ),
+        Field::new("lat", DataType::Float32, false),
+        Field::new("long", DataType::Float32, false),
+    ]));
+
+    let custom_batch = RecordBatch::try_new(
+        custom_schema.clone(),
+        vec![
+            Arc::new(
+                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                    vec![
+                        Some(vec![Some(1.1), Some(1.2), Some(1.3), Some(1.4)]),
+                        Some(vec![Some(0.2), Some(1.8), Some(0.4), Some(3.6)]),
+                    ],
+                    4,
+                ),
+            ),
+            Arc::new(Float32Array::from(vec![45.5, 40.1])),
+            Arc::new(Float32Array::from(vec![-122.7, -74.1])),
+        ],
+    )
+    .unwrap();
+    let custom_reader: Box<dyn RecordBatchReader + Send> =
+        Box::new(RecordBatchIterator::new(vec![Ok(custom_batch)].into_iter(), custom_schema.clone()));
+    let custom_table = db
+        .create_table("my_table_custom_schema", custom_reader)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:create_table_custom_schema]
+    assert_eq!(custom_table.count_rows(None).await.unwrap(), 2);
+
+    // --8<-- [start:create_table_from_arrow]
+    let arrow_schema = Arc::new(Schema::new(vec![
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 16),
+            false,
+        ),
+        Field::new("text", DataType::Utf8, false),
+    ]));
+
+    let arrow_batch = RecordBatch::try_new(
+        arrow_schema.clone(),
+        vec![
+            Arc::new(
+                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                    vec![Some(vec![Some(0.1); 16]), Some(vec![Some(0.2); 16])],
+                    16,
+                ),
+            ),
+            Arc::new(StringArray::from(vec!["foo", "bar"])),
+        ],
+    )
+    .unwrap();
+    let arrow_reader: Box<dyn RecordBatchReader + Send> =
+        Box::new(RecordBatchIterator::new(vec![Ok(arrow_batch)].into_iter(), arrow_schema.clone()));
+    let arrow_table = db
+        .create_table("arrow_table_example", arrow_reader)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:create_table_from_arrow]
+    assert_eq!(arrow_table.count_rows(None).await.unwrap(), 2);
+
+    // --8<-- [start:create_table_from_iterator]
+    let batch_schema = Arc::new(Schema::new(vec![
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4),
+            false,
+        ),
+        Field::new("item", DataType::Utf8, false),
+        Field::new("price", DataType::Float32, false),
+    ]));
+
+    let batches = (0..5)
+        .map(|i| {
+            RecordBatch::try_new(
+                batch_schema.clone(),
+                vec![
+                    Arc::new(
+                        FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                            vec![
+                                Some(vec![Some(3.1 + i as f32), Some(4.1), Some(5.1), Some(6.1)]),
+                                Some(vec![
+                                    Some(5.9),
+                                    Some(26.5 + i as f32),
+                                    Some(4.7),
+                                    Some(32.8),
+                                ]),
+                            ],
+                            4,
+                        ),
+                    ),
+                    Arc::new(StringArray::from(vec![
+                        format!("item{}", i * 2 + 1),
+                        format!("item{}", i * 2 + 2),
+                    ])),
+                    Arc::new(Float32Array::from(vec![
+                        ((i * 2 + 1) * 10) as f32,
+                        ((i * 2 + 2) * 10) as f32,
+                    ])),
+                ],
+            )
+            .unwrap()
+        })
+        .collect::<Vec<_>>();
+
+    let batch_reader: Box<dyn RecordBatchReader + Send> =
+        Box::new(RecordBatchIterator::new(batches.into_iter().map(Ok), batch_schema.clone()));
+    let batch_table = db
+        .create_table("batched_table", batch_reader)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:create_table_from_iterator]
+    assert_eq!(batch_table.count_rows(None).await.unwrap(), 10);
+
+    // --8<-- [start:open_existing_table]
+    let open_schema = Arc::new(Schema::new(vec![
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 2),
+            false,
+        ),
+        Field::new("lat", DataType::Float32, false),
+        Field::new("long", DataType::Float32, false),
+    ]));
+    let open_batch = RecordBatch::try_new(
+        open_schema.clone(),
+        vec![
+            Arc::new(
+                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                    vec![Some(vec![Some(1.1), Some(1.2)])],
+                    2,
+                ),
+            ),
+            Arc::new(Float32Array::from(vec![45.5])),
+            Arc::new(Float32Array::from(vec![-122.7])),
+        ],
+    )
+    .unwrap();
+    let open_reader: Box<dyn RecordBatchReader + Send> =
+        Box::new(RecordBatchIterator::new(vec![Ok(open_batch)].into_iter(), open_schema.clone()));
+    db.create_table("test_table", open_reader)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+
+    println!("{:?}", db.table_names().execute().await.unwrap());
+
+    let opened_table = db.open_table("test_table").execute().await.unwrap();
+    // --8<-- [end:open_existing_table]
+    assert_eq!(opened_table.count_rows(None).await.unwrap(), 1);
+
+    // --8<-- [start:create_empty_table]
+    let empty_schema = Arc::new(Schema::new(vec![
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 2),
+            false,
+        ),
+        Field::new("item", DataType::Utf8, false),
+        Field::new("price", DataType::Float32, false),
+    ]));
+    let empty_table = db
+        .create_empty_table("test_empty_table", empty_schema)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:create_empty_table]
+    assert_eq!(empty_table.count_rows(None).await.unwrap(), 0);
+
+    // --8<-- [start:drop_table]
+    let drop_schema = Arc::new(Schema::new(vec![
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 2),
+            false,
+        ),
+        Field::new("lat", DataType::Float32, false),
+    ]));
+    let drop_batch = RecordBatch::try_new(
+        drop_schema.clone(),
+        vec![
+            Arc::new(
+                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                    vec![Some(vec![Some(1.1), Some(1.2)])],
+                    2,
+                ),
+            ),
+            Arc::new(Float32Array::from(vec![45.5])),
+        ],
+    )
+    .unwrap();
+    let drop_reader: Box<dyn RecordBatchReader + Send> =
+        Box::new(RecordBatchIterator::new(vec![Ok(drop_batch)].into_iter(), drop_schema.clone()));
+    db.create_table("my_table", drop_reader)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+
+    db.drop_table("my_table", &[]).await.unwrap();
+    // --8<-- [end:drop_table]
+    assert!(
+        !db.table_names()
+            .execute()
+            .await
+            .unwrap()
+            .contains(&"my_table".to_string())
+    );
+
+    // --8<-- [start:schema_add_setup]
+    let schema_add_schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new("name", DataType::Utf8, false),
+        Field::new("price", DataType::Float64, false),
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 128),
+            false,
+        ),
+    ]));
+    let schema_add_batch = RecordBatch::try_new(
+        schema_add_schema.clone(),
+        vec![
+            Arc::new(Int64Array::from(vec![1, 2, 3])),
+            Arc::new(StringArray::from(vec!["Laptop", "Smartphone", "Headphones"])),
+            Arc::new(Float64Array::from(vec![1200.0, 800.0, 150.0])),
+            Arc::new(
+                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                    vec![
+                        Some(vec![Some(0.1_f32); 128]),
+                        Some(vec![Some(0.2_f32); 128]),
+                        Some(vec![Some(0.3_f32); 128]),
+                    ],
+                    128,
+                ),
+            ),
+        ],
+    )
+    .unwrap();
+    let schema_add_reader: Box<dyn RecordBatchReader + Send> = Box::new(RecordBatchIterator::new(
+        vec![Ok(schema_add_batch)].into_iter(),
+        schema_add_schema.clone(),
+    ));
+    let schema_add_table = db
+        .create_table("schema_evolution_add_example", schema_add_reader)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:schema_add_setup]
+    assert_eq!(schema_add_table.count_rows(None).await.unwrap(), 3);
+
+    // --8<-- [start:add_columns_calculated]
+    // Add a discounted price column (10% discount)
+    schema_add_table
+        .add_columns(
+            NewColumnTransform::SqlExpressions(vec![(
+                "discounted_price".to_string(),
+                "cast((price * 0.9) as float)".to_string(),
+            )]),
+            None,
+        )
+        .await
+        .unwrap();
+    // --8<-- [end:add_columns_calculated]
+
+    // --8<-- [start:add_columns_default_values]
+    // Add a stock status column with default value
+    schema_add_table
+        .add_columns(
+            NewColumnTransform::SqlExpressions(vec![(
+                "in_stock".to_string(),
+                "cast(true as boolean)".to_string(),
+            )]),
+            None,
+        )
+        .await
+        .unwrap();
+    // --8<-- [end:add_columns_default_values]
+
+    // --8<-- [start:add_columns_nullable]
+    // Add a nullable timestamp column
+    schema_add_table
+        .add_columns(
+            NewColumnTransform::SqlExpressions(vec![(
+                "last_ordered".to_string(),
+                "cast(NULL as timestamp)".to_string(),
+            )]),
+            None,
+        )
+        .await
+        .unwrap();
+    // --8<-- [end:add_columns_nullable]
+
+    // --8<-- [start:add_feature_columns_sql]
+    schema_add_table
+        .add_columns(
+            NewColumnTransform::SqlExpressions(vec![
+                (
+                    "price_per_id".to_string(),
+                    "cast(price / id as float)".to_string(),
+                ),
+                ("price_log".to_string(), "ln(price)".to_string()),
+                (
+                    "price_score".to_string(),
+                    "cast(price / (price + 100.0) as float)".to_string(),
+                ),
+            ]),
+            None,
+        )
+        .await
+        .unwrap();
+    // --8<-- [end:add_feature_columns_sql]
+
+    // --8<-- [start:schema_alter_setup]
+    let schema_alter_schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new("name", DataType::Utf8, false),
+        Field::new("price", DataType::Int32, false),
+        Field::new("discount_price", DataType::Float64, false),
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 128),
+            false,
+        ),
+    ]));
+    let schema_alter_batch = RecordBatch::try_new(
+        schema_alter_schema.clone(),
+        vec![
+            Arc::new(Int64Array::from(vec![1, 2])),
+            Arc::new(StringArray::from(vec!["Laptop", "Smartphone"])),
+            Arc::new(Int32Array::from(vec![1200, 800])),
+            Arc::new(Float64Array::from(vec![1080.0, 720.0])),
+            Arc::new(
+                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                    vec![Some(vec![Some(0.1_f32); 128]), Some(vec![Some(0.2_f32); 128])],
+                    128,
+                ),
+            ),
+        ],
+    )
+    .unwrap();
+    let schema_alter_reader: Box<dyn RecordBatchReader + Send> = Box::new(RecordBatchIterator::new(
+        vec![Ok(schema_alter_batch)].into_iter(),
+        schema_alter_schema.clone(),
+    ));
+    let schema_alter_table = db
+        .create_table("schema_evolution_alter_example", schema_alter_reader)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:schema_alter_setup]
+    assert_eq!(schema_alter_table.count_rows(None).await.unwrap(), 2);
+
+    // --8<-- [start:alter_columns_rename]
+    // Rename discount_price to sale_price
+    schema_alter_table
+        .alter_columns(&[ColumnAlteration::new("discount_price".to_string())
+            .rename("sale_price".to_string())])
+        .await
+        .unwrap();
+    // --8<-- [end:alter_columns_rename]
+
+    // --8<-- [start:alter_columns_data_type]
+    // Change price from int32 to int64 for larger numbers
+    schema_alter_table
+        .alter_columns(&[ColumnAlteration::new("price".to_string()).cast_to(DataType::Int64)])
+        .await
+        .unwrap();
+    // --8<-- [end:alter_columns_data_type]
+
+    // --8<-- [start:alter_columns_nullable]
+    // Make the name column nullable
+    schema_alter_table
+        .alter_columns(&[ColumnAlteration::new("name".to_string()).set_nullable(true)])
+        .await
+        .unwrap();
+    // --8<-- [end:alter_columns_nullable]
+
+    // --8<-- [start:alter_columns_multiple]
+    // Rename, change type, and make nullable in one operation
+    schema_alter_table
+        .alter_columns(&[ColumnAlteration::new("sale_price".to_string())
+            .rename("final_price".to_string())
+            .cast_to(DataType::Float64)
+            .set_nullable(true)])
+        .await
+        .unwrap();
+    // --8<-- [end:alter_columns_multiple]
+
+    // --8<-- [start:alter_columns_with_expression]
+    // For custom transforms, create a new column from a SQL expression.
+    let expression_schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new("price_text", DataType::Utf8, false),
+    ]));
+    let expression_batch = RecordBatch::try_new(
+        expression_schema.clone(),
+        vec![
+            Arc::new(Int64Array::from(vec![1])),
+            Arc::new(StringArray::from(vec!["$100"])),
+        ],
+    )
+    .unwrap();
+    let expression_reader: Box<dyn RecordBatchReader + Send> = Box::new(RecordBatchIterator::new(
+        vec![Ok(expression_batch)].into_iter(),
+        expression_schema.clone(),
+    ));
+    let expression_table = db
+        .create_table("schema_evolution_expression_example", expression_reader)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+
+    expression_table
+        .add_columns(
+            NewColumnTransform::SqlExpressions(vec![(
+                "price_numeric".to_string(),
+                "cast(replace(price_text, '$', '') as int)".to_string(),
+            )]),
+            None,
+        )
+        .await
+        .unwrap();
+    expression_table.drop_columns(&["price_text"]).await.unwrap();
+    expression_table
+        .alter_columns(&[ColumnAlteration::new("price_numeric".to_string())
+            .rename("price".to_string())])
+        .await
+        .unwrap();
+    // --8<-- [end:alter_columns_with_expression]
+    assert_eq!(expression_table.count_rows(None).await.unwrap(), 1);
+
+    // --8<-- [start:schema_drop_setup]
+    let schema_drop_schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new("name", DataType::Utf8, false),
+        Field::new("price", DataType::Float64, false),
+        Field::new("temp_col1", DataType::Utf8, false),
+        Field::new("temp_col2", DataType::Int32, false),
+        Field::new(
+            "vector",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 128),
+            false,
+        ),
+    ]));
+    let schema_drop_batch = RecordBatch::try_new(
+        schema_drop_schema.clone(),
+        vec![
+            Arc::new(Int64Array::from(vec![1, 2, 3])),
+            Arc::new(StringArray::from(vec!["Laptop", "Smartphone", "Headphones"])),
+            Arc::new(Float64Array::from(vec![1200.0, 800.0, 150.0])),
+            Arc::new(StringArray::from(vec!["X", "Y", "Z"])),
+            Arc::new(Int32Array::from(vec![100, 200, 300])),
+            Arc::new(
+                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                    vec![
+                        Some(vec![Some(0.1_f32); 128]),
+                        Some(vec![Some(0.2_f32); 128]),
+                        Some(vec![Some(0.3_f32); 128]),
+                    ],
+                    128,
+                ),
+            ),
+        ],
+    )
+    .unwrap();
+    let schema_drop_reader: Box<dyn RecordBatchReader + Send> = Box::new(RecordBatchIterator::new(
+        vec![Ok(schema_drop_batch)].into_iter(),
+        schema_drop_schema.clone(),
+    ));
+    let schema_drop_table = db
+        .create_table("schema_evolution_drop_example", schema_drop_reader)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:schema_drop_setup]
+    assert_eq!(schema_drop_table.count_rows(None).await.unwrap(), 3);
+
+    // --8<-- [start:drop_columns_single]
+    // Remove the first temporary column
+    schema_drop_table.drop_columns(&["temp_col1"]).await.unwrap();
+    // --8<-- [end:drop_columns_single]
+
+    // --8<-- [start:drop_columns_multiple]
+    // Remove the second temporary column
+    schema_drop_table.drop_columns(&["temp_col2"]).await.unwrap();
+    // --8<-- [end:drop_columns_multiple]
+
+    // --8<-- [start:alter_vector_column]
+    let old_dim = 384;
+    let new_dim = 1024;
+    let vector_schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new(
+            "embedding",
+            DataType::FixedSizeList(
+                Arc::new(Field::new("item", DataType::Float32, true)),
+                old_dim,
+            ),
+            true,
+        ),
+    ]));
+    let vector_batch = RecordBatch::try_new(
+        vector_schema.clone(),
+        vec![
+            Arc::new(Int64Array::from(vec![1])),
+            Arc::new(
+                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                    vec![Some(vec![Some(0.1_f32); old_dim as usize])],
+                    old_dim,
+                ),
+            ),
+        ],
+    )
+    .unwrap();
+    let vector_reader: Box<dyn RecordBatchReader + Send> =
+        Box::new(RecordBatchIterator::new(vec![Ok(vector_batch)].into_iter(), vector_schema.clone()));
+    let vector_table = db
+        .create_table("vector_alter_example", vector_reader)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+
+    // Changing FixedSizeList dimensions (384 -> 1024) is not supported via alter_columns.
+    // Use add_columns + drop_columns + alter_columns(rename) to replace the column.
+    vector_table
+        .add_columns(
+            NewColumnTransform::SqlExpressions(vec![(
+                "embedding_v2".to_string(),
+                format!("arrow_cast(NULL, 'FixedSizeList({}, Float32)')", new_dim),
+            )]),
+            None,
+        )
+        .await
+        .unwrap();
+    vector_table.drop_columns(&["embedding"]).await.unwrap();
+    vector_table
+        .alter_columns(&[ColumnAlteration::new("embedding_v2".to_string())
+            .rename("embedding".to_string())])
+        .await
+        .unwrap();
+    // --8<-- [end:alter_vector_column]
+    assert_eq!(vector_table.count_rows(None).await.unwrap(), 1);
+
+    let field_metadata_schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        Field::new("category", DataType::Utf8, false),
+    ]));
+    let field_metadata_batch = RecordBatch::try_new(
+        field_metadata_schema.clone(),
+        vec![
+            Arc::new(Int64Array::from(vec![0, 1])),
+            Arc::new(StringArray::from(vec!["a", "b"])),
+        ],
+    )
+    .unwrap();
+    let field_metadata_reader: Box<dyn RecordBatchReader + Send> = Box::new(
+        RecordBatchIterator::new(vec![Ok(field_metadata_batch)].into_iter(), field_metadata_schema),
+    );
+    let field_metadata_table = db
+        .create_table("schema_field_metadata_example", field_metadata_reader)
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+
+    // --8<-- [start:schema_field_metadata_merge]
+    // Set two metadata keys on the `category` field.
+    let res = field_metadata_table
+        .update_field_metadata(&[FieldMetadataUpdate::new("category")
+            .set("unit", "label")
+            .set("pii", "false")])
+        .await
+        .unwrap();
+    println!("version: {}", res.version);
+
+    // Merge: add a new key, delete one with `.remove`, keep the rest.
+    field_metadata_table
+        .update_field_metadata(&[FieldMetadataUpdate::new("category")
+            .set("source", "import")
+            .remove("pii")])
+        .await
+        .unwrap();
+    // --8<-- [end:schema_field_metadata_merge]
+
+    // --8<-- [start:schema_field_metadata_replace]
+    field_metadata_table
+        .update_field_metadata(&[FieldMetadataUpdate::new("category")
+            .set("owner", "search-team")
+            .replace()])
+        .await
+        .unwrap();
+    // --8<-- [end:schema_field_metadata_replace]
+
+    // --8<-- [start:update_example_table_setup]
+    let table = db
+        .create_table(
+            "users_example",
+            make_users_reader(vec![1, 2], vec!["Alice", "Bob"], Some(vec![10, 20])),
+        )
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:update_example_table_setup]
+    let _ = table;
+
+    // --8<-- [start:update_operation]
+    let table = db
+        .create_table(
+            "users_example",
+            make_users_reader(vec![1, 2], vec!["Alice", "Bob"], Some(vec![10, 20])),
+        )
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    table
+        .update()
+        .only_if("id = 2")
+        .column("name", "'Bobby'")
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:update_operation]
+
+    // --8<-- [start:update_using_sql]
+    let table = db
+        .create_table(
+            "users_example",
+            make_users_reader(vec![1, 2], vec!["Alice", "Bob"], Some(vec![10, 20])),
+        )
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    table
+        .update()
+        .only_if("id = 2")
+        .column("login_count", "login_count + 1")
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:update_using_sql]
+
+    // --8<-- [start:merge_matched_update_only]
+    let table = db
+        .create_table(
+            "users_example",
+            make_users_reader(vec![1, 2], vec!["Alice", "Bob"], Some(vec![10, 20])),
+        )
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+
+    let mut merge_insert = table.merge_insert(&["id"]);
+    merge_insert.when_matched_update_all(None);
+    merge_insert
+        .execute(make_users_reader(
+            vec![2, 3],
+            vec!["Bobby", "Charlie"],
+            Some(vec![21, 5]),
+        ))
+        .await
+        .unwrap();
+    // --8<-- [end:merge_matched_update_only]
+
+    // --8<-- [start:insert_if_not_exists]
+    let table = db
+        .create_table(
+            "users_example",
+            make_users_reader(vec![1, 2], vec!["Alice", "Bob"], Some(vec![10, 20])),
+        )
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+
+    let mut merge_insert = table.merge_insert(&["id"]);
+    merge_insert.when_not_matched_insert_all();
+    merge_insert
+        .execute(make_users_reader(
+            vec![2, 3],
+            vec!["Bobby", "Charlie"],
+            Some(vec![21, 5]),
+        ))
+        .await
+        .unwrap();
+    // --8<-- [end:insert_if_not_exists]
+
+    // --8<-- [start:merge_update_insert]
+    let table = db
+        .create_table(
+            "users_example",
+            make_users_reader(vec![1, 2], vec!["Alice", "Bob"], Some(vec![10, 20])),
+        )
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+
+    let mut merge_insert = table.merge_insert(&["id"]);
+    merge_insert
+        .when_matched_update_all(None)
+        .when_not_matched_insert_all();
+    merge_insert
+        .execute(make_users_reader(
+            vec![2, 3],
+            vec!["Bobby", "Charlie"],
+            Some(vec![21, 5]),
+        ))
+        .await
+        .unwrap();
+    // --8<-- [end:merge_update_insert]
+
+    // --8<-- [start:merge_delete_missing_by_source]
+    let table = db
+        .create_table(
+            "users_example",
+            make_users_reader(
+                vec![1, 2, 3],
+                vec!["Alice", "Bob", "Charlie"],
+                Some(vec![10, 20, 5]),
+            ),
+        )
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+
+    let mut merge_insert = table.merge_insert(&["id"]);
+    merge_insert
+        .when_matched_update_all(None)
+        .when_not_matched_insert_all()
+        .when_not_matched_by_source_delete(None);
+    merge_insert
+        .execute(make_users_reader(
+            vec![2, 3],
+            vec!["Bobby", "Charlie"],
+            Some(vec![21, 5]),
+        ))
+        .await
+        .unwrap();
+    // --8<-- [end:merge_delete_missing_by_source]
+
+    // --8<-- [start:merge_partial_columns]
+    let table = db
+        .create_table(
+            "users_example",
+            make_users_reader(vec![1, 2], vec!["Alice", "Bob"], Some(vec![10, 20])),
+        )
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+
+    let mut merge_insert = table.merge_insert(&["id"]);
+    merge_insert
+        .when_matched_update_all(None)
+        .when_not_matched_insert_all();
+    merge_insert
+        .execute(make_users_reader(vec![2, 3], vec!["Bobby", "Charlie"], None))
+        .await
+        .unwrap();
+    // --8<-- [end:merge_partial_columns]
+
+    let table = db
+        .create_table(
+            "users_example",
+            make_users_reader(
+                vec![1, 2, 3],
+                vec!["Alice", "Bob", "Charlie"],
+                Some(vec![10, 20, 5]),
+            ),
+        )
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+
+    // --8<-- [start:delete_operation]
+    // delete data
+    let predicate = "id = 3";
+    table.delete(predicate).await.unwrap();
+    // --8<-- [end:delete_operation]
+
+    let table = db
+        .create_table(
+            "users_cleanup_example",
+            make_users_reader(
+                vec![1, 2, 3],
+                vec!["Alice", "Bob", "Charlie"],
+                Some(vec![10, 20, 5]),
+            ),
+        )
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+
+    // --8<-- [start:update_optimize_cleanup]
+    table
+        .optimize(OptimizeAction::Prune {
+            older_than: Some(Duration::days(1)),
+            delete_unverified: None,
+            error_if_tagged_old_versions: None,
+        })
+        .await
+        .unwrap();
+    // --8<-- [end:update_optimize_cleanup]
+
+    // --8<-- [start:consistency_strong]
+    let strong_writer_db = connect(&db_uri).execute().await.unwrap();
+    let strong_reader_db = connect(&db_uri)
+        .read_consistency_interval(StdDuration::from_secs(0))
+        .execute()
+        .await
+        .unwrap();
+    let strong_writer_table = strong_writer_db
+        .create_table(
+            "consistency_strong_table",
+            make_users_reader(vec![1], vec!["Alice"], None),
+        )
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    let strong_reader_table = strong_reader_db
+        .open_table("consistency_strong_table")
+        .execute()
+        .await
+        .unwrap();
+    strong_writer_table
+        .add(make_users_reader(vec![2], vec!["Bob"], None))
+        .execute()
+        .await
+        .unwrap();
+    let strong_rows_after_write = strong_reader_table.count_rows(None).await.unwrap();
+    println!(
+        "Rows visible with strong consistency: {}",
+        strong_rows_after_write
+    );
+    // --8<-- [end:consistency_strong]
+    assert_eq!(strong_rows_after_write, 2);
+
+    // --8<-- [start:consistency_eventual]
+    let eventual_writer_db = connect(&db_uri).execute().await.unwrap();
+    let eventual_reader_db = connect(&db_uri)
+        .read_consistency_interval(StdDuration::from_secs(3600))
+        .execute()
+        .await
+        .unwrap();
+    let eventual_writer_table = eventual_writer_db
+        .create_table(
+            "consistency_eventual_table",
+            make_users_reader(vec![1], vec!["Alice"], None),
+        )
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    let eventual_reader_table = eventual_reader_db
+        .open_table("consistency_eventual_table")
+        .execute()
+        .await
+        .unwrap();
+    eventual_writer_table
+        .add(make_users_reader(vec![2], vec!["Bob"], None))
+        .execute()
+        .await
+        .unwrap();
+    let eventual_rows_after_write = eventual_reader_table.count_rows(None).await.unwrap();
+    println!(
+        "Rows visible before eventual refresh interval: {}",
+        eventual_rows_after_write
+    );
+    // --8<-- [end:consistency_eventual]
+    assert_eq!(eventual_rows_after_write, 1);
+
+    // --8<-- [start:consistency_checkout_latest]
+    let checkout_writer_db = connect(&db_uri).execute().await.unwrap();
+    let checkout_reader_db = connect(&db_uri).execute().await.unwrap();
+    let checkout_writer_table = checkout_writer_db
+        .create_table(
+            "consistency_checkout_latest_table",
+            make_users_reader(vec![1], vec!["Alice"], None),
+        )
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    let checkout_reader_table = checkout_reader_db
+        .open_table("consistency_checkout_latest_table")
+        .execute()
+        .await
+        .unwrap();
+    checkout_writer_table
+        .add(make_users_reader(vec![2], vec!["Bob"], None))
+        .execute()
+        .await
+        .unwrap();
+    let rows_before_refresh = checkout_reader_table.count_rows(None).await.unwrap();
+    println!("Rows before checkout_latest: {}", rows_before_refresh);
+    checkout_reader_table.checkout_latest().await.unwrap();
+    let rows_after_refresh = checkout_reader_table.count_rows(None).await.unwrap();
+    println!("Rows after checkout_latest: {}", rows_after_refresh);
+    // --8<-- [end:consistency_checkout_latest]
+    assert_eq!(rows_before_refresh, 1);
+    assert_eq!(rows_after_refresh, 2);
+
+    // --8<-- [start:versioning_basic_setup]
+    let table_name = "quotes_versioning_example";
+    let data = vec![
+        (1, "Richard", "Wubba Lubba Dub Dub!"),
+        (2, "Morty", "Rick, what's going on?"),
+        (3, "Richard", "I turned myself into a pickle, Morty!"),
+    ];
+
+    let table = db
+        .create_table(table_name, make_quotes_reader(data))
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:versioning_basic_setup]
+    assert_eq!(table.count_rows(None).await.unwrap(), 3);
+
+    // --8<-- [start:versioning_check_initial_version]
+    let versions = table.list_versions().await.unwrap();
+    let current_version = table.version().await.unwrap();
+    println!("Number of versions after creation: {}", versions.len());
+    println!("Current version: {}", current_version);
+    // --8<-- [end:versioning_check_initial_version]
+    assert_eq!(versions.len(), 1);
+    assert_eq!(current_version, versions.last().unwrap().version);
+
+    // --8<-- [start:versioning_update_data]
+    table
+        .update()
+        .only_if("author = 'Richard'")
+        .column("author", "'Richard Daniel Sanchez'")
+        .execute()
+        .await
+        .unwrap();
+    let rows_after_update = table
+        .count_rows(Some("author = 'Richard Daniel Sanchez'".to_string()))
+        .await
+        .unwrap();
+    println!(
+        "Rows updated to Richard Daniel Sanchez: {}",
+        rows_after_update
+    );
+    // --8<-- [end:versioning_update_data]
+    assert_eq!(rows_after_update, 2);
+
+    // --8<-- [start:versioning_add_data]
+    let more_data = vec![
+        (4, "Richard Daniel Sanchez", "That's the way the news goes!"),
+        (5, "Morty", "Aww geez, Rick!"),
+    ];
+    table
+        .add(make_quotes_reader(more_data))
+        .execute()
+        .await
+        .unwrap();
+    // --8<-- [end:versioning_add_data]
+    assert_eq!(table.count_rows(None).await.unwrap(), 5);
+
+    // --8<-- [start:versioning_check_versions_after_mod]
+    let versions_after_mod = table.list_versions().await.unwrap();
+    let version_count_after_mod = versions_after_mod.len();
+    let version_after_mod = table.version().await.unwrap();
+    println!(
+        "Number of versions after modifications: {}",
+        version_count_after_mod
+    );
+    println!("Current version: {}", version_after_mod);
+    // --8<-- [end:versioning_check_versions_after_mod]
+    assert!(version_count_after_mod >= 2);
+    assert_eq!(version_after_mod, versions_after_mod.last().unwrap().version);
+
+    // --8<-- [start:versioning_list_all_versions]
+    let all_versions = table.list_versions().await.unwrap();
+    for v in &all_versions {
+        println!("Version {}, created at {}", v.version, v.timestamp);
+    }
+    // --8<-- [end:versioning_list_all_versions]
+    assert!(!all_versions.is_empty());
+
+    // --8<-- [start:versioning_rollback]
+    table.checkout(version_after_mod).await.unwrap();
+    table.restore().await.unwrap();
+    let versions_after_rollback = table.list_versions().await.unwrap();
+    let version_count_after_rollback = versions_after_rollback.len();
+    println!(
+        "Total number of versions after rollback: {}",
+        version_count_after_rollback
+    );
+    // --8<-- [end:versioning_rollback]
+    assert_eq!(version_count_after_rollback, version_count_after_mod + 1);
+    assert_eq!(table.count_rows(None).await.unwrap(), 5);
+
+    // --8<-- [start:versioning_checkout_latest]
+    table.checkout_latest().await.unwrap();
+    // --8<-- [end:versioning_checkout_latest]
+    let latest_version = table.version().await.unwrap();
+    let versions_after_checkout = table.list_versions().await.unwrap();
+    assert_eq!(latest_version, versions_after_checkout.last().unwrap().version);
+
+    // --8<-- [start:versioning_delete_data]
+    table.delete("author = 'Morty'").await.unwrap();
+    let rows_after_deletion = table.count_rows(None).await.unwrap();
+    println!("Number of rows after deletion: {}", rows_after_deletion);
+    // --8<-- [end:versioning_delete_data]
+    assert_eq!(rows_after_deletion, 3);
+
+    // Setup: build a table with three versions to operate on with tags.
+    let tags_table = db
+        .create_table(
+            "quotes_tags_example",
+            make_quotes_reader(vec![(1, "Richard", "Wubba Lubba Dub Dub!")]),
+        )
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap(); // v1
+    tags_table
+        .add(make_quotes_reader(vec![(2, "Morty", "Aww geez, Rick!")]))
+        .execute()
+        .await
+        .unwrap(); // v2
+    tags_table
+        .add(make_quotes_reader(vec![(3, "Summer", "Whatever, Grandpa")]))
+        .execute()
+        .await
+        .unwrap(); // v3
+
+    // --8<-- [start:versioning_tags]
+    let mut tags = tags_table.tags().await.unwrap();
+
+    // Create a tag pointing at a specific version
+    tags.create("baseline", 1).await.unwrap();
+    let current_version = tags_table.version().await.unwrap();
+    tags.create("with-edits", current_version).await.unwrap();
+
+    // List all tags on this table
+    let all_tags = tags.list().await.unwrap();
+    println!("Tags: {:?}", all_tags);
+
+    // Look up the version a tag points at
+    let baseline_version = tags.get_version("baseline").await.unwrap();
+    println!("baseline -> v{}", baseline_version);
+
+    // Move an existing tag to a different version
+    tags.update("baseline", 2).await.unwrap();
+
+    // Check out a version by tag name (separate method in Rust)
+    tags_table.checkout_tag("baseline").await.unwrap();
+    println!("Current version: {}", tags_table.version().await.unwrap());
+
+    // Delete a tag (does not delete the underlying version)
+    tags.delete("with-edits").await.unwrap();
+
+    // Return to the latest version
+    tags_table.checkout_latest().await.unwrap();
+    // --8<-- [end:versioning_tags]
+    assert_eq!(tags_table.version().await.unwrap(), 3);
+    let remaining = tags.list().await.unwrap();
+    assert!(remaining.contains_key("baseline"));
+    assert!(!remaining.contains_key("with-edits"));
+
+    // Setup: a fresh quotes table to demonstrate branches on.
+    let branches_table = db
+        .create_table(
+            "quotes_branches_example",
+            make_quotes_reader(vec![
+                (1, "Lancelot", "My lance never fails."),
+                (2, "Arthur", "Long live Camelot!"),
+                (3, "Merlin", "Magic always has a price."),
+            ]),
+        )
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+
+    use lancedb::table::Ref;
+
+    // --8<-- [start:branch_create]
+    // Fork an isolated, writable branch from main's latest version.
+    // `create_branch` returns a table handle scoped to the new branch.
+    let branch = branches_table
+        .create_branch("exp", Ref::Version(None, None))
+        .await
+        .unwrap();
+    // --8<-- [end:branch_create]
+
+    // --8<-- [start:branch_write]
+    // Writes land on the branch handle only; main is left untouched.
+    branch
+        .add(make_quotes_reader(vec![(4, "Lancelot", "For the realm!")]))
+        .execute()
+        .await
+        .unwrap();
+    println!("Branch rows: {}", branch.count_rows(None).await.unwrap()); // 4
+    println!("Main rows: {}", branches_table.count_rows(None).await.unwrap()); // 3
+
+    // List every branch, each mapped to its metadata (including its fork point).
+    println!("Branches: {:?}", branches_table.list_branches().await.unwrap());
+    // --8<-- [end:branch_write]
+
+    // --8<-- [start:branch_reopen]
+    // Reopen an existing branch by name from the table handle...
+    let checked_out = branches_table.checkout_branch("exp", None).await.unwrap();
+    // ...or open it directly via the connection's builder.
+    let opened = db
+        .open_table("quotes_branches_example")
+        .branch("exp")
+        .execute()
+        .await
+        .unwrap();
+    println!(
+        "Reopened rows: {}, {}",
+        checked_out.count_rows(None).await.unwrap(),
+        opened.count_rows(None).await.unwrap()
+    ); // both 4
+    // --8<-- [end:branch_reopen]
+
+    // --8<-- [start:branch_delete]
+    // Delete the branch and its branch-local history. Data on main is safe.
+    branches_table.delete_branch("exp").await.unwrap();
+    // --8<-- [end:branch_delete]
+
+    assert_eq!(branches_table.count_rows(None).await.unwrap(), 3);
+    assert!(!branches_table.list_branches().await.unwrap().contains_key("exp"));
+
+    // Setup: a branch with row results that we want to apply to main.
+    let candidate = branches_table
+        .create_branch("candidate", Ref::Version(None, None))
+        .await
+        .unwrap();
+    candidate
+        .update()
+        .only_if("id = 1")
+        .column("quote", "'Revised on the branch'")
+        .execute()
+        .await
+        .unwrap();
+    candidate
+        .add(make_quotes_reader(vec![(
+            4,
+            "Galahad",
+            "The grail awaits.",
+        )]))
+        .execute()
+        .await
+        .unwrap();
+
+    // --8<-- [start:branch_upsert_to_main]
+    // This is a row-level upsert, not a merge of branch histories.
+    // `merge_insert` updates matching rows and inserts new rows using a stable
+    // unique key. Filter the branch read if you only want to apply some results.
+    let schema = candidate.schema().await.unwrap();
+    let batches = candidate
+        .query()
+        .execute()
+        .await
+        .unwrap()
+        .try_collect::<Vec<_>>()
+        .await
+        .unwrap();
+    let rows_to_apply = RecordBatchIterator::new(batches.into_iter().map(Ok), schema);
+
+    let mut merge = branches_table.merge_insert(&["id"]);
+    merge
+        .when_matched_update_all(None) // update rows that already exist on main
+        .when_not_matched_insert_all(); // insert rows that are new on the branch
+    merge.execute(Box::new(rows_to_apply)).await.unwrap();
+    // --8<-- [end:branch_upsert_to_main]
+
+    assert_eq!(branches_table.count_rows(None).await.unwrap(), 4);
+    branches_table.delete_branch("candidate").await.unwrap();
+
+    // Setup: a larger table with a vector and a text column to index.
+    let products = db
+        .create_table("products_branch_index", make_products_reader(512))
+        .mode(CreateTableMode::Overwrite)
+        .execute()
+        .await
+        .unwrap();
+
+    // --8<-- [start:branch_index]
+    use lancedb::index::scalar::FtsIndexBuilder;
+    use lancedb::index::Index;
+
+    // Build and validate indexes on a branch before using the configuration on
+    // main.
+    let dev = products
+        .create_branch("index-dev", Ref::Version(None, None))
+        .await
+        .unwrap();
+
+    // A vector (ANN) index and a full-text search index, both branch-scoped.
+    dev.create_index(&["vector"], Index::Auto)
+        .execute()
+        .await
+        .unwrap();
+    dev.create_index(&["text"], Index::FTS(FtsIndexBuilder::default()))
+        .execute()
+        .await
+        .unwrap();
+
+    // Both indexes live only on the branch; main still has none.
+    println!("Branch indexes: {}", dev.list_indices().await.unwrap().len()); // 2
+    println!("Main indexes: {}", products.list_indices().await.unwrap().len()); // 0
+    // --8<-- [end:branch_index]
+
+    assert_eq!(dev.list_indices().await.unwrap().len(), 2);
+    assert_eq!(products.list_indices().await.unwrap().len(), 0);
+    products.delete_branch("index-dev").await.unwrap();
+}
diff --git a/docs/web-tests/ts/ann_indexes.test.ts b/docs/web-tests/ts/ann_indexes.test.ts
new file mode 100644
index 000000000..a37872c9f
--- /dev/null
+++ b/docs/web-tests/ts/ann_indexes.test.ts
@@ -0,0 +1,57 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+import { expect, test } from "@jest/globals";
+// --8<-- [start:import]
+import * as lancedb from "@lancedb/lancedb";
+import type { VectorQuery } from "@lancedb/lancedb";
+// --8<-- [end:import]
+import { withTempDirectory } from "./util.ts";
+
+test("ann index examples", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    // --8<-- [start:ingest]
+    const db = await lancedb.connect(databaseDir);
+
+    const data = Array.from({ length: 5_000 }, (_, i) => ({
+      vector: Array(128).fill(i),
+      id: `${i}`,
+      content: "",
+      longId: `${i}`,
+    }));
+
+    const table = await db.createTable("my_vectors", data, {
+      mode: "overwrite",
+    });
+    await table.createIndex("vector", {
+      config: lancedb.Index.ivfPq({
+        numPartitions: 10,
+        numSubVectors: 16,
+      }),
+    });
+    // --8<-- [end:ingest]
+
+    // --8<-- [start:search1]
+    const search = table.search(Array(128).fill(1.2)).limit(2) as VectorQuery;
+    const results1 = await search.nprobes(20).refineFactor(10).toArray();
+    // --8<-- [end:search1]
+    expect(results1.length).toBe(2);
+
+    // --8<-- [start:search2]
+    const results2 = await table
+      .search(Array(128).fill(1.2))
+      .where("id != '1141'")
+      .limit(2)
+      .toArray();
+    // --8<-- [end:search2]
+    expect(results2.length).toBe(2);
+
+    // --8<-- [start:search3]
+    const results3 = await table
+      .search(Array(128).fill(1.2))
+      .select(["id"])
+      .limit(2)
+      .toArray();
+    // --8<-- [end:search3]
+    expect(results3.length).toBe(2);
+  });
+}, 100_000);
diff --git a/docs/web-tests/ts/basic_usage.test.ts b/docs/web-tests/ts/basic_usage.test.ts
new file mode 100644
index 000000000..f00e69ba4
--- /dev/null
+++ b/docs/web-tests/ts/basic_usage.test.ts
@@ -0,0 +1,161 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+import { expect, test } from "@jest/globals";
+import * as fs from "node:fs";
+import * as path from "node:path";
+// --8<-- [start:basic_imports]
+import * as lancedb from "@lancedb/lancedb";
+import * as arrow from "apache-arrow";
+// --8<-- [end:basic_imports]
+import { withTempDirectory } from "./util.ts";
+
+const dataPath = path.join(__dirname, "..", "camelot.json");
+
+test("basic usage examples (async)", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    const db = await lancedb.connect(databaseDir);
+
+    // --8<-- [start:data_load]
+    const data = JSON.parse(fs.readFileSync(dataPath, "utf-8"));
+    // --8<-- [end:data_load]
+
+    // --8<-- [start:basic_create_table]
+    let table = await db.createTable("camelot", data, {
+       mode: "overwrite",
+    });
+    // --8<-- [end:basic_create_table]
+    expect(await table.countRows()).toBe(8);
+
+    // --8<-- [start:basic_open_table]
+    table = await db.openTable("camelot");
+    // --8<-- [end:basic_open_table]
+
+    // --8<-- [start:basic_create_empty_table]
+    const schema = new arrow.Schema([
+      new arrow.Field("id", new arrow.Int16()),
+      new arrow.Field("name", new arrow.Utf8()),
+      new arrow.Field("role", new arrow.Utf8()),
+      new arrow.Field("description", new arrow.Utf8()),
+      new arrow.Field(
+        "vector",
+        new arrow.FixedSizeList(
+          4,
+          new arrow.Field("item", new arrow.Float32(), true),
+        ),
+      ),
+      new arrow.Field(
+        "stats",
+        new arrow.Struct([
+          new arrow.Field("strength", new arrow.Int8()),
+          new arrow.Field("courage", new arrow.Int8()),
+          new arrow.Field("magic", new arrow.Int8()),
+          new arrow.Field("wisdom", new arrow.Int8()),
+        ]),
+      ),
+    ]);
+    await db.createEmptyTable("camelot_empty", schema, { mode: "overwrite" });
+    // --8<-- [end:basic_create_empty_table]
+    expect(await db.tableNames()).toContain("camelot_empty");
+    await db.dropTable("camelot_empty");
+
+    // --8<-- [start:basic_add_data]
+    const magicalCharacters = [
+      {
+        id: 9,
+        name: "Morgan le Fay",
+        role: "Sorceress",
+        description:
+          "A powerful enchantress, Arthur's half-sister, and a complex figure who oscillates between aiding and opposing Camelot.",
+        vector: [0.1, 0.84, 0.25, 0.7],
+        stats: { strength: 2, courage: 3, magic: 5, wisdom: 4 },
+      },
+      {
+        id: 10,
+        name: "The Lady of the Lake",
+        role: "Mystical Guardian",
+        description:
+          "A mysterious supernatural figure associated with Avalon, known for giving Arthur the sword Excalibur.",
+        vector: [0.0, 0.9, 0.58, 0.88],
+        stats: { strength: 2, courage: 3, magic: 5, wisdom: 5 },
+      },
+    ];
+    await table.add(magicalCharacters);
+    // --8<-- [end:basic_add_data]
+    expect(await table.countRows()).toBe(10);
+
+    // --8<-- [start:basic_vector_search]
+    const queryVector = [0.03, 0.85, 0.61, 0.9];
+    const result = await table.search(queryVector).limit(5).toArray();
+    console.log(result);
+    // --8<-- [end:basic_vector_search]
+
+    // --8<-- [start:basic_add_columns]
+    await table.addColumns([
+      {
+        name: "power",
+        valueSql:
+          "cast(((stats.strength + stats.courage + stats.magic + stats.wisdom) / 4.0) as float)",
+      },
+    ]);
+    // --8<-- [end:basic_add_columns]
+    const schemaWithPower = await table.schema();
+    expect(schemaWithPower.fields.some((f) => f.name === "power")).toBe(true);
+
+    // --8<-- [start:basic_vector_search_q1]
+    // Who are the characters similar to  "wizard"?
+    const queryVector1 = [0.03, 0.85, 0.61, 0.9];
+    const r1 = await table
+      .search(queryVector1)
+      .limit(5)
+      .select(["name", "role", "description"])
+      .toArray();
+    console.log(r1);
+    // --8<-- [end:basic_vector_search_q1]
+
+    // --8<-- [start:basic_vector_search_q2]
+    // Who are the characters similar to "wizard" with high magic stats?
+    const queryVector2 = [0.03, 0.85, 0.61, 0.9];
+    const r2 = await table
+      .search(queryVector2)
+      .where("stats.magic > 3")
+      .select(["name", "role", "description"])
+      .limit(5)
+      .toArray();
+    console.log(r2);
+    // --8<-- [end:basic_vector_search_q2]
+
+    // --8<-- [start:basic_vector_search_q3]
+    // Who are the strongest characters?
+    const r3 = await table
+      .query()
+      .where("stats.strength > 3")
+      .select(["name", "role", "description"])
+      .limit(5)
+      .toArray();
+    console.log(r3);
+    // --8<-- [end:basic_vector_search_q3]
+
+    // --8<-- [start:basic_vector_search_q4]
+    // Who are the strongest characters?
+    const r4 = await table
+      .query()
+      .select(["name", "role", "description", "power"])
+      .toArray();
+    console.log(r4);
+    // --8<-- [end:basic_vector_search_q4]
+
+    // --8<-- [start:basic_drop_columns]
+    await table.dropColumns(["power"]);
+    // --8<-- [end:basic_drop_columns]
+
+    // --8<-- [start:basic_delete_rows]
+    await table.delete('role = "Traitor Knight"');
+    // --8<-- [end:basic_delete_rows]
+    expect(await table.countRows()).toBe(9);
+
+    // --8<-- [start:basic_drop_table]
+    await db.dropTable("camelot");
+    // --8<-- [end:basic_drop_table]
+    expect(await db.tableNames()).not.toContain("camelot");
+  });
+});
diff --git a/docs/web-tests/ts/biome.json b/docs/web-tests/ts/biome.json
new file mode 100644
index 000000000..f8fb5b200
--- /dev/null
+++ b/docs/web-tests/ts/biome.json
@@ -0,0 +1,52 @@
+{
+  "$schema": "https://biomejs.dev/schemas/1.9.4/schema.json",
+  "vcs": {
+    "enabled": false,
+    "clientKind": "git",
+    "useIgnoreFile": false
+  },
+  "files": {
+    "ignoreUnknown": false,
+    "ignore": []
+  },
+  "formatter": {
+    "enabled": true,
+    "indentStyle": "space"
+  },
+  "organizeImports": {
+    "enabled": true
+  },
+  "linter": {
+    "enabled": true,
+    "rules": {
+      "recommended": true
+    }
+  },
+  "javascript": {
+    "formatter": {
+      "quoteStyle": "double"
+    }
+  },
+  "overrides": [
+    {
+      "include": ["*"],
+      "linter": {
+        "rules": {
+          "style": {
+            "noNonNullAssertion": "off"
+          }
+        }
+      }
+    },
+    {
+      "include": ["merge_insert.test.ts"],
+      "linter": {
+        "rules": {
+          "style": {
+            "useNamingConvention": "off"
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/docs/web-tests/ts/connection.test.ts b/docs/web-tests/ts/connection.test.ts
new file mode 100644
index 000000000..891501d4c
--- /dev/null
+++ b/docs/web-tests/ts/connection.test.ts
@@ -0,0 +1,167 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+import { afterEach, describe, expect, jest, test } from "@jest/globals";
+import * as lancedb from "@lancedb/lancedb";
+import { withTempDirectory } from "./util.ts";
+
+test("connect to a local database", async () => {
+  await withTempDirectory(async (tempDir) => {
+    const cwd = process.cwd();
+    process.chdir(tempDir);
+    try {
+      // --8<-- [start:connect]
+      const db = await lancedb.connect("ex_lancedb");
+      // --8<-- [end:connect]
+      await db.createTable("probe", [{ id: 1 }]);
+      expect((await db.listTables()).tables).toEqual(["probe"]);
+    } finally {
+      process.chdir(cwd);
+    }
+    const reopened = await lancedb.connect(`${tempDir}/ex_lancedb`);
+    expect((await reopened.listTables()).tables).toEqual(["probe"]);
+  });
+});
+
+async function connectEnterpriseQuickstart() {
+  // --8<-- [start:connect_enterprise_quickstart]
+  const setting = (name: string) => {
+    const value = process.env[name];
+    if (!value) throw new Error(`Set ${name}`);
+    return value;
+  };
+
+  const db = await lancedb.connect(setting("LANCEDB_URI"), {
+    apiKey: setting("LANCEDB_API_KEY"),
+    region: process.env.LANCEDB_REGION ?? "us-east-1",
+    hostOverride: setting("LANCEDB_HOST_OVERRIDE"),
+  });
+  // --8<-- [end:connect_enterprise_quickstart]
+  return db;
+}
+
+describe("enterprise quickstart connect", () => {
+  const saved = { ...process.env };
+  afterEach(() => {
+    process.env = { ...saved };
+  });
+
+  test("reads the connection from the environment", async () => {
+    const mockDb = { __mock: true } as unknown as Awaited<
+      ReturnType<typeof lancedb.connect>
+    >;
+    const spy = jest.spyOn(lancedb, "connect").mockResolvedValue(mockDb);
+    process.env.LANCEDB_URI = "db://your-database";
+    process.env.LANCEDB_API_KEY = "your-api-key";
+    delete process.env.LANCEDB_REGION;
+    process.env.LANCEDB_HOST_OVERRIDE = "https://your-endpoint";
+
+    const db = await connectEnterpriseQuickstart();
+    expect(db).toBe(mockDb);
+    expect(spy).toHaveBeenCalledWith("db://your-database", {
+      apiKey: "your-api-key",
+      region: "us-east-1",
+      hostOverride: "https://your-endpoint",
+    });
+    spy.mockRestore();
+  });
+
+  test("requires an endpoint", async () => {
+    process.env.LANCEDB_URI = "db://your-database";
+    process.env.LANCEDB_API_KEY = "your-api-key";
+    delete process.env.LANCEDB_HOST_OVERRIDE;
+
+    await expect(connectEnterpriseQuickstart()).rejects.toThrow(
+      "Set LANCEDB_HOST_OVERRIDE",
+    );
+  });
+});
+
+async function connectObjectStorageExample() {
+  // --8<-- [start:connect_object_storage]
+  const uri = "s3://your-bucket/path";
+  // You can also use "gs://your-bucket/path" or "az://your-container/path".
+  const db = await lancedb.connect(uri);
+  // --8<-- [end:connect_object_storage]
+  return db;
+}
+
+async function namespaceTableOpsExample() {
+  // --8<-- [start:namespace_table_ops]
+  const db = await lancedb.connectNamespace("dir", { root: "./local_lancedb" });
+
+  // Create namespace tree: prod/search and prod/recommendations
+  await db.createNamespace(["prod"], { mode: "exist_ok" });
+  await db.createNamespace(["prod", "search"], { mode: "exist_ok" });
+  await db.createNamespace(["prod", "recommendations"], { mode: "exist_ok" });
+
+  await db.createTable(
+    "user",
+    [{ id: 1, vector: [0.1, 0.2], name: "alice" }],
+    ["prod", "search"],
+    { mode: "create" }, // use "overwrite" only if you want to replace existing table
+  );
+
+  await db.createTable(
+    "user",
+    [{ id: 2, vector: [0.3, 0.4], name: "bob" }],
+    ["prod", "recommendations"],
+    { mode: "create" },
+  );
+
+  // Verify
+  console.log((await db.listNamespaces()).namespaces); // ["prod"]
+  console.log((await db.listNamespaces(["prod"])).namespaces); // ["recommendations", "search"]
+  console.log(await db.tableNames(["prod", "search"])); // ["user"]
+  console.log(await db.tableNames(["prod", "recommendations"])); // ["user"]
+  // --8<-- [end:namespace_table_ops]
+}
+
+async function namespaceAdminOpsExample() {
+  // --8<-- [start:namespace_admin_ops]
+  const db = await lancedb.connectNamespace("dir", { root: "./local_lancedb" });
+  const namespace = ["prod", "search"];
+
+  await db.createNamespace(["prod"]);
+  await db.createNamespace(["prod", "search"]);
+
+  const childNamespaces = (await db.listNamespaces(["prod"])).namespaces;
+  console.log(`Child namespaces under ${JSON.stringify(namespace)}:`, childNamespaces);
+  // Child namespaces under ["prod","search"]: [ 'search' ]
+
+  const metadata = await db.describeNamespace(["prod", "search"]);
+  console.log(`Metadata for namespace ${JSON.stringify(namespace)}:`, metadata);
+
+  await db.dropNamespace(["prod", "search"], { mode: "skip" });
+  await db.dropNamespace(["prod"], { mode: "skip" });
+  // --8<-- [end:namespace_admin_ops]
+  return { childNamespaces, metadata };
+}
+
+void [
+  connectObjectStorageExample,
+  connectEnterpriseQuickstart,
+  namespaceTableOpsExample,
+  namespaceAdminOpsExample,
+];
+
+test("enterprise placeholders use an explicit endpoint", async () => {
+  const mockDb = {} as Awaited<ReturnType<typeof lancedb.connect>>;
+  const spy = jest.spyOn(lancedb, "connect").mockResolvedValue(mockDb);
+  try {
+    // --8<-- [start:connect_enterprise_placeholders]
+    const db = await lancedb.connect("db://your-database", {
+      apiKey: "your-api-key",
+      region: "us-east-1",
+      hostOverride: "https://your-endpoint",
+    });
+    // --8<-- [end:connect_enterprise_placeholders]
+    expect(db).toBe(mockDb);
+    expect(spy).toHaveBeenCalledWith("db://your-database", {
+      apiKey: "your-api-key",
+      region: "us-east-1",
+      hostOverride: "https://your-endpoint",
+    });
+  } finally {
+    spy.mockRestore();
+  }
+});
diff --git a/docs/web-tests/ts/custom_embedding_function.test.ts b/docs/web-tests/ts/custom_embedding_function.test.ts
new file mode 100644
index 000000000..528928a9d
--- /dev/null
+++ b/docs/web-tests/ts/custom_embedding_function.test.ts
@@ -0,0 +1,79 @@
+import {
+  type FeatureExtractionPipeline,
+  pipeline,
+} from "@huggingface/transformers";
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+import { expect, test } from "@jest/globals";
+// --8<-- [start:imports]
+import * as lancedb from "@lancedb/lancedb";
+import {
+  LanceSchema,
+  TextEmbeddingFunction,
+  getRegistry,
+  register,
+} from "@lancedb/lancedb/embedding";
+// --8<-- [end:imports]
+import { withTempDirectory } from "./util.ts";
+
+// --8<-- [start:embedding_impl]
+@register("sentence-transformers")
+class SentenceTransformersEmbeddings extends TextEmbeddingFunction {
+  name = "Xenova/all-miniLM-L6-v2";
+  #ndims!: number;
+  extractor!: FeatureExtractionPipeline;
+
+  async init() {
+    this.extractor = await pipeline("feature-extraction", this.name, {
+      dtype: "fp32",
+    });
+    this.#ndims = await this.generateEmbeddings(["hello"]).then(
+      (e) => e[0].length,
+    );
+  }
+
+  ndims() {
+    return this.#ndims;
+  }
+
+  toJSON() {
+    return {
+      name: this.name,
+    };
+  }
+  async generateEmbeddings(texts: string[]) {
+    const output = await this.extractor(texts, {
+      pooling: "mean",
+      normalize: true,
+    });
+    return output.tolist();
+  }
+}
+// --8<-- [end:embedding_impl]
+
+test("Registry examples", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    // --8<-- [start:call_custom_function]
+    const registry = getRegistry();
+
+    const sentenceTransformer = await registry
+      .get<SentenceTransformersEmbeddings>("sentence-transformers")!
+      .create();
+
+    const schema = LanceSchema({
+      vector: sentenceTransformer.vectorField(),
+      text: sentenceTransformer.sourceField(),
+    });
+
+    const db = await lancedb.connect(databaseDir);
+    const table = await db.createEmptyTable("table", schema, {
+      mode: "overwrite",
+    });
+
+    await table.add([{ text: "hello" }, { text: "world" }]);
+
+    const results = await table.search("greeting").limit(1).toArray();
+    // --8<-- [end:call_custom_function]
+    expect(results.length).toBe(1);
+  });
+}, 100_000);
diff --git a/docs/web-tests/ts/embedding.test.ts b/docs/web-tests/ts/embedding.test.ts
new file mode 100644
index 000000000..a73b808ca
--- /dev/null
+++ b/docs/web-tests/ts/embedding.test.ts
@@ -0,0 +1,160 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+import { expect, test } from "@jest/globals";
+// --8<-- [start:imports]
+import * as lancedb from "@lancedb/lancedb";
+import "@lancedb/lancedb/embedding/openai";
+import { LanceSchema, getRegistry, register } from "@lancedb/lancedb/embedding";
+import { EmbeddingFunction } from "@lancedb/lancedb/embedding";
+import { type Float, Float32, Utf8 } from "apache-arrow";
+// --8<-- [end:imports]
+import { withTempDirectory } from "./util.ts";
+
+const openAiTest = process.env.OPENAI_API_KEY == null ? test.skip : test;
+
+openAiTest("create embedding function", () => {
+  // --8<-- [start:create_embedding_function]
+  const func = getRegistry().get("openai")!.create({
+    model: "text-embedding-3-small",
+  });
+  // --8<-- [end:create_embedding_function]
+  expect(func).toBeDefined();
+});
+
+openAiTest("openai embeddings", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    // --8<-- [start:openai_embeddings]
+    const db = await lancedb.connect(databaseDir);
+    const func = getRegistry()
+      .get("openai")
+      ?.create({ model: "text-embedding-ada-002" }) as EmbeddingFunction;
+
+    const wordsSchema = LanceSchema({
+      text: func.sourceField(new Utf8()),
+      vector: func.vectorField(),
+    });
+    const tbl = await db.createEmptyTable("words", wordsSchema, {
+      mode: "overwrite",
+    });
+    await tbl.add([{ text: "hello world" }, { text: "goodbye world" }]);
+
+    const query = "greetings";
+    const actual = (await tbl.search(query).limit(1).toArray())[0];
+    // --8<-- [end:openai_embeddings]
+    expect(actual).toHaveProperty("text");
+  });
+});
+
+openAiTest("manual query embeddings", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    // --8<-- [start:manual_query_embeddings]
+    const db = await lancedb.connect(databaseDir);
+    const func = getRegistry()
+      .get("openai")
+      ?.create({ model: "text-embedding-ada-002" }) as EmbeddingFunction;
+
+    const wordsSchema = LanceSchema({
+      text: func.sourceField(new Utf8()),
+      vector: func.vectorField(),
+    });
+    const tbl = await db.createEmptyTable("words", wordsSchema, {
+      mode: "overwrite",
+    });
+    await tbl.add([{ text: "hello world" }, { text: "goodbye world" }]);
+
+    const queryVector = await func.computeQueryEmbeddings("greetings");
+    // --8<-- [start:manual_query_search]
+    // queryVector is assumed to already be generated by your embedding function
+    const actual = (await tbl.search(queryVector).limit(1).toArray())[0];
+    // --8<-- [end:manual_query_search]
+    // --8<-- [end:manual_query_embeddings]
+    expect(actual).toHaveProperty("text");
+  });
+});
+
+test("custom embedding function", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    // --8<-- [start:embedding_function]
+    const db = await lancedb.connect(databaseDir);
+
+    @register("my_embedding")
+    class MyEmbeddingFunction extends EmbeddingFunction<string> {
+      constructor(optionsRaw = {}) {
+        super();
+        const options = this.resolveVariables(optionsRaw);
+        // Initialize using options
+      }
+      ndims() {
+        return 3;
+      }
+      protected getSensitiveKeys(): string[] {
+        return [];
+      }
+      embeddingDataType(): Float {
+        return new Float32();
+      }
+      async computeQueryEmbeddings(_data: string) {
+        // This is a placeholder for a real embedding function
+        return [1, 2, 3];
+      }
+      async computeSourceEmbeddings(data: string[]) {
+        // This is a placeholder for a real embedding function
+        return Array.from({ length: data.length }).fill([
+          1, 2, 3,
+        ]) as number[][];
+      }
+    }
+
+    const func = new MyEmbeddingFunction();
+
+    const data = [{ text: "pepperoni" }, { text: "pineapple" }];
+
+    // Option 1: manually specify the embedding function
+    const table = await db.createTable("vectors", data, {
+      embeddingFunction: {
+        function: func,
+        sourceColumn: "text",
+        vectorColumn: "vector",
+      },
+      mode: "overwrite",
+    });
+
+    // Option 2: provide the embedding function through a schema
+
+    const schema = LanceSchema({
+      text: func.sourceField(new Utf8()),
+      vector: func.vectorField(),
+    });
+
+    const table2 = await db.createTable("vectors2", data, {
+      schema,
+      mode: "overwrite",
+    });
+    // --8<-- [end:embedding_function]
+    expect(await table.countRows()).toBe(2);
+    expect(await table2.countRows()).toBe(2);
+  });
+});
+
+test("embedding function api_key", async () => {
+  // --8<-- [start:register_secret]
+  const registry = getRegistry();
+  registry.setVar("api_key", "sk-...");
+
+  const func = registry.get("openai")!.create({
+    apiKey: "$var:api_key",
+  });
+  // --8<-- [end:register_secret]
+});
+
+openAiTest("embedding function variable fallback", () => {
+  // --8<-- [start:register_model_fallback]
+  const registry = getRegistry();
+  registry.setVar("openai_model", "text-embedding-3-large");
+
+  const func = registry.get("openai")!.create({
+    model: "$var:openai_model:text-embedding-3-small",
+  });
+  // --8<-- [end:register_model_fallback]
+  expect(func).toBeDefined();
+});
diff --git a/docs/web-tests/ts/filtering.test.ts b/docs/web-tests/ts/filtering.test.ts
new file mode 100644
index 000000000..1c35d5120
--- /dev/null
+++ b/docs/web-tests/ts/filtering.test.ts
@@ -0,0 +1,42 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+import { expect, test } from "@jest/globals";
+import * as lancedb from "@lancedb/lancedb";
+import { withTempDirectory } from "./util.ts";
+
+test("filtering examples", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    const db = await lancedb.connect(databaseDir);
+
+    const data = Array.from({ length: 10_000 }, (_, i) => ({
+      vector: Array(1536).fill(i),
+      id: i,
+      item: `item ${i}`,
+      strId: `${i}`,
+    }));
+
+    const tbl = await db.createTable("myVectors", data, { mode: "overwrite" });
+
+    // --8<-- [start:search]
+    const _result = await tbl
+      .search(Array(1536).fill(0.5))
+      .limit(1)
+      .where("id = 10")
+      .toArray();
+    // --8<-- [end:search]
+
+    // --8<-- [start:vec_search]
+    const result = await (
+      tbl.search(Array(1536).fill(0)) as lancedb.VectorQuery
+    )
+      .where("(item IN ('item 0', 'item 2')) AND (id > 10)")
+      .postfilter()
+      .toArray();
+    // --8<-- [end:vec_search]
+    expect(result.length).toBe(0);
+
+    // --8<-- [start:sql_search]
+    await tbl.query().where("id = 10").limit(10).toArray();
+    // --8<-- [end:sql_search]
+  });
+});
diff --git a/docs/web-tests/ts/full_text_search.test.ts b/docs/web-tests/ts/full_text_search.test.ts
new file mode 100644
index 000000000..9777bda7f
--- /dev/null
+++ b/docs/web-tests/ts/full_text_search.test.ts
@@ -0,0 +1,45 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+import { expect, test } from "@jest/globals";
+import * as lancedb from "@lancedb/lancedb";
+import { withTempDirectory } from "./util.ts";
+
+test("full text search", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    const db = await lancedb.connect(databaseDir);
+
+    const words = [
+      "apple",
+      "banana",
+      "cherry",
+      "date",
+      "elderberry",
+      "fig",
+      "grape",
+    ];
+
+    const data = Array.from({ length: 10_000 }, (_, i) => ({
+      vector: Array(1536).fill(i),
+      id: i,
+      item: `item ${i}`,
+      strId: `${i}`,
+      doc: words[i % words.length],
+    }));
+
+    const tbl = await db.createTable("myVectors", data, { mode: "overwrite" });
+
+    await tbl.createIndex("doc", {
+      config: lancedb.Index.fts(),
+    });
+
+    // --8<-- [start:full_text_search]
+    const result = await tbl
+      .query()
+      .nearestToText("apple")
+      .select(["id", "doc"])
+      .limit(10)
+      .toArray();
+    expect(result.length).toBe(10);
+    // --8<-- [end:full_text_search]
+  });
+}, 10_000);
diff --git a/docs/web-tests/ts/ingestion_examples.test.ts b/docs/web-tests/ts/ingestion_examples.test.ts
new file mode 100644
index 000000000..4c6b88412
--- /dev/null
+++ b/docs/web-tests/ts/ingestion_examples.test.ts
@@ -0,0 +1,117 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+import { expect, test } from "@jest/globals";
+
+test("complete ingestion workflow", async () => {
+  // --8<-- [start:write_workflow_body]
+  const lancedb = await import("@lancedb/lancedb");
+  const { mkdtemp, rm } = await import("node:fs/promises");
+  const { tmpdir } = await import("node:os");
+  const { join } = await import("node:path");
+
+  const uri = await mkdtemp(join(tmpdir(), "lancedb-ingestion-"));
+  try {
+    const db = await lancedb.connect(uri);
+    const table = await db.createTable("users", [
+      { id: 1, name: "Alice", login_count: 10 },
+      { id: 2, name: "Bob", login_count: 20 },
+    ]);
+    await table.add([{ id: 3, name: "Charlie", login_count: 5 }]);
+    await table.update({ where: "id = 2", values: { name: "Bobby" } });
+    await table.update({
+      where: "id = 2",
+      valuesSql: { login_count: "login_count + 1" },
+    });
+    await table
+      .mergeInsert("id")
+      .whenMatchedUpdateAll()
+      .whenNotMatchedInsertAll()
+      .execute([
+        { id: 3, name: "Charlie", login_count: 6 },
+        { id: 4, name: "Dana", login_count: 1 },
+      ]);
+    console.log(
+      "Rows selected for deletion:",
+      await table.query().where("id = 4").toArray(),
+    );
+    await table.delete("id = 4");
+    const rows = (await table.query().toArray()).sort((a, b) => a.id - b.id);
+    console.log(rows);
+    // --8<-- [end:write_workflow_body]
+    expect(rows.map((row) => [row.id, row.name, row.login_count])).toEqual([
+      [1, "Alice", 10],
+      [2, "Bobby", 21],
+      [3, "Charlie", 6],
+    ]);
+    // --8<-- [start:write_workflow_tail]
+  } finally {
+    await rm(uri, { recursive: true, force: true });
+  }
+  // --8<-- [end:write_workflow_tail]
+});
+
+test("complete delete_rows", async () => {
+  // --8<-- [start:delete_rows_body]
+  const lancedb = await import("@lancedb/lancedb");
+  const { mkdtemp, rm } = await import("node:fs/promises");
+  const { tmpdir } = await import("node:os");
+  const { join } = await import("node:path");
+  const uri = await mkdtemp(join(tmpdir(), "lancedb-ingestion-"));
+  try {
+    const db = await lancedb.connect(uri);
+    const table = await db.createTable("users", [
+      { id: 1, name: "Alice" },
+      { id: 2, name: "Bob" },
+      { id: 3, name: "Charlie" },
+    ]);
+    const predicate = "id = 3";
+    console.log("Selected:", await table.query().where(predicate).toArray());
+    await table.delete(predicate);
+    const rows = (await table.query().toArray()).sort((a, b) => a.id - b.id);
+    console.log("Remaining:", rows);
+    // --8<-- [end:delete_rows_body]
+    expect(rows.map((row) => [row.id, row.name])).toEqual([
+      [1, "Alice"],
+      [2, "Bob"],
+    ]);
+    // --8<-- [start:delete_rows_tail]
+  } finally {
+    await rm(uri, { recursive: true, force: true });
+  }
+  // --8<-- [end:delete_rows_tail]
+});
+
+test("complete add_progress", async () => {
+  // --8<-- [start:add_progress_body]
+  const lancedb = await import("@lancedb/lancedb");
+  const { mkdtemp, rm } = await import("node:fs/promises");
+  const { tmpdir } = await import("node:os");
+  const { join } = await import("node:path");
+  const uri = await mkdtemp(join(tmpdir(), "lancedb-ingestion-"));
+  try {
+    const db = await lancedb.connect(uri);
+    const table = await db.createTable("users", [{ id: 1, name: "Alice" }]);
+    await table.add(
+      [
+        { id: 2, name: "Bob" },
+        { id: 3, name: "Charlie" },
+      ],
+      {
+        progress: (progress) =>
+          console.log(`Rows written: ${progress.outputRows}`),
+      },
+    );
+    const rows = (await table.query().toArray()).sort((a, b) => a.id - b.id);
+    console.log("After insertion:", rows);
+    // --8<-- [end:add_progress_body]
+    expect(rows.map((row) => [row.id, row.name])).toEqual([
+      [1, "Alice"],
+      [2, "Bob"],
+      [3, "Charlie"],
+    ]);
+    // --8<-- [start:add_progress_tail]
+  } finally {
+    await rm(uri, { recursive: true, force: true });
+  }
+  // --8<-- [end:add_progress_tail]
+});
diff --git a/docs/web-tests/ts/integrations.ts b/docs/web-tests/ts/integrations.ts
new file mode 100644
index 000000000..3ae7967e9
--- /dev/null
+++ b/docs/web-tests/ts/integrations.ts
@@ -0,0 +1,134 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+// --8<-- [start:frameworks_genkit_usage]
+import { lancedbIndexerRef, lancedb, lancedbRetrieverRef, WriteMode } from "genkitx-lancedb";
+import { textEmbedding004, vertexAI } from "@genkit-ai/vertexai";
+import { gemini } from "@genkit-ai/vertexai";
+import { z, genkit } from "genkit";
+import { Document } from "genkit/retriever";
+import { chunk } from "llm-chunk";
+import { readFile } from "fs/promises";
+import path from "path";
+import pdf from "pdf-parse/lib/pdf-parse";
+
+const ai = genkit({
+  plugins: [
+    // vertexAI provides the textEmbedding004 embedder
+    vertexAI(),
+
+    // the local vector store requires an embedder to translate from text to vector
+    lancedb([
+      {
+        dbUri: ".db", // optional lancedb uri, default to .db
+        tableName: "table", // optional table name, default to table
+        embedder: textEmbedding004,
+      },
+    ]),
+  ],
+});
+// --8<-- [end:frameworks_genkit_usage]
+
+// --8<-- [start:frameworks_genkit_custom_indexer]
+export const menuPdfIndexer = lancedbIndexerRef({
+  // Using all defaults, for dbUri, tableName, and embedder, etc
+});
+
+const chunkingConfig = {
+  minLength: 1000,
+  maxLength: 2000,
+  splitter: "sentence",
+  overlap: 100,
+  delimiters: "",
+} as any;
+
+async function extractTextFromPdf(filePath: string) {
+  const pdfFile = path.resolve(filePath);
+  const dataBuffer = await readFile(pdfFile);
+  const data = await pdf(dataBuffer);
+  return data.text;
+}
+
+export const indexMenu = ai.defineFlow(
+  {
+    name: "indexMenu",
+    inputSchema: z.string().describe("PDF file path"),
+    outputSchema: z.void(),
+  },
+  async (filePath: string) => {
+    filePath = path.resolve(filePath);
+
+    // Read the pdf.
+    const pdfTxt = await ai.run("extract-text", () => extractTextFromPdf(filePath));
+
+    // Divide the pdf text into segments.
+    const chunks = await ai.run("chunk-it", async () => chunk(pdfTxt, chunkingConfig));
+
+    // Convert chunks of text into documents to store in the index.
+    const documents = chunks.map((text) => {
+      return Document.fromText(text, { filePath });
+    });
+
+    // Add documents to the index.
+    await ai.index({
+      indexer: menuPdfIndexer,
+      documents,
+      options: {
+        writeMode: WriteMode.Overwrite,
+      } as any,
+    });
+  },
+);
+// --8<-- [end:frameworks_genkit_custom_indexer]
+
+// --8<-- [start:frameworks_genkit_custom_retriever]
+export const menuRetriever = lancedbRetrieverRef({
+  tableName: "table", // Use the same table name as the indexer.
+  displayName: "Menu", // Use a custom display name.
+});
+
+export const menuQAFlow = ai.defineFlow(
+  { name: "Menu", inputSchema: z.string(), outputSchema: z.string() },
+  async (input: string) => {
+    // retrieve relevant documents
+    const docs = await ai.retrieve({
+      retriever: menuRetriever,
+      query: input,
+      options: {
+        k: 3,
+      },
+    });
+
+    const extractedContent = docs.map((doc) => {
+      if (doc.content && Array.isArray(doc.content) && doc.content.length > 0) {
+        if (doc.content[0].media && doc.content[0].media.url) {
+          return doc.content[0].media.url;
+        }
+      }
+      return "No content found";
+    });
+
+    console.log("Extracted content:", extractedContent);
+
+    const { text } = await ai.generate({
+      model: gemini("gemini-2.0-flash"),
+      prompt: `
+You are acting as a helpful AI assistant that can answer 
+questions about the food available on the menu at Genkit Grub Pub.
+
+Use only the context provided to answer the question.
+If you don't know, do not make up an answer.
+Do not add or change items on the menu.
+
+Context:
+${extractedContent.join("\n\n")}
+
+Question: ${input}`,
+      docs,
+    });
+
+    return text;
+  },
+);
+// --8<-- [end:frameworks_genkit_custom_retriever]
+
diff --git a/docs/web-tests/ts/jest.config.cjs b/docs/web-tests/ts/jest.config.cjs
new file mode 100644
index 000000000..db83d639c
--- /dev/null
+++ b/docs/web-tests/ts/jest.config.cjs
@@ -0,0 +1,6 @@
+/** @type {import('ts-jest').JestConfigWithTsJest} */
+module.exports = {
+  preset: "ts-jest",
+  testEnvironment: "node",
+  testPathIgnorePatterns: ["./dist"],
+};
diff --git a/docs/web-tests/ts/merge_insert.test.ts b/docs/web-tests/ts/merge_insert.test.ts
new file mode 100644
index 000000000..1b33dab6a
--- /dev/null
+++ b/docs/web-tests/ts/merge_insert.test.ts
@@ -0,0 +1,68 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import { expect, test } from "@jest/globals";
+import * as lancedb from "@lancedb/lancedb";
+
+test("basic upsert", async () => {
+  const db = await lancedb.connect("memory://");
+
+  // --8<-- [start:upsert_basic]
+  const table = await db.createTable("users", [
+    { id: 0, name: "Alice" },
+    { id: 1, name: "Bob" },
+  ]);
+
+  const newUsers = [
+    { id: 1, name: "Bobby" },
+    { id: 2, name: "Charlie" },
+  ];
+  await table
+    .mergeInsert("id")
+    .whenMatchedUpdateAll()
+    .whenNotMatchedInsertAll()
+    .execute(newUsers);
+
+  await table.countRows(); // 3
+  // --8<-- [end:upsert_basic]
+  expect(await table.countRows()).toBe(3);
+
+  // --8<-- [start:insert_if_not_exists]
+  const table2 = await db.createTable("domains", [
+    { domain: "google.com", name: "Google" },
+    { domain: "github.com", name: "GitHub" },
+  ]);
+
+  const newDomains = [
+    { domain: "google.com", name: "Google" },
+    { domain: "facebook.com", name: "Facebook" },
+  ];
+  await table2
+    .mergeInsert("domain")
+    .whenNotMatchedInsertAll()
+    .execute(newDomains);
+  await table2.countRows(); // 3
+  // --8<-- [end:insert_if_not_exists]
+  expect(await table2.countRows()).toBe(3);
+
+  // --8<-- [start:replace_range]
+  const table3 = await db.createTable("chunks", [
+    { doc_id: 0, chunk_id: 0, text: "Hello" },
+    { doc_id: 0, chunk_id: 1, text: "World" },
+    { doc_id: 1, chunk_id: 0, text: "Foo" },
+    { doc_id: 1, chunk_id: 1, text: "Bar" },
+  ]);
+
+  const newChunks = [{ doc_id: 1, chunk_id: 0, text: "Baz" }];
+
+  await table3
+    .mergeInsert(["doc_id", "chunk_id"])
+    .whenMatchedUpdateAll()
+    .whenNotMatchedInsertAll()
+    .whenNotMatchedBySourceDelete({ where: "doc_id = 1" })
+    .execute(newChunks);
+
+  await table3.countRows("doc_id = 1"); // 1
+  // --8<-- [end:replace_range]
+  expect(await table3.countRows("doc_id = 1")).toBe(1);
+});
diff --git a/docs/web-tests/ts/multimodal.test.ts b/docs/web-tests/ts/multimodal.test.ts
new file mode 100644
index 000000000..ac9a8201e
--- /dev/null
+++ b/docs/web-tests/ts/multimodal.test.ts
@@ -0,0 +1,104 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+import { expect, test } from "@jest/globals";
+// --8<-- [start:multimodal_imports]
+import * as arrow from "apache-arrow";
+import { Buffer } from "node:buffer";
+import * as lancedb from "@lancedb/lancedb";
+// --8<-- [end:multimodal_imports]
+import { withTempDirectory } from "./util.ts";
+
+test("multimodal snippets (async)", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    const db = await lancedb.connect(databaseDir);
+
+    // --8<-- [start:create_dummy_data]
+    const createDummyImage = (color: string): Uint8Array => {
+      const pngHeader = Uint8Array.from([137, 80, 78, 71, 13, 10, 26, 10]);
+      return Buffer.concat([Buffer.from(pngHeader), Buffer.from(color, "utf8")]);
+    };
+
+    const data = [
+      {
+        id: 1,
+        filename: "red_square.png",
+        vector: Array.from({ length: 128 }, (_, i) => (i % 16) / 16),
+        image_blob: createDummyImage("red"),
+        label: "red",
+      },
+      {
+        id: 2,
+        filename: "blue_square.png",
+        vector: Array.from({ length: 128 }, (_, i) => ((i + 8) % 16) / 16),
+        image_blob: createDummyImage("blue"),
+        label: "blue",
+      },
+    ];
+    // --8<-- [end:create_dummy_data]
+
+    // --8<-- [start:define_schema]
+    const schema = new arrow.Schema([
+      new arrow.Field("id", new arrow.Int32()),
+      new arrow.Field("filename", new arrow.Utf8()),
+      new arrow.Field(
+        "vector",
+        new arrow.FixedSizeList(
+          128,
+          new arrow.Field("item", new arrow.Float32(), true),
+        ),
+      ),
+      new arrow.Field("image_blob", new arrow.Binary()),
+      new arrow.Field("label", new arrow.Utf8()),
+    ]);
+    // --8<-- [end:define_schema]
+
+    // --8<-- [start:ingest_data]
+    const multimodalData = lancedb.makeArrowTable(data, { schema });
+    const tbl = await db.createTable("images", multimodalData, {
+      mode: "overwrite",
+    });
+    // --8<-- [end:ingest_data]
+    expect(await tbl.countRows()).toBe(2);
+
+    // --8<-- [start:search_data]
+    const queryVector = Array.from({ length: 128 }, (_, i) => (i % 16) / 16);
+    const results = await tbl.search(queryVector).limit(1).toArray();
+    // --8<-- [end:search_data]
+
+    // --8<-- [start:process_results]
+    for (const row of results) {
+      const imageBytes = row.image_blob as Uint8Array;
+      console.log(
+        `Retrieved image: ${row.filename}, Byte length: ${imageBytes.length}`,
+      );
+    }
+    // --8<-- [end:process_results]
+    expect(results).toHaveLength(1);
+
+    // --8<-- [start:blob_api_schema]
+    const blobSchema = new arrow.Schema([
+      new arrow.Field("id", new arrow.Int64()),
+      new arrow.Field(
+        "video",
+        new arrow.LargeBinary(),
+        true,
+        new Map([["lance-encoding:blob", "true"]]),
+      ),
+    ]);
+    // --8<-- [end:blob_api_schema]
+
+    // --8<-- [start:blob_api_ingest]
+    const blobData = lancedb.makeArrowTable(
+      [
+        { id: 1, video: Buffer.from("fake_video_bytes_1") },
+        { id: 2, video: Buffer.from("fake_video_bytes_2") },
+      ],
+      { schema: blobSchema },
+    );
+    const blobTable = await db.createTable("videos", blobData, {
+      mode: "overwrite",
+    });
+    // --8<-- [end:blob_api_ingest]
+    expect(await blobTable.countRows()).toBe(2);
+  });
+});
diff --git a/docs/web-tests/ts/package-lock.json b/docs/web-tests/ts/package-lock.json
new file mode 100644
index 000000000..5acc87907
--- /dev/null
+++ b/docs/web-tests/ts/package-lock.json
@@ -0,0 +1,5400 @@
+{
+  "name": "examples",
+  "version": "1.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "examples",
+      "version": "1.0.0",
+      "license": "Apache-2.0",
+      "dependencies": {
+        "@huggingface/transformers": "^3.0.2",
+        "@lancedb/lancedb": "^0.31.0",
+        "openai": "^4.29.2",
+        "sharp": "^0.35.5"
+      },
+      "devDependencies": {
+        "@biomejs/biome": "^1.7.3",
+        "@jest/globals": "^29.7.0",
+        "jest": "^29.7.0",
+        "jest-environment-node-single-context": "^29.4.0",
+        "ts-jest": "^29.2.5",
+        "typescript": "^5.5.4"
+      }
+    },
+    "..": {
+      "name": "@lancedb/lancedb",
+      "version": "0.12.0",
+      "cpu": [
+        "x64",
+        "arm64"
+      ],
+      "extraneous": true,
+      "license": "Apache 2.0",
+      "os": [
+        "darwin",
+        "linux",
+        "win32"
+      ],
+      "dependencies": {
+        "reflect-metadata": "^0.2.2"
+      },
+      "devDependencies": {
+        "@aws-sdk/client-dynamodb": "^3.33.0",
+        "@aws-sdk/client-kms": "^3.33.0",
+        "@aws-sdk/client-s3": "^3.33.0",
+        "@biomejs/biome": "^1.7.3",
+        "@jest/globals": "^29.7.0",
+        "@napi-rs/cli": "^2.18.3",
+        "@types/axios": "^0.14.0",
+        "@types/jest": "^29.1.2",
+        "@types/node": "^22.7.4",
+        "@types/tmp": "^0.2.6",
+        "apache-arrow-13": "npm:apache-arrow@13.0.0",
+        "apache-arrow-14": "npm:apache-arrow@14.0.0",
+        "apache-arrow-15": "npm:apache-arrow@15.0.0",
+        "apache-arrow-16": "npm:apache-arrow@16.0.0",
+        "apache-arrow-17": "npm:apache-arrow@17.0.0",
+        "eslint": "^8.57.0",
+        "jest": "^29.7.0",
+        "shx": "^0.3.4",
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+        "ts-jest": "^29.1.2",
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+        "typedoc-plugin-markdown": "^4.2.1",
+        "typescript": "^5.5.4",
+        "typescript-eslint": "^7.1.0"
+      },
+      "engines": {
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+      },
+      "optionalDependencies": {
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+        "openai": "^4.29.2"
+      },
+      "peerDependencies": {
+        "apache-arrow": ">=13.0.0 <=17.0.0"
+      }
+    },
+    "node_modules/@babel/code-frame": {
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+      }
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diff --git a/docs/web-tests/ts/package.json b/docs/web-tests/ts/package.json
new file mode 100644
index 000000000..0a5e9639c
--- /dev/null
+++ b/docs/web-tests/ts/package.json
@@ -0,0 +1,36 @@
+{
+  "name": "examples",
+  "version": "1.0.0",
+  "description": "Examples for LanceDB",
+  "main": "index.js",
+  "type": "module",
+  "scripts": {
+    "//1": "--experimental-vm-modules is needed to run jest with sentence-transformers",
+    "//2": "--testEnvironment is needed to run jest with sentence-transformers",
+    "//3": "See: https://github.com/huggingface/transformers.js/issues/57",
+    "test": "node --experimental-vm-modules node_modules/.bin/jest --testEnvironment jest-environment-node-single-context --verbose",
+    "lint": "biome check *.ts && biome format *.ts",
+    "lint-ci": "biome ci .",
+    "lint-fix": "biome check --write *.ts && npm run format",
+    "format": "biome format --write *.ts"
+  },
+  "author": "Lance Devs",
+  "license": "Apache-2.0",
+  "dependencies": {
+    "@huggingface/transformers": "^3.0.2",
+    "@lancedb/lancedb": "^0.31.0",
+    "openai": "^4.29.2",
+    "sharp": "^0.35.5"
+  },
+  "devDependencies": {
+    "@biomejs/biome": "^1.7.3",
+    "@jest/globals": "^29.7.0",
+    "jest": "^29.7.0",
+    "jest-environment-node-single-context": "^29.4.0",
+    "ts-jest": "^29.2.5",
+    "typescript": "^5.5.4"
+  },
+  "overrides": {
+    "sharp": "$sharp"
+  }
+}
diff --git a/docs/web-tests/ts/quickstart.test.ts b/docs/web-tests/ts/quickstart.test.ts
new file mode 100644
index 000000000..e32a14f86
--- /dev/null
+++ b/docs/web-tests/ts/quickstart.test.ts
@@ -0,0 +1,263 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+import { describe, expect, test } from "@jest/globals";
+import { randomUUID } from "node:crypto";
+import * as fs from "node:fs";
+import * as path from "node:path";
+import * as lancedb from "@lancedb/lancedb";
+import { withTempDirectory } from "./util.ts";
+
+// What the quickstart's download step leaves in an application's directory.
+const IMAGE = path.join(
+  __dirname,
+  "../../web/static/assets/images/quickstart/sir-lancelot.jpg",
+);
+const deployment = ["LANCEDB_URI", "LANCEDB_HOST_OVERRIDE", "LANCEDB_API_KEY"];
+// The connection settings alone never send these tests to a deployment.
+const optIn = "LANCEDB_DOCS_TEST_DEPLOYMENT";
+const connections =
+  process.env[optIn] === "1" ? ["local", "enterprise"] : ["local"];
+
+async function tableNames(db: lancedb.Connection) {
+  const names: string[] = [];
+  let pageToken: string | undefined;
+  do {
+    const page = await db.listTables({ pageToken });
+    names.push(...page.tables);
+    pageToken = page.pageToken;
+  } while (pageToken);
+  return names;
+}
+
+// A deployment connection on which a test creates only tables of its own:
+// each name gets a suffix unique to the test and is claimed only while no
+// table has it; the test drops what it claimed, whether or not it passed.
+async function withOwnedTables(
+  db: lancedb.Connection,
+  fn: (db: lancedb.Connection) => Promise<void>,
+) {
+  const suffix = randomUUID().replaceAll("-", "").slice(0, 12);
+  const claimed: string[] = [];
+  const createTable = async (
+    name: string,
+    data: Parameters<lancedb.Connection["createTable"]>[1],
+    options: Partial<lancedb.CreateTableOptions> = {},
+  ) => {
+    const owned = `${name}_${suffix}`;
+    let mode = options.mode;
+    if (!claimed.includes(owned)) {
+      if ((await tableNames(db)).includes(owned)) {
+        throw new Error(`${owned} already exists and is not this test's`);
+      }
+      claimed.push(owned);
+      mode = "create";
+    }
+    return db.createTable(owned, data, { ...options, mode });
+  };
+  try {
+    await fn(
+      new Proxy(db, {
+        get: (target, property, receiver) =>
+          property === "createTable"
+            ? createTable
+            : Reflect.get(target, property, receiver),
+      }),
+    );
+  } finally {
+    const existing = await tableNames(db);
+    for (const name of claimed) {
+      if (existing.includes(name)) {
+        await db.dropTable(name);
+      }
+    }
+  }
+}
+
+async function withDatabase(
+  connection: string,
+  fn: (db: lancedb.Connection) => Promise<void>,
+) {
+  if (connection === "enterprise") {
+    const missing = deployment.filter((name) => !process.env[name]);
+    if (missing.length) {
+      throw new Error(`${optIn}=1 needs ${missing.join(", ")}`);
+    }
+    const db = await lancedb.connect(process.env.LANCEDB_URI!, {
+      apiKey: process.env.LANCEDB_API_KEY!,
+      region: process.env.LANCEDB_REGION ?? "us-east-1",
+      hostOverride: process.env.LANCEDB_HOST_OVERRIDE!,
+    });
+    await withOwnedTables(db, fn);
+    return;
+  }
+  await withTempDirectory(async (databaseDir) => {
+    await fn(await lancedb.connect(databaseDir));
+  });
+}
+
+async function createCharacters(db: lancedb.Connection) {
+  // --8<-- [start:quickstart_data]
+  const data = [
+    {
+      id: "1",
+      name: "King Arthur",
+      role: "King",
+      description: "Leader of Camelot and wielder of Excalibur.",
+      stats: { strength: 4, magic: 1, leadership: 5, wisdom: 4 },
+      vector: [0.7, 0.1, 0.9, 0.7],
+    },
+    {
+      id: "2",
+      name: "Merlin",
+      role: "Wizard",
+      description: "Advisor and prophet with deep magical knowledge.",
+      stats: { strength: 2, magic: 5, leadership: 4, wisdom: 5 },
+      vector: [0.2, 0.9, 0.4, 0.9],
+    },
+    {
+      id: "3",
+      name: "Sir Lancelot",
+      role: "Knight",
+      description: "Legendary knight known for courage and combat skill.",
+      stats: { strength: 5, magic: 1, leadership: 3, wisdom: 3 },
+      vector: [0.9, 0.1, 0.5, 0.4],
+    },
+  ];
+  // --8<-- [end:quickstart_data]
+
+  // --8<-- [start:quickstart_create_table]
+  const table = await db.createTable("characters", data, { mode: "overwrite" });
+  // --8<-- [end:quickstart_create_table]
+  expect(await table.countRows()).toBe(3);
+
+  // --8<-- [start:quickstart_add_data]
+  await table.add([
+    {
+      id: "4",
+      name: "Morgana",
+      role: "Sorceress",
+      description: "Powerful sorceress of Avalon.",
+      stats: { strength: 2, magic: 5, leadership: 4, wisdom: 4 },
+      vector: [0.3, 0.9, 0.6, 0.8],
+    },
+  ]);
+  // --8<-- [end:quickstart_add_data]
+  expect(await table.countRows()).toBe(4);
+  return table;
+}
+
+describe.each(connections)("quickstart (%s)", (connection) => {
+  test("create, add and search", async () => {
+    await withDatabase(connection, async (db) => {
+      const table = await createCharacters(db);
+
+      // --8<-- [start:quickstart_vector_search_1]
+      // Search for examples similar to a "wise magical advisor"
+      const queryVector = [0.2, 0.8, 0.4, 0.9];
+
+      const result = await table
+        .search(queryVector)
+        .select(["name", "role", "description", "_distance"])
+        .limit(2)
+        .toArray();
+      console.table(result);
+      // --8<-- [end:quickstart_vector_search_1]
+      expect(result.map((row) => row.name)).toEqual(["Merlin", "Morgana"]);
+
+      // --8<-- [start:quickstart_curate_with_metadata]
+      const curated = await table
+        .search(queryVector)
+        .where("stats.magic >= 4")
+        .select(["name", "role", "description", "_distance"])
+        .limit(2)
+        .toArray();
+      console.table(curated);
+      // --8<-- [end:quickstart_curate_with_metadata]
+      expect(curated.map((row) => row.name)).toEqual(["Merlin", "Morgana"]);
+    });
+  });
+
+  test("a derived feature", async () => {
+    await withDatabase(connection, async (db) => {
+      const table = await createCharacters(db);
+
+      // --8<-- [start:quickstart_add_feature]
+      await table.addColumns([
+        {
+          name: "power_score",
+          valueSql:
+            "cast(((stats.strength + stats.magic + stats.leadership + stats.wisdom) / 4.0) as float)",
+        },
+      ]);
+      // --8<-- [end:quickstart_add_feature]
+
+      // --8<-- [start:quickstart_query_feature]
+      const features = await table
+        .query()
+        .select(["name", "role", "power_score"])
+        .toArray();
+      console.table(features);
+      // --8<-- [end:quickstart_query_feature]
+      expect(
+        features.map((row) => [row.name, row.power_score]).sort(),
+      ).toEqual([
+        ["King Arthur", 3.5],
+        ["Merlin", 4],
+        ["Morgana", 3.75],
+        ["Sir Lancelot", 3],
+      ]);
+    });
+  });
+
+  test("multimodal data", async () => {
+    await withDatabase(connection, async (db) => {
+      await withTempDirectory(async (app) => {
+        fs.mkdirSync(path.join(app, "data"));
+        fs.copyFileSync(IMAGE, path.join(app, "data/sir-lancelot.jpg"));
+        const cwd = process.cwd();
+        process.chdir(app);
+        try {
+          // --8<-- [start:quickstart_multimodal_bytes]
+          const arrow = await import("apache-arrow");
+          const { readFile } = await import("node:fs/promises");
+
+          const imageBytes = await readFile("data/sir-lancelot.jpg");
+          const imageSchema = new arrow.Schema([
+            new arrow.Field("id", new arrow.Utf8()),
+            new arrow.Field("description", new arrow.Utf8()),
+            new arrow.Field("image", new arrow.Binary()),
+            new arrow.Field(
+              "vector",
+              new arrow.FixedSizeList(
+                4,
+                new arrow.Field("item", new arrow.Float32(), true),
+              ),
+            ),
+          ]);
+          const imageData = lancedb.makeArrowTable(
+            [
+              {
+                id: "lancelot",
+                description: "Portrait of Sir Lancelot",
+                image: imageBytes,
+                vector: [0.9, 0.1, 0.5, 0.4],
+              },
+            ],
+            { schema: imageSchema },
+          );
+          const multimodalTable = await db.createTable(
+            "character_images",
+            imageData,
+            { mode: "overwrite" },
+          );
+          // --8<-- [end:quickstart_multimodal_bytes]
+          expect(await multimodalTable.countRows()).toBe(1);
+          const [row] = await multimodalTable.query().select(["image"]).toArray();
+          expect(Buffer.from(row.image)).toEqual(fs.readFileSync(IMAGE));
+        } finally {
+          process.chdir(cwd);
+        }
+      });
+    });
+  });
+});
diff --git a/docs/web-tests/ts/search.test.ts b/docs/web-tests/ts/search.test.ts
new file mode 100644
index 000000000..bc50968cc
--- /dev/null
+++ b/docs/web-tests/ts/search.test.ts
@@ -0,0 +1,355 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+import { expect, test } from "@jest/globals";
+// --8<-- [start:import]
+import * as lancedb from "@lancedb/lancedb";
+// --8<-- [end:import]
+// --8<-- [start:import_bin_util]
+import { Field, FixedSizeList, Int32, Schema, Uint8 } from "apache-arrow";
+// --8<-- [end:import_bin_util]
+import { Float32, Struct } from "apache-arrow";
+import { withTempDirectory } from "./util.ts";
+
+async function buildIndexedTable(db: lancedb.Connection, name: string) {
+  const data = Array.from({ length: 512 }, (_, i) => ({
+    vector: Array.from({ length: 128 }, () => Math.random()),
+    id: i,
+  }));
+  const tbl = await db.createTable(name, data, { mode: "overwrite" });
+  await tbl.createIndex("vector", {
+    config: lancedb.Index.ivfPq({ numPartitions: 4, numSubVectors: 8 }),
+  });
+  return tbl;
+}
+
+test("vector search", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    {
+      const db = await lancedb.connect(databaseDir);
+
+      const data = Array.from({ length: 10_000 }, (_, i) => ({
+        vector: Array(128).fill(i),
+        id: `${i}`,
+      }));
+
+      await db.createTable("my_vectors", data);
+    }
+
+    // --8<-- [start:search1]
+    const db = await lancedb.connect(databaseDir);
+    const tbl = await db.openTable("my_vectors");
+
+    const results1 = await tbl.search(Array(128).fill(1.2)).limit(10).toArray();
+    // --8<-- [end:search1]
+    expect(results1.length).toBe(10);
+
+    // --8<-- [start:search2]
+    const results2 = await (
+      tbl.search(Array(128).fill(1.2)) as lancedb.VectorQuery
+    )
+      .distanceType("cosine")
+      .limit(10)
+      .toArray();
+    // --8<-- [end:search2]
+    expect(results2.length).toBe(10);
+
+    // --8<-- [start:distance_range]
+    const results3 = await (
+      tbl.search(Array(128).fill(1.2)) as lancedb.VectorQuery
+    )
+      .distanceType("cosine")
+      .distanceRange(0.1, 0.2)
+      .limit(10)
+      .toArray();
+    // --8<-- [end:distance_range]
+    for (const r of results3) {
+      expect(r.distance).toBeGreaterThanOrEqual(0.1);
+      expect(r.distance).toBeLessThan(0.2);
+    }
+
+    {
+      // --8<-- [start:ingest_binary_data]
+      const schema = new Schema([
+        new Field("id", new Int32(), true),
+        new Field("vec", new FixedSizeList(32, new Field("item", new Uint8()))),
+      ]);
+      const data = lancedb.makeArrowTable(
+        Array(1_000)
+          .fill(0)
+          .map((_, i) => ({
+            // the 256 bits would be store in 32 bytes,
+            // if your data is already in this format, you can skip the packBits step
+            id: i,
+            vec: lancedb.packBits(Array(256).fill(i % 2)),
+          })),
+        { schema: schema },
+      );
+
+      const tbl = await db.createTable("binary_table", data);
+      await tbl.createIndex("vec", {
+        config: lancedb.Index.ivfFlat({
+          numPartitions: 10,
+          distanceType: "hamming",
+        }),
+      });
+      // --8<-- [end:ingest_binary_data]
+
+      // --8<-- [start:search_binary_data]
+      const query = Array(32)
+        .fill(1)
+        .map(() => Math.floor(Math.random() * 255));
+      const results = await tbl.query().nearestTo(query).limit(10).toArrow();
+      // --8<-- [end:search_binary_data]
+      expect(results.numRows).toBe(10);
+    }
+  });
+});
+
+test("vector search docs snippets", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    const db = await lancedb.connect(databaseDir);
+
+    // ---- Nested vector column: inference and explicit selection ----
+    {
+      const nestedSchema = new Schema([
+        new Field("id", new Int32()),
+        new Field(
+          "image",
+          new Struct([
+            new Field(
+              "embedding",
+              new FixedSizeList(2, new Field("item", new Float32(), true)),
+            ),
+          ]),
+        ),
+      ]);
+      const arrowTbl = lancedb.makeArrowTable(
+        [{ id: 0, image: { embedding: [0.0, 1.0] } }],
+        { schema: nestedSchema },
+      );
+      await db.createTable("nested", arrowTbl, { mode: "overwrite" });
+
+      // --8<-- [start:select_vector_column]
+      const table = await db.openTable("nested");
+
+      // Inferred: LanceDB finds the single nested vector leaf automatically.
+      await table.query().nearestTo([0.0, 1.0]).limit(1).toArray();
+
+      // Explicit: required when more than one vector column matches.
+      await table
+        .query()
+        .nearestTo([0.0, 1.0])
+        .column("image.embedding")
+        .limit(1)
+        .toArray();
+      // --8<-- [end:select_vector_column]
+    }
+
+    // ---- Index a nested vector column ----
+    {
+      const dim = 16;
+      const nestedSchema = new Schema([
+        new Field("id", new Int32()),
+        new Field(
+          "image",
+          new Struct([
+            new Field(
+              "embedding",
+              new FixedSizeList(dim, new Field("item", new Float32(), true)),
+            ),
+          ]),
+        ),
+      ]);
+      const rows = Array.from({ length: 512 }, (_, i) => ({
+        id: i,
+        image: { embedding: Array.from({ length: dim }, () => Math.random()) },
+      }));
+      const arrowTbl = lancedb.makeArrowTable(rows, { schema: nestedSchema });
+      const table = await db.createTable("nested_index", arrowTbl, {
+        mode: "overwrite",
+      });
+
+      // --8<-- [start:index_nested_column]
+      await table.createIndex("image.embedding");
+      // --8<-- [end:index_nested_column]
+    }
+
+    // ---- Indexed queries: refine, fast search, bypass index ----
+    {
+      const table = await buildIndexedTable(db, "ann_table");
+      const embedding = Array.from({ length: 128 }, () => Math.random());
+
+      // --8<-- [start:exact_vs_approximate]
+      // Indexed ANN search without refinement (fast, approximate `_distance`)
+      const fastResults = await (table.search(embedding) as lancedb.VectorQuery)
+        .limit(10)
+        .toArray();
+
+      // Recompute distances on full vectors for reranked candidates
+      const exactDistanceResults = await (
+        table.search(embedding) as lancedb.VectorQuery
+      )
+        .limit(10)
+        .refineFactor(1)
+        .toArray();
+
+      // Rerank a larger candidate set for better recall (higher latency)
+      const higherRecallResults = await (
+        table.search(embedding) as lancedb.VectorQuery
+      )
+        .limit(10)
+        .refineFactor(20)
+        .toArray();
+      // --8<-- [end:exact_vs_approximate]
+      expect(fastResults.length).toBe(10);
+      expect(exactDistanceResults.length).toBe(10);
+      expect(higherRecallResults.length).toBe(10);
+
+      // --8<-- [start:fast_search]
+      await table
+        .query()
+        .nearestTo(embedding)
+        .fastSearch()
+        .limit(5)
+        .toArray();
+      // --8<-- [end:fast_search]
+
+      // --8<-- [start:bypass_vector_index]
+      await table
+        .query()
+        .nearestTo(embedding)
+        .bypassVectorIndex()
+        .limit(5)
+        .toArray();
+      // --8<-- [end:bypass_vector_index]
+    }
+
+    // ---- Brute force search (no index) ----
+    {
+      const data = Array.from({ length: 64 }, (_, i) => ({
+        vector: Array(128).fill(i),
+        id: `${i}`,
+      }));
+      await db.createTable("my_vectors", data, { mode: "overwrite" });
+
+      // --8<-- [start:brute_force_search]
+      const tbl = await db.openTable("my_vectors");
+
+      const results1 = await tbl.search(Array(128).fill(1.2)).limit(3).toArray();
+      // --8<-- [end:brute_force_search]
+      expect(results1.length).toBe(3);
+    }
+
+    // ---- Binary (hamming) vector search ----
+    {
+      const schema = new Schema([
+        new Field("id", new Int32(), true),
+        new Field("vector", new FixedSizeList(32, new Field("item", new Uint8()))),
+      ]);
+      const data = lancedb.makeArrowTable(
+        Array(1000)
+          .fill(0)
+          .map((_, i) => ({
+            // the 256 bits are stored in 32 bytes; if your data is already in
+            // this format, you can skip the packBits step
+            id: i,
+            vector: lancedb.packBits(Array(256).fill(i % 2)),
+          })),
+        { schema },
+      );
+
+      // --8<-- [start:binary_search]
+      const tbl = await db.createTable("binary_vectors", data, {
+        mode: "overwrite",
+      });
+      await tbl.createIndex("vector", {
+        config: lancedb.Index.ivfFlat({
+          numPartitions: 10,
+          distanceType: "hamming",
+        }),
+      });
+
+      const query = Array(32)
+        .fill(1)
+        .map(() => Math.floor(Math.random() * 255));
+      const results = await tbl.query().nearestTo(query).limit(10).toArray();
+      // --8<-- [end:binary_search]
+      expect(results.length).toBeLessThanOrEqual(10);
+    }
+
+    // ---- Enterprise-style prefilter / postfilter / batch search ----
+    {
+      const dimensions = 768;
+      const rows = Array.from({ length: 50 }, (_, i) => ({
+        vector: Array.from({ length: dimensions }, () => Math.random() * 2 - 1),
+        text: `story ${i}`,
+        keywords: `kw${i}`,
+        label: i % 4,
+      }));
+      await db.createTable("lancedb-enterprise-quickstart", rows, {
+        mode: "overwrite",
+      });
+
+      {
+        // --8<-- [start:vector_search_prefilter]
+        // Generate a sample 768-dimension embedding vector (typical for BERT-based models)
+        // In real applications, you would get this from an embedding model
+        const dimensions = 768;
+        const queryEmbed = Array.from(
+          { length: dimensions },
+          () => Math.random() * 2 - 1,
+        );
+
+        // Open table and perform search
+        const tableName = "lancedb-enterprise-quickstart";
+        const table = await db.openTable(tableName);
+
+        // Vector search with filters (pre-filtering is the default)
+        const vectorResults = await table
+          .search(queryEmbed)
+          .where("label > 2")
+          .select(["text", "keywords", "label"])
+          .limit(5)
+          .toArray();
+
+        console.log("Search results (with pre-filtering):");
+        console.log(vectorResults);
+        // --8<-- [end:vector_search_prefilter]
+
+        // --8<-- [start:vector_search_postfilter]
+        const vectorResultsWithPostFilter = await (
+          table.search(queryEmbed) as lancedb.VectorQuery
+        )
+          .where("label > 2")
+          .postfilter()
+          .select(["text", "keywords", "label"])
+          .limit(5)
+          .toArray();
+
+        console.log("Vector search results with post-filter:");
+        console.log(vectorResultsWithPostFilter);
+        // --8<-- [end:vector_search_postfilter]
+
+        // --8<-- [start:batch_search]
+        // Batch query
+        console.log("Performing batch vector search...");
+        const batchSize = 5;
+        const queryVectors = Array.from({ length: batchSize }, () =>
+          Array.from({ length: dimensions }, () => Math.random() * 2 - 1),
+        );
+        let batchQuery = table.search(queryVectors[0]) as lancedb.VectorQuery;
+        for (let i = 1; i < batchSize; i++) {
+          batchQuery = batchQuery.addQueryVector(queryVectors[i]);
+        }
+        const batchResults = await batchQuery
+          .select(["text", "keywords", "label"])
+          .limit(5)
+          .toArray();
+        console.log("Batch vector search results:");
+        console.log(batchResults);
+        // --8<-- [end:batch_search]
+        expect(batchResults.length).toBeGreaterThan(0);
+      }
+    }
+  });
+});
diff --git a/docs/web-tests/ts/search_examples.test.ts b/docs/web-tests/ts/search_examples.test.ts
new file mode 100644
index 000000000..be4c388ee
--- /dev/null
+++ b/docs/web-tests/ts/search_examples.test.ts
@@ -0,0 +1,222 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import { expect, test } from "@jest/globals";
+
+test("complete vector_search", async () => {
+  // --8<-- [start:vector_search_body]
+  const lancedb = await import("@lancedb/lancedb");
+  const { mkdtemp, rm } = await import("node:fs/promises");
+  const { tmpdir } = await import("node:os");
+  const { join } = await import("node:path");
+  const uri = await mkdtemp(join(tmpdir(), "lancedb-search-"));
+  try {
+    const db = await lancedb.connect(uri);
+    const table = await db.createTable("documents", [
+      {
+        id: 1,
+        text: "A puppy playing in the park",
+        category: "animals",
+        vector: [1.0, 0.0],
+      },
+      {
+        id: 2,
+        text: "A dog resting at home",
+        category: "animals",
+        vector: [0.9, 0.1],
+      },
+      {
+        id: 3,
+        text: "A guide to growing tomatoes",
+        category: "gardening",
+        vector: [0.0, 1.0],
+      },
+    ]);
+    const rows = await table
+      .vectorSearch([1.0, 0.0])
+      .distanceType("l2")
+      .select(["id", "text", "_distance"])
+      .limit(2)
+      .toArray();
+    console.log(rows);
+    // --8<-- [end:vector_search_body]
+    expect(rows.map((row) => row.id)).toEqual([1, 2]);
+    expect(rows[0]._distance).toBe(0);
+    // --8<-- [start:vector_search_tail]
+  } finally {
+    await rm(uri, { recursive: true, force: true });
+  }
+  // --8<-- [end:vector_search_tail]
+});
+
+test("complete text_search", async () => {
+  // --8<-- [start:text_search_body]
+  const lancedb = await import("@lancedb/lancedb");
+  const { mkdtemp, rm } = await import("node:fs/promises");
+  const { tmpdir } = await import("node:os");
+  const { join } = await import("node:path");
+  const uri = await mkdtemp(join(tmpdir(), "lancedb-search-"));
+  try {
+    const db = await lancedb.connect(uri);
+    const table = await db.createTable("documents", [
+      { id: 1, text: "A puppy playing in the park", category: "animals" },
+      { id: 2, text: "A dog resting at home", category: "animals" },
+      { id: 3, text: "A guide to growing tomatoes", category: "gardening" },
+    ]);
+    await table.createIndex("text", { config: lancedb.Index.fts() });
+    const rows = await table
+      .query()
+      .fullTextSearch("puppy", { columns: ["text"] })
+      .select(["id", "text", "_score"])
+      .limit(2)
+      .toArray();
+    console.log(rows);
+    // --8<-- [end:text_search_body]
+    expect(rows.map((row) => row.id)).toEqual([1]);
+    expect(rows[0]._score).toBeGreaterThan(0);
+    // --8<-- [start:text_search_tail]
+  } finally {
+    await rm(uri, { recursive: true, force: true });
+  }
+  // --8<-- [end:text_search_tail]
+});
+
+test("complete hybrid_search", async () => {
+  // --8<-- [start:hybrid_search_body]
+  const lancedb = await import("@lancedb/lancedb");
+  const { mkdtemp, rm } = await import("node:fs/promises");
+  const { tmpdir } = await import("node:os");
+  const { join } = await import("node:path");
+  const uri = await mkdtemp(join(tmpdir(), "lancedb-search-"));
+  try {
+    const db = await lancedb.connect(uri);
+    const table = await db.createTable("documents", [
+      {
+        id: 1,
+        text: "A puppy playing in the park",
+        category: "animals",
+        vector: [1.0, 0.0],
+      },
+      {
+        id: 2,
+        text: "A dog resting at home",
+        category: "animals",
+        vector: [0.9, 0.1],
+      },
+      {
+        id: 3,
+        text: "A guide to growing tomatoes",
+        category: "gardening",
+        vector: [0.0, 1.0],
+      },
+    ]);
+    await table.createIndex("text", { config: lancedb.Index.fts() });
+    const reranker = await lancedb.rerankers.RRFReranker.create();
+    const rows = await table
+      .vectorSearch([1.0, 0.0])
+      .fullTextSearch("puppy", { columns: ["text"] })
+      .rerank(reranker)
+      .limit(2)
+      .toArray();
+    console.log(rows);
+    // --8<-- [end:hybrid_search_body]
+    expect(rows.map((row) => row.id)).toEqual([1, 2]);
+    expect(rows[0]._relevance_score).toBeGreaterThan(rows[1]._relevance_score);
+    // --8<-- [start:hybrid_search_tail]
+  } finally {
+    await rm(uri, { recursive: true, force: true });
+  }
+  // --8<-- [end:hybrid_search_tail]
+});
+
+test("complete filter_search", async () => {
+  // --8<-- [start:filter_search_body]
+  const lancedb = await import("@lancedb/lancedb");
+  const { mkdtemp, rm } = await import("node:fs/promises");
+  const { tmpdir } = await import("node:os");
+  const { join } = await import("node:path");
+  const uri = await mkdtemp(join(tmpdir(), "lancedb-search-"));
+  try {
+    const db = await lancedb.connect(uri);
+    const table = await db.createTable("documents", [
+      {
+        id: 1,
+        text: "A puppy playing in the park",
+        category: "animals",
+        vector: [1.0, 0.0],
+      },
+      {
+        id: 2,
+        text: "A dog resting at home",
+        category: "animals",
+        vector: [0.9, 0.1],
+      },
+      {
+        id: 3,
+        text: "A guide to growing tomatoes",
+        category: "gardening",
+        vector: [0.0, 1.0],
+      },
+    ]);
+    const rows = await table
+      .vectorSearch([0.0, 1.0])
+      .where("category = 'animals'")
+      .select(["id", "text", "category", "_distance"])
+      .limit(2)
+      .toArray();
+    console.log(rows);
+    // --8<-- [end:filter_search_body]
+    expect(rows.map((row) => row.id).sort()).toEqual([1, 2]);
+    expect(rows.every((row) => row.category === "animals")).toBe(true);
+    // --8<-- [start:filter_search_tail]
+  } finally {
+    await rm(uri, { recursive: true, force: true });
+  }
+  // --8<-- [end:filter_search_tail]
+});
+
+test("complete query_plan", async () => {
+  // --8<-- [start:query_plan_body]
+  const lancedb = await import("@lancedb/lancedb");
+  const { mkdtemp, rm } = await import("node:fs/promises");
+  const { tmpdir } = await import("node:os");
+  const { join } = await import("node:path");
+  const uri = await mkdtemp(join(tmpdir(), "lancedb-search-"));
+  try {
+    const db = await lancedb.connect(uri);
+    const table = await db.createTable("documents", [
+      {
+        id: 1,
+        text: "A puppy playing in the park",
+        category: "animals",
+        vector: [1.0, 0.0],
+      },
+      {
+        id: 2,
+        text: "A dog resting at home",
+        category: "animals",
+        vector: [0.9, 0.1],
+      },
+      {
+        id: 3,
+        text: "A guide to growing tomatoes",
+        category: "gardening",
+        vector: [0.0, 1.0],
+      },
+    ]);
+    const query = table
+      .vectorSearch([1.0, 0.0])
+      .select(["id", "text", "_distance"])
+      .limit(2);
+    console.log("Plan:", await query.explainPlan());
+    console.log("Measured execution:", await query.analyzePlan());
+    const rows = await query.toArray();
+    console.log(rows);
+    // --8<-- [end:query_plan_body]
+    expect(rows.map((row) => row.id)).toEqual([1, 2]);
+    // --8<-- [start:query_plan_tail]
+  } finally {
+    await rm(uri, { recursive: true, force: true });
+  }
+  // --8<-- [end:query_plan_tail]
+});
diff --git a/docs/web-tests/ts/sentence-transformers.test.ts b/docs/web-tests/ts/sentence-transformers.test.ts
new file mode 100644
index 000000000..a407e192b
--- /dev/null
+++ b/docs/web-tests/ts/sentence-transformers.test.ts
@@ -0,0 +1,96 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+import { expect, test } from "@jest/globals";
+import { withTempDirectory } from "./util.ts";
+
+// --8<-- [start:quickstart_imports]
+import * as lancedb from "@lancedb/lancedb";
+import "@lancedb/lancedb/embedding/transformers";
+import { Utf8 } from "apache-arrow";
+// --8<-- [end:quickstart_imports]
+
+test("full text search", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    const db = await lancedb.connect(databaseDir);
+    const func = (await lancedb.embedding
+      .getRegistry()
+      .get("huggingface")
+      ?.create()) as lancedb.embedding.EmbeddingFunction;
+
+    const facts = [
+      "Albert Einstein was a theoretical physicist.",
+      "The capital of France is Paris.",
+      "The Great Wall of China is one of the Seven Wonders of the World.",
+      "Python is a popular programming language.",
+      "Mount Everest is the highest mountain in the world.",
+      "Leonardo da Vinci painted the Mona Lisa.",
+      "Shakespeare wrote Hamlet.",
+      "The human body has 206 bones.",
+      "The speed of light is approximately 299,792 kilometers per second.",
+      "Water boils at 100 degrees Celsius.",
+      "The Earth orbits the Sun.",
+      "The Pyramids of Giza are located in Egypt.",
+      "Coffee is one of the most popular beverages in the world.",
+      "Tokyo is the capital city of Japan.",
+      "Photosynthesis is the process by which plants make their food.",
+      "The Pacific Ocean is the largest ocean on Earth.",
+      "Mozart was a prolific composer of classical music.",
+      "The Internet is a global network of computers.",
+      "Basketball is a sport played with a ball and a hoop.",
+      "The first computer virus was created in 1983.",
+      "Artificial neural networks are inspired by the human brain.",
+      "Deep learning is a subset of machine learning.",
+      "IBM's Watson won Jeopardy! in 2011.",
+      "The first computer programmer was Ada Lovelace.",
+      "The first chatbot was ELIZA, created in the 1960s.",
+    ].map((text) => ({ text }));
+
+    const factsSchema = lancedb.embedding.LanceSchema({
+      text: func.sourceField(new Utf8()),
+      vector: func.vectorField(),
+    });
+
+    const tbl = await db.createTable("facts", facts, {
+      mode: "overwrite",
+      schema: factsSchema,
+    });
+
+    const query = "How many bones are in the human body?";
+    const actual = await tbl.search(query).limit(1).toArray();
+
+    expect(actual[0].text).toBe("The human body has 206 bones.");
+  });
+}, 100_000);
+
+test.skip("embedding quickstart snippets", async () => {
+  // --8<-- [start:quickstart_connect]
+  const db = await lancedb.connect("data/sample-lancedb");
+  // --8<-- [end:quickstart_connect]
+
+  // --8<-- [start:quickstart_init_model]
+  const model = (await lancedb.embedding
+    .getRegistry()
+    .get("huggingface")
+    ?.create()) as lancedb.embedding.EmbeddingFunction;
+  // --8<-- [end:quickstart_init_model]
+
+  // --8<-- [start:quickstart_schema]
+  const wordsSchema = lancedb.embedding.LanceSchema({
+    text: model.sourceField(new Utf8()),
+    vector: model.vectorField(),
+  });
+  // --8<-- [end:quickstart_schema]
+
+  // --8<-- [start:quickstart_create_table]
+  const table = await db.createEmptyTable("words", wordsSchema, {
+    mode: "overwrite",
+  });
+  await table.add([{ text: "hello world" }, { text: "goodbye world" }]);
+  // --8<-- [end:quickstart_create_table]
+
+  // --8<-- [start:quickstart_query]
+  const query = "greetings";
+  const actual = (await table.search(query).limit(1).toArray())[0];
+  console.log(actual.text);
+  // --8<-- [end:quickstart_query]
+});
diff --git a/docs/web-tests/ts/storage.test.ts b/docs/web-tests/ts/storage.test.ts
new file mode 100644
index 000000000..38c27cf27
--- /dev/null
+++ b/docs/web-tests/ts/storage.test.ts
@@ -0,0 +1,187 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+import { expect, test } from "@jest/globals";
+import * as lancedb from "@lancedb/lancedb";
+
+// --8<-- [start:storage_connect_s3]
+async function storageConnectS3() {
+  const db = await lancedb.connect("s3://bucket/path");
+  return db;
+}
+// --8<-- [end:storage_connect_s3]
+
+// --8<-- [start:storage_connect_gcs]
+async function storageConnectGcs() {
+  const db = await lancedb.connect("gs://bucket/path");
+  return db;
+}
+// --8<-- [end:storage_connect_gcs]
+
+// --8<-- [start:storage_connect_azure]
+async function storageConnectAzure() {
+  const db = await lancedb.connect("az://bucket/path");
+  return db;
+}
+// --8<-- [end:storage_connect_azure]
+
+// --8<-- [start:storage_connect_timeout]
+async function storageConnectTimeout() {
+  const db = await lancedb.connect("s3://bucket/path", {
+    storageOptions: { timeout: "60s" },
+  });
+  return db;
+}
+// --8<-- [end:storage_connect_timeout]
+
+// --8<-- [start:storage_table_timeout]
+async function storageTableTimeout() {
+  const db = await lancedb.connect("s3://bucket/path");
+  const table = await db.createTable(
+    "table",
+    [{ a: 1, b: 2 }],
+    { storageOptions: { timeout: "60s" } },
+  );
+  return table;
+}
+// --8<-- [end:storage_table_timeout]
+
+// --8<-- [start:storage_s3_ddb]
+async function storageS3Ddb() {
+  const db = await lancedb.connect(
+    "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
+  );
+  return db;
+}
+// --8<-- [end:storage_s3_ddb]
+
+// --8<-- [start:storage_s3_ddb_local]
+async function storageS3DdbLocal() {
+  const db = await lancedb.connect(
+    "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
+    {
+      storageOptions: {
+        endpoint: "http://localhost:4566",
+        dynamodbEndpoint: "http://localhost:4566",
+        allowHttp: "true",
+      },
+    },
+  );
+  return db;
+}
+// --8<-- [end:storage_s3_ddb_local]
+
+// --8<-- [start:storage_s3_sse_kms]
+async function storageS3SseKms() {
+  const db = await lancedb.connect("s3://bucket/path", {
+    storageOptions: {
+      awsServerSideEncryption: "aws:kms",
+      awsSseKmsKeyId: "<your-kms-key-id-or-arn>",
+    },
+  });
+  return db;
+}
+// --8<-- [end:storage_s3_sse_kms]
+
+// --8<-- [start:storage_azure_sas]
+async function storageAzureSas() {
+  const db = await lancedb.connect(
+    "az://my-container/my-database",
+    {
+      storageOptions: {
+        azureStorageAccountName: "some-account",
+        azureStorageSasToken: "<sas-token>",
+      },
+    },
+  );
+  return db;
+}
+// --8<-- [end:storage_azure_sas]
+
+// --8<-- [start:storage_s3_minio]
+async function storageS3Minio() {
+  const db = await lancedb.connect("s3://bucket/path", {
+    storageOptions: {
+      region: "us-east-1",
+      endpoint: "http://minio:9000",
+    },
+  });
+  return db;
+}
+// --8<-- [end:storage_s3_minio]
+
+// --8<-- [start:storage_s3_express]
+async function storageS3Express() {
+  const db = await lancedb.connect(
+    "s3://my-bucket--use1-az4--x-s3/path",
+    {
+      storageOptions: {
+        region: "us-east-1",
+        s3Express: "true",
+      },
+    },
+  );
+  return db;
+}
+// --8<-- [end:storage_s3_express]
+
+// --8<-- [start:storage_gcs_service_account]
+async function storageGcsServiceAccount() {
+  const db = await lancedb.connect(
+    "gs://my-bucket/my-database",
+    {
+      storageOptions: {
+        serviceAccount: "path/to/service-account.json",
+      },
+    },
+  );
+  return db;
+}
+// --8<-- [end:storage_gcs_service_account]
+
+// --8<-- [start:storage_azure_account]
+async function storageAzureAccount() {
+  const db = await lancedb.connect(
+    "az://my-container/my-database",
+    {
+      storageOptions: {
+        accountName: "some-account",
+        accountKey: "some-key",
+      },
+    },
+  );
+  return db;
+}
+// --8<-- [end:storage_azure_account]
+
+// --8<-- [start:storage_tigris_connect]
+async function storageTigrisConnect() {
+  const db = await lancedb.connect(
+    "s3://your-bucket/path",
+    {
+      storageOptions: {
+        endpoint: "https://t3.storage.dev",
+        region: "auto",
+      },
+    },
+  );
+  return db;
+}
+// --8<-- [end:storage_tigris_connect]
+
+test("storage ts snippets compile", async () => {
+  expect(storageConnectS3).toBeDefined();
+  expect(storageConnectGcs).toBeDefined();
+  expect(storageConnectAzure).toBeDefined();
+  expect(storageConnectTimeout).toBeDefined();
+  expect(storageTableTimeout).toBeDefined();
+  expect(storageS3Ddb).toBeDefined();
+  expect(storageS3DdbLocal).toBeDefined();
+  expect(storageS3Minio).toBeDefined();
+  expect(storageS3Express).toBeDefined();
+  expect(storageS3SseKms).toBeDefined();
+  expect(storageGcsServiceAccount).toBeDefined();
+  expect(storageAzureAccount).toBeDefined();
+  expect(storageAzureSas).toBeDefined();
+  expect(storageTigrisConnect).toBeDefined();
+});
+
diff --git a/docs/web-tests/ts/tables.test.ts b/docs/web-tests/ts/tables.test.ts
new file mode 100644
index 000000000..16fa4545c
--- /dev/null
+++ b/docs/web-tests/ts/tables.test.ts
@@ -0,0 +1,1061 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+import { expect, jest, test } from "@jest/globals";
+import * as arrow from "apache-arrow";
+import * as lancedb from "@lancedb/lancedb";
+import { withTempDirectory } from "./util.ts";
+
+async function updateConnectEnterpriseExample() {
+  // --8<-- [start:update_connect_enterprise]
+  const db = await lancedb.connect("db://your-project-slug", {
+    apiKey: "your-api-key",
+    region: "us-east-1",
+  });
+  // --8<-- [end:update_connect_enterprise]
+  return db;
+}
+
+async function updateConnectLocalExample() {
+  // --8<-- [start:update_connect_local]
+  const db = await lancedb.connect("./data");
+  // --8<-- [end:update_connect_local]
+  return db;
+}
+
+test("table creation snippets (async)", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    const db = await lancedb.connect(databaseDir);
+
+    // --8<-- [start:create_table_from_dicts]
+    type Location = {
+      vector: number[];
+      lat: number;
+      long: number;
+    };
+
+    const data: Location[] = [
+      { vector: [1.1, 1.2], lat: 45.5, long: -122.7 },
+      { vector: [0.2, 1.8], lat: 40.1, long: -74.1 },
+    ];
+    const table = await db.createTable("test_table", data, {
+      mode: "overwrite",
+    });
+    // --8<-- [end:create_table_from_dicts]
+    expect(await table.countRows()).toBe(2);
+
+    await db.createTable("conflict_table", data);
+    // --8<-- [start:create_table_conflict_handling]
+    // Idempotent open: reuse the existing table if it exists.
+    // The provided data is ignored; the schema is validated against the
+    // existing table and a mismatch raises an error.
+    let conflictTable = await db.createTable("conflict_table", data, {
+      existOk: true,
+    });
+
+    // Overwrite: drop the existing table and create a new one with the
+    // provided data. This permanently discards the old table's data.
+    conflictTable = await db.createTable("conflict_table", data, {
+      mode: "overwrite",
+    });
+    // --8<-- [end:create_table_conflict_handling]
+    expect(await conflictTable.countRows()).toBe(2);
+
+    // --8<-- [start:create_table_custom_schema]
+    const customSchema = new arrow.Schema([
+      new arrow.Field(
+        "vector",
+        new arrow.FixedSizeList(
+          4,
+          new arrow.Field("item", new arrow.Float32(), true),
+        ),
+      ),
+      new arrow.Field("lat", new arrow.Float32()),
+      new arrow.Field("long", new arrow.Float32()),
+    ]);
+
+    const customSchemaData = lancedb.makeArrowTable(
+      [
+        { vector: [1.1, 1.2, 1.3, 1.4], lat: 45.5, long: -122.7 },
+        { vector: [0.2, 1.8, 0.4, 3.6], lat: 40.1, long: -74.1 },
+      ],
+      { schema: customSchema },
+    );
+    const customSchemaTable = await db.createTable(
+      "my_table_custom_schema",
+      customSchemaData,
+      { mode: "overwrite" },
+    );
+    // --8<-- [end:create_table_custom_schema]
+    expect(await customSchemaTable.countRows()).toBe(2);
+
+    // --8<-- [start:create_table_from_arrow]
+    const arrowSchema = new arrow.Schema([
+      new arrow.Field(
+        "vector",
+        new arrow.FixedSizeList(
+          16,
+          new arrow.Field("item", new arrow.Float32(), true),
+        ),
+      ),
+      new arrow.Field("text", new arrow.Utf8()),
+    ]);
+    const arrowData = lancedb.makeArrowTable(
+      [
+        { vector: Array(16).fill(0.1), text: "foo" },
+        { vector: Array(16).fill(0.2), text: "bar" },
+      ],
+      { schema: arrowSchema },
+    );
+    const arrowTable = await db.createTable("f32_tbl", arrowData, {
+      mode: "overwrite",
+    });
+    // --8<-- [end:create_table_from_arrow]
+    expect(await arrowTable.countRows()).toBe(2);
+
+    // --8<-- [start:create_table_from_iterator]
+    const batchSchema = new arrow.Schema([
+      new arrow.Field(
+        "vector",
+        new arrow.FixedSizeList(
+          4,
+          new arrow.Field("item", new arrow.Float32(), true),
+        ),
+      ),
+      new arrow.Field("item", new arrow.Utf8()),
+      new arrow.Field("price", new arrow.Float32()),
+    ]);
+
+    const tableForBatches = await db.createEmptyTable(
+      "batched_table",
+      batchSchema,
+      {
+        mode: "overwrite",
+      },
+    );
+
+    const rows = Array.from({ length: 10 }, (_, i) => ({
+      vector: [i + 0.1, i + 0.2, i + 0.3, i + 0.4],
+      item: `item-${i + 1}`,
+      price: (i + 1) * 10,
+    }));
+
+    const chunkSize = 2;
+    for (let i = 0; i < rows.length; i += chunkSize) {
+      const batch = lancedb.makeArrowTable(rows.slice(i, i + chunkSize), {
+        schema: batchSchema,
+      });
+      await tableForBatches.add(batch);
+    }
+    // --8<-- [end:create_table_from_iterator]
+    expect(await tableForBatches.countRows()).toBe(10);
+
+    // --8<-- [start:open_existing_table]
+    const openTableData = [{ vector: [1.1, 1.2], lat: 45.5, long: -122.7 }];
+    await db.createTable("test_table_open", openTableData, {
+      mode: "overwrite",
+    });
+
+    console.log(await db.tableNames());
+
+    const openedTable = await db.openTable("test_table_open");
+    // --8<-- [end:open_existing_table]
+    expect(await openedTable.countRows()).toBe(1);
+
+    // --8<-- [start:create_empty_table]
+    const emptySchema = new arrow.Schema([
+      new arrow.Field(
+        "vector",
+        new arrow.FixedSizeList(
+          2,
+          new arrow.Field("item", new arrow.Float32(), true),
+        ),
+      ),
+      new arrow.Field("item", new arrow.Utf8()),
+      new arrow.Field("price", new arrow.Float32()),
+    ]);
+    const emptyTable = await db.createEmptyTable(
+      "test_empty_table",
+      emptySchema,
+      {
+        mode: "overwrite",
+      },
+    );
+    // --8<-- [end:create_empty_table]
+    expect(await emptyTable.countRows()).toBe(0);
+
+    // --8<-- [start:drop_table]
+    await db.createTable("my_table", [{ vector: [1.1, 1.2], lat: 45.5 }], {
+      mode: "overwrite",
+    });
+
+    await db.dropTable("my_table");
+    // --8<-- [end:drop_table]
+    expect(await db.tableNames()).not.toContain("my_table");
+  });
+});
+
+test("add progress snippet (async)", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    const db = await lancedb.connect(databaseDir);
+    const table = await db.createTable("progress_table", [
+      { vector: [1.1, 1.2], item: "foo" },
+    ]);
+    const moreData = [
+      { vector: [2.1, 2.2], item: "bar" },
+      { vector: [3.1, 3.2], item: "baz" },
+    ];
+    const log = jest.spyOn(console, "log").mockImplementation(() => {});
+    try {
+      // --8<-- [start:add_progress]
+      // Track ingestion progress as batches are written.
+      await table.add(moreData, {
+        progress: (p) => {
+          const rows = `${p.outputRows}/${p.totalRows ?? "?"} rows`;
+          const size = `${p.outputBytes} bytes, ${p.elapsedSeconds.toFixed(1)}s`;
+          const tasks = `${p.activeTasks}/${p.totalTasks} tasks active`;
+          const done = p.done ? " \u2014 done" : "";
+          console.log(`wrote ${rows} (${size}, ${tasks})${done}`);
+        },
+      });
+      // --8<-- [end:add_progress]
+      expect(log.mock.calls.at(-1)?.[0]).toMatch(/^wrote 2\/2 rows .* — done$/);
+    } finally {
+      log.mockRestore();
+    }
+    expect(await table.countRows()).toBe(3);
+  });
+});
+
+test("schema evolution snippets (async)", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    const db = await lancedb.connect(databaseDir);
+
+    // --8<-- [start:schema_add_setup]
+    const schemaAddData = [
+      {
+        id: 1,
+        name: "Laptop",
+        price: 1200.0,
+        vector: Array.from({ length: 128 }, () => Math.random()),
+      },
+      {
+        id: 2,
+        name: "Smartphone",
+        price: 800.0,
+        vector: Array.from({ length: 128 }, () => Math.random()),
+      },
+      {
+        id: 3,
+        name: "Headphones",
+        price: 150.0,
+        vector: Array.from({ length: 128 }, () => Math.random()),
+      },
+    ];
+    const schemaAddTable = await db.createTable(
+      "schema_evolution_add_example",
+      schemaAddData,
+      { mode: "overwrite" },
+    );
+    // --8<-- [end:schema_add_setup]
+    expect(await schemaAddTable.countRows()).toBe(3);
+
+    // --8<-- [start:add_columns_calculated]
+    // Add a discounted price column (10% discount)
+    await schemaAddTable.addColumns([
+      {
+        name: "discounted_price",
+        valueSql: "cast((price * 0.9) as float)",
+      },
+    ]);
+    // --8<-- [end:add_columns_calculated]
+
+    // --8<-- [start:add_columns_default_values]
+    // Add a stock status column with default value
+    await schemaAddTable.addColumns([
+      {
+        name: "in_stock",
+        valueSql: "cast(true as boolean)",
+      },
+    ]);
+    // --8<-- [end:add_columns_default_values]
+
+    // --8<-- [start:add_columns_nullable]
+    // Add a nullable timestamp column
+    await schemaAddTable.addColumns([
+      {
+        name: "last_ordered",
+        valueSql: "cast(NULL as timestamp)",
+      },
+    ]);
+    // --8<-- [end:add_columns_nullable]
+
+    // --8<-- [start:add_feature_columns_sql]
+    await schemaAddTable.addColumns([
+      {
+        name: "price_per_id",
+        valueSql: "cast(price / id as float)",
+      },
+      {
+        name: "price_log",
+        valueSql: "ln(price)",
+      },
+      {
+        name: "price_score",
+        valueSql: "cast(price / (price + 100.0) as float)",
+      },
+    ]);
+    // --8<-- [end:add_feature_columns_sql]
+    expect((await schemaAddTable.schema()).fields.map((field) => field.name)).toEqual(
+      expect.arrayContaining(["price_per_id", "price_log", "price_score"]),
+    );
+
+    // --8<-- [start:schema_alter_setup]
+    const schemaAlter = new arrow.Schema([
+      new arrow.Field("id", new arrow.Int64()),
+      new arrow.Field("name", new arrow.Utf8()),
+      new arrow.Field("price", new arrow.Int32()),
+      new arrow.Field("discount_price", new arrow.Float64()),
+      new arrow.Field(
+        "vector",
+        new arrow.FixedSizeList(
+          128,
+          new arrow.Field("item", new arrow.Float32(), true),
+        ),
+      ),
+    ]);
+    const schemaAlterData = lancedb.makeArrowTable(
+      [
+        {
+          id: 1,
+          name: "Laptop",
+          price: 1200,
+          discount_price: 1080.0,
+          vector: Array.from({ length: 128 }, () => Math.random()),
+        },
+        {
+          id: 2,
+          name: "Smartphone",
+          price: 800,
+          discount_price: 720.0,
+          vector: Array.from({ length: 128 }, () => Math.random()),
+        },
+      ],
+      { schema: schemaAlter },
+    );
+    const schemaAlterTable = await db.createTable(
+      "schema_evolution_alter_example",
+      schemaAlterData,
+      { mode: "overwrite" },
+    );
+    // --8<-- [end:schema_alter_setup]
+    expect(await schemaAlterTable.countRows()).toBe(2);
+
+    // --8<-- [start:alter_columns_rename]
+    // Rename discount_price to sale_price
+    await schemaAlterTable.alterColumns([
+      { path: "discount_price", rename: "sale_price" },
+    ]);
+    // --8<-- [end:alter_columns_rename]
+
+    // --8<-- [start:alter_columns_data_type]
+    // Change price from int32 to int64 for larger numbers
+    await schemaAlterTable.alterColumns([
+      { path: "price", dataType: new arrow.Int64() },
+    ]);
+    // --8<-- [end:alter_columns_data_type]
+
+    // --8<-- [start:alter_columns_nullable]
+    // Make the name column nullable
+    await schemaAlterTable.alterColumns([{ path: "name", nullable: true }]);
+    // --8<-- [end:alter_columns_nullable]
+
+    // --8<-- [start:alter_columns_multiple]
+    // Rename, change type, and make nullable in one operation
+    await schemaAlterTable.alterColumns([
+      {
+        path: "sale_price",
+        rename: "final_price",
+        dataType: new arrow.Float64(),
+        nullable: true,
+      },
+    ]);
+    // --8<-- [end:alter_columns_multiple]
+
+    // --8<-- [start:alter_columns_with_expression]
+    // For custom transforms, create a new column from a SQL expression.
+    const expressionTable = await db.createTable(
+      "schema_evolution_expression_example",
+      [{ id: 1, price_text: "$100" }],
+      { mode: "overwrite" },
+    );
+
+    await expressionTable.addColumns([
+      {
+        name: "price_numeric",
+        valueSql: "cast(replace(price_text, '$', '') as int)",
+      },
+    ]);
+    await expressionTable.dropColumns(["price_text"]);
+    await expressionTable.alterColumns([
+      { path: "price_numeric", rename: "price" },
+    ]);
+    // --8<-- [end:alter_columns_with_expression]
+    expect(await expressionTable.countRows()).toBe(1);
+
+    // --8<-- [start:schema_drop_setup]
+    const schemaDropData = [
+      {
+        id: 1,
+        name: "Laptop",
+        price: 1200.0,
+        temp_col1: "X",
+        temp_col2: 100,
+        vector: Array.from({ length: 128 }, () => Math.random()),
+      },
+      {
+        id: 2,
+        name: "Smartphone",
+        price: 800.0,
+        temp_col1: "Y",
+        temp_col2: 200,
+        vector: Array.from({ length: 128 }, () => Math.random()),
+      },
+      {
+        id: 3,
+        name: "Headphones",
+        price: 150.0,
+        temp_col1: "Z",
+        temp_col2: 300,
+        vector: Array.from({ length: 128 }, () => Math.random()),
+      },
+    ];
+    const schemaDropTable = await db.createTable(
+      "schema_evolution_drop_example",
+      schemaDropData,
+      { mode: "overwrite" },
+    );
+    // --8<-- [end:schema_drop_setup]
+    expect(await schemaDropTable.countRows()).toBe(3);
+
+    // --8<-- [start:drop_columns_single]
+    // Remove the first temporary column
+    await schemaDropTable.dropColumns(["temp_col1"]);
+    // --8<-- [end:drop_columns_single]
+
+    // --8<-- [start:drop_columns_multiple]
+    // Remove the second temporary column
+    await schemaDropTable.dropColumns(["temp_col2"]);
+    // --8<-- [end:drop_columns_multiple]
+
+    // --8<-- [start:alter_vector_column]
+    const oldDim = 384;
+    const newDim = 1024;
+    const vectorSchema = new arrow.Schema([
+      new arrow.Field("id", new arrow.Int64()),
+      new arrow.Field(
+        "embedding",
+        new arrow.FixedSizeList(
+          oldDim,
+          new arrow.Field("item", new arrow.Float16(), true),
+        ),
+        true,
+      ),
+    ]);
+    const vectorData = lancedb.makeArrowTable(
+      [{ id: 1, embedding: Array.from({ length: oldDim }, () => Math.random()) }],
+      { schema: vectorSchema },
+    );
+    const vectorTable = await db.createTable("vector_alter_example", vectorData, {
+      mode: "overwrite",
+    });
+
+    // Changing FixedSizeList dimensions (384 -> 1024) is not supported via alterColumns.
+    // Use addColumns + dropColumns + alterColumns(rename) to replace the column.
+    await vectorTable.addColumns([
+      {
+        name: "embedding_v2",
+        valueSql: `arrow_cast(NULL, 'FixedSizeList(${newDim}, Float16)')`,
+      },
+    ]);
+    await vectorTable.dropColumns(["embedding"]);
+    await vectorTable.alterColumns([{ path: "embedding_v2", rename: "embedding" }]);
+    // --8<-- [end:alter_vector_column]
+    expect(await vectorTable.countRows()).toBe(1);
+
+    const fieldMetadataTable = await db.createTable(
+      "schema_field_metadata_example",
+      [
+        { id: 0, category: "a" },
+        { id: 1, category: "b" },
+      ],
+      { mode: "overwrite" },
+    );
+
+    // --8<-- [start:schema_field_metadata_merge]
+    // Set two metadata keys on the `category` field.
+    const res = await fieldMetadataTable.updateFieldMetadata([
+      { path: "category", metadata: { unit: "label", pii: "false" } },
+    ]);
+    console.log(res.version);
+
+    // Merge: add a new key, delete one via null, keep the rest.
+    await fieldMetadataTable.updateFieldMetadata([
+      { path: "category", metadata: { source: "import", pii: null } },
+    ]);
+    // --8<-- [end:schema_field_metadata_merge]
+
+    // --8<-- [start:schema_field_metadata_replace]
+    await fieldMetadataTable.updateFieldMetadata([
+      {
+        path: "category",
+        metadata: { owner: "search-team" },
+        replace: true,
+      },
+    ]);
+    // --8<-- [end:schema_field_metadata_replace]
+  });
+});
+
+test("update snippets (async)", async () => {
+  // Keep connection snippets in this file, but do not run enterprise/local examples in CI.
+  void updateConnectEnterpriseExample;
+  void updateConnectLocalExample;
+
+  await withTempDirectory(async (databaseDir) => {
+    const db = await lancedb.connect(databaseDir);
+
+    {
+      // --8<-- [start:update_example_table_setup]
+      const table = await db.createTable(
+        "users_example",
+        [
+          { id: 1, name: "Alice", login_count: 10 },
+          { id: 2, name: "Bob", login_count: 20 },
+        ],
+        { mode: "overwrite" },
+      );
+      // --8<-- [end:update_example_table_setup]
+      await table.countRows();
+    }
+
+    {
+      // --8<-- [start:update_operation]
+      const table = await db.createTable(
+        "users_example",
+        [
+          { id: 1, name: "Alice", login_count: 10 },
+          { id: 2, name: "Bob", login_count: 20 },
+        ],
+        { mode: "overwrite" },
+      );
+      await table.update({ where: "id = 2", values: { name: "Bobby" } });
+      // --8<-- [end:update_operation]
+      await table.countRows();
+    }
+
+    {
+      // --8<-- [start:update_using_sql]
+      const table = await db.createTable(
+        "users_example",
+        [
+          { id: 1, name: "Alice", login_count: 10 },
+          { id: 2, name: "Bob", login_count: 20 },
+        ],
+        { mode: "overwrite" },
+      );
+      await table.update({
+        where: "id = 2",
+        valuesSql: { login_count: "login_count + 1" },
+      });
+      // --8<-- [end:update_using_sql]
+      await table.countRows();
+    }
+
+    {
+      // --8<-- [start:merge_matched_update_only]
+      const table = await db.createTable(
+        "users_example",
+        [
+          { id: 1, name: "Alice", login_count: 10 },
+          { id: 2, name: "Bob", login_count: 20 },
+        ],
+        { mode: "overwrite" },
+      );
+
+      const incomingUsers = [
+        { id: 2, name: "Bobby", login_count: 21 },
+        { id: 3, name: "Charlie", login_count: 5 },
+      ];
+
+      await table
+        .mergeInsert("id")
+        .whenMatchedUpdateAll()
+        .execute(incomingUsers);
+      // --8<-- [end:merge_matched_update_only]
+      await table.countRows();
+    }
+
+    {
+      // --8<-- [start:insert_if_not_exists]
+      const table = await db.createTable(
+        "users_example",
+        [
+          { id: 1, name: "Alice", login_count: 10 },
+          { id: 2, name: "Bob", login_count: 20 },
+        ],
+        { mode: "overwrite" },
+      );
+
+      const incomingUsers = [
+        { id: 2, name: "Bobby", login_count: 21 },
+        { id: 3, name: "Charlie", login_count: 5 },
+      ];
+
+      await table
+        .mergeInsert("id")
+        .whenNotMatchedInsertAll()
+        .execute(incomingUsers);
+      // --8<-- [end:insert_if_not_exists]
+      await table.countRows();
+    }
+
+    {
+      // --8<-- [start:merge_update_insert]
+      const table = await db.createTable(
+        "users_example",
+        [
+          { id: 1, name: "Alice", login_count: 10 },
+          { id: 2, name: "Bob", login_count: 20 },
+        ],
+        { mode: "overwrite" },
+      );
+
+      const incomingUsers = [
+        { id: 2, name: "Bobby", login_count: 21 },
+        { id: 3, name: "Charlie", login_count: 5 },
+      ];
+
+      await table
+        .mergeInsert("id")
+        .whenMatchedUpdateAll()
+        .whenNotMatchedInsertAll()
+        .execute(incomingUsers);
+      // --8<-- [end:merge_update_insert]
+      await table.countRows();
+    }
+
+    {
+      // --8<-- [start:merge_delete_missing_by_source]
+      const table = await db.createTable(
+        "users_example",
+        [
+          { id: 1, name: "Alice", login_count: 10 },
+          { id: 2, name: "Bob", login_count: 20 },
+          { id: 3, name: "Charlie", login_count: 5 },
+        ],
+        { mode: "overwrite" },
+      );
+
+      const incomingUsers = [
+        { id: 2, name: "Bobby", login_count: 21 },
+        { id: 3, name: "Charlie", login_count: 5 },
+      ];
+
+      await table
+        .mergeInsert("id")
+        .whenMatchedUpdateAll()
+        .whenNotMatchedInsertAll()
+        .whenNotMatchedBySourceDelete()
+        .execute(incomingUsers);
+      // --8<-- [end:merge_delete_missing_by_source]
+      await table.countRows();
+    }
+
+    {
+      // --8<-- [start:merge_partial_columns]
+      const table = await db.createTable(
+        "users_example",
+        [
+          { id: 1, name: "Alice", login_count: 10 },
+          { id: 2, name: "Bob", login_count: 20 },
+        ],
+        { mode: "overwrite" },
+      );
+
+      const incomingUsers = [
+        { id: 2, name: "Bobby" },
+        { id: 3, name: "Charlie" },
+      ];
+
+      await table
+        .mergeInsert("id")
+        .whenMatchedUpdateAll()
+        .whenNotMatchedInsertAll()
+        .execute(incomingUsers);
+      // --8<-- [end:merge_partial_columns]
+      await table.countRows();
+    }
+
+    {
+      const table = await db.createTable(
+        "users_example",
+        [
+          { id: 1, name: "Alice", login_count: 10 },
+          { id: 2, name: "Bob", login_count: 20 },
+          { id: 3, name: "Charlie", login_count: 5 },
+        ],
+        { mode: "overwrite" },
+      );
+
+      // --8<-- [start:delete_operation]
+      // delete data
+      const predicate = "id = 3";
+      await table.delete(predicate);
+      // --8<-- [end:delete_operation]
+      await table.countRows();
+    }
+
+    {
+      const table = await db.createTable(
+        "users_cleanup_example",
+        [
+          { id: 1, name: "Alice", login_count: 10 },
+          { id: 2, name: "Bob", login_count: 20 },
+          { id: 3, name: "Charlie", login_count: 5 },
+        ],
+        { mode: "overwrite" },
+      );
+
+      // --8<-- [start:update_optimize_cleanup]
+      const olderThan = new Date();
+      olderThan.setDate(olderThan.getDate() - 1);
+      await table.optimize({ cleanupOlderThan: olderThan });
+      // --8<-- [end:update_optimize_cleanup]
+    }
+  });
+});
+
+test("versioning snippets (async)", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    const db = await lancedb.connect(databaseDir);
+
+    // --8<-- [start:versioning_basic_setup]
+    const tableName = "quotes_versioning_example";
+    const data = [
+      { id: 1, author: "Richard", quote: "Wubba Lubba Dub Dub!" },
+      { id: 2, author: "Morty", quote: "Rick, what's going on?" },
+      {
+        id: 3,
+        author: "Richard",
+        quote: "I turned myself into a pickle, Morty!",
+      },
+    ];
+    const table = await db.createTable(tableName, data, { mode: "overwrite" });
+    // --8<-- [end:versioning_basic_setup]
+    expect(await table.countRows()).toBe(3);
+
+    // --8<-- [start:versioning_check_initial_version]
+    const versions = await table.listVersions();
+    const currentVersion = await table.version();
+    console.log(`Number of versions after creation: ${versions.length}`);
+    console.log(`Current version: ${currentVersion}`);
+    // --8<-- [end:versioning_check_initial_version]
+    expect(versions.length).toBe(1);
+    expect(currentVersion).toBe(versions[versions.length - 1].version);
+
+    // --8<-- [start:versioning_update_data]
+    await table.update({
+      where: "author = 'Richard'",
+      values: { author: "Richard Daniel Sanchez" },
+    });
+    const rowsAfterUpdate = await table.countRows(
+      "author = 'Richard Daniel Sanchez'",
+    );
+    console.log(`Rows updated to Richard Daniel Sanchez: ${rowsAfterUpdate}`);
+    // --8<-- [end:versioning_update_data]
+    expect(rowsAfterUpdate).toBe(2);
+
+    // --8<-- [start:versioning_add_data]
+    const moreData = [
+      {
+        id: 4,
+        author: "Richard Daniel Sanchez",
+        quote: "That's the way the news goes!",
+      },
+      { id: 5, author: "Morty", quote: "Aww geez, Rick!" },
+    ];
+    await table.add(moreData);
+    // --8<-- [end:versioning_add_data]
+    expect(await table.countRows()).toBe(5);
+
+    // --8<-- [start:versioning_check_versions_after_mod]
+    const versionsAfterMod = await table.listVersions();
+    const versionCountAfterMod = versionsAfterMod.length;
+    const versionAfterMod = await table.version();
+    console.log(
+      `Number of versions after modifications: ${versionCountAfterMod}`,
+    );
+    console.log(`Current version: ${versionAfterMod}`);
+    // --8<-- [end:versioning_check_versions_after_mod]
+    expect(versionCountAfterMod).toBeGreaterThanOrEqual(2);
+    expect(versionAfterMod).toBe(versionsAfterMod[versionsAfterMod.length - 1].version);
+
+    // --8<-- [start:versioning_list_all_versions]
+    const allVersions = await table.listVersions();
+    for (const v of allVersions) {
+      console.log(`Version ${v.version}, created at ${v.timestamp}`);
+    }
+    // --8<-- [end:versioning_list_all_versions]
+    expect(allVersions.length).toBeGreaterThanOrEqual(1);
+
+    // --8<-- [start:versioning_rollback]
+    await table.checkout(versionAfterMod);
+    await table.restore();
+    const versionsAfterRollback = await table.listVersions();
+    const versionCountAfterRollback = versionsAfterRollback.length;
+    console.log(
+      `Total number of versions after rollback: ${versionCountAfterRollback}`,
+    );
+    // --8<-- [end:versioning_rollback]
+    expect(versionCountAfterRollback).toBe(versionCountAfterMod + 1);
+    expect(await table.countRows()).toBe(5);
+
+    // --8<-- [start:versioning_checkout_latest]
+    await table.checkoutLatest();
+    // --8<-- [end:versioning_checkout_latest]
+    const latestVersion = await table.version();
+    const versionsAfterCheckout = await table.listVersions();
+    expect(latestVersion).toBe(
+      versionsAfterCheckout[versionsAfterCheckout.length - 1].version,
+    );
+
+    // --8<-- [start:versioning_delete_data]
+    await table.delete("author = 'Morty'");
+    const rowsAfterDeletion = await table.countRows();
+    console.log(`Number of rows after deletion: ${rowsAfterDeletion}`);
+    // --8<-- [end:versioning_delete_data]
+    expect(rowsAfterDeletion).toBe(3);
+
+    const tagsTable = await db.createTable(
+      "quotes_tags_example",
+      [{ id: 1, author: "Richard", quote: "Wubba Lubba Dub Dub!" }],
+      { mode: "overwrite" },
+    ); // v1
+    await tagsTable.add([
+      { id: 2, author: "Morty", quote: "Aww geez, Rick!" },
+    ]); // v2
+    await tagsTable.add([
+      { id: 3, author: "Summer", quote: "Whatever, Grandpa" },
+    ]); // v3
+
+    // --8<-- [start:versioning_tags]
+    const tags = await tagsTable.tags();
+
+    // Create a tag pointing at a specific version
+    await tags.create("baseline", 1);
+    await tags.create("with-edits", await tagsTable.version());
+
+    // List all tags on this table
+    console.log(await tags.list());
+
+    // Look up the version a tag points at
+    console.log(await tags.getVersion("baseline"));
+
+    // Move an existing tag to a different version
+    await tags.update("baseline", 2);
+
+    // Check out a version by tag name
+    await tagsTable.checkout("baseline");
+    console.log(await tagsTable.version());
+
+    // Delete a tag (does not delete the underlying version)
+    await tags.delete("with-edits");
+
+    // Return to the latest version
+    await tagsTable.checkoutLatest();
+    // --8<-- [end:versioning_tags]
+    expect(await tagsTable.version()).toBe(3);
+    const remainingTags = await tags.list();
+    expect(remainingTags).toHaveProperty("baseline");
+    expect(remainingTags).not.toHaveProperty("with-edits");
+  });
+});
+
+test("branch snippets (async)", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    const db = await lancedb.connect(databaseDir);
+    const table = await db.createTable(
+      "quotes_branches_example",
+      [
+        { id: 1, author: "Lancelot", quote: "My lance never fails." },
+        { id: 2, author: "Arthur", quote: "Long live Camelot!" },
+        { id: 3, author: "Merlin", quote: "Magic always has a price." },
+      ],
+      { mode: "overwrite" },
+    );
+
+    const branches = await table.branches();
+
+    // --8<-- [start:branch_create]
+    // Fork an isolated, writable branch from main's latest version.
+    // `create` returns a table handle scoped to the new branch.
+    const branch = await branches.create("exp");
+    // --8<-- [end:branch_create]
+
+    // --8<-- [start:branch_write]
+    // Writes land on the branch handle only; main is left untouched.
+    await branch.add([{ id: 4, author: "Lancelot", quote: "For the realm!" }]);
+    console.log(await branch.countRows()); // 4 rows on the branch
+    console.log(await table.countRows()); // 3 rows; main is unaffected
+
+    // List every branch, each mapped to its metadata (including its fork point).
+    console.log(await branches.list());
+    // --8<-- [end:branch_write]
+
+    // --8<-- [start:branch_reopen]
+    // Reopen an existing branch by name from the table handle...
+    const checkedOut = await branches.checkout("exp");
+    // ...or open it directly from the database connection.
+    const branchHandle = await db.openTable(
+      "quotes_branches_example",
+      undefined,
+      { branch: "exp" },
+    );
+    console.log(await checkedOut.countRows(), await branchHandle.countRows()); // both 4
+    // --8<-- [end:branch_reopen]
+
+    // --8<-- [start:branch_delete]
+    // Delete the branch and its branch-local history. Data on main is safe.
+    await branches.delete("exp");
+    // --8<-- [end:branch_delete]
+
+    expect(await table.countRows()).toBe(3);
+    expect(await branches.list()).not.toHaveProperty("exp");
+
+    // Setup: a branch with row results that we want to apply to main.
+    const candidate = await branches.create("candidate");
+    await candidate.update({
+      where: "id = 1",
+      values: { quote: "Revised on the branch" },
+    });
+    await candidate.add([
+      { id: 4, author: "Galahad", quote: "The grail awaits." },
+    ]);
+
+    // --8<-- [start:branch_upsert_to_main]
+    // This is a row-level upsert, not a merge of branch histories.
+    // `mergeInsert` updates matching rows and inserts new rows using a stable
+    // unique key. Filter the branch read if you only want to apply some results.
+    const rowsToApply = await candidate.toArrow();
+    await table
+      .mergeInsert("id")
+      .whenMatchedUpdateAll() // update rows that already exist on main
+      .whenNotMatchedInsertAll() // insert rows that are new on the branch
+      .execute(rowsToApply);
+    // --8<-- [end:branch_upsert_to_main]
+
+    expect(await table.countRows()).toBe(4);
+    await branches.delete("candidate");
+
+    // Setup: a larger table with a vector and a text column to index.
+    const products = await db.createTable(
+      "products_branch_index",
+      Array.from({ length: 512 }, (_, i) => ({
+        id: i,
+        vector: Array.from({ length: 4 }, () => Math.random()),
+        text: `product number ${i}`,
+      })),
+      { mode: "overwrite" },
+    );
+    const productBranches = await products.branches();
+
+    // --8<-- [start:branch_index]
+    // Build and validate indexes on a branch before using the configuration on
+    // main.
+    const dev = await productBranches.create("index-dev");
+
+    // A vector (ANN) index and a full-text search index, both branch-scoped.
+    await dev.createIndex("vector", {
+      config: lancedb.Index.ivfPq({
+        distanceType: "cosine",
+        numPartitions: 1,
+        numSubVectors: 2,
+      }),
+    });
+    await dev.createIndex("text", { config: lancedb.Index.fts() });
+
+    // Both indexes live only on the branch; main still has none.
+    console.log((await dev.listIndices()).map((ix) => ix.name)); // branch: two indexes
+    console.log((await products.listIndices()).map((ix) => ix.name)); // main: [] (untouched)
+    // --8<-- [end:branch_index]
+
+    expect(await dev.listIndices()).toHaveLength(2);
+    expect(await products.listIndices()).toHaveLength(0);
+    await productBranches.delete("index-dev");
+  });
+});
+
+test("consistency snippets (async)", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    // --8<-- [start:consistency_strong]
+    const strongWriterDb = await lancedb.connect(databaseDir);
+    const strongReaderDb = await lancedb.connect(databaseDir, {
+      readConsistencyInterval: 0,
+    });
+    const strongWriterTable = await strongWriterDb.createTable(
+      "consistency_strong_table",
+      [{ id: 1 }],
+      { mode: "overwrite" },
+    );
+    const strongReaderTable = await strongReaderDb.openTable(
+      "consistency_strong_table",
+    );
+    await strongWriterTable.add([{ id: 2 }]);
+    const strongRowsAfterWrite = await strongReaderTable.countRows();
+    console.log(`Rows visible with strong consistency: ${strongRowsAfterWrite}`);
+    // --8<-- [end:consistency_strong]
+    expect(strongRowsAfterWrite).toBe(2);
+
+    // --8<-- [start:consistency_eventual]
+    const eventualWriterDb = await lancedb.connect(databaseDir);
+    const eventualReaderDb = await lancedb.connect(databaseDir, {
+      readConsistencyInterval: 3600,
+    });
+    const eventualWriterTable = await eventualWriterDb.createTable(
+      "consistency_eventual_table",
+      [{ id: 1 }],
+      { mode: "overwrite" },
+    );
+    const eventualReaderTable = await eventualReaderDb.openTable(
+      "consistency_eventual_table",
+    );
+    await eventualWriterTable.add([{ id: 2 }]);
+    const eventualRowsAfterWrite = await eventualReaderTable.countRows();
+    console.log(
+      `Rows visible before eventual refresh interval: ${eventualRowsAfterWrite}`,
+    );
+    // --8<-- [end:consistency_eventual]
+    expect(eventualRowsAfterWrite).toBe(1);
+
+    // --8<-- [start:consistency_checkout_latest]
+    const checkoutWriterDb = await lancedb.connect(databaseDir);
+    const checkoutReaderDb = await lancedb.connect(databaseDir);
+    const checkoutWriterTable = await checkoutWriterDb.createTable(
+      "consistency_checkout_latest_table",
+      [{ id: 1 }],
+      { mode: "overwrite" },
+    );
+    const checkoutReaderTable = await checkoutReaderDb.openTable(
+      "consistency_checkout_latest_table",
+    );
+    await checkoutWriterTable.add([{ id: 2 }]);
+    const rowsBeforeRefresh = await checkoutReaderTable.countRows();
+    console.log(`Rows before checkoutLatest: ${rowsBeforeRefresh}`);
+    await checkoutReaderTable.checkoutLatest();
+    const rowsAfterRefresh = await checkoutReaderTable.countRows();
+    console.log(`Rows after checkoutLatest: ${rowsAfterRefresh}`);
+    // --8<-- [end:consistency_checkout_latest]
+    expect(rowsBeforeRefresh).toBe(1);
+    expect(rowsAfterRefresh).toBe(2);
+  });
+});
diff --git a/docs/web-tests/ts/tsconfig.json b/docs/web-tests/ts/tsconfig.json
new file mode 100644
index 000000000..49a04ac10
--- /dev/null
+++ b/docs/web-tests/ts/tsconfig.json
@@ -0,0 +1,17 @@
+{
+  "include": ["*.test.ts"],
+  "compilerOptions": {
+    "target": "es2022",
+    "module": "NodeNext",
+    "declaration": true,
+    "outDir": "./dist",
+    "strict": true,
+    "allowJs": true,
+    "resolveJsonModule": true,
+    "emitDecoratorMetadata": true,
+    "experimentalDecorators": true,
+    "moduleResolution": "NodeNext",
+    "allowImportingTsExtensions": true,
+    "emitDeclarationOnly": true
+  }
+}
diff --git a/docs/web-tests/ts/util.ts b/docs/web-tests/ts/util.ts
new file mode 100644
index 000000000..404abedf7
--- /dev/null
+++ b/docs/web-tests/ts/util.ts
@@ -0,0 +1,16 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+import * as fs from "node:fs";
+import { tmpdir } from "node:os";
+import * as path from "node:path";
+
+export async function withTempDirectory(
+  fn: (tempDir: string) => Promise<void>,
+) {
+  const tmpDirPath = fs.mkdtempSync(path.join(tmpdir(), "temp-dir-"));
+  try {
+    await fn(tmpDirPath);
+  } finally {
+    fs.rmSync(tmpDirPath, { recursive: true });
+  }
+}
diff --git a/docs/web/.mintignore b/docs/web/.mintignore
new file mode 100644
index 000000000..b8da6c17a
--- /dev/null
+++ b/docs/web/.mintignore
@@ -0,0 +1,6 @@
+# Functions are documented before their release. At release, remove these
+# entries, add the pages back to the navigation in docs.json, restore the
+# /feature-engineering/functions redirect to /management/function, and publish
+# the drafts marked "Draft for the Functions release".
+management/function.mdx
+sql/ddl/function.mdx
diff --git a/docs/web/agent-branch-experiments.mdx b/docs/web/agent-branch-experiments.mdx
new file mode 100644
index 000000000..3f4f1cbfa
--- /dev/null
+++ b/docs/web/agent-branch-experiments.mdx
@@ -0,0 +1,100 @@
+---
+title: "Run experiments on branches"
+sidebarTitle: "Branch Experiments"
+description: "Use LanceDB branches to isolate agent-driven experiments from main, evaluate them on a fixed test set, and promote only the winner."
+icon: "flask"
+keywords: ["AI agents", "branches", "experiments", "embeddings", "evaluation"]
+---
+
+LanceDB supports [branching](/tables/branching) to isolate experiments from the main table when working with agents.
+Branches are useful when you instruct an agent to try several approaches without
+affecting the production data on the `main` branch.
+Each experiment gets its own writable table history.
+
+For example, you can generate and compare embeddings from two text embedding
+models on the given table:
+
+| Branch | Model | Added column |
+| --- | --- | --- |
+| `embed-minilm` | `sentence-transformers/all-MiniLM-L6-v2` | `vector_minilm` |
+| `embed-nomic` | `nomic-embed-text` from Ollama | `vector_nomic` |
+
+The branches share a logical table, but each has its own schema and history.
+The original table handle still points to `main`. LanceDB does not have a
+process-wide current branch.
+
+Install the model packages, then download the `nomic-embed-text` model from Ollama
+(or any other model you prefer):
+
+```bash
+uv add sentence-transformers ollama
+ollama pull nomic-embed-text
+```
+
+It's always recommended to measure the results of an experiment on a fixed evaluation set.
+Here's an example of how you could ask the agent to run both experiments and compare the results:
+
+```text Agent prompt
+Use the lancedb plugin and the LanceDB branching documentation to compare two
+embedding experiments on the `camelot_multimodal` table.
+
+1. Fork `embed-minilm` from `main`. Build normalized embeddings from
+   `"{role}. {description}"` with
+   `sentence-transformers/all-MiniLM-L6-v2` and add them as `vector_minilm`.
+2. Fork `embed-nomic` from `main`. Embed the identical text with Ollama
+   `nomic-embed-text` and add the results as `vector_nomic`.
+3. Process rows in batches and write only through each branch-scoped table
+   handle. Verify that neither new column appears on `main`.
+4. Evaluate both branches with the queries "wise magical advisor",
+   "treacherous rebel", and "virtuous Grail knight". Report the top three names,
+   latency, and whether the expected character ranks first.
+5. Do not modify `main` and do not delete either branch. Recommend a winner
+   based on the results.
+```
+
+<Note>
+The vector dimensions may differ because the columns live on separate branches.
+Use a different column name for each model. Within an experiment, use the same
+model for the stored text and the search queries.
+</Note>
+
+## Apply the winning experiment to `main` {#apply-the-winning-experiment-to-main}
+
+The branch experiments leave you with the results side by side, and they
+deliberately never touch `main`. Once you've picked a winner, apply it to
+`main` yourself by rerunning that experiment's validated transformation
+directly against the `main` table. Rerunning the reviewed operation is the
+reliable path on both OSS and Enterprise: it replays exactly the transformation
+you validated on the branch, and it works the same regardless of how the branch
+evolved.
+
+Suppose `nomic-embed-text` wins. Rerun the same Nomic embedding transformation
+you validated on `embed-nomic` against `main` in batches, then call
+`table.optimize()` on OSS. Verify a bounded vector search on `main`, and keep
+the losing branch around until you're confident in the result.
+
+See [Branches](/tables/branching) for more on how branches, versions, and tags
+relate.
+
+## More experiments you can run {#more-experiments-you-can-run}
+
+Swapping embedding models on a branch while working with agents is only one example
+of what you can do with the LanceDB plugin. The table below shows other experiments
+you can run with the plugin.
+
+| Hypothesis | Change on the branch | What to evaluate |
+| --- | --- | --- |
+| A different embedding model improves retrieval | Add a new vector column | Recall, ranking quality, latency, and cost |
+| A new parser or OCR model improves source data | Reprocess files and backfill metadata | Validation failures and a reviewed sample |
+| A new search setup works better | Build an index or change hybrid-search and reranking settings | Relevance and latency on a fixed query set |
+| A curation rule improves the corpus | Deduplicate, classify, or filter records | False positives, false negatives, and row counts |
+| A migration is safe to deploy | Add columns or run a backfill | Schema compatibility and application checks |
+
+For each experiment, ask the agent to state the hypothesis, use a fixed
+evaluation set, report the comparison, and, if you want, work on an alternate
+branch that's isolated from `main` until you decide you want the new derived
+column in `main`.
+
+Once you've chosen a winner, rerun that validated transformation to ingest it into `main`
+yourself, on either LanceDB OSS or Enterprise. See our documentation on [branches](/tables/branching)
+for more information.
diff --git a/docs/web/api-reference/rest/index.mdx b/docs/web/api-reference/rest/index.mdx
new file mode 100644
index 000000000..b2dd10016
--- /dev/null
+++ b/docs/web/api-reference/rest/index.mdx
@@ -0,0 +1,94 @@
+---
+title: REST API Reference
+sidebarTitle: "Overview"
+description: "API reference for LanceDB"
+
+---
+
+[Lance REST Namespace](https://lance.org/format/namespace/) spec
+is an OpenAPI protocol that enables reading, writing and managing Lance tables by connecting
+those metadata services or building a custom metadata server in a standardized way.
+
+LanceDB OSS allows you to interface with Lance tables via the REST Namespace.
+LanceDB Enterprise provides an extended REST API with
+additional endpoints for managing tables and data.
+If you have specific needs or questions about the Enterprise REST API Namespace,
+please [contact us](mailto:support@lancedb.com).
+
+## Authentication {#authentication}
+
+<Badge color="red">Enterprise</Badge>
+
+All HTTP requests to LanceDB APIs must contain an <u>x-api-key</u> header that specifies a valid API key and
+must be encoded as JSON or Arrow RPC.
+
+To authenticate to the Enterprise REST API, you need the endpoint for your deployment and a valid API key for that deployment.
+
+### Get your Enterprise credentials {#get-your-enterprise-credentials}
+
+1. Obtain the following values from your LanceDB administrator or the LanceDB team that provisioned your Enterprise deployment:
+   - an API key
+   - your Enterprise REST endpoint
+   - your database name, if your deployment uses a private endpoint or `host_override`
+
+2. Export those values in your terminal:
+
+```bash
+export LANCEDB_API_KEY="<your-api-key>"
+export LANCEDB_HOST_OVERRIDE="https://your-enterprise-endpoint.com"
+export LANCEDB_DATABASE="your-database-name"
+```
+
+`LANCEDB_HOST_OVERRIDE` is the endpoint the SDKs take as `host_override`; the
+database name is the part of its `db://` URI after `db://`.
+
+3. If your Enterprise deployment is private, connect through the private network endpoint provided for your deployment. For example, Azure Private Link deployments commonly use a private IP or an internal DNS name as the endpoint.
+
+### Verify authentication {#verify-authentication}
+
+4. Check that you can reach the deployment and list tables:
+
+```bash
+curl -X GET "$LANCEDB_HOST_OVERRIDE/v1/table/" \
+   -H "x-api-key: $LANCEDB_API_KEY" \
+   -H "x-lancedb-database: $LANCEDB_DATABASE"
+```
+
+The response lists one page of table names, with a token for the next page when
+there are more.
+
+If your deployment endpoint already includes the database host name, you can omit the `x-lancedb-database` header.
+
+5. Create a table to confirm write access. Let's call it `words`. A table is
+created from an Arrow IPC stream, which this writes with PyArrow:
+
+```bash
+python -c "import pyarrow as pa, sys; t = pa.table({'word': ['hello', 'world']}); w = pa.ipc.new_stream(sys.stdout.buffer, t.schema); w.write_table(t); w.close()" > words.arrow
+
+curl -X POST "$LANCEDB_HOST_OVERRIDE/v1/table/words/create" \
+   -H "Content-Type: application/vnd.apache.arrow.stream" \
+   -H "x-api-key: $LANCEDB_API_KEY" \
+   -H "x-lancedb-database: $LANCEDB_DATABASE" \
+   --data-binary @words.arrow
+```
+
+6. Check that the table has been created:
+
+```bash
+curl -X POST "$LANCEDB_HOST_OVERRIDE/v1/table/words/describe" \
+   -H "Content-Type: application/json" \
+   -H "x-api-key: $LANCEDB_API_KEY" \
+   -H "x-lancedb-database: $LANCEDB_DATABASE" \
+   -d '{}'
+```
+
+That's it -- you're connected! Now, you can start adding data and querying it.
+You can visit the tutorial section to build your own applications with LanceDB.
+
+<Card
+   title="Tutorials"
+   icon="book"
+   href="/tutorials/"
+>
+   Check out our tutorials on building various applications with LanceDB.
+</Card>
diff --git a/docs/web/architecture.mdx b/docs/web/architecture.mdx
new file mode 100644
index 000000000..997bb95b0
--- /dev/null
+++ b/docs/web/architecture.mdx
@@ -0,0 +1,124 @@
+---
+title: "Architecture"
+sidebarTitle: "Architecture"
+description: "How LanceDB Enterprise separates query serving, background work, and storage, and how it compares with embedded LanceDB OSS."
+icon: cubes
+keywords: ["architecture", "embedded", "distributed", "object storage", "compute", "deployment"]
+---
+
+LanceDB OSS is an embedded library for working with Lance tables. LanceDB
+Enterprise is a multimodal lakehouse for data curation, feature engineering,
+search, and training data access. Its architecture separates query serving,
+read execution, caching, and background work. Both use the open
+[Lance](/basics/lance) format.
+
+Teams can curate datasets, derive features, search multimodal records, and
+read the resulting tables into training frameworks. Enterprise separates the
+resources that serve shared data from those that build indexes and run
+background work, so teams can size them for these different workloads.
+
+![LanceDB OSS runs the application and embedded library in one process, using local disk or object storage. Enterprise applications connect to query serving, with separate read execution and caching, indexing and background workers, and durable object storage. Both use the Lance format, but they do not share a storage location automatically.](/static/assets/images/overview/lancedb-architecture.svg)
+
+The diagram shows the main compute and storage boundaries, not every request
+path. The control plane governs the Enterprise service.
+
+## A shared format, different storage choices {#storage}
+
+Lance stores table data, versions, and indexes. OSS can read and write tables on
+local disk or in object storage. Enterprise keeps durable table data and index
+artifacts in object storage, independently of the machines serving requests.
+Its caches accelerate reads; they are not the durable source of truth.
+
+Sharing a format does not mean that an Enterprise deployment automatically
+opens an existing OSS database. Plan how to move or connect your data and
+configure access for your deployment.
+
+## OSS: an embedded library inside your application {#embedded}
+
+With an embedded connection, the LanceDB library plans and executes queries,
+builds indexes, and updates tables inside your process. There is no separate
+database server to deploy.
+
+This fits local retrieval applications, notebooks, development, and
+single-node data pipelines. Your application controls the compute resources and
+when to run [table maintenance](/maintenance/compaction). You also manage access
+to the local files or object storage that hold your tables.
+
+## Enterprise: a multimodal lakehouse for shared workloads {#distributed}
+
+Applications connect to remote tables through a LanceDB SDK. The service handles
+requests across a fleet, with components sized for different kinds of work:
+
+- **Query serving** receives requests, resolves tables, and plans operations.
+  Add serving capacity as more applications and users depend on your data.
+- **Read execution and caching** use separately provisioned resources. Executors
+  cache table data to reduce repeated reads from object storage.
+- **Indexing and background workers** build indexes and perform maintenance
+  outside the query request path. Size this work separately from query serving
+  as your data grows.
+
+The control plane manages configuration, identity, policy, and cluster
+lifecycle. It governs the service rather than executing table queries.
+[Authorization](/governance/authorization) lets teams control access to catalog
+objects when enabled and configured by the administrator. Deployment settings govern background
+[compaction](/maintenance/compaction) and
+[cleanup](/maintenance/cleanup), including retention.
+
+These boundaries support different parts of an AI data workflow.
+[SQL curation](/curation) requires a deployment with SQL enabled.
+[Feature engineering](/feature-engineering) can produce derived columns and
+tables; computed columns and materialized views require a deployment that
+enables them. [Geneva](/geneva) runs distributed Python feature backfills while
+its capabilities continue to transition to LanceDB APIs.
+[Training data loaders](/training/data-loading) read tables into model training
+frameworks in both offerings. LanceDB provides data access for training; the
+training framework runs the model.
+
+Size and monitor each tier for your workload. Read the
+[Enterprise architecture](/enterprise/architecture) for the component details
+and request flows.
+
+## Application changes for Enterprise {#differences}
+
+Core operations remain familiar: create a table, add data, and run a search.
+With OSS, the application executes those operations. With Enterprise, the SDK
+sends requests to the service, and the service manages execution and configured
+background maintenance.
+
+To use Enterprise, configure the cluster endpoint, database URI and credentials,
+and make your data available in the Enterprise database. Review SDK methods
+that behave differently on remote tables, including result retrieval and
+background jobs. [Moving to LanceDB Enterprise](/basics/sdk#moving-to-enterprise)
+describes these application changes. If you plan to use materialized views, check the
+[stable row ID requirements](/basics/limits#schema) before creating source tables.
+
+## Choose for the workload you need to run {#choosing}
+
+Choose OSS when you want an embedded library and can manage its resources and
+maintenance in your application.
+
+Choose Enterprise when multiple applications or teams need a shared multimodal
+lakehouse for curation, feature engineering, search, and training data access.
+It also fits workloads whose query and indexing demand grow at different rates,
+or that need configured service-level access controls and background operations.
+You can begin with Enterprise.
+
+[Talk to us about your workload](https://www.lancedb.com/contact) to plan capacity,
+deployment options, and the path from your existing data to Enterprise.
+
+## Where to go next {#next}
+
+<CardGroup cols={2}>
+  <Card title="Enterprise architecture" icon="cubes" href="/enterprise/architecture">
+    Query serving, execution, and background work in detail.
+  </Card>
+  <Card title="Connect to Enterprise" icon="plug" href="/basics/connect#enterprise">
+    Connect an application to your deployment.
+  </Card>
+  <Card title="Guarantees" icon="shield-check" href="/basics/guarantees">
+    What a write promises, and what a reader sees.
+  </Card>
+  <Card title="Lance format" icon="layer-group" href="/basics/lance">
+    The open format underneath both offerings.
+  </Card>
+</CardGroup>
diff --git a/docs/web/basics/agentic-skills.mdx b/docs/web/basics/agentic-skills.mdx
new file mode 100644
index 000000000..bda6e1f65
--- /dev/null
+++ b/docs/web/basics/agentic-skills.mdx
@@ -0,0 +1,292 @@
+---
+title: "Agentic skills"
+sidebarTitle: "Agentic Skills"
+description: "Give a coding agent a current, maintained reference for the LanceDB APIs."
+icon: robot
+keywords: ["agents", "plugin", "claude code", "codex", "cursor", "copilot"]
+---
+
+import {
+  PyCamelotSchema,
+  PyCamelotBatches,
+  PyCamelotOssIngestion,
+} from '/snippets/build_with_ai_agents.mdx';
+
+A coding agent writing LanceDB code from memory writes whatever was in its
+training data -- often an older API, sometimes one that never existed. The
+LanceDB agent plugin fixes that by supplying the current reference at the point
+the agent needs it.
+
+## The plugin {#plugin}
+
+It ships as the `lancedb` plugin in
+the [lancedb-agent-plugins](https://github.com/lancedb/lancedb-agent-plugins)
+repository, which is also a plugin marketplace. Install it with your agent's
+plugin manager:
+
+<CodeGroup>
+```text Claude Code
+/plugin marketplace add lancedb/lancedb-agent-plugins
+/plugin install lancedb@lancedb
+```
+
+```bash Codex icon="terminal"
+codex plugin marketplace add lancedb/lancedb-agent-plugins
+codex plugin add lancedb@lancedb
+```
+
+```bash Other agents icon="terminal"
+npx plugins add lancedb/lancedb-agent-plugins
+```
+</CodeGroup>
+
+The [`plugins`](https://www.npmjs.com/package/plugins) installer shown under
+**Other agents** is a cross-tool option. It detects which agent CLIs are on your
+`PATH` — Cursor, GitHub Copilot CLI, VS Code, Grok Build, and Kimi Code, as well
+as Claude Code and Codex — and installs through each one's native plugin system.
+
+The plugin supplements the agent's training data with current LanceDB
+instructions. To pick up later revisions, refresh the marketplace:
+
+<CodeGroup>
+```text Claude Code
+/plugin marketplace update lancedb
+```
+
+```bash Codex icon="terminal"
+codex plugin marketplace upgrade
+```
+
+```bash Other agents icon="terminal"
+npx plugins add lancedb/lancedb-agent-plugins
+```
+</CodeGroup>
+
+<Accordion title="Where the plugin gets installed">
+Each plugin manager clones the repository, reads its marketplace manifest to find
+the `lancedb` plugin, and hands the plugin to the agent's own plugin store rather
+than copying files into your project. Claude Code and Codex keep it under
+`~/.claude/plugins` and `~/.codex/plugins` respectively, so the plugin is available
+in every project on the machine.
+
+The `npx plugins` installer defaults to the same user-wide scope. Pass
+`--scope project` to record the plugin in the current repository instead, so that
+everyone working in it gets the same plugin, or `-t <target>` to install for a
+single agent rather than every one it detects:
+
+```bash
+npx plugins add lancedb/lancedb-agent-plugins --scope project -t cursor
+```
+
+Not every agent supports project scope; `npx plugins targets` lists what it
+found and what each target supports.
+</Accordion>
+
+### Get started with the LanceDB agent plugin {#get-started-with-the-lancedb-agent-plugin}
+
+This tutorial uses the Camelot dataset from the [quickstart](/quickstart), with
+a portrait added for each character. Each LanceDB row contains validated
+metadata and raw JPEG bytes. Text, images, and any embeddings you add later
+remain in the same table.
+
+### 1\. Download the multimodal dataset {#1-download-the-multimodal-dataset}
+
+From a new project directory, download the JSON file and portraits:
+
+```bash
+mkdir -p data/img
+
+BASE_URL="https://docs.lancedb.com/static/assets/tutorials/build-with-ai-agents/camelot/data"
+curl -fsSL "$BASE_URL/camelot.json" -o data/camelot.json
+
+for image in \
+  arthur.jpg \
+  guinevere.jpg \
+  merlin.jpg \
+  mordred.jpg \
+  sir_galahad.jpg \
+  sir_gawain.jpg \
+  sir_lancelot.jpg \
+  sir_percival.jpg
+do
+  curl -fsSL "$BASE_URL/img/$image" -o "data/img/$image"
+done
+```
+
+Each JSON record has this shape:
+
+```json
+{
+  "id": 2,
+  "name": "Merlin",
+  "role": "Wizard and Advisor",
+  "description": "A powerful wizard and prophet who mentors Arthur.",
+  "stats": {
+    "strength": 2,
+    "courage": 4,
+    "magic": 5,
+    "wisdom": 5
+  },
+  "img": "data/img/merlin.jpg"
+}
+```
+
+JSON input may have missing fields, unexpected fields, or values of the wrong
+type. The LanceDB plugin tells the agent to validate each record with strict
+Pydantic models before writing it.
+
+After the agent writes the pipeline, inspect the schema, batching, and write
+path rather than assuming it followed the plugin's guidance correctly.
+
+### 2\. Prompt your agent to build the pipeline {#2-prompt-your-agent-to-build-the-pipeline}
+
+Install the Python packages used by the example:
+
+```bash icon="terminal"
+uv init
+uv add lancedb pyarrow pydantic
+```
+
+If you're using LanceDB Enterprise, ask the agent to ingest into an Enterprise table,
+provide the relevant environment variables for connecting to your Enterprise deployment
+in a local `.env` file, and point the agent to it.
+
+```text .env
+LANCEDB_URI=db://your_project_name
+LANCEDB_API_KEY=your_api_key_here
+LANCEDB_REGION=us-east-1
+LANCEDB_HOST_OVERRIDE=https://your-enterprise-endpoint.com
+```
+
+If you're using LanceDB OSS, no connection settings are required, as it runs as an
+embedded retrieval library. A simple prompt like this should work:
+
+```text Agent prompt
+# If using OSS
+Use the lancedb plugin to ingest the dataset in `data/` into a LanceDB
+OSS table.
+
+# If using enterprise
+Use the lancedb plugin to ingest the dataset in `data/` into a LanceDB
+Enterprise table using the connection information in `.env`.
+```
+
+Because the plugin is registered with the agent's own plugin system, the agent
+should pick it up on its own once you restart the session. If it does not,
+simply ask it to use the `lancedb` plugin in the prompt, as shown above.
+
+That should be enough! The agent will create `ingest_multimodal.py`, or similar.
+The following sections inspect the script to verify that it follows the plugin's guidance.
+
+#### Data validation {#data-validation}
+
+The plugin encourages the agent to validate each record with Pydantic before writing it.
+The agent should ideally define a schema for the table and a nested schema for the
+`stats` field.
+
+<CodeBlock filename="ingest_multimodal.py" language="Python" icon="python">
+  {PyCamelotSchema}
+</CodeBlock>
+
+In this case, our agent correctly defined `Character` and `Stats` Pydantic models
+and validated the JSON before adding it to the table.
+
+#### Batched ingestion {#batched-ingestion}
+
+Naively calling `table.add()` once per row is slow, and is considered an anti-pattern
+in LanceDB. The plugin encourages the agent to collect incoming rows into batches and
+write them with a single `table.add()` call. When you use the plugin, the agent should
+produce something like this:
+
+<CodeBlock filename="ingest_multimodal.py" language="Python" icon="python">
+  {PyCamelotBatches}
+</CodeBlock>
+
+The script calls `Character.model_validate(...)` before adding a record to the
+batch. If validation fails, that batch is never written. The function yields up
+to `batch_size` rows at a time, providing an iterable of batches for the ingestion step,
+shown next.
+
+#### Table maintenance {#table-maintenance}
+For LanceDB OSS, the plugin instructs the agent to call
+`table.optimize()` after the ingestion loop. This compacts small fragments, cleans up
+old versions according to the retention policy, and incorporates new data into indexes.
+
+<CodeBlock filename="ingest_multimodal.py" language="Python" icon="python">
+  {PyCamelotOssIngestion}
+</CodeBlock>
+
+If you're using LanceDB Enterprise, the plugin mentions that this step is not needed
+because LanceDB Enterprise handles maintenance automatically.
+
+
+<Note>
+This example dataset has only eight rows, so the default batch size writes it in
+one call. Larger inputs should still avoid single-row write commits.
+</Note>
+
+### 3\. Run the OSS pipeline {#3-run-the-oss-pipeline}
+
+```bash
+uv run python ingest_multimodal.py
+```
+
+The table now contains the validated character records and their JPEG bytes.
+Here are the first three rows:
+
+| Image | Character | Role |
+| --- | --- | --- |
+| <img src="/static/assets/tutorials/build-with-ai-agents/camelot/data/img/arthur.jpg" alt="King Arthur" width="128" height="128" /> | King Arthur | King of Camelot |
+| <img src="/static/assets/tutorials/build-with-ai-agents/camelot/data/img/merlin.jpg" alt="Merlin" width="128" height="128" /> | Merlin | Wizard and Advisor |
+| <img src="/static/assets/tutorials/build-with-ai-agents/camelot/data/img/guinevere.jpg" alt="Queen Guinevere" width="128" height="128" /> | Queen Guinevere | Queen of Camelot |
+
+Once your pipeline works, you can [run experiments on branches](/agent-branch-experiments)
+to try new embedding models, parsers, or search settings without touching `main`.
+
+### Takeaways {#takeaways}
+
+The example in this tutorial was small, but similar ideas apply to other workflows, too.
+Give the agent the data source, the constraints it must respect, and the
+artifacts it should return.
+
+The plugin supplies LanceDB-specific guidance, but it's the user's responsibility to
+ensure the output makes sense for the application.
+
+### Try the plugin with your own dataset {#try-the-plugin-with-your-own-dataset}
+
+The plugin shown in this tutorial should generalize reasonably well to other use cases.
+If you find any issues, open [an issue](https://github.com/lancedb/lancedb-agent-plugins/issues)
+on GitHub, clearly describing the intended behavior.
+
+You can choose OSS or Enterprise based on how the work will run:
+
+Start with [LanceDB OSS](/quickstart) during the early stages of a project
+when an agent is helping you prototype,
+explore a dataset, or run small workflows on a subset of the data. The application owns
+the storage and lifecycle work, so ask the agent to validate inputs, write in
+batches, and it will include table maintenance operations such as `optimize()`
+where appropriate.
+
+Choose [LanceDB Enterprise](/enterprise) when the resulting table becomes
+shared production infrastructure, and the workload needs distributed capacity,
+private deployment, or platform-managed operations. The data format and the core
+table API stay the same, so the pipeline's table and search code carries over:
+the agent connects to a remote `db://` table, leaves maintenance to the
+deployment instead of calling `optimize()`, and submits long-running work as
+jobs. See [Moving to LanceDB Enterprise](/basics/sdk#moving-to-enterprise).
+
+## What agents are good at here {#workflows}
+
+The walkthrough above is one pattern. The other has its own tutorial.
+
+<Columns cols={2}>
+  <Card title="Run experiments on branches" icon="flask" href="/agent-branch-experiments">
+    Isolate agent-driven experiments on a branch, evaluate them on a fixed test
+    set, and promote only the winner.
+  </Card>
+</Columns>
+
+It is the more interesting pattern. [Branches](/management/table#versioning)
+give an agent somewhere to be wrong safely: it can rewrite a schema, recompute a
+column, or try three embedding models without touching `main`, and you keep only
+what measured better.
diff --git a/docs/web/basics/catalog.mdx b/docs/web/basics/catalog.mdx
new file mode 100644
index 000000000..c6950b9de
--- /dev/null
+++ b/docs/web/basics/catalog.mdx
@@ -0,0 +1,66 @@
+---
+title: "Lance Catalog"
+sidebarTitle: "Lance Catalog"
+description: "Organize, find and control access to your data in LanceDB."
+icon: sitemap
+keywords: ["catalog", "namespace", "hierarchy", "governance", "database", "table", "view"]
+---
+
+## Catalog overview {#catalog}
+
+A catalog keeps track of named data objects, such as tables and views, so
+applications can discover and access them. In LanceDB, it organizes those
+objects into databases and namespaces.
+
+## Databases, namespaces and tables {#objects}
+
+A database contains namespaces that group related tables and views. Namespaces
+can also contain other namespaces, allowing teams to organize data by project,
+environment or purpose.
+
+For example, a search application can keep production and staging data in
+separate namespaces within the same database:
+
+```mermaid
+flowchart TD
+    database["ai_data · database"] --> prod["prod · namespace"]
+    database --> staging["staging · namespace"]
+    prod --> prod_docs["documents · table"]
+    staging --> staging_docs["documents · table"]
+```
+
+<span id="views" />
+
+| Object | What it represents |
+| --- | --- |
+| [Database](/management/database) | A container for namespaces and their data objects. |
+| [Namespace](/management/namespace) | A named group of tables, views or nested namespaces. |
+| [Table](/management/table) | Data stored as rows and columns. |
+| [View](/management/view) | A saved query, evaluated when read. Available in Enterprise. |
+| [Materialized view](/management/materialized-view) | The stored results of a query, updated by a refresh. |
+
+{/*
+Draft for the Functions release. Add this row to the table above:
+| [Function](/management/function) | Registered code used to compute column values. Available in Enterprise. |
+*/}
+
+<span id="resolution" />
+
+## Organization and access control {#governance}
+
+- **Separate projects and environments.** Keep production and staging tables
+  in distinct namespaces, even when they use the same table names.
+- **Find shared datasets.** Give teams a consistent place to discover and
+  access the data they work with.
+- **Control access in Enterprise.** Use permissions on databases, namespaces
+  and tables to manage who can access shared data and what they can do with it.
+
+<span id="elsewhere" />
+
+## Learn more {#next}
+
+- [Databases](/management/database): connect to and manage a database.
+- [Namespaces](/management/namespace): create groups and address tables within them.
+- [Tables](/management/table): create, inspect and manage your data.
+- [Views](/management/view) and [materialized views](/management/materialized-view): work with query-defined data.
+- [Authorization](/governance/authorization): configure Enterprise access permissions.
diff --git a/docs/web/basics/connect.mdx b/docs/web/basics/connect.mdx
new file mode 100644
index 000000000..ac7061bbc
--- /dev/null
+++ b/docs/web/basics/connect.mdx
@@ -0,0 +1,132 @@
+---
+title: "Connect to LanceDB"
+sidebarTitle: "Connect"
+description: "Use the embedded LanceDB OSS library with tables in a local directory or object storage, or connect to LanceDB Enterprise."
+icon: plug
+keywords: ["connect", "uri", "embedded", "object storage", "s3", "enterprise", "db://"]
+---
+
+import {
+    PyConnect,
+    PyConnectAsync,
+    PyConnectEnterprisePlaceholders,
+    PyConnectEnterpriseQuickstart,
+    PyConnectObjectStorage,
+    PyConnectObjectStorageAsync,
+    RsConnect,
+    TsConnect,
+    TsConnectEnterprisePlaceholders,
+    TsConnectEnterpriseQuickstart,
+    TsConnectObjectStorage,
+} from '/snippets/connection.mdx';
+
+The URI you pass to `connect` decides where the work runs. The same Python,
+TypeScript and Rust SDKs open all three kinds;
+[OSS and Enterprise](/basics/offerings) describes how the two offerings differ.
+
+| URI | Where queries run |
+| --- | --- |
+| A local path, such as `./data` | OSS: in your process |
+| `s3://`, `gs://` or `az://` | OSS: in your process, reading object storage |
+| `db://` | LanceDB Enterprise: queries run on your cluster |
+
+## A local directory {#local}
+
+Embedded in your application, LanceDB reads and writes the tables in a
+directory on disk.
+
+<CodeGroup>
+    <CodeBlock filename="Python (sync)" language="Python" icon="python">
+    {PyConnect}
+    </CodeBlock>
+
+    <CodeBlock filename="Python (async)" language="Python" icon="python">
+    {PyConnectAsync}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    { "import * as lancedb from \"@lancedb/lancedb\";\n\n" }
+    {TsConnect}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    { "use lancedb::connect;\n" }
+    { "\n" }
+    {RsConnect}
+    </CodeBlock>
+</CodeGroup>
+
+## Object storage {#object-storage}
+
+With an object storage URI the library is still embedded: your process runs
+every query and reads and writes the bucket directly, with the credentials from
+its environment or its storage options. [Configuring storage](/deployment/storage)
+covers credentials, endpoints and provider options.
+
+<CodeGroup>
+    <CodeBlock filename="Python (sync)" language="Python" icon="python">
+    {PyConnectObjectStorage}
+    </CodeBlock>
+
+    <CodeBlock filename="Python (async)" language="Python" icon="python">
+    {PyConnectObjectStorageAsync}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    { "import * as lancedb from \"@lancedb/lancedb\";\n\n" }
+    {TsConnectObjectStorage}
+    </CodeBlock>
+</CodeGroup>
+
+In Rust, pass the same URI to `connect`.
+
+## LanceDB Enterprise {#enterprise}
+
+A `db://` URI connects to LanceDB Enterprise, where your cluster runs queries
+and background work. Get the database URI, API key, region and endpoint from
+your administrator, then replace the placeholders below. Use your cluster's region
+in place of `us-east-1`. In application code, load credentials from environment
+variables or a secrets manager.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyConnectEnterprisePlaceholders}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    { "import * as lancedb from \"@lancedb/lancedb\";\n\n" }
+    {TsConnectEnterprisePlaceholders}
+    </CodeBlock>
+</CodeGroup>
+
+In Rust, enable the crate's `remote` feature; the connection builder then takes
+the same settings through `api_key`, `region` and `host_override`. To use OAuth
+instead of an API key, see
+[Authentication](/enterprise/authentication); for every connection option, see
+[Database](/management/database#deployment).
+
+
+<Accordion title="Load production credentials from your environment">
+
+Set these values in the shell that starts your application. Replace the URI, API
+key, region and endpoint with your Enterprise connection settings. In production, a secrets
+manager can supply these environment variables for you.
+
+```shell
+export LANCEDB_URI="db://your-database"
+export LANCEDB_API_KEY="your-api-key"
+export LANCEDB_REGION="us-east-1"
+export LANCEDB_HOST_OVERRIDE="https://your-endpoint"
+```
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyConnectEnterpriseQuickstart}
+  </CodeBlock>
+  <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    { "import * as lancedb from \"@lancedb/lancedb\";\n\n" }
+    {TsConnectEnterpriseQuickstart}
+  </CodeBlock>
+</CodeGroup>
+
+</Accordion>
diff --git a/docs/web/basics/guarantees.mdx b/docs/web/basics/guarantees.mdx
new file mode 100644
index 000000000..840ea2d50
--- /dev/null
+++ b/docs/web/basics/guarantees.mdx
@@ -0,0 +1,91 @@
+---
+title: "Guarantees"
+sidebarTitle: "Guarantees"
+description: "Understand table commits, concurrent writes and the boundaries of recovery."
+icon: shield-check
+keywords: ["guarantees", "consistency", "atomicity", "durability", "concurrency"]
+---
+
+## Atomic table writes {#writes}
+
+A table transaction commits its changes atomically: readers see the committed
+table version, not a partially applied transaction. For example, a committed
+append adds its rows together.
+
+This boundary is one table transaction. A workflow that updates several tables
+or runs a background job can contain multiple commits. Do not assume the whole
+workflow is atomic.
+
+<span id="schema" />
+
+Schema changes are also part of table history. Changing a column's type can
+rewrite data; adding a derived column can write the new column independently.
+
+## Consistent reads {#reads}
+
+A read of a table uses a consistent snapshot. A consistent snapshot can still
+be older than another writer's latest commit. [Consistency](/tables/consistency)
+explains how OSS and Enterprise choose the version a reader sees.
+
+For an experiment that must keep using the same data, select a specific retained
+table version rather than relying on a connection's default freshness.
+
+## Concurrent writes {#concurrency}
+
+Lance uses optimistic concurrency control. Writers prepare changes, then check
+compatibility with intervening commits. Compatible changes can be committed;
+conflicts may be retried or returned as errors.
+
+Applications must handle failed writes. Concurrent appends are compatible, but
+that does not promise success through storage failures or exhausted retries.
+Do not rely on a particular ordering between independent writers.
+
+The [Lance transaction specification](https://lance.org/format/table/transaction/)
+describes commit behavior and compatibility between operations.
+
+## Storage durability {#durability}
+
+A committed table version is stored in the table’s configured storage. LanceDB
+OSS uses your local filesystem or object store; LanceDB Enterprise uses the
+cluster’s configured storage.
+
+Table version history does not replace backups. Configure storage redundancy
+and backups to meet your recovery requirements.
+
+## Background job completion {#jobs}
+
+In LanceDB Enterprise, operations such as index builds and column refreshes
+can run as background jobs. Receiving a job handle confirms submission. Wait
+for the job to succeed before relying on its result.
+
+Some operations save results in batches. If a job fails or is cancelled,
+batches already committed can remain in the table. Each operation’s guide
+explains whether it can leave partial results.
+
+## Writes after a client timeout {#timeouts}
+
+If a client times out while waiting for a write, the write may already have
+committed. Retrying an append can therefore insert the same records again.
+
+Check whether the intended records were written before retrying. For an
+operation submitted as a background job, check its status using the job handle.
+
+## Version history and recovery {#history}
+
+Retained table versions let you inspect earlier data or restore a table to a
+previous version. Cleanup can remove those versions and their underlying files,
+so configure retention to cover the history you need.
+
+Dropping a table is different from deleting records. Table version history
+cannot undo `drop_table`.
+
+<span id="not-guaranteed" />
+
+## Learn more {#next}
+
+- [Consistency](/tables/consistency): control when reads observe updates.
+- [Table versioning](/management/table#versioning): inspect and restore history.
+- [Cleanup](/maintenance/cleanup): configure retention and understand protected versions.
+- [Jobs](/basics/jobs): distinguish submission, completion and cancellation.
+- [Column refresh](/feature-engineering/refresh-column): understand partial progress.
+- [Architecture](/architecture): understand storage and background maintenance.
diff --git a/docs/web/basics/jobs.mdx b/docs/web/basics/jobs.mdx
new file mode 100644
index 000000000..0577cd120
--- /dev/null
+++ b/docs/web/basics/jobs.mdx
@@ -0,0 +1,75 @@
+---
+title: "Jobs"
+sidebarTitle: "Job"
+description: "Track background work from submission to completion."
+icon: hourglass-half
+keywords: ["job", "background", "progress", "events", "cancel"]
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Job handles track operations in OSS and Enterprise. Enterprise also
+    supports listing server jobs, reopening them by ID and reading their events.
+---
+
+## Job handles {#convention}
+
+A job handle lets you track an operation, such as an index build or a computed
+column refresh. Use it to check progress, wait for completion and inspect
+failures.
+
+Methods such as Python's `create_index_async` and TypeScript's
+`createIndexAsync` return job handles. In OSS, the work runs in your application
+process; in Enterprise, it runs on server workers. A returned handle can
+represent an operation that has already finished, so check its status before
+waiting or requesting cancellation. See the operation's API reference for when
+the method returns.
+
+## Submit and wait for a job {#waiting}
+
+1. **Submit the operation.** Keep the returned job handle. For an Enterprise
+   job, also keep its ID if another process needs to look it up later.
+2. **Check progress.** Read the current status while the operation runs.
+3. **Wait for completion.** Check that the job succeeded before using its
+   result. If it failed or was cancelled, inspect the failure details and any
+   results it committed before stopping.
+
+In Python, `job.status()` reports the current state and `job.wait()` waits for
+completion, raising if the job fails or is cancelled. If `job.wait()` times out, the client stops waiting.
+The timeout does not cancel the server job.
+
+<span id="inspecting" />
+<span id="events" />
+
+## Inspect and cancel jobs {#managing}
+
+| Task | What to do |
+| --- | --- |
+| Inspect progress | Check status and refresh the handle to retrieve details. Enterprise job events can help explain ongoing work or failures. |
+| Reattach from another process | Open an existing server job with its ID, such as Python's `db.open_job(job_id)`. The server must still retain the job record. |
+| Diagnose a failure | Inspect failure details and relevant events. Enterprise SQL also provides `SHOW ERRORS`. |
+| Request cancellation | Call the job's cancellation method. Cancellation may take time, and work already committed is not rolled back. |
+
+Reopening a job retrieves the existing operation; it does not restart failed
+work. Treat the ID as an opaque identifier and pass it unchanged to the jobs API.
+
+Event responses have [row limits](/basics/limits#jobs). An explicit limit can
+request more than the default, but it does not guarantee a complete event
+history.
+
+## OSS and Enterprise execution {#in-process}
+
+| | OSS | Enterprise |
+| --- | --- | --- |
+| Execution | In your application process | On server workers |
+| Job identity | No server ID | A server ID for looking up the job |
+| Event history | No server event history | Available through the jobs API |
+| Reattaching after a client restart | No server job to reopen | Reopen by ID while the record is retained |
+
+## Learn more {#next}
+
+- [Build and repair indexes](/indexing/reindexing): submit index work.
+- [Column refresh](/feature-engineering/refresh-column): populate derived values.
+- [Background jobs](/maintenance/background-jobs): understand Enterprise maintenance.
+- [SQL job commands](/sql/ddl/job): list jobs and inspect errors.
+- [SDKs](/basics/sdk): find the job API for your client.
diff --git a/docs/web/basics/lakehouse.mdx b/docs/web/basics/lakehouse.mdx
new file mode 100644
index 000000000..04e07afaf
--- /dev/null
+++ b/docs/web/basics/lakehouse.mdx
@@ -0,0 +1,75 @@
+---
+title: "LanceDB Lakehouse"
+sidebarTitle: "LanceDB Lakehouse"
+description: "Bring multimodal data, search and feature engineering together for AI workloads."
+icon: layer-group
+keywords: ["lakehouse", "multimodal", "search", "compute", "training", "feature engineering"]
+---
+
+## Multimodal storage and processing {#multimodal}
+
+LanceDB brings storage, search and data processing together for AI workloads.
+You can store multimodal data, derive features, search your records and prepare
+training datasets using tables built on the open [Lance format](/basics/lance).
+
+The format provides the storage foundation. The [catalog](/basics/catalog)
+organizes your tables. LanceDB provides the tools to work with that data.
+
+## Example: a video search workflow {#capabilities}
+
+Consider a team building a video search application:
+
+<span id="ingest" />
+<span id="compute" />
+<span id="search" />
+
+1. **Ingest the clips.** Store video clips with metadata such as timestamps,
+   source and labels.
+2. **Add features.** Generate captions and embeddings with your models, and
+   store them alongside each clip.
+3. **Use the dataset.** Search for relevant clips using embeddings and metadata
+   filters, or select a dataset to read into a training framework.
+
+```mermaid
+flowchart TD
+    clips["Video clips and metadata"] --> table["Store in a Lance table"]
+    table --> features["Add captions and embeddings"]
+    features --> search["Search for relevant clips"]
+    features --> training["Select data for training"]
+```
+
+The clips, metadata and derived features stay associated in the same table,
+so retrieval and training workflows can use the same dataset. Your training
+framework runs the model; LanceDB provides access to the training data.
+
+## Shared compute and maintenance in Enterprise {#where}
+
+LanceDB OSS runs as an embedded library in your application. LanceDB Enterprise
+provides shared infrastructure for applications and teams working with larger
+multimodal workloads.
+
+<span id="maintenance" />
+
+- **Scale resources for different workloads.** Provision query serving and
+  background workers independently as search traffic and indexing demand grow.
+- **Share data across teams.** Give applications access to shared tables, with
+  Enterprise permissions controlling who can read or change them.
+- **Manage ongoing maintenance.** Configure background indexing, compaction
+  and cleanup as your datasets grow.
+
+See [Architecture](/architecture) for how Enterprise separates storage, query
+serving and background work.
+
+## Learn more {#next}
+
+- [Data ingestion](/ingestion): load and update your data.
+- [Search](/search): retrieve records with vector, full-text and hybrid search.
+- [Feature engineering](/feature-engineering): derive columns and datasets.
+- [Training data access](/training/data-loading): read datasets into training workflows.
+- [Enterprise](/enterprise): explore deployment options for shared workloads.
+
+{/*
+Draft for the Functions release. Add this item to the list above, after
+Feature engineering:
+- [Functions](/management/function): run Python to compute column values.
+*/}
diff --git a/docs/web/basics/lance.mdx b/docs/web/basics/lance.mdx
new file mode 100644
index 000000000..f2dd0ecfa
--- /dev/null
+++ b/docs/web/basics/lance.mdx
@@ -0,0 +1,45 @@
+---
+title: "Lance format"
+sidebarTitle: "Lance Format"
+description: "The open format for multimodal data behind LanceDB."
+icon: "/static/assets/logo/lance-logo-gray.svg"
+keywords: ["lance", "format", "multimodal", "versioning", "column evolution"]
+---
+
+## Lance format overview {#standard}
+
+[Lance](https://lance.org/) is an open-source, columnar format designed for
+multimodal AI. It defines how data is stored and organized into versioned tables.
+
+<span id="embedded" />
+<span id="enterprise" />
+
+LanceDB OSS and LanceDB Enterprise both use Lance as their storage foundation.
+The format is developed as an independent open-source project, with its own
+specification and tools.
+
+## Multimodal data {#storage}
+
+A Lance table can hold images, video, audio, text, embeddings and structured
+metadata together. For example, a video dataset can keep each clip alongside
+its caption, labels and embeddings, so applications can search and work with
+those fields together.
+
+## Data access, versioning and column updates {#capabilities}
+
+- **Efficient data access.** Read individual rows for retrieval and training,
+  or scan columns for analytics.
+- **Versioned data.** Read an earlier table version to reproduce an experiment
+  or inspect previous results, while that version is retained.
+- **Independent column updates.** Add embeddings or other derived features
+  without rewriting the existing image, video or text columns.
+
+## Learn more {#next}
+
+<span id="versioning" />
+<span id="evolution" />
+
+- [Lance format specification](https://lance.org/format): how the format works.
+- [Table versioning](/management/table#versioning): read and manage table versions.
+- [Computed columns](/feature-engineering/add-computed-column): add derived features to your data.
+- [Architecture](/architecture): how LanceDB OSS and Enterprise build on Lance.
diff --git a/docs/web/basics/limits.mdx b/docs/web/basics/limits.mdx
new file mode 100644
index 000000000..0d4f1b028
--- /dev/null
+++ b/docs/web/basics/limits.mdx
@@ -0,0 +1,71 @@
+---
+title: "Limits"
+sidebarTitle: "Limits"
+description: "Find fixed constraints, configurable bounds and what happens when you reach them."
+icon: ruler
+keywords: ["limits", "maximum", "quota", "constraints", "bounds"]
+---
+
+<span id="scope" />
+
+This page lists limits for job events, SQL error reports, sampling and
+full-text indexing. Each table identifies the affected API and what happens
+when a request reaches its limit. SQL and server job limits apply to Enterprise.
+
+<span id="names" />
+
+[Naming Rules](/basics/naming) is the reference for name lengths, characters and
+namespace depth. Client and server versions can differ in which rules they
+enforce.
+
+<span id="jobs" />
+<span id="sql" />
+
+## Fixed API limits {#fixed-limits}
+
+| Operation | Scope | Limit and behavior |
+| --- | --- | --- |
+| Job event retrieval | Enterprise | At most 10,000 rows per response. Results can be truncated without a truncation indicator. |
+| `SHOW ERRORS ... LIMIT` | Enterprise SQL | Accepts 1 through 100,000. Values outside the range are rejected. |
+| `SHOW ERRORS` without `FOR JOB` | Enterprise SQL | Inspects up to the 32 most recent failed jobs. Use `FOR JOB` to inspect a specific job. |
+| Percentage sampling | Enterprise SQL | `TABLESAMPLE` accepts percentages from 0 through 100. Values outside the range are rejected. |
+| Row-count sampling | Enterprise SQL | `TABLESAMPLE (... ROWS)` requires a whole number. |
+
+See [SQL job commands](/sql/ddl/job#errors) and
+[Sampling](/sql/dql/sampling) for syntax and restrictions on supported table types.
+
+<span id="schema" />
+
+Data-type and index constraints belong to the operation you choose. Check
+[Data types](/sql/data-types) and [Vector indexes](/indexing/vector-index)
+before choosing a schema or index. For materialized views, check
+[stable row ID requirements](/feature-engineering/materialized-views#stable-row-ids)
+before creating source tables.
+
+## Configurable limits {#not-quotas}
+
+| Setting | What it controls |
+| --- | --- |
+| Job event `limit` | Requests more or fewer event rows. The default is 1,000; the 10,000-row maximum still applies. |
+| FTS `max_token_length` | The maximum indexed token length. The default is 40; longer tokens are excluded from the index. |
+| Enterprise capacity and quotas | Storage, request rate and concurrency are governed by your deployment's resources and configuration. Confirm these with your administrator or LanceDB support. |
+
+## Handling rejected or incomplete results {#behavior}
+
+- **Rejected requests:** correct the input before retrying, such as a sampling
+  percentage outside the accepted range.
+- **Truncated responses:** do not assume that a returned event list is the full
+  history. Increasing `limit` cannot exceed the maximum; use filters to narrow
+  the events you inspect.
+- **Excluded data:** an FTS token-length setting can affect which words are
+  searchable. Choose it for your text and indexing requirements.
+
+<span id="availability" />
+
+## Learn more {#next}
+
+- [Naming Rules](/basics/naming): choose portable names and paths.
+- [Jobs](/basics/jobs): track work and inspect events.
+- [Full-text indexes](/indexing/fts-index): configure tokenization.
+- [SQL](/basics/sql) and [SDKs](/basics/sdk): check interface support.
+- [Enterprise support](/deployment/support): discuss deployment capacity.
diff --git a/docs/web/basics/naming.mdx b/docs/web/basics/naming.mdx
new file mode 100644
index 000000000..af61c2b6c
--- /dev/null
+++ b/docs/web/basics/naming.mdx
@@ -0,0 +1,69 @@
+---
+title: "Naming rules"
+sidebarTitle: "Naming Rules"
+description: "Choose portable names for your data objects and namespace paths."
+icon: signature
+keywords: ["names", "naming", "table name", "namespace", "identifier", "limits"]
+---
+
+## Naming rules {#rules}
+
+<span id="objects" />
+
+Databases, namespaces, tables, views and branches all have names. Choose names
+that describe their purpose, such as `documents`, `prod` or `training_2026`.
+Consistent names make data easier to find and application code easier to read.
+
+Use the following rules for names that work across LanceDB OSS and Enterprise.
+
+| Object | Characters | Length |
+| --- | --- | --- |
+| Table, materialized view, view | ASCII letters and digits, `-`, `_`, `.` | 1 to 128 characters |
+| Namespace component | ASCII letters and digits, `-`, `_`, `.` | 1 to 64 characters; at most 10 components in a path |
+| Database | ASCII letters and digits, `-`, `_`, `.`, with `/` between components | 1 to 64 characters for the whole name |
+| Branch | Avoid `$` and `:`; use ASCII letters, digits, `-` and `_` for portability | Keep to 1 to 64 characters |
+
+- Names and individual namespace or database components must not be empty.
+- Table, view, namespace and database components cannot be `.` or `..`.
+- Avoid `$` and `:` because they separate parts of qualified identifiers.
+- A namespace path beginning with `public` cannot contain another component.
+
+{/*
+Draft for the Functions release. Add these rows to the end of the table above:
+| Secret | ASCII letters and digits, `-`, `_`, `.` | 1 to 128 characters |
+| Function | ASCII letters and digits, `_` | 1 to 128 characters |
+Add this item to the list above, after the rule for `.` and `..`:
+- Secret names cannot be `.` or `..`.
+*/}
+
+## Namespace paths {#paths}
+
+A namespace path is a list of names. `["prod", "search"]` identifies the
+`search` namespace within `prod`. A table named `documents` belongs to that
+namespace; its table name remains `documents`.
+
+Use the SDK's namespace-path argument to supply the list. A period inside an
+SDK name is a character in that name, not a separator that creates another
+namespace. SQL uses its own qualified-name syntax; see
+[SQL namespace paths](/sql/dql/select#namespaces).
+
+The empty path `[]` identifies the root namespace.
+
+## Examples {#examples}
+
+| Name or path | Guidance |
+| --- | --- |
+| `documents`, `user-events.v2`, `train_2026` | Portable table names |
+| `["prod", "search"]` | A two-component namespace path |
+| `my table` | Use `my_table` instead of a space |
+| `orders$2026` | Use `orders_2026`; `$` is reserved |
+| `résumés` | Use an ASCII name such as `resumes` |
+| `["public", "search"]` | Invalid nesting under `public`; use a different parent namespace |
+
+<span id="which-versions-check-them" />
+
+## Learn more {#next}
+
+- [Namespaces](/management/namespace): create namespaces and address tables.
+- [Branches](/tables/branching): name and manage separate lines of table history.
+- [Limits](/basics/limits): check other constraints before designing your schema.
diff --git a/docs/web/basics/offerings.mdx b/docs/web/basics/offerings.mdx
new file mode 100644
index 000000000..c66b77e78
--- /dev/null
+++ b/docs/web/basics/offerings.mdx
@@ -0,0 +1,70 @@
+---
+title: "LanceDB OSS or Enterprise?"
+sidebarTitle: "OSS and Enterprise"
+description: "Start with an embedded library, or use the LanceDB Enterprise multimodal lakehouse for shared AI workloads."
+icon: compass
+keywords: ["oss", "enterprise", "embedded", "deployment", "availability", "comparison"]
+---
+
+LanceDB gives you two ways to work with multimodal data, both built on the open
+[Lance](/basics/lance) format.
+
+**LanceDB OSS** is an embedded library. Run it inside your application
+with tables on local disk or in object storage. You control the application,
+its resources, and table maintenance, without a separate database service.
+[Try the quickstart](/quickstart) to create a table and run your first search.
+
+**LanceDB Enterprise** is a multimodal lakehouse for data curation, feature
+engineering, search, and training data access. It adds query-serving capacity,
+data caching, and background operations for teams working with shared
+multimodal datasets.
+[Connect to Enterprise](/basics/connect#enterprise) when your cluster is ready.
+
+<span id="labels" />
+<span id="availability" />
+
+## Why choose Enterprise? {#why-enterprise}
+
+### Serve growing workloads {#serving}
+
+Add query-serving capacity as more applications and users depend on your data.
+Enterprise spreads query traffic across a fleet and caches frequently read
+table data near compute, reducing repeated reads from object storage. Query
+serving and background work use separate resources, so you can size each for
+your workload. See [Enterprise architecture](/enterprise/architecture).
+
+### Reduce operational work {#operations}
+
+Let the service handle routine table maintenance instead of scheduling it in
+application code. Enterprise handles background [compaction](/maintenance/compaction) and can
+[clean up old table versions](/maintenance/cleanup) according to your retention
+policy. Configure these maintenance tasks for your Enterprise cluster so each
+application does not have to schedule them.
+
+### Fit your deployment requirements {#deployment}
+
+Choose a managed deployment or bring LanceDB into your own cloud account.
+Enterprise integrates with OpenTelemetry for metrics and traces, helping your
+team monitor the service alongside your applications. Talk to us about your
+cloud, networking, and operational requirements to plan the right deployment.
+The [Enterprise overview](/enterprise) explains the available options.
+
+### Connect your data workflows {#workflows}
+
+Use the same tables for retrieval, dataset preparation, and training data access.
+With its [SQL endpoint](/basics/sql) enabled, Enterprise supports analytical
+[curation](/curation), including joins, grouping, and sampling.
+[Feature engineering](/feature-engineering) adds derived columns and tables;
+computed columns and materialized views require the corresponding features to
+be enabled for your Enterprise cluster.
+[Geneva](/geneva) runs Python feature backfills as distributed jobs.
+[Training data loaders](/training/data-loading), available in both offerings,
+read the resulting datasets for model training.
+
+## Plan your Enterprise deployment {#get-started}
+
+[Talk to us about your workload](https://www.lancedb.com/contact) to discuss your
+data, query traffic, pipelines, and deployment requirements.
+
+For a closer look at how the service runs, read the
+[Enterprise architecture](/enterprise/architecture).
diff --git a/docs/web/basics/query.mdx b/docs/web/basics/query.mdx
new file mode 100644
index 000000000..624617918
--- /dev/null
+++ b/docs/web/basics/query.mdx
@@ -0,0 +1,55 @@
+---
+title: "Queries"
+sidebarTitle: "Query"
+description: "Choose how to query your data and read the results."
+icon: magnifying-glass
+keywords: ["query", "search", "SQL", "async", "streaming", "results"]
+---
+
+## Querying tables {#queries}
+
+A query selects data from your tables. You can filter rows, choose columns,
+search for similar content or combine those operations. For example, a video
+application might find clips similar to a sample and return only those recorded
+in the last month.
+
+## SDK searches and SQL queries {#searches}
+
+| Interface | When to use it |
+| --- | --- |
+| **SDK query builders** | Build vector, full-text and hybrid searches, apply filters and choose the fields to return. Available in OSS and Enterprise. |
+| **Enterprise SQL** | Write SQL queries for filtering, aggregation, joins and other data curation tasks. Connect through Python or a Flight SQL client. |
+
+Both interfaces work with LanceDB tables. Choose the one that supports your
+operation and fits your application. See [SQL](/basics/sql) for connection
+instructions and SQL search support, and [Search](/search) for the query builders.
+
+## Collect or stream results {#results}
+
+- **Collect the results** when you want a list, Arrow table or DataFrame that
+  fits in memory. Use a result limit and select only the columns you need.
+- **Read batches** when you want to process a large result incrementally.
+  Reading batches avoids loading the entire result into application memory.
+  It does not guarantee that the query can return its first batch immediately.
+
+### Asynchronous clients {#async-client}
+
+An async client lets your application do other work while a request is waiting.
+That is separate from whether you collect results or read them in batches.
+Python offers synchronous and asynchronous clients; TypeScript uses async APIs.
+Check the SDK reference for the methods available in your client.
+
+### Query handles and background jobs {#jobs}
+
+An Enterprise SQL query handle lets you inspect query execution and request
+cancellation. Background operations such as index builds return a
+[job](/basics/jobs) handle instead. Use the API for the returned handle to
+check status and retrieve results.
+
+## Learn more {#next}
+
+- [Quickstart](/quickstart): run a complete create, insert and search example.
+- [Search](/search): choose vector, full-text or hybrid retrieval.
+- [Filtering](/search/filtering): narrow results with predicates.
+- [SQL](/basics/sql): connect a client and run SQL queries.
+- [SDKs](/basics/sdk): choose a language and find its API reference.
diff --git a/docs/web/basics/sdk.mdx b/docs/web/basics/sdk.mdx
new file mode 100644
index 000000000..fc386bc7a
--- /dev/null
+++ b/docs/web/basics/sdk.mdx
@@ -0,0 +1,117 @@
+---
+title: "SDKs"
+sidebarTitle: "SDKs"
+description: "Choose a LanceDB SDK and find the API reference for your language."
+icon: code-compare
+keywords: ["python", "typescript", "rust", "java", "sdk", "rest", "api reference"]
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Python, TypeScript and Rust support both LanceDB OSS and LanceDB Enterprise.
+    Java provides a client for the Enterprise REST API.
+---
+
+Use a LanceDB SDK to create tables, add data, build indexes and run searches
+from your application. Python, TypeScript and Rust work with both the embedded
+OSS library and LanceDB Enterprise.
+
+The [quickstart](/quickstart) includes installation instructions and complete
+examples in all three languages. For connection settings, see
+[Connect to LanceDB](/basics/connect).
+
+## Official SDKs {#supported-sdks}
+
+| SDK | Use it with | API reference |
+| --- | --- | --- |
+| Python | Python applications, notebooks, pandas and PyArrow | [Python reference](https://lancedb.github.io/lancedb/python/python/) |
+| TypeScript | Node.js and TypeScript applications | [TypeScript reference](https://lancedb.github.io/lancedb/js/) |
+| Rust | Rust applications using the native library | [Rust reference](https://docs.rs/lancedb/latest/lancedb/index.html) |
+| Java | Applications using the Enterprise REST API | [Java reference](https://lancedb.github.io/lancedb/java/java/) |
+
+Guides show the available language examples together. Use the API reference for
+method signatures and options; SDKs do not expose every feature through the same
+methods. In Rust, enable the `remote` feature to connect to Enterprise.
+
+For SQL joins, aggregations and window functions, use
+[SQL](/basics/sql). Python provides a SQL client, and applications in other
+languages can use a Flight SQL client. TypeScript's table query API does not
+provide a SQL execution method.
+
+## REST API {#rest}
+
+LanceDB Enterprise also exposes an HTTP API for catalog and table operations.
+Use it when integrating a language or tool without a LanceDB SDK. The
+[REST API reference](/api-reference/rest) describes its endpoints and request
+formats. For SQL queries, connect through [Flight SQL](/basics/sql).
+
+## Community SDKs {#community-sdks}
+
+These clients are maintained by the community. Check each project's supported
+features, installation instructions and releases before choosing one.
+
+| Language | Project |
+| --- | --- |
+| Go | [lancedb-go](https://pkg.go.dev/github.com/lancedb/lancedb-go/pkg/lancedb) |
+| Ruby | [Lancelot](https://github.com/scientist-labs/lancelot) |
+| Swift | [LanceDbSwiftKit](https://github.com/RyanLisse/LanceDbSwiftKit) |
+| R | [lancedb](https://github.com/CathalByrneGit/lancedb) |
+| Flutter | [flutter_lancedb](https://github.com/Alexcn/flutter_lancedb) |
+
+## Python multiprocessing {#multiprocessing}
+
+<Warning>
+Use `spawn` when starting Python worker processes with `multiprocessing`.
+LanceDB uses threads internally, and forking a process with active threads can
+cause deadlocks. Create a connection inside each worker instead of sharing an
+open connection from the parent process.
+</Warning>
+
+For PyTorch worker setup and connection factories, see
+[PyTorch integration](/training/torch).
+
+## Moving to LanceDB Enterprise {#moving-to-enterprise}
+
+The core table workflow stays familiar: connect, create or open a table, write
+records, and query them. Enterprise runs those operations on remote compute and
+manages background maintenance.
+
+- **Configure the connection.** Replace the local path with your database URI,
+  and supply the endpoint, region and credentials provided by your administrator.
+  [Connect to Enterprise](/basics/connect#enterprise) shows these settings.
+- **Retrieve query results.** Python's remote tables do not support whole-table
+  `to_arrow()` or `to_pandas()` calls. Use a query and select the columns and
+  number of rows you need, for example `table.search().limit(10).to_arrow()`.
+- **Handle table listings and namespaces.** Remote table listings are paginated.
+  Use the namespace and pagination options described in
+  [Namespaces](/management/namespace).
+- **Use server-side jobs.** Index builds and feature refreshes can run as
+  [jobs](/basics/jobs). Each operation's guide explains how to submit work and
+  track completion.
+- **Let Enterprise manage maintenance.** Use
+  [background maintenance](/maintenance/background-jobs) rather than calling
+  the embedded table's `optimize()` method.
+
+For SQL connection details, see [SQL](/basics/sql). For the operating model and
+deployment options, see [LanceDB Enterprise](/enterprise).
+
+## Shared data across languages {#invariants}
+
+LanceDB uses Arrow schemas across its SDKs. A fixed-size vector column stores
+vectors of a consistent dimension; the [data types reference](/sql/data-types)
+describes the supported element types and other column types.
+
+Applications in different languages can work with the same tables. Connect to
+the same database and namespace, use the appropriate credentials, and account
+for [read consistency](/tables/consistency) when another process writes data.
+
+## Related guides {#next}
+
+<CardGroup cols={2}>
+  <Card title="SQL" icon="table-list" href="/basics/sql">
+    Query Enterprise tables with SQL from Python or a Flight SQL client.
+  </Card>
+  <Card title="Agentic Skills" icon="robot" href="/basics/agentic-skills">
+    Use the LanceDB plugin to help a coding agent work with your data.
+  </Card>
+</CardGroup>
diff --git a/docs/web/basics/sql.mdx b/docs/web/basics/sql.mdx
new file mode 100644
index 000000000..1c4ad46cd
--- /dev/null
+++ b/docs/web/basics/sql.mdx
@@ -0,0 +1,159 @@
+---
+title: "SQL"
+sidebarTitle: "SQL"
+description: "Query LanceDB Enterprise with SQL from Python or an Arrow Flight SQL client."
+icon: table-list
+keywords: ["sql", "flight sql", "datafusion", "reference", "dialect"]
+availability:
+  oss: unavailable
+  enterprise: available
+  summary: >-
+    Enterprise supports SQL through Arrow Flight SQL. Use the SDK table APIs for embedded OSS tables. See [Connection settings](#sql-is-remote) to connect.
+---
+
+Use SQL in LanceDB Enterprise to filter records, join tables, calculate
+aggregates, and prepare datasets. SQL queries work with the same Lance tables
+you create and update through the SDKs, without exporting the data to another
+system.
+
+You can run SQL through the Python SDK or an Arrow Flight SQL client. This
+guide covers connection settings, a first query, and how SQL fits alongside
+the SDK table APIs.
+
+## Connect a SQL client {#flight-sql}
+
+Enterprise exposes an
+[Arrow Flight SQL](https://arrow.apache.org/docs/format/FlightSql.html)
+endpoint. A compatible client sends SQL statements to this endpoint and
+receives results as Arrow record batches.
+
+- **Python:** use `execute_query()` on an Enterprise connection, as shown below.
+- **TypeScript:** use a separate Flight SQL client. The LanceDB TypeScript SDK
+  does not expose a SQL execution method.
+- **Other languages and tools:** use a Flight SQL client or a tool with Flight
+  SQL integration. A LanceDB-specific SDK is not required to submit SQL.
+
+The [SQL reference](/sql#connecting) shows a query from Python. See
+[SDKs](/basics/sdk) for the LanceDB language clients.
+
+### Connection settings {#sql-is-remote}
+
+Get the following values from your Enterprise administrator:
+
+- Database URI, such as `db://your-database`.
+- API key and region.
+- API endpoint for `host_override`.
+- Flight SQL endpoint for `sql_host_override`, including its scheme and port.
+
+The Flight SQL endpoint must be enabled and reachable from your client. Pass
+it explicitly as `sql_host_override` in Python, as in the example below. The
+API and SQL endpoints may use different hosts or ports.
+
+See [Connect to Enterprise](/basics/connect#enterprise) for other connection options.
+
+SQL runs in Enterprise. Connections to a local directory or an object-storage
+bucket use the embedded OSS library and cannot execute SQL statements. Use
+SDK table operations to query those tables.
+
+## Run a query in Python {#sql-from-python}
+
+Install the SDK with `pip install lancedb`. Replace the connection placeholders
+with your Enterprise settings, including the region and both endpoint URLs.
+This example reads an existing table named `my_docs` with `id` and `text`
+columns. Change the query to match your table.
+
+```python
+import lancedb
+
+db = lancedb.connect(
+    "db://your-database",
+    api_key="your-api-key",
+    region="us-east-1",
+    host_override="https://your-api-endpoint",
+    sql_host_override="https://your-sql-endpoint:443",
+)
+
+reader = db.execute_query("SELECT id, text FROM my_docs LIMIT 10")
+table = reader.read_all()
+print(table.to_pylist())
+```
+
+In application code, load credentials from environment variables or a secrets
+manager. The [connection guide](/basics/connect#enterprise) shows that setup.
+
+`execute_query()` returns a blocking Arrow reader when results start arriving.
+The example uses `read_all()` to collect the small result in memory. For larger
+results, consume the reader in batches. To submit a query and check its status
+separately, use `execute_query_async()`. See [Queries](/basics/query) for query
+handles, streaming, and cancellation.
+
+## Choose SQL or SDK table operations {#choosing}
+
+Use **SQL** for joins, aggregations, window functions, and analytical queries
+written in SQL. Use **SDK table operations** to build applications around table
+management, ingestion, filtering, and search, including applications that run
+with the embedded OSS library.
+
+You can combine them. For example, create and populate a table with an SDK,
+then query it with SQL to prepare a training dataset. In Python, both workflows
+can use the same Enterprise connection.
+
+### Common SQL operations {#sdk-parity}
+
+Several tasks are available through both interfaces, but the methods and SQL
+statements do not have identical coverage. SDK support also differs by
+language. These SQL reference pages describe the supported statements:
+
+| Task | SQL reference |
+| --- | --- |
+| Create a table | [CREATE TABLE](/sql/ddl/table) |
+| Insert or change rows | [INSERT](/sql/dml/insert), [UPDATE](/sql/dml/update), [DELETE](/sql/dml/delete) |
+| Build an index | [CREATE INDEX](/sql/ddl/index) |
+| Join and aggregate data | [SELECT](/sql/dql/select) |
+| Save a query as a view | [CREATE VIEW](/sql/ddl/view) |
+
+[Computed columns](/feature-engineering/add-computed-column) store SQL
+expressions whose values are populated by a refresh. {/* Draft for the Functions release:
+They are separate from registered Python Functions. */}
+See the computed-column and
+[materialized-view](/management/materialized-view) guides for their SDK and SQL
+operations and Enterprise setup requirements.
+
+## Vector and full-text search {#no-vector-sql}
+
+SQL vector search support depends on the Enterprise server version and query
+form. Versions that support `ORDER BY distance(vector, query_vector) LIMIT k`
+over a table scan can plan indexed or flat vector search. Check that your
+server version supports this form, then use
+[`EXPLAIN`](/sql/dql/explain) to inspect the query plan.
+
+The [SDK vector search API](/search/vector-search) is also available in
+Enterprise and OSS. Use it when you prefer the SDK query builder or need to
+search embedded tables.
+
+For full-text search in SQL, use the [`fts()`](/sql/functions#fts) table
+function or the [`contains_tokens`](/sql/scalar-functions#contains-tokens)
+predicate.
+
+## SQL syntax and API references {#reference}
+
+The [SQL reference](/sql) documents statement syntax, supported expressions,
+and data types. For SDK methods and their arguments, use the
+[Python](https://lancedb.github.io/lancedb/python/python/),
+[TypeScript](https://lancedb.github.io/lancedb/js/), or
+[Rust](https://docs.rs/lancedb/latest/lancedb/index.html) API reference.
+
+<Card title="SQL Reference" icon="table-list" href="/sql">
+  Statement syntax, expressions, and data types for LanceDB SQL.
+</Card>
+
+## Next steps {#next}
+
+<CardGroup cols={2}>
+  <Card title="Queries" icon="magnifying-glass" href="/basics/query">
+    Read results, track long-running queries, and cancel work.
+  </Card>
+  <Card title="SDKs" icon="code-compare" href="/basics/sdk">
+    Compare language clients and find their API references.
+  </Card>
+</CardGroup>
diff --git a/docs/web/curation/aggregate.mdx b/docs/web/curation/aggregate.mdx
new file mode 100644
index 000000000..b6d02d7fb
--- /dev/null
+++ b/docs/web/curation/aggregate.mdx
@@ -0,0 +1,144 @@
+---
+title: "Profiling a dataset"
+sidebarTitle: "Aggregate"
+description: "Measure coverage, missing values and class balance before selecting a dataset."
+icon: chart-column
+keywords: ["aggregate", "group by", "class balance", "count", "profiling", "distinct"]
+availability:
+  oss: unavailable
+  enterprise: available
+  summary: >-
+    Enterprise supports SQL aggregation.
+---
+
+Use SQL aggregates to summarize a dataset: count records, find missing values,
+and compare groups. These summaries help you understand the data before
+selecting records for search, evaluation or training.
+
+## Example data {#example-data}
+
+Connect with an [Enterprise SQL client](/basics/sql). The examples use a small
+document table and a separate label table. Expand the setup below to create them.
+
+<Accordion title="Create the example tables">
+
+`curation_documents` has six records describing four documents.
+`curation_labels` has one label for each of three documents. `row_id` identifies
+a record; `doc_id` identifies the document it describes.
+
+Run each statement separately in a scratch database or namespace. Create these
+tables once for the whole section, using fresh names if they already exist.
+
+```sql
+CREATE TABLE curation_documents (
+    row_id BIGINT, doc_id BIGINT, version BIGINT,
+    source VARCHAR, caption VARCHAR, score DOUBLE
+);
+INSERT INTO curation_documents VALUES
+    (1, 10, 1, 'web', NULL, 0.80),
+    (2, 10, 2, 'web', 'A cat on a chair', 0.95),
+    (3, 20, 1, 'books', 'A dog in a field', 0.90),
+    (4, 30, 1, 'web', NULL, 0.70),
+    (5, 30, 1, 'web', 'A cat by a window', 0.85),
+    (6, 40, 1, 'books', 'A bird in a tree', 0.60);
+
+CREATE TABLE curation_labels (doc_id BIGINT, label VARCHAR);
+INSERT INTO curation_labels VALUES
+    (10, 'cat'), (20, 'dog'), (30, 'cat');
+```
+
+`score` is a sample quality score, not a search result score. Document 10 has
+two versions. Document 30 has two records at the same version. Document 40
+has no label. These cases let you check the effect of each operation.
+
+</Accordion>
+
+## Count records and missing values {#size}
+
+Use `count(*)` to count rows and `count(column)` to count non-null values.
+Add `DISTINCT` to count unique values:
+
+```sql
+SELECT count(*) AS records,
+       count(DISTINCT doc_id) AS documents,
+       count(caption) AS captioned,
+       count(*) FILTER (WHERE caption IS NULL) AS missing_caption
+FROM curation_documents;
+```
+
+The example returns **6 records, 4 documents, 4 populated captions and 2 missing
+captions**. `count(column)` excludes null values; it does not check whether a
+string is empty or whether a value is useful for the task.
+
+## Group records by label {#balance}
+
+Use `GROUP BY` to calculate a summary for each label:
+
+```sql
+SELECT label, count(*) AS documents
+FROM curation_labels
+GROUP BY label
+ORDER BY documents DESC, label;
+```
+
+There are **2 cats and 1 dog** in the label table. Document 40 is absent, so this
+query does not measure labeling coverage across all source documents. Use a
+[left join](/curation/join#join-type) to find documents without labels.
+
+Decide whether the distribution matches your intended use. A uniform random
+sample tends to preserve the source distribution; it does not balance classes.
+To cap the number selected per label, use
+[ranking within a group](/curation/windowing#per-group).
+
+## Count records that match a condition {#conditional}
+
+```sql
+SELECT source,
+       count(*) AS records,
+       count(*) FILTER (WHERE score >= 0.9) AS high_score,
+       count(*) FILTER (WHERE caption IS NULL) AS missing_caption
+FROM curation_documents
+GROUP BY source
+ORDER BY source;
+```
+
+`FILTER` applies a condition to one aggregate. Here, `books` has 2 records,
+1 high-score record and no missing captions. `web` has 4 records, 1 high-score
+record and 2 missing captions. Counts are per record, so repeated document IDs
+still contribute more than once.
+
+<span id="distinct"></span>
+## Group numeric values into ranges {#buckets}
+
+Use `count(DISTINCT column)` for the number of distinct non-null values.
+For numeric fields, group by a range or summarize their distribution. For
+example, this query counts records in score buckets of width 0.1:
+
+```sql
+SELECT floor(score * 10) / 10 AS score_bucket, count(*) AS records
+FROM curation_documents
+GROUP BY score_bucket
+ORDER BY score_bucket;
+```
+
+For time-based data, group by the time interval relevant to the task. The
+[aggregation reference](/sql/dql/aggregation) and
+[scalar function reference](/sql/scalar-functions) cover additional summaries,
+including approximate counts and percentiles. Use an approximate aggregate when an estimate is sufficient; use an exact
+count when the result must be precise.
+
+## Profile a sample {#sampling}
+
+A [sample](/curation/sampling) can help you explore a large dataset before a
+full scan. Report the sample size and method alongside the result. A sample can miss rare classes, and fragment sampling can reflect the order
+in which records were written.
+
+Before accepting a curated dataset, check its required fields and key
+constraints directly rather than inferring them from a small sample.
+
+## Continue curating {#reference}
+
+Use these measurements to choose a [sample](/curation/sampling),
+[attach missing metadata](/curation/join), or
+[select one record per document](/curation/windowing).
+See [Aggregation](/sql/dql/aggregation) for complete SQL syntax.
diff --git a/docs/web/curation/index.mdx b/docs/web/curation/index.mdx
new file mode 100644
index 000000000..fd8ea73aa
--- /dev/null
+++ b/docs/web/curation/index.mdx
@@ -0,0 +1,125 @@
+---
+title: "Data curation"
+sidebarTitle: "Overview"
+description: "Prepare datasets for search, evaluation and training by profiling, selecting and combining records."
+icon: filter
+keywords: ["curation", "dataset", "training set", "sampling", "deduplication", "class balance"]
+---
+
+Data curation turns a collection of records into a dataset suited to a task.
+You might select labeled examples for training, prepare a fixed evaluation
+set, or remove duplicate documents from a search corpus.
+
+Start with the result you need. Then inspect the source data, choose which
+records belong, and save the selected data or the rules used to produce it.
+
+## Choose a curation task {#questions}
+
+| Task | What it helps you decide |
+| --- | --- |
+| [Profile a dataset](/curation/aggregate) | Whether coverage, missing values and class balance suit the task. |
+| [Sample records](/curation/sampling) | Which subset to inspect or use for an experiment. |
+| [Join datasets](/curation/join) | How to attach labels, metadata or scores stored elsewhere. |
+| [Rank and deduplicate](/curation/windowing) | Which records to keep when several describe the same item. |
+
+The analytical examples in this section use **Enterprise SQL**. Connect with
+the [SQL guide](/basics/sql). For sampling through the Python SDK in OSS or
+Enterprise, see [Sample without SQL](/curation/sampling#training).
+
+## Prepare a labeled document dataset {#shape}
+
+Suppose you need one labeled record per document for a training experiment.
+Inspect the source records, choose the latest version of each document, then
+join its label. Finally, check the class balance of the selected records.
+
+### Example data {#example-data}
+
+Connect with an [Enterprise SQL client](/basics/sql). The examples use a small
+document table and a separate label table. Expand the setup below to create them.
+
+<Accordion title="Create the example tables">
+
+`curation_documents` has six records describing four documents.
+`curation_labels` has one label for each of three documents. `row_id` identifies
+a record; `doc_id` identifies the document it describes.
+
+Run each statement separately in a scratch database or namespace. Create these
+tables once for the whole section, using fresh names if they already exist.
+
+```sql
+CREATE TABLE curation_documents (
+    row_id BIGINT, doc_id BIGINT, version BIGINT,
+    source VARCHAR, caption VARCHAR, score DOUBLE
+);
+INSERT INTO curation_documents VALUES
+    (1, 10, 1, 'web', NULL, 0.80),
+    (2, 10, 2, 'web', 'A cat on a chair', 0.95),
+    (3, 20, 1, 'books', 'A dog in a field', 0.90),
+    (4, 30, 1, 'web', NULL, 0.70),
+    (5, 30, 1, 'web', 'A cat by a window', 0.85),
+    (6, 40, 1, 'books', 'A bird in a tree', 0.60);
+
+CREATE TABLE curation_labels (doc_id BIGINT, label VARCHAR);
+INSERT INTO curation_labels VALUES
+    (10, 'cat'), (20, 'dog'), (30, 'cat');
+```
+
+`score` is a sample quality score, not a search result score. Document 10 has
+two versions. Document 30 has two records at the same version. Document 40
+has no label. These cases let you check the effect of each operation.
+
+</Accordion>
+
+The following query chooses the latest record for each document, then keeps
+only documents with a label:
+
+```sql
+WITH ranked AS (
+    SELECT *, row_number() OVER (
+        PARTITION BY doc_id ORDER BY version DESC, row_id DESC
+    ) AS rn
+    FROM curation_documents
+)
+SELECT d.row_id, d.doc_id, d.caption, l.label
+FROM ranked AS d
+JOIN curation_labels AS l ON l.doc_id = d.doc_id
+WHERE d.rn = 1
+ORDER BY d.doc_id;
+```
+
+The result contains documents **10, 20 and 30**, with labels **cat, dog and
+cat**. Document 40 is excluded because this particular task requires a label.
+The `row_id` tie-breaker makes the choice for document 30 deterministic;
+choose a tie-breaker that fits your own data.
+
+The example assumes one label per document. Duplicate keys in the label table
+would multiply the result rows. The [joining guide](/curation/join#labels)
+shows how to check that assumption.
+
+## Check the selected dataset {#check-result}
+
+Compare the result with the task's requirements: are the expected documents
+present, are required fields populated, and is the class distribution suitable?
+This example has two cats and one dog. That distribution may be appropriate
+for exploration, but it does not establish a representative evaluation set.
+
+[Profiling](/curation/aggregate) provides the counts to inspect.
+[Sampling](/curation/sampling) and [ranking](/curation/windowing) help you
+adjust the selection without changing the original records.
+
+## Save and reuse the result {#next}
+
+Choose the output according to how you will use it:
+
+- **A fixed dataset:** save the query result in a new table with
+  [`CREATE TABLE AS SELECT`](/sql/ddl/table#create). Keep that output unchanged
+  when comparing experiments.
+- **A reusable selection:** save the query as a [view](/management/view).
+  A view stores the query definition, so it is not a frozen evaluation set.
+- **Stored results that you refresh:** use a
+  [materialized view](/management/materialized-view) when its supported query
+  and refresh behavior fit the workflow.
+
+Record the source versions and selection rules alongside the output. Continue
+with [data loading](/training/data-loading) to use the curated data in a
+training pipeline.
diff --git a/docs/web/curation/join.mdx b/docs/web/curation/join.mdx
new file mode 100644
index 000000000..03a45326e
--- /dev/null
+++ b/docs/web/curation/join.mdx
@@ -0,0 +1,133 @@
+---
+title: "Joining datasets"
+sidebarTitle: "Join"
+description: "Combine records with labels or metadata, and check for missing or duplicate matches."
+icon: link
+keywords: ["join", "labels", "metadata", "enrichment", "namespace"]
+availability:
+  oss: unavailable
+  enterprise: available
+  summary: >-
+    Enterprise supports SQL joins.
+---
+
+A SQL join combines records from two tables using a shared key. Use joins to
+attach labels, metadata or model scores to your documents. Choose an inner join
+to keep matching records, or a left join to retain records without a match.
+
+## Example data {#example-data}
+
+Connect with an [Enterprise SQL client](/basics/sql). The examples use a small
+document table and a separate label table. Expand the setup below to create them.
+
+<Accordion title="Create the example tables">
+
+`curation_documents` has six records describing four documents.
+`curation_labels` has one label for each of three documents. `row_id` identifies
+a record; `doc_id` identifies the document it describes.
+
+Run each statement separately in a scratch database or namespace. Create these
+tables once for the whole section, using fresh names if they already exist.
+
+```sql
+CREATE TABLE curation_documents (
+    row_id BIGINT, doc_id BIGINT, version BIGINT,
+    source VARCHAR, caption VARCHAR, score DOUBLE
+);
+INSERT INTO curation_documents VALUES
+    (1, 10, 1, 'web', NULL, 0.80),
+    (2, 10, 2, 'web', 'A cat on a chair', 0.95),
+    (3, 20, 1, 'books', 'A dog in a field', 0.90),
+    (4, 30, 1, 'web', NULL, 0.70),
+    (5, 30, 1, 'web', 'A cat by a window', 0.85),
+    (6, 40, 1, 'books', 'A bird in a tree', 0.60);
+
+CREATE TABLE curation_labels (doc_id BIGINT, label VARCHAR);
+INSERT INTO curation_labels VALUES
+    (10, 'cat'), (20, 'dog'), (30, 'cat');
+```
+
+`score` is a sample quality score, not a search result score. Document 10 has
+two versions. Document 30 has two records at the same version. Document 40
+has no label. These cases let you check the effect of each operation.
+
+</Accordion>
+
+## Basic join {#join-type}
+
+An inner join keeps matching records:
+
+```sql
+SELECT d.row_id, d.doc_id, l.label
+FROM curation_documents AS d
+JOIN curation_labels AS l ON l.doc_id = d.doc_id
+ORDER BY d.row_id;
+```
+
+This returns **5 rows**: both records for document 10, one for document 20,
+and both records for document 30. To produce one row per document,
+[deduplicate the source records](/curation/windowing#dedup).
+
+### Find records without a match
+
+A left join keeps every record from the left table and fills missing right-side
+values with `NULL`. Add `WHERE l.doc_id IS NULL` to return only unlabeled documents:
+
+```sql
+SELECT DISTINCT d.doc_id
+FROM curation_documents AS d
+LEFT JOIN curation_labels AS l ON l.doc_id = d.doc_id
+WHERE l.doc_id IS NULL
+ORDER BY d.doc_id;
+```
+
+The result is **document 40**. You might send it for annotation, retain it for
+an unlabeled-data workflow, or exclude it from a task that requires labels.
+
+## Handle duplicate keys {#labels}
+
+This example joins `curation_documents.doc_id` to `curation_labels.doc_id`.
+First check whether the label table contains more than one row per document:
+
+```sql
+SELECT doc_id, count(*) AS labels
+FROM curation_labels
+GROUP BY doc_id
+HAVING count(*) > 1;
+```
+
+The example returns no rows. If your data returns duplicates, decide whether
+they represent valid multiple labels or records that need consolidation.
+A join does not enforce one-to-one matching.
+
+## Join tables across namespaces {#namespaces}
+
+Use a table's qualified path to address it in another
+[namespace](/management/namespace). For example, a table named `images` in
+`prod.media` is addressed as `prod.media.images`. The same matching-key and
+join-type rules apply; your identity needs access to the source objects.
+
+## Combine search results with metadata {#search}
+
+A [SQL full-text search](/sql/dql/fts) returns rows that can be joined to other
+tables. Join using an identifier retained in the search result, then check
+whether the metadata has one or several matches per result.
+
+For an SDK search, retain the matching IDs and combine them with metadata in
+your application or save the selected records to a table for further SQL work.
+See [SQL search interfaces](/basics/sql#no-vector-sql) for supported query forms.
+
+## Join performance {#performance}
+
+Check the row count, unmatched keys and duplicate matches before using a
+joined dataset. Select only the columns needed for the next step, especially
+when source records include large media or embedding columns.
+
+Use [Explain a query plan](/search/explain) to see how the query reads and
+combines data. Check the plan to see whether the query uses indexes and how it performs the
+join. An index on a key does not guarantee that the join will use it.
+
+## Save the result or continue curating {#reference}
+
+Keep the query for reuse or [save the selected data](/curation#next).
+See [Joins](/sql/dql/join) for join types and exact SQL behavior.
diff --git a/docs/web/curation/sampling.mdx b/docs/web/curation/sampling.mdx
new file mode 100644
index 000000000..643b06bdf
--- /dev/null
+++ b/docs/web/curation/sampling.mdx
@@ -0,0 +1,172 @@
+---
+title: "SQL sampling"
+sidebarTitle: "SQL Sampling"
+description: "Choose a sample for exploration or evaluation and preserve the selected records when reproducibility matters."
+icon: dice
+keywords: ["sampling", "tablesample", "subset", "eval set", "reproducible", "shuffle"]
+availability:
+  oss: unavailable
+  enterprise: available
+  summary: >-
+    Enterprise supports SQL `TABLESAMPLE`. For sampling in OSS and Enterprise through the Python SDK, see [Sample without SQL](#training).
+---
+
+import { PySampleDataset } from '/snippets/curation_sampling.mdx';
+
+Sampling selects records from a larger table. Use it to explore unfamiliar
+data, run a smaller experiment, or prepare an evaluation dataset. Enterprise
+SQL provides `TABLESAMPLE` for selecting a percentage or number of rows.
+
+This page covers Enterprise SQL sampling and a complete
+[Python SDK example](#training) that runs locally.
+
+## Example data {#example-data}
+
+Connect with an [Enterprise SQL client](/basics/sql). The examples use a small
+document table and a separate label table. Expand the setup below to create them.
+
+<Accordion title="Create the example tables">
+
+`curation_documents` has six records describing four documents.
+`curation_labels` has one label for each of three documents. `row_id` identifies
+a record; `doc_id` identifies the document it describes.
+
+Run each statement separately in a scratch database or namespace. Create these
+tables once for the whole section, using fresh names if they already exist.
+
+```sql
+CREATE TABLE curation_documents (
+    row_id BIGINT, doc_id BIGINT, version BIGINT,
+    source VARCHAR, caption VARCHAR, score DOUBLE
+);
+INSERT INTO curation_documents VALUES
+    (1, 10, 1, 'web', NULL, 0.80),
+    (2, 10, 2, 'web', 'A cat on a chair', 0.95),
+    (3, 20, 1, 'books', 'A dog in a field', 0.90),
+    (4, 30, 1, 'web', NULL, 0.70),
+    (5, 30, 1, 'web', 'A cat by a window', 0.85),
+    (6, 40, 1, 'books', 'A bird in a tree', 0.60);
+
+CREATE TABLE curation_labels (doc_id BIGINT, label VARCHAR);
+INSERT INTO curation_labels VALUES
+    (10, 'cat'), (20, 'dog'), (30, 'cat');
+```
+
+`score` is a sample quality score, not a search result score. Document 10 has
+two versions. Document 30 has two records at the same version. Document 40
+has no label. These cases let you check the effect of each operation.
+
+</Accordion>
+
+## Basic sampling {#quick}
+
+A percentage selects a fraction of the source; `ROWS` requests a row count:
+
+```sql
+SELECT row_id, doc_id FROM curation_documents TABLESAMPLE (50);
+SELECT row_id, doc_id FROM curation_documents TABLESAMPLE (3 ROWS);
+```
+
+A percentage sample does not guarantee an exact number of rows. Use a count
+when the desired sample size is the main constraint, and inspect the result
+rather than assuming it contains every category you need.
+
+`TABLESAMPLE` requires a table that is not WAL-backed. See the
+[sampling reference](/sql/dql/sampling#refused) for this restriction; do not
+change a production table's write configuration just to try an example.
+
+## Sampling methods {#methods}
+
+Choose a method according to how records should be selected:
+
+| Method | Selection | When to use it |
+| --- | --- | --- |
+| `BERNOULLI` (default) | Select individual rows | When records should be sampled independently of their storage fragments |
+| `SYSTEM` | Select whole Lance fragments | When reducing reads matters and grouping by fragment is acceptable |
+
+```sql
+SELECT row_id, doc_id
+FROM curation_documents TABLESAMPLE BERNOULLI (50);
+
+SELECT row_id, doc_id
+FROM curation_documents TABLESAMPLE SYSTEM (50);
+```
+
+If write order groups records by label, date or source, a fragment sample can
+reflect that grouping. On a tiny table stored in one fragment, it may include
+all records or none. Row sampling also needs an adequate sample size to
+represent rare groups; check that the resulting sample covers the groups you need.
+
+## Repeatable samples {#fixed}
+
+A seed requests a repeatable draw:
+
+```sql
+SELECT row_id, doc_id
+FROM curation_documents TABLESAMPLE (50) REPEATABLE (42);
+```
+
+Keep the source data and sampling configuration fixed when comparing draws.
+For a lasting evaluation set, save the selected records rather than relying
+on a seed to preserve membership as the source changes:
+
+```sql
+CREATE TABLE curation_eval AS
+SELECT * FROM curation_documents TABLESAMPLE (50) REPEATABLE (42);
+```
+
+Create this output once with a fresh name and keep it unchanged. Record the
+source version and selection policy. Sampling records independently can place
+revisions of the same document into different splits; decide the
+[grouping policy](/curation/windowing#splits) before using the result for
+training or evaluation.
+
+## Sampling with joins {#joins}
+
+Sampling both input tables independently changes which pairs can match.
+For a one-to-one match, independent 10% row samples retain a given pair with
+about 1% probability, not 10%.
+
+To sample documents while retaining their available labels, sample only the
+document table:
+
+```sql
+SELECT curation_documents.row_id, curation_documents.doc_id, l.label
+FROM curation_documents TABLESAMPLE (50)
+JOIN curation_labels AS l ON l.doc_id = curation_documents.doc_id;
+```
+
+This samples source records, not distinct documents. If you need to sample the
+result of a join or deduplication, first save that result as a table suitable
+for sampling. `TABLESAMPLE` cannot be applied directly to a subquery result.
+
+## Sample without SQL {#training}
+
+The Python SDK provides a permutation builder for selecting and ordering rows
+in OSS and Enterprise. A split selects a subset; a shuffle changes the order
+in which records are read. `Permutation.identity(table)` retains stored order.
+
+<Accordion title="Complete Python example: read a random subset">
+
+Install the Python SDK with `pip install lancedb`, save the program as
+`sample_dataset.py`, and run `python sample_dataset.py`. It creates a temporary
+local database with ten rows and reads a random split containing three rows.
+The temporary database is removed when the program finishes.
+
+<CodeBlock filename="sample_dataset.py" language="python" icon="python">
+{PySampleDataset}
+</CodeBlock>
+
+The program prints three distinct IDs. It uses a fixed seed, but does not
+promise the same IDs across SDK versions or changes to the source dataset.
+
+</Accordion>
+
+For shuffled training reads, worker configuration and data loaders, see
+[Loading data for model training](/training/data-loading).
+
+## Related guides {#reference}
+
+Count the sampled rows and [inspect their distribution](/curation/aggregate)
+before using them. Preserve the selected data when the exact membership matters.
+See [SQL sampling](/sql/dql/sampling) for syntax and restrictions.
diff --git a/docs/web/curation/windowing.mdx b/docs/web/curation/windowing.mdx
new file mode 100644
index 000000000..fa84ed5fe
--- /dev/null
+++ b/docs/web/curation/windowing.mdx
@@ -0,0 +1,162 @@
+---
+title: "Ranking and deduplication"
+sidebarTitle: "Windowing"
+description: "Choose records with explicit ranking rules and deterministic tie handling."
+icon: chart-simple
+keywords: ["window", "deduplication", "row_number", "rank", "train test split", "per class"]
+availability:
+  oss: unavailable
+  enterprise: available
+  summary: >-
+    Enterprise supports SQL window functions.
+---
+
+Window functions calculate values across related rows while keeping each row
+in the result. Use them to rank documents, keep one record per item, or compare
+a record with the one before it.
+
+## Example data {#example-data}
+
+Connect with an [Enterprise SQL client](/basics/sql). The examples use a small
+document table and a separate label table. Expand the setup below to create them.
+
+<Accordion title="Create the example tables">
+
+`curation_documents` has six records describing four documents.
+`curation_labels` has one label for each of three documents. `row_id` identifies
+a record; `doc_id` identifies the document it describes.
+
+Run each statement separately in a scratch database or namespace. Create these
+tables once for the whole section, using fresh names if they already exist.
+
+```sql
+CREATE TABLE curation_documents (
+    row_id BIGINT, doc_id BIGINT, version BIGINT,
+    source VARCHAR, caption VARCHAR, score DOUBLE
+);
+INSERT INTO curation_documents VALUES
+    (1, 10, 1, 'web', NULL, 0.80),
+    (2, 10, 2, 'web', 'A cat on a chair', 0.95),
+    (3, 20, 1, 'books', 'A dog in a field', 0.90),
+    (4, 30, 1, 'web', NULL, 0.70),
+    (5, 30, 1, 'web', 'A cat by a window', 0.85),
+    (6, 40, 1, 'books', 'A bird in a tree', 0.60);
+
+CREATE TABLE curation_labels (doc_id BIGINT, label VARCHAR);
+INSERT INTO curation_labels VALUES
+    (10, 'cat'), (20, 'dog'), (30, 'cat');
+```
+
+`score` is a sample quality score, not a search result score. Document 10 has
+two versions. Document 30 has two records at the same version. Document 40
+has no label. These cases let you check the effect of each operation.
+
+</Accordion>
+
+## Keep one record per document {#dedup}
+
+`row_number()` numbers rows within each group. `PARTITION BY` defines the group,
+and `ORDER BY` determines which record comes first. This query selects the
+latest version of each document, using `row_id` to break ties:
+
+```sql
+WITH ranked AS (
+    SELECT *, row_number() OVER (
+        PARTITION BY doc_id ORDER BY version DESC, row_id DESC
+    ) AS rn
+    FROM curation_documents
+)
+SELECT row_id, doc_id, version, caption
+FROM ranked
+WHERE rn = 1
+ORDER BY doc_id;
+```
+
+This returns row IDs **2, 3, 5 and 6**. For document 30, rows 4 and 5 have the
+same version; the example deliberately chooses the larger `row_id`.
+Without a complete ordering, the selected row among ties is not determined by
+the query.
+
+Use `rank()` instead of `row_number()` when tied records should all survive,
+and order only by the fields that define a tie. Adding a unique tie-breaker
+would make every rank distinct. The outer query filters the calculated rank
+because window functions are evaluated after the inner query's `WHERE` clause.
+
+## Select the highest-scoring records per class {#per-group}
+
+Deduplicate first, then rank the labeled documents within each class:
+
+```sql
+WITH latest AS (
+    SELECT *, row_number() OVER (
+        PARTITION BY doc_id ORDER BY version DESC, row_id DESC
+    ) AS version_rank
+    FROM curation_documents
+), ranked AS (
+    SELECT d.doc_id, l.label, d.score,
+           row_number() OVER (
+               PARTITION BY l.label ORDER BY d.score DESC, d.doc_id
+           ) AS class_rank
+    FROM latest AS d
+    JOIN curation_labels AS l ON l.doc_id = d.doc_id
+    WHERE d.version_rank = 1
+)
+SELECT doc_id, label, score
+FROM ranked
+WHERE class_rank <= 1
+ORDER BY label;
+```
+
+The result is **document 10 for cat and document 20 for dog**. Change the cap
+from `1` to the maximum number you want per class. A class with fewer records
+cannot supply the full cap.
+
+This selects by score, not at random. For a random subset, use
+[sampling](/curation/sampling) instead of ordering by score or ID.
+
+## Training and evaluation splits {#splits}
+
+Ranking can divide ordered records into groups, but ordering by ID does not
+make those groups random or representative. `ntile()` produces approximately
+equal-sized buckets within a partition; small classes may not populate every
+bucket.
+
+Choose the split policy for the task. Keep related records, such as revisions
+of one document or frames from one video, in the same split to avoid leakage.
+A time-based evaluation may instead require a cutoff date. Save the final
+assignments or resulting datasets so later updates do not silently change the
+evaluation population.
+
+## Compare neighboring records {#offset}
+
+`lag()` and `lead()` read neighboring rows in the specified order. For example,
+inspect how scores change across a document's records:
+
+```sql
+SELECT doc_id, row_id, score,
+       lag(score) OVER (
+           PARTITION BY doc_id ORDER BY version, row_id
+       ) AS previous_score
+FROM curation_documents
+ORDER BY doc_id, version, row_id;
+```
+
+The first record in each document has no previous score. In this dataset,
+row 2 follows a score of 0.80 and row 5 follows a score of 0.70.
+
+<span id="running"></span>
+## Window query performance {#cost}
+
+Window operations can require sorting. Filter out records you do not need
+before ranking, and inspect the
+[query plan](/search/explain) when cost matters.
+
+For cumulative counts, sums and other running summaries, use an aggregate
+with an explicit window order and frame. The
+[window reference](/sql/dql/window#window) covers those expressions.
+
+## Continue with the curated data {#reference}
+
+[Profile the selected records](/curation/aggregate) and
+[save the result](/curation#next) for downstream use.
+See [Window functions](/sql/dql/window) for the full syntax and function list.
diff --git a/docs/web/demos/fineweb-edu-search.mdx b/docs/web/demos/fineweb-edu-search.mdx
new file mode 100644
index 000000000..64d4decd4
--- /dev/null
+++ b/docs/web/demos/fineweb-edu-search.mdx
@@ -0,0 +1,20 @@
+---
+title: "Large scale vector search (1.5B)"
+sidebarTitle: "Large Scale Vector Search"
+description: "Semantic search over 1.5B+ FineWeb EDU embeddings."
+icon: database
+---
+
+<Badge color="purple">vector-search</Badge>
+<Badge color="blue">education</Badge>
+<Badge color="gray">bge-embeddings</Badge>
+
+<Card
+  title="Large Scale Vector Search (1.5B)"
+  img="/static/assets/images/demos/fineweb_edu_hero.svg"
+  href="https://lancedb-demos.vercel.app/demo/fineweb-edu-search"
+>
+Semantic search over curated FineWeb EDU pages, represented as 1.5B+ BGE embeddings. Inspect the
+query plan to see how LanceDB serves vector search at billion scale.
+
+</Card>
diff --git a/docs/web/demos/index.mdx b/docs/web/demos/index.mdx
new file mode 100644
index 000000000..fb1bdc49a
--- /dev/null
+++ b/docs/web/demos/index.mdx
@@ -0,0 +1,17 @@
+---
+title: "Demo application gallery"
+sidebarTitle: "Overview"
+description: "End-to-end applications built with LanceDB."
+icon: grid-2
+---
+
+Applications built on LanceDB, each with a live demo and a write-up of how it
+works.
+
+| App | What it shows |
+| --- | --- |
+| [Semantic.art](/demos/semantic-art) | Multimodal art discovery using feelings, phrases, and images. |
+| [Wikipedia 41M hybrid search](/demos/wikipedia-search) | Full-text and vector search combined, over 41M+ entries. |
+| [Video search](/demos/video-search) | Natural-language search across a video library. |
+| [Large scale vector search (1.5B)](/demos/fineweb-edu-search) | Semantic search over 1.5B+ FineWeb EDU embeddings. |
+| [Robot data curation (DROID)](/demos/robot-data-curation) | Searching, scoring, and selecting robotics training examples. |
diff --git a/docs/web/demos/robot-data-curation.mdx b/docs/web/demos/robot-data-curation.mdx
new file mode 100644
index 000000000..b61f53afe
--- /dev/null
+++ b/docs/web/demos/robot-data-curation.mdx
@@ -0,0 +1,21 @@
+---
+title: "Robot data curation (DROID)"
+sidebarTitle: "Robot Data Curation"
+description: "Search, score, and select robotics training examples from DROID."
+icon: robot
+---
+
+<Badge color="green">multimodal</Badge>
+<Badge color="yellow">robotics</Badge>
+<Badge color="purple">vector-search</Badge>
+<Badge color="gray">fts</Badge>
+
+<Card
+  title="Robot Data Curation (DROID)"
+  img="/static/assets/images/demos/droid_hero.jpg"
+  href="https://lancedb-demos.vercel.app/demo/robot-data-curation"
+>
+Search robot video frames from the DROID dataset, compute features, and select training examples in LanceDB.
+Add captions and quality scores as columns, then write model errors back to guide the next training run.
+
+</Card>
diff --git a/docs/web/demos/semantic-art.mdx b/docs/web/demos/semantic-art.mdx
new file mode 100644
index 000000000..a7ed3859f
--- /dev/null
+++ b/docs/web/demos/semantic-art.mdx
@@ -0,0 +1,28 @@
+---
+title: "Semantic.art"
+sidebarTitle: "Semantic.art"
+description: "Multimodal art discovery through feelings, phrases, and images."
+icon: palette
+---
+
+<Badge color="green">multimodal</Badge>
+<Badge color="red">hybrid-search</Badge>
+<Badge color="purple">vector-search</Badge>
+<Badge color="surface-destructive">semantic-routing</Badge>
+
+<Card
+  title="Semantic.art"
+  img="/static/assets/images/demos/sda_hero.jpg"
+  href="https://www.semantic.art/"
+>
+Semantic.art turns real, human-made art discovery into a multimodal search experience using
+feelings, phrases, and images. It's built with LanceDB hybrid search and semantic routing.
+
+<Card
+    title="Learn how it's built"
+    href="https://lancedb.com/blog/semanticdotart/"
+>
+Read in detail about how Semantic.art is built in this blog post.
+</Card>
+
+</Card>
diff --git a/docs/web/demos/video-search.mdx b/docs/web/demos/video-search.mdx
new file mode 100644
index 000000000..4a8ec3d4c
--- /dev/null
+++ b/docs/web/demos/video-search.mdx
@@ -0,0 +1,20 @@
+---
+title: "Video search"
+sidebarTitle: "Video Search"
+description: "Search a video library with natural language queries."
+icon: video
+---
+
+<Badge color="green">multimodal</Badge>
+<Badge color="orange">video-search</Badge>
+<Badge color="purple">vector-search</Badge>
+
+<Card
+  title="Video Search"
+  img="/static/assets/images/demos/video_demo_hero.png"
+  href="https://lancedb-demos.vercel.app/demo/video-search"
+>
+Search through a library of videos using natural language queries. This demo showcases how to use LanceDB
+to perform semantic search on video content.
+
+</Card>
diff --git a/docs/web/demos/wikipedia-search.mdx b/docs/web/demos/wikipedia-search.mdx
new file mode 100644
index 000000000..73dac4998
--- /dev/null
+++ b/docs/web/demos/wikipedia-search.mdx
@@ -0,0 +1,28 @@
+---
+title: "Wikipedia 41M hybrid search"
+sidebarTitle: "Wikipedia Search"
+description: "Hybrid full-text and vector search across 41M+ Wikipedia entries."
+icon: magnifying-glass
+---
+
+<Badge color="green">multimodal</Badge>
+<Badge color="red">hybrid-search</Badge>
+<Badge color="purple">vector-search</Badge>
+<Badge color="gray">fts</Badge>
+
+<Card
+  title="Wikipedia 41M Hybrid Search"
+  img="/static/assets/images/demos/wiki_hero.png"
+  href="https://lancedb-demos.vercel.app/demo/wikipedia-search"
+>
+Interactive hybrid search, full-text search (FTS) and vector search demo with 41M+ Wikipedia entries.
+Explore the power of combining FTS with vector search for more relevant results.
+
+<Card
+    title="Learn how it's built"
+    href="https://lancedb.com/blog/feature-full-text-search/"
+>
+Read in detail about how the Wikipedia 41M hybrid search demo is built in this blog post.
+</Card>
+
+</Card>
diff --git a/docs/web/deployment/monitoring.mdx b/docs/web/deployment/monitoring.mdx
new file mode 100644
index 000000000..5ad56ce1c
--- /dev/null
+++ b/docs/web/deployment/monitoring.mdx
@@ -0,0 +1,130 @@
+---
+title: "Monitor LanceDB with OpenTelemetry"
+sidebarTitle: "Monitoring"
+description: "Export LanceDB object store request counts, bytes, latency, errors, and throttles to any OpenTelemetry backend."
+icon: "chart-line"
+keywords: ["monitoring", "observability", "opentelemetry", "otel", "metrics", "prometheus", "object store"]
+---
+
+LanceDB emits internal metrics (currently object store request counts, bytes transferred, request latency, retryable errors, and throttles) and can bridge them into any [OpenTelemetry](https://opentelemetry.io/) backend. Use this to watch how your application interacts with S3, GCS, Azure Blob, or the local filesystem in production: spot latency regressions, catch retry storms, and size your storage tier from real workload data.
+
+The bridge is available in the Python and TypeScript SDKs. It is a thin wrapper over LanceDB's `metrics` recorder; your application supplies and configures the OpenTelemetry SDK.
+
+<Note>
+This page covers LanceDB OSS instrumentation. LanceDB Enterprise clusters emit their own metrics from the server side. See [Enterprise monitoring](#enterprise) below.
+</Note>
+
+## Available metrics {#what-you-get}
+
+Once instrumented, LanceDB registers one observable instrument per metric on your `MeterProvider`. The current catalog covers the object store layer:
+
+| Metric | Kind | Description |
+|--------|------|-------------|
+| `lance_object_store_requests_total` | Counter | Total object store requests, labelled by `operation` and `base` (store scheme). |
+| `lance_object_store_request_duration_seconds` | Histogram | Request latency in seconds. |
+| `lance_object_store_request_bytes_total` | Counter | Bytes read from or written to the store. |
+| `lance_object_store_errors_total` | Counter | Object store requests that failed. |
+| `lance_object_store_retryable_responses_total` | Counter | Retryable HTTP responses (5xx, 429 and 408) from a store reached over HTTP. |
+| `lance_object_store_throttle_total` | Counter | Throttle responses (429 and 503) from a store reached over HTTP. |
+| `lance_object_store_in_flight_requests` | Gauge | Currently outstanding object store requests. |
+
+The recorder is process-global and pull-based: your configured `MetricReader` collects on its own schedule, so there is no hot-path overhead beyond the atomic aggregation that LanceDB does anyway.
+
+<Note>
+**Histograms are exported Prometheus-style.** OpenTelemetry has no asynchronous histogram instrument, so each histogram surfaces as three observable counters: `<name>_bucket` (with an `le` attribute per bucket boundary, including `+Inf`), `<name>_count`, and `<name>_sum`. Only `_sum` carries the histogram's unit; `_bucket` and `_count` are cumulative sample counts.
+</Note>
+
+## Python {#python}
+
+Install LanceDB with the `otel` extra to pull in the OpenTelemetry API, plus an OpenTelemetry SDK of your choice. The SDK is intentionally not bundled, so you configure it and its readers and exporters however your platform expects.
+
+```bash
+pip install "lancedb[otel]" opentelemetry-sdk opentelemetry-exporter-otlp-proto-grpc
+```
+
+Call `instrument_lancedb_metrics()` once at startup, before opening any tables. It returns `True` when the recorder is installed and instruments are registered.
+
+```python Python icon="python"
+import lancedb
+from lancedb.otel import instrument_lancedb_metrics
+from opentelemetry.sdk.metrics import MeterProvider
+from opentelemetry.sdk.metrics.export import (
+    PeriodicExportingMetricReader,
+)
+from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import (
+    OTLPMetricExporter,
+)
+
+reader = PeriodicExportingMetricReader(OTLPMetricExporter())
+provider = MeterProvider(metric_readers=[reader])
+
+instrument_lancedb_metrics(provider)
+
+# Any object store activity from this point on is now recorded.
+db = lancedb.connect("s3://my-bucket/lancedb")
+```
+
+If you omit `meter_provider`, LanceDB uses the global provider returned by `opentelemetry.metrics.get_meter_provider()`.
+
+<Warning>
+`instrument_lancedb_metrics()` returns `False` and emits a warning if another `metrics`-crate recorder is already installed in the process. Only one global recorder is permitted, so instrument LanceDB before any other library that installs its own recorder.
+</Warning>
+
+## TypeScript {#typescript}
+
+Install the OpenTelemetry SDK and exporter in your application:
+
+```bash
+npm install @opentelemetry/sdk-metrics @opentelemetry/exporter-metrics-otlp-grpc
+```
+
+```typescript TypeScript icon="square-js"
+import { connect, instrumentLanceDbMetrics } from "@lancedb/lancedb";
+import { MeterProvider, PeriodicExportingMetricReader } from "@opentelemetry/sdk-metrics";
+import { OTLPMetricExporter } from "@opentelemetry/exporter-metrics-otlp-grpc";
+
+const reader = new PeriodicExportingMetricReader({
+  exporter: new OTLPMetricExporter(),
+});
+const provider = new MeterProvider({ readers: [reader] });
+
+instrumentLanceDbMetrics(provider);
+
+const db = await connect("s3://my-bucket/lancedb");
+```
+
+`instrumentLanceDbMetrics()` also accepts no arguments, in which case it uses the global provider from `@opentelemetry/api`. Calling it more than once is safe: instruments are created only on the first successful call.
+
+## Build dashboards and alerts {#what-to-watch}
+
+A few starting points for dashboards and alerts:
+
+- **Request rate by operation:** `rate(lance_object_store_requests_total[1m])` broken down by `operation` shows read vs. write pressure and helps size ingestion and serving traffic separately.
+- **Tail latency:** histogram quantiles over `lance_object_store_request_duration_seconds_bucket` catch object store slowdowns before they surface as query timeouts.
+- **Retryable responses:** a rising `lance_object_store_retryable_responses_total` typically means you are being throttled and should back off or shard writes.
+- **In-flight requests:** a growing `lance_object_store_in_flight_requests` gauge without a matching rise in throughput indicates queueing.
+
+## Enterprise monitoring {#enterprise}
+
+<Badge color="red">Enterprise</Badge> A deployment emits its own metrics from
+the server side, using Prometheus for collection and OpenTelemetry (OTel) to
+export them with data enrichment -- no client-side instrumentation needed.
+
+| Component | Metrics |
+| --- | --- |
+| Query node | RPS, query latency, error codes, slow-query count, CPU/memory utilization |
+| Plan executor | SSD cache hit/miss rate, CPU/memory utilization |
+
+[Contact support](/deployment/support) for the comprehensive metric list and
+help integrating them with your monitoring dashboard.
+
+## Related guides {#where-to-go-next}
+
+<Columns cols={2}>
+  <Card title="Performance tips" icon="gauge-high" href="/search#performance">
+    Tune ingestion, indexing, and query patterns once metrics highlight a hot spot.
+  </Card>
+  <Card title="Storage configuration" icon="wrench" href="/storage/configuration">
+    Configure the object store backends whose requests these metrics measure.
+  </Card>
+</Columns>
diff --git a/docs/web/deployment/storage.mdx b/docs/web/deployment/storage.mdx
new file mode 100644
index 000000000..4994e8f0c
--- /dev/null
+++ b/docs/web/deployment/storage.mdx
@@ -0,0 +1,425 @@
+---
+title: "Embedded storage configuration"
+sidebarTitle: "Embedded Storage"
+description: "Choose and configure local disk or object storage for tables accessed by the embedded LanceDB OSS library."
+icon: database
+keywords: ["storage", "s3", "gcs", "azure", "object store", "configuration", "deploy"]
+availability:
+  oss: available
+  enterprise: unavailable
+  summary: >-
+    The embedded library chooses and configures its storage backend from the
+    connection URI and `storage_options`. In LanceDB Enterprise, storage is part of
+    the deployment's own configuration instead.
+---
+
+This page is about the embedded library, where your connection chooses and
+configures the storage backend. A LanceDB Enterprise deployment sets up its own
+storage when it is deployed, so a `db://` connection passes none of these
+options except an Azure storage account name -- see
+[Deployment](/enterprise/deployment) for how a deployment is set up.
+
+LanceDB's storage layer is built on modular, disk-first components. That design makes it flexible enough to run across local NVMe, EBS, EFS, and any object store that exposes an S3-compatible API. It also supports region-specific backends such as [Tencent COS](/storage/configuration#tencent-cos) and cache-acceleration layers such as [GooseFS](/storage/configuration#goosefs).
+
+Choosing a backend is a balance between latency, scalability, cost, and operational complexity. Use this guide to pick the right fit for your workload.
+
+## Choosing a backend {#choosing}
+
+![](/static/assets/images/storage/lancedb_storage_tradeoffs.png)
+
+When architecting your system, ask yourself:
+
+- **Latency**: How fast do I need results? What do the p50 and p95 look like?
+- **Scalability**: Can I scale data volume and QPS easily?
+- **Cost**: What is the all-in cost of storage plus serving?
+- **Reliability/Availability**: How will replication and disaster recovery work?
+
+### Comparison {#comparison}
+
+Below is a high-level comparison ordered from lowest cost to lowest latency.
+
+### 1\. Object storage (S3, GCS, Azure Blob) {#1-object-storage-s3-gcs-azure-blob}
+- **Latency**: Highest; expect hundreds of milliseconds and higher p95.
+- **Scalability**: Effectively unlimited storage; QPS bound by concurrency limits.
+- **Cost**: Lowest overall.
+- **Reliability/Availability**: Highly available, backed by cloud SLAs.
+
+LanceDB separates storage and compute and writes immutable fragments, making it a strong fit for stateless, horizontally scalable deployments.
+
+<Note>
+**Concurrent writers on S3**
+
+S3 and S3 Express now support atomic writes natively, so LanceDB handles concurrent writers against the same table out-of-the-box — no external commit coordinator is required. Bucket-level [server-side encryption with KMS](/storage/configuration#server-side-encryption-with-kms) and [S3 Express One Zone](/storage/configuration#s3-express) are also supported on this tier.
+</Note>
+
+### 2\. File storage (EFS, GCS Filestore, Azure File) {#2-file-storage-efs-gcs-filestore-azure-file}
+- **Latency**: Better than object storage; p95 under ~&lt;100ms is typical.
+- **Scalability**: High, but limited by provisioned IOPS per volume.
+- **Cost**: More than object storage but cheaper than in-memory options; cold data can tier down automatically.
+- **Reliability/Availability**: Highly available; replication/backup must be managed separately.
+
+Keep a copy of data in object storage for disaster recovery. If zero downtime is required, provision a second network file system with replicated data.
+
+### 3\. Third-party storage (e.g., MinIO, WekaFS) {#3-third-party-storage-e-g--minio-wekafs}
+
+- **Latency**: Similar to EFS; typically under &lt;100ms.
+- **Scalability**: Determined by the chosen vendor’s cluster sizing.
+- **Cost**: Higher than S3; may edge above EFS at larger scales.
+- **Reliability/Availability**: Shareable across many nodes; replication depends on vendor capabilities.
+
+### 4\. Block storage (EBS, GCP Persistent Disk, Azure Managed Disk) {#4-block-storage-ebs-gcp-persistent-disk-azure-managed-disk}
+- **Latency**: Near-local performance; often &lt;30ms.
+- **Scalability**: Not shareable across instances; shard or copy data when scaling.
+- **Cost**: Higher than networked file systems, plus potential I/O charges.
+- **Reliability/Availability**: Persists through instance restarts; backups and sharding must be managed.
+
+### 5\. Local storage (SSD, NVMe) {#5-local-storage-ssd-nvme}
+- **Latency**: Fastest; p95 often under &lt;10ms.
+- **Scalability**: Hard to scale in cloud environments; requires sharding or additional copies for higher QPS.
+- **Cost**: Highest; tightly coupling compute and storage makes horizontal scaling difficult.
+- **Reliability/Availability**: Data is tied to the instance; backups must be rigorous.
+
+Use local disk only when you need extremely low latency and are comfortable owning the operational overhead.
+
+### File-format choices that interact with the backend {#file-format-choices}
+
+A few `storage_options` keys shape new tables in ways that depend on the backend you picked above. They are documented in full on [below](#new-table-configuration); the architecture-level summary is:
+
+- `new_table_enable_v2_manifest_paths` matters most on object stores, where opening a table with many versions is dominated by listing cost. Leave it off for backward compatibility with clients older than LanceDB 0.10.0.
+- `new_table_enable_stable_row_ids` keeps row IDs stable across compaction, delete, and merge. The choice is independent of the backend but affects any system that joins on row ID.
+- `new_table_data_storage_version` selects the on-disk format. The default `stable` is recommended for all new tables; pick `legacy` only when older readers must keep working.
+
+import {
+    PyStorageAzureAccount,
+    PyStorageAzureSas,
+    PyStorageConnectAzure,
+    PyStorageConnectGcs,
+    PyStorageConnectS3,
+    PyStorageConnectTimeout,
+    PyStorageGcsServiceAccount,
+    PyStorageS3Express,
+    PyStorageS3Minio,
+    PyStorageS3SseKms,
+    PyStorageTableTimeout,
+    PyStorageTigrisConnect,
+    PyStorageCosConnect,
+    PyStorageGoosefsConnect,
+    TsStorageAzureAccount,
+    TsStorageAzureSas,
+    TsStorageConnectAzure,
+    TsStorageConnectGcs,
+    TsStorageConnectS3,
+    TsStorageConnectTimeout,
+    TsStorageGcsServiceAccount,
+    TsStorageS3Express,
+    TsStorageS3Minio,
+    TsStorageS3SseKms,
+    TsStorageTableTimeout,
+    TsStorageTigrisConnect,
+    TsStorageGoosefsConnect,
+} from '/snippets/storage.mdx';
+
+### Configuring a backend {#configuring}
+
+<Note>
+**LanceDB Enterprise storage configuration**
+
+In LanceDB Enterprise, you connect with `db://...` and the cluster owns the storage credentials, so `storage_options` are not passed at runtime. Cloud auth is set at deployment time. For federated databases, the namespace service vends per-request credentials automatically. See the [quickstart](/quickstart), [Enterprise overview](/enterprise/), and [Azure deployment guide](/enterprise/deployment/azure) for the Enterprise flow.
+</Note>
+
+### Object stores {#object-stores}
+
+LanceDB supports AWS S3 (and compatible stores), Azure Blob Storage, and Google Cloud Storage. The URI scheme in your `connect` call selects the backend.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyStorageConnectS3}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsStorageConnectS3}
+    </CodeBlock>
+</CodeGroup>
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyStorageConnectGcs}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsStorageConnectGcs}
+    </CodeBlock>
+</CodeGroup>
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyStorageConnectAzure}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsStorageConnectAzure}
+    </CodeBlock>
+</CodeGroup>
+
+### Configuration options {#configuration-options}
+
+When running inside the target cloud with correct IAM bindings, LanceDB often needs no extra configuration. When running elsewhere, provide credentials via environment variables or `storage_options`.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyStorageConnectTimeout}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsStorageConnectTimeout}
+    </CodeBlock>
+</CodeGroup>
+
+<Info>
+**Storage option casing**
+
+Keys are case-insensitive. Use lowercase in `storage_options` and uppercase in environment variables.
+</Info>
+
+Table-level `storage_options` inherit every key from the connection and override on a per-key basis. Pass them to `create_table` or `open_table` for options that should apply to a single table:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyStorageTableTimeout}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsStorageTableTimeout}
+    </CodeBlock>
+</CodeGroup>
+
+<Tip>
+**Inspect a table's own options**
+
+On `AsyncTable`, `await table.initial_storage_options()` returns the options passed when the table was opened, and `await table.latest_storage_options()` returns them after any provider-driven refresh. Neither includes what the table inherits from its connection's `storage_options`, which still apply. The deprecated `table.storage_options()` method will be removed in a future release.
+</Tip>
+
+#### General object store options {#general-object-store-options}
+
+| Key | Description |
+| :-- | :-- |
+| `allow_http` | Allow non-TLS connections. |
+| `allow_invalid_certificates` | Skip certificate validation for TLS connections. |
+| `connect_timeout` | Timeout for the connect phase. |
+| `timeout` | Timeout for the full request. |
+| `user_agent` | User agent string sent with requests. |
+| `proxy_url` | Proxy URL to route requests through. |
+| `proxy_ca_certificate` | PEM-formatted CA certificate for proxy connections. |
+| `proxy_excludes` | Comma-separated hosts that bypass the proxy (domains or CIDR). |
+| `download_retry_count` | Number of retries when downloading objects. |
+| `client_max_retries` | Maximum retries for object-store client requests. |
+| `client_retry_timeout` | Total retry timeout (seconds) for object-store client requests. |
+
+<Info>
+**Option support varies by backend**
+
+These are commonly used options. Cloud-specific keys (for example `region`, `endpoint`, `service_account`, and Azure credential keys) are backend-dependent and can be provided in `storage_options` as needed.
+</Info>
+
+#### New table configuration {#new-table-configuration}
+
+These options control the Lance file format and features used when creating new tables. Pass them via `storage_options` at connection or table level. They are evaluated only at table creation; setting them on an existing connection does not rewrite or alter tables that already exist.
+
+| Key | Values | Default | Description |
+| :-- | :-- | :-- | :-- |
+| `new_table_data_storage_version` | `legacy`, `stable` | `stable` | Lance file format version for new tables. Use `legacy` for backward compatibility with older clients, or `stable` for the current format with better performance. |
+| `new_table_enable_v2_manifest_paths` | `true`, `false` | `false` | Use v2 manifest path naming. Requires LanceDB >= 0.10.0 to read. |
+| `new_table_enable_stable_row_ids` | `true`, `false` | `false` | Keep row IDs stable across compaction, delete, and merge operations. |
+
+```mermaid
+flowchart TD
+    A[Creating a new table] --> B{Need backward compatibility\nwith older LanceDB clients?}
+    B -->|Yes| C[new_table_data_storage_version: legacy]
+    B -->|No| D[new_table_data_storage_version: stable\nDefault — recommended]
+    D --> E{Need stable row IDs\nacross compaction and deletes?}
+    E -->|Yes| F[new_table_enable_stable_row_ids: true]
+    E -->|No| G[Default: false]
+    D --> H{All clients on LanceDB >= 0.10.0?}
+    H -->|Yes| I[new_table_enable_v2_manifest_paths: true]
+    H -->|No| J[Default: false]
+```
+
+<CodeGroup>
+```python Python icon="python"
+import lancedb
+
+# Set the Lance file format version at connection level
+db = lancedb.connect(
+    "s3://bucket/path",
+    storage_options={
+        "new_table_data_storage_version": "stable",
+    },
+)
+```
+
+```typescript TypeScript icon="square-js"
+import * as lancedb from "@lancedb/lancedb";
+
+// Set the Lance file format version at connection level
+const db = await lancedb.connect("s3://bucket/path", {
+  storageOptions: {
+    newTableDataStorageVersion: "stable",
+  },
+});
+```
+</CodeGroup>
+
+<Warning>
+**Deprecated parameter**
+
+The `data_storage_version` parameter on `create_table()` is deprecated. Use `new_table_data_storage_version` in `storage_options` instead.
+</Warning>
+
+### AWS S3 {#aws-s3}
+
+![](/static/assets/images/storage/aws.jpg)
+
+Set `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and optionally `AWS_SESSION_TOKEN` as environment variables or pass them in `storage_options`. Region is optional for AWS but required for most S3-compatible stores.
+
+Minimum permissions usually include `s3:PutObject`, `s3:GetObject`, `s3:DeleteObject`, `s3:ListBucket`, and `s3:GetBucketLocation` scoped to the relevant bucket/prefix.
+
+### S3-compatible stores {#s3-compatible-stores}
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyStorageS3Minio}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsStorageS3Minio}
+    </CodeBlock>
+</CodeGroup>
+
+If the endpoint is `http://` (common in local development), also set `ALLOW_HTTP=true` or pass `allow_http=True` in `storage_options`.
+
+### S3 Express {#s3-express}
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyStorageS3Express}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsStorageS3Express}
+    </CodeBlock>
+</CodeGroup>
+
+Consult AWS networking requirements for S3 Express before enabling.
+
+<Tip>
+**Clean up failed multipart uploads**
+
+LanceDB aborts multipart uploads on graceful shutdown, but crashes can leave incomplete uploads. Add an S3 lifecycle rule to delete in-progress uploads after a few days.
+</Tip>
+
+### Server-side encryption with KMS {#server-side-encryption-with-kms}
+
+To encrypt at rest with an AWS KMS key, set `aws_server_side_encryption` to `aws:kms` and `aws_sse_kms_key_id` to the key ID or ARN. The same options apply at connection or table level and combine with bucket-level default encryption.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyStorageS3SseKms}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsStorageS3SseKms}
+    </CodeBlock>
+</CodeGroup>
+
+The IAM principal needs `kms:Encrypt`, `kms:Decrypt`, and `kms:GenerateDataKey` on the configured KMS key.
+
+### Google Cloud Storage {#google-cloud-storage}
+
+![](/static/assets/images/storage/gcp.jpg)
+
+Provide credentials via `GOOGLE_SERVICE_ACCOUNT` (path to JSON) or include the path in `storage_options`. GCS defaults to HTTP/1; set `HTTP1_ONLY=false` if you need HTTP/2.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyStorageGcsServiceAccount}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsStorageGcsServiceAccount}
+    </CodeBlock>
+</CodeGroup>
+
+### Azure Blob Storage {#azure-blob-storage}
+
+![](/static/assets/images/storage/azure.jpg)
+
+Set `AZURE_STORAGE_ACCOUNT_NAME` and `AZURE_STORAGE_ACCOUNT_KEY` as environment variables, or pass them via `storage_options`.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyStorageAzureAccount}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsStorageAzureAccount}
+    </CodeBlock>
+</CodeGroup>
+
+For SAS-token auth, set `azure_storage_account_name` and `azure_storage_sas_token`:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyStorageAzureSas}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsStorageAzureSas}
+    </CodeBlock>
+</CodeGroup>
+
+Other supported keys include service principal credentials (`azure_client_id`, `azure_client_secret`, `azure_tenant_id`), managed identities, and custom endpoints.
+
+### Tigris Object Storage {#tigris-object-storage}
+
+![](/static/assets/images/storage/tigris.jpg)
+
+Tigris exposes an S3-compatible API. Configure the endpoint and region:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyStorageTigrisConnect}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsStorageTigrisConnect}
+    </CodeBlock>
+</CodeGroup>
+
+Environment variables `AWS_ENDPOINT=https://t3.storage.dev` and `AWS_DEFAULT_REGION=auto` achieve the same configuration.
+
+### Tencent COS {#tencent-cos}
+
+[Tencent Cloud Object Storage (COS)](https://www.tencentcloud.com/products/cos) is the primary object store for workloads running in the China region. Use the `cos://` URI scheme to connect directly to a COS bucket.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyStorageCosConnect}
+    </CodeBlock>
+</CodeGroup>
+
+Supported keys include `secret_id`, `secret_key`, `region`, and `endpoint`. You can also authenticate via the `TENCENTCLOUD_SECRET_ID` and `TENCENTCLOUD_SECRET_KEY` environment variables.
+
+<Note>
+**Availability**
+
+COS is bundled in the Python wheel by default. To use it from Rust or the Node binding, build with the `cos` Cargo feature enabled.
+</Note>
+
+### GooseFS {#goosefs}
+
+[GooseFS](https://www.tencentcloud.com/document/product/1424) is Tencent Cloud's distributed cache acceleration layer for COS and S3. It is a common choice when the same hot dataset is read repeatedly, such as vector search and AI training workloads. Connect using the `goosefs://` URI scheme.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyStorageGoosefsConnect}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsStorageGoosefsConnect}
+    </CodeBlock>
+</CodeGroup>
+
+GooseFS reads credentials and endpoint configuration from the GooseFS client environment. See the [GooseFS documentation](https://www.tencentcloud.com/document/product/1424) for cluster setup.
+
+<Note>
+**Availability**
+
+GooseFS is bundled by default in the Python wheel and the Node binding. To use it from Rust, build with the `goosefs` Cargo feature enabled.
+</Note>
diff --git a/docs/web/deployment/support.mdx b/docs/web/deployment/support.mdx
new file mode 100644
index 000000000..5e35d9131
--- /dev/null
+++ b/docs/web/deployment/support.mdx
@@ -0,0 +1,36 @@
+---
+title: "Support"
+sidebarTitle: "Support"
+description: "Where to get help: community Discord for OSS, dedicated support for Enterprise."
+icon: circle-question
+keywords: ["support", "help", "discord", "community"]
+---
+
+## LanceDB OSS {#oss}
+
+For a quick question, the
+[Discord community](https://discord.gg/AUEWnJ7Txb) has channels for
+Python, TypeScript and Rust where you can ask the community for help.
+
+Use GitHub for discussions and issues that others can search and follow:
+
+| | |
+| --- | --- |
+| [GitHub Discussions](https://github.com/lancedb/lancedb/discussions) | Open-ended questions, design questions, "what's the right way to do X" |
+| [GitHub Issues](https://github.com/lancedb/lancedb/issues) | A specific bug, or a feature request |
+
+## LanceDB Enterprise {#enterprise}
+
+<Badge color="red">Enterprise</Badge> Contact your support team directly at
+[support@lancedb.com](mailto:support@lancedb.com) for dedicated assistance.
+
+## What to include {#what-to-include}
+
+A faster answer usually depends on the same handful of details:
+
+| | |
+| --- | --- |
+| SDK and version | `lancedb.__version__` (Python), `package.json` (TypeScript), the crate version (Rust) |
+| Connection type | embedded path, or a `db://` deployment |
+| The exact error | full traceback or error message, not a paraphrase |
+| A minimal repro | the smallest table schema and call that reproduces it |
diff --git a/docs/web/docs.json b/docs/web/docs.json
new file mode 100644
index 000000000..a8fb44007
--- /dev/null
+++ b/docs/web/docs.json
@@ -0,0 +1,598 @@
+{
+  "$schema": "https://mintlify.com/docs.json",
+  "appearance": {
+    "strict": false
+  },
+  "theme": "mint",
+  "name": "LanceDB",
+  "banner": {
+    "content": "[Why Multimodal Data Needs a Better Lakehouse? \u2014 Download the Research Study](https://lancedb.com/download/)",
+    "dismissible": true
+  },
+  "colors": {
+    "primary": "#FF6B35",
+    "light": "#FF8A5C",
+    "dark": "#E55A2B"
+  },
+  "fonts": {
+    "family": "Inter"
+  },
+  "styling": {
+    "codeblocks": {
+      "theme": {
+        "light": "vitesse-light",
+        "dark": "catppuccin-mocha"
+      }
+    }
+  },
+  "favicon": "/static/favicon.ico",
+  "redirects": [
+    {
+      "source": "/tables/update",
+      "destination": "/ingestion/update"
+    },
+    {
+      "source": "/search/fts-examples",
+      "destination": "/search/full-text-search"
+    },
+    {
+      "source": "/search/optimize-queries",
+      "destination": "/search/explain"
+    },
+    {
+      "source": "/lance",
+      "destination": "/basics/lance"
+    },
+    {
+      "source": "/tables-and-namespaces",
+      "destination": "/basics/catalog"
+    },
+    {
+      "source": "/catalog",
+      "destination": "/basics/catalog"
+    },
+    {
+      "source": "/catalog/database",
+      "destination": "/basics/catalog"
+    },
+    {
+      "source": "/api-reference",
+      "destination": "/basics/apis"
+    },
+    {
+      "source": "/feature-engineering/computed-columns",
+      "destination": "/feature-engineering/add-computed-column"
+    },
+    {
+      "source": "/tables/create",
+      "destination": "/ingestion"
+    },
+    {
+      "source": "/tables/multimodal",
+      "destination": "/management/table#multimodal"
+    },
+    {
+      "source": "/search/select",
+      "destination": "/sql/select"
+    },
+    {
+      "source": "/tutorials/search",
+      "destination": "/tutorials/search/hybrid-search-beir"
+    },
+    {
+      "source": "/tutorials/agents",
+      "destination": "/tutorials/agents/contextual-rag"
+    },
+    {
+      "source": "/tutorials/feature-engineering",
+      "destination": "/tutorials/feature-engineering/feature-engineering-101"
+    },
+    {
+      "source": "/tables",
+      "destination": "/management/table"
+    },
+    {
+      "source": "/namespaces",
+      "destination": "/management/namespace"
+    },
+    {
+      "source": "/namespaces/usage",
+      "destination": "/management/namespace"
+    },
+    {
+      "source": "/basics/data-types",
+      "destination": "/sql/data-types"
+    },
+    {
+      "source": "/tables/schema",
+      "destination": "/management/table#schema"
+    },
+    {
+      "source": "/tables/versioning",
+      "destination": "/management/table#versioning"
+    },
+    {
+      "source": "/search/multivector-search",
+      "destination": "/search/vector-search"
+    },
+    {
+      "source": "/search/explain-analyze",
+      "destination": "/search/explain"
+    },
+    {
+      "source": "/indexing/quantization",
+      "destination": "/indexing/vector-index"
+    },
+    {
+      "source": "/performance",
+      "destination": "/search#performance"
+    },
+    {
+      "source": "/reranking/:slug*",
+      "destination": "/reranking"
+    },
+    {
+      "source": "/training",
+      "destination": "/training/data-loading"
+    },
+    {
+      "source": "/training/lerobot",
+      "destination": "/integrations/lerobotdataset"
+    },
+    {
+      "source": "/build-with-ai-agents",
+      "destination": "/basics/agentic-skills"
+    },
+    {
+      "source": "/core/update",
+      "destination": "/ingestion/update"
+    },
+    {
+      "source": "/storage",
+      "destination": "/deployment/storage"
+    },
+    {
+      "source": "/storage/configuration",
+      "destination": "/deployment/storage"
+    },
+    {
+      "source": "/storage/monitoring",
+      "destination": "/deployment/monitoring"
+    },
+    {
+      "source": "/search/sql",
+      "destination": "/sql"
+    },
+    {
+      "source": "/search/sql/index",
+      "destination": "/sql"
+    },
+    {
+      "source": "/search/sql/fts-sql",
+      "destination": "/sql/dql/fts"
+    },
+    {
+      "source": "/sql/select",
+      "destination": "/sql/dql/select"
+    },
+    {
+      "source": "/sql/subqueries",
+      "destination": "/sql/dql/subqueries"
+    },
+    {
+      "source": "/sql/join",
+      "destination": "/sql/dql/join"
+    },
+    {
+      "source": "/sql/aggregation",
+      "destination": "/sql/dql/aggregation"
+    },
+    {
+      "source": "/sql/window",
+      "destination": "/sql/dql/window"
+    },
+    {
+      "source": "/sql/sampling",
+      "destination": "/sql/dql/sampling"
+    },
+    {
+      "source": "/sql/explain",
+      "destination": "/sql/dql/explain"
+    },
+    {
+      "source": "/sql/ddl",
+      "destination": "/sql/ddl/table"
+    },
+    {
+      "source": "/sql/dml",
+      "destination": "/sql/dml/insert"
+    },
+    {
+      "source": "/sql/refresh",
+      "destination": "/sql/dml/refresh"
+    },
+    {
+      "source": "/sql/show",
+      "destination": "/sql/ddl/job"
+    },
+    {
+      "source": "/faq",
+      "destination": "/deployment/support"
+    },
+    {
+      "source": "/faq/faq-oss",
+      "destination": "/deployment/support"
+    },
+    {
+      "source": "/faq/faq-enterprise",
+      "destination": "/deployment/support"
+    },
+    {
+      "source": "/troubleshooting",
+      "destination": "/deployment/support"
+    },
+    {
+      "source": "/basics/apis",
+      "destination": "/basics/sdk"
+    }
+  ],
+  "navigation": {
+    "tabs": [
+      {
+        "tab": "Documentation",
+        "groups": [
+          {
+            "group": "Getting Started",
+            "pages": [
+              "index",
+              "basics/offerings",
+              "basics/connect",
+              "quickstart",
+              "architecture",
+              "basics/sdk",
+              "basics/sql",
+              "basics/agentic-skills"
+            ]
+          },
+          {
+            "group": "Concepts",
+            "pages": [
+              "basics/lance",
+              "basics/catalog",
+              "basics/lakehouse",
+              "basics/query",
+              "basics/jobs",
+              "tables/consistency",
+              "basics/guarantees",
+              "basics/limits",
+              "basics/naming"
+            ]
+          },
+          {
+            "group": "Data Ingestion",
+            "pages": [
+              "ingestion/index",
+              "ingestion/create-table",
+              "ingestion/insert",
+              "ingestion/merge-insert",
+              "ingestion/update",
+              "ingestion/delete"
+            ]
+          },
+          {
+            "group": "Semantic Search",
+            "pages": [
+              "search/index",
+              "search/vector-search",
+              "search/full-text-search",
+              "search/hybrid-search",
+              "search/filtering",
+              "reranking/index",
+              "search/explain"
+            ]
+          },
+          {
+            "group": "Data Curation",
+            "pages": [
+              "curation/index",
+              "curation/aggregate",
+              "curation/sampling",
+              "curation/join",
+              "curation/windowing"
+            ]
+          },
+          {
+            "group": "Feature Engineering",
+            "pages": [
+              "feature-engineering/index",
+              "feature-engineering/data-evolution",
+              "feature-engineering/add-computed-column",
+              "feature-engineering/refresh-column",
+              "feature-engineering/materialized-views"
+            ]
+          },
+          {
+            "group": "Training",
+            "pages": [
+              "training/why-lancedb",
+              "training/data-loading",
+              "training/torch"
+            ]
+          },
+          {
+            "group": "Management",
+            "pages": [
+              "management/index",
+              "management/database",
+              "management/namespace",
+              "management/table",
+              "tables/branching",
+              "management/materialized-view",
+              "management/view"
+            ]
+          },
+          {
+            "group": "Maintenance",
+            "pages": [
+              "indexing/index",
+              "indexing/vector-index",
+              "indexing/fts-index",
+              "indexing/scalar-index",
+              "indexing/reindexing",
+              "maintenance/compaction",
+              "maintenance/cleanup",
+              "maintenance/background-jobs"
+            ]
+          },
+          {
+            "group": "Deployment",
+            "pages": [
+              "enterprise/index",
+              "enterprise/architecture",
+              "enterprise/deployment/index",
+              "deployment/storage",
+              "deployment/monitoring",
+              "enterprise/deployment/azure",
+              "enterprise/benchmarks",
+              "deployment/support"
+            ]
+          },
+          {
+            "group": "Security",
+            "pages": [
+              "enterprise/authentication",
+              "governance/authorization",
+              "enterprise/security"
+            ]
+          },
+          {
+            "group": "SQL Reference",
+            "pages": [
+              "sql/index",
+              "sql/data-types",
+              {
+                "group": "DDL",
+                "pages": [
+                  "sql/ddl/namespace",
+                  "sql/ddl/table",
+                  "sql/ddl/view",
+                  "sql/ddl/materialized-view",
+                  "sql/ddl/index",
+                  "sql/ddl/job"
+                ],
+                "icon": "sitemap"
+              },
+              {
+                "group": "DML",
+                "pages": [
+                  "sql/dml/insert",
+                  "sql/dml/update",
+                  "sql/dml/delete",
+                  "sql/dml/copy",
+                  "sql/dml/refresh"
+                ],
+                "icon": "database"
+              },
+              {
+                "group": "DQL",
+                "pages": [
+                  "sql/dql/select",
+                  "sql/dql/subqueries",
+                  "sql/dql/join",
+                  "sql/dql/aggregation",
+                  "sql/dql/window",
+                  "sql/dql/sampling",
+                  "sql/dql/fts",
+                  "sql/dql/explain"
+                ],
+                "icon": "filter"
+              },
+              {
+                "group": "Expressions",
+                "pages": [
+                  "sql/operators",
+                  "sql/functions",
+                  "sql/scalar-functions",
+                  "sql/prepared-statements"
+                ],
+                "icon": "code"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "tab": "Integrations",
+        "pages": [
+          "integrations/index",
+          {
+            "group": "Embedding Registry",
+            "pages": [
+              "embedding/index",
+              "embedding/quickstart"
+            ]
+          },
+          {
+            "group": "Embedding Providers",
+            "pages": [
+              "integrations/embedding/huggingface",
+              "integrations/embedding/aws",
+              "integrations/embedding/cohere",
+              "integrations/embedding/colpali",
+              "integrations/embedding/gemini",
+              "integrations/embedding/ibm",
+              "integrations/embedding/imagebind",
+              "integrations/embedding/instructor",
+              "integrations/embedding/jina",
+              "integrations/embedding/ollama",
+              "integrations/embedding/openai",
+              "integrations/embedding/openclip",
+              "integrations/embedding/sentence-transformers",
+              "integrations/embedding/voyageai",
+              "integrations/embedding/superlinked"
+            ]
+          },
+          {
+            "group": "Rerankers",
+            "pages": [
+              "integrations/reranking/answerdotai",
+              "integrations/reranking/cohere",
+              "integrations/reranking/colbert",
+              "integrations/reranking/jina",
+              "integrations/reranking/openai",
+              "integrations/reranking/voyageai",
+              "integrations/reranking/watsonx"
+            ]
+          },
+          {
+            "group": "Data Platforms & Frameworks",
+            "expanded": true,
+            "pages": [
+              "integrations/data/pydantic",
+              "integrations/data/duckdb",
+              "integrations/data/pandas_and_pyarrow",
+              "integrations/data/polars_arrow",
+              "integrations/data/dlt",
+              "integrations/data/voxel51"
+            ]
+          },
+          {
+            "group": "AI Platforms & Frameworks",
+            "expanded": true,
+            "pages": [
+              "integrations/ai/agno",
+              "integrations/ai/hermes-agent",
+              "integrations/ai/huggingface",
+              "integrations/ai/langchain",
+              "integrations/ai/llamaIndex",
+              "integrations/ai/genkit",
+              "integrations/ai/kiln",
+              "integrations/ai/prompttools",
+              "integrations/ai/synthetic-data-kit"
+            ]
+          }
+        ]
+      },
+      {
+        "tab": "Tutorials",
+        "pages": [
+          "tutorials/index",
+          {
+            "group": "Search",
+            "pages": [
+              "tutorials/search/hybrid-search-beir",
+              "tutorials/search/multivector-xtr",
+              "tutorials/search/multivector-needle-in-a-haystack",
+              "tutorials/search/video-search",
+              "tutorials/search/vector-arithmetic",
+              "tutorials/search/ner-search",
+              "tutorials/search/reddit-summarizer"
+            ]
+          },
+          {
+            "group": "RAG",
+            "pages": [
+              "tutorials/agents/contextual-rag",
+              "tutorials/agents/nvidia-rag-blueprint/index",
+              "tutorials/agents/time-travel-rag/index",
+              "tutorials/agents/multimodal-agent/index",
+              "tutorials/agents/matryoshka-embeddings",
+              "tutorials/agents/hyde",
+              "tutorials/agents/late-chunking",
+              "tutorials/agents/agentic-rag"
+            ]
+          },
+          {
+            "group": "Feature Engineering",
+            "pages": [
+              "tutorials/feature-engineering/feature-engineering-101",
+              "tutorials/feature-engineering/materialized-views",
+              "agent-branch-experiments"
+            ]
+          },
+          {
+            "group": "Training",
+            "pages": [
+              "training/object-detection",
+              "training/vlm-finetuning",
+              "integrations/stable-worldmodel"
+            ]
+          }
+        ]
+      },
+      {
+        "tab": "Demos",
+        "pages": [
+          "demos/index",
+          "demos/semantic-art",
+          "demos/wikipedia-search",
+          "demos/video-search",
+          "demos/fineweb-edu-search",
+          "demos/robot-data-curation"
+        ]
+      }
+    ]
+  },
+  "logo": {
+    "light": "/static/assets/logo/dark-lancedb-logo.svg",
+    "dark": "/static/assets/logo/light-lancedb-logo.svg",
+    "href": "https://lancedb.com"
+  },
+  "background": {
+    "color": {
+      "light": "#FAF5F0",
+      "dark": "#000000"
+    }
+  },
+  "navbar": {
+    "links": [
+      {
+        "label": "Support",
+        "href": "https://www.lancedb.com/contact"
+      }
+    ]
+  },
+  "footer": {
+    "socials": {
+      "github": "https://github.com/lancedb",
+      "linkedin": "https://www.linkedin.com/company/lancedb",
+      "x": "https://x.com/lancedb",
+      "discord": "https://discord.gg/AUEWnJ7Txb"
+    },
+    "links": [
+      {
+        "items": [
+          {
+            "label": "Cookie settings",
+            "href": "#cookie-settings"
+          }
+        ]
+      }
+    ]
+  },
+  "integrations": {
+    "ga4": {
+      "measurementId": "G-1TMC2PR69E"
+    }
+  }
+}
diff --git a/docs/web/embedding/index.mdx b/docs/web/embedding/index.mdx
new file mode 100644
index 000000000..9f1531c05
--- /dev/null
+++ b/docs/web/embedding/index.mdx
@@ -0,0 +1,250 @@
+---
+title: "Managing Embeddings"
+sidebarTitle: "Managing Embeddings"
+description: "Use the embedding API in LanceDB -- registry, functions, schemas, and multi-language SDK support."
+icon: "bars"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Embedding functions run in your process, whether the table is local or on a
+    deployment, which stores their configuration but never runs them. Python
+    and TypeScript also embed query text; in Rust you embed queries yourself.
+---
+
+import {
+    PyOpenaiEmbeddings,
+    PyCreateEmbeddingFunction,
+    PyManualQuerySearch,
+    PyEmbeddingFunction,
+    PyRegisterDevice,
+    PyRegisterSecret,
+    TsOpenaiEmbeddings,
+    TsCreateEmbeddingFunction,
+    TsManualQuerySearch,
+    TsEmbeddingFunction,
+    TsRegisterModelFallback,
+    TsRegisterSecret,
+    RsOpenaiEmbeddings,
+    RsCreateEmbeddingFunction,
+    RsManualQuerySearch,
+    RsEmbeddingFunction,
+} from '/snippets/embedding.mdx';
+
+Modern machine learning models can be trained to convert raw data into embeddings, which are vectors
+of floating point numbers. The position of an embedding in vector space captures the semantics of
+the data, so vectors that are close to each other are considered similar.
+
+LanceDB provides an embedding function registry in OSS as well as its Enterprise versions
+([see below](#embeddings-in-lancedb-enterprise))
+that automatically generates vector embeddings during data ingestion. Query text is embedded
+automatically too, in the client, for local and Enterprise tables alike, with SDK-specific query
+ergonomics. The API abstracts
+embedding generation, allowing you to focus on your application logic.
+
+## Embedding Registry {#embedding-registry}
+
+You can get a supported embedding function from the registry, and then use it in your table schema. 
+Once configured, the embedding function will automatically generate embeddings when you insert data
+into the table. Query-time behavior depends on SDK: Python/TypeScript can query with text directly,
+while Rust examples typically compute query embeddings explicitly before vector search.
+
+In the Rust examples, replace `your-openai-api-key` with your OpenAI API key. In
+application code, load credentials from environment variables or a secrets manager.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="python" icon="python">
+    {PyOpenaiEmbeddings}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+    {TsOpenaiEmbeddings}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="rust" icon="rust">
+    {RsOpenaiEmbeddings}
+    </CodeBlock>
+</CodeGroup>
+
+### Using an embedding function {#using-an-embedding-function}
+
+Create an embedding function before you attach it to table or schema metadata. Python and TypeScript fetch
+provider implementations from the embedding registry, while Rust constructs the provider embedding function
+directly and registers it on the connection before using it in an `EmbeddingDefinition`.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="python" icon="python">
+    {PyCreateEmbeddingFunction}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+    {TsCreateEmbeddingFunction}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="rust" icon="rust">
+    {RsCreateEmbeddingFunction}
+    </CodeBlock>
+</CodeGroup>
+
+Provider configuration is SDK-specific, so copy the option names from the provider page for the SDK you use.
+For example, the OpenAI model is selected with `name` in Python, `model` in TypeScript, and the model argument
+to `OpenAIEmbeddingFunction::new_with_model` in Rust.
+
+| Concept | Python | TypeScript | Rust |
+| --- | --- | --- | --- |
+| Model | `name="text-embedding-3-small"` | `{ model: "text-embedding-3-small" }` | `new_with_model(api_key, "text-embedding-3-small")` |
+| Retry count | `max_retries=7` | Provider/client-specific | Provider/client-specific |
+| API key | `api_key="..."`, environment variables, or `$var:` | `apiKey: "..."`, environment variables, or `$var:` | Constructor argument or environment variable |
+| Device | Provider-specific, for example `device="cuda"` | Provider-specific | Provider-specific |
+
+When ingesting data with an embedding definition, LanceDB only computes the vector column if that
+column is missing from the incoming batch or present but entirely null. If you provide any non-null
+values in the vector column, LanceDB treats the column as user-supplied and does not backfill the
+remaining rows in that batch.
+
+For reusable runtime configuration, the registry also supports `$var:` placeholders in embedding-function config.
+This is useful for provider secrets and environment-specific settings in Python and TypeScript.
+
+- Python uses `registry.set_var(...)`.
+- TypeScript uses `registry.setVar(...)`.
+- You can provide a fallback with `$var:name:default`.
+- Sensitive values such as API keys should be passed through registry variables instead of hardcoding them in config.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="python" icon="python">
+    {PyRegisterSecret}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+    {TsRegisterSecret}
+    </CodeBlock>
+</CodeGroup>
+
+For non-sensitive settings such as inference device selection, you can also use a default fallback:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="python" icon="python">
+    {PyRegisterDevice}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+    {TsRegisterModelFallback}
+    </CodeBlock>
+</CodeGroup>
+
+Find the full list of arguments for each provider in the [integrations](/integrations/embedding) section.
+
+## Multiple embedding columns {#multiple-embedding-columns}
+
+A single table can include more than one embedding definition when you want to store multiple semantic views
+of the same data, or generate embeddings from different source columns. In practice, each embedding definition
+maps one source column to one vector column, and the table schema can contain multiple such pairs.
+
+The exact setup differs by SDK, but the underlying pattern is the same: define a distinct source/vector pair
+for each embedding function you want applied during ingest.
+
+In TypeScript, automatic query embedding currently uses the first embedding function stored in the
+table metadata. If a table has multiple embedding definitions and you need to query a specific vector
+column, compute the query embedding explicitly and pass the vector to the search builder.
+
+## Embedding model providers {#embedding-model-providers}
+
+LanceDB supports most popular embedding providers.
+
+### Text embeddings {#text-embeddings}
+
+| Provider | Model ID | Default Model |
+|----------|----------|---------------|
+| OpenAI | `openai` | `text-embedding-ada-002` |
+| Sentence Transformers | `sentence-transformers` | `all-MiniLM-L6-v2` |
+| Hugging Face | `huggingface` | `colbert-ir/colbertv2.0` |
+| Cohere | `cohere` | `embed-english-v3.0` |
+| ... | ... | ... |
+
+### Multimodal embedding {#multimodal-embedding}
+
+| Provider | Model ID | Supported Inputs |
+|----------|----------|------------------|
+| OpenCLIP | `open-clip` | Text, Images |
+| ImageBind | `imagebind` | Text, Images, Audio, Video |
+| ... | ... | ... |
+
+You can find all supported embedding models in the [integrations](/integrations/embedding) section.
+
+## Embeddings in LanceDB Enterprise {#embeddings-in-lancedb-enterprise}
+<Badge color="red">Enterprise</Badge>
+
+In LanceDB Enterprise, embedding generation during data ingestion is client-side and the resulting vectors are
+stored on the remote table.
+
+<Info>
+The Enterprise server does not currently generate embeddings from query text on its own. Any automatic
+query-time embedding happens on the client side.
+</Info>
+
+### How string queries are interpreted {#how-string-queries-are-interpreted}
+
+For the Python remote client, `table.search("hello")` can take two different paths:
+
+- If the selected vector column has embedding metadata
+  (i.e., the table schema stores the source-column, vector-column, and
+  embedding-function mapping created from fields like `SourceField()` and
+  `VectorField()` during table creation), then the embeddings are computed in the Python client process.
+  The client uses the same local LanceDB embedding registry used by OSS tables to
+  reconstruct the embedding function from schema metadata, compute the query vector in
+  the client process, and send that vector to Enterprise for search.
+  ```python
+  result = table.search("hello").limit(1).to_list()
+  # The Python client computes the query embedding locally, then sends a vector search.
+  ```
+- If the table does not have embedding metadata for that search, `table.search("hello")` in `auto` mode is
+  treated as an FTS query instead.
+  ```python
+  result = table.search("hello").limit(5).to_list()
+  # In auto mode this is treated as an FTS query, not a vector query.
+  ```
+
+If you want explicit vector or hybrid behavior and the client cannot resolve an embedding function from the
+table metadata, generate the query embedding yourself and pass the vector directly.
+
+TypeScript has a similar string-query distinction in `auto` mode: if no embedding providers have been
+registered in the process, `search("text")` falls back to FTS. Once an embedding provider has been
+imported and registered, the client expects table embedding metadata for automatic vector search and
+raises an error if the table has none.
+
+The manual query-embedding flow below works across Enterprise SDKs and is an explicit path you can use when you
+want full control over query-time behavior.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="python" icon="python">
+    {PyManualQuerySearch}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+    {TsManualQuerySearch}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="rust" icon="rust">
+    {RsManualQuerySearch}
+    </CodeBlock>
+</CodeGroup>
+
+## Custom Embedding Functions {#custom-embedding-functions}
+
+You can always implement your own embedding function:
+- Python/TypeScript: subclass `TextEmbeddingFunction` (text) or `EmbeddingFunction` (multimodal).
+- Rust: implement the `EmbeddingFunction` trait.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="python" icon="python">
+    {PyEmbeddingFunction}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+    {TsEmbeddingFunction}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="rust" icon="rust">
+    {RsEmbeddingFunction}
+    </CodeBlock>
+</CodeGroup>
diff --git a/docs/web/embedding/quickstart.mdx b/docs/web/embedding/quickstart.mdx
new file mode 100644
index 000000000..bcaf90f6f
--- /dev/null
+++ b/docs/web/embedding/quickstart.mdx
@@ -0,0 +1,155 @@
+---
+title: "Embeddings: Quickstart"
+sidebarTitle: "Quickstart"
+description: "Quickstart guide for generating and working with embeddings."
+icon: "rocket"
+---
+
+import {
+    TsQuickstartConnect,
+    TsQuickstartCreateTable,
+    TsQuickstartImports,
+    TsQuickstartInitModel,
+    TsQuickstartQuery,
+    TsQuickstartSchema,
+} from '/snippets/sentence-transformers.mdx';
+
+LanceDB will automatically vectorize the data both at ingestion and query time. All you need to do is specify which model to use.
+Popular embedding models like OpenAI, Hugging Face, Sentence Transformers, CLIP, and more, are supported.
+
+## Step 1: Import Required Libraries {#step-1-import-required-libraries}
+
+First, import the necessary LanceDB components:
+
+<CodeGroup>
+```python Python icon="python"
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+```
+
+    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+    {TsQuickstartImports}
+    </CodeBlock>
+</CodeGroup>
+
+- `lancedb`: The main database connection and operations
+- `LanceModel`: Pydantic model for defining table schemas
+- `Vector`: Field type for storing vector embeddings
+- `get_registry()`: Access to the embedding function registry. It has all the supported as well as custom embedding functions registered by the user
+- TypeScript uses `lancedb.embedding.getRegistry()` and `lancedb.embedding.LanceSchema()` for the same registry/schema workflow
+- In TypeScript, import the provider module before calling `getRegistry().get(...)`; the provider import is what registers names such as `"huggingface"` or `"openai"`
+
+## Step 2: Connect to LanceDB {#step-2-connect-to-lancedb}
+
+Establish a connection to your LanceDB OSS directory or Enterprise cluster:
+
+<CodeGroup>
+```python Python icon="python"
+# Enter your LanceDB connection URI for OSS or Enterprise here
+db = lancedb.connect(...)
+```
+
+    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+    {TsQuickstartConnect}
+    </CodeBlock>
+</CodeGroup>
+
+## Step 3: Initialize the Embedding Function {#step-3-initialize-the-embedding-function}
+
+Choose and configure your embedding model:
+
+<CodeGroup>
+```python Python icon="python"
+model = get_registry().get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", )
+```
+
+    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+    {TsQuickstartInitModel}
+    </CodeBlock>
+</CodeGroup>
+
+This creates an embedding function from the local embedding registry. The Python snippet uses the
+`sentence-transformers` provider with the BGE model; the TypeScript snippet uses the Transformers-backed
+`huggingface` provider. You can:
+- Change `"sentence-transformers"` to other providers like `"openai"`, `"cohere"`, etc.
+- Modify the model name for different embedding models
+- Set `device="cuda"` for GPU acceleration if available
+
+## Step 4: Define Your Schema {#step-4-define-your-schema}
+
+Create a Pydantic model that defines your table structure:
+
+<CodeGroup>
+```python Python icon="python"
+class Words(LanceModel):
+    text: str = model.SourceField()  
+    vector: Vector(model.ndims()) = model.VectorField()
+```
+
+    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+    {TsQuickstartSchema}
+    </CodeBlock>
+</CodeGroup>
+
+- `SourceField()`: This field will be embedded
+- `VectorField()`: This stores the embeddings
+- `model.ndims()`: Sets vector dimensions for your model
+- In TypeScript, use `model.sourceField(...)` and `model.vectorField()` inside `LanceSchema(...)`
+
+## Step 5: Create Table and Ingest Data {#step-5-create-table-and-ingest-data}
+
+Create a table with your schema and add data:
+
+<CodeGroup>
+```python Python icon="python"
+table = db.create_table("words", schema=Words)
+table.add([
+    {"text": "hello world"},
+    {"text": "goodbye world"}
+])
+```
+
+    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+    {TsQuickstartCreateTable}
+    </CodeBlock>
+</CodeGroup>
+
+The `table.add()` call automatically:
+- Takes the text from each document
+- Generates embeddings using your chosen model
+- Stores both the original text and the vector embeddings
+
+If your input already includes the vector column, automatic embedding only runs when that column is
+absent or entirely null for the batch. Partially supplied vectors are treated as manual data, so
+LanceDB preserves them instead of filling only the missing rows.
+
+## Step 6: Query with Automatic Embedding {#step-6-query-with-automatic-embedding}
+
+Note: On LanceDB Enterprise, the server does not generate embeddings from query text. In the Python remote
+client, `table.search("greetings")` can still work when the table schema includes embedding metadata, because
+the client computes the query embedding before sending the vector search. If there is no embedding metadata for
+that search, `search("greetings")` in `auto` mode is treated as FTS instead.
+
+Search your data using natural language queries:
+
+<CodeGroup>
+```python Python icon="python"
+query = "greetings"
+actual = table.search(query).limit(1).to_pydantic(Words)[0]
+print(actual.text)
+```
+
+    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+    {TsQuickstartQuery}
+    </CodeBlock>
+</CodeGroup>
+
+The search process:
+1. Automatically converts your query text to embeddings
+2. Finds the most similar vectors in your table
+3. Returns the matching documents
+
+Automatic text search depends on the table's embedding metadata. If the client cannot reconstruct an
+embedding function from that metadata, compute the query embedding yourself and search with the
+vector directly.
diff --git a/docs/web/enterprise/architecture.mdx b/docs/web/enterprise/architecture.mdx
new file mode 100644
index 000000000..fc1c1c7db
--- /dev/null
+++ b/docs/web/enterprise/architecture.mdx
@@ -0,0 +1,20 @@
+---
+title: "Enterprise architecture"
+sidebarTitle: "Architecture"
+description: "The tiers a deployment runs, and how they scale independently."
+icon: cubes
+keywords: ["architecture", "control plane", "data plane", "indexer"]
+---
+
+<Badge color="red">Enterprise</Badge>
+
+This page describes a LanceDB Enterprise capability. The embedded form
+of LanceDB has no server to provide it -- see
+[Architecture](/architecture) for what differs between the two.
+
+A deployment separates compute from storage so each scales on its own: table
+data and indexes live in object storage, while query serving and background work
+run on machines that can be replaced without any of them owning the data.
+
+[Architecture](/architecture) covers this alongside the embedded form, which is
+the comparison most readers want first.
diff --git a/docs/web/enterprise/authentication.mdx b/docs/web/enterprise/authentication.mdx
new file mode 100644
index 000000000..4d04120fc
--- /dev/null
+++ b/docs/web/enterprise/authentication.mdx
@@ -0,0 +1,175 @@
+---
+title: "Authentication"
+sidebarTitle: "Authentication"
+description: "Authentication modes for LanceDB Enterprise: API keys and OAuth credentials."
+icon: key
+keywords: ["authentication", "oauth", "api key", "azure managed identity", "client credentials"]
+availability:
+  oss: unavailable
+  enterprise: available
+  summary: >-
+    Requests to a deployment carry an API key or an OAuth token, whichever it is
+    configured to accept. Embedded LanceDB has no server to sign in to; see
+    [Architecture](/architecture).
+---
+
+A deployment authenticates every request before
+[authorization](/governance/authorization) decides what it may do, against a
+`db://` remote table, in one of two ways:
+
+- **API keys** -- a long-lived shared secret passed as `api_key` on connect. Works in every SDK.
+- **OAuth 2.0** -- short-lived bearer tokens obtained from your identity provider, refreshed automatically by the client.
+
+OAuth is the recommended option when you want to rotate credentials centrally,
+plug into an existing identity provider, or run on Azure with managed
+identities so no secret material lives on the client.
+
+## API key {#api-key}
+
+Pass the API key from your Enterprise tenant on `connect`. This works with
+both the synchronous and asynchronous Python clients, as well as TypeScript
+and Rust.
+
+```python
+import lancedb
+
+db = lancedb.connect(
+    uri="db://your-database-uri",
+    api_key="your-api-key",
+    region="us-east-1",
+    host_override="https://your-enterprise-endpoint.com",
+)
+```
+
+An API key belongs in the environment rather than in source. See
+[Database](/management/database#deployment) for the connection options.
+
+## OAuth {#oauth}
+
+The async Python client and the TypeScript client can obtain bearer tokens
+from an OIDC issuer and attach them to every request. Token acquisition,
+caching, and refresh are handled inside the client -- your application code
+only provides the configuration.
+
+A deployment accepts these tokens only when its administrator has configured
+it with your identity provider. Ask them for the issuer URL, client ID and
+scopes to use.
+
+<Info>
+In Python, OAuth is supported through `lancedb.connect_async`. The synchronous
+`connect` entry point continues to use API key authentication for `db://`
+URIs. In TypeScript, `lancedb.connect` accepts `oauthConfig` directly.
+</Info>
+
+### Supported flows {#supported-flows}
+
+`OAuthFlowType` selects how the client acquires tokens:
+
+| Flow | Python value | TypeScript value | When to use |
+| --- | --- | --- | --- |
+| Client Credentials | `OAuthFlowType.CLIENT_CREDENTIALS` | `OAuthFlowType.ClientCredentials` | Service-to-service / machine-to-machine. Requires a client ID and client secret registered with your identity provider. |
+| Azure Managed Identity | `OAuthFlowType.AZURE_MANAGED_IDENTITY` | `OAuthFlowType.AzureManagedIdentity` | Workloads running on Azure compute (VMs, AKS, App Service, Container Apps). Tokens are fetched from the Azure IMDS endpoint, so no client secret is stored on the client. |
+
+### Configure OAuth {#configure-oauth}
+
+Build an OAuth config and pass it on connect. Use `oauth_config` in Python and
+`oauthConfig` in TypeScript.
+
+<CodeGroup>
+```python Python icon="python"
+import lancedb
+from lancedb.remote import OAuthConfig, OAuthFlowType
+
+# Client Credentials (service-to-service)
+oauth_config = OAuthConfig(
+    issuer_url="https://login.microsoftonline.com/{tenant}/v2.0",
+    client_id="your-application-client-id",
+    client_secret="your-application-client-secret",
+    scopes=["api://lancedb-api/.default"],
+    flow=OAuthFlowType.CLIENT_CREDENTIALS,
+)
+
+db = await lancedb.connect_async(
+    uri="db://your-database-uri",
+    region="us-east-1",
+    host_override="https://your-enterprise-endpoint.com",
+    oauth_config=oauth_config,
+)
+```
+
+```typescript TypeScript icon="square-js"
+import * as lancedb from "@lancedb/lancedb";
+import { OAuthConfig, OAuthFlowType } from "@lancedb/lancedb";
+
+// Client Credentials (service-to-service)
+const oauthConfig: OAuthConfig = {
+  issuerUrl: "https://login.microsoftonline.com/{tenant}/v2.0",
+  clientId: "your-application-client-id",
+  clientSecret: "your-application-client-secret",
+  scopes: ["api://lancedb-api/.default"],
+  flow: OAuthFlowType.ClientCredentials,
+};
+
+const db = await lancedb.connect("db://your-database-uri", {
+  region: "us-east-1",
+  hostOverride: "https://your-enterprise-endpoint.com",
+  oauthConfig,
+});
+```
+</CodeGroup>
+
+For workloads running on Azure compute, use a managed identity instead of a
+client secret:
+
+<CodeGroup>
+```python Python icon="python"
+from lancedb.remote import OAuthConfig, OAuthFlowType
+
+oauth_config = OAuthConfig(
+    issuer_url="https://login.microsoftonline.com/{tenant}/v2.0",
+    client_id="your-application-client-id",
+    scopes=["api://lancedb-api/.default"],
+    flow=OAuthFlowType.AZURE_MANAGED_IDENTITY,
+    # Optional: required only for user-assigned managed identities.
+    managed_identity_client_id="your-user-assigned-identity-client-id",
+)
+```
+
+```typescript TypeScript icon="square-js"
+import { OAuthConfig, OAuthFlowType } from "@lancedb/lancedb";
+
+const oauthConfig: OAuthConfig = {
+  issuerUrl: "https://login.microsoftonline.com/{tenant}/v2.0",
+  clientId: "your-application-client-id",
+  scopes: ["api://lancedb-api/.default"],
+  flow: OAuthFlowType.AzureManagedIdentity,
+  // Optional: required only for user-assigned managed identities.
+  managedIdentityClientId: "your-user-assigned-identity-client-id",
+};
+```
+</CodeGroup>
+
+### Configuration reference {#configuration-reference}
+
+The same configuration is available in both SDKs. Python uses `snake_case`
+field names; TypeScript uses `camelCase`.
+
+| Python field / TypeScript field | Required | Description |
+| --- | --- | --- |
+| `issuer_url` / `issuerUrl` | Yes | OIDC issuer URL or OAuth authority URL. For Azure, use `https://login.microsoftonline.com/{tenant_id}/v2.0`. |
+| `client_id` / `clientId` | Yes | Application / client ID registered with your identity provider. |
+| `scopes` | Yes | List of OAuth scopes to request. For Azure managed identity, provide exactly one scope or resource, for example `["api://your-app-id/.default"]`. |
+| `flow` | No | Selects the OAuth flow. Defaults to Client Credentials. |
+| `client_secret` / `clientSecret` | Conditional | Required for Client Credentials. Redacted from the config's `repr` (Python) and `Debug` output (TypeScript native binding) so it does not leak into logs. |
+| `managed_identity_client_id` / `managedIdentityClientId` | No | Client ID of a user-assigned managed identity. Only used with Azure Managed Identity; omit for system-assigned identities. |
+| `refresh_buffer_secs` / `refreshBufferSecs` | No | How many seconds before token expiry to proactively refresh. Defaults to 300. Keep this well below the token TTL -- setting it greater than or equal to the TTL forces a refresh on every request. |
+
+<Warning>
+Treat the client secret like any other production credential. Load it from a
+secret manager or environment variable rather than committing it to source
+control. Both SDKs deliberately omit the secret from their debug/repr output
+so accidental log lines do not expose the value.
+</Warning>
+
+Embedded LanceDB has nothing to authenticate to -- your process opens the files
+directly, and the object store's own credentials are the only gate.
diff --git a/docs/web/enterprise/benchmarks.mdx b/docs/web/enterprise/benchmarks.mdx
new file mode 100644
index 000000000..3626f7b81
--- /dev/null
+++ b/docs/web/enterprise/benchmarks.mdx
@@ -0,0 +1,20 @@
+---
+title: "Benchmarks"
+sidebarTitle: "Benchmarks"
+description: "Published performance results for LanceDB Enterprise."
+icon: chart-line
+keywords: ["benchmarks", "performance", "throughput", "latency"]
+---
+
+<Badge color="red">Enterprise</Badge>
+
+This page describes a LanceDB Enterprise capability. The embedded form
+of LanceDB has no server to provide it -- see
+[Architecture](/architecture) for what differs between the two.
+
+Benchmark results are specific to a deployment's shape -- cluster size, storage
+class, dataset -- so they are published with LanceDB Enterprise.
+
+For tuning your own workload rather than comparing published numbers, see
+[Query plans](/search/explain) and the performance notes on
+[Search](/search#performance) and [Insert](/ingestion/insert#performance).
diff --git a/docs/web/enterprise/deployment/azure.mdx b/docs/web/enterprise/deployment/azure.mdx
new file mode 100644
index 000000000..349393249
--- /dev/null
+++ b/docs/web/enterprise/deployment/azure.mdx
@@ -0,0 +1,17 @@
+---
+title: "Azure deployment guide"
+sidebarTitle: "Azure"
+description: "Deploying LanceDB Enterprise on Azure."
+icon: cloud
+keywords: ["azure", "deployment", "aks"]
+---
+
+<Badge color="red">Enterprise</Badge>
+
+This page describes a LanceDB Enterprise capability. The embedded form
+of LanceDB has no server to provide it -- see
+[Architecture](/architecture) for what differs between the two.
+
+The Azure deployment procedure is published with LanceDB Enterprise. See
+[Deployment](/enterprise/deployment) for the general shape, and
+[contact us](https://www.lancedb.com/contact) to get started.
diff --git a/docs/web/enterprise/deployment/index.mdx b/docs/web/enterprise/deployment/index.mdx
new file mode 100644
index 000000000..c718cc32b
--- /dev/null
+++ b/docs/web/enterprise/deployment/index.mdx
@@ -0,0 +1,20 @@
+---
+title: "Deployment guide"
+sidebarTitle: "Deployment Models"
+description: "Standing up a LanceDB Enterprise deployment."
+icon: server
+keywords: ["deployment", "install", "cluster", "kubernetes"]
+---
+
+<Badge color="red">Enterprise</Badge>
+
+This page describes a LanceDB Enterprise capability. The embedded form
+of LanceDB has no server to provide it -- see
+[Architecture](/architecture) for what differs between the two.
+
+Deployment is specific to your cloud and account, so the procedure is published
+with LanceDB Enterprise. [Contact us](https://www.lancedb.com/contact) to get
+started.
+
+Embedded LanceDB has nothing to deploy -- it is a library your process links
+against. See [Architecture](/architecture#embedded).
diff --git a/docs/web/enterprise/index.mdx b/docs/web/enterprise/index.mdx
new file mode 100644
index 000000000..a601e850d
--- /dev/null
+++ b/docs/web/enterprise/index.mdx
@@ -0,0 +1,33 @@
+---
+title: "LanceDB Enterprise"
+sidebarTitle: "Overview"
+description: "What a deployment adds over embedded LanceDB."
+icon: building
+keywords: ["enterprise", "deployment", "cluster"]
+---
+
+<Badge color="red">Enterprise</Badge>
+
+This page describes a LanceDB Enterprise capability. The embedded form
+of LanceDB has no server to provide it -- see
+[Architecture](/architecture) for what differs between the two.
+
+LanceDB Enterprise runs the lakehouse as a service: a control plane for
+configuration, identity and policy; a data plane serving reads and writes; and
+indexers building indexes and compacting outside the request path.
+
+What it adds over the embedded form:
+
+| | |
+| --- | --- |
+| [SQL](/sql) | an Arrow Flight SQL endpoint |
+| [Jobs](/basics/jobs) | server-side work with ids, status and events |
+| [Authorization](/governance/authorization) | per-object grants |
+| Scale | a cluster rather than your process |
+
+{/*
+Draft for the Functions release. Add this row to the table above, after SQL:
+| [Functions](/management/function) | Python running on workers |
+*/}
+
+See [Architecture](/architecture) for the two shapes side by side.
diff --git a/docs/web/enterprise/security.mdx b/docs/web/enterprise/security.mdx
new file mode 100644
index 000000000..a2b60a242
--- /dev/null
+++ b/docs/web/enterprise/security.mdx
@@ -0,0 +1,20 @@
+---
+title: "Compliance"
+sidebarTitle: "Compliance"
+description: "Certifications, data handling, and the controls a deployment provides."
+icon: certificate
+keywords: ["compliance", "security", "soc2", "encryption", "audit"]
+---
+
+<Badge color="red">Enterprise</Badge>
+
+This page describes a LanceDB Enterprise capability. The embedded form
+of LanceDB has no server to provide it -- see
+[Architecture](/architecture) for what differs between the two.
+
+Compliance posture -- certifications, data residency, encryption at rest and in
+transit, audit logging -- is a property of a deployment, so it is documented
+with LanceDB Enterprise.
+
+For embedded LanceDB, these are properties of the object store you point it at
+and the process you run it in; LanceDB adds no layer of its own.
diff --git a/docs/web/feature-engineering/add-computed-column.mdx b/docs/web/feature-engineering/add-computed-column.mdx
new file mode 100644
index 000000000..331918715
--- /dev/null
+++ b/docs/web/feature-engineering/add-computed-column.mdx
@@ -0,0 +1,124 @@
+---
+title: "Add a computed column"
+sidebarTitle: "Computed Columns"
+description: "Declare a column from a SQL expression. Nothing is evaluated until you refresh it."
+icon: table-columns
+keywords: ["computed columns", "add_columns", "transforms", "schema"]
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Both offerings support computed columns. Enterprise requires the deployment to enable them; the embedded library evaluates them in your process.
+---
+
+A computed column is declared with an expression and starts with no values.
+Declaring it is cheap on any table size, because nothing is evaluated until
+[a refresh](/feature-engineering/refresh-column) asks for it.
+
+## Add a computed column {#declare}
+
+```python
+import lancedb
+
+db = lancedb.connect("./.lancedb")
+table = db.create_table("computed_demo", [{"x": 1}, {"x": 2}])
+
+table.add_columns(computed={"doubled": "x * 2"})
+# AddColumnsResult(version=2)
+```
+
+That commits a new table version with the column present and empty. It is an
+ordinary table version, so the usual [versioning](/management/table#versioning) applies --
+including that you can travel back to before the column existed.
+
+## Compute now or on refresh {#computed-vs-transforms}
+
+`add_columns` takes either, and the difference matters:
+
+- **SQL expressions in `transforms`** are evaluated *now*, for every row, as
+  part of the call. Good for a small table, or a column you want populated
+  immediately.
+  {/* Draft for the Functions release:
+  A `FunctionApplication` passed through `transforms` instead
+  declares empty Function-bound columns, which a refresh fills; that needs a
+  deployment with Functions enabled, and the embedded library refuses it. */}
+- **`computed`** stores the expression and evaluates nothing. The column is
+  committed empty and filled later by
+  [`refresh_column`](/feature-engineering/refresh-column).
+
+`computed` and `transforms` cannot be combined in one call. Reach for `computed` when the table is
+large enough that you would rather control when the work happens.
+
+## The expression {#expressions}
+
+The expression is SQL, evaluated over the row. It can reference any column in
+the table, including another computed column:
+
+```python
+table.add_columns(computed={
+    "power_score": "cast(x as double) / 2.0",
+    "is_strong":   "power_score > 0.5",
+})
+```
+
+Declaration order is dependency order -- a computed column may reference one
+declared earlier in the same mapping, but not one declared after it.
+
+The functions available are the ones the
+[SQL dialect](/sql/functions) documents.
+
+A literal in the expression has to fit the column it meets: `x / 2.0` is
+refused when `x` is an integer column, so write `cast(x as double) / 2.0`.
+
+<Warning>
+In LanceDB Enterprise, keep the expression to top-level columns. A column
+refresh can fail on an expression that reads a field inside a struct, such as
+`stats.magic + stats.strength`, which the embedded library computes. Try a new
+expression on a small table first.
+</Warning>
+
+## Change columns used by an expression {#input-constraints}
+
+<Warning>
+While a computed column's declaration references another column, that column
+cannot be renamed, retyped, or dropped. Drop the computed column first
+if you need to change one of its inputs.
+</Warning>
+
+This is worth knowing before deriving a column from something you expect to
+reshape later. Dropping the computed column releases the constraint.
+
+## Filling with Python instead of SQL {#functions}
+
+<Badge color="red">Enterprise</Badge>
+
+When the value cannot be expressed in SQL -- an embedding, a model call, an
+image decode -- [Geneva](/geneva), the feature engineering package for LanceDB
+Enterprise, fills the column with a Python function instead.
+
+{/*
+Draft for the Functions release. Replaces the paragraph above:
+
+When the value cannot be expressed in SQL -- an embedding, a model call, an
+image decode -- the column is filled by a registered
+[function](/management/function) instead of an expression. Declaring it looks
+the same; what differs is what fills it. It needs a deployment with Functions
+enabled.
+*/}
+
+## Related guides {#next}
+
+<CardGroup cols={2}>
+  <Card title="Refresh a column" icon="arrows-rotate" href="/feature-engineering/refresh-column">
+    Fill the rows that lack values.
+  </Card>
+  <Card title="Geneva" icon="layer-group" href="/geneva">
+    Fill a column with Python on LanceDB Enterprise.
+  </Card>
+  {/*
+  Draft for the Functions release. Replaces the Geneva card above:
+  <Card title="Functions" icon="code" href="/management/function">
+    Fill a column with Python.
+  </Card>
+  */}
+</CardGroup>
diff --git a/docs/web/feature-engineering/data-evolution.mdx b/docs/web/feature-engineering/data-evolution.mdx
new file mode 100644
index 000000000..f70612cf9
--- /dev/null
+++ b/docs/web/feature-engineering/data-evolution.mdx
@@ -0,0 +1,134 @@
+---
+title: "Data evolution"
+sidebarTitle: "Data Evolution"
+description: "Derive new columns from data already in the table, without rewriting what is already there."
+icon: wand-magic-sparkles
+keywords: ["data evolution", "derived columns", "add_columns", "zero copy", "feature engineering"]
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Adding columns computed from SQL expressions works in the embedded library
+    and in LanceDB Enterprise.
+---
+
+import {
+    PySchemaAddSetup,
+    TsSchemaAddSetup as TsSchemaAddSetup,
+    RsSchemaAddSetup as RsSchemaAddSetup,
+    PyAddColumnsCalculated as AddColumnsCalculated,
+    TsAddColumnsCalculated as TsAddColumnsCalculated,
+    RsAddColumnsCalculated as RsAddColumnsCalculated,
+    PyAddFeatureColumnsSql as AddFeatureColumnsSql,
+    TsAddFeatureColumnsSql as TsAddFeatureColumnsSql,
+    RsAddFeatureColumnsSql as RsAddFeatureColumnsSql,
+} from '/snippets/tables.mdx';
+
+Add derived columns to calculate features from data already in a table.
+For example, compute a product score from existing fields without reading
+and rewriting the table in your application.
+
+Lance can write the new columns without rewriting the existing columns.
+This is useful when the table also contains large images, video or embeddings.
+See [Lance format](/basics/lance) for the storage model.
+
+<Note>
+In SQL, on an Enterprise cluster that enables computed columns, this is [`ALTER TABLE ... ADD COLUMN ... AS`](/sql/ddl/table#computed-column).
+</Note>
+
+## Set up the example table {#set-up-the-example-table}
+
+The examples use `db`, a connection opened as in
+[Connect to LanceDB](/basics/connect), and create a small product table in it:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PySchemaAddSetup}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsSchemaAddSetup}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsSchemaAddSetup}
+    </CodeBlock>
+</CodeGroup>
+
+## Add derived columns {#add-derived-columns}
+
+You can add new columns that are derived from existing data using SQL expressions.
+For feature engineering on large existing tables, group related derived features into
+one `add_columns` operation instead of running many separate writes. This creates one
+new table version for the schema change and computes the new columns from the existing
+rows, which avoids growing the table's version history with many small updates.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {AddColumnsCalculated}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsAddColumnsCalculated}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsAddColumnsCalculated}
+    </CodeBlock>
+</CodeGroup>
+
+The same call can add multiple derived columns at once. For example, if you are
+building several lightweight features from existing product fields, pass all of the
+new column expressions together:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {AddFeatureColumnsSql}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsAddFeatureColumnsSql}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsAddFeatureColumnsSql}
+    </CodeBlock>
+</CodeGroup>
+
+<Info>
+LanceDB `add_columns` does not currently accept Python callables, batch UDFs, or
+PyArrow `RecordBatch` iterators for populating new columns. New column values must be
+defined with SQL expressions, or added as NULL columns from an Arrow field or schema.
+If your transformation cannot be expressed in SQL, compute the values outside
+`add_columns` before writing them back through another workflow.
+</Info>
+
+## When SQL is not enough {#beyond-sql}
+
+The expressions above are evaluated once, as part of the call. For other workflows:
+
+- A [computed column](/feature-engineering/add-computed-column) declares the
+  expression and leaves the column empty, so you choose when the work happens.
+  Use it when you want to schedule the computation separately.
+- On LanceDB Enterprise, [Geneva](/geneva) fills a column with Python, for
+  values SQL cannot express -- an embedding, a model call, an image decode.
+
+{/*
+Draft for the Functions release. Replaces the Geneva item in the list above:
+- A [function](/management/function) fills a column with Python, for values SQL
+  cannot express -- an embedding, a model call, an image decode.
+*/}
+
+A deployment has to enable computed columns before you use them.
+{/* Draft for the Functions release: Functions need enabling there too. */}
+
+## Related guides {#next}
+
+<CardGroup cols={2}>
+  <Card title="Add a computed column" icon="table-columns" href="/feature-engineering/add-computed-column">
+    Declare now, fill later.
+  </Card>
+  <Card title="Schema evolution" icon="boxes-stacked" href="/management/table#schema">
+    Change the table's shape instead of its contents.
+  </Card>
+</CardGroup>
diff --git a/docs/web/feature-engineering/index.mdx b/docs/web/feature-engineering/index.mdx
new file mode 100644
index 000000000..7fe699648
--- /dev/null
+++ b/docs/web/feature-engineering/index.mdx
@@ -0,0 +1,172 @@
+---
+title: "Feature engineering"
+sidebarTitle: "Overview"
+description: "Derive columns and tables from data already in the lakehouse, without moving it somewhere else to compute."
+icon: layer-group
+keywords: ["feature engineering", "computed columns", "materialized views", "lakehouse", "jobs"]
+---
+
+Use feature engineering to turn raw data into features for search, analysis
+and model training. LanceDB stores derived columns and materialized views
+alongside the source data, so you can reuse the results across those workflows.
+
+[Materialized views](/feature-engineering/materialized-views) are ordinary
+[catalog objects](/basics/catalog). Create them through the SDK or SQL,
+then refresh the derived data when its inputs change.
+
+{/*
+Draft for the Functions release. Replaces the paragraph above:
+
+[Functions](/management/function) and
+[materialized views](/feature-engineering/materialized-views) are ordinary
+[catalog objects](/basics/catalog). Create them through the SDK or SQL,
+then refresh the derived data when its inputs change.
+*/}
+
+<Note>
+In LanceDB Enterprise, every operation here also has a SQL form -- see [DDL](/sql/ddl/table#alter) to declare a column and [REFRESH](/sql/dml/refresh) to fill it.
+</Note>
+
+## Computed columns and materialized views {#the-shape}
+
+Computed columns and materialized views both keep derived data in the
+lakehouse, but they fill it differently, and the difference decides what a
+refresh costs:
+
+| | Computed column | Materialized view |
+| --- | --- | --- |
+| Declaring or creating it | records the expression; no values are written | runs the query and stores its rows, unless created empty |
+| A refresh | fills rows with no value and recomputes rows whose inputs changed | applies source changes or rebuilds the view, depending on the query and SDK version |
+| A refresh when nothing changed | fills nothing | can be a no-op when incremental refresh is supported |
+| In LanceDB Enterprise | runs as a [job](/basics/jobs) | runs as a server-side materialization job |
+
+### Computed columns fill incrementally {#incremental}
+
+A [computed column](/feature-engineering/add-computed-column) separates
+**declaring** from **filling**:
+
+- Declaring is cheap and immediate. It records the expression in the schema
+  and commits a new table version, with no data written. It does not evaluate the expression over existing rows.
+- Fill the column with a separate refresh call that you can retry. In LanceDB
+  Enterprise, submit the refresh as a [job](/basics/jobs) and track its progress.
+
+**Refresh new and changed rows.** A
+[refresh](/feature-engineering/refresh-column) gives values to rows that lack
+them and recomputes rows whose inputs changed since they were computed. It
+leaves the rest alone, so calling it twice is not twice the work, and appending
+rows means the next refresh handles only the new ones.
+
+**Computed values update when you refresh.** If a column derives from `price` and a row's
+`price` is later updated, the derived value keeps its old value until the next
+refresh recomputes it. Schedule refreshes after changing the source data when consumers need
+up-to-date features.
+
+### Materialized views store query results {#views}
+
+A [materialized view](/feature-engineering/materialized-views) has no separate
+declaration step: creating one runs its query and stores the result, so the view
+is ready to read. A refresh updates those stored results. Python SDK 0.40.0 can apply
+changes incrementally for supported queries; other queries require a full rebuild.
+See [refresh behavior](/feature-engineering/materialized-views#incremental) for
+version-specific details.
+
+Enterprise runs materialization on remote compute. Use the SDK and refresh
+options supported by your server version; see
+[Enterprise refreshes](/feature-engineering/materialized-views#availability).
+
+## Choose a feature engineering workflow {#which-one}
+
+| | What it produces | Filled by | Available |
+| --- | --- | --- | --- |
+| [Computed column](/feature-engineering/add-computed-column) | a new column on an existing table | a SQL expression | OSS and Enterprise |
+| [Materialized view](/feature-engineering/materialized-views) | a new table derived by a query | a refresh | OSS and Enterprise |
+| [Geneva](/geneva) | columns filled by Python | the `geneva` package | <Badge color="red">Enterprise</Badge> |
+
+{/*
+Draft for the Functions release. Replace the Geneva row of the table above with:
+| [Function](/management/function) | a computed column filled by Python | registered code | <Badge color="red">Enterprise</Badge> |
+| [Geneva](/geneva) | columns filled by Python, for existing pipelines | the `geneva` package | <Badge color="red">Enterprise</Badge> |
+*/}
+
+**Computed columns** are the common case: a column declared with a SQL
+expression, filled by [a refresh](/feature-engineering/refresh-column).
+
+**Materialized views** derive a whole table -- a filtered, projected, or
+aggregated view of a source -- and bring it up to date on each refresh. The
+result is an ordinary table: queryable, indexable, searchable.
+
+**Geneva** is LanceDB Enterprise's Python package for work SQL cannot express:
+embedding a column, calling a model, decoding an image. It defines features as
+Python functions and backfills them as distributed jobs.
+
+{/*
+Draft for the Functions release. Replaces the Geneva paragraph above:
+
+**Functions** run Python where the data is, for work SQL cannot express:
+embedding a column, calling a model, decoding an image. Registered once,
+versioned, then applied as a column. They are the way to do that on a
+deployment; [Geneva](/geneva) remains available for existing pipelines.
+*/}
+
+A materialized view can select computed columns.
+{/* Draft for the Functions release: A computed column can be filled by a registered function. */}
+
+## Where the work runs {#where}
+
+<Badge color="red">Enterprise</Badge>
+
+Locally, a refresh runs in your process. With LanceDB Enterprise it runs across the
+cluster, and the API reflects that: `refresh_column` raises, and
+`refresh_column_async` returns a [job](/basics/jobs) handle instead. Computed
+columns are off by default in LanceDB Enterprise, and its administrator has to enable
+them before a column can be declared there.
+
+```python
+job = table.refresh_column_async("power_score")
+result = job.wait()
+result.rows_assigned
+```
+
+A backfill over a large table can therefore be started, left, and checked on
+from a different process -- the connection does not have to stay open.
+
+Enterprise manages the compute for the refresh. You still control when to
+submit it; declaring a computed column does not schedule recurring refreshes.
+{/* Draft for the Functions release: That is also why a function must declare the environment it needs. */}
+
+## Recovering from a failure {#failures}
+
+{/*
+Draft for the Functions release.
+
+A function that raises fails the job. There is no per-row retry or skip policy
+in the function definition, so a function calling a flaky external service
+should handle that itself, inside the function body.
+*/}
+
+When a refresh fails, fix the cause and run the refresh
+again. The new refresh fills what is still empty or stale. How much of the
+failed run was kept depends on where it ran (see
+[Refresh a column](/feature-engineering/refresh-column#failures)), and a retry
+can recompute values that were already written, so do not count on it costing
+only the rows that did not complete.
+
+## Related guides {#next}
+
+<CardGroup cols={2}>
+  <Card title="Add a computed column" icon="table-columns" href="/feature-engineering/add-computed-column">
+    Declare a column from a SQL expression.
+  </Card>
+  <Card title="Refresh a column" icon="arrows-rotate" href="/feature-engineering/refresh-column">
+    Fill the rows that lack values, locally or as a job.
+  </Card>
+  <Card title="Refresh a materialized view" icon="table" href="/feature-engineering/materialized-views">
+    Bring a derived table up to date with its source.
+  </Card>
+  {/*
+  Draft for the Functions release:
+  <Card title="Functions" icon="code" href="/management/function">
+    Register Python to fill a column with.
+  </Card>
+  */}
+</CardGroup>
diff --git a/docs/web/feature-engineering/materialized-views.mdx b/docs/web/feature-engineering/materialized-views.mdx
new file mode 100644
index 000000000..ffe4fc959
--- /dev/null
+++ b/docs/web/feature-engineering/materialized-views.mdx
@@ -0,0 +1,117 @@
+---
+title: "Refresh a materialized view"
+sidebarTitle: "Materialized View Refresh"
+description: "Create a materialized view and refresh it when the source data changes."
+icon: table
+keywords: ["materialized view", "create_materialized_view", "refresh", "full refresh"]
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Both offerings support materialized views. OSS refreshes run in your process; Enterprise runs the work on remote compute. See [Enterprise refreshes](#availability) for server compatibility requirements.
+---
+
+import {
+    PyMaterializedViewCreate as MaterializedViewCreate,
+    PyMaterializedViewRefresh as MaterializedViewRefresh,
+} from '/snippets/materialized_view.mdx';
+
+A materialized view stores the result of a query as a table. Use it to keep a
+filtered or transformed dataset that you can query, index and search repeatedly.
+Refresh the view after its source data changes.
+
+<Note>
+In SQL this is [`REFRESH MATERIALIZED VIEW`](/sql/refresh#refresh-materialized-view).
+</Note>
+
+## Create and refresh {#create-and-refresh}
+
+<CodeBlock filename="Python" language="Python" icon="python">
+{MaterializedViewCreate}
+</CodeBlock>
+
+Creating the view computes its rows, so it is ready to read. Pass
+`with_no_data=True` to create it empty instead. Later changes to the source
+reach the view through a refresh:
+
+<CodeBlock filename="Python" language="Python" icon="python">
+{MaterializedViewRefresh}
+</CodeBlock>
+
+The example works with Python SDK 0.40.0. It creates the source with stable
+row IDs, which that release requires for ordinary materialized-view sources.
+
+`select` takes column names, `(alias, expression)` pairs, or a dict of the same.
+Omitting it selects every source column, expanded against the schema as it was
+at creation time. `where` filters source rows; `limit` caps the view.
+
+## Prepare the source table {#stable-row-ids}
+
+In Python SDK 0.40.0, create the source with
+`storage_options={"new_table_enable_stable_row_ids": "true"}`, as the example
+above does. Stable row IDs let the view track source rows across updates and
+compaction. Set this option when creating the table; it cannot be added later.
+
+A source with a MemWAL write spec or retained, uncompacted write-ahead-log
+rows is not accepted by the local materialized-view implementation.
+
+## Refresh behavior {#incremental}
+
+With Python SDK 0.40.0, a refresh applies source changes incrementally when the
+query supports it, and rebuilds the view otherwise. In the example, adding Bea
+writes one new view row. Refreshing again without source changes writes none.
+Use `refresh(full=True)` to force a rebuild.
+
+`rows_written` counts rows written by that refresh, not all rows in the view.
+Use `view.table.count_rows()` to get the total. Pass `source_version=N` to
+refresh against a particular source version instead of the latest.
+
+<Note>
+Refresh behavior has changed between SDK versions. The earlier 0.40.0b13
+development build rebuilt every local view on refresh and did not accept
+`full`; 0.39.0 created views empty. Use the reference for your installed SDK
+when maintaining an existing pipeline.
+</Note>
+
+## Enterprise refreshes {#availability}
+
+<Badge color="red">Enterprise</Badge>
+
+Enterprise runs materialized-view creation and refresh on remote compute.
+For SQL, see [`CREATE MATERIALIZED VIEW`](/sql/ddl/materialized-view) and
+[`REFRESH MATERIALIZED VIEW`](/sql/dml/refresh#refresh-materialized-view).
+
+Use an SDK version supported by your Enterprise deployment. Materialized-view
+APIs and stored definitions have changed across releases; a local SDK example
+alone does not establish compatibility with an older server. Before upgrading
+an existing pipeline, confirm the supported refresh options and whether its
+views need to be recreated with your administrator.
+
+Stable row IDs and feature configuration for Enterprise sources are managed on
+the server. The source-table storage option in the local example does not
+configure the Enterprise deployment, and Python SDK 0.40.0's `create_table` on a
+`db://` connection does not accept `storage_options`: leave it out, and confirm
+with your administrator that the deployment gives new tables stable row IDs.
+
+`list_materialized_views` requests the server's view listing. On a local
+database it reads table schemas to find views.
+
+## Working with the view {#working-with-the-view}
+
+`view.table` is the underlying table, for querying, indexing, or search.
+`view.definition` is the recorded query: the select list, predicate, and
+limit. Use it to inspect the query without inferring it from the rows. Both are properties; on an async view, the definition is read
+with `await view.definition()`.
+
+An existing view is reopened with `db.open_materialized_view(name)`.
+
+## Related guides {#next}
+
+<CardGroup cols={2}>
+  <Card title="Refresh a column" icon="arrows-rotate" href="/feature-engineering/refresh-column">
+    Derive a column instead of a whole table.
+  </Card>
+  <Card title="Versioning" icon="clock-rotate-left" href="/management/table#versioning">
+    Inspect the table history and read a particular version.
+  </Card>
+</CardGroup>
diff --git a/docs/web/feature-engineering/refresh-column.mdx b/docs/web/feature-engineering/refresh-column.mdx
new file mode 100644
index 000000000..ae430e5f8
--- /dev/null
+++ b/docs/web/feature-engineering/refresh-column.mdx
@@ -0,0 +1,147 @@
+---
+title: "Refresh a column"
+sidebarTitle: "Column Refresh"
+description: "Populate a computed column and update it when its inputs change."
+icon: arrows-rotate
+keywords: ["refresh_column", "backfill", "job", "idempotent", "REFRESH COLUMN"]
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Both offerings support column refresh. The embedded library runs it in your process; Enterprise runs it as a server job that must be enabled for your Enterprise cluster.
+---
+
+Refresh a [computed column](/feature-engineering/add-computed-column) to fill
+new rows and recompute values whose inputs changed. The example below continues
+with the `doubled` column from the computed-column guide.
+
+<CodeGroup>
+```python Python icon="python"
+table.refresh_column("doubled")
+# RefreshColumnResult(rows_filled=2, version=4)
+
+table.to_arrow().sort_by("x").to_pandas()
+#    x  doubled
+# 0  1        2
+# 1  2        4
+```
+
+```sql SQL
+REFRESH COLUMN doubled ON my_table
+```
+</CodeGroup>
+
+The refresh commits ordinary table versions -- the values, then a
+metadata-only record of the inputs they were computed from -- so
+[versioning](/management/table#versioning) applies to it like any other write.
+
+<Note>
+The SQL form is [`REFRESH COLUMN`](/sql/refresh#refresh-column), with the same resource options.
+</Note>
+
+## What a refresh recomputes {#idempotent}
+
+A refresh fills rows that have no value and recomputes rows whose inputs
+changed since they were computed. Every other row is left alone, so a second
+refresh with nothing changed does nothing. This supports two common workflows.
+
+**Appending is cheap.** Rows added since the last refresh are filled by the
+next one; filled rows whose inputs are unchanged are skipped. Backfilling a
+column and then ingesting more data does not mean recomputing the backfill.
+
+**A filled value catches up with its input at the next refresh.** If `doubled`
+derives from `x` and a row's `x` is later updated, `doubled` keeps its old value
+until the next refresh recomputes it.
+
+That is what makes the call idempotent and safe to retry -- but unlike a
+spreadsheet formula, nothing is recomputed until you refresh, so a derived
+column is stale between an update and the next refresh.
+
+## Refreshing part of a table {#partial}
+
+<Badge color="red">Enterprise</Badge> In SQL a refresh can be scoped to a
+predicate, which is useful for filling a slice first and checking it before
+committing to the whole table:
+
+```sql
+REFRESH COLUMN embedding ON documents WHERE created_at > '2026-01-01'
+```
+
+<Note>
+**Enterprise job type:** `computed_refresh` for a SQL-expression column.
+{/* Draft for the Functions release: `function_refresh` for one filled by a registered [function](/management/function). */}
+</Note>
+
+## Run a refresh on Enterprise {#async}
+
+<Badge color="red">Enterprise</Badge>
+
+With an Enterprise cluster that enables computed columns, the refresh runs on the
+cluster. `refresh_column` raises
+there; use `refresh_column_async`, which returns a [job](/basics/jobs) you can
+wait on or poll instead of holding a connection open:
+
+```python
+job = table.refresh_column_async("doubled")
+result = job.wait()
+result.rows_assigned
+# 2
+job.status()
+# 'finished'
+```
+
+Poll with `status()`, resolve with `wait()`. `status()` is a point snapshot and
+does not raise, which is what makes it safe in a loop; `wait()` raises
+`JobFailedError`, `JobCancelledError` or `TimeoutError`.
+
+A long backfill can therefore be started, left, and checked on from a different
+process.
+
+### Configure workers and resources {#resources}
+
+SQL exposes the worker budget directly, which is the lever when a backfill is
+too slow or too expensive:
+
+```sql
+REFRESH COLUMN embedding ON documents
+  WITH (cpu_millis = 8000, memory_bytes = 17179869184, gpu_count = 2)
+```
+
+`num_workers` sets the count instead of a per-worker budget; the two are
+alternatives rather than a pair. `batch_size` controls how many rows a worker
+takes at a time, and `error_policy = 'skip'` continues past rows that fail
+rather than failing the job.
+
+## When a refresh fails {#failures}
+
+What a failure keeps depends on where the refresh ran. `refresh_column` in your
+process writes the column in one commit, so a failure leaves the column as it
+was. A refresh job in LanceDB Enterprise commits finished batches as it goes, so some
+values can remain after a failure.
+
+Either way, recover by fixing the cause and refreshing again. The new refresh
+fills rows that are still empty or whose inputs changed, and normally skips
+values that were kept. That is not guaranteed: a refresh can recompute values
+that were already written -- for example when the record of the inputs they
+were computed from was not saved -- so do not count on a retry costing only the
+rows that did not complete.
+
+{/*
+Draft for the Functions release.
+
+For a column filled by a [function](/management/function), the usual cause is
+the worker environment rather than the data. See
+[environment mismatches](/management/function#mismatch-symptoms) for the
+symptoms, which rarely name their cause.
+*/}
+
+## Related guides {#next}
+
+<CardGroup cols={2}>
+  <Card title="Jobs" icon="hourglass-half" href="/basics/jobs">
+    Waiting on, inspecting, and cancelling server-side work.
+  </Card>
+  <Card title="Refresh a materialized view" icon="table" href="/feature-engineering/materialized-views">
+    The same idea, for a whole derived table.
+  </Card>
+</CardGroup>
diff --git a/docs/web/governance/authorization.mdx b/docs/web/governance/authorization.mdx
new file mode 100644
index 000000000..9ef978c38
--- /dev/null
+++ b/docs/web/governance/authorization.mdx
@@ -0,0 +1,149 @@
+---
+title: "Authorization"
+sidebarTitle: "Authorization"
+description: "Who may do what: privileges granted on catalog objects to principals, groups, roles, and API keys."
+icon: shield-halved
+keywords: ["authorization", "authz", "privileges", "rbac", "access control"]
+availability:
+  oss: unavailable
+  enterprise: available
+  summary: >-
+    Enterprise supports catalog privileges when its administrator configures enforcement. Embedded LanceDB has no catalog authorization layer: access is whatever the object store allows.
+---
+
+[Authentication](/enterprise/authentication) establishes who is calling.
+Authorization decides what they may do.
+
+An access control entry grants **one privilege** on **one object** to **one
+subject**. Everything below is a consequence of that shape.
+
+## Subjects {#subjects}
+
+A grant is held by one of four kinds of subject, distinguished by a prefix:
+
+| Prefix | Subject |
+| --- | --- |
+| `p:` | a principal, by ID -- `P:` by name |
+| `g:` | a group, by ID -- `G:` by name |
+| `r:` | a role |
+| `a:` | the principal holding an API key, named by the key |
+
+Granting to a group or role rather than a principal is what keeps the entry
+count manageable: a principal's effective privileges are the union of what it
+holds directly and what it inherits.
+
+`P:`, `G:` and `a:` are ways of naming a subject when you make a request. The
+entry is stored against the ID they resolve to, so a grant to `a:` belongs to
+the principal holding that key and carries that principal's full authority,
+not a narrower one.
+
+## Objects {#objects}
+
+Objects are addressed by kind and path:
+
+```
+system
+database:db
+namespace:db:ns
+table:db:ns$table
+view:db:ns$view
+```
+
+{/*
+Draft for the Functions release. Replaces the object kinds above:
+
+```
+system
+database:db
+namespace:db:ns
+table:db:ns$table
+view:db:ns$view
+function:db:ns$function
+secret:db:ns$secret
+```
+*/}
+
+A nested namespace joins its path with `$`, as in `namespace:db:parent$child`
+or `table:db:parent$child$table`.
+
+The hierarchy is not decorative. Reaching a table requires `USAGE` on the
+namespace containing it and on the database containing that -- a grant on the
+table alone is not enough to traverse to it.
+
+## Privileges {#privileges}
+
+| Privilege | Granted on | Allows |
+| --- | --- | --- |
+| `OWNERSHIP` | any object but `system` | ownership, conveying full authority |
+| `USAGE` | system | listing jobs and their events, and looking up other identities |
+| `USAGE` | database | accessing the namespaces inside |
+| `USAGE` | namespace | accessing the tables inside |
+| `SELECT` | table, view | reading rows |
+| `INSERT` | table | appending rows |
+| `UPDATE` | table | modifying existing rows |
+| `DELETE` | table | deleting rows |
+| `CREATE_DATABASE` | system | creating a database |
+| `CREATE_NAMESPACE` | database, namespace | creating immediate child namespaces |
+| `CREATE_TABLE` | namespace | creating a table in it |
+| `CREATE_MATERIALIZED_VIEW` | namespace | creating a materialized view in it |
+| `CREATE_VIEW` | namespace | creating a non-materialized view in it |
+| `OPERATE` | system | describing and controlling jobs, managing caches, and reading server configuration |
+
+{/*
+Draft for the Functions release. Add these rows to the table above.
+After the `USAGE` row for namespace:
+| `USAGE` | function | binding it to a column, and refreshing a column it computes |
+| `USAGE` | secret | binding and resolving it |
+After the `UPDATE` row for table:
+| `UPDATE` | secret | rotating it, without the right to bind or read it |
+After the `CREATE_VIEW` row:
+| `CREATE_FUNCTION` | namespace | registering a function in it |
+| `CREATE_SECRET` | namespace | creating a secret in it |
+*/}
+
+The four data privileges map onto the four
+[ingestion operations](/ingestion), so a principal that may append but not
+modify is expressed by granting `INSERT` without `UPDATE`.
+
+<Note>
+`CREATE_MATERIALIZED_VIEW` and `CREATE_VIEW` are separate privileges because
+[the two objects differ](/basics/catalog): one stores rows and is refreshed,
+the other stores only a query. Granting the ability to create one does not
+grant the other.
+</Note>
+
+## Creation privileges resolve upward {#upward}
+
+A subtlety worth knowing, because it explains grants that appear to work on
+objects that do not exist yet.
+
+When you create an object, the deployment checks the database and each
+namespace that will contain it. One that **has no owner** -- because it does
+not exist yet, or predates per-object authorization -- is treated as one you are
+creating: the check falls back to `CREATE_DATABASE` on `system` for the
+database, or to `CREATE_NAMESPACE` on its parent for a namespace, and you become
+its owner. One that has an owner needs `USAGE` as usual.
+
+So a principal holding `CREATE_DATABASE` can create a table in a database that
+has not been created -- which is what makes creation possible at all, and why
+an empty ACL set is not the same as a deny. The fallback applies only when
+creating; other operations on a database or namespace need `USAGE` on it.
+
+## Listing grants {#listing}
+
+Entries can be listed filtered by object, by subject, or both:
+
+```
+object  = table:db:ns$documents
+subject = G:data-science
+```
+
+Results paginate through a continuation token. A page holds at most 100 stored
+`(object, subject)` tuples, and a tuple carrying several privileges expands
+into several entries in the response, so a page can return more than 100
+entries: the count reflects privileges rather than tuples. Keep requesting with
+the returned token until a page comes back without one.
+
+Listing by subject answers "what can this team reach"; listing by object
+answers "who can reach this table". Both are worth running before changing a
+grant.
diff --git a/docs/web/index.mdx b/docs/web/index.mdx
new file mode 100644
index 000000000..75044ad0d
--- /dev/null
+++ b/docs/web/index.mdx
@@ -0,0 +1,124 @@
+---
+title: LanceDB
+sidebarTitle: "LanceDB"
+description: "Multimodal lakehouse for AI."
+icon: "/static/assets/logo/lancedb-icon-gray.svg"
+keywords: ["multimodal lakehouse", "training", "feature engineering", "search", "open source", "oss"]
+---
+
+**LanceDB** is a [multimodal lakehouse](https://lancedb.com/blog/multimodal-lakehouse/) for AI teams that need
+one data layer for curation, feature engineering, search and retrieval, and model training.
+It is built on top of [Lance](/basics/lance), an open-source lakehouse format designed for multimodal AI data.
+
+Move from data exploration to model training on one, unified platform without needing to manage a
+fragmented stack of storage, feature, retrieval, and training systems.
+
+## Build better models, faster {#build-better-models-faster}
+
+Training data and experimentation slow down when raw data, metadata, embeddings, features, and governance
+artifacts live in separate systems. LanceDB keeps them together in one versioned multimodal table, so AI teams spend less
+time stitching infrastructure together and more time improving datasets, testing features, and keeping GPUs fed.
+
+![Training data lifecycle: Curation, Feature Engineering, Search and Retrieval, Training](/static/assets/images/overview/training-data-lifecycle.svg)
+
+Use the same table to curate training data, add derived features, retrieve examples, and feed training jobs that rely on expensive GPUs.
+Training workloads can sample, shuffle, and scan projected columns from local storage or object storage, then assemble
+GPU-ready batches from a tagged dataset version.
+
+For a deeper look at how this works in training pipelines, start with [Why LanceDB for training](/training/why-lancedb).
+
+## LanceDB suite {#lancedb-suite}
+
+The LanceDB suite includes LanceDB OSS, an open-source embedded retrieval library, and LanceDB Enterprise,
+a multimodal lakehouse platform for the full AI data lifecycle.
+OSS is easy to set up on a local machine for search and regular-scale workflows. LanceDB Enterprise is built
+for teams that need scale without building bespoke infrastructure for curation,
+feature engineering, search and retrieval, and efficient training data access.
+
+![LanceDB suite: OSS search and Enterprise multimodal lakehouse on Lance format](/static/assets/images/overview/lancedb-suite.svg)
+
+## Why teams use LanceDB {#why-teams-use-lancedb}
+
+<Steps>
+  <Step title="One table for the whole AI data loop">
+    Store images, video, audio, text, annotations, embeddings, and model-generated features together in one schema-enforced table.
+    The same table can support dataset curation, feature backfills, experiment splits, retrieval, and training.
+  </Step>
+  <Step title="High-throughput data access for training">
+    Training workloads mix fast random access with high-throughput sequential scans. LanceDB is designed for both, so
+    teams can shuffle data into GPU-ready batches more efficiently, improve input throughput, and iterate on experiments faster.
+  </Step>
+  <Step title="Fast, versatile search and retrieval">
+    Whether the end user is a human or an agent, LanceDB powers production retrieval workloads such as semantic search,
+    hybrid search, RAG, agent memory, and recommendation systems. Retrieval runs against the same LanceDB tables used
+    for curation, feature engineering, and training workflows.
+  </Step>
+</Steps>
+
+## Start with your workload {#start-with-your-workload}
+
+<CardGroup cols={2}>
+  <Card title="Train and fine-tune models" icon="fire" href="/training/why-lancedb">
+    Learn why LanceDB works well as the data layer for training workloads.
+  </Card>
+  <Card title="Load data into PyTorch" icon="boxes-stacked" href="/training/data-loading">
+    Use LanceDB tables and permutations for projected, shuffled, random-access training reads.
+  </Card>
+  <Card title="Browse ready-to-use datasets" icon="database" href="/datasets">
+    Explore Lance-formatted multimodal datasets with raw bytes, metadata, embeddings, and indices.
+  </Card>
+  <Card title="Build search and retrieval" icon="search" href="/search/">
+    Use vector search, full-text search, hybrid search, reranking, filtering, and SQL.
+  </Card>
+</CardGroup>
+
+## From local development to production scale {#from-local-development-to-production-scale}
+
+LanceDB OSS and LanceDB Enterprise share the same Lance format and table model. Start locally with the embedded OSS
+library, then move to Enterprise when your team needs distributed scale, managed infrastructure, private deployment,
+or higher-throughput curation, feature engineering, search and retrieval, and training workflows.
+
+### 1\. LanceDB OSS {#1-lancedb-oss}
+The fastest way to get started is the open-source embedded library, with client SDKs in Python, TypeScript
+and Rust. Run it locally in just a few steps, which lets you explore datasets, curate data, and run search and retrieval workloads
+for agents. Start here:
+
+<Columns cols={2}>
+  <Card
+    title="Quickstart"
+    icon="rocket"
+    href="/quickstart"
+  >
+    Get started with LanceDB in minutes.
+  </Card>
+  <Card
+    title="Basic Table Operations"
+    icon="table"
+    href="/management/table"
+  >
+    Create tables, evolve schemas, version data, and modify rows in LanceDB.
+  </Card>
+</Columns>
+
+### 2\. LanceDB Enterprise {#2-lancedb-enterprise}
+
+[LanceDB Enterprise](/enterprise) is a petabyte-scale (and beyond), distributed **multimodal lakehouse** platform built for
+search, curation, feature engineering, and high-throughput training data access workflows on top of the same core table
+abstraction. This eliminates the need for teams to build bespoke infrastructure to manage large multimodal datasets.
+To set up LanceDB Enterprise in your organization,
+[reach out to us](https://www.lancedb.com/contact).
+
+<Info>
+**Built with scale, performance, and security in mind.**
+
+LanceDB Enterprise is designed for very large-scale, high-performance, distributed workloads in
+private deployments, and can operate under strict [security requirements](/enterprise/security).
+</Info>
+
+<Card
+  title="Quickstart"
+  icon="rocket"
+  href="/quickstart"
+>
+  Get started with LanceDB in minutes, including Enterprise `db://` connections.
+</Card>
diff --git a/docs/web/indexing/fts-index.mdx b/docs/web/indexing/fts-index.mdx
new file mode 100644
index 000000000..832b67315
--- /dev/null
+++ b/docs/web/indexing/fts-index.mdx
@@ -0,0 +1,172 @@
+---
+title: "Full-Text Search (FTS) Index"
+sidebarTitle: "FTS Index"
+description: "Create and tune BM25-based full-text search indexes in LanceDB."
+icon: "book"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Full-text indexes are built in the embedded library and in LanceDB Enterprise.
+    The embedded library builds the index before the call returns; a
+    deployment builds it in the background.
+---
+import { PyFtsIndexAsync as FtsIndexAsync, PyFtsIndexCreate as FtsIndexCreate, PyFtsIndexNested as FtsIndexNested, PyFtsIndexWait as FtsIndexWait } from '/snippets/indexing.mdx';
+
+LanceDB provides performant full-text search based on BM25, allowing you to incorporate keyword-based search in your retrieval solutions. This page shows
+examples on how to create and configure FTS indexes in LanceDB OSS and Enterprise, using the synchronous and asynchronous APIs.
+
+<Note>
+In LanceDB Enterprise, `create_fts_index` API returns immediately, but index building happens asynchronously.
+</Note>
+
+## Creating FTS Indexes {#creating-fts-indexes}
+
+### Synchronous API {#synchronous-api}
+
+Use `create_fts_index` with synchronous LanceDB connections:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {FtsIndexCreate}
+    </CodeBlock>
+</CodeGroup>
+
+Check FTS index status using the API:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {FtsIndexWait}
+    </CodeBlock>
+</CodeGroup>
+
+`wait_for_index(...)` waits until the named FTS index exists and `index_stats(...)` reports `num_unindexed_rows == 0`. It can time out if writes keep adding rows faster than the index catches up. If a table has multiple FTS indexes, specify the target text column when querying instead of relying on implicit selection.
+
+### Asynchronous API {#asynchronous-api}
+
+When using async connections (`connect_async`), use `create_index` with the `FTS` configuration:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {FtsIndexAsync}
+    </CodeBlock>
+</CodeGroup>
+
+<Note>
+The `create_fts_index` method is not available on `AsyncTable`. Use `create_index` with `FTS` config instead.
+</Note>
+
+The current FTS implementation is Lance-native. Legacy Tantivy-only options, including
+`use_tantivy`, are no longer accepted by the index creation APIs.
+
+## Nested field paths {#nested-field-paths}
+
+FTS indexes can target text leaves inside struct columns by passing a dotted path (for example, `payload.text`). The same path works for [`MatchQuery`](/search/full-text-search) and [`PhraseQuery`](/search/full-text-search), and for the `columns` argument on async `nearest_to_text` queries.
+
+You can point an index at any string leaf nested in a struct, regardless of depth. The struct container itself isn't indexable: you have to name a specific text field.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {FtsIndexNested}
+    </CodeBlock>
+</CodeGroup>
+
+LanceDB rejects paths that don't resolve to a text leaf:
+
+- A struct container (for example, `payload`): raises `ValueError: FTS index cannot be created ...`.
+- A non-text leaf such as an integer or float (for example, `payload.count`): raises the same error.
+- A path that doesn't exist in the schema (for example, `payload.missing`): raises `ValueError: Field path ... not found`.
+
+The async API accepts the same dotted paths through `create_index`:
+
+```python Python icon="python"
+from lancedb.index import FTS
+
+await async_table.create_index("payload.text", config=FTS(with_position=True))
+```
+
+## Configuration Options {#configuration-options}
+
+### FTS Parameters {#fts-parameters}
+
+| Parameter | Type | Default | Description |
+|:----------|:-----|:--------|:------------|
+| `with_position` | bool | `False` | Store token positions (required for phrase queries) |
+| `base_tokenizer` | str | `"simple"` | Text splitting method (`simple`, `whitespace`, `raw`, `ngram`, `icu`, `jieba/*`, or `lindera/*`) |
+| `language` | str | `"English"` | Language for stemming and stop-word filters. Choose CJK and mixed-language segmentation with `base_tokenizer`. |
+| `max_token_length` | int | `40` | Maximum token size; longer tokens are omitted |
+| `lower_case` | bool | `True` | Lowercase tokens |
+| `stem` | bool | `True` | Apply stemming (`running` → `run`) |
+| `remove_stop_words` | bool | `True` | Drop common stop words |
+| `ascii_folding` | bool | `True` | Normalize accented characters |
+| `custom_stop_words` | list[str] | `None` | Extra stop words to drop in addition to the language defaults. Requires `remove_stop_words=True`. |
+| `ngram_min_length` | int | `3` | Minimum n-gram length. Applies only when `base_tokenizer="ngram"`. |
+| `ngram_max_length` | int | `3` | Maximum n-gram length. Applies only when `base_tokenizer="ngram"`. |
+| `prefix_only` | bool | `False` | Index only prefix n-grams rather than all substrings. Applies only when `base_tokenizer="ngram"`. |
+| `block_size` | int | `128` | Number of documents per compressed posting block. Supported values are `128` and `256`. Setting this to `256` opts in to the experimental FTS V3 layout. |
+
+<Note title="Key parameters">
+- `max_token_length` can filter out base64 blobs or long URLs.
+- Disabling `with_position` reduces index size but disables phrase queries.
+- `ascii_folding` helps with international text (e.g., “café” → “cafe”).
+</Note>
+
+### Tokenizer choices {#tokenizer-choices}
+
+`base_tokenizer` controls segmentation before token filters run:
+
+- `simple`, `whitespace`, and `raw` cover common tokenization strategies for space-delimited text.
+- `ngram` indexes overlapping character spans for substring-style matching.
+- `icu` uses bundled ICU4X word segmentation for mixed-language text and scripts where whitespace splitting is not enough. ICU stands for [International Components for Unicode](https://icu.unicode.org/), and this tokenizer does not need external model files.
+- `jieba/*` is for Chinese word segmentation with Jieba.
+- `lindera/*` loads a compiled Lindera dictionary, such as `lindera/ipadic` for Japanese or `lindera/ko-dic` for Korean.
+
+Model-backed tokenizers such as `jieba/default`, `lindera/ipadic`, and `lindera/ko-dic` require tokenizer model files in Lance's language model home. Lance looks under the default platform data directory for `lance/language_models`, or you can set `LANCE_LANGUAGE_MODEL_HOME` to point to another model root. For example, `jieba/default` is resolved under `<model-home>/jieba/default/...`. For a table in LanceDB Enterprise, the files must be present where the deployment builds and queries the index, so ask its administrator whether they are installed.
+
+`language` is used by token filters, not by the base tokenizer. Stemming supports Arabic, Danish, Dutch, English, Finnish, French, German, Greek, Hungarian, Italian, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish, Tamil, and Turkish. Built-in stop-word removal supports Danish, Dutch, English, Finnish, French, German, Hungarian, Italian, Norwegian, Portuguese, Russian, Spanish, and Swedish. For other stemming languages, set `remove_stop_words=False` or pass `custom_stop_words`.
+
+### Posting block size {#posting-block-size}
+
+`block_size` controls the number of documents packed into each compressed posting block on disk. The default of `128` matches the current FTS layout and is the right choice for most workloads. Setting it to `256` opts in to the experimental FTS V3 format, which changes how postings are encoded and may introduce breaking changes in future releases. Any other value is rejected at index creation time.
+
+You can set the option through either the synchronous or asynchronous API. In the async Python API, pass it on the `FTS` config, and in the TypeScript API use the camelCase `blockSize` field on `FtsOptions`:
+
+```python Python icon="python"
+from lancedb.index import FTS
+
+await async_table.create_index("text", config=FTS(block_size=256))
+```
+
+```typescript TypeScript icon="square-js"
+await table.createIndex("text", {
+  config: lancedb.Index.fts({ blockSize: 256 }),
+});
+```
+
+### Phrase Query Configuration {#phrase-query-configuration}
+
+Enable phrase queries by setting:
+
+| Parameter | Required Value | Purpose |
+|:----------|:---------------|:--------|
+| `with_position` | `True` | Track token positions for phrase matching |
+| `remove_stop_words` | `False` | Preserve stop words for exact phrase matching |
+
+## Indexing nested string fields {#indexing-nested-string-fields}
+
+You can build an FTS index on a string field inside a struct by passing its full dotted path, like `nested.text`. The same path is used when you query the index through `fts_columns`, and the indexed column is reported back as the full path from `list_indices()`.
+
+```python
+# Schema: pa.struct([pa.field("text", pa.string())]) stored under the `nested` column.
+table.create_fts_index("nested.text")
+
+results = (
+    table.search("puppy", query_type="fts", fts_columns="nested.text")
+    .limit(5)
+    .to_list()
+)
+```
+
+<Note>
+Use the canonical Lance path: dot-separate each struct field from root to leaf (for example, `metadata.author.name`). The same convention applies to scalar and vector indexes.
+</Note>
diff --git a/docs/web/indexing/index.mdx b/docs/web/indexing/index.mdx
new file mode 100644
index 000000000..78844527f
--- /dev/null
+++ b/docs/web/indexing/index.mdx
@@ -0,0 +1,82 @@
+---
+title: "Indexing Data"
+sidebarTitle: "Overview"
+description: "Optimize search performance with LanceDB using vector indexes, full-text search, scalar indexes, and more."
+icon: "list"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Indexes are built in the embedded library and in LanceDB Enterprise. Enterprise builds them in the background, and builds fewer vector index types than the
+    embedded library.
+---
+
+Indexes speed up searches and filters by organizing data for specific query
+patterns. LanceDB supports three main index families:
+
+- **Vector Index**: Efficiently searches for similar vectors across high-dimensional data (e.g. images, audio, or text embeddings)
+- **Full-Text Search Index**: Enables fast keyword-based searches by indexing words and phrases
+- **Scalar Index**: Accelerates filtering of structured numeric or categorical data
+
+## Supported Indexes
+
+| Index | Use Case | Description |
+| :--------- | :------- | :---------- |
+| `IVF` (Vector) | Large-scale vector search with configurable accuracy/speed trade-offs. | Inverted File Index: a partition-based approximate nearest neighbor algorithm that groups similar vectors into partitions for efficient search. <br />**Quantizations**: `None/Flat` `PQ` `SQ` `RQ`|
+| `IVF_HNSW` (Vector) | Large-scale vector search requiring both high recall and efficient partitioning. Combines the scalability of IVF with the search quality of HNSW. | Hybrid index combining IVF partitioning with HNSW graphs in each partition. Provides improved search quality over pure IVF while maintaining scalability.<br />**Quantizations**: `None/Flat` `SQ` `PQ`|
+| `FTS` (Full-text search) | String columns (e.g., title, description, content) requiring keyword-based search with BM25 ranking. | Full-text search index using BM25 ranking algorithm. Tokenizes text with configurable tokenization, stemming, stop word removal, and language-specific processing. |
+| `BTree` (Scalar) | Numeric, temporal, and string columns with mostly distinct values. Best for selective equality, inequality, and range predicates. | Sorted index storing sorted copies of scalar columns with block headers in a btree cache. Header entries map to blocks of rows (4096 rows per block) for efficient disk reads. |
+| `Bitmap` (Scalar) | Low-cardinality columns with few thousand or fewer distinct values. Accelerates equality and range filters. | Stores a bitmap for each distinct value in the column, with one bit per row indicating presence. Memory-efficient for low-cardinality data. |
+| `LabelList` (Scalar) | List columns (e.g., tags, categories, keywords) requiring `array_contains_all` or `array_contains_any` filters. | Scalar index for `List<T>` and `LargeList<T>` columns of primitive values, using an underlying bitmap index structure to enable fast array membership lookups. |
+| `FM` (Scalar) | String or binary columns that need raw substring search. | FM-Index over `Utf8`, `LargeUtf8`, `Binary`, or `LargeBinary` data for filters such as `contains(path, 'needle')`. Use FTS instead for tokenized word search and BM25 ranking. |
+
+A deployment builds the vector indexes `IVF_FLAT`, `IVF_PQ`, `IVF_RQ` and
+`IVF_HNSW_SQ`. `IVF_SQ`, `IVF_HNSW_FLAT` and `IVF_HNSW_PQ` are built only by the
+embedded library.
+
+## Quantization
+LanceDB also supports several [quantization](/indexing/vector-index) methods, used by vector indexes to compress vectors and reduce storage requirements:
+
+| Quantization | Use Case | Description |
+| :----------- | :------- | :---------- |
+| `PQ` (Product Quantization) | Default choice for most vector search scenarios. Use when you need to balance index size and recall. | Divides vectors into subvectors and quantizes each subvector independently. Provides a good balance between compression ratio and search accuracy. |
+| `RQ` (RabitQ Quantization) | Use when you need maximum compression or have specific per-dimension requirements. | Per-dimension quantization using a RabitQ codebook. Provides fine-grained control over compression per dimension. For `IVF_RQ`, vector dimensions must be divisible by `8`. |
+| `SQ` (Scalar Quantization) | Use when you need faster indexing or when vector dimensions have consistent value ranges. | Quantizes each dimension independently. Simpler than PQ but typically provides less compression. |
+| `None/Flat` | Use for binary vectors (with `hamming` distance) or when you need maximum recall and have sufficient storage. | No quantization; stores raw vectors. Provides the highest accuracy but requires more storage and memory. |
+
+## Performance {#performance}
+
+#### Vector indexes {#vector-indexes}
+
+If vector search latency climbs with table size (i.e., queries that ran in milliseconds on a small table take seconds as it grows to millions of rows), check whether the query is scanning every vector without an index. The point at which an index helps depends on vector dimensions, hardware and
+your latency target. Measure your workload before choosing an index. Pick the type by your data shape:
+
+| Index | When to use |
+|-------|-------------|
+| `IVF_PQ` | General-purpose default, and what a deployment builds when it is set to index a column automatically. Higher accuracy than RQ at small dimensions (≤ 256). |
+| `IVF_RQ` | Maximum compression on high-dim vectors, faster builds than PQ. |
+| `IVF_HNSW_SQ` | Best recall/latency for unfiltered search; higher latency variance under selective filters. |
+| `IVF_FLAT` | Required for binary vectors with `hamming`. |
+
+The distance metric is fixed once the index is built. Pick the distance metric based on how the embedding model was trained: `cosine` (unnormalized), `dot` (already-normalized, best performance), `l2` (general-purpose, default), `hamming` (binary). If a query explicitly requests a different metric, the index cannot be used and the query falls back to a brute-force scan; a deployment can be configured to reject that scan on a large table. For parameter tuning, see [Vector Indexing](/indexing/vector-index).
+
+#### Scalar indexes {#scalar-indexes}
+
+Scalar indexes can reduce the rows scanned for selective filters and speed up
+`merge_insert` key lookups. Start with columns used frequently in those operations,
+then use [query plans](/search/explain) to check whether the index helps. Choose
+the index type based on the column's values:
+
+| Index | Best for |
+|-------|----------|
+| `BTREE` (default) | Numeric, string, temporal columns with mostly distinct values |
+| `BITMAP` | Boolean and low-cardinality columns (< ~1,000 distinct values) |
+| `LABEL_LIST` | `List<T>` columns queried with `array_has_any` / `array_has_all` |
+
+See [Scalar Indexing](/indexing/scalar-index).
+
+#### Full-text search {#full-text-search}
+
+Token positions and retained stop words increase the information stored in a
+full-text index. Keep the defaults for basic keyword search; enable
+`with_position=True` and retain the needed stop words for phrase queries. See [FTS Indexing](/indexing/fts-index) configuration options for the full set of options.
diff --git a/docs/web/indexing/reindexing.mdx b/docs/web/indexing/reindexing.mdx
new file mode 100644
index 000000000..fdbfe3b5f
--- /dev/null
+++ b/docs/web/indexing/reindexing.mdx
@@ -0,0 +1,147 @@
+---
+title: "Building and repairing indexes"
+sidebarTitle: "Build & Repair"
+description: "Learn how to keep your indexes up-to-date in LanceDB using incremental indexing, including best practices for adding new records without full reindexing."
+icon: "refresh"
+keywords: ["reindexing", "re-index", "incremental indexing", "reindex"]
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Indexes are kept up to date in the embedded library and in LanceDB Enterprise. In
+    the embedded library you add new rows to them with `optimize()`; a
+    deployment does it in the background, and `optimize()` is not available
+    there.
+---
+
+import {
+    PyReindexingIncremental as ReindexingIncremental,
+    PyGpuIndexCuda as GpuIndexCuda,
+    PyGpuIndexMps as GpuIndexMps,
+} from '/snippets/indexing.mdx';
+
+As you add new data to your LanceDB tables, your indexes may become outdated.
+Reindexing is the process of updating the index to account for new data -- this applies to either a full-text search (FTS) index or a vector index. Reindexing is an important operation to run periodically as your data grows, as it has performance implications.
+
+As data is being added and a reindex operation is running, LanceDB will combine results from the existing index with exhaustive/flat search on the new data. This is done to ensure that you're still retrieving results over all your data, but it does come at a performance cost. The more data that you add without reindexing, the impact on latency (due to exhaustive search) can be noticeable.
+
+Rather than dropping an existing index entirely and reindexing from scratch, LanceDB supports **incremental indexing**.
+
+<Note>
+**Enterprise job types:** an index build runs as `reindex_` followed by the
+algorithm, such as `reindex_ivf_pq`, `reindex_btree` or `reindex_fts`, and so
+does folding new rows into an existing index. A create-only build, one that must
+not replace an existing index, runs as `create_` followed by the algorithm
+instead; Python's `create_scalar_index` and `create_fts_index` ask for that by
+default. The name says which algorithm is building, not a separate feature.
+</Note>
+
+## Incremental Reindexing {#incremental-reindexing}
+
+In the embedded library, you trigger an incremental indexing operation on
+updated data with the `optimize()` method on a table. A deployment does this
+for you and does not support `optimize()`, as described below.
+
+Table optimization performs three maintenance operations:
+
+1. **Compaction**: merges small fragments into larger ones to improve read performance
+2. **Pruning/Cleanup**: removes files from versions older than a retention window (7 days by default)
+3. **Index update**: adds newly-ingested data to existing vector, scalar, and FTS indexes
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {ReindexingIncremental}
+    </CodeBlock>
+</CodeGroup>
+
+<Badge color="red">Enterprise</Badge>
+
+LanceDB Enterprise supports incremental reindexing through an automated background process. Once enough new rows have been added to a table, the system automatically adds them to its indexes. As the dataset grows, indexes are asynchronously updated in the background.
+
+- While indexes are being rebuilt, queries use brute force methods on unindexed rows, which may temporarily increase latency. If you can omit unindexed rows, set `fast_search=True` to search only indexed data.
+  This can return incomplete results while indexing catches up.
+- Use `index_stats()` to view the number of unindexed rows. This will be zero when indexes are fully up-to-date. If you call `wait_for_index(...)`, it polls the same status and can time out while continuous writes keep adding unindexed rows.
+- Rows below the deployment's threshold stay unindexed until more arrive, so the count can stay above zero without further writes. `FM` indexes are not updated in the background.
+
+The benefit of using LanceDB Enterprise is that it automates the reindexing process
+and operates continuously in the background, minimizing the impact on latency under high loads.
+In OSS, you must manually manage the reindexing cadence based on your data growth and performance needs.
+
+## Disk utilization {#disk-utilization}
+
+Compaction by itself does not immediately free disk space, and can temporarily increase it because new
+compacted files are written before old-version files are deleted. Disk space is reclaimed when old versions
+are pruned during cleanup. Set retention only as low as your rollback and time-travel requirements allow.
+
+If you need to reclaim space more aggressively in OSS, use a shorter retention window:
+<CodeGroup>
+    ```python Python icon=Python
+    from datetime import timedelta
+
+    table.optimize(cleanup_older_than=timedelta(days=1))
+    ```
+</CodeGroup>
+
+## Building on GPUs {#gpu}
+
+GPU acceleration can reduce the time needed to build vector indexes on large
+datasets. Actual build time depends on the index configuration, vector
+dimensions, storage throughput and available GPU resources.
+
+<Info>
+**Automatic GPU indexing**
+<Badge color="red">Enterprise-only</Badge>
+
+Automatic GPU Indexing is currently only available in [LanceDB Enterprise](/enterprise/).
+Please [contact us](https://www.lancedb.com/contact) to enable this feature for your deployment.
+
+The vector index is created when you call `create_index`. The backend uses GPU resources
+to build `IVF_PQ` indexes, with the number of GPUs set by the deployment's configuration.
+
+This process is also asynchronous by default, but you can use `wait_for_index` to convert it
+into a synchronous process by waiting until the index is built.
+
+</Info>
+
+`wait_for_index(...)` waits for the index to exist and for `index_stats(...)` to report `num_unindexed_rows == 0`. It can time out if the table is receiving continuous writes while the build is trying to catch up.
+
+GPU acceleration changes how the vector index is built, not the lifecycle after the build. In the
+embedded library, rows appended later still need `optimize()` before they are part of the index; a
+deployment adds them in the background. `fast_search()` only searches indexed rows.
+
+### Manual GPU indexing in LanceDB OSS {#gpu-manual-gpu-indexing-in-lancedb-oss}
+
+You can use the Python SDK to manually create the `IVF_PQ` index on a GPU. You'll need
+[PyTorch>2.0](https://pytorch.org/). Note that GPU-based indexing is currently only
+supported by the synchronous SDK in LanceDB OSS.
+
+Specify the values `cuda` or `mps` (on Apple Silicon) for the `accelerator` parameter
+to enable GPU training on your device.
+
+#### GPU indexing on Linux {#gpu-gpu-indexing-on-linux}
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {GpuIndexCuda}
+    </CodeBlock>
+</CodeGroup>
+
+#### GPU indexing on macOS (Apple Silicon) {#gpu-gpu-indexing-on-macos-apple-silicon}
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {GpuIndexMps}
+    </CodeBlock>
+</CodeGroup>
+
+### Performance considerations {#gpu-performance-considerations}
+
+- GPU memory usage scales with `num_partitions` and vector dimensions
+- Size GPU memory for the training batches and index configuration
+- Monitor memory use while choosing training batch sizes
+- Measure build time and recall before changing training settings
+
+### Troubleshooting {#gpu-troubleshooting}
+
+If you encounter the error `AssertionError: Torch not compiled with CUDA enabled`,
+you need to [install PyTorch with CUDA support](https://pytorch.org/get-started/locally/).
diff --git a/docs/web/indexing/scalar-index.mdx b/docs/web/indexing/scalar-index.mdx
new file mode 100644
index 000000000..9f1f9a176
--- /dev/null
+++ b/docs/web/indexing/scalar-index.mdx
@@ -0,0 +1,264 @@
+---
+title: "Scalar Indexes"
+sidebarTitle: "Scalar Index"
+description: "Learn how to use scalar indexes in LanceDB for efficient metadata filtering and query optimization."
+icon: "tree"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Scalar indexes are built in the embedded library and in LanceDB Enterprise. A
+    deployment builds them in the background and keeps most of them up to date
+    itself; in the embedded library you update them with `optimize()`.
+---
+
+import {
+    PyScalarIndexBuild as ScalarIndexBuild,
+    PyScalarIndexWait as ScalarIndexWait,
+    PyScalarIndexOptimize as ScalarIndexOptimize,
+    PyScalarIndexFilter as ScalarIndexFilter,
+    PyScalarIndexPrefilter as ScalarIndexPrefilter,
+    PyScalarIndexUuidType as ScalarIndexUuidType,
+    PyScalarIndexUuidData as ScalarIndexUuidData,
+    PyScalarIndexUuidTable as ScalarIndexUuidTable,
+    PyScalarIndexUuidWait as ScalarIndexUuidWait,
+    PyScalarIndexUuidUpsert as ScalarIndexUuidUpsert,
+    PyScalarIndexNestedFields as ScalarIndexNestedFields,
+} from '/snippets/indexing.mdx';
+
+Scalar indexes accelerate filters on values such as numbers, categories and
+strings. Use them for selective table queries or metadata filters combined
+with vector and full-text search.
+
+LanceDB supports four types of scalar indexes:
+
+- `BTREE`: Stores column data in sorted order for binary search. Best for columns with many unique values.
+- `BITMAP`: Uses bitmaps to track value presence. Ideal for columns with few unique values (e.g., categories, tags).
+- `LABEL_LIST`: Special index for `List<T>` and `LargeList<T>` columns of primitive values supporting `array_contains_all` and `array_contains_any` queries.
+- `FM`: FM-Index over string or binary columns that accelerates substring search via `contains(col, 'needle')`.
+
+<Tip title="Working with scalar indices">
+Use scalar indexes with:
+
+- Vector search (prefilter results using metadata)
+- Full-text search (combining keyword matching with structured filters) 
+- SQL scans (optimizing WHERE clauses on scalar columns)
+- Key-value lookups (enabling rapid primary key-based retrievals)
+</Tip>
+
+## Choosing the Right Index Type
+
+| Data Type                                                       | Filter                                    | Index Type   |
+|:----------------------------------------------------------------|:------------------------------------------|:-------------|
+| Numeric, String, Temporal                                       | `<`, `=`, `>`, `in`, `between`, `is null` | `BTREE`      |
+| Boolean, numbers or strings with fewer than 1,000 unique values | `<`, `=`, `>`, `in`, `between`, `is null` | `BITMAP`     |
+| List of low cardinality of numbers or strings                   | `array_has_any`, `array_has_all`          | `LABEL_LIST` |
+| String or binary (`Utf8`, `LargeUtf8`, `Binary`, `LargeBinary`) | `contains(col, 'needle')`                 | `FM`         |
+
+## Scalar Index Operations
+
+### 1. Build the Index
+
+You can create multiple scalar indexes within a table. By default, the index will be `BTREE`, but you can always configure another type like `BITMAP`
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {ScalarIndexBuild}
+    </CodeBlock>
+</CodeGroup>
+
+<Note title="LanceDB Enterprise">
+If you are using LanceDB Enterprise, the `create_scalar_index` API returns immediately, but the building of the scalar index is asynchronous. To wait until all data is fully indexed, you can specify the `wait_timeout` parameter on `create_scalar_index()` or call `wait_for_index()` on the table.
+</Note>
+
+### 2. Check Index Status
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {ScalarIndexWait}
+    </CodeBlock>
+</CodeGroup>
+
+`wait_for_index(...)` waits until the named scalar indexes exist and `index_stats(...)` reports `num_unindexed_rows == 0`. If a table is receiving steady writes, that fully indexed state may not stabilize before the timeout. In LanceDB Enterprise, rows added after the index was built are indexed only once enough have accumulated, so a small remainder can also keep the wait from finishing.
+
+### 3. Update the Index
+
+Updating the table data (adding, deleting, or modifying records) requires that you also update the scalar index. In the embedded library, call `optimize`, which will trigger an update to the existing scalar index.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {ScalarIndexOptimize}
+    </CodeBlock>
+</CodeGroup>
+
+<Note title="LanceDB Enterprise">
+New data added after creating the scalar index will still appear in search results if optimize is not used, but with increased latency due to a flat search on the unindexed portion. LanceDB Enterprise does not support `optimize()`; instead it adds new rows to `BTREE`, `BITMAP` and `LABEL_LIST` indexes in the background once enough have accumulated. It does not update `FM` indexes automatically.
+</Note>
+
+### 4. Run Indexed Searches
+
+The following scan will be faster if the column `book_id` has a scalar index:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {ScalarIndexFilter}
+    </CodeBlock>
+</CodeGroup>
+
+Scalar indexes can also speed up scans containing a vector search or full text search, and a prefilter:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {ScalarIndexPrefilter}
+    </CodeBlock>
+</CodeGroup>
+
+## Indexing nested fields
+
+Scalar indexes can target a scalar field inside a struct by passing its full dotted path. The path is preserved end to end: it's the value you pass to `create_scalar_index`, it's what `list_indices()` reports under `columns`, and it's the column reference you use in filter predicates.
+
+```python
+# Schema: pa.struct([pa.field("user_id", pa.int32())]) stored under the `metadata` column.
+table.create_scalar_index("metadata.user_id", name="metadata_user_id_idx")
+
+# The same dotted path works in WHERE clauses.
+table.search().where("metadata.user_id = 42").limit(1).to_list()
+```
+
+<Note>
+Nested paths follow Lance field-path semantics: dot-separate each struct field from root to leaf (for example, `metadata.author.name`). The same convention applies to FTS and vector indexes.
+</Note>
+
+## FM-Index for substring search
+
+The `FM` index is a scalar index built over string or binary columns that
+accelerates substring lookups expressed as `contains(col, 'needle')`. Unlike the
+tokenized [FTS index](/indexing/fts-index), which matches whole words after
+tokenization, the FM-Index matches arbitrary substrings of the raw bytes. It
+works well for URLs, file paths, identifiers, log lines, or any column where you
+search for a fragment rather than a word.
+
+Use the FM-Index when:
+
+- Filters use `contains(col, 'needle')` (substring), not equality or word search.
+- The column is `Utf8`, `LargeUtf8`, `Binary`, or `LargeBinary`.
+- You want substring matches without paying for tokenization, language analysis,
+  or BM25 scoring.
+
+Pick `FTS` instead when you need word-level relevance ranking, phrase queries,
+or language-aware tokenization.
+
+### Create an FM-Index
+
+Build an FM-Index with the async `create_index` API by passing the `Fm` config in
+Python or `Index.fm()` in TypeScript. In Rust, use `Index::Fm(FmIndexBuilder::default())`.
+A deployment builds FM-Indexes too, but does not add new rows to them in the background as it
+does for the other scalar indexes.
+
+<CodeGroup>
+```python Python icon="python"
+from lancedb.index import Fm
+
+await tbl.create_index("text", config=Fm())
+```
+
+```typescript TypeScript icon="square-js"
+import * as lancedb from "@lancedb/lancedb";
+import { Index } from "@lancedb/lancedb";
+
+await tbl.createIndex("text", { config: Index.fm() });
+```
+
+```rust Rust icon="rust"
+use lancedb::index::Index;
+use lancedb::index::scalar::FmIndexBuilder;
+
+tbl.create_index(&["text"], Index::Fm(FmIndexBuilder::default()))
+    .execute()
+    .await?;
+```
+</CodeGroup>
+
+After the index is built, substring filters use it automatically:
+
+```python
+table.search().where("contains(text, 'needle')").limit(10).to_pandas()
+```
+
+`list_indices()` reports the index type as `"Fm"`.
+
+## Index UUID Columns
+
+LanceDB supports scalar indexes on UUID columns (stored as `FixedSizeBinary(16)`), enabling efficient lookups and filtering on UUID-based primary keys.
+
+<Note>
+**To use `FixedSizeBinary`, ensure you have:**
+
+- Python SDK version `0.22.0` or later
+- TypeScript SDK version `0.19.0` or later
+</Note>
+
+### 1. Define UUID Type
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {ScalarIndexUuidType}
+    </CodeBlock>
+</CodeGroup>
+
+### 2. Generate UUID Data
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {ScalarIndexUuidData}
+    </CodeBlock>
+</CodeGroup>
+
+### 3. Create Table with UUID Column
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {ScalarIndexUuidTable}
+    </CodeBlock>
+</CodeGroup>
+
+### 4. Create and Wait for the Index
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {ScalarIndexUuidWait}
+    </CodeBlock>
+</CodeGroup>
+
+### 5. Perform Operations with the UUID Index
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {ScalarIndexUuidUpsert}
+    </CodeBlock>
+</CodeGroup>
+
+## Index nested fields
+
+You can build a scalar index on a field inside a struct column by passing the
+canonical dot-separated path to `create_index`. This is useful when filters
+target attributes nested under a `metadata`-style column, for example
+`metadata.user_id` or `metadata.event.type`.
+
+If a literal segment of the path itself contains a dot (for example a column
+named `user.id` nested inside `metadata`), wrap that segment in backticks so
+LanceDB can tell the dot apart from the path separator: `` metadata.`user.id` ``.
+
+`list_indices()` echoes the same canonical path back, so the column you pass in
+round-trips through index metadata regardless of nesting depth or escaping.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {ScalarIndexNestedFields}
+    </CodeBlock>
+</CodeGroup>
+
+<Note>
+Composite indexes that cover multiple columns aren't supported yet. Each
+`create_index` call must target a single (possibly nested) field path.
+</Note>
diff --git a/docs/web/indexing/vector-index.mdx b/docs/web/indexing/vector-index.mdx
new file mode 100644
index 000000000..e9768efa6
--- /dev/null
+++ b/docs/web/indexing/vector-index.mdx
@@ -0,0 +1,421 @@
+---
+title: "Vector Indexes"
+sidebarTitle: "Vector Index"
+description: "Build and manage LanceDB vector indexes."
+icon: "arrow-up-right-dots"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Vector indexes are built in the embedded library and in LanceDB Enterprise. The
+    embedded library builds an index before `create_index()` returns; a
+    deployment builds it in the background, keeps it up to date, and builds
+    only `IVF_FLAT`, `IVF_PQ`, `IVF_RQ` and `IVF_HNSW_SQ`.
+---
+
+import {
+    PyVectorIndexConfigureIvf as VectorIndexConfigureIvf,
+    PyVectorIndexSetup as VectorIndexSetup,
+    PyVectorIndexBuildIvf as VectorIndexBuildIvf,
+    PyVectorIndexNestedField as VectorIndexNestedField,
+    PyVectorIndexAsyncConfig as VectorIndexAsyncConfig,
+    PyVectorIndexQueryIvf as VectorIndexQueryIvf,
+    PyVectorIndexBuildHnsw as VectorIndexBuildHnsw,
+    PyVectorIndexQueryHnsw as VectorIndexQueryHnsw,
+    PyVectorIndexBinarySchema as VectorIndexBinarySchema,
+    PyVectorIndexBinaryAddData as VectorIndexBinaryAddData,
+    PyVectorIndexBinaryBuildIndex as VectorIndexBinaryBuildIndex,
+    PyVectorIndexBinarySearch as VectorIndexBinarySearch,
+    PyVectorIndexCheckStatus as VectorIndexCheckStatus,
+    PyVectorIndexNprobes as VectorIndexNprobes,
+    PyVectorIndexDistanceRange as VectorIndexDistanceRange,
+    PyVectorIndexBypassRecall as VectorIndexBypassRecall,
+    PyVectorIndexCustomName as VectorIndexCustomName,
+    PyQuantizationCustomParams as QuantizationCustomParams,
+} from '/snippets/indexing.mdx';
+
+Vector indexes are robust tools in facilitating fast searches across large numeric datasets. 
+LanceDB implements **ANN (Approximate Nearest-Neighbor)** queries with several techniques that provide benefits across a variety of use cases.
+
+## Do you need one? {#do-you-need-one}
+
+Not always. LanceDB's disk-based brute-force kNN search is fast even without an
+index -- computing 100K pairs of 1000-dimension vectors takes under 20ms in
+benchmarks. For datasets around 100K rows or fewer, or applications that can
+accept ~100ms latency, a vector index is usually unnecessary overhead.
+
+Build one once a table passes roughly a million rows, dimensionality is high,
+or query latency matters more than that. See
+[`bypass_vector_index`](#search-time-parameters) to run an exact search
+side-by-side and measure whether an index is worth it for your data.
+
+## Choosing the Right Index
+
+LanceDB vector indexes are combined with several [quantization](/indexing/vector-index) techniques to admit efficient storage.
+The following table lists provided quantized vector indexes and their common use cases. You can specify index type manually in
+Lance with `index_type`.
+
+
+| If your priority is... | Use this index | Why | Approx. compression ratio | Python config class |
+| :--- | :--- | :--- | :--- | :--- |
+| Maximum compression | `IVF_RQ` | IVF indexing with RaBitQ quantization | Around `1/32` of raw size | `IvfRq` |
+| Higher accuracy at small dimensions (`dimension <= 256`) | `IVF_PQ` | IVF indexing with product quantization | Usually `1/64` to `1/16` of raw size (depends on `num_sub_vectors`) | `IvfPq` |
+| Best recall/latency trade-off | `IVF_HNSW_SQ` | IVF-HNSW indexing with scalar | Typically a little larger than `1/4` of raw size | `IvfHnswSq` |
+| Highest recall / no quantization | `IVF_HNSW_FLAT` | IVF-HNSW indexing with no quantization; embedded library only | Around raw vector size plus HNSW graph overhead | `IvfHnswFlat` |
+
+<Warning>
+If your vector search frequently includes metadata filters (`where(...)`), use `IVF_RQ` or `IVF_PQ`. In filtered workloads, HNSW-backed IVF indexes such as `IVF_HNSW_FLAT` and `IVF_HNSW_SQ` can show higher latency variance.
+</Warning>
+
+## Understanding Vector Indexes
+
+LanceDB offers two vector indexes, which can be created on any numeric Lance dataset: 
+**Inverted File (IVF)** and **Hierarchical Navigable Small World (HNSW)**.
+
+### IVF
+
+The **Inverted File Index (IVF)** accelerates ANN searches by drastically reducing the search space. The index consists of
+a small set of *centroids* corresponding to an approximate solution to the 
+[$k$-means clustering](https://en.wikipedia.org/wiki/K-means_clustering) problem. 
+Each vector remembers its nearest centroid, and each centroid remembers its associated set of vectors, 
+called its *partition*. 
+<Frame caption="Example partition of a geometric space, with colored marks denoting centroids.">
+  <img src="/static/assets/images/indexing/ivfpq_ivf_desc.webp" alt="IVF vector-space partitioning" />
+</Frame>
+
+At query time, we can compare the queried vector to the smaller set of *centroids* (as opposed to the entire dataset)
+for a closest match, then run a brute-force comparison against its resulting partition. This technique quickly prunes a large search space,
+giving an approximate ANN result.
+
+However, observe that a queried vector may lie near the boundary of $2$ or more partitions; thus, the true nearest neighbors are scattered across several different partitions.
+ To address this, LanceDB exposes the `nprobes` parameter, which specifies the number of partitions searched. 
+A high `nprobes` parameter will yield more accurate results at slightly higher runtime.
+
+<Frame caption="Edge case for single-partition brute force.">
+  <img src="/static/assets/images/indexing/ivfpq_query_vector.webp" alt="IVF vector-space partitioning" />
+</Frame>
+
+### HNSW
+**Hierarchical Navigable Small World (HNSW)** constructs a layered graph hierarchy on the vector set, with edges representing distances. 
+We can visualize an HNSW index as a vertical stack of graphs, with the top layer having very few edges and
+each other layer having a multiplicative factor more edges than the layer above it.
+
+High layers of a HNSW hierarchy represent sparse, higher-distance networks, and lower layers represent finer, lower-distance networks. 
+To query a vector $q$, the index proceeds iteratively through layers, first finding $q$'s nearest neighbor in the graph, then proceeding recursively
+through the induced subhierarchy until the lowest layer is reached.
+
+<Frame caption="HNSW hierarchy: sparse network at the top layer, fine network at the bottom.">
+  <img src="/static/assets/images/indexing/hnsw_layered_graph.png" alt="HNSW layered graph hierarchy" />
+</Frame>
+
+To visualize this process, imagine that you must drive your car from San Francisco to a specific house in Boston. Initially, you must first drive thousands of miles
+on the interstate freeway I-90 E. Eventually, you merge onto the Massachusetts Turnpike, the center
+of the greater Boston highway system. From there, you use a series of increasingly smaller, narrower roads within the city (Charles River Bridge, St. Paul St,
+Thatcher St) before finally reaching the house. 
+
+A key observation is that you must initially travel far distances through long-distance road networks 
+(the interstate freeway system), before proceeding to finer and finer road networks (greater Boston highway system, central Brookline neighborhood connectors) before finally reaching
+your destination. This iterative series of road networks mimics the layered graph traversals performed by a HNSW query.
+<Info>
+**IVF + HNSW**
+
+In LanceDB, HNSW is not exposed as a top-level vector index. Instead, it's available as a substructure which 
+further indexes the selected vectors inside each IVF partition. 
+This combines the scalability of IVF with the high recall of HNSW.
+LanceDB supports IVF-HNSW-based quantized indexes `IVF_HNSW_FLAT`, `IVF_HNSW_PQ`, and `IVF_HNSW_SQ`; a deployment builds only `IVF_HNSW_SQ`.
+</Info>
+
+
+## Using Vector Indexes
+Learn how to configure, build, and search LanceDB vector indexes, including build and search time parameters, asynchronous objects, and several examples.
+
+### Configuration
+
+#### Build-time Parameters
+
+| Parameter | Description |
+| :--- | :--- |
+| `distance_type` | Default is `l2`, others available are `cosine` and `dot`. 
+| `num_partitions` | The number of IVF partitions constructed (corresponds to the $k$ in $k$-means clustering). This takes precedence over `target_partition_size` if both are set. |
+| `target_partition_size` | An alternative IVF sizing knob that derives the partition count by setting the number of rows per partition. `num_partitions` takes precedence over `target_partition_size` if both are set. If neither is set the number of partitions is automatically calculated based on the number of rows and the index type. |
+| `num_sub_vectors` | Applies to `IVF_PQ`; defaults to `dimension // 16` (or `dimension // 8` if not a multiple of 16). Larger values produce better recall and slower search. |
+| `max_iterations` | Maximum number of k-means training iterations, for every IVF/HNSW index type. Default `50`. Increase for larger datasets or to improve training quality. |
+| `sample_rate` | Number of k-means training samples per partition, for every IVF/HNSW index type. Default `256`. Higher values increase both accuracy and training time. |
+
+#### Search-time Parameters
+
+| Parameter | Description |
+| :--- | :--- |
+| `limit` | Number of results to return (the `k` in `k-ANN`). |
+| `nprobes` | Sets both `minimum_nprobes` and `maximum_nprobes` to the same value in Lance. Leave unset to use Lance's adaptive defaults. At very high scales you will likely need to experiment to find your preferred recall / latency balance. |
+| `minimum_nprobes` | Minimum number of partitions scanned. Leave unset to use Lance's default. |
+| `maximum_nprobes` | Maximum number of partitions scanned. Only scans more than `minimum_nprobes` if an initial pass does not return enough results, which is useful for narrow filters. Leave unset, or set to `0`, to remove the cap. |
+| `ef` | HNSW search-time exploration factor. Start around `1.5 * k` and increase up to `10 * k` for higher recall. |
+| `refine_factor` | Reads and reranks additional candidates in memory to recover recall lost to quantization. Start between `5` and `50` (a factor of `25` reads and reranks the top `25 * k` candidates) and measure the latency cost, typically a few milliseconds, against the recall gained. |
+| `distance_range(lower_bound, upper_bound)` | Return only rows whose distance falls within `[lower_bound, upper_bound)`. Either bound is optional. Useful for near-duplicate detection or "close-enough" matching. |
+| `bypass_vector_index()` | Ignore the ANN index entirely and perform an exact (flat) scan. Can be used to measure ANN `recall@k`, or to query with a metric the index was not built for (e.g., a non-cosine query on a multivector column). |
+
+<Note>
+The `distance_type` can be specified at search time in addition to build time. If
+you do this, set it to the same value used to build the vector index. If the search
+metric does not match the index metric, LanceDB cannot use the index and falls
+back to a costly flat search. On LanceDB Cloud, the brute-force KNN safety limit
+can reject that search on a large table instead of executing it. An Enterprise
+deployment applies the same limit only when it is configured to.
+
+When the search metric is omitted, current SDKs use the index metric if an index
+is available. Some older SDK versions sent `l2` when it was omitted, so explicitly
+set the matching metric or upgrade the SDK when querying a non-`l2` index with an
+older client.
+</Note>
+
+**Recommended `nprobes` behavior by index type:**
+
+| Index type | Guidance |
+| :--- | :--- |
+| `IVF_HNSW_FLAT`, `IVF_HNSW_SQ` | Leave the probe settings unset, then tune `ef` first. Expect higher latency variance under filtered search. |
+| `IVF_RQ`, `IVF_PQ` | Leave the probe settings unset and configure them only when recall is insufficient. If using a selective filter you may need to increase `maximum_nprobes` if you do not receive enough results. |
+
+
+<Note>
+**Filtered queries and adaptive `nprobes`.** When a `where(...)` filter is active, LanceDB initially scans `minimum_nprobes` 
+partitions and uses a wider scan if too few rows are found. 
+Set `minimum_nprobes == maximum_nprobes` or call `nprobes(n)` to instead fix the partition count.
+Whether a deployment widens the scan this way depends on its version; one that does not scans exactly
+`minimum_nprobes` partitions.
+</Note>
+
+Here is an example of a vector search exercising several of the above parameters.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VectorIndexNprobes}
+    </CodeBlock>
+</CodeGroup>
+
+
+LanceDB also supports advanced search-time controls for thresholded retrieval, recall measurement, and working around index-level metric constraints.
+
+**Thresholding with `distance_range`:**
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VectorIndexDistanceRange}
+    </CodeBlock>
+</CodeGroup>
+
+**Using `bypass_vector_index`:**
+
+Use `bypass_vector_index` to compute an exact **kNN** result. Note that exact queries may be prohibitively slow on production scales.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VectorIndexBypassRecall}
+    </CodeBlock>
+</CodeGroup>
+
+<Note title="Multivector distance constraint">
+Multivector indexing currently requires `distance_type="cosine"`. Use `bypass_vector_index()` for non-`cosine` queries on a multivector column. See [Multivector Search](/search/vector-search) for the full rules.
+</Note>
+
+#### Async API and Config Objects
+
+Create vector indexes asynchronously with `await table.create_index("vector", config=...)`. The `config` object 
+admits the same build-time parameters described above. Pass an instance of a Python config class from the table in [Choosing the Right Index](#choosing-the-right-index):
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VectorIndexAsyncConfig}
+    </CodeBlock>
+</CodeGroup>
+
+### IVF Indexes
+
+This example creates and queries an `IVF_PQ` index for a table of vectors with respect to `cosine` similarity. 
+Specify `vector_column_name` if you have multiple vector columns or non-default names. 
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VectorIndexBuildIvf + VectorIndexQueryIvf}
+    </CodeBlock>
+</CodeGroup>
+
+<Note>
+TypeScript currently doesn't support `IvfSq` (IVF with Scalar Quantization).
+</Note>
+
+For a vector field nested inside a struct, pass its full dotted path as `vector_column_name` (e.g. `image.embedding`). The same path is used at query time and is what `list_indices()` reports under `columns`. See [Selecting the vector column](/search/vector-search#selecting-the-vector-column) for the full path syntax.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VectorIndexNestedField}
+    </CodeBlock>
+</CodeGroup>
+
+### IVF-HNSW Indexes
+
+Beyond the general build-time parameters above, two additional parameters are specific to IVF-HNSW indexes:
+
+| Parameter | Description |
+| :--- | :--- |
+| `m` | The number of neighbors to select for each vector in the HNSW graph. |
+| `ef_construction` | The number of candidates to evaluate during the construction of the HNSW graph. Start at `150`; increase for better recall, decrease for faster indexing. |
+
+Partition sizing follows the general `num_partitions`/`target_partition_size` guidance above.
+The snippet below builds and queries an `IVF_HNSW_SQ` index. 
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VectorIndexBuildHnsw + VectorIndexQueryHnsw}
+    </CodeBlock>
+</CodeGroup>
+
+### Managing Vector Indexes
+
+ <Badge color="red">Enterprise-only</Badge> 
+In LanceDB Enterprise, vector indexes are managed **automatically**. The system asynchronously updates and optimizes indexes as a background process:
+- Automatically manages indexing parameters and storage
+- Can build an `IVF_PQ` index automatically on a vector column the deployment is configured to index
+- GPU-based `IVF_PQ` builds are available on request -- [contact support](/deployment/support) to enable them for your deployment. Build time depends on dataset size, dimensions and GPU resources.
+
+ <Badge color="gray">Open-Source</Badge> 
+ LanceDB OSS users can manually create vector indexes by calling `table.create_index()`. 
+ See the above sections for guidance on manually tuning index parameters as data changes.
+
+<Note>
+In LanceDB Enterprise, `create_index()` returns immediately and the vector index builds asynchronously.
+To wait until all data is indexed, specify the `wait_timeout` parameter, or call `wait_for_index(...)` afterward.
+It waits for the named index to exist and for `index_stats(...)` to report `num_unindexed_rows == 0`.
+In the embedded library the index is built before `create_index()` returns.
+</Note>
+
+<Note>
+Rows appended after an initial index build remain outside the index until refreshed manually (OSS) or automatically (Enterprise). Normal
+search still checks those unindexed rows with a slower fallback path; `fast_search()` skips that
+fallback and searches only indexed rows.
+</Note>
+
+<Info>
+**Operational checks**
+
+In the embedded library, use `optimize()` after appends or other writes to fold new rows into existing indexes.
+A deployment does this in the background, and `optimize()` is not available there.
+</Info>
+#### Check Index Status
+
+In LanceDB Enterprise, vector index creation runs in the background and may take some time to complete.
+While it is ongoing, you can check its status through the API or the **LanceDB Enterprise UI**.
+
+In the LanceDB Enterprise UI, navigate to your table page - the "Index" column reflects each column's index status: it is blank when no index exists, shows an "in progress" label while the index is being built, and shows the index type once the build completes.
+
+To check status programmatically, use `list_indices()` and `index_stats()`. **By default**, the index name is formed by appending `_idx` to the column name (e.g., a `keywords_embeddings` column produces `keywords_embeddings_idx`). Note that `list_indices()` only returns information after the index is fully built.
+
+Each entry returned by `list_indices()` also carries detailed per-index metadata, so you can inspect an index without a follow-up `index_stats()` call. Node.js exposes the same fields in camelCase (`num_indexed_rows` → `numIndexedRows`):
+
+| Parameter | Description |
+| :--- | :--- |
+| `num_indexed_rows`, `num_unindexed_rows` | Index coverage over the table |
+| `size_bytes` | Total size of the index files on disk |
+| `num_segments`, `index_version` | On-disk layout and format version |
+| `created_at` | Creation time (ms since the Unix epoch in Node.js) |
+| `index_uuid`, `type_url` | Internal identifiers for the index segment |
+| `index_details` | Type-specific details (e.g. IVF partition counts or quantization settings) |
+
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VectorIndexCheckStatus}
+    </CodeBlock>
+</CodeGroup>
+
+
+<Note>
+These fields are populated for local tables. In LanceDB Enterprise, `list_indices()` returns them except `num_unindexed_rows`; use `index_stats()` for index coverage.
+</Note>
+#### Custom Index Names
+
+The `{column}_idx` suffix is the default naming convention. 
+Pass `name=...` to `create_index()` to override it. Once set, the custom name will be reflected in `list_indices()`, `index_stats(name)`, and `wait_for_index([name])`.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VectorIndexCustomName}
+    </CodeBlock>
+</CodeGroup>
+
+## Quantization {#quantization}
+
+**Quantization** is used in LanceDB to efficiently compress and store vector indexes. We discuss only the quantization techniques here;
+discussion of LanceDB vector indexes and quantized vector indexes can be found [here](/indexing/vector-index).
+
+### Quantization Techniques
+LanceDB provides $3$ distinct quantization techniques: Product Quantization (PQ), RaBitQ Quantization (RQ), and Scalar Quantization (SQ).
+Recall that all quantizations perform **lossy** compression, in that they irreversibly lose some degree of precision in order to 
+compactly store an index.
+
+#### Product Quantization (PQ)
+To visualize PQ, assume a vector dataset has $d$ dimensions, with a **chunk** of a vector denoting a contiguous block of entries. Imagine that each vector has
+ $m$ disjoint chunks of $d/m$ entries each, where the first chunk contains entries $0$ through $d/m-1$, the second contains
+entries $d/m$ through $2d/m-1$, and so on.
+
+Now, for each $i$, let $S_i$ represent the set containing chunk $i$ of each vector (entries $(i-1)d/m$ to $id/m-1$).
+For each $S_i$ independently, a small set of **centroids** is computed corresponding to an approximate solution to the $k$-means clustering problem.
+
+For each original vector in the dataset, every chunk is associated to its nearest centroid; thus, the vector itself is associated with the concatenation of $m$ centroids.
+We then gather all our centroids (from all chunks) into a single lookup table, and for each vector, we store the concatenation of IDs of its corresponding centroids. 
+At query time, we need only to compare each chunk of the queried vector with our set of centroids.
+
+<Frame caption="">
+  <img src="/static/assets/images/indexing/ivfpq_pq_desc.png" alt="IVF vector-space partitioning" />
+</Frame>
+
+In the above example, the original vector is split into chunks (subvectors), each of which is associated to a centroid. The stored quantization code for this vector 
+is the concatenation $2 || 1 || 4 || 3$. The original vector required $128$ dimensions $\times 32$-bit floats $ =4096$ bits total,
+which has been compressed to $4$ chunks $\times 8$-bit integers $= 32$ bits of quantized storage.
+
+#### RaBitQ Quantization (RQ)
+
+RaBitQ is an advanced quantization technique that outperforms PQ in several ways.
+It needs no codebook to train, estimates distances very quickly at query-time, 
+and crucially, quantizes each vector in (with some small overhead) just **one bit per dimension!** 
+In practice, RaBitQ compresses a $1024$-dimensional `float32` vector into just a few thousand bits, while maintaining good recall.
+
+The inner workings of RaBitQ quantization are rather mathematically dense. It generates a quantization codebook by 
+applying a uniformly random, approximately distance-preserving orthogonal transformation of the vertices of the $d$-dimensional hypercube, 
+where $d$ is the dimensionality of the dataset. We defer the details, and an elegant theoretical error bound, to [the original paper.](https://arxiv.org/pdf/2405.12497)
+
+##### Using RaBitQ
+Use RaBitQ quantization by selecting quantized index types ending in the suffix `RQ`. For example, call `create_index` with `index_type="IVF_RQ"`. 
+Note that when using `IVF_RQ`, the dimension of the dataset must be a multiple of `8`.
+
+`num_bits` determines how many bits are used to quantize each dimension.
+`1` is the standard RaBitQ setting. Increase to `2`, `4`, or `8` bits to achieve better recall for additional storage and query-time compute.
+
+<Warning title="Reading multi-bit indexes across versions">
+RaBitQ-quantized indexes computed with `num_bits >= 2` use a newer on-disk layout, and cannot be read by some older LanceDB versions. 
+</Warning>
+
+See this [blog post](https://lancedb.com/blog/feature-rabitq-quantization/) for further discussion and benchmarking of LanceDB's RaBitQ implementation.
+
+#### Scalar Quantization (SQ)
+Scalar quantization quantizes each entry of a vector independently, by simply replacing it with the closest of a pre-defined set of values. 
+In practice, it often uses $8$ bits per dimension of a vector, and supports very fast encoding and decoding.
+
+For example, suppose we know all vector entries across our dataset lie in the range $[-128 \times 10^5, 127 \times 10^5]$. 
+In this case, we could quantize a given value $v$ as an $8$-bit representation of the integer $j$, where $j \times 10^5$ is the closest value to $v$
+among all integral multiples $\{j \times 10^5: -128 \leq j \leq 127 \}$.
+
+### Quantization API Reference
+
+
+| Parameter | Description |
+| :--- | :--- |
+| `num_bits` | Bits per dimension for quantization. Only applies to `IVF_PQ`/`IVF_HNSW_PQ` (default `8`) and `IVF_RQ` (default `1`, RaBitQ); not used by `IVF_FLAT`/`IVF_SQ`/`IVF_HNSW_FLAT`/`IVF_HNSW_SQ`. Higher values improve accuracy at the cost of additional storage. |
+
+`max_iterations` and `sample_rate` also affect quantizer training, but since they apply to every IVF/HNSW index type (not just quantized ones), they're documented as general [Build-time Parameters](/indexing/vector-index#build-time-parameters) instead. All three are passed as keyword arguments to `create_index`, alongside `index_type`:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {QuantizationCustomParams}
+    </CodeBlock>
+</CodeGroup>
diff --git a/docs/web/ingestion/create-table.mdx b/docs/web/ingestion/create-table.mdx
new file mode 100644
index 000000000..7d4363be7
--- /dev/null
+++ b/docs/web/ingestion/create-table.mdx
@@ -0,0 +1,391 @@
+---
+title: "Creating a table"
+sidebarTitle: "Create Table"
+description: "Create a table from records or a schema and choose input formats."
+icon: table
+keywords: ["create table", "ingest", "arrow", "pandas", "polars", "pydantic", "schema"]
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Create and open tables in OSS and Enterprise.
+---
+
+import {
+    TsConnect,
+    RsConnect,
+} from '/snippets/connection.mdx';
+import {
+    TsCreateTableFromDicts as TsCreateTableFromDicts,
+    RsCreateTableFromDicts as RsCreateTableFromDicts,
+    TsCreateTableConflictHandling as TsCreateTableConflictHandling,
+    RsCreateTableConflictHandling as RsCreateTableConflictHandling,
+    TsCreateTableCustomSchema as TsCreateTableCustomSchema,
+    RsCreateTableCustomSchema as RsCreateTableCustomSchema,
+    TsCreateTableFromArrow as TsCreateTableFromArrow,
+    RsCreateTableFromArrow as RsCreateTableFromArrow,
+    TsOpenExistingTable as TsOpenExistingTable,
+    RsOpenExistingTable as RsOpenExistingTable,
+    TsCreateEmptyTable as TsCreateEmptyTable,
+    RsCreateEmptyTable as RsCreateEmptyTable,
+    TsDropTable as TsDropTable,
+    RsDropTable as RsDropTable,
+    PyTablesBasicConnect as TablesBasicConnect,
+    PyTablesDocumentModel as TablesDocumentModel,
+    PyTablesTzValidator as TablesTzValidator,
+} from '/snippets/tables.mdx';
+import {
+    PyCreateTableFromDicts as CreateTableFromDicts,
+    PyCreateTableConflictHandling as CreateTableConflictHandling,
+    PyCreateTableFromPandas as CreateTableFromPandas,
+    PyCreateTableCustomSchema as CreateTableCustomSchema,
+    PyCreateTableFromPolars as CreateTableFromPolars,
+    PyCreateTableFromArrow as CreateTableFromArrow,
+    PyCreateTableFromPydantic as CreateTableFromPydantic,
+    PyCreateTableNestedSchema as CreateTableNestedSchema,
+    PyOpenExistingTable as OpenExistingTable,
+    PyCreateEmptyTable as CreateEmptyTable,
+    PyCreateEmptyTablePydantic as CreateEmptyTablePydantic,
+    PyDropTable as DropTable,
+} from '/snippets/ingestion_examples.mdx';
+
+import { PyWriteWorkflow, TsWriteWorkflowBody, TsWriteWorkflowTail, RsWriteWorkflow } from '/snippets/ingestion_examples.mdx';
+
+Create a table from existing records, or define an empty table before data
+arrives. Define a schema to specify column types, allow or disallow missing
+values, and set the size of your vectors.
+
+## Create a table with data {#create-a-table-with-data}
+
+The Python input examples below are complete programs. Install `lancedb` and
+any additional libraries imported by the example, save one as a Python file,
+and run it. Each uses its own temporary local database.
+
+The TypeScript and Rust blocks illustrate the corresponding input formats.
+For a complete connection-to-result program in either language, expand
+[the write workflow](#complete-example). Table names follow the
+[naming rules](/basics/naming).
+
+### From list of objects {#from-list-of-objects}
+
+You can provide a list of objects to create a table. The Python and TypeScript SDKs
+support lists/arrays of records. The Rust example converts structs to Arrow batches.
+In Python, pass a list or other batch-like object; a single bare `dict` or single
+`LanceModel` is rejected.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {CreateTableFromDicts}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsCreateTableFromDicts}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsCreateTableFromDicts}
+    </CodeBlock>
+</CodeGroup>
+
+### If the table already exists {#handle-existing-tables}
+
+By default, creating a table fails if its name is already in use. Choose whether
+to open that table or overwrite it.
+
+#### Open the existing table
+
+Use `exist_ok=True` in Python or `existOk: true` in TypeScript to return the
+existing table without changing its contents. This is useful for setup code
+that may run more than once. Any data supplied to the create call is ignored
+when the table already exists.
+
+Inspect the returned table's schema if your code depends on specific columns
+or types. Opening an existing table does not change its schema to match the
+data supplied in the create call.
+
+#### Overwrite the table
+
+Use `mode="overwrite"` in Python or `mode: "overwrite"` in TypeScript to replace
+the existing table with the supplied data and schema. Choose this only when
+you intend to replace its previous contents.
+
+The following Python program demonstrates both choices:
+
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {CreateTableConflictHandling}
+    </CodeBlock>
+
+
+
+
+<Note>
+`exist_ok` / `existOk` does not append the provided data to an existing table. Use
+[`table.add()`](/ingestion/insert) for that. If you need to ensure a table exists *and*
+contains specific rows, choose an explicit [merge policy](/ingestion/merge-insert). Creating an empty table
+and repeatedly appending the same batch does not deduplicate it.
+</Note>
+
+### From a custom schema {#from-a-custom-schema}
+
+You can define a custom Arrow schema for the table. This is useful when you want to have more control over the column types and metadata.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {CreateTableCustomSchema}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsCreateTableCustomSchema}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsCreateTableCustomSchema}
+    </CodeBlock>
+</CodeGroup>
+
+An explicit schema also determines which columns accept missing values (`null`). If later writes omit a
+non-nullable column, or provide actual nulls for it, ingestion fails; nullable columns can be
+omitted or written with null values. Without an explicit schema, Python infers list-like vector
+values as fixed-size `float32` vector fields from the observed dimension.
+
+For Python ingest, malformed vector values fail by default. If you expect occasional wrong-length,
+null, or NaN vectors, choose an `on_bad_vectors` policy: `"drop"` removes those rows, `"fill"` writes
+`fill_value`, and `"null"` writes nulls.
+
+### From an Arrow Table {#from-an-arrow-table}
+You can also create LanceDB tables directly from Arrow tables.
+Rust uses an Arrow `RecordBatchReader` for the same Arrow-native ingest flow.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {CreateTableFromArrow}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsCreateTableFromArrow}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsCreateTableFromArrow}
+    </CodeBlock>
+</CodeGroup>
+
+
+### From a Pandas DataFrame {#from-a-pandas-dataframe}
+<Badge color="green">Python Only</Badge>
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {CreateTableFromPandas}
+    </CodeBlock>
+</CodeGroup>
+
+<Note title="Note">
+Data is converted to Arrow before being written to disk. For maximum control over how data is saved, either provide the PyArrow schema to convert to or else provide a PyArrow Table directly.
+</Note>
+
+<Note title="Vector Column Type">
+The **`vector`** column needs to be a [Vector](/integrations/data/pydantic#vector-field) (defined as [pyarrow.FixedSizeList](https://arrow.apache.org/docs/python/generated/pyarrow.list_.html)) type.
+</Note>
+
+### From a Polars DataFrame {#from-a-polars-dataframe}
+<Badge color="green">Python Only</Badge>
+
+Pass a Polars DataFrame as input. Install `polars` for this example; the
+conversion preserves the table structure through Arrow.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {CreateTableFromPolars}
+    </CodeBlock>
+</CodeGroup>
+
+### From Pydantic Models {#from-pydantic-models}
+<Badge color="green">Python Only</Badge>
+
+When you create an empty table without data, you must specify the table schema.
+LanceDB supports creating tables by specifying a PyArrow schema or a specialized
+Pydantic model called `LanceModel`.
+
+For example, the following Content model specifies a table with 5 columns:
+`movie_id`, `vector`, `genres`, `title`, and `imdb_id`. When you create a table, you can
+pass the class as the value of the `schema` parameter to `create_table`.
+The `vector` column is a `Vector` type, which is a specialized Pydantic type that
+can be configured with the vector dimensions. It is also important to note that
+LanceDB only understands subclasses of `lancedb.pydantic.LanceModel`
+(which itself derives from `pydantic.BaseModel`).
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {CreateTableFromPydantic}
+    </CodeBlock>
+</CodeGroup>
+
+#### Nested schemas {#nested-schemas}
+
+Sometimes your data model may contain nested objects. For example, you may want to store the document string and the document source name as a nested Document object:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {TablesDocumentModel}
+    </CodeBlock>
+</CodeGroup>
+
+This can be used as the type of a LanceDB table column:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {CreateTableNestedSchema}
+    </CodeBlock>
+</CodeGroup>
+
+This creates a struct column called "document" that has two subfields
+called "content" and "source":
+
+```bash
+In [28]: tbl.schema
+Out[28]:
+id: string not null
+vector: fixed_size_list<item: float>[1536] not null
+    child 0, item: float
+document: struct<content: string not null, source: string not null> not null
+    child 0, content: string not null
+    child 1, source: string not null
+```
+
+#### Validators {#validators}
+
+Because `LanceModel` inherits from Pydantic's `BaseModel`, you can combine them with Pydantic's
+[field validators](https://docs.pydantic.dev/latest/concepts/validators). The example
+below shows how to add a validator to ensure that only valid timezone-aware datetime objects are used
+for a `created_at` field.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {TablesTzValidator}
+    </CodeBlock>
+</CodeGroup>
+
+The example catches a `ValidationError` for the timestamp with a different
+timezone. A validator runs when the Pydantic model is constructed; declaring a
+model as the table schema does not make every later write run that validator.
+
+<Note>
+Datasets too large to pass to `create_table` at once are covered separately;
+see [Insert data](/ingestion/insert#large-datasets) for batches, file-backed
+input and write parallelism.
+</Note>
+
+## Create empty table {#create-empty-table}
+You can create an empty table for scenarios where you want to add data to the table later.
+An example would be when you want to collect data from a stream/external file and then add it to a table in
+batches.
+
+An empty table can be initialized via an Arrow schema.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {CreateEmptyTable}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsCreateEmptyTable}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsCreateEmptyTable}
+    </CodeBlock>
+</CodeGroup>
+
+Alternatively, define the schema with `LanceModel`, a Pydantic model class
+provided by `lancedb.pydantic`. Its `Vector` type declares a fixed vector dimension.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {CreateEmptyTablePydantic}
+    </CodeBlock>
+</CodeGroup>
+
+Once the empty table has been created, you can append to it or modify its contents,
+as explained in [Ingesting data](/ingestion).
+
+## Open an existing table {#open-an-existing-table}
+
+You can open an existing table by specifying the name of the table to the `open_table` / `openTable` method.
+If you forget the name of your table, you can always get a listing of all table names.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {OpenExistingTable}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsOpenExistingTable}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsOpenExistingTable}
+    </CodeBlock>
+</CodeGroup>
+
+## Drop a table {#drop-a-table}
+
+Use the `drop_table()` method on the database to remove a table.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {DropTable}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsDropTable}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsDropTable}
+    </CodeBlock>
+</CodeGroup>
+
+Dropping a table removes the table object. Deleting rows keeps the table and
+changes its contents; retained versions and cleanup are described in
+[Cleanup](/maintenance/cleanup).
+On a local database, dropping a table that does not exist raises an error; in
+Python, pass `ignore_missing=True` to suppress it. A deployment may report
+success instead, depending on how it stores its catalog, so do not rely on the
+error to tell you a table was missing. Python's deployment connection does not
+take `ignore_missing`.
+
+<span id="verify"></span>
+
+## Complete write workflow {#complete-example}
+
+<Accordion title="Complete examples: create, append, update, upsert and delete">
+
+Each program creates a temporary local database, performs the five operations,
+prints the result, and removes its temporary data when finished.
+
+- **Python:** install `lancedb`, save the code as `ingestion.py`, and run
+  `python ingestion.py`.
+- **TypeScript:** install `@lancedb/lancedb`, save the code as `ingestion.mts`,
+  and run `node ingestion.mts` with Node.js 22.18 or later.
+- **Rust:** download the [complete Rust project](/static/code/ingestion-rust.zip),
+  extract it, and run `cargo run --locked`. It includes `src/main.rs`, the Cargo
+  manifest and a lockfile for the example's dependencies. The code is shown below.
+
+<CodeGroup>
+<CodeBlock filename="ingestion.py" language="python" icon="python">
+{PyWriteWorkflow}
+</CodeBlock>
+<CodeBlock filename="ingestion.mts" language="typescript" icon="square-js">
+{TsWriteWorkflowBody}
+{TsWriteWorkflowTail}
+</CodeBlock>
+<CodeBlock filename="src/main.rs" language="rust" icon="rust">
+{RsWriteWorkflow}
+</CodeBlock>
+</CodeGroup>
+
+After the operations, Alice has 10 logins, Bobby has 21, and Charlie has 6.
+Dana is selected for deletion and is absent from the final result.
+
+</Accordion>
diff --git a/docs/web/ingestion/delete.mdx b/docs/web/ingestion/delete.mdx
new file mode 100644
index 000000000..b7b1f9432
--- /dev/null
+++ b/docs/web/ingestion/delete.mdx
@@ -0,0 +1,110 @@
+---
+title: "Delete rows"
+sidebarTitle: "Delete"
+description: "Select and delete records, verify the remaining rows, and understand storage retention."
+icon: trash
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Delete rows through the SDKs in OSS and Enterprise. Storage reclamation follows compaction and retention policy.
+---
+
+import { RsExample as RsDeleteRows } from '/snippets/ingestion_delete.mdx';
+import { TsDeleteRowsBody, TsDeleteRowsTail } from '/snippets/ingestion_examples.mdx';
+import { PyDeleteRows } from '/snippets/ingestion_examples.mdx';
+import { PyWriteWorkflow, TsWriteWorkflowBody, TsWriteWorkflowTail, RsWriteWorkflow } from '/snippets/ingestion_examples.mdx';
+
+Deleting rows removes matching records from the current table contents. It
+keeps the table and schema so you can continue reading and writing. To remove
+the table itself, see [Drop a table](/ingestion/create-table#drop-a-table).
+
+## Inspect the rows you intend to delete {#select-rows}
+
+Use the same predicate for inspection and deletion. The example below creates
+Alice, Bob and Charlie, prints Charlie's row, and deletes it by `id = 3`.
+Inspection and deletion are separate operations; concurrent writes can change
+which rows match between them.
+
+## Delete matching records {#delete-rows}
+
+Each program creates Alice, Bob and Charlie in a temporary local database,
+deletes Charlie, and prints the remaining rows: **Alice and Bob**. The temporary
+database is removed when the program finishes.
+
+- **Python:** install `lancedb`, save the code as `delete_rows.py`, and run
+  `python delete_rows.py`.
+- **TypeScript:** install `@lancedb/lancedb`, save the code as `delete_rows.mts`,
+  and run `node delete_rows.mts` with Node.js 22.18 or later.
+- **Rust:** download the [Rust examples](/static/code/ingestion-feedback-rust.zip),
+  extract the project, and run `cargo run --locked --bin delete`.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{PyDeleteRows}
+</CodeBlock>
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsDeleteRowsBody}
+{TsDeleteRowsTail}
+</CodeBlock>
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsDeleteRows}
+</CodeBlock>
+</CodeGroup>
+
+Enterprise also supports [`DELETE`](/sql/dml/delete).
+
+## Verify that the rows are absent {#verify}
+
+Query the same predicate after deletion and check the remaining row count.
+A reader still using an older snapshot may see the earlier contents; see
+[Consistency](/tables/consistency).
+
+Deleted rows are excluded from current query results, including indexed
+searches. Deletion does not imply immediate physical erasure of every stored
+copy of the data.
+
+## Understand storage reclamation {#storage}
+
+Compaction rewrites the live data layout; cleanup removes eligible retained
+versions and files. Retained history can continue to reference data that no
+longer appears in the latest table version.
+
+In OSS, `optimize()` provides maintenance operations. In Enterprise, managed
+maintenance and retention follow the service configuration. Choose retention
+according to the history you need, rather than shortening it as part of a
+routine delete example. See [Compaction](/maintenance/compaction) and
+[Cleanup](/maintenance/cleanup) for the separate maintenance workflow.
+
+## Complete write workflow {#complete-example}
+
+<Accordion title="Complete examples: create, append, update, upsert and delete">
+
+Each program creates a temporary local database, performs the five operations,
+prints the result, and removes its temporary data when finished.
+
+- **Python:** install `lancedb`, save the code as `ingestion.py`, and run
+  `python ingestion.py`.
+- **TypeScript:** install `@lancedb/lancedb`, save the code as `ingestion.mts`,
+  and run `node ingestion.mts` with Node.js 22.18 or later.
+- **Rust:** download the [complete Rust project](/static/code/ingestion-rust.zip),
+  extract it, and run `cargo run --locked`. It includes `src/main.rs`, the Cargo
+  manifest and a lockfile for the example's dependencies. The code is shown below.
+
+<CodeGroup>
+<CodeBlock filename="ingestion.py" language="python" icon="python">
+{PyWriteWorkflow}
+</CodeBlock>
+<CodeBlock filename="ingestion.mts" language="typescript" icon="square-js">
+{TsWriteWorkflowBody}
+{TsWriteWorkflowTail}
+</CodeBlock>
+<CodeBlock filename="src/main.rs" language="rust" icon="rust">
+{RsWriteWorkflow}
+</CodeBlock>
+</CodeGroup>
+
+After the operations, Alice has 10 logins, Bobby has 21, and Charlie has 6.
+Dana is selected for deletion and is absent from the final result.
+
+</Accordion>
diff --git a/docs/web/ingestion/index.mdx b/docs/web/ingestion/index.mdx
new file mode 100644
index 000000000..c37b46a62
--- /dev/null
+++ b/docs/web/ingestion/index.mdx
@@ -0,0 +1,97 @@
+---
+title: "Ingesting data"
+sidebarTitle: "Overview"
+description: "Choose how to create a table, append records or change existing data."
+icon: inbox
+keywords: ["ingest", "create table", "add", "insert", "arrow", "pandas", "polars", "pydantic"]
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Create tables and write records through the SDKs in OSS and Enterprise. Enterprise also provides SQL write statements.
+---
+
+import { PyWriteWorkflow, TsWriteWorkflowBody, TsWriteWorkflowTail, RsWriteWorkflow } from '/snippets/ingestion_examples.mdx';
+
+Ingestion brings data into a LanceDB table. Start by choosing a schema and
+creating the table, then choose a write operation according to how incoming
+records relate to data already stored.
+
+## Choose a write operation {#choose-a-write-method}
+
+| Task | Operation | What happens |
+| --- | --- | --- |
+| [Create a table](/ingestion/create-table) | `create_table` / `createTable` | Create a table from data or an explicit schema. |
+| [Append records](/ingestion/insert) | `add` | Add rows without matching existing keys. |
+| [Upsert by key](/ingestion/merge-insert) | `merge_insert` / `mergeInsert` | Choose whether matching rows are updated and new keys inserted. |
+| [Update selected rows](/ingestion/update) | `update` | Change values in rows selected by a predicate. |
+| [Delete selected rows](/ingestion/delete) | `delete` | Remove matching rows from the current table contents. |
+
+Appending the same input twice can create duplicate records. Use merge insert
+with a stable key when the incoming data should update existing records.
+
+## Connect and prepare the data {#connect-to-lancedb}
+
+Use the [connection guide](/basics/connect) to connect to OSS or Enterprise.
+The SDK write operations in this section work with both. Enterprise also
+supports [SQL write statements](/sql#dml); an SDK filter expression does not
+require a separate SQL connection.
+
+Choose the column types, required fields and vector dimensions before a large
+load. SDKs support different input representations, including row objects and
+Arrow batches. The [table creation guide](/ingestion/create-table) covers
+those choices.
+
+## Try a complete write workflow {#create-the-example-table}
+
+This example starts with Alice and Bob, appends Charlie, updates Bob's name
+and login count, then merges a batch containing Charlie and Dana. Finally, it
+selects Dana's row for inspection and deletes it.
+
+<Accordion title="Complete examples: create, append, update, upsert and delete">
+
+Each program creates a temporary local database, performs the five operations,
+prints the result, and removes its temporary data when finished.
+
+- **Python:** install `lancedb`, save the code as `ingestion.py`, and run
+  `python ingestion.py`.
+- **TypeScript:** install `@lancedb/lancedb`, save the code as `ingestion.mts`,
+  and run `node ingestion.mts` with Node.js 22.18 or later.
+- **Rust:** download the [complete Rust project](/static/code/ingestion-rust.zip),
+  extract it, and run `cargo run --locked`. It includes `src/main.rs`, the Cargo
+  manifest and a lockfile for the example's dependencies. The code is shown below.
+
+<CodeGroup>
+<CodeBlock filename="ingestion.py" language="python" icon="python">
+{PyWriteWorkflow}
+</CodeBlock>
+<CodeBlock filename="ingestion.mts" language="typescript" icon="square-js">
+{TsWriteWorkflowBody}
+{TsWriteWorkflowTail}
+</CodeBlock>
+<CodeBlock filename="src/main.rs" language="rust" icon="rust">
+{RsWriteWorkflow}
+</CodeBlock>
+</CodeGroup>
+
+After the operations, Alice has 10 logins, Bobby has 21, and Charlie has 6.
+Dana is selected for deletion and is absent from the final result.
+
+</Accordion>
+
+## Verify the result {#verify}
+
+After a write completes, inspect the affected keys and fields and check the row
+count. SDK return values differ by operation and language; use the relevant
+API reference when relying on returned counts or version metadata.
+
+Readers with older snapshots may not see another handle's writes immediately.
+See [Consistency](/tables/consistency). A table checked out at a historical
+version must return to the latest version before it can be modified.
+
+## Load larger datasets {#large-loads}
+
+Use batches or file-backed input when the dataset is too large to hold in
+memory. The [insert guide](/ingestion/insert#large-datasets) covers input
+formats, progress reporting and write parallelism. Keep retry behavior in mind:
+repeating an append is not the same as retrying a key-based upsert.
diff --git a/docs/web/ingestion/insert.mdx b/docs/web/ingestion/insert.mdx
new file mode 100644
index 000000000..f2ee0d7db
--- /dev/null
+++ b/docs/web/ingestion/insert.mdx
@@ -0,0 +1,237 @@
+---
+title: "Insert data"
+sidebarTitle: "Insert"
+description: "Append records, load file-backed data or batches, and verify ingestion."
+icon: plus
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Append records through the SDKs in OSS and Enterprise. For large Enterprise writes, see [large upload settings](#bulk-ingests-into-a-remote-table).
+---
+
+import {
+    TsCreateTableFromIterator as TsCreateTableFromIterator,
+    RsCreateTableFromIterator as RsCreateTableFromIterator,
+} from '/snippets/tables.mdx';
+import {
+    PyAddFromDataset as AddFromDataset,
+    PyCreateTableFromIterator as CreateTableFromIterator,
+} from '/snippets/ingestion_examples.mdx';
+
+import { RsExample as RsAddProgress } from '/snippets/ingestion_progress.mdx';
+import { PyAddProgress, TsAddProgressBody, TsAddProgressTail } from '/snippets/ingestion_examples.mdx';
+import { PyAppendRows } from '/snippets/ingestion_examples.mdx';
+import { PyWriteWorkflow, TsWriteWorkflowBody, TsWriteWorkflowTail, RsWriteWorkflow } from '/snippets/ingestion_examples.mdx';
+
+Use `add()` to append records to an existing table. It does not compare incoming
+keys with stored keys. Choose [merge insert](/ingestion/merge-insert) when the
+same record may arrive again and should update an existing row.
+
+## Append a first batch {#append}
+
+Install the Python SDK with `pip install lancedb`. This complete example starts
+with Alice, appends Bob and Charlie, and prints all three records.
+
+<CodeBlock filename="append_rows.py" language="python" icon="python">
+{PyAppendRows}
+</CodeBlock>
+
+The program uses temporary local storage. For a complete TypeScript or Rust
+example, see [Complete write workflow](#complete-example).
+Enterprise SQL offers [`INSERT`](/sql/dml/insert), including `INSERT ... SELECT`.
+
+## Prepare the input {#prepare}
+
+Match the table's schema, including required fields and vector dimensions.
+Supported input formats differ by SDK; see
+[Create a table](/ingestion/create-table). Inspect conversion errors before
+retrying, and verify the final row count and representative values.
+
+## Choose input for a large load {#large-datasets}
+
+Avoid building one large in-memory list when records already live in files
+or arrive as batches. Create a table with the intended schema, then append
+through the appropriate input path.
+
+<span id="bulk-ingestion-for-data-you-already-have"></span>
+### Read from files {#from-files-parquet-csv-etc}
+
+Python accepts a `pyarrow.dataset.Dataset`. This example writes a small Parquet
+file locally, creates an empty table with its schema, and appends the two records.
+For your own data, point the dataset reader at the input files instead.
+
+<CodeBlock filename="load_parquet.py" language="python" icon="python">
+{AddFromDataset}
+</CodeBlock>
+
+A file-backed source can be scanned without first loading the whole dataset
+into application memory. This Python input API is distinct from converting
+files to Arrow batches yourself in another language.
+
+<span id="iterator-ingestion-for-data-you-transform-on-the-fly"></span>
+### Produce batches during ingestion {#from-iterators-custom-batch-generation}
+
+Use batches when processing the input as it arrives. The Python example creates
+an empty table and appends ten records produced by a batch iterator.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{CreateTableFromIterator}
+</CodeBlock>
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsCreateTableFromIterator}
+</CodeBlock>
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsCreateTableFromIterator}
+</CodeBlock>
+</CodeGroup>
+
+The language APIs differ: the TypeScript example appends each constructed Arrow
+batch, while the Rust example supplies an Arrow reader when creating the table.
+These are input-format examples, not equivalent parallel-write benchmarks.
+
+### Control write parallelism {#write-parallelism}
+
+Python's `add()` can estimate parallelism for materialized inputs. A streaming
+reader may not expose its total size or support replay. Use `write_parallelism`
+when controlling the number of concurrent partitions; increasing it can also
+increase memory use. TypeScript does not expose that manual option.
+
+Measure with realistic row sizes and storage. Avoid one write per row when a
+batch will do, and avoid a large worker count for a small input.
+
+## Track completion and progress {#tracking-ingestion-progress}
+
+Python, TypeScript and Rust support progress callbacks when adding data.
+These examples start with Alice, append Bob and Charlie, and print the number
+of rows written as progress updates arrive. After the write succeeds, they
+query the table to show all three records.
+
+Each program uses a temporary local database and removes it when finished.
+
+- **Python:** install `lancedb`, save the code as `insert_progress.py`, and run
+  `python insert_progress.py`.
+- **TypeScript:** install `@lancedb/lancedb`, save the code as `insert_progress.mts`,
+  and run `node insert_progress.mts` with Node.js 22.18 or later.
+- **Rust:** download the [Rust examples](/static/code/ingestion-feedback-rust.zip),
+  extract the project, and run `cargo run --locked --bin progress`.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{PyAddProgress}
+</CodeBlock>
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsAddProgressBody}
+{TsAddProgressTail}
+</CodeBlock>
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsAddProgress}
+</CodeBlock>
+</CodeGroup>
+
+For a progress bar in Python, install `tqdm` and pass `progress=True` instead
+of a callback.
+
+### Read progress updates
+
+Updates include row and byte counts, elapsed time, and parallel write activity.
+The field names differ by language:
+
+| Meaning | Python dictionary key | TypeScript property | Rust method |
+|:--------|:----------------------|:--------------------|:------------|
+| Rows written so far | `output_rows` | `outputRows` | `output_rows()` |
+| Bytes written so far | `output_bytes` | `outputBytes` | `output_bytes()` |
+| Expected row count, when known | `total_rows` | `totalRows` | `total_rows()` |
+| Elapsed time | `elapsed_seconds` | `elapsedSeconds` | `elapsed()` |
+| Active write tasks | `active_tasks` | `activeTasks` | `active_tasks()` |
+| Total write tasks | `total_tasks` | `totalTasks` | `total_tasks()` |
+| Final update | `done` | `done` | `done()` |
+
+Python and TypeScript report elapsed time in seconds; Rust returns a `Duration`.
+The expected row count may be unknown for streaming input. On the final update,
+it falls back to the number of rows written.
+
+Keep callbacks short and use the write operation's result to determine success.
+The final `done` update means progress reporting has ended; it can also occur
+after a write fails or is cancelled. Input errors before writing starts may
+produce no updates.
+
+TypeScript delivers callbacks asynchronously. Slow callbacks can queue up,
+and the `add()` promise may resolve before every callback has run. Exceptions
+from a TypeScript callback are logged and do not abort the write.
+
+## Verify writes and handle retries {#performance}
+
+Wait for the write operation's result, then check the expected records.
+Progress callbacks describe activity; they do not establish that a write
+succeeded. If a request fails after the server may have committed it, blindly
+repeating an append can duplicate rows. The client's automatic retries can do
+the same: on a remote table, the Python and TypeScript 0.40.0 clients resend an
+append after a retryable status such as `504`, even when the server had already
+committed it, and `add()` still returns success. Reconcile using your record
+IDs, or choose an upsert policy designed for replay.
+
+## Configure large uploads {#bulk-ingests-into-a-remote-table}
+
+<Badge color="red">Enterprise</Badge>
+
+This section applies only to remote tables (`db://` connections). Embedded (local) writes are unaffected.
+
+When you write to a remote LanceDB Enterprise table, the client sends the write as one HTTP `insert` request unless it splits the data into more than one partition, which it does for a large input it can measure or when you set [`write_parallelism`](#write-parallelism). A partitioned write goes up as a multipart write: the client cuts each partition into one or more `insert` parts and uploads them under a single upload id. Each part is streamed. Its size and duration still affect resource use and
+timeouts: it has to finish within the client read timeout while the server writes it to object storage, so on very large ingests an oversized part can run past that timeout and surface as:
+
+```text
+lancedb.remote.errors.HttpError: operation timed out
+```
+
+Two environment variables control how a multipart write's parts are cut. Both are picked up automatically by the Python and TypeScript clients, for clients that support these settings:
+
+| Variable | Default | What it controls |
+|----------|---------|------------------|
+| `LANCE_CLIENT_MAX_BYTES_PER_REQUEST` | 8 GiB | Maximum size of a single insert part, in LZ4-compressed Arrow IPC bytes. Set to `0` to disable splitting and send one request per partition. |
+| `LANCE_CLIENT_MAX_REQUEST_DURATION` | Half the read timeout | Maximum wall-clock time, in whole seconds, that any one insert part may stay open. Set to `0` to disable the time-based cut. |
+
+A part is cut after the input batch that reaches either limit, so cuts fall between batches. Lower the byte budget, with smaller input batches, when large ingests hit the read timeout; lower the duration when uploads are slow or throttled and the byte budget isn't the limiting factor.
+
+```bash
+export LANCE_CLIENT_MAX_BYTES_PER_REQUEST=1073741824  # 1 GiB
+export LANCE_CLIENT_MAX_REQUEST_DURATION=120          # 2 minutes
+```
+
+Splitting into more parts does not change the final table. The server stages the parts under their shared upload ID before committing
+the write. This upload protocol is separate from repeating an application-level
+`add()` after a timeout.
+
+## Complete write workflow {#complete-example}
+
+<Accordion title="Complete examples: create, append, update, upsert and delete">
+
+Each program creates a temporary local database, performs the five operations,
+prints the result, and removes its temporary data when finished.
+
+- **Python:** install `lancedb`, save the code as `ingestion.py`, and run
+  `python ingestion.py`.
+- **TypeScript:** install `@lancedb/lancedb`, save the code as `ingestion.mts`,
+  and run `node ingestion.mts` with Node.js 22.18 or later.
+- **Rust:** download the [complete Rust project](/static/code/ingestion-rust.zip),
+  extract it, and run `cargo run --locked`. It includes `src/main.rs`, the Cargo
+  manifest and a lockfile for the example's dependencies. The code is shown below.
+
+<CodeGroup>
+<CodeBlock filename="ingestion.py" language="python" icon="python">
+{PyWriteWorkflow}
+</CodeBlock>
+<CodeBlock filename="ingestion.mts" language="typescript" icon="square-js">
+{TsWriteWorkflowBody}
+{TsWriteWorkflowTail}
+</CodeBlock>
+<CodeBlock filename="src/main.rs" language="rust" icon="rust">
+{RsWriteWorkflow}
+</CodeBlock>
+</CodeGroup>
+
+After the operations, Alice has 10 logins, Bobby has 21, and Charlie has 6.
+Dana is selected for deletion and is absent from the final result.
+
+</Accordion>
diff --git a/docs/web/ingestion/merge-insert.mdx b/docs/web/ingestion/merge-insert.mdx
new file mode 100644
index 000000000..6fa59fd9c
--- /dev/null
+++ b/docs/web/ingestion/merge-insert.mdx
@@ -0,0 +1,266 @@
+---
+title: "Merge insert"
+sidebarTitle: "Merge Insert"
+description: "Match incoming records by key and choose which rows to update, insert or remove."
+icon: code-merge
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Merge insert is available through the SDKs in OSS and Enterprise. Use it for key-based updates and inserts.
+---
+
+import {
+    TsMergeMatchedUpdateOnly,
+    TsInsertIfNotExists,
+    TsMergeUpdateInsert,
+    TsMergeDeleteMissingBySource,
+    TsMergePartialColumns,
+    RsMergeMatchedUpdateOnly,
+    RsInsertIfNotExists,
+    RsMergeUpdateInsert,
+    RsMergeDeleteMissingBySource,
+    RsMergePartialColumns,
+} from '/snippets/tables.mdx';
+import {
+    PyMergeMatchedUpdateOnly as MergeMatchedUpdateOnly,
+    PyInsertIfNotExists as InsertIfNotExists,
+    PyMergeUpdateInsert as MergeUpdateInsert,
+    PyMergeDeleteMissingBySource as MergeDeleteMissingBySource,
+    PyMergePartialColumns as MergePartialColumns,
+} from '/snippets/ingestion_examples.mdx';
+
+import { PyWriteWorkflow, TsWriteWorkflowBody, TsWriteWorkflowTail, RsWriteWorkflow } from '/snippets/ingestion_examples.mdx';
+
+Use merge insert when incoming records should be compared with existing rows.
+Combine update and insert policies for an **upsert**. Use [append](/ingestion/insert)
+when every incoming row should simply be added.
+
+## Merge incoming rows by key {#merge-incoming-rows-by-key}
+
+Merging is different from updating because it involves comparing incoming rows to existing rows by key, and then choosing what to do based on whether the key exists in the target table or not.
+Use `merge_insert("id")` in Python or the corresponding SDK builder.
+
+In merge operations, rows are split into three groups:
+
+- **Matched**: key exists in both source and target.
+- **Not matched**: key exists only in source.
+- **Not matched by source**: key exists only in target.
+
+Conditional merge clauses can compare old and new values. Use the `target.` prefix for the
+existing table row and `source.` for the incoming row, for example
+`target.last_update < source.last_update`.
+
+Choose a key that identifies the same record in the source and target. Check
+for duplicate and null keys before merging. A primary-key declaration does not
+enforce uniqueness on ordinary writes, and append operations do not deduplicate
+records for you.
+
+A scalar index on the key can help matching. Keep it current and measure the
+actual workload rather than assuming every merge uses an index. Enterprise may
+reject a merge when the unindexed scan exceeds its configured limit; inspect
+that error and the index state before retrying.
+
+## Choose a merge policy {#policies}
+
+| Source and target relationship | Available action |
+| --- | --- |
+| Key appears in both | Update matching target rows, optionally with a condition. |
+| Key appears only in the source | Insert it, or leave it out. |
+| Key appears only in the target | Keep it, or explicitly delete it under a synchronization policy. |
+
+The following examples are independent. Each starts from the table shown in
+its code, so the expected results do not depend on running the previous example.
+Python blocks include their own temporary connection. For a full TypeScript or
+Rust program, see [Complete write workflow](#complete-example).
+
+### Update matched rows only {#update-matched-rows-only}
+
+This updates keys that already exist in the target table. Source rows with new keys are ignored.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {MergeMatchedUpdateOnly}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsMergeMatchedUpdateOnly}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsMergeMatchedUpdateOnly}
+    </CodeBlock>
+</CodeGroup>
+
+Expected table contents:
+
+| id | name | login_count |
+| --- | --- | --- |
+| 1 | Alice | 10 |
+| 2 | Bobby | 21 |
+
+### Insert unmatched rows only {#insert-unmatched-rows-only}
+
+This inserts only brand-new keys from the source. Existing keys are left unchanged.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {InsertIfNotExists}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsInsertIfNotExists}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsInsertIfNotExists}
+    </CodeBlock>
+</CodeGroup>
+
+Expected table contents:
+
+| id | name | login_count |
+| --- | --- | --- |
+| 1 | Alice | 10 |
+| 2 | Bob | 20 |
+| 3 | Charlie | 5 |
+
+### Update matched rows and insert unmatched rows {#update-matched-rows-and-insert-unmatched-rows}
+
+Use both `when_matched_update_all()` and `when_not_matched_insert_all()` when you want to update existing keys and insert missing keys in one operation.
+
+<Info>
+This is a conventional **upsert**.
+</Info>
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {MergeUpdateInsert}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsMergeUpdateInsert}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsMergeUpdateInsert}
+    </CodeBlock>
+</CodeGroup>
+
+Expected table contents:
+
+| id | name | login_count |
+| --- | --- | --- |
+| 1 | Alice | 10 |
+| 2 | Bobby | 21 |
+| 3 | Charlie | 5 |
+
+### Delete target rows that are missing from source {#delete-target-rows-that-are-missing-from-source}
+
+Use `when_not_matched_by_source_delete()` only when the incoming data is the
+complete set for the target scope you intend to synchronize. An incremental
+batch is usually not a complete source: deleting everything absent from it
+could remove unrelated target records. Inspect the unmatched target keys first.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {MergeDeleteMissingBySource}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsMergeDeleteMissingBySource}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsMergeDeleteMissingBySource}
+    </CodeBlock>
+</CodeGroup>
+
+Expected table contents:
+
+| id | name | login_count |
+| --- | --- | --- |
+| 2 | Bobby | 21 |
+| 3 | Charlie | 5 |
+
+Here, IDs 2 and 3 are updated and ID 1 is deleted. This policy synchronizes
+the target to the supplied source set.
+
+### Use partial columns in merge updates {#use-partial-columns-in-merge-updates}
+
+Merge updates do not require you to provide values for all columns. You can provide only a subset of columns in source rows. For matched rows, only the provided columns are updated.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {MergePartialColumns}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsMergePartialColumns}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsMergePartialColumns}
+    </CodeBlock>
+</CodeGroup>
+
+Expected table contents:
+
+| id | name | login_count |
+| --- | --- | --- |
+| 1 | Alice | 10 |
+| 2 | Bobby | 20 |
+| 3 | Charlie | null |
+
+Note that in the example above, when `merge_insert` creates a new row, any missing columns are written as `null`. If a missing column is non-nullable in your schema, the insert will fail.
+
+## Check the merged records {#verify}
+
+Inspect the keys that should have changed, confirm that unrelated rows remain,
+and check inserted or deleted counts when the SDK returns them. Repeating a
+merge is only safe when its keys, conditions and update values produce the
+intended result on retry.
+
+### Embeddings and expression predicates {#advanced}
+
+When a table uses an [embedding definition](/embedding), behavior depends on
+which source and vector fields the incoming records provide. Do not assume a
+partial metadata update recomputes embeddings. Inspect the resulting vector
+when changing the input used to generate it.
+
+Rust's `when_matched_update_all_expr` and
+`when_not_matched_by_source_delete_expr` accept DataFusion expressions for
+local tables. Use the string predicate variants for Enterprise tables.
+
+## Complete write workflow {#complete-example}
+
+<Accordion title="Complete examples: create, append, update, upsert and delete">
+
+Each program creates a temporary local database, performs the five operations,
+prints the result, and removes its temporary data when finished.
+
+- **Python:** install `lancedb`, save the code as `ingestion.py`, and run
+  `python ingestion.py`.
+- **TypeScript:** install `@lancedb/lancedb`, save the code as `ingestion.mts`,
+  and run `node ingestion.mts` with Node.js 22.18 or later.
+- **Rust:** download the [complete Rust project](/static/code/ingestion-rust.zip),
+  extract it, and run `cargo run --locked`. It includes `src/main.rs`, the Cargo
+  manifest and a lockfile for the example's dependencies. The code is shown below.
+
+<CodeGroup>
+<CodeBlock filename="ingestion.py" language="python" icon="python">
+{PyWriteWorkflow}
+</CodeBlock>
+<CodeBlock filename="ingestion.mts" language="typescript" icon="square-js">
+{TsWriteWorkflowBody}
+{TsWriteWorkflowTail}
+</CodeBlock>
+<CodeBlock filename="src/main.rs" language="rust" icon="rust">
+{RsWriteWorkflow}
+</CodeBlock>
+</CodeGroup>
+
+After the operations, Alice has 10 logins, Bobby has 21, and Charlie has 6.
+Dana is selected for deletion and is absent from the final result.
+
+</Accordion>
diff --git a/docs/web/ingestion/update.mdx b/docs/web/ingestion/update.mdx
new file mode 100644
index 000000000..d44a2561e
--- /dev/null
+++ b/docs/web/ingestion/update.mdx
@@ -0,0 +1,123 @@
+---
+title: "Update rows"
+sidebarTitle: "Update"
+description: "Select existing rows, change values or expressions, and verify the changes."
+icon: pen
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Update rows through the SDKs in OSS and Enterprise. Enterprise also supports the SQL UPDATE statement.
+---
+
+import {
+    TsUpdateOperation,
+    TsUpdateUsingSql,
+    RsUpdateOperation,
+    RsUpdateUsingSql,
+} from '/snippets/tables.mdx';
+import {
+    PyUpdateOperation as UpdateOperation,
+    PyUpdateUsingSql as UpdateUsingSql,
+} from '/snippets/ingestion_examples.mdx';
+
+import { PyWriteWorkflow, TsWriteWorkflowBody, TsWriteWorkflowTail, RsWriteWorkflow } from '/snippets/ingestion_examples.mdx';
+
+Use `update` to change rows selected by a predicate. Use
+[merge insert](/ingestion/merge-insert) instead when matching an incoming dataset
+against existing keys.
+
+## Select the rows to change {#select-rows}
+
+Check the predicate with a [filtered query](/search/filtering) before applying
+the update. These examples target the row with `id = 2`. Review the scope
+carefully when omitting a predicate, since that can update the entire table.
+
+Each example below starts from Alice with 10 logins and Bob with 20. The
+Python programs create and remove their own temporary database. TypeScript
+and Rust operation blocks use `db`; a complete program is provided below.
+
+## Assign a new value {#update-rows}
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{UpdateOperation}
+</CodeBlock>
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsUpdateOperation}
+</CodeBlock>
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsUpdateOperation}
+</CodeBlock>
+</CodeGroup>
+
+The result contains Alice and **Bobby**, with their login counts unchanged.
+The Python `values` and TypeScript `values` arguments take literal values.
+Rust's `column` argument is an expression, so the string value includes SQL quotes.
+
+## Calculate a value from the existing row {#update-rows-with-sql-expressions}
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{UpdateUsingSql}
+</CodeBlock>
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsUpdateUsingSql}
+</CodeBlock>
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsUpdateUsingSql}
+</CodeBlock>
+</CodeGroup>
+
+In this separate example, Bob keeps his name and his login count becomes **21**.
+Python's `values_sql` and TypeScript's `valuesSql` accept expressions.
+These SDK expressions work with embedded OSS tables; they do not require an
+Enterprise SQL endpoint. Enterprise also offers [`UPDATE`](/sql/dml/update).
+
+## Verify the affected data {#verify}
+
+Read the selected rows again and inspect the changed fields. For another
+connection or table handle, account for its
+[read consistency](/tables/consistency) settings.
+
+Nested-field updates are not supported by these operations. For schema changes,
+see [schema evolution](/management/table#schema). Treat derived values separately:
+changing an input field is not a guarantee that every stored feature has been
+recomputed. See [Refresh a column](/feature-engineering/refresh-column).
+
+Updates can change index coverage and query cost. After a large update,
+inspect the workload and follow [index maintenance](/indexing/reindexing)
+guidance for the backend you use.
+
+## Complete write workflow {#complete-example}
+
+<Accordion title="Complete examples: create, append, update, upsert and delete">
+
+Each program creates a temporary local database, performs the five operations,
+prints the result, and removes its temporary data when finished.
+
+- **Python:** install `lancedb`, save the code as `ingestion.py`, and run
+  `python ingestion.py`.
+- **TypeScript:** install `@lancedb/lancedb`, save the code as `ingestion.mts`,
+  and run `node ingestion.mts` with Node.js 22.18 or later.
+- **Rust:** download the [complete Rust project](/static/code/ingestion-rust.zip),
+  extract it, and run `cargo run --locked`. It includes `src/main.rs`, the Cargo
+  manifest and a lockfile for the example's dependencies. The code is shown below.
+
+<CodeGroup>
+<CodeBlock filename="ingestion.py" language="python" icon="python">
+{PyWriteWorkflow}
+</CodeBlock>
+<CodeBlock filename="ingestion.mts" language="typescript" icon="square-js">
+{TsWriteWorkflowBody}
+{TsWriteWorkflowTail}
+</CodeBlock>
+<CodeBlock filename="src/main.rs" language="rust" icon="rust">
+{RsWriteWorkflow}
+</CodeBlock>
+</CodeGroup>
+
+After the operations, Alice has 10 logins, Bobby has 21, and Charlie has 6.
+Dana is selected for deletion and is absent from the final result.
+
+</Accordion>
diff --git a/docs/web/integrations/ai/agno.mdx b/docs/web/integrations/ai/agno.mdx
new file mode 100644
index 000000000..b5edef031
--- /dev/null
+++ b/docs/web/integrations/ai/agno.mdx
@@ -0,0 +1,151 @@
+---
+title: "Agno"
+sidebarTitle: "Agno"
+description: "Build a search assistant using the Agno agent framework with LanceDB as the knowledge backend."
+---
+
+import {
+  PyFrameworksAgnoAgent,
+  PyFrameworksAgnoCliChat,
+  PyFrameworksAgnoIngestYoutube,
+  PyFrameworksAgnoSetup,
+} from '/snippets/integrations.mdx';
+
+[Agno](https://docs.agno.com/introduction) is a framework for building agentic AI applications.
+It supports LanceDB as a knowledge backend, allowing you to easily ingest and retrieve external content for your agents.
+
+When you pair Agno's `Knowledge` system with LanceDB, you get a clean Agentic RAG setup.
+We'll walk through the steps below to build a YouTube transcript-aware Agno assistant that can:
+- Ingest a transcript from a YouTube video via the YouTube API
+- Store embeddings and metadata in LanceDB
+- Retrieve context during responses with hybrid search
+- Ask questions about the video content in a CLI chat loop
+
+## Prerequisites {#prerequisites}
+
+Install dependencies:
+
+<CodeGroup>
+```bash pip icon="terminal"
+pip install -U agno openai lancedb youtube-transcript-api beautifulsoup4
+```
+
+```bash uv icon="terminal"
+uv add agno openai lancedb youtube-transcript-api beautifulsoup4
+```
+</CodeGroup>
+
+## Step 1: Configure LanceDB-backed knowledge {#step-1-configure-lancedb-backed-knowledge}
+
+First, you can initialize the core `Knowledge` object that your agent will use for retrieval.
+It configures LanceDB as the vector store, enables hybrid search with native LanceDB FTS, and sets the embedding model.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksAgnoSetup}
+</CodeBlock>
+
+## Step 2: Fetch and ingest the YouTube transcript {#step-2-fetch-and-ingest-the-youtube-transcript}
+
+Next, extract a YouTube video ID, fetch the full transcript, and flatten it into text for indexing.
+The snippet shown below then inserts that transcript text into the Agno knowledge base, which writes vectors and metadata to LanceDB.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksAgnoIngestYoutube}
+</CodeBlock>
+
+<Info>
+This path explicitly fetches the transcript first, then inserts transcript text into LanceDB through Agno.
+</Info>
+
+## Step 3: Create an Agno agent with knowledge search {#step-3-create-an-agno-agent-with-knowledge-search}
+
+The next step is to construct an Agno `Agent` and attach the knowledge base you just populated.
+With `search_knowledge=True`, the agent performs retrieval before answering, so responses stay grounded in transcript context.
+
+In Agno, retrieval is exposed as a tool call that the model can invoke at runtime.
+When `search_knowledge=True`, Agno makes a knowledge-search tool (shown in output as `search_knowledge_base(...)`) available to the model; the model decides when to call it, Agno executes the tool, and the returned context is fed back into the final answer.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksAgnoAgent}
+</CodeBlock>
+
+## Step 4: Start a CLI chat loop {#step-4-start-a-cli-chat-loop}
+
+You can now ask an initial question and then start an interactive loop for follow-up queries.
+Each prompt runs through the same retrieval pipeline, so you can iteratively inspect what the transcript contains.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksAgnoCliChat}
+</CodeBlock>
+
+<Info>
+Want local-first inference? Replace OpenAI model/embedder classes with Agno's Ollama providers. See Agno's Ollama knowledge examples: [docs.agno.com/examples/models/ollama/chat/knowledge](https://docs.agno.com/examples/models/ollama/chat/knowledge).
+</Info>
+
+### Question 1 {#question-1}
+
+The following question is asked in the CLI chat loop:
+```
+┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃                                                                                  ┃
+┃ Q: What kinds of data can LanceDB handle?                                        ┃
+┃                                                                                  ┃
+┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
+┏━ Tool Calls ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃                                                                                  ┃
+┃ • search_knowledge_base(query=What kinds of data can LanceDB handle?)            ┃
+┃ • search_knowledge_base(query=LanceDB images audio video handle kinds of data    ┃
+┃ can handle 'LanceDB can handle' 'kinds of data' 'images audio video' transcript) ┃
+┃                                                                                  ┃
+┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
+┏━ Response (19.1s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃                                                                                  ┃
+┃                                                                                  ┃
+┃  • Images, audio, video — i.e., multimodal AI data and “all manners of things    ┃
+┃    you don't put into traditional databases” (per the transcript).               ┃
+┃                                                                                  ┃
+┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
+```
+
+We get the response based on the transcript's contents as expected.
+
+### Question 2 {#question-2}
+
+Let's ask a more specific question about the CEO of LanceDB, which is also in the transcript:
+
+```
+You: What is the name of the CEO of LanceDB? 
+INFO Found 10 documents                                                             
+┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃                                                                                  ┃
+┃ What is the name of the CEO of LanceDB?                                          ┃
+┃                                                                                  ┃
+┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
+┏━ Tool Calls ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃                                                                                  ┃
+┃ • search_knowledge_base(query=CEO of LanceDB)                                    ┃
+┃                                                                                  ┃
+┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
+┏━ Response (16.7s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃                                                                                  ┃
+┃                                                                                  ┃
+┃  • According to the retrieved YouTube transcript/title, the CEO of LanceDB is    ┃
+┃    Chang She.                                                                    ┃
+┃                                                                                  ┃
+┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
+```
+
+We get the response based on the transcript's contents and title as expected.
+
+## Why this works well {#why-this-works-well}
+
+To start, LanceDB OSS can run from a local directory, so transcript data can stay on your machine when you are using the OSS stack.
+
+- You do not need to maintain a separate transcript parser in your application code.
+- You do not need to hand-roll chunking and retrieval orchestration across multiple modules.
+- One explicit Agno `Knowledge` object, backed by LanceDB, defines both ingestion and search behavior in one place.
+- Fewer moving parts means the tutorial stays readable and the same pattern is easier to carry into production code.
+
+As your application needs grow, you can migrate to LanceDB [Enterprise](/enterprise) for
+convenience features like automatic compaction and reindexing and the ability to scale to
+really large datasets.
diff --git a/docs/web/integrations/ai/genkit.mdx b/docs/web/integrations/ai/genkit.mdx
new file mode 100644
index 000000000..6314842b8
--- /dev/null
+++ b/docs/web/integrations/ai/genkit.mdx
@@ -0,0 +1,73 @@
+---
+title: "GenKit"
+sidebarTitle: "GenKit"
+
+---
+
+import {
+  TsFrameworksGenkitCustomIndexer,
+  TsFrameworksGenkitCustomRetriever,
+  TsFrameworksGenkitUsage,
+} from '/snippets/integrations.mdx';
+
+### genkitx-lancedb {#genkitx-lancedb}
+Genkit is an open-source framework for building end-to-end AI and RAG pipelines with a clean, TypeScript-first
+developer experience. The genkitx-lancedb plugin lets you use LanceDB as a high-performance vector store
+inside your Genkit flows, so you can index, search, and retrieve data efficiently as part of your AI
+applications.
+
+### Installation {#installation}
+```bash
+pnpm install genkitx-lancedb
+```
+
+### Usage {#usage}
+
+Adding LanceDB plugin to your genkit instance.
+
+<CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+  {TsFrameworksGenkitUsage}
+</CodeBlock>
+
+You can run this app with the following command:
+```bash
+genkit start -- tsx --watch src/index.ts
+```
+
+This'll add LanceDB as a retriever and indexer to the genkit instance. You can see it in the GUI view
+<img width="1710" alt="Screenshot 2025-05-11 at 7 21 05 PM" src="https://github.com/user-attachments/assets/e752f7f4-785b-4797-a11e-72ab06a531b7" />
+
+**Testing retrieval on a sample table**
+Let's see the raw retrieval results
+
+<img width="1710" alt="Screenshot 2025-05-11 at 7 21 05 PM" src="https://github.com/user-attachments/assets/b8d356ed-8421-4790-8fc0-d6af563b9657" />
+On running this query, you'll get 5 results fetched from the lancedb table, where each result looks something like this:
+<img width="1417" alt="Screenshot 2025-05-11 at 7 21 18 PM" src="https://github.com/user-attachments/assets/77429525-36e2-4da6-a694-e58c1cf9eb83" />
+
+
+
+## Creating a custom RAG flow {#creating-a-custom-rag-flow}
+
+Now that we've seen how you can use LanceDB in a Genkit pipeline, let's refine the flow and create a RAG. A RAG flow will consist of an index and a retriever with its outputs postprocessed and fed into an LLM for final response
+
+### Creating custom indexer flows {#creating-custom-indexer-flows}
+You can also create custom indexer flows, utilizing more options and features provided by LanceDB.
+
+<CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+  {TsFrameworksGenkitCustomIndexer}
+</CodeBlock>
+
+<img width="1316" alt="Screenshot 2025-05-11 at 8 35 56 PM" src="https://github.com/user-attachments/assets/e2a20ce4-d1d0-4fa2-9a84-f2cc26e3a29f" />
+
+In your console, you can see the logs
+
+<img width="511" alt="Screenshot 2025-05-11 at 7 19 14 PM" src="https://github.com/user-attachments/assets/243f26c5-ed38-40b6-b661-002f40f0423a" />
+
+### Creating custom retriever flows {#creating-custom-retriever-flows}
+You can also create custom retriever flows, utilizing more options and features provided by LanceDB.
+<CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+  {TsFrameworksGenkitCustomRetriever}
+</CodeBlock>
+Now using our retrieval flow, we can ask a question about the ingested PDF
+<img width="1306" alt="Screenshot 2025-05-11 at 7 18 45 PM" src="https://github.com/user-attachments/assets/86c66b13-7c12-4d5f-9d81-ae36bfb1c346" />
+
diff --git a/docs/web/integrations/ai/hermes-agent.mdx b/docs/web/integrations/ai/hermes-agent.mdx
new file mode 100644
index 000000000..621b53129
--- /dev/null
+++ b/docs/web/integrations/ai/hermes-agent.mdx
@@ -0,0 +1,327 @@
+---
+title: "Hermes Agent"
+sidebarTitle: "Hermes Agent"
+description: "Use LanceDB as a persistent, semantic memory backend for Hermes Agent. Get durable recall across sessions with vector and hybrid search."
+---
+
+[Hermes Agent](https://github.com/NousResearch/hermes-agent) is a self-hosted, open-source
+personal agent from [Nous Research](https://nousresearch.com). You can talk to it from a
+terminal UI or reach the same agent from Telegram, Discord, and Slack, and it exposes a
+dedicated slot for external *memory providers* that run alongside its built-in notes.
+
+The [LanceDB memory plugin](https://github.com/lancedb/hermes-agent-memory) fills that slot.
+It gives Hermes durable, semantic recall across sessions: state a preference or a project
+convention once, and the agent can retrieve it weeks later in a brand-new session — even when
+you ask for it in completely different words. Everything runs inside Hermes' own Python
+process, storing a single LanceDB table on local disk. There's no memory server to operate.
+
+<Info>
+**The mental model is clean**
+
+- Hermes owns the agent loop
+- LanceDB manages the durable long-term memory and offers semantic recall.
+</Info>
+
+## Why LanceDB fits agent memory {#why-lancedb-fits-agent-memory}
+
+Out of the box, Hermes remembers with a small curated notes file frozen into the system
+prompt, plus lexical (keyword) search over past sessions. Both are useful, but keyword search
+misses paraphrases of what you originally typed — the exact thing you need when recalling a
+fact you phrased differently months ago.
+
+LanceDB OSS is an embedded retrieval library, which makes it a natural fit here:
+
+- **No server to stand up** — it reads and writes a table on local disk, so the plugin ships
+  as a dependency rather than a service to operate.
+- **One table holds everything** — content, metadata, and embeddings live together. A memory
+  becomes a structured row with a category, tags, timestamps, and provenance, not just a text
+  blob.
+- **Query it any way you need** — vector similarity for meaning, BM25 full-text for exact
+  names and jargon, a hybrid of the two, or plain metadata filters to keep recall scoped to
+  the right workspace.
+- **It scales up** — the same table abstraction carries over to larger LanceDB deployments
+  later, so the local setup is never a dead end.
+
+## Install and activate {#install-and-activate}
+
+<Tip>
+Want to try this without touching your existing Hermes setup? Run everything in an isolated
+profile: `hermes profile create demo`, then add `-p demo` to the commands below. When you're
+done, `rm -rf ~/.hermes/profiles/demo` removes all trace.
+</Tip>
+
+<Steps>
+<Step title="Install Hermes Agent">
+Skip this if you already have Hermes installed.
+
+```bash
+curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
+```
+</Step>
+
+<Step title="Install the plugin">
+This shallow-clones the plugin into `~/.hermes/plugins/lancedb/`.
+
+```bash
+hermes plugins install lancedb/hermes-agent-memory
+```
+</Step>
+
+<Step title="Install runtime dependencies into Hermes' environment">
+Hermes loads plugins inside its own Python interpreter, so the dependencies go *there* — not
+into a separate virtualenv. (This interpreter is shared across profiles, so you only install
+once.)
+
+```bash
+uv pip install --python ~/.hermes/hermes-agent/venv/bin/python3 lancedb openai pyyaml
+```
+</Step>
+
+<Step title="Set your embeddings API key">
+The plugin turns conversations into embeddings, so it needs an embeddings key. By default that
+is OpenAI, so set `OPENAI_API_KEY` in your environment or in `~/.hermes/.env`.
+
+<Info>
+Prefer a local or non-OpenAI model? The plugin uses an OpenAI-compatible client, so you can
+point it at any compatible endpoint (OpenRouter, Ollama, vLLM, …) in your config — no code
+change needed. See [Configuration](#configuration) below.
+</Info>
+</Step>
+
+<Step title="Activate and verify">
+Switch memory on and pick this plugin:
+
+```bash
+hermes memory setup     # choose "lancedb"
+```
+
+Then confirm it's actually active before you start chatting — this is the one step worth not
+skipping, because Hermes quietly falls back to its built-in notes if the provider isn't set:
+
+```bash
+hermes memory status
+```
+
+```text
+Memory status
+────────────────────────────────────────
+  Built-in:  always active
+  Provider:  lancedb
+
+  Plugin:    installed ✓
+  Status:    available ✓
+```
+
+You want to see `Provider: lancedb` with both `installed ✓` and `available ✓`.
+</Step>
+</Steps>
+
+## The memory tools {#the-memory-tools}
+
+Once activated, the agent has four tools for working with long-term memory:
+
+| Tool | What it does |
+|:--|:--|
+| `lancedb_recall` | Semantic (vector, the default) or hybrid search over your workspace memory. Returns matching facts with scores and provenance. |
+| `lancedb_remember` | Stores a durable fact when you explicitly ask. Deduplicated by content hash, so remembering the same thing twice doesn't pile up rows. |
+| `lancedb_read` | Fetches a single memory by ID, optionally with the original conversation messages it was distilled from. |
+| `lancedb_forget` | Deletes safely: previews candidates first, then deletes by exact ID, so nothing disappears by accident. |
+
+Beyond these tools, the plugin also captures durable facts from your conversations
+automatically — an auxiliary model distills them before context is compressed and again when a
+session ends, so insights survive even when the raw messages are summarized away.
+
+## Walkthrough {#walkthrough}
+
+"_Teach it your project preferences_"
+
+Let's make this concrete with the pain we opened on: re-explaining your setup to the agent every session.
+We'll save a convention once and then prove a brand-new session can recall it. This example will touch all four
+tools along the way.
+
+### Remember {#remember}
+
+Ask Hermes to commit a convention to long-term memory. Saying "remember in long-term memory"
+makes sure it lands in the LanceDB store, which shows up as the `⚡ lancedb_r` (`lancedb_remember`)
+line below:
+
+```text
+● Remember in long-term memory: for this project I only use uv, never pip, and I always add type hints to Python functions.
+
+  ┊ 🧠 memory    +memory: "For this project, the user only uses uv for Python package management, never pip, and always adds type hints to Python functions."
+  ┊ ⚡ lancedb_r   0.0s
+ ─  ⚕ Hermes  ────────────────────────────────────────────────────────────────
+
+     Remembered. I've stored that project convention: use uv only, never pip, and always add type hints to Python functions.
+```
+
+### Recall {#recall}
+
+First, take Hermes' built-in notes out of the picture so recall can *only* come from LanceDB —
+the two layers run side by side otherwise, and either could answer:
+
+```bash
+hermes config set memory.memory_enabled false
+```
+
+Now start a fresh session (`/new`) and ask for the convention back, worded completely
+differently from how you saved it:
+
+```text
+● /new
+● Look through your long-term memory for my Python project preferences.
+
+ ─  ⚕ Hermes  ────────────────────────────────────────────────────────────────
+
+     I found one durable memory about Python project preferences:
+
+     - Project Python tooling preference is uv; pip is not used; type hints are required.
+```
+
+This is the whole point in one exchange. You asked about your *preferences* using none of the
+words you stored the fact with — no *uv*, no *pip*, no *type hints* — yet a fresh session, with
+none of the earlier conversation in context, pulls it straight back. Semantic search matches on
+meaning rather than exact wording, which is precisely where keyword search falls short.
+
+<Tip>
+Turn the built-in layer back on for everyday use with `hermes config set memory.memory_enabled true`
+— the two memory layers happily coexist. We only disabled it here to prove the recall came from
+LanceDB.
+</Tip>
+
+### Read {#read}
+
+You can also ask where a fact came from. Hermes attributes the answer to its stored memory
+rather than guessing from a file in the repo (under the hood, `lancedb_read` can also return
+the exact source messages a fact was distilled from):
+
+```text
+● Where did that uv preference come from?
+
+ ─  ⚕ Hermes  ────────────────────────────────────────────────────────────────
+
+     It came from the project's persistent memory, not from a codebase file I inspected.
+
+     Specifically, the recalled memory says:
+     - "For this project, the user only uses uv for Python package management, never pip, and always adds type hints to Python functions."
+```
+
+### Forget {#forget}
+
+When a preference changes, ask Hermes to drop the old fact. The tool calls tell the whole
+story: the two `⚡ lancedb_f` (`lancedb_forget`) lines are it previewing matches and then
+deleting, and the trailing `⚡ lancedb_r` is it saving the replacement in the same breath:
+
+```text
+● I've switched from uv to pixi. Forget the uv preference.
+
+  ┊ ⚡ lancedb_f uv preference python package management never pip type hints  0.3s
+  ┊ ⚡ lancedb_f   0.0s
+  ┊ ⚡ lancedb_r   0.2s
+ ─  ⚕ Hermes  ────────────────────────────────────────────────────────────────
+
+     Done — I removed the uv preference from memory and replaced it with:
+
+     - This project uses pixi for Python package management
+     - Type hints on Python functions are still preferred
+```
+
+Remember, recall, read, forget: four small operations that between them cover the entire
+lifecycle of a durable memory.
+
+## Retrieval modes {#retrieval-modes}
+
+Recall ships in `vector` mode by default — pure semantic search, which is what survives the
+paraphrasing you saw above. If you also need exact name or jargon matching, switch to `hybrid`
+(vector + BM25) and choose how the two legs are fused: RRF, a vector-biased linear blend, or a
+cross-encoder reranker. Mode is set per call; fusion is a config setting.
+
+```yaml
+# ~/.hermes/config.yaml
+plugins:
+  lancedb:
+    retrieval:
+      mode: hybrid          # vector (default) | hybrid
+      reranker:
+        type: rrf           # how the vector + BM25 legs are fused
+        # Swap RRF for a reranking pass (pulls in sentence-transformers + torch):
+        # type: cross-encoder
+        # model: cross-encoder/ettin-reranker-17m-v1
+        # rerank_top_n: 50
+```
+
+The cross-encoder is the one path that pulls in a local ML stack, so it stays opt-in. It
+defaults to the compact 17M-parameter [ettin reranker](https://huggingface.co/cross-encoder/ettin-reranker-17m-v1).
+
+## Inspect the store {#inspect-the-store}
+
+Everything lives in one table named `memories` at `~/.hermes/lancedb/memories.lance`. Because
+it's a plain LanceDB table, you can open it directly and see exactly what the agent has stored
+— a `kind` column separates extracted `fact` rows from the raw `turn` rows they were drawn
+from:
+
+```python
+import lancedb
+
+db = lancedb.connect("~/.hermes/lancedb")
+tbl = db.open_table("memories")
+print(tbl.to_pandas()[["kind", "category", "content"]].head())
+```
+
+## Configuration {#configuration}
+
+The plugin runs on sensible defaults once activated — you don't have to configure anything.
+`~/.hermes/config.yaml` is purely for overrides. Two common ones:
+
+Use a cheaper model for the auxiliary fact-extraction calls:
+
+```yaml
+# ~/.hermes/config.yaml
+auxiliary:
+  lancedb_extraction:
+    provider: openrouter
+    model: google/gemini-3-flash
+```
+
+Point embeddings at a fully local endpoint (for example, Ollama) so nothing leaves your
+machine:
+
+```yaml
+# ~/.hermes/config.yaml
+plugins:
+  lancedb:
+    embedding:
+      model: nomic-embed-text
+      base_url: http://localhost:11434/v1
+      api_key_env: OLLAMA_API_KEY      # any value works for local Ollama
+```
+
+<Info>
+Changing the embedding model (or its dimension) against an existing store requires recreating
+the table — the plugin fails loudly on a dimension mismatch rather than silently returning
+nothing. Every option is documented in the plugin's [`default_config.yaml`](https://github.com/lancedb/hermes-agent-memory/blob/main/src/default_config.yaml).
+</Info>
+
+## Benchmark {#benchmark}
+
+On [LongMemEval-S](https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned), a
+long-conversation QA benchmark, LanceDB's semantic recall clearly beat Hermes' built-in lexical
+search (0.66 vs. 0.53 answer accuracy) by finding the right messages even when the question was
+worded differently from the original conversation. For the full methodology, the
+per-question-type breakdown, and a reproducible harness, see the
+[blog post](https://www.lancedb.com/blog/semantic-memory-for-hermes-agent-with-lancedb) and the
+[benchmark harness](https://github.com/lancedb/hermes-agent-memory/tree/main/benchmarks).
+
+## Why this works well {#why-this-works-well}
+
+- **It's local-first and embedded.** The LanceDB memory table lives on your disk with no server to run;
+  the plugin installs as a dependency of Hermes' own environment.
+- **Recall survives paraphrasing.** Semantic search matches meaning, not spelling, which is the
+  failure mode that sinks keyword-only session search.
+- **Memories are structured and traceable.** Each fact is a row with metadata and a link back
+  to the messages it came from, and `forget` always previews before it deletes.
+- **Nothing about it is a dead end.** As your needs grow, the same table abstraction carries
+  over to LanceDB [Enterprise](/enterprise) for automatic compaction, reindexing, and scale.
+
+To try it, install the plugin, enable it with `hermes memory setup`, and run the kind of
+workflow we walked through above.
diff --git a/docs/web/integrations/ai/huggingface.mdx b/docs/web/integrations/ai/huggingface.mdx
new file mode 100644
index 000000000..0a38c208b
--- /dev/null
+++ b/docs/web/integrations/ai/huggingface.mdx
@@ -0,0 +1,347 @@
+---
+title: "Hugging Face Hub"
+sidebarTitle: "Hugging Face"
+description: "Use LanceDB directly on Lance datasets hosted on the Hugging Face Hub for multimodal search and retrieval."
+availability:
+  oss: available
+  enterprise: unavailable
+  summary: >-
+    The embedded library opens Lance datasets on the Hugging Face Hub in place
+    through `hf://` URIs. A deployment does not read `hf://` datasets.
+---
+
+[Hugging Face Hub](https://huggingface.co/datasets?format=format:lance&sort=trending) is a popular platform for sharing machine learning datasets, models, and other resources.
+
+LanceDB can directly scan Lance datasets hosted on the [Hugging Face Hub](https://huggingface.co/datasets?format=format:lance) with `hf://` URIs.
+This is enabled under the hood by the [lance-huggingface](https://lance.org/integrations/huggingface/)
+integration that allows users to stream Lance datasets directly from Hugging Face without needing to
+download them first.
+
+For ML and AI engineers working in LanceDB, this capability is incredibly useful for quickly exploring
+multimodal datasets and reusing Lance datasets shared by others, without writing custom data loaders
+or preprocessing pipelines.
+
+The snippets below use the [`lance-format/laion-1m`](https://huggingface.co/datasets/lance-format/laion-1m)
+dataset published in Lance format. The dataset includes a million image-caption pairs, and the
+Lance dataset can package image embeddings alongside the metadata. This makes it useful for
+demonstrating LanceDB's multimodal search capabilities in combination with easy sharing via the
+Hugging Face Hub.
+
+The LAION table includes multimodal columns such as:
+
+- `image` (inline JPEG bytes)
+- `caption` (text)
+- `img_emb` (image embedding vector)
+- metadata fields such as `url` and `similarity`
+
+## Install dependencies {#install-dependencies}
+
+```bash
+pip install lancedb pillow
+```
+
+## Open the dataset with LanceDB {#open-the-dataset-with-lancedb}
+
+LanceDB can open the dataset directly from the Hub, without needing to download it first.
+Note that in LanceDB, you need to specify the table name when opening a Lance dataset,
+and the Hugging Face convention is to use `train` and `test` splits for datasets.
+The LAION dataset is uploaded as a single split named `train`, so we specify the table name
+that contains the `*.lance` files when opening the dataset.
+
+```python
+import lancedb
+
+db = lancedb.connect("hf://datasets/lance-format/laion-1m/data")
+table = db.open_table("train")
+
+print(f"Opened table: {table.name}")
+print(f"Rows: {len(table)}")
+```
+
+## Inspect schema and available indexes {#inspect-schema-and-available-indexes}
+
+```python
+print(table.schema)
+```
+This prints the schema of the LAION table. Note that there's an image embedding column that's
+a fixed-size list of 768-dimensional floats, and a binary column containing the raw JPEG bytes of the image.
+```
+image_path: string
+caption: string
+NSFW: string
+similarity: double
+LICENSE: string
+url: string
+key: string
+status: string
+error_message: null
+width: int64
+height: int64
+original_width: int64
+original_height: int64
+exif: string
+md5: string
+img_emb: fixed_size_list<item: float>[768]
+  child 0, item: float
+image: binary
+```
+
+When inspecting Lance datasets from Hugging Face, it's also a good idea to check whether the dataset author included
+any pre-built indexes that you can use for search. You can check the available indexes with:
+
+```python
+print(table.list_indices())
+```
+```
+[
+    Index(IvfPq, columns=["img_emb"], name="img_emb_idx"),
+    Index(FTS, columns=["caption"], name="caption_idx")
+]
+```
+
+In this case, we see that we have an IVF_PQ vector index on the `img_emb` column, and an FTS index on the `caption`
+column, which means we can directly do vector search on the image embeddings and keyword search on the captions
+without needing to build the indexes ourselves!
+
+<Info>
+If you see an empty list, it may be because the dataset author did not include the index files when uploading
+to Hugging Face. You can download the dataset locally, and build the indexes yourself. See the [indexing guide](/indexing/)
+for instructions on building different types of indexes with LanceDB.
+</Info>
+
+## Projection scan {#projection-scan}
+
+Run a simple scan by projecting relevant columns to get a feel for the dataset. For example, we
+can run a search without any filters or input parameters to get a small subset of the data:
+
+```python
+rows = (
+    table.search()
+    .select(["caption", "url", "similarity"])
+    .limit(3)
+    .to_list()
+)
+
+for i, row in enumerate(rows, start=1):
+    print(f"{i}. {row['caption']}")
+    print(f"   url={row['url']}")
+    print(f"   similarity={row['similarity']}")
+```
+
+We get the first three rows and their metadata printed out, which look like this:
+```
+1. Cordelia and Dudley on their wedding  day last year
+   url=https://i.dailymail.co.uk/i/pix/2012/01/05/article-2082728-0EF8956600000578-53_233x315.jpg
+   similarity=0.2926466464996338
+2. Statistics on challenges for automation in 2021
+   url=https://verloop.io/wp-content/uploads/2021/02/Challenges.jpg
+   similarity=0.30174341797828674
+3. Teacher Gifts / Great gifts for your child's teacher.  Don't know what to get?  Take a look at these gifts that the teacher in your life will love!
+   url=https://i.pinimg.com/custom_covers/216x146/550494823141083777_1487893945.jpg
+   similarity=0.3362061381340027
+```
+
+## Scan and filter data {#scan-and-filter-data}
+
+Filtered search is a common pattern to narrow down interesting subsets of the data during early
+exploration. Here's an example:
+
+```python
+filtered = (
+    table.search()
+    .where("height > 600")
+    .select(["caption", "url", "width", "height"])
+    .limit(3)
+    .to_list()
+)
+
+for row in filtered:
+    print(row["caption"], row["url"], row["width"], row["height"])
+```
+
+This prints out the metadata for large images with height greater than 600 pixels:
+```
+Luca Trousers, mustard stripe https://cdn.shopify.com/s/files/1/0151/5333/products/IMG_0791_1024x1024.jpg?v=1585142190 384 766
+Baby Blue Fitted Short Sleeve T Shirt 3 https://cdn-img.prettylittlething.com/a/d/d/1/add198cab3ec30a61102437275573f4963642528_cmf6022_3.jpg 384 612
+pattern cutting made easy pdf https://i.pinimg.com/736x/7c/6c/a7/7c6ca7361815a8929b3dd6ad34a03ab9.jpg 384 1045
+```
+
+## Export image bytes to local files {#export-image-bytes-to-local-files}
+
+To work with a subset of the data locally, you can export the image bytes from the table and save them as JPEG files.
+```python
+from pathlib import Path
+
+sample = (
+    table.search()
+    .select(["image", "caption"])
+    .limit(3)
+    .to_list()
+)
+
+out_dir = Path("samples")
+out_dir.mkdir(exist_ok=True)
+
+for i, row in enumerate(sample):
+    out_path = out_dir / f"laion_{i}.jpg"
+    with open(out_path, "wb") as f:
+        f.write(row["image"])
+    print(f"Saved {out_path} | caption={row['caption']}")
+```
+
+You can now preview the images you just exported on your local machine to get a better sense of the data.
+
+## Vector search {#vector-search}
+
+You can use LanceDB to run vector search directly on the data on the Hub, **without needing to download the dataset
+or build your own vector index**. This makes it incredibly easy to explore the dataset and iterate on your search queries
+before you decide to download a local copy for further experimentation on your end.
+
+```python
+# Pick an arbitrary image embedding from the dataset
+query_embedding = (
+    table.search()
+    .select(["img_emb"])
+    .limit(1)
+    .to_list()[0]["img_emb"]
+)
+
+results = (
+    table.search(query_embedding, vector_column_name="img_emb")
+    .select(["caption", "url", "_distance"])
+    .limit(3)
+    .to_list()
+)
+
+for row in results:
+    print(row["_distance"], row["caption"])
+```
+
+| distance | caption |
+| --- | --- |
+| 0.17765313386917114 | Cordelia and Dudley on their wedding  day last year |
+| 0.17765313386917114 | Cordelia and Dudley on their wedding  day last year |
+| 0.17765313386917114 | Cordelia and Dudley on their wedding  day last year |
+
+<Warning>
+Note that the LAION dataset is known to contain a lot of duplicate images, so you may see the same image
+showing up multiple times in the search results.
+</Warning>
+
+## Full-text search {#full-text-search}
+
+Run an FTS search query that uses BM25 ranking on the `caption` column (on which we already have an FTS index):
+
+```python
+fts_results = (
+    table.search("dog running on beach", query_type="fts")
+    .select(["caption", "url", "_score"])
+    .limit(3)
+    .to_list()
+)
+```
+
+| caption | url | _score |
+| --- | --- | --- |
+| running with dog | https://www.doggytastic.com/wp… | 15.73168 |
+| Dog Running in Water | https://static.wixstatic.com/m… | 14.756516 |
+| Dogs on the run by heidiannemo… | http://ih2.redbubble.net/image… | 14.756516 |
+
+## Download the full dataset {#download-the-full-dataset}
+
+<Warning>
+You may hit Hugging Face rate limits when streaming large samples from `hf://`, despite using a Hugging Face token.
+For repeated queries or queries that operate on the full dataset, it's recommended to
+download the dataset locally and query from disk.
+</Warning>
+
+Here's how to download the entire dataset via the [Hugging Face CLI](https://huggingface.co/docs/huggingface_hub/en/guides/cli):
+
+```bash
+huggingface-cli download lance-format/laion-1m --repo-type dataset --local-dir ./laion-1m
+```
+
+## Upload your own datasets to Hugging Face in Lance format {#upload-your-own-datasets-to-hugging-face-in-lance-format}
+
+This section shows how you can upload your own Lance datasets to the Hugging Face Hub to share with the community.
+
+First, install the [Hugging Face CLI](https://huggingface.co/docs/huggingface_hub/en/guides/cli) and export both `OPENAI_API_KEY` and `HF_TOKEN`.
+Then, create a Lance dataset using LanceDB on a local machine, and then proceed to upload it to the Hub via a CLI command.
+
+```bash
+export OPENAI_API_KEY=...
+export HF_TOKEN=hf_...
+hf auth login --token "$HF_TOKEN"
+```
+
+A typical sequence of steps is given below.
+
+### 1\. Upload your local directory to the Hub {#1-upload-your-local-directory-to-the-hub}
+
+Upload the full local directory to a specified repository on the Hugging Face Hub. The command below uploads the contents of your local LanceDB directory at `/path/to/your_local_dir` to a new repository named `your_hf_org/repo_name` under your Hugging Face account.
+
+```bash bash icon="code"
+hf upload-large-folder /path/to/your_local_dir your_hf_org/repo_name \
+  --repo-type dataset \
+  --revision main
+```
+
+<Info>
+The `upload-large-folder` command is designed for [uploading large datasets](https://huggingface.co/docs/huggingface_hub/en/guides/upload) (potentially terabytes in size) and will handle multipart uploads, retries, and resuming interrupted uploads.
+</Info>
+
+### 2\. Inspect dataset versions {#2-inspect-dataset-versions}
+
+Because you can query your remote dataset directly from Hugging Face with `hf://` URIs in LanceDB, you can easily inspect the dataset versions and updates on the Hub without needing to download the data locally. This is very useful to keep track of changes to the dataset and iterate on your data collection and curation process.
+
+```python Python icon="python"
+import lancedb
+
+db = lancedb.connect("hf://datasets/your_hf_org/repo_name")
+table = db.open_table("table_name")
+
+versions = table.list_versions()
+print(versions)
+```
+This will print out the list of versions available for the dataset on the Hub, along with their metadata such as creation date and description.
+
+### 3\. Add a dataset card {#3-add-a-dataset-card}
+
+The Hub dataset card allows you to communicate the schema and usage of the dataset to other developers. It sits at the repo's root in a file named `README.md` on the Hub.
+This project keeps the source card text in `HF_DATASET_CARD.md`, so you can publish updates
+to the dataset there and upload it as `README.md` using the following command on the HF CLI:
+this requires a regular `hf upload` because it is a single-file upload to a specific target path (a custom commit message can be added if you wish).
+
+```bash
+hf upload lancedb/magical_kingdom HF_DATASET_CARD.md README.md \
+  --repo-type dataset \
+  --commit-message "Update dataset card"
+```
+
+### 4\. Update the dataset {#4-update-the-dataset}
+
+Over time, you may want to add new rows (append) or columns (backfill) to your dataset as your needs evolve. You can make the necessary updates to your local dataset using LanceDB, and then upload the updated version back to the Hub with the same `hf upload-large-folder` command.
+
+```bash bash icon="code"
+hf upload-large-folder /path/to/your_local_dir your_hf_org/repo_name \
+  --repo-type dataset \
+  --revision main
+```
+The CLI will only upload the new data that has changed since the last upload, avoiding wasted I/O while making it easy to keep your dataset up-to-date on the Hub.
+
+That's it! Your dataset is now updated on the Hub with the new data and schema changes, and other users can query the latest version of the dataset directly from Hugging Face with `hf://` URIs in LanceDB.
+
+## Explore more Lance datasets on Hugging Face {#explore-more-lance-datasets-on-hugging-face}
+
+The LanceDB team is actively uploading useful and interesting datasets in Lance format to the Hugging Face Hub
+under the [lance-format](https://huggingface.co/lance-format) organization. We actively encourage the Hugging Face
+and LanceDB communities to upload their own Lance datasets to the Hub to share with others!
+
+In the meantime, feel free to check out the Hugging Face Hub to discover more Lance datasets uploaded by the community.
+
+<Card
+  icon="/static/assets/logo/huggingface-logo.svg"
+  href="https://huggingface.co/datasets?format=format:lance&sort=trending"
+>
+Click here to explore the latest trending Lance datasets on 🤗 Hugging Face!
+</Card>
diff --git a/docs/web/integrations/ai/kiln.mdx b/docs/web/integrations/ai/kiln.mdx
new file mode 100644
index 000000000..c93f31e17
--- /dev/null
+++ b/docs/web/integrations/ai/kiln.mdx
@@ -0,0 +1,48 @@
+---
+title: "Kiln AI"
+sidebarTitle: "Kiln"
+
+---
+
+[**Kiln**](https://kiln.tech) is a free tool for building production-ready AI systems, combining an intuitive desktop application and an open-source Python library. It supports RAG pipelines, evaluations, agents, MCP tool-calling, synthetic data generation, and fine-tuning. Kiln provides deep integration with LanceDB for vector search, full-text search (BM25), and hybrid search. 
+
+## Quick Start: Build a RAG Pipeline in 5 Minutes with Kiln and LanceDB {#quick-start-build-a-rag-pipeline-in-5-minutes-with-kiln-and-lancedb}
+
+Watch the [quick start overview on Vimeo](https://vimeo.com/1119945690).
+
+Kiln's [app](https://kiln.tech/download) makes it easy to:
+
+ - Build a RAG pipeline with a simple drag-and-drop interface
+ - [Compare](#find-the-best-rag-pipeline-for-your-use-case) search index options (powered by LanceDB), document extractors, embedding models, and chunking strategies
+ - Create end-to-end [evaluations](https://docs.kiln.tech/docs/evaluations) to determine which search configuration works best for your use case
+ - Load your data from Kiln into [LanceDB Enterprise](/enterprise) for production use
+ - Iterate with confidence by evaluating new content, prompts, models, and embeddings in minutes instead of weeks
+
+## Find the Best RAG Pipeline for Your Use Case {#find-the-best-rag-pipeline-for-your-use-case}
+
+There is no universal best RAG solution—only the best solution for your specific use case. Kiln makes it easy to compare state-of-the-art configurations and find which works best for you.
+
+Start with pre-configured templates for state-of-the-art RAG at various performance/quality/cost levels, or experiment with any combination of options:
+
+|Area|Technologies|Description|
+|:----|:----|:----|
+|Search Index|LanceDB|Compare LanceDB's vector search, full-text search (BM25), and hybrid search to find the best approach for your use case.|
+|Content|Kiln Document Library|Collaborate on a document library with your team to find the best content for your RAG. Track every revision and tag document sets.|
+|Document Extraction|Gemini, OpenAI GPT, Qwen VL, and more|Find the most accurate document extraction models for converting PDFs, images, audio, video, and other formats into textual data for RAG.|
+|Embeddings|Embedding models from Gemini, OpenAI, Nomic, Qwen, and more|Find the embedding model best suited to your use case.|
+|Chunking|LlamaIndex|Find the ideal chunk size and method.|
+
+## Get Started {#get-started}
+
+To get started, download the [Kiln App](https://kiln.tech/download), create a project, and navigate to "Docs & Search".
+
+See the [Kiln documentation for creating a RAG system](https://docs.kiln.tech/docs/documents-and-search-rag) for details on each step of the process.
+
+## More Information {#more-information}
+
+ - [Kiln Homepage](https://kiln.tech)
+ - [Download the Kiln App](https://kiln.tech/download)
+ - [Kiln GitHub Repository](https://github.com/Kiln-AI/Kiln)
+ - [Building RAG Systems - Kiln Documentation](https://docs.kiln.tech/docs/documents-and-search-rag)
+ - [Python Library](https://pypi.org/project/kiln-ai/) or `pip install kiln_ai`
+
diff --git a/docs/web/integrations/ai/langchain.mdx b/docs/web/integrations/ai/langchain.mdx
new file mode 100644
index 000000000..14ed628a8
--- /dev/null
+++ b/docs/web/integrations/ai/langchain.mdx
@@ -0,0 +1,229 @@
+---
+title: "LangChain"
+sidebarTitle: "LangChain"
+
+---
+
+import {
+  PyFrameworksLangchainAddImages,
+  PyFrameworksLangchainAddTexts,
+  PyFrameworksLangchainCreateIndex,
+  PyFrameworksLangchainMaxMarginalRelevance,
+  PyFrameworksLangchainQuickStart,
+  PyFrameworksLangchainSimilaritySearch,
+  PyFrameworksLangchainSimilaritySearchByVector,
+  PyFrameworksLangchainSimilaritySearchByVectorWithScores,
+  PyFrameworksLangchainSimilaritySearchWithScores,
+  PyFrameworksLangchainVectorStoreConfig,
+} from '/snippets/integrations.mdx';
+
+**LangChain** is a framework designed for building applications with large language models (LLMs) by chaining together various components. It supports a range of functionalities including memory, agents, and chat models, enabling developers to create context-aware applications.
+
+![Illustration](https://raw.githubusercontent.com/lancedb/assets/refs/heads/main/docs/assets/integration/langchain_rag.png)
+
+LangChain streamlines these stages (in figure above) by providing pre-built components and tools for integration, memory management, and deployment, allowing developers to focus on application logic rather than underlying complexities.
+
+Integration of **Langchain** with **LanceDB** enables applications to retrieve the most relevant data by comparing query vectors against stored vectors, facilitating effective information retrieval. It results in better and context aware replies and actions by the LLMs.
+
+## Quick Start {#quick-start}
+You can load your document data using langchain's loaders, for this example we are using `TextLoader` and `OpenAIEmbeddings` as the embedding model.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksLangchainQuickStart}
+</CodeBlock>
+
+## Documentation {#documentation}
+In the above example `LanceDB` vector store class object is created using `from_documents()` method  which is a `classmethod` and returns the initialized class object. 
+
+You can also use `LanceDB.from_texts(texts: List[str],embedding: Embeddings)` class method.  
+
+The exhaustive list of parameters for `LanceDB` vector store are : 
+
+|Name|type|Purpose|default|
+|:----|:----|:----|:----|
+|`connection`| (Optional) `Any` |`lancedb.db.LanceDBConnection` connection object to use.  If not provided, a new connection will be created.|`None`|
+|`embedding`| (Optional) `Embeddings` | Langchain embedding model.|Provided by user.|
+|`uri`| (Optional) `str` |It specifies the directory location of **LanceDB database** and establishes a connection that can be used to interact with the database. |`/tmp/lancedb`|
+|`vector_key` |(Optional) `str`| Column name to use for vector's in the table.|`'vector'`|
+|`id_key` |(Optional) `str`| Column name to use for id's in the table.|`'id'`|
+|`text_key` |(Optional) `str` |Column name to use for text in the table.|`'text'`|
+|`table_name` |(Optional) `str`| Name of your table in the database.|`'vectorstore'`|
+|`api_key` |(Optional `str`) |API key to use for LanceDB Enterprise deployment.|`None`|
+|`region` |(Optional) `str`| Region to use for LanceDB Enterprise deployment.| Only for LanceDB Enterprise : `None`.|
+|`mode` |(Optional) `str` | Mode to use for adding data to the table. Valid values are "append" and "overwrite".|`'overwrite'`|
+|`table`| (Optional) `Any`| You can connect to an existing table of LanceDB, created outside of langchain, and utilize it.|`None`|
+|`distance`|(Optional) `str`| The choice of distance metric used to calculate the similarity between vectors.|`'l2'`|
+|`reranker` |(Optional) `Any`| The reranker to use for LanceDB.|`None`|
+|`relevance_score_fn` |(Optional) `Callable[[float], float]` | Langchain relevance score function to be used.|`None`|
+|`limit`|`int`|Set the maximum number of results to return.| `DEFAULT_K` (it is 4)|
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksLangchainVectorStoreConfig}
+</CodeBlock>
+
+### Methods {#methods}
+
+##### `add_texts()`
+
+This method turn texts into embedding and add it to the database.
+
+|Name|Purpose|defaults|
+|:---|:---|:---|
+|`texts`|`Iterable` of strings to add to the vectorstore.|Provided by user|
+|`metadatas`|Optional `list[dict()]` of metadatas associated with the texts.|`None`|
+|`ids`|Optional `list` of ids to associate with the texts.|`None`|
+|`kwargs`| Other keyworded arguments provided by the user. |-|
+
+It returns list of ids of the added texts.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksLangchainAddTexts}
+</CodeBlock>
+
+With LanceDB Enterprise, `tbl.to_pandas()` raises in the synchronous Python client;
+read the rows with a query instead, such as `tbl.search().limit(100).to_pandas()`.
+
+------
+
+
+##### create_index() 
+
+This method creates a scalar(for non-vector cols) or a vector index on a table.
+
+|Name|type|Purpose|defaults|
+|:---|:---|:---|:---|
+|`vector_col`|`Optional[str]`| Provide if you want to create index on a vector column. |`None`|
+|`col_name`|`Optional[str]`| Provide if you want to create index on a non-vector column. |`None`|
+|`metric`|`Optional[str]` |Provide the metric to use for vector index. choice of metrics: 'l2', 'dot', 'cosine'. |`l2`|
+|`num_partitions`|`Optional[int]`|Number of partitions to use for the index.|`256`|
+|`num_sub_vectors`|`Optional[int]` |Number of sub-vectors to use for the index.|`96`|
+|`index_cache_size`|`Optional[int]` |Size of the index cache.|`None`|
+|`name`|`Optional[str]` |Name of the table to create index on.|`None`|
+
+For index creation make sure your table has enough data in it. An ANN index is usually not needed for datasets ~100K vectors. For large-scale (>1M) or higher dimension vectors, it is beneficial to create an ANN index.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksLangchainCreateIndex}
+</CodeBlock>
+
+------
+
+##### similarity_search()
+
+This method performs similarity search based on **text query**.
+
+| Name    | Type                 | Purpose | Default |
+|---------|----------------------|---------|---------|
+| `query` | `str`                |  A `str` representing the text query that you want to search for in the vector store.         | N/A     |
+| `k`         | `Optional[int]`           | It specifies the number of documents to return.        | `None`    |
+| `filter`    | `Optional[Dict[str, str]]`| It is used to filter the search results by specific metadata criteria.        | `None`    |
+| `fts`   | `Optional[bool]`     |   It indicates whether to perform a full-text search (FTS).      | `False`   |
+| `name`      | `Optional[str]`           | It is used for specifying the name of the table to query. If not provided, it uses the default table set during the initialization of the LanceDB instance.        | `None`    |
+| `kwargs`    | `Any`                     | Other keyworded arguments provided by the user.        | N/A     |
+
+Return documents most similar to the query **without relevance scores**.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksLangchainSimilaritySearch}
+</CodeBlock>
+
+------
+
+##### similarity_search_by_vector()
+
+The method returns documents that are most similar to the specified **embedding (query) vector**. 
+
+| Name        | Type                      | Purpose | Default |
+|-------------|---------------------------|---------|---------|
+| `embedding` | `List[float]`             | The embedding vector you want to use to search for similar documents in the vector store.         | N/A     |
+| `k`         | `Optional[int]`           | It specifies the number of documents to return.        | `None`    |
+| `filter`    | `Optional[Dict[str, str]]`| It is used to filter the search results by specific metadata criteria.        | `None`    |
+| `name`      | `Optional[str]`           | It is used for specifying the name of the table to query. If not provided, it uses the default table set during the initialization of the LanceDB instance.        | `None`    |
+| `kwargs`    | `Any`                     | Other keyworded arguments provided by the user.        | N/A     |
+
+**It does not provide relevance scores.**
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksLangchainSimilaritySearchByVector}
+</CodeBlock>
+
+------
+
+##### similarity_search_with_score()
+
+Returns documents most similar to the **query string** along with their relevance scores.
+
+| Name     | Type                      | Purpose | Default |
+|----------|---------------------------|---------|---------|
+| `query`  | `str`                     |A `str` representing the text query you want to search for in the vector store. This query will be converted into an embedding using the specified embedding function.         | N/A     |
+| `k`      | `Optional[int]`           | It specifies the number of documents to return.  | `None`    |
+| `filter` | `Optional[Dict[str, str]]`|  It is used to filter the search results by specific metadata criteria. This allows you to narrow down the search results based on certain metadata attributes associated with the documents.       | `None`    |
+| `kwargs` | `Any`                     |  Other keyworded arguments provided by the user.       | N/A     |
+
+It gets called by base class's `similarity_search_with_relevance_scores` which selects relevance score based on our `_select_relevance_score_fn`.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksLangchainSimilaritySearchWithScores}
+</CodeBlock>
+
+------
+
+##### similarity_search_by_vector_with_relevance_scores()
+
+Similarity search using **query vector**.
+
+| Name        | Type                      | Purpose | Default |
+|-------------|---------------------------|---------|---------|
+| `embedding` | `List[float]`             | The embedding vector you want to use to search for similar documents in the vector store.        | N/A     |
+| `k`         | `Optional[int]`           |   It specifies the number of documents to return.       | `None`    |
+| `filter`    | `Optional[Dict[str, str]]`|  It is used to filter the search results by specific metadata criteria.       | `None`    |
+| `name`      | `Optional[str]`           |   It is used for specifying the name of the table to query.       | `None`    |
+| `kwargs`    | `Any`                     |   Other keyworded arguments provided by the user.      | N/A     |
+
+The method returns documents most similar to the specified embedding (query) vector, along with their relevance scores.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksLangchainSimilaritySearchByVectorWithScores}
+</CodeBlock>
+
+------
+
+##### max_marginal_relevance_search()
+
+This method returns docs selected using the maximal marginal relevance(MMR).
+Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
+
+| Name | Type | Purpose | Default |
+|---------------|-----------------|-----------|---------|
+| `query`       | `str` | Text to look up documents similar to. | N/A     |
+| `k`   | `Optional[int]` | Number of Documents to return.| `4`       |
+| `fetch_k`| `Optional[int]`| Number of Documents to fetch to pass to MMR algorithm.| `None`    |
+| `lambda_mult` | `float`                   | Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. | `0.5`     |
+| `filter`| `Optional[Dict[str, str]]`| Filter by metadata. | `None`    |
+|`kwargs`| Other keyworded arguments provided by the user. |-|
+
+Similarly, `max_marginal_relevance_search_by_vector()` function returns docs most similar to the embedding passed to the function using MMR. instead of a string query you need to pass the embedding to be searched for. 
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksLangchainMaxMarginalRelevance}
+</CodeBlock>
+
+------
+
+##### add_images()
+
+This method adds images by automatically creating their embeddings and adds them to the vectorstore.
+
+| Name       | Type                          | Purpose                        | Default |
+|------------|-------------------------------|--------------------------------|---------|
+| `uris`     | `List[str]`                   | File path to the image         | N/A     |
+| `metadatas`| `Optional[List[dict]]`        | Optional list of metadatas     | `None`    |
+| `ids`      | `Optional[List[str]]`         | Optional list of IDs           | `None`    |
+
+It returns list of IDs of the added images.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksLangchainAddImages}
+</CodeBlock>
+
+
diff --git a/docs/web/integrations/ai/llamaIndex.mdx b/docs/web/integrations/ai/llamaIndex.mdx
new file mode 100644
index 000000000..a38e2c61e
--- /dev/null
+++ b/docs/web/integrations/ai/llamaIndex.mdx
@@ -0,0 +1,55 @@
+---
+title: "LlamaIndex"
+sidebarTitle: "LlamaIndex"
+
+---
+
+import {
+  PyFrameworksLlamaindexAddReranker,
+  PyFrameworksLlamaindexFiltering,
+  PyFrameworksLlamaindexHybridSearch,
+  PyFrameworksLlamaindexQuickStart,
+} from '/snippets/integrations.mdx';
+
+## Quickstart {#quickstart}
+
+LlamaIndex is a well-known framework for building LLM-powered agents over your data with LLMs and workflows.
+You can build your LlamaIndex pipeline and persist your metadata and embeddings in LanceDB via the `LanceDBVectorStore` class.
+
+First, install the LlamaIndex-LanceDB integration.
+
+<CodeBlock filename="bash" language="bash" icon="terminal">
+pip install llama-index-vector-stores-LanceDB
+</CodeBlock>
+
+Run the below script as an example.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksLlamaindexQuickStart}
+</CodeBlock>
+
+The vector store connector will open an existing LanceDB directory or create the directory if it does not exist.
+
+### Filtering {#filtering}
+For metadata filtering, you can use a Lance SQL-like string filter as demonstrated in the example above. Additionally, you can also filter using the `MetadataFilters` class from LlamaIndex:
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksLlamaindexFiltering}
+</CodeBlock>
+
+### Hybrid Search {#hybrid-search}
+For complete documentation, refer [here](https://docs.lancedb.com/search/hybrid-search). This example uses the `colbert` reranker. Make sure to install necessary dependencies for the reranker you choose.
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksLlamaindexHybridSearch}
+</CodeBlock>
+
+In the snippet above, you can change/specify `query_type` when creating the engine/retriever
+to use different search strategies, such as vector search or FTS.
+
+## API reference {#api-reference}
+
+<Card
+  title="LlamaIndex Vector Stores API reference"
+  href="https://developers.llamaindex.ai/python/framework-api-reference/storage/vector_store/lancedb/"
+>
+See the official LlamaIndex Vector Stores API reference for more details.
+</Card>
\ No newline at end of file
diff --git a/docs/web/integrations/ai/prompttools.mdx b/docs/web/integrations/ai/prompttools.mdx
new file mode 100644
index 000000000..b0edd575a
--- /dev/null
+++ b/docs/web/integrations/ai/prompttools.mdx
@@ -0,0 +1,15 @@
+---
+title: "PromptTools"
+sidebarTitle: "PromptTools"
+
+---
+
+[PromptTools](https://github.com/hegelai/prompttools) offers a set of free, open-source tools for testing and experimenting with models, prompts, and configurations. The core idea is to enable developers to evaluate prompts using familiar interfaces like code and notebooks. You can use it to experiment with different configurations of LanceDB, and test how LanceDB integrates with the LLM of your choice.
+{/*
+[Evaluating Prompts with PromptTools](./examples/prompttools-eval-prompts/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/prompttools-eval-prompts/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+*/}
+
+<a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/prompttools-eval-prompts/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" /></a>
+
+![Alt text](https://prompttools.readthedocs.io/en/latest/_images/demo.gif "a title")
+
diff --git a/docs/web/integrations/ai/synthetic-data-kit.mdx b/docs/web/integrations/ai/synthetic-data-kit.mdx
new file mode 100644
index 000000000..18b90674a
--- /dev/null
+++ b/docs/web/integrations/ai/synthetic-data-kit.mdx
@@ -0,0 +1,81 @@
+---
+title: "Meta Llama Synthetic Data Kit"
+sidebarTitle: "Synthetic Data Kit"
+description: "Use Meta Llama's Synthetic Data Kit with LanceDB to generate high-quality synthetic datasets for LLM fine-tuning and training."
+
+---
+
+
+
+[Synthetic Data Kit](https://github.com/meta-llama/synthetic-data-kit) is a tool from Meta LLAMA that helps you generate high-quality synthetic datasets for fine-tuning large language models (LLMs). It simplifies the process of preparing data for fine-tuning by providing a command-line interface (CLI) with a modular four-command flow.
+
+One of the key features of the `synthetic-data-kit` is its use of the Lance format for storing and ingesting datasets. This allows for efficient storage and retrieval of data, which is crucial when working with large datasets.
+
+### Key Features: {#key-features}
+
+*   **Data Ingestion:** The toolkit can ingest various file formats, including PDF, HTML, YouTube transcripts, DOCX, PPT, and TXT.
+*   **Fine-tuning Format Creation:** It can create different fine-tuning formats, such as question-answer (QA) pairs, QA pairs with Chain-of-Thought (CoT), and summarization formats.
+*   **Data Curation:** The tool uses Llama as a judge to curate high-quality examples, ensuring the quality of the generated dataset.
+*   **Flexible Saving Options:** You can save the generated datasets in various formats compatible with your fine-tuning workflow, including Hugging Face, JSONL, and JSON.
+
+### How it Works: {#how-it-works}
+
+The synthetic-data-kit follows a simple four-step process:
+
+1.  **Ingest:** Import your input files into the toolkit. The data is stored in the Lance format for efficient processing.
+2.  **Create:** Generate diverse fine-tuning datasets, such as reasoning, summarization, and QA pairs, from the ingested documents.
+3.  **Curate:** Use Llama to filter and select high-quality examples from the generated dataset.
+4.  **Save-as:** Export the curated dataset in your preferred format.
+
+### Usage {#usage}
+
+The `synthetic-data-kit` uses Lance format to store and manage the data that you ingest. The workflow is a series of commands that build on each other, starting with the `ingest` command.
+
+Here is an example of the end-to-end workflow:
+
+1.  **Ingest Data into a LanceDB dataset**
+
+    This command takes a directory of source files and creates a LanceDB dataset from them.
+
+    ```bash
+    synthetic-data-kit ingest docs/report.pdf --multimodal
+    # This will create a Lance dataset at data/parsed/report.lance
+    # with 'text' and 'image' columns.
+    
+    #Generate multimodal-qa pairs from the ingested data
+    synthetic-data-kit create data/parsed/report.lance --type multimodal-qa
+    ```
+
+2.  **Create fine-tuning data**
+
+    This command uses the LanceDB dataset created in the previous step to generate synthetic data in the desired format.
+
+    ```bash
+    synthetic_data create data/parsed/report.lance
+    ```
+
+3.  **Curate the data**
+
+    This step uses a language model to curate the generated data and ensure its quality.
+
+    ```bash
+    synthetic_data curate report.json
+    ```
+
+4.  **Save the final dataset**
+
+    Finally, save the curated data to a file in the desired format.
+
+    ```bash
+    synthetic_data save-as  report.json --save_path ./my_finetuning_data.jsonl
+    ```
+
+This workflow allows you to go from a collection of documents to a high-quality, fine-tuning dataset with just a few commands. The use of LanceDB in the background makes the process efficient and scalable.
+
+### Getting Started: {#getting-started}
+
+To get started with the synthetic-data-kit, you can clone the [GitHub Repository](https://github.com/meta-llama/synthetic-data-kit) and install the necessary dependencies. 
+
+> **Note:** You will also need access to a Llama model, either running locally or via a hosted API.
+
+
diff --git a/docs/web/integrations/data/dlt.mdx b/docs/web/integrations/data/dlt.mdx
new file mode 100644
index 000000000..f15a4b776
--- /dev/null
+++ b/docs/web/integrations/data/dlt.mdx
@@ -0,0 +1,97 @@
+---
+title: "dlt"
+sidebarTitle: "DLT"
+
+---
+
+import {
+  PyPlatformsDltAdapterImport,
+  PyPlatformsDltAdapterUsage,
+  PyPlatformsDltPipeline,
+} from '/snippets/integrations.mdx';
+
+[dlt](https://dlthub.com/docs/intro) is an open-source library that you can add to your Python scripts to load data from various and often messy data sources into well-structured, live datasets. dlt's [integration with LanceDB](https://dlthub.com/docs/dlt-ecosystem/destinations/lancedb) lets you ingest data from any source (databases, APIs, CSVs, dataframes, JSONs, and more) into LanceDB with a few lines of simple python code. The integration enables automatic normalization of nested data, schema inference, incremental loading and embedding the data. dlt also has integrations with several other tools like dbt, airflow, dagster etc. that can be inserted into your LanceDB workflow.
+
+## How to ingest data into LanceDB {#how-to-ingest-data-into-lancedb}
+
+In this example, we will be fetching movie information from the [Open Movie Database (OMDb) API](https://www.omdbapi.com/) and loading it into a local LanceDB instance. To implement it, you will need an API key for the OMDb API (which can be created freely [here](https://www.omdbapi.com/apikey.aspx)).
+  
+1. **Install `dlt` with LanceDB extras:**  
+    ```sh 
+    pip install dlt[lancedb]
+    ```
+
+2. **Inside an empty directory, initialize a `dlt` project with:**  
+    ```sh
+    dlt init rest_api lancedb
+    ```
+    This will add all the files necessary to create a `dlt` pipeline that can ingest data from any REST API (ex: OMDb API) and load into LanceDB.
+    ```text
+    ├── .dlt
+    │   ├── config.toml
+    │   └── secrets.toml
+    ├── rest_api
+    ├── rest_api_pipeline.py
+    └── requirements.txt
+    ```
+  
+    dlt has a list of pre-built [sources](https://dlthub.com/docs/dlt-ecosystem/verified-sources/) like [SQL databases](https://dlthub.com/docs/dlt-ecosystem/verified-sources/sql_database), [REST APIs](https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api), [Google Sheets](https://dlthub.com/docs/dlt-ecosystem/verified-sources/google_sheets), [Notion](https://dlthub.com/docs/dlt-ecosystem/verified-sources/notion) etc., that can be used out-of-the-box by running `dlt init <source_name> lancedb`. Since dlt is a python library, it is also very easy to modify these pre-built sources or to write your own custom source from scratch.
+
+
+3. **Specify necessary credentials and/or embedding model details:**  
+    
+    In order to fetch data from the OMDb API, you will need to pass a valid API key into your pipeline. Depending on whether you're using LanceDB OSS or LanceDB Enterprise, you also may need to provide the necessary credentials to connect to the LanceDB instance. These can be pasted inside `.dlt/secrets.toml`. 
+
+    dlt's LanceDB integration also allows you to automatically embed the data during ingestion. Depending on the embedding model chosen, you may need to paste the necessary credentials inside `.dlt/secrets.toml`:
+    ```toml
+    [sources.rest_api]
+    api_key = "api_key" # Enter the API key for the OMDb API
+
+    [destination.lancedb]
+    embedding_model_provider = "sentence-transformers"
+    embedding_model = "all-MiniLM-L6-v2"
+    [destination.lancedb.credentials]
+    uri = ".lancedb"
+    api_key = "api_key" # API key to connect to LanceDB Enterprise. Leave out if you are using LanceDB OSS.
+    embedding_model_provider_api_key = "embedding_model_provider_api_key" # Not needed for providers that don't need authentication (ollama, sentence-transformers).
+    ```
+    See [here](https://dlthub.com/docs/dlt-ecosystem/destinations/lancedb#configure-the-destination) for more information and for a list of available models and model providers.  
+
+
+4. **Write the pipeline code inside `rest_api_pipeline.py`:**  
+
+    The following code shows how you can configure dlt's REST API source to connect to the [OMDb API](https://www.omdbapi.com/), fetch all movies with the word "godzilla" in the title, and load it into a LanceDB table. The REST API source allows you to pull data from any API with minimal code, to learn more read the [dlt docs](https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api).
+
+    <CodeBlock filename="Python" language="Python" icon="python">
+      {PyPlatformsDltPipeline}
+    </CodeBlock>
+
+    The script above will ingest the data into LanceDB as it is, i.e. without creating any embeddings. If we want to embed one of the fields (for example, `"Title"` that contains the movie titles), then we will use dlt's `lancedb_adapter` and modify the script as follows:  
+    
+    - Add the following import statement:
+        <CodeBlock filename="Python" language="Python" icon="python">
+          {PyPlatformsDltAdapterImport}
+        </CodeBlock>
+    - Modify the pipeline run like this:
+        <CodeBlock filename="Python" language="Python" icon="python">
+          {PyPlatformsDltAdapterUsage}
+        </CodeBlock>
+    This will use the embedding model specified inside `.dlt/secrets.toml` to embed the field `"Title"`.
+
+5. **Install necessary dependencies:**  
+    ```sh
+    pip install -r requirements.txt
+    ```  
+
+    Note: You may need to install the dependencies for your embedding models separately.
+    ```sh
+    pip install sentence-transformers
+    ```
+
+6. **Run the pipeline:**
+    Finally, running the following command will ingest the data into your LanceDB instance.
+    ```sh
+    python custom_source.py
+    ```
+
+For more information and advanced usage of dlt's LanceDB integration, read [the dlt documentation](https://dlthub.com/docs/dlt-ecosystem/destinations/lancedb).
diff --git a/docs/web/integrations/data/duckdb.mdx b/docs/web/integrations/data/duckdb.mdx
new file mode 100644
index 000000000..23c4891f9
--- /dev/null
+++ b/docs/web/integrations/data/duckdb.mdx
@@ -0,0 +1,119 @@
+---
+title: "DuckDB"
+sidebarTitle: "DuckDB"
+description: "Learn how to use the DuckDB-Lance extension to query Lance tables with SQL."
+---
+
+LanceDB integrates with [DuckDB](https://duckdb.org/) through the [Lance extension](https://github.com/lance-format/lance-duckdb) for DuckDB. In this page, we'll show how LanceDB manages table lifecycle, and DuckDB provides SQL analytics (including joins) and search over those tables.
+
+Note that earlier versions of LanceDB used to recommend converting Lance tables to Arrow tables via `table.to_arrow()`. Although this method is still available for local tables (because DuckDB [natively scans Arrow tables](https://duckdb.org/2021/12/03/duck-arrow)), it is no longer the recommended workflow for working with Lance tables in DuckDB. This page shows how to use the Lance extension with namespace-attached LanceDB tables, allowing you to pushdown SQL queries directly to the Lance layer.
+
+
+## Install {#install}
+
+Install the DuckDB CLI as per [their docs](https://duckdb.org/install) and alternatively, their Python package with `pip install duckdb`.
+
+Then, open the DuckDB CLI and install and load the Lance extension as follows:
+
+```sql SQL icon="database"
+INSTALL lance;
+LOAD lance;
+```
+
+## Attach the directory namespace in DuckDB {#attach-the-directory-namespace-in-duckdb}
+
+Attach the LanceDB root directory as a Lance namespace:
+
+```sql SQL icon="database"
+ATTACH './local_lancedb' AS lance_ns (TYPE LANCE);
+```
+
+In this page, tables are referenced using `lance_ns.main.<table_name>`, so the table path is `lance_ns.main.lance_duck`.
+
+## Write Lance table {#write-lance-table}
+
+Create the `lance_duck` table using SQL and populate it with sample data:
+
+```sql SQL icon="database"
+CREATE OR REPLACE TABLE lance_ns.main.lance_duck AS
+SELECT *
+FROM (
+  VALUES
+    ('duck', 'quack', [0.9, 0.7, 0.1]::FLOAT[]),
+    ('horse', 'neigh', [0.3, 0.1, 0.5]::FLOAT[]),
+    ('dragon', 'roar', [0.5, 0.2, 0.7]::FLOAT[])
+) AS t(animal, noise, vector);
+```
+
+This table is the source of truth for all DuckDB queries below.
+
+<Info>
+The examples below show SQL entered in the DuckDB CLI. You can run the same SQL from
+Python as well, using LanceDB and DuckDB's Python clients in your application code.
+</Info>
+
+## Query the table with SQL {#query-the-table-with-sql}
+
+```sql SQL icon="database"
+SELECT *
+  FROM lance_ns.main.lance_duck
+  LIMIT 5;
+```
+
+## Vector search {#vector-search}
+
+```sql SQL icon="database"
+SELECT animal, noise, vector, _distance
+  FROM lance_vector_search(
+    'lance_ns.main.lance_duck',
+    'vector',
+    [0.8, 0.7, 0.2]::FLOAT[],
+    k = 1,
+    prefilter = true
+  )
+  ORDER BY _distance ASC;
+```
+
+## Full-text search {#full-text-search}
+
+```sql SQL icon="database"
+SELECT animal, noise, vector, _score
+  FROM lance_fts(
+    'lance_ns.main.lance_duck',
+    'animal',
+    'the brave knight faced the dragon',
+    k = 1,
+    prefilter = true
+  )
+  ORDER BY _score DESC;
+```
+
+## Hybrid search {#hybrid-search}
+
+```sql SQL icon="database"
+SELECT animal, noise, vector, _hybrid_score, _distance, _score
+  FROM lance_hybrid_search(
+    'lance_ns.main.lance_duck',
+    'vector',
+    [0.8, 0.7, 0.2]::FLOAT[],
+    'animal',
+    'the duck surprised the dragon',
+    k = 2,
+    prefilter = false,
+    alpha = 0.5,
+    oversample_factor = 4
+  )
+  ORDER BY _hybrid_score DESC;
+```
+
+## Directory namespace model {#directory-namespace-model}
+
+A directory namespace maps a LanceDB catalog root to namespace-qualified table identifiers in DuckDB. This keeps table discovery and table naming stable as your project grows.
+
+To learn more about the catalog and namespace model, see [Namespaces and the Catalog Model](/management/namespace).
+
+
+## Advanced usage {#advanced-usage}
+
+See the [docs](https://github.com/lance-format/lance-duckdb) directory in the Lance-DuckDB extension repo
+for more advanced usage on SQL and REST API clients.
diff --git a/docs/web/integrations/data/pandas_and_pyarrow.mdx b/docs/web/integrations/data/pandas_and_pyarrow.mdx
new file mode 100644
index 000000000..c646e7f3b
--- /dev/null
+++ b/docs/web/integrations/data/pandas_and_pyarrow.mdx
@@ -0,0 +1,52 @@
+---
+title: "Pandas and PyArrow"
+sidebarTitle: "Pandas & PyArrow"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    DataFrames go into `create_table` and come back from query results the same
+    way for local and deployment tables.
+
+---
+
+import {
+  PyPlatformsPandasAsyncExample,
+  PyPlatformsPandasCreateTable,
+  PyPlatformsPandasImports,
+  PyPlatformsPandasVectorSearch,
+} from '/snippets/integrations.mdx';
+
+Because Lance is built on top of [Apache Arrow](https://arrow.apache.org/),
+LanceDB fits naturally into Pandas-first workflows. You can ingest a `DataFrame`,
+query it with LanceDB's vector operators, and keep working in Pandas without any glue code.
+
+## Create a dataset {#create-a-dataset}
+
+Start by importing LanceDB alongside your usual Pandas utilities and connect to a temporary database.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyPlatformsPandasImports}
+</CodeBlock>
+
+Use the familiar `pd.DataFrame` API to prepare your rows, then pass the entire frame to `db.create_table`.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyPlatformsPandasCreateTable}
+</CodeBlock>
+
+## Vector search {#vector-search}
+
+Queries can return Pandas frames as well, so you can immediately inspect the results or pipe them into downstream analytics.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyPlatformsPandasVectorSearch}
+</CodeBlock>
+
+## Async API {#async-api}
+
+For web services or background jobs that already rely on `asyncio`, use the asynchronous helpers to keep everything non-blocking.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyPlatformsPandasAsyncExample}
+</CodeBlock>
diff --git a/docs/web/integrations/data/polars_arrow.mdx b/docs/web/integrations/data/polars_arrow.mdx
new file mode 100644
index 000000000..3acfcfea5
--- /dev/null
+++ b/docs/web/integrations/data/polars_arrow.mdx
@@ -0,0 +1,58 @@
+---
+title: "Polars"
+sidebarTitle: "Polars & Arrow"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Polars data goes into `create_table`, and query results come back as Polars
+    DataFrames, for local and deployment tables alike; a LazyFrame over a whole
+    table is local only.
+
+---
+
+import {
+  PyPlatformsPolarsCreateTable,
+  PyPlatformsPolarsImports,
+  PyPlatformsPolarsLazyframe,
+  PyPlatformsPolarsPydantic,
+  PyPlatformsPolarsVectorSearch,
+} from '/snippets/integrations.mdx';
+
+LanceDB supports [Polars](https://github.com/pola-rs/polars), a blazingly fast DataFrame library for Python written in Rust. Under the hood, both Lance and Polars speak Arrow, so passing data back and forth stays zero-copy and ergonomic.
+
+## Create and Query a Table {#create-and-query-a-table}
+
+Import the required libraries, including the optional Pydantic helpers if you plan to define schemas.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyPlatformsPolarsImports}
+</CodeBlock>
+
+Build a Polars `DataFrame`, convert it to Arrow, and use it directly when creating a LanceDB table.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyPlatformsPolarsCreateTable}
+</CodeBlock>
+
+Run vector search and keep the results as a Polars `DataFrame` for further processing or visualization.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyPlatformsPolarsVectorSearch}
+</CodeBlock>
+
+## Work with LazyFrames {#work-with-lazyframes}
+
+When you want to operate on the entire table (potentially larger than RAM), convert to a Polars `LazyFrame` so you can chain transformations without loading everything at once. This works for local tables only; with LanceDB Enterprise, `search(...).to_polars()` returns an eager DataFrame of query results.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyPlatformsPolarsLazyframe}
+</CodeBlock>
+
+## Define Schemas with Pydantic {#define-schemas-with-pydantic}
+
+You can also describe your table via `LanceModel` and continue ingesting data from Polars. This is useful when multiple teams share a schema or when you want validation.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyPlatformsPolarsPydantic}
+</CodeBlock>
diff --git a/docs/web/integrations/data/pydantic.mdx b/docs/web/integrations/data/pydantic.mdx
new file mode 100644
index 000000000..3ac660774
--- /dev/null
+++ b/docs/web/integrations/data/pydantic.mdx
@@ -0,0 +1,97 @@
+---
+title: "Pydantic"
+sidebarTitle: "Pydantic"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    The SDK converts `LanceModel` schemas to Arrow, and query rows back to
+    models, in your process, so they work the same with local and deployment
+    tables.
+
+---
+
+import {
+  PyFrameworksPydanticBaseExample,
+  PyFrameworksPydanticBaseModel,
+  PyFrameworksPydanticImports,
+  PyFrameworksPydanticSetUrl,
+  PyFrameworksPydanticTypeConversion,
+  PyFrameworksPydanticVectorField,
+} from '/snippets/integrations.mdx';
+
+[Pydantic](https://docs.pydantic.dev/latest/) is a data validation library in Python.
+LanceDB integrates with Pydantic for schema inference, data ingestion, and query result casting.
+Using `lancedb.pydantic.LanceModel`, users can seamlessly
+integrate Pydantic with the rest of the LanceDB APIs.
+
+First, import the necessary LanceDB and Pydantic modules:
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksPydanticImports}
+</CodeBlock>
+
+Next, define your Pydantic model by inheriting from `LanceModel` and specifying your fields including a vector field:
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksPydanticBaseModel}
+</CodeBlock>
+
+Set the database connection URL:
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksPydanticSetUrl}
+</CodeBlock>
+
+Now you can create a table, add data, and perform vector search operations:
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksPydanticBaseExample}
+</CodeBlock>
+
+
+## Vector Field {#vector-field}
+
+LanceDB provides a `lancedb.pydantic.Vector` method to define a
+vector Field in a Pydantic Model.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksPydanticVectorField}
+</CodeBlock>
+
+This example demonstrates how LanceDB automatically converts Pydantic field types to their corresponding Apache Arrow data types. The `pydantic_to_schema()` function takes a Pydantic model and generates an Arrow schema where:
+- `int` fields become `pa.int64()` (64-bit integers)
+- `str` fields become `pa.utf8()` (UTF-8 encoded strings)  
+- `Vector(768)` becomes `pa.list_(pa.float32(), 768)` (fixed-size list of 768 float32 values)
+- The `False` parameter indicates that the fields are not nullable
+
+## Type Conversion {#type-conversion}
+
+LanceDB automatically convert Pydantic fields to
+[Apache Arrow DataType](https://arrow.apache.org/docs/python/generated/pyarrow.DataType.html#pyarrow.DataType).
+
+Current supported type conversions:
+
+| Pydantic Field Type | PyArrow Data Type |
+| ------------------- | ----------------- |
+| `int`               | `pyarrow.int64`   |
+| `float`              | `pyarrow.float64`  |
+| `bool`              | `pyarrow.bool`    |
+| `str`               | `pyarrow.utf8()`    |
+| `list`              | `pyarrow.List`    |
+| `BaseModel`         | `pyarrow.Struct`    |
+| `Vector(n)`         | `pyarrow.FixedSizeList(float32, n)` |
+
+LanceDB supports to create Apache Arrow Schema from a
+`pydantic.BaseModel`
+via `lancedb.pydantic.pydantic_to_schema` method.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksPydanticTypeConversion}
+</CodeBlock>
+
+This example shows a more complex Pydantic model with various field types and demonstrates how LanceDB handles:
+- Basic types: `int` and `str` fields
+- Vector fields: `Vector(1536)` creates a fixed-size list of 1536 float32 values
+- List fields: `List[int]` becomes a variable-length list of int64 values
+- Schema generation: The `pydantic_to_schema()` function automatically converts all these types to their Arrow equivalents
\ No newline at end of file
diff --git a/docs/web/integrations/data/voxel51.mdx b/docs/web/integrations/data/voxel51.mdx
new file mode 100644
index 000000000..1ee782353
--- /dev/null
+++ b/docs/web/integrations/data/voxel51.mdx
@@ -0,0 +1,212 @@
+---
+title: "Voxel51"
+sidebarTitle: "Voxel51"
+---
+
+import {
+  PyPlatformsVoxel51BackendFlag,
+  PyPlatformsVoxel51BackendParams,
+  PyPlatformsVoxel51BrainConfig,
+  PyPlatformsVoxel51Cleanup,
+  PyPlatformsVoxel51ComputeSimilarity,
+  PyPlatformsVoxel51LoadDataset,
+  PyPlatformsVoxel51SortBySimilarity,
+} from '/snippets/integrations.mdx';
+
+# FiftyOne
+
+[FiftyOne](https://docs.voxel51.com/) is an open source toolkit that enables users to curate better data and build better models. It includes tools for data exploration, visualization, and management, as well as features for collaboration and sharing. 
+
+Any developers, data scientists, and researchers who work with computer vision and machine learning can use FiftyOne to improve the quality of their datasets and deliver insights about their models.
+
+![example](/static/assets/images/integrations/voxel.gif)
+
+**FiftyOne** provides an API to create LanceDB tables and run similarity queries, both **programmatically in Python** and via **point-and-click in the App**.
+
+Let's get started and see how to use **LanceDB** to create a **similarity index** on your FiftyOne datasets.
+
+## Overview {#overview}
+
+[Embeddings](/embedding/) are foundational to all of the **vector search** features. In FiftyOne, embeddings are managed by the [**FiftyOne Brain**](https://docs.voxel51.com/user_guide/brain.html) that provides powerful machine learning techniques designed to transform how you curate your data from an art into a measurable science. 
+
+> _Have you ever wanted to find the images most similar to an image in your dataset?_
+
+The **FiftyOne Brain** makes computing **visual similarity** really easy. You can compute the similarity of samples in your dataset using an embedding model and store the results in the **brain key**. 
+You can then sort your samples by similarity or use this information to find potential duplicate images.
+
+
+We'll be doing the following : 
+
+1. **Create Index** - In order to run similarity queries against our media, we need to **index** the data. We can do this via the `compute_similarity()` function. 
+  -  In the function, specify the **model** you want to use to generate the embedding vectors, and what **vector search engine** you want to use on the **backend** (here LanceDB). 
+  <Tip>
+  You can also give the similarity index a name(`brain_key`), which is useful if you want to run vector searches against multiple indexes.
+  </Tip>
+
+2. **Query** - Once you have generated your similarity index, you can query your dataset with `sort_by_similarity()`. The query can be any of the following:
+
+  - An ID (sample or patch)
+  - A query vector of same dimension as the index
+  - A list of IDs (samples or patches)
+  - A text prompt (search semantically)
+
+## Prerequisites: install necessary dependencies {#prerequisites-install-necessary-dependencies}
+
+1. **Create and activate a virtual environment**
+
+Install virtualenv package and run the following command in your project directory.
+<CodeBlock filename="bash" language="bash" icon="terminal">
+python -m venv fiftyone_
+</CodeBlock>
+From inside the project directory run the following to activate the virtual environment.
+
+<CodeGroup>
+    <CodeBlock filename="macOS/Linux" language="bash" icon="linux">
+    source fiftyone_/Scripts/activate
+    </CodeBlock>
+    <CodeBlock filename="Windows" language="bash" icon="windows">
+    fiftyone_/Scripts/activate
+    </CodeBlock>
+</CodeGroup>
+
+2. **Install the following packages in the virtual environment**
+
+    To install FiftyOne, ensure you have activated any virtual environment that you are using, then run
+<CodeBlock filename="bash" language="bash" icon="terminal">
+pip install fiftyone
+</CodeBlock>
+
+
+## Understand basic workflow {#understand-basic-workflow}
+
+The basic workflow shown below uses LanceDB to create a similarity index on your FiftyOne datasets:
+
+1. Load a dataset into FiftyOne.
+
+2. Compute embedding vectors for samples or patches in your dataset, or select a model to use to generate embeddings.
+
+3. Use the `compute_similarity()` method to generate a LanceDB table for the samples or object patches embeddings in a dataset by setting the parameter `backend="lancedb"` and specifying a `brain_key` of your choice.
+
+4. Use this LanceDB table to query your data with `sort_by_similarity()`.
+
+5. If desired, delete the table.
+
+## Quick Example {#quick-example}
+
+Let's jump on a quick example that demonstrates this workflow.
+
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyPlatformsVoxel51LoadDataset}
+</CodeBlock>
+Make sure you install torch ([guide here](https://pytorch.org/get-started/locally/)) before proceeding. 
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyPlatformsVoxel51ComputeSimilarity}
+</CodeBlock>
+
+!!! note
+    Running the code above will download the clip model (2.6Gb)
+
+Once the similarity index has been generated, we can query our data in FiftyOne by specifying the `brain_key`:
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyPlatformsVoxel51SortBySimilarity}
+</CodeBlock>
+
+The returned result are of type - `DatasetView`.
+
+<Note>
+`DatasetView` does not hold its contents in-memory. Views simply store the rule(s) that are applied to extract the content of interest from the underlying Dataset when the view is iterated/aggregated on.
+
+This means, for example, that the contents of a `DatasetView` may change as the underlying Dataset is modified.
+</Note>
+
+> _Can you query a view instead of dataset?_
+
+Yes, you can also query a view.
+
+Performing a similarity search on a `DatasetView` will only return results from the view; if the view contains samples that were not included in the index, they will never be included in the result.
+
+This means that you can index an entire Dataset once and then perform searches on subsets of the dataset by constructing views that contain the images of interest.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyPlatformsVoxel51Cleanup}
+</CodeBlock>
+
+
+## Using LanceDB backend {#using-lancedb-backend}
+By default, calling `compute_similarity()` or `sort_by_similarity()` will use an sklearn backend.
+
+To use the LanceDB backend, simply set the optional `backend` parameter of `compute_similarity()` to `"lancedb"`:
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyPlatformsVoxel51BackendFlag}
+</CodeBlock>
+
+Alternatively, you can configure FiftyOne to use the LanceDB backend by setting the following environment variable.
+
+In your terminal, set the environment variable using:
+
+<CodeGroup>
+    <CodeBlock filename="macOS/Linux" language="bash" icon="linux">
+    export FIFTYONE_BRAIN_DEFAULT_SIMILARITY_BACKEND=lancedb
+    </CodeBlock>
+
+    <CodeBlock filename="Windows" language="powershell" icon="windows">
+    $Env:FIFTYONE_BRAIN_DEFAULT_SIMILARITY_BACKEND="lancedb" //powershell
+
+    set FIFTYONE_BRAIN_DEFAULT_SIMILARITY_BACKEND=lancedb //cmd
+    </CodeBlock>
+</CodeGroup>
+
+<Warning>
+This will only run during the terminal session. Once terminal is closed, environment variable is deleted.
+</Warning>
+
+Alternatively, you can **permanently** configure FiftyOne to use the LanceDB backend creating a `brain_config.json` at `~/.fiftyone/brain_config.json`. The JSON file may contain any desired subset of config fields that you wish to customize.
+
+```json
+{
+  "default_similarity_backend": "lancedb"
+}
+```
+This will override the default `brain_config` and will set it according to your customization. You can check the configuration by running the following code : 
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyPlatformsVoxel51BrainConfig}
+</CodeBlock>
+
+## LanceDB config parameters {#lancedb-config-parameters}
+
+The LanceDB backend supports query parameters that can be used to customize your similarity queries. These parameters include:
+
+| Name           | Purpose                                                                                                          | Default          |
+| :------------- | :--------------------------------------------------------------------------------------------------------------- | :--------------- |
+| **table_name** | The name of the LanceDB table to use. If none is provided, a new table will be created                           | `None`           |
+| **metric**     | The embedding distance metric to use when creating a new table. The supported values are ("cosine", "euclidean") | `"cosine"`       |
+| **uri**        | The database URI to use. In this Database URI, tables will be created.                                           | `"/tmp/lancedb"` |
+
+There are two ways to specify/customize the parameters:
+
+1. **Using `brain_config.json` file** 
+
+```json
+{
+    "similarity_backends": {
+        "lancedb": {
+            "table_name": "your-table",
+            "metric": "euclidean",
+            "uri": "/tmp/lancedb"
+        }
+    }
+}
+```
+
+2. **Directly passing to `compute_similarity()` to configure a specific new index** : 
+
+<CodeBlock filename="Python" language="Python" icon="python">
+    {PyPlatformsVoxel51BackendParams}
+</CodeBlock>
+
+For a much more in depth walkthrough of the integration, visit the LanceDB x Voxel51 [docs page](https://docs.voxel51.com/integrations/lancedb.html).
diff --git a/docs/web/integrations/embedding/aws.mdx b/docs/web/integrations/embedding/aws.mdx
new file mode 100644
index 000000000..4e1c4fb90
--- /dev/null
+++ b/docs/web/integrations/embedding/aws.mdx
@@ -0,0 +1,42 @@
+---
+title: AWS Bedrock
+sidebarTitle: AWS
+---
+
+import { PyEmbeddingAwsUsage } from '/snippets/integrations.mdx';
+
+AWS Bedrock supports multiple base models for generating text embeddings. You need to setup the AWS credentials to use this embedding function.
+You can do so by using `awscli` and also add your session_token:
+```shell
+aws configure
+aws configure set aws_session_token "<your_session_token>"
+```
+to ensure that the credentials are set up correctly, you can run the following command:
+```shell
+aws sts get-caller-identity
+```
+
+Supported Embedding modelIDs are:
+* `amazon.titan-embed-text-v1`
+* `cohere.embed-english-v3`
+* `cohere.embed-multilingual-v3`
+
+Supported parameters (to be passed in `create` method) are:
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| **name** | str | "amazon.titan-embed-text-v1" | The model ID of the bedrock model to use. Supported base models for Text Embeddings: amazon.titan-embed-text-v1, cohere.embed-english-v3, cohere.embed-multilingual-v3 |
+| **region** | str | "us-east-1" | Optional name of the AWS Region in which the service should be called (e.g., "us-east-1"). |
+| **profile_name** | str | None | Optional name of the AWS profile to use for calling the Bedrock service. If not specified, the default profile will be used. |
+| **assumed_role** | str | None | Optional ARN of an AWS IAM role to assume for calling the Bedrock service. If not specified, the current active credentials will be used. |
+| **role_session_name** | str | "lancedb-embeddings" | Optional name of the AWS IAM role session to use for calling the Bedrock service. If not specified, a "lancedb-embeddings" name will be used. |
+| **runtime** | bool | True | Optional choice of getting different client to perform operations with the Amazon Bedrock service. |
+| **max_retries** | int | 7 | Optional number of retries to perform when a request fails. |
+
+Usage Example:
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingAwsUsage}
+  </CodeBlock>
+</CodeGroup>
diff --git a/docs/web/integrations/embedding/cohere.mdx b/docs/web/integrations/embedding/cohere.mdx
new file mode 100644
index 000000000..b9a8e3378
--- /dev/null
+++ b/docs/web/integrations/embedding/cohere.mdx
@@ -0,0 +1,55 @@
+---
+title: Cohere
+sidebarTitle: Cohere
+---
+
+import { PyEmbeddingCohereUsage } from '/snippets/integrations.mdx';
+
+Using cohere API requires cohere package, which can be installed using `pip install cohere`. Cohere embeddings are used to generate embeddings for text data. The embeddings can be used for various tasks like semantic search, clustering, and classification.
+You also need to set the `COHERE_API_KEY` environment variable to use the Cohere API.
+
+```shell
+export COHERE_API_KEY="your-api-key"
+```
+
+Supported models are:
+
+- embed-english-v3.0
+- embed-multilingual-v3.0
+- embed-english-light-v3.0
+- embed-multilingual-light-v3.0
+- embed-english-v2.0
+- embed-english-light-v2.0
+- embed-multilingual-v2.0
+
+
+Supported parameters (to be passed in `create` method) are:
+
+| Parameter | Type | Default Value | Description |
+|---|---|--------|---------|
+| `name` | `str` | `"embed-english-v2.0"` | The model ID of the cohere model to use. Supported base models for Text Embeddings: embed-english-v3.0, embed-multilingual-v3.0, embed-english-light-v3.0, embed-multilingual-light-v3.0, embed-english-v2.0, embed-english-light-v2.0, embed-multilingual-v2.0 |
+| `source_input_type` | `str` | `"search_document"` | The type of input data to be used for the source column. |
+| `query_input_type` | `str` | `"search_query"` | The type of input data to be used for the query. |
+
+Cohere supports following input types:
+
+| Input Type               | Description                          |
+|-------------------------|---------------------------------------|
+| "`search_document`"     | Used for embeddings stored in a vector|
+|                         | database for search use-cases.        |
+| "`search_query`"        | Used for embeddings of search queries |
+|                         | run against a vector DB               |
+| "`semantic_similarity`" | Specifies the given text will be used |
+|                         | for Semantic Textual Similarity (STS) |
+| "`classification`"      | Used for embeddings passed through a  |
+|                         | text classifier.                      |
+| "`clustering`"          | Used for the embeddings run through a |
+|                         | clustering algorithm                  |
+
+Usage Example:
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingCohereUsage}
+  </CodeBlock>
+</CodeGroup>
\ No newline at end of file
diff --git a/docs/web/integrations/embedding/colpali.mdx b/docs/web/integrations/embedding/colpali.mdx
new file mode 100644
index 000000000..dcd65a017
--- /dev/null
+++ b/docs/web/integrations/embedding/colpali.mdx
@@ -0,0 +1,53 @@
+---
+title: ColPali
+sidebarTitle: ColPali
+---
+
+import {
+  PyEmbeddingColpaliSetup,
+  PyEmbeddingColpaliTextSearch,
+} from '/snippets/integrations.mdx';
+
+We support [ColPali](https://github.com/illuin-tech/colpali) model embeddings for multimodal multi-vector retrieval. ColPali produces multiple embedding vectors per input (multi-vector), enabling more nuanced similarity matching between text queries and image documents.
+
+Using ColPali requires the colpali-engine package, which can be installed using `pip install colpali-engine`.
+
+<Info>
+ColPali produces **multi-vector** embeddings, meaning each input generates multiple embedding vectors rather than a single vector. Use `MultiVector(func.ndims())` instead of `Vector(func.ndims())` when defining your schema.
+</Info>
+
+Supported models are:
+
+- Metric-AI/ColQwen2.5-3b-multilingual-v1.0 (default)
+- vidore/colpali-v1.3
+- vidore/colqwen2-v1.0
+- vidore/colSmol-256M
+
+Supported parameters (to be passed in `create` method) are:
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| `model_name` | `str` | `"Metric-AI/ColQwen2.5-3b-multilingual-v1.0"` | The name of the model to use. |
+| `device` | `str` | `"auto"` | The device for inference. Can be `"auto"`, `"cpu"`, `"cuda"`, or `"mps"`. |
+| `dtype` | `str` | `"bfloat16"` | Data type for model weights (bfloat16, float16, float32, float64). |
+| `pooling_strategy` | `str` | `"hierarchical"` | Token pooling strategy: `"hierarchical"`, `"lambda"`, or `None`. |
+| `pool_factor` | `int` | `2` | Factor to reduce sequence length when pooling is enabled. |
+| `batch_size` | `int` | `2` | Batch size for processing inputs. |
+| `quantization_config` | `Optional[BitsAndBytesConfig]` | `None` | Quantization configuration for the model (requires bitsandbytes). |
+
+This embedding function supports ingesting images as both bytes and URLs. You can query them using text.
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingColpaliSetup}
+  </CodeBlock>
+</CodeGroup>
+
+Now we can search using text queries:
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingColpaliTextSearch}
+  </CodeBlock>
+</CodeGroup>
+
diff --git a/docs/web/integrations/embedding/gemini.mdx b/docs/web/integrations/embedding/gemini.mdx
new file mode 100644
index 000000000..e3359ec0d
--- /dev/null
+++ b/docs/web/integrations/embedding/gemini.mdx
@@ -0,0 +1,26 @@
+---
+title: Gemini
+sidebarTitle: Gemini
+---
+
+import { PyEmbeddingGeminiUsage } from '/snippets/integrations.mdx';
+
+With Google's Gemini, you can represent text (words, sentences, and blocks of text) in a vectorized form, making it easier to compare and contrast embeddings. For example, two texts that share a similar subject matter or sentiment should have similar embeddings, which can be identified through mathematical comparison techniques such as cosine similarity. For more on how and why you should use embeddings, refer to the Embeddings guide.
+The Gemini Embedding Model API supports various task types:
+
+| Task Type               | Description                                                                                                                                                |
+|-------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| "`retrieval_query`"     | Specifies the given text is a query in a search/retrieval setting.                                                                                         |
+| "`retrieval_document`"  | Specifies the given text is a document in a search/retrieval setting. Using this task type requires a title but is automatically provided by Embeddings API |
+| "`semantic_similarity`" | Specifies the given text will be used for Semantic Textual Similarity (STS).                                                                               |
+| "`classification`"      | Specifies that the embeddings will be used for classification.                                                                                             |
+| "`clustering`"           | Specifies that the embeddings will be used for clustering.                                                                                                 |
+
+
+Usage Example:
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingGeminiUsage}
+  </CodeBlock>
+</CodeGroup>
\ No newline at end of file
diff --git a/docs/web/integrations/embedding/huggingface.mdx b/docs/web/integrations/embedding/huggingface.mdx
new file mode 100644
index 000000000..69cf85599
--- /dev/null
+++ b/docs/web/integrations/embedding/huggingface.mdx
@@ -0,0 +1,16 @@
+---
+title: Hugging Face
+sidebarTitle: Hugging Face
+---
+
+import { PyEmbeddingHuggingfaceUsage } from '/snippets/integrations.mdx';
+
+We offer support for all Hugging Face models (which can be loaded via [transformers](https://huggingface.co/docs/transformers/en/index) library). The default model is `colbert-ir/colbertv2.0` which also has its own special callout - `registry.get("colbert")`. Some Hugging Face models might require custom models defined on the HuggingFace Hub in their own modeling files. You may enable this by setting `trust_remote_code=True`. This option should only be set to True for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. 
+
+Example usage:
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingHuggingfaceUsage}
+  </CodeBlock>
+</CodeGroup>
\ No newline at end of file
diff --git a/docs/web/integrations/embedding/ibm.mdx b/docs/web/integrations/embedding/ibm.mdx
new file mode 100644
index 000000000..6dd3d57e3
--- /dev/null
+++ b/docs/web/integrations/embedding/ibm.mdx
@@ -0,0 +1,68 @@
+---
+title: IBM watsonx
+sidebarTitle: IBM WatsonX
+---
+
+import { PyEmbeddingIbmUsage } from '/snippets/provider_examples.mdx';
+
+Generate text embeddings using IBM's watsonx.ai platform.
+
+## Supported Models {#supported-models}
+
+You can find a list of supported models at [IBM watsonx.ai Documentation](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx). The currently supported model names are:
+
+| Model ID                                     | Dimensions |
+|----------------------------------------------|------------|
+| `ibm/granite-embedding-278m-multilingual`    | 768        |
+| `ibm/slate-125m-english-rtrvr-v2`            | 768        |
+| `ibm/slate-30m-english-rtrvr-v2`             | 384        |
+| `intfloat/multilingual-e5-large`             | 1024       |
+| `sentence-transformers/all-minilm-l6-v2`     | 384        |
+
+<Info>
+For new tables, `ibm/granite-embedding-278m-multilingual` is the recommended default. Older model IDs (such as `ibm/slate-125m-english-rtrvr` and `sentence-transformers/all-minilm-l12-v2`) remain resolvable for tables whose stored metadata references them, but they are no longer advertised for new use.
+</Info>
+
+## Parameters {#parameters}
+
+The following parameters can be passed to the `create` method:
+
+| Parameter  | Type     | Default Value                    | Description                                               |
+|------------|----------|----------------------------------|-----------------------------------------------------------|
+| name       | str      | `"ibm/slate-125m-english-rtrvr"` | The model ID of the watsonx.ai model to use. Pass one of the current supported IDs above (e.g. `"ibm/granite-embedding-278m-multilingual"`) when creating new tables. |
+| api_key    | str      | None                             | Optional IBM Cloud API key (or set `WATSONX_API_KEY`)     |
+| project_id | str      | None                             | Optional watsonx project ID (or set `WATSONX_PROJECT_ID`). Mutually exclusive with `space_id`. |
+| space_id   | str      | None                             | Optional watsonx deployment space ID (or set `WATSONX_SPACE_ID`). Mutually exclusive with `project_id`. |
+| url        | str      | None                             | Optional custom URL for the watsonx.ai instance           |
+| params     | dict     | None                             | Optional additional parameters for the embedding model (e.g. `{"truncate_input_tokens": 512}`) |
+
+<Info>
+You must supply exactly one of `project_id` or `space_id` (either as an argument or via its environment variable). Setting both, or neither, raises a `ValueError`.
+</Info>
+
+## Usage Example {#usage-example}
+
+First, the watsonx.ai library is an optional dependency, so must be installed separately:
+
+```
+pip install ibm-watsonx-ai
+```
+
+Optionally set environment variables (if not passing credentials to `create` directly):
+
+```sh
+export WATSONX_API_KEY="YOUR_WATSONX_API_KEY"
+# Provide exactly one of the following:
+export WATSONX_PROJECT_ID="YOUR_WATSONX_PROJECT_ID"
+# export WATSONX_SPACE_ID="YOUR_WATSONX_SPACE_ID"
+```
+
+Replace the credential placeholders with your provider credentials. The registry
+variable keeps the API key out of stored embedding configuration. In application
+code, load its value from environment variables or a secrets manager.
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingIbmUsage}
+  </CodeBlock>
+</CodeGroup>
diff --git a/docs/web/integrations/embedding/imagebind.mdx b/docs/web/integrations/embedding/imagebind.mdx
new file mode 100644
index 000000000..4010de634
--- /dev/null
+++ b/docs/web/integrations/embedding/imagebind.mdx
@@ -0,0 +1,54 @@
+---
+title: ImageBind
+sidebarTitle: ImageBind
+---
+
+import {
+  PyEmbeddingImagebindSetup,
+  PyEmbeddingImagebindImageSearch,
+  PyEmbeddingImagebindAudioSearch,
+  PyEmbeddingImagebindTextSearch,
+} from '/snippets/integrations.mdx';
+
+We have support for [imagebind](https://github.com/facebookresearch/ImageBind) model embeddings. You can download our version of the packaged model via - `pip install imagebind-packaged==0.1.2`.
+
+This function is registered as `imagebind` and supports Audio, Video and Text modalities(extending to Thermal,Depth,IMU data):
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| `name` | `str` | `"imagebind_huge"` | Name of the model. |
+| `device` | `str` | `"cpu"` | The device to run the model on. Can be `"cpu"` or `"gpu"`. |
+| `normalize` | `bool` | `False` | set to `True` to normalize your inputs before model ingestion. |
+
+Below is an example demonstrating how the API works:
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingImagebindSetup}
+  </CodeBlock>
+</CodeGroup>
+
+Now, we can search using any modality:
+
+#### image search {#image-search}
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingImagebindImageSearch}
+  </CodeBlock>
+</CodeGroup>
+#### audio search {#audio-search}
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingImagebindAudioSearch}
+  </CodeBlock>
+</CodeGroup>
+#### Text search {#text-search}
+You can add any input query and fetch the result as follows:
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingImagebindTextSearch}
+  </CodeBlock>
+</CodeGroup>
+
+If you have any questions about the embeddings API, supported models, or see a relevant model missing, please raise an issue [on GitHub](https://github.com/lancedb/lancedb/issues).
diff --git a/docs/web/integrations/embedding/instructor.mdx b/docs/web/integrations/embedding/instructor.mdx
new file mode 100644
index 000000000..aea4d443e
--- /dev/null
+++ b/docs/web/integrations/embedding/instructor.mdx
@@ -0,0 +1,39 @@
+---
+title: Instructor
+sidebarTitle: Instructor
+---
+
+import { PyEmbeddingInstructorUsage } from '/snippets/integrations.mdx';
+
+[Instructor](https://instructor-embedding.github.io/) is an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g. classification, retrieval, clustering, text evaluation, etc.) and domains (e.g. science, finance, etc.) by simply providing the task instruction, without any finetuning.
+
+If you want to calculate customized embeddings for specific sentences, you can follow the unified template to write instructions.
+
+<Info>
+Represent the `domain` `text_type` for `task_objective`:
+
+- `domain` is optional, and it specifies the domain of the text, e.g. science, finance, medicine, etc.
+- `text_type` is required, and it specifies the encoding unit, e.g. sentence, document, paragraph, etc.
+- `task_objective` is optional, and it specifies the objective of embedding, e.g. retrieve a document, classify the sentence, etc.
+</Info>
+
+More information about the model can be found at the [source URL](https://github.com/xlang-ai/instructor-embedding).
+
+| Argument | Type | Default | Description |
+|---|---|---|---|
+| `name` | `str` | "hkunlp/instructor-base" | The name of the model to use |
+| `batch_size` | `int` | `32` | The batch size to use when generating embeddings |
+| `device` | `str` | `"cpu"` | The device to use when generating embeddings |
+| `show_progress_bar` | `bool` | `True` | Whether to show a progress bar when generating embeddings |
+| `normalize_embeddings` | `bool` | `True` | Whether to normalize the embeddings |
+| `quantize` | `bool` | `False` | Whether to quantize the model |
+| `source_instruction` | `str` | `"represent the document for retrieval"` | The instruction for the source column |
+| `query_instruction` | `str` | `"represent the document for retrieving the most similar documents"` | The instruction for the query |
+
+
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingInstructorUsage}
+  </CodeBlock>
+</CodeGroup>
\ No newline at end of file
diff --git a/docs/web/integrations/embedding/jina.mdx b/docs/web/integrations/embedding/jina.mdx
new file mode 100644
index 000000000..588500d36
--- /dev/null
+++ b/docs/web/integrations/embedding/jina.mdx
@@ -0,0 +1,51 @@
+---
+title: Jina
+sidebarTitle: Jina
+---
+
+import {
+  PyEmbeddingJinaText,
+  PyEmbeddingJinaMultimodal,
+} from '/snippets/provider_examples.mdx';
+
+## Text Embedding Models {#text-embedding-models}
+
+Jina embeddings are used to generate embeddings for text and image data.
+You also need to set the `JINA_API_KEY` environment variable to use the Jina API.
+
+You can find a list of supported models under [https://jina.ai/embeddings/](https://jina.ai/embeddings/)
+
+Supported parameters (to be passed in `create` method) are:
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| `name` | `str` | `"jina-clip-v1"` | The model ID of the jina model to use |
+
+Usage Example:
+
+Replace `your-api-key` with your Jina API key. This provider reads it from `JINA_API_KEY`; the example sets it explicitly. In application code, supply it through your environment or a secrets manager.
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingJinaText}
+  </CodeBlock>
+</CodeGroup>
+
+## Multimodal Embedding Models {#multimodal-embedding-models}
+
+Jina embeddings can also be used to embed both text and image data, only some of the models support image data and you can check the list
+under [https://jina.ai/embeddings/](https://jina.ai/embeddings/)
+
+Supported parameters (to be passed in `create` method) are:
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| `name` | `str` | `"jina-clip-v1"` | The model ID of the jina model to use |
+
+Usage Example:
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingJinaMultimodal}
+  </CodeBlock>
+</CodeGroup>
diff --git a/docs/web/integrations/embedding/ollama.mdx b/docs/web/integrations/embedding/ollama.mdx
new file mode 100644
index 000000000..120cde8f2
--- /dev/null
+++ b/docs/web/integrations/embedding/ollama.mdx
@@ -0,0 +1,25 @@
+---
+title: Ollama
+sidebarTitle: Ollama
+---
+
+import { PyEmbeddingOllamaUsage } from '/snippets/integrations.mdx';
+
+Generate embeddings via the [ollama](https://github.com/ollama/ollama-python) python library. More details:
+
+- [Ollama docs on embeddings](https://github.com/ollama/ollama/blob/main/docs/api.md#generate-embeddings)
+- [Ollama blog on embeddings](https://ollama.com/blog/embedding-models)
+
+| Parameter              | Type                       | Default Value            | Description                                                                                                                                    |
+|------------------------|----------------------------|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------|
+| `name`                 | `str`                      | `nomic-embed-text`       | The name of the model.                                                                                                                         |
+| `host`                 | `str`                      | `http://localhost:11434` | The Ollama host to connect to.                                                                                                                 |
+| `options`              | `ollama.Options` or `dict` | `None`                   | Additional model parameters listed in the documentation for the Modelfile such as `temperature`. |
+| `keep_alive`           | `float` or `str`           | `"5m"`                   | Controls how long the model will stay loaded into memory following the request.                                                                |
+| `ollama_client_kwargs` | `dict`                     | `{}`                     | kwargs that can be past to the `ollama.Client`.                                                                                                |
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingOllamaUsage}
+  </CodeBlock>
+</CodeGroup>
diff --git a/docs/web/integrations/embedding/openai.mdx b/docs/web/integrations/embedding/openai.mdx
new file mode 100644
index 000000000..fbfa62d9d
--- /dev/null
+++ b/docs/web/integrations/embedding/openai.mdx
@@ -0,0 +1,26 @@
+---
+title: OpenAI
+sidebarTitle: OpenAI
+---
+
+import { PyEmbeddingOpenaiBasic } from '/snippets/integrations.mdx';
+
+LanceDB registers the OpenAI embeddings function in the registry by default, as `openai`. Below are the parameters that you can customize when creating the instances:
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| `name` | `str` | `"text-embedding-ada-002"` | The name of the model. |
+| `dim` | `int` |  Model default   | Requested output dimensions for models that support shortening embeddings. Use `func.ndims()` when defining the vector field. |
+| `use_azure` | bool | `False` | Set true to use Azure OpenAI SDK |
+
+Set your OpenAI API key before running the example:
+
+```shell
+export OPENAI_API_KEY="your-api-key"
+```
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingOpenaiBasic}
+  </CodeBlock>
+</CodeGroup>
\ No newline at end of file
diff --git a/docs/web/integrations/embedding/openclip.mdx b/docs/web/integrations/embedding/openclip.mdx
new file mode 100644
index 000000000..118bb26c1
--- /dev/null
+++ b/docs/web/integrations/embedding/openclip.mdx
@@ -0,0 +1,46 @@
+---
+title: OpenCLIP
+sidebarTitle: OpenCLIP
+---
+
+import {
+  PyEmbeddingOpenclipSetup,
+  PyEmbeddingOpenclipTextSearch,
+  PyEmbeddingOpenclipImageSearch,
+} from '/snippets/integrations.mdx';
+
+We support CLIP model embeddings using the open source alternative, [open-clip](https://github.com/mlfoundations/open_clip) which supports various customizations. It is registered as `open-clip` and supports the following customizations:
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| `name` | `str` | `"ViT-B-32"` | The name of the model. |
+| `pretrained` | `str` | `"laion2b_s34b_b79k"` | The name of the pretrained model to load. |
+| `device` | `str` | `"cpu"` | The device to run the model on. Can be `"cpu"` or `"gpu"`. |
+| `batch_size` | `int` | `64` | The number of images to process in a batch. |
+| `normalize` | `bool` | `True` | Whether to normalize the input images before feeding them to the model. |
+
+This embedding function supports ingesting images as both bytes and urls. You can query them using both text and other images.
+
+<Info>
+LanceDB supports ingesting images directly from accessible links.
+</Info>
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingOpenclipSetup}
+  </CodeBlock>
+</CodeGroup>
+Now we can search using text from both the default vector column and the custom vector column
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingOpenclipTextSearch}
+  </CodeBlock>
+</CodeGroup>
+
+Because we're using a multimodal embedding function, we can also search using images
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingOpenclipImageSearch}
+  </CodeBlock>
+</CodeGroup>
diff --git a/docs/web/integrations/embedding/sentence-transformers.mdx b/docs/web/integrations/embedding/sentence-transformers.mdx
new file mode 100644
index 000000000..aa0c9d1ca
--- /dev/null
+++ b/docs/web/integrations/embedding/sentence-transformers.mdx
@@ -0,0 +1,169 @@
+---
+title: Sentence Transformers
+sidebarTitle: Sentence Transformers
+---
+
+import { PyEmbeddingSentenceTransformersBaai } from '/snippets/integrations.mdx';
+
+Allows you to set parameters when registering a `sentence-transformers` object.
+
+<Info>
+Sentence transformer embeddings are normalized by default. It is recommended to use normalized embeddings for similarity search.
+</Info>
+
+<Warning>
+The `trust_remote_code` parameter defaults to `True`, which allows models to execute arbitrary code from their Hugging Face repository during loading. If you are loading untrusted models, set `trust_remote_code=False` to prevent remote code execution.
+</Warning>
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| `name` | `str` | `all-MiniLM-L6-v2` | The name of the model |
+| `device` | `str` | `cpu` | The device to run the model on (can be `cpu` or `gpu`) |
+| `normalize` | `bool` | `True` | Whether to normalize the input text before feeding it to the model |
+| `trust_remote_code` | `bool` | `True` | Whether to trust and execute remote code from the model's Huggingface repository |
+
+
+<Details title="Check out available sentence-transformer models here!">
+```markdown
+    - sentence-transformers/all-MiniLM-L12-v2
+    - sentence-transformers/paraphrase-mpnet-base-v2
+    - sentence-transformers/gtr-t5-base
+    - sentence-transformers/LaBSE
+    - sentence-transformers/all-MiniLM-L6-v2
+    - sentence-transformers/bert-base-nli-max-tokens
+    - sentence-transformers/bert-base-nli-mean-tokens
+    - sentence-transformers/bert-base-nli-stsb-mean-tokens
+    - sentence-transformers/bert-base-wikipedia-sections-mean-tokens
+    - sentence-transformers/bert-large-nli-cls-token
+    - sentence-transformers/bert-large-nli-max-tokens
+    - sentence-transformers/bert-large-nli-mean-tokens
+    - sentence-transformers/bert-large-nli-stsb-mean-tokens
+    - sentence-transformers/distilbert-base-nli-max-tokens
+    - sentence-transformers/distilbert-base-nli-mean-tokens
+    - sentence-transformers/distilbert-base-nli-stsb-mean-tokens
+    - sentence-transformers/distilroberta-base-msmarco-v1
+    - sentence-transformers/distilroberta-base-msmarco-v2
+    - sentence-transformers/nli-bert-base-cls-pooling
+    - sentence-transformers/nli-bert-base-max-pooling
+    - sentence-transformers/nli-bert-base
+    - sentence-transformers/nli-bert-large-cls-pooling
+    - sentence-transformers/nli-bert-large-max-pooling
+    - sentence-transformers/nli-bert-large
+    - sentence-transformers/nli-distilbert-base-max-pooling
+    - sentence-transformers/nli-distilbert-base
+    - sentence-transformers/nli-roberta-base
+    - sentence-transformers/nli-roberta-large
+    - sentence-transformers/roberta-base-nli-mean-tokens
+    - sentence-transformers/roberta-base-nli-stsb-mean-tokens
+    - sentence-transformers/roberta-large-nli-mean-tokens
+    - sentence-transformers/roberta-large-nli-stsb-mean-tokens
+    - sentence-transformers/stsb-bert-base
+    - sentence-transformers/stsb-bert-large
+    - sentence-transformers/stsb-distilbert-base
+    - sentence-transformers/stsb-roberta-base
+    - sentence-transformers/stsb-roberta-large
+    - sentence-transformers/xlm-r-100langs-bert-base-nli-mean-tokens
+    - sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens
+    - sentence-transformers/xlm-r-base-en-ko-nli-ststb
+    - sentence-transformers/xlm-r-bert-base-nli-mean-tokens
+    - sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens
+    - sentence-transformers/xlm-r-large-en-ko-nli-ststb
+    - sentence-transformers/bert-base-nli-cls-token
+    - sentence-transformers/all-distilroberta-v1
+    - sentence-transformers/multi-qa-MiniLM-L6-dot-v1
+    - sentence-transformers/multi-qa-distilbert-cos-v1
+    - sentence-transformers/multi-qa-distilbert-dot-v1
+    - sentence-transformers/multi-qa-mpnet-base-cos-v1
+    - sentence-transformers/multi-qa-mpnet-base-dot-v1
+    - sentence-transformers/nli-distilroberta-base-v2
+    - sentence-transformers/all-MiniLM-L6-v1
+    - sentence-transformers/all-mpnet-base-v1
+    - sentence-transformers/all-mpnet-base-v2
+    - sentence-transformers/all-roberta-large-v1
+    - sentence-transformers/allenai-specter
+    - sentence-transformers/average_word_embeddings_glove.6B.300d
+    - sentence-transformers/average_word_embeddings_glove.840B.300d
+    - sentence-transformers/average_word_embeddings_komninos
+    - sentence-transformers/average_word_embeddings_levy_dependency
+    - sentence-transformers/clip-ViT-B-32-multilingual-v1
+    - sentence-transformers/clip-ViT-B-32
+    - sentence-transformers/distilbert-base-nli-stsb-quora-ranking
+    - sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking
+    - sentence-transformers/distilroberta-base-paraphrase-v1
+    - sentence-transformers/distiluse-base-multilingual-cased-v1
+    - sentence-transformers/distiluse-base-multilingual-cased-v2
+    - sentence-transformers/distiluse-base-multilingual-cased
+    - sentence-transformers/facebook-dpr-ctx_encoder-multiset-base
+    - sentence-transformers/facebook-dpr-ctx_encoder-single-nq-base
+    - sentence-transformers/facebook-dpr-question_encoder-multiset-base
+    - sentence-transformers/facebook-dpr-question_encoder-single-nq-base
+    - sentence-transformers/gtr-t5-large
+    - sentence-transformers/gtr-t5-xl
+    - sentence-transformers/gtr-t5-xxl
+    - sentence-transformers/msmarco-MiniLM-L-12-v3
+    - sentence-transformers/msmarco-MiniLM-L-6-v3
+    - sentence-transformers/msmarco-MiniLM-L12-cos-v5
+    - sentence-transformers/msmarco-MiniLM-L6-cos-v5
+    - sentence-transformers/msmarco-bert-base-dot-v5
+    - sentence-transformers/msmarco-bert-co-condensor
+    - sentence-transformers/msmarco-distilbert-base-dot-prod-v3
+    - sentence-transformers/msmarco-distilbert-base-tas-b
+    - sentence-transformers/msmarco-distilbert-base-v2
+    - sentence-transformers/msmarco-distilbert-base-v3
+    - sentence-transformers/msmarco-distilbert-base-v4
+    - sentence-transformers/msmarco-distilbert-cos-v5
+    - sentence-transformers/msmarco-distilbert-dot-v5
+    - sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned
+    - sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch
+    - sentence-transformers/msmarco-distilroberta-base-v2
+    - sentence-transformers/msmarco-roberta-base-ance-firstp
+    - sentence-transformers/msmarco-roberta-base-v2
+    - sentence-transformers/msmarco-roberta-base-v3
+    - sentence-transformers/multi-qa-MiniLM-L6-cos-v1
+    - sentence-transformers/nli-mpnet-base-v2
+    - sentence-transformers/nli-roberta-base-v2
+    - sentence-transformers/nq-distilbert-base-v1
+    - sentence-transformers/paraphrase-MiniLM-L12-v2
+    - sentence-transformers/paraphrase-MiniLM-L3-v2
+    - sentence-transformers/paraphrase-MiniLM-L6-v2
+    - sentence-transformers/paraphrase-TinyBERT-L6-v2
+    - sentence-transformers/paraphrase-albert-base-v2
+    - sentence-transformers/paraphrase-albert-small-v2
+    - sentence-transformers/paraphrase-distilroberta-base-v1
+    - sentence-transformers/paraphrase-distilroberta-base-v2
+    - sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+    - sentence-transformers/paraphrase-multilingual-mpnet-base-v2
+    - sentence-transformers/paraphrase-xlm-r-multilingual-v1
+    - sentence-transformers/quora-distilbert-base
+    - sentence-transformers/quora-distilbert-multilingual
+    - sentence-transformers/sentence-t5-base
+    - sentence-transformers/sentence-t5-large
+    - sentence-transformers/sentence-t5-xxl
+    - sentence-transformers/sentence-t5-xl
+    - sentence-transformers/stsb-distilroberta-base-v2
+    - sentence-transformers/stsb-mpnet-base-v2
+    - sentence-transformers/stsb-roberta-base-v2
+    - sentence-transformers/stsb-xlm-r-multilingual
+    - sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1
+    - sentence-transformers/clip-ViT-L-14
+    - sentence-transformers/clip-ViT-B-16
+    - sentence-transformers/use-cmlm-multilingual
+    - sentence-transformers/all-MiniLM-L12-v1
+```
+</Details>
+
+<Info>
+You can also load many other model architectures from the library. For example models from sources such as BAAI, Nomic, Salesforce Research, etc. See this HF hub page for all [supported models](https://huggingface.co/models?library=sentence-transformers).
+</Info>
+
+<Note title="BAAI embeddings example">
+Here is an example that uses the BAAI embedding model from the Hugging Face Hub [supported models](https://huggingface.co/models?library=sentence-transformers).
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingSentenceTransformersBaai}
+  </CodeBlock>
+</CodeGroup>
+</Note>
+Visit sentence-transformers [HuggingFace HUB](https://huggingface.co/sentence-transformers) page for more information on the available models.
+
diff --git a/docs/web/integrations/embedding/superlinked.mdx b/docs/web/integrations/embedding/superlinked.mdx
new file mode 100644
index 000000000..bb2768d74
--- /dev/null
+++ b/docs/web/integrations/embedding/superlinked.mdx
@@ -0,0 +1,149 @@
+---
+title: Superlinked
+sidebarTitle: Superlinked
+---
+
+[Superlinked](https://superlinked.com) is a self-hosted inference engine (SIE) for embedding, reranking, and extraction. The `sie-lancedb` package registers SIE as a first-class embedding function in LanceDB's embeddings registry, so embeddings are computed automatically on insert and search. You need a running SIE instance - see the [Superlinked quickstart](https://superlinked.com/docs) for deployment options.
+
+## Installation {#installation}
+
+<CodeGroup>
+```bash Python icon=Python
+pip install sie-lancedb
+```
+
+```bash TypeScript icon=js
+npm install @superlinked/sie-lancedb @lancedb/lancedb
+```
+</CodeGroup>
+
+## Registered functions {#registered-functions}
+
+Importing `sie_lancedb` registers two embedding functions in LanceDB's registry:
+
+| Name | Purpose |
+|---|---|
+| `"sie"` | Dense text embeddings |
+| `"sie-multivector"` | ColBERT-style late interaction with MaxSim scoring |
+
+Supported parameters on `.create()`:
+
+| Parameter | Type | Description |
+|---|---|---|
+| `model` | `str` | Any of 85+ SIE-supported models (e.g. `BAAI/bge-m3`, `NovaSearch/stella_en_400M_v5`, `jinaai/jina-colbert-v2`) |
+| `base_url` | `str` | URL of the SIE endpoint (e.g. `http://localhost:8080`) |
+
+## Usage {#usage}
+
+```py Python icon=Python
+import tempfile
+from pathlib import Path
+
+import lancedb
+from lancedb.embeddings import get_registry
+from lancedb.pydantic import LanceModel, Vector
+import sie_lancedb  # registers "sie" and "sie-multivector"
+
+sie = get_registry().get("sie").create(
+    model="BAAI/bge-m3",
+    base_url="http://localhost:8080",
+)
+
+class Documents(LanceModel):
+    id: int
+    text: str = sie.SourceField()
+    vector: Vector(sie.ndims()) = sie.VectorField()
+
+db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "superlinked-demo"))
+table = db.create_table("docs", schema=Documents)
+
+table.add([
+    {"id": 1, "text": "Machine learning is a subset of AI."},
+    {"id": 2, "text": "Neural networks use multiple layers."},
+    {"id": 3, "text": "Python is popular for ML development."},
+])
+
+results = table.search("What is deep learning?").limit(3).to_list()
+```
+
+LanceDB handles embedding generation for both inserts and queries automatically, based on the `SourceField` / `VectorField` declarations on the schema. The `id` column gives each document a stable key, which [entity extraction](#entity-extraction) uses to merge its results back.
+
+## Hybrid search with reranker {#hybrid-search-with-reranker}
+
+`SIEReranker` plugs into LanceDB's hybrid search pipeline. It uses SIE's cross-encoder `score()` to rerank combined vector + full-text search results. You need a full-text search index on the column first:
+
+```py Python icon=Python
+from sie_lancedb import SIEReranker
+
+# Create FTS index for hybrid search
+table.create_fts_index("text", replace=True)
+
+results = (
+    table.search("What is deep learning?", query_type="hybrid")
+    .rerank(SIEReranker(model="jinaai/jina-reranker-v2-base-multilingual"))
+    .limit(5)
+    .to_list()
+)
+
+for r in results:
+    print(f"{r['_relevance_score']:.3f}  {r['text']}")
+```
+
+The reranker also works with pure vector or pure FTS search via `.rerank()`.
+
+## ColBERT and multivector {#colbert-and-multivector}
+`SIEMultiVectorEmbeddingFunction` (registered as `"sie-multivector"`) works with LanceDB's native `MultiVector` type and MaxSim scoring for ColBERT and ColPali models. In LanceDB Python 0.40.0, a multivector that LanceDB generates on insert is stored as null unless the document's token count happens to equal the per-token dimension, so this example computes the document multivectors with `compute_source_embeddings()` and passes them in the `vector` column. LanceDB still generates the query multivector automatically:
+
+```py Python icon=Python
+from lancedb.pydantic import MultiVector
+
+sie_colbert = get_registry().get("sie-multivector").create(
+    model="jinaai/jina-colbert-v2",
+    base_url="http://localhost:8080",
+)
+
+class ColBERTDocs(LanceModel):
+    text: str = sie_colbert.SourceField()
+    vector: MultiVector(sie_colbert.ndims()) = sie_colbert.VectorField()
+
+table = db.create_table("colbert_docs", schema=ColBERTDocs)
+
+# Compute the document multivectors and store them with their text
+texts = ["Machine learning is a subset of AI."]
+vectors = sie_colbert.compute_source_embeddings(texts)
+table.add([{"text": text, "vector": vector} for text, vector in zip(texts, vectors)])
+
+# MaxSim search - query and document multivectors compared token-by-token
+results = table.search("What is ML?").limit(5).to_list()
+```
+
+## Entity extraction {#entity-extraction}
+
+`SIEExtractor` adds entity extraction to LanceDB's data-enrichment workflows. Extract entities from a text column and merge the results back as a structured Arrow column - enabling filtered search on extracted entities. This example enriches the `docs` table from [Usage](#usage), matching rows on its `id` column:
+
+```py Python icon=Python
+from sie_lancedb import SIEExtractor
+
+extractor = SIEExtractor(
+    base_url="http://localhost:8080",
+    model="urchade/gliner_multi-v2.1",
+)
+
+docs = db.open_table("docs")
+extractor.enrich_table(
+    docs,
+    source_column="text",
+    target_column="entities",
+    labels=["person", "technology", "organization"],
+    id_column="id",
+)
+```
+
+The `entities` column stores structured Arrow data (`list<struct<text, label, score, start, end, bbox>>`), so you can filter on extracted entities in queries.
+
+## Links {#links}
+
+- [`sie-lancedb` on PyPI](https://pypi.org/project/sie-lancedb/)
+- [`@superlinked/sie-lancedb` on npm](https://www.npmjs.com/package/@superlinked/sie-lancedb)
+- [Superlinked on GitHub](https://github.com/superlinked/sie)
+- [Superlinked docs](https://superlinked.com/docs)
diff --git a/docs/web/integrations/embedding/voyageai.mdx b/docs/web/integrations/embedding/voyageai.mdx
new file mode 100644
index 000000000..8babfc214
--- /dev/null
+++ b/docs/web/integrations/embedding/voyageai.mdx
@@ -0,0 +1,64 @@
+---
+title: VoyageAI
+sidebarTitle: VoyageAI
+---
+
+import { PyEmbeddingVoyageaiUsage, PyEmbeddingVoyageaiMultimodal } from '/snippets/integrations.mdx';
+
+Voyage AI provides cutting-edge embedding and rerankers.
+
+
+Using voyageai API requires voyageai package, which can be installed using `pip install voyageai`. Voyage AI embeddings are used to generate embeddings for text data. The embeddings can be used for various tasks like semantic search, clustering, and classification.
+You also need to set the `VOYAGE_API_KEY` environment variable to use the VoyageAI API.
+
+```shell
+export VOYAGE_API_KEY="your-api-key"
+```
+
+Supported models are:
+
+- voyage-4-large (best retrieval quality, 1024 default dimensions, supports 256/512/1024/2048)
+- voyage-4 (balanced general-purpose, 1024 default dimensions, supports 256/512/1024/2048)
+- voyage-4-lite (optimized for latency/cost, 1024 default dimensions, supports 256/512/1024/2048)
+- voyage-context-3
+- voyage-3.5
+- voyage-3.5-lite
+- voyage-3
+- voyage-3-lite
+- voyage-finance-2
+- voyage-multilingual-2
+- voyage-law-2
+- voyage-code-2
+- voyage-multimodal-3.5 (multimodal - supports text, images, and video)
+
+<Info>
+**Multimodal Model:** `voyage-multimodal-3.5` supports text, images, and video inputs. It outputs 1024-dimensional embeddings by default, configurable via the `output_dimension` parameter (256, 512, 1024, 2048). See the [VoyageAI multimodal embeddings documentation](https://docs.voyageai.com/docs/multimodal-embeddings) for more details.
+</Info>
+
+Supported parameters (to be passed in `create` method) are:
+
+| Parameter | Type | Default Value | Description |
+|---|---|--------|---------|
+| `name` | `str` | `None` | The model ID of the model to use. Supported models: voyage-4-large, voyage-4, voyage-4-lite, voyage-3, voyage-3-lite, voyage-3.5, voyage-3.5-lite, voyage-context-3, voyage-finance-2, voyage-multilingual-2, voyage-law-2, voyage-code-2, voyage-multimodal-3.5 |
+| `input_type` | `str` | `None` | Type of the input text. Default to None. Other options: query, document. |
+| `truncation` | `bool` | `True` | Whether to truncate the input texts to fit within the context length. |
+| `output_dimension` | `int` | `None` | Output embedding dimension. Only supported by `voyage-multimodal-3.5`. Valid options: 256, 512, 1024 (default), 2048. |
+
+
+Usage Example:
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingVoyageaiUsage}
+  </CodeBlock>
+</CodeGroup>
+
+### Multimodal Example {#multimodal-example}
+
+The `voyage-multimodal-3.5` model can embed text alongside images. You can use image URLs, file paths, or PIL Image objects:
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyEmbeddingVoyageaiMultimodal}
+  </CodeBlock>
+</CodeGroup>
\ No newline at end of file
diff --git a/docs/web/integrations/index.mdx b/docs/web/integrations/index.mdx
new file mode 100644
index 000000000..640628684
--- /dev/null
+++ b/docs/web/integrations/index.mdx
@@ -0,0 +1,17 @@
+---
+title: Integrations
+sidebarTitle: Overview
+description: Connect LanceDB with popular AI providers, frameworks, and data platforms
+---
+
+LanceDB seamlessly plugs into the rest of your AI and data engineering stack. Use the sections
+below to jump straight into the guides that matter for your workflow.
+
+
+| Group | Description |
+|:----------------|:-------------|
+| [Embedding models](/integrations/embedding/) | Connect with popular embedding model providers including OpenAI, Cohere, Hugging Face, and more |
+| [Reranking models](/integrations/reranking/) | Enhance search results with advanced reranking models and techniques |
+| [AI platforms & frameworks](/integrations/ai/) | Integrate with LangChain, LlamaIndex, Kiln, and other AI development frameworks |
+| [Data platforms & frameworks](/integrations/data/) | Integrate LanceDB with popular data tools and platforms like DuckDB, Pydantic and dlt |
+
diff --git a/docs/web/integrations/lerobotdataset.mdx b/docs/web/integrations/lerobotdataset.mdx
new file mode 100644
index 000000000..cfb66b69b
--- /dev/null
+++ b/docs/web/integrations/lerobotdataset.mdx
@@ -0,0 +1,164 @@
+---
+title: "LeRobotDataset"
+sidebarTitle: "LeRobotDataset"
+description: "Train LeRobot policies straight from object storage, and curate, search and visualize robotics datasets as Lance tables."
+keywords: ["lerobot", "robotics", "droid", "training", "dataloader"]
+---
+
+import {
+  PyFrameworksLerobotFilterFrames,
+  PyFrameworksLerobotLoad,
+  PyFrameworksLerobotOpenLanceTables,
+  PyFrameworksLerobotTrainReady,
+} from '/snippets/integrations.mdx';
+
+[LeRobot](https://huggingface.co/docs/lerobot/index) is Hugging Face's open-source robotics stack. Since [lerobot#4363](https://github.com/huggingface/lerobot/pull/4363) it reads Lance datasets natively: same `LeRobotDataset` class, same items, same training pipeline. The dataset's `"storage_format": "lance"` picks the reader. No subclasses, no wrapper library.
+
+What you get:
+
+- **Train from object storage while keeping your GPUs fed.** Lance reads random frames straight from S3, GCS, the HF Hub or HF Storage Buckets, fetching only the bytes each batch needs, so every batch is a global shuffle: any frame, from anywhere in the dataset. In the table below (samples/s, 8 workers, batch 64), Lance and upstream local both draw every batch from the whole dataset. Upstream streaming does a reservoir shuffle, reading the dataset in order and drawing each batch from a pool of recently seen frames, so those two columns are not the same job:
+
+| dataset | window shuffle, Lance from object store | window shuffle, upstream streaming from the Hub | Lance (object store) vs upstream streaming | RAM, Lance | RAM, streaming | global shuffle, Lance from object store | global shuffle, upstream from local NVMe | Lance (object store) vs upstream (local NVMe) |
+|---|---|---|---|---|---|---|---|---|
+| DROID | 904 | 478 | 1.9x | 8 GB | 122 GB | 257 | 224 | 1.1x |
+| roboturk | 778 | 152 | 5.1x | 6 GB | 9 GB | 1,074 | 983 | 1.1x |
+| aloha_mobile_cabinet | 772 | 51 | 15.2x | 9 GB | 30 GB | 693 | 573 | 1.2x |
+| koch_pick_place_5_lego | 701 | 54 | 13.0x | 8 GB | 15 GB | 876 | 526 | 1.7x |
+| pusht | 5,340 | 648 | 8.2x | 3 GB | 7 GB | 5,508 | 4,224 | 1.3x |
+| aloha_static_cups_open | 597 | 45 | 13.2x | 10 GB | 38 GB | 575 | 483 | 1.2x |
+
+The shuffle window is 0.05% of the dataset, at least 5,000 frames: 5,000 on the small sets, 13,815 on DROID. Streaming DROID at that window in upstream default needs 122 GB, so those two runs used a 256 GB machine with the same decode speed. The DROID pair is noisy: Lance 684 and 1,124, streaming 303 and 653.
+
+Same batches: Lance from an object store is 1.9x faster than upstream streaming on DROID and 5x to 15x on the other datasets, with 8 GB of RAM against 122 GB on DROID. Global shuffle: Lance with nothing downloaded is 1.1x to 1.7x faster than upstream reading local NVMe after the download. berkeley_rpt, toto and soarm101 are left out because their metadata lists frames the videos do not have; `lerobot-lance-doctor` reports that before you train.
+
+- **Faster iteration, end to end.** 10,000 steps of SmolVLA on DROID, 8xH100, lerobot's default `num_workers=4`, same seed:
+
+| | Lance, S3, nothing downloaded | upstream, local NVMe after a 384 GB download |
+|---|---|---|
+| wall clock | **1 h 27 m** | 2 h 00 m |
+| steady rate | 495 samples/s | 361 samples/s |
+| of each step spent waiting on data | 1.7% | 37.4% |
+| final loss | 0.2380 | 0.2380 |
+
+Same model, same loss, 33 minutes sooner, and no 384 GB download first. The difference is time the GPUs spend waiting on data, so it grows with the run: at 100k steps it is over five hours, and every re-run and sweep point pays it again.
+
+- **The training table is also the index.** Parquet has no secondary indexes, so finding frames instead of scanning them usually means another system per question: a vector database for embeddings, a search service for instructions, a feature store for scores. With Lance those are columns and indexes on the same table the DataLoader reads. One query spans all of them, at a version the trainer can reopen.
+
+## Install
+
+```bash
+pip install "lerobot[lancedb]"   # the reader, inside lerobot itself
+pip install lerobot-lancedb      # the converter
+```
+
+## Convert a dataset
+
+Any LeRobot v3.0 dataset, from the Hub or a local directory:
+
+```bash
+lerobot-lance-convert --repo-id lerobot/pusht --out ./pusht-lance
+```
+
+**Migration costs about as much as a file copy.** Videos are stored as blobs without re-encoding, so items are bit-identical to the source: 392,578 frames convert in 10 seconds, DROID's 27.6M in 34 minutes. Nothing about your recording or training code changes. The output keeps `meta/` as is and adds three Lance tables (`frames`, `videos`, `meta`). `lerobot-lance-doctor` audits the result and often finds problems in the source: DROID carries 44% orphaned rows, and three of eight public datasets we converted have frame-count defects that only random access exposes.
+
+Host the result anywhere: an HF dataset repo (`hf upload`, tag it `v3.0`), an HF Storage Bucket (`hf buckets sync`), or any object store (`aws s3 sync`).
+
+## Load and train
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksLerobotLoad}
+</CodeBlock>
+
+An explicit `root` works for any object store: `LeRobotDataset("lerobot/pusht", root="s3://my-bucket/pusht-lance")`. Remote roots download only `meta/`. Data is fetched per batch. Temporal windows, `DataLoader` and the rest of the training stack work unchanged:
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksLerobotTrainReady}
+</CodeBlock>
+
+Or from the CLI. Held-out evaluation on a remote dataset needs random access, so it only works on the Lance path:
+
+```bash
+lerobot-train \
+  --dataset.repo_id=my-org/my-bucket/pusht-lance \
+  --dataset.repo_type=bucket \
+  --dataset.eval_split=0.02 \
+  --policy.type=act
+```
+
+## Visualize TB-scale datasets in Foxglove
+
+LeRobot's dataset viewer can serve an episode to [Foxglove](https://foxglove.dev) over its WebSocket protocol ([lerobot#4542](https://github.com/huggingface/lerobot/pull/4542)), and with a Lance dataset it works wherever the data lives:
+
+```bash
+lerobot-dataset-viz --repo-id lance-format/pusht-lance --episode-index 3 --display-mode foxglove
+```
+
+Open the Foxglove app, connect to `ws://localhost:8765`, and scrub. Cameras, joint state and actions show up as topics you can lay out however you like:
+
+![Foxglove showing episode 7 of DROID served from S3: three camera panels and the joint state, scrubbed to 19.4 s](/static/assets/images/integrations/lerobot-foxglove-droid.png)
+
+Frames are fetched as you scrub, so looking at one episode of the 373 GB DROID from S3 moves a few MB, not the dataset. No MCAP export, no download, and nothing new to install on the dataset side: this is the same reader the trainer uses.
+
+## Query, curate, enrich
+
+The frames table is a LanceDB table like any other:
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksLerobotOpenLanceTables}
+</CodeBlock>
+
+Filter with SQL over any feature, using the indexes built at conversion:
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksLerobotFilterFrames}
+</CodeBlock>
+
+From here it is standard LanceDB. The examples below are from our DROID walkthrough, all on the table the trainer reads.
+
+**Feature engineering costs nothing until you use it.** With [Geneva](/geneva/udfs/udfs), `add_columns` registers a `@udf` function and its input columns as a new column and computes nothing; `backfill` computes and stores the values later, in parallel. No copy of the dataset, and the videos are never touched:
+
+```python
+@udf(data_type=pa.float32(), input_columns=["action_joint_velocity"])
+def jerk_score(v):                      # per-frame motion roughness
+    ...
+
+tbl.add_columns({"jerk_score": jerk_score})   # nothing computed yet
+tbl.backfill("jerk_score", concurrency=8)     # 610,403 rows in 16.7 s
+```
+
+Embeddings work the same way, reading frames straight from the video blobs in the same table:
+
+```python
+@udf(data_type=pa.list_(pa.float32(), 768), num_gpus=1,
+     input_columns=["episode_index", "frame_index"])
+class EmbedFromBlob:                    # SigLIP2 loads once per worker
+    def __call__(self, episode_index, frame_index):
+        blob = self.videos.take_blobs("video_bytes", indices=[vrow])[0]
+        frame = VideoDecoder(blob).get_frames_at([pos]).data[0]
+        ...
+```
+
+**Search** is an index on those columns: a vector index over the embeddings built in 29.7 s across 27.6M rows, a full-text index over the instructions in 7.0 s, queries in 10 to 54 ms.
+
+**Curation** is one query across all of it. Semantic search with a predicate on the derived score, no join, no second system:
+
+```python
+results = (
+    tbl.search(vec("a gripper closing on an object"), vector_column_name="emb_siglip2")
+    .where("jerk_score > 1.2875")  # the roughest 1% of frames
+    .limit(4)
+    .to_arrow()
+)
+```
+
+**Mining** is the same query pointed at the rare cases. `tbl.search().where("jerk_score > 1.2875 AND success = false")` pulls the rough, failed frames out of 27.6M rows in milliseconds, ready to become a fine-tuning set or a labeling queue.
+
+**EDA** is a scan of the tabular columns: a pass over all 27.6M rows reads 2.52 GB in 190 s and decodes no video.
+
+Then drop what you found from training with `episodes=[...]` instead of rewriting files. The reader ignores columns it does not know, and the table is versioned, so curation and training share one copy of the data at a version you can reopen.
+
+<Info>
+Lance datasets are read-only in LeRobot: record and edit in the default format, then convert.
+</Info>
+
+Public examples to try everything above: [`lance-format/pusht-lance`](https://huggingface.co/datasets/lance-format/pusht-lance) and friends under the [`lance-format`](https://huggingface.co/lance-format) org.
diff --git a/docs/web/integrations/reranking/answerdotai.mdx b/docs/web/integrations/reranking/answerdotai.mdx
new file mode 100644
index 000000000..e3f500d49
--- /dev/null
+++ b/docs/web/integrations/reranking/answerdotai.mdx
@@ -0,0 +1,52 @@
+---
+title: Answer.AI Rerankers
+sidebarTitle: "Answer.AI"
+description: Use AnswerDotAI's lightweight reranking library with LanceDB. Features unified API for common reranking models, configurable model selection, and comprehensive scoring options.
+
+---
+
+import { PyRerankingAnswerdotaiUsage } from '/snippets/integrations.mdx';
+
+# Answer.AI Rerankers
+
+This integration uses [AnswersDotAI's rerankers](https://github.com/AnswerDotAI/rerankers) to rerank the search results, providing a lightweight, low-dependency, unified API to use all common reranking and cross-encoder models.
+
+> **Note:** Supported query types – Hybrid, Vector, and FTS.
+
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyRerankingAnswerdotaiUsage}
+  </CodeBlock>
+</CodeGroup>
+
+## Accepted Arguments {#accepted-arguments}
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `model_type` | `str` | `"colbert"` | The type of model to use. Supported model types can be found here: https://github.com/AnswerDotAI/rerankers. |
+| `model_name` | `str` | `"answerdotai/answerai-colbert-small-v1"` | The name of the reranker model to use. |
+| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. |
+
+
+
+## Supported Scores for each query type {#supported-scores-for-each-query-type}
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+### Hybrid Search {#hybrid-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
+| `all` | ❌ Not Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
+
+### Vector Search {#vector-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
+| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). |
+
+### FTS Search {#fts-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
+| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
diff --git a/docs/web/integrations/reranking/cohere.mdx b/docs/web/integrations/reranking/cohere.mdx
new file mode 100644
index 000000000..26b98a7c8
--- /dev/null
+++ b/docs/web/integrations/reranking/cohere.mdx
@@ -0,0 +1,59 @@
+---
+title: Cohere Reranker
+sidebarTitle: "Cohere"
+description: Integrate Cohere's powerful reranking API with LanceDB for enhanced search results. Supports English and multilingual models with configurable scoring options for vector, FTS, and hybrid search.
+
+---
+
+import { PyRerankingCohereUsage } from '/snippets/provider_examples.mdx';
+
+# Cohere Reranker
+
+This reranker uses the [Cohere](https://cohere.ai/) API to rerank the search results. You can use this reranker by passing `CohereReranker()` to the `rerank()` method. Note that you'll either need to set the `COHERE_API_KEY` environment variable or pass the `api_key` argument to use this reranker.
+
+
+> **Note:** Supported query types – Hybrid, Vector, and FTS.
+
+```shell
+pip install cohere
+```
+
+Replace the credential placeholders with your provider credentials. In application code, load credentials from environment variables or a secrets manager.
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyRerankingCohereUsage}
+  </CodeBlock>
+</CodeGroup>
+
+## Accepted Arguments {#accepted-arguments}
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `model_name` | `str` | `"rerank-english-v2.0"` | The name of the reranker model to use. Available cohere models are: rerank-english-v2.0, rerank-multilingual-v2.0 |
+| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
+| `top_n` | `str` | `None` | The number of results to return. If None, will return all results. |
+| `api_key` | `str` | `None` | The API key for the Cohere API. If not provided, the `COHERE_API_KEY` environment variable is used. |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type |
+
+
+
+## Supported Scores for each query type {#supported-scores-for-each-query-type}
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+### Hybrid Search {#hybrid-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column |
+| `all` | ❌ Not Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
+
+### Vector Search {#vector-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column |
+| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |
+
+### FTS Search {#fts-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column |
+| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
diff --git a/docs/web/integrations/reranking/colbert.mdx b/docs/web/integrations/reranking/colbert.mdx
new file mode 100644
index 000000000..463b3d78d
--- /dev/null
+++ b/docs/web/integrations/reranking/colbert.mdx
@@ -0,0 +1,50 @@
+---
+title: ColBERT Reranker
+sidebarTitle: "ColBERT"
+description: Enhance search results with ColBERT's contextual reranking in LanceDB. Features efficient model deployment, device optimization, and flexible scoring options for vector, FTS, and hybrid search.
+
+---
+
+import { PyRerankingColbertUsage } from '/snippets/integrations.mdx';
+
+# ColBERT Reranker
+
+This reranker uses ColBERT model to rerank the search results. You can use this reranker by passing `ColbertReranker()` to the `rerank()` method. 
+> **Note:** Supported query types – Hybrid, Vector, and FTS.
+
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyRerankingColbertUsage}
+  </CodeBlock>
+</CodeGroup>
+
+## Accepted Arguments {#accepted-arguments}
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `model_name` | `str` | `"colbert-ir/colbertv2.0"` | The name of the reranker model to use.|
+| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
+| `device` | `str` | `None` | The device to use for the cross encoder model. If None, will use "cuda" if available, otherwise "cpu". |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. |
+
+
+## Supported Scores for each query type {#supported-scores-for-each-query-type}
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+### Hybrid Search {#hybrid-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
+| `all` | ❌ Not Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
+
+### Vector Search {#vector-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
+| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). |
+
+### FTS Search {#fts-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
+| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
diff --git a/docs/web/integrations/reranking/jina.mdx b/docs/web/integrations/reranking/jina.mdx
new file mode 100644
index 000000000..450a18ecf
--- /dev/null
+++ b/docs/web/integrations/reranking/jina.mdx
@@ -0,0 +1,56 @@
+---
+title: Jina Reranker
+sidebarTitle: "Jina AI"
+description: Integrate Jina's multilingual reranking API with LanceDB for improved search results. Features model selection, API key management, and flexible scoring options for all search types.
+
+---
+
+import { PyRerankingJinaUsage } from '/snippets/provider_examples.mdx';
+
+# Jina Reranker
+
+This reranker uses the [Jina](https://jina.ai/reranker/) API to rerank the search results. You can use this reranker by passing `JinaReranker()` to the `rerank()` method. Note that you'll either need to set the `JINA_API_KEY` environment variable or pass the `api_key` argument to use this reranker.
+
+
+> **Note:** Supported query types – Hybrid, Vector, and FTS.
+
+
+Replace the credential placeholders with your provider credentials. In application code, load credentials from environment variables or a secrets manager.
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyRerankingJinaUsage}
+  </CodeBlock>
+</CodeGroup>
+
+## Accepted Arguments {#accepted-arguments}
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `model_name` | `str` | `"jina-reranker-v2-base-multilingual"` | The name of the reranker model to use. You can find the list of available models in https://jina.ai/reranker. |
+| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
+| `top_n` | `str` | `None` | The number of results to return. If None, will return all results. |
+| `api_key` | `str` | `None` | The API key for the Jina API. If not provided, the `JINA_API_KEY` environment variable is used. |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. |
+
+
+
+## Supported Scores for each query type {#supported-scores-for-each-query-type}
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+### Hybrid Search {#hybrid-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
+| `all` | ❌ Not Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
+
+### Vector Search {#vector-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
+| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). |
+
+### FTS Search {#fts-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
+| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
diff --git a/docs/web/integrations/reranking/openai.mdx b/docs/web/integrations/reranking/openai.mdx
new file mode 100644
index 000000000..22d2af2cc
--- /dev/null
+++ b/docs/web/integrations/reranking/openai.mdx
@@ -0,0 +1,54 @@
+---
+title: OpenAI Reranker (Experimental)
+sidebarTitle: "OpenAI"
+description: Explore experimental search reranking using OpenAI's GPT models in LanceDB. Features configurable model selection, API key management, and comprehensive scoring options for all search types.
+
+---
+
+import { PyRerankingOpenaiUsage } from '/snippets/provider_examples.mdx';
+
+# OpenAI Reranker (Experimental)
+
+This reranker uses OpenAI chat model to rerank the search results. You can use this reranker by passing `OpenAI()` to the `rerank()` method. 
+> **Note:** Supported query types – Hybrid, Vector, and FTS.
+
+> **Warning:** This reranker is experimental. OpenAI does not have a dedicated reranking model, so it uses a chat model under the hood.
+
+Replace `your-api-key` with your provider API key. In application code, load
+credentials from environment variables or a secrets manager.
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyRerankingOpenaiUsage}
+  </CodeBlock>
+</CodeGroup>
+
+## Accepted Arguments {#accepted-arguments}
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `model_name` | `str` | `"gpt-4-turbo-preview"` | The name of the reranker model to use.|
+| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. |
+| `api_key` | `str` | `None` | The API key to use. If None, will use the OPENAI_API_KEY environment variable.
+
+
+## Supported Scores for each query type {#supported-scores-for-each-query-type}
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+### Hybrid Search {#hybrid-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
+| `all` | ❌ Not Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
+
+### Vector Search {#vector-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
+| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). |
+
+### FTS Search {#fts-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
+| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
diff --git a/docs/web/integrations/reranking/voyageai.mdx b/docs/web/integrations/reranking/voyageai.mdx
new file mode 100644
index 000000000..80a666f56
--- /dev/null
+++ b/docs/web/integrations/reranking/voyageai.mdx
@@ -0,0 +1,59 @@
+---
+title: VoyageAI Reranker
+sidebarTitle: "Voyage AI"
+description: Integrate VoyageAI's cutting-edge reranking models with LanceDB. Features model selection, API key management, and comprehensive scoring options for all search types.
+
+---
+
+import { PyRerankingVoyageaiUsage } from '/snippets/provider_examples.mdx';
+
+# VoyageAI Reranker
+
+Voyage AI provides cutting-edge embedding and rerankers.
+
+This reranker uses the [VoyageAI](https://docs.voyageai.com/docs/) API to rerank the search results. You can use this reranker by passing `VoyageAIReranker()` to the `rerank()` method. Note that you'll either need to set the `VOYAGE_API_KEY` environment variable or pass the `api_key` argument to use this reranker.
+
+
+> **Note:** Supported query types – Hybrid, Vector, and FTS.
+
+
+Replace `your-api-key` with your provider API key. In application code, load
+credentials from environment variables or a secrets manager.
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyRerankingVoyageaiUsage}
+  </CodeBlock>
+</CodeGroup>
+
+## Accepted Arguments {#accepted-arguments}
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `model_name` | `str` | `None` | The name of the reranker model to use. Available models are: rerank-2, rerank-2-lite |
+| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
+| `top_n` | `str` | `None` | The number of results to return. If None, will return all results. |
+| `api_key` | `str` | `None` | The API key for the Voyage AI API. If not provided, the `VOYAGE_API_KEY` environment variable is used. |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type |
+| `truncation` | `bool` | `None` | Whether to truncate the input to satisfy the "context length limit" on the query and the documents. |
+
+
+## Supported Scores for each query type {#supported-scores-for-each-query-type}
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+### Hybrid Search {#hybrid-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ❌ Not Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
+
+### Vector Search {#vector-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ✅ Supported | Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |
+
+### FTS Search {#fts-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
+| `all` | ✅ Supported | Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
diff --git a/docs/web/integrations/reranking/watsonx.mdx b/docs/web/integrations/reranking/watsonx.mdx
new file mode 100644
index 000000000..5e4c6322c
--- /dev/null
+++ b/docs/web/integrations/reranking/watsonx.mdx
@@ -0,0 +1,65 @@
+---
+title: Watsonx Reranker
+sidebarTitle: "IBM watsonx.ai"
+description: Rerank LanceDB search results with the IBM watsonx.ai text rerank API. Supports vector, FTS, and hybrid search with configurable models, projects, and spaces.
+
+---
+
+import { PyRerankingWatsonxUsage } from '/snippets/provider_examples.mdx';
+
+# Watsonx Reranker
+
+This reranker uses the [IBM watsonx.ai](https://cloud.ibm.com/docs/apis/watsonx-ai#text-rerank) text rerank API to reorder search results. Pass `WatsonxReranker()` to the `rerank()` method on a query. Credentials come from the `WATSONX_API_KEY` and `WATSONX_PROJECT_ID` (or `WATSONX_SPACE_ID`) environment variables, or can be passed explicitly as arguments.
+
+> **Note:** Supported query types – Hybrid, Vector, and FTS.
+
+```shell
+pip install ibm-watsonx-ai
+```
+
+Replace the credential placeholders with your provider credentials. In application code, load credentials from environment variables or a secrets manager.
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyRerankingWatsonxUsage}
+  </CodeBlock>
+</CodeGroup>
+
+## Accepted Arguments {#accepted-arguments}
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `model_name` | `str` | `"cross-encoder/ms-marco-minilm-l-12-v2"` | The rerank model ID. See [supported rerank models](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx#rerank). |
+| `column` | `str` | `"text"` | The name of the table column to use as document input. |
+| `top_n` | `int` | `None` | The number of results to return. If `None`, all results are returned. |
+| `return_score` | `str` | `"relevance"` | Options are `"relevance"` or `"all"`. Controls which score columns are kept on the result. |
+| `api_key` | `str` | `None` | IBM Cloud API key. Falls back to the `WATSONX_API_KEY` environment variable. |
+| `project_id` | `str` | `None` | watsonx.ai project ID. Falls back to `WATSONX_PROJECT_ID`. Mutually exclusive with `space_id`. |
+| `space_id` | `str` | `None` | watsonx.ai deployment space ID. Falls back to `WATSONX_SPACE_ID`. Mutually exclusive with `project_id`. |
+| `url` | `str` | `"https://us-south.ml.cloud.ibm.com"` | watsonx.ai service URL. |
+| `truncate_input_tokens` | `int` | `None` | Truncate each document to this many tokens before scoring. |
+
+<Info>
+You must supply exactly one of `project_id` or `space_id` (either as an argument or via its environment variable). Setting both, or neither, raises a `ValueError`.
+</Info>
+
+## Supported scores for each query type {#supported-scores-for-each-query-type}
+
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+### Hybrid Search {#hybrid-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
+| `all` | ❌ Not Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
+
+### Vector Search {#vector-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
+| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). |
+
+### FTS Search {#fts-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
+| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
diff --git a/docs/web/integrations/stable-worldmodel.mdx b/docs/web/integrations/stable-worldmodel.mdx
new file mode 100644
index 000000000..ee35e2159
--- /dev/null
+++ b/docs/web/integrations/stable-worldmodel.mdx
@@ -0,0 +1,107 @@
+---
+title: "Stable World Model"
+sidebarTitle: "Stable World Model"
+description: "Use Stable World Model with LanceDB-backed datasets for reproducible world model research, fast data loading, and compact storage."
+---
+
+import {
+  PyFrameworksStableWorldmodelCollectLance,
+  PyFrameworksStableWorldmodelConvert,
+  PyFrameworksStableWorldmodelEvaluate,
+  PyFrameworksStableWorldmodelLoadLance,
+} from '/snippets/integrations.mdx';
+
+[Stable World Model](https://github.com/galilai-group/stable-worldmodel) is a research platform for collecting data, training world models, and evaluating policies with model-predictive control across standardized environments.
+
+The LanceDB integration is built into Stable World Model's data format registry. Lance is the default backend for collected datasets, so a path ending in `.lance` gives you an append-friendly LanceDB table with episode-contiguous rows and fast indexed reads.
+
+Random access speed is the bottleneck for world model training, since the loop repeatedly samples temporal windows from high-dimensional observations, actions, and rewards. The faster those windows arrive, the more GPU time goes into training rather than waiting on the data loader.
+
+## Install {#install}
+
+```bash
+pip install stable-worldmodel
+```
+
+Datasets and checkpoints are stored under `$STABLEWM_HOME`, which defaults to `~/.stable_worldmodel/`.
+
+## Collect data into Lance {#collect-data-into-lance}
+
+Stable World Model uses Lance by default when you collect to a `.lance` path.
+Replace `your_expert_policy` with the expert or scripted policy you use to collect demonstrations.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksStableWorldmodelCollectLance}
+</CodeBlock>
+
+Every writer accepts a `mode` argument such as `append`, `overwrite`, or `error`. The default is append, so re-running collection extends the existing dataset.
+
+## Load a Lance dataset for training {#load-a-lance-dataset-for-training}
+
+The dataset loader autodetects the Lance format from the path.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksStableWorldmodelLoadLance}
+</CodeBlock>
+
+Your model code stays focused on the world model objective while LanceDB handles the storage layout and read path.
+
+## Evaluate with model-predictive control {#evaluate-with-model-predictive-control}
+
+After training a world model on the Lance-backed dataset, Stable World Model can evaluate it with planning solvers such as CEM.
+Replace `world_model` with the trained model object from your training loop.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksStableWorldmodelEvaluate}
+</CodeBlock>
+
+## Convert between formats {#convert-between-formats}
+
+Stable World Model can convert between registered dataset formats. A common workflow is to collect in Lance for fast training reads, then export to the video layout for compact inspection artifacts.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+  {PyFrameworksStableWorldmodelConvert}
+</CodeBlock>
+
+## Throughput {#throughput}
+
+The Stable World Model README reports the following PushT benchmark results from `scripts/benchmark/compare_h5_lance.py`:
+
+| Format | Source | Cache | samples/s | ms/step |
+|:---|:---|:---|---:|---:|
+| HDF5 | local | no-cache | 1,416.1 | 45.2 |
+| HDF5 | local | cached | 1,474.0 | 43.4 |
+| LanceDB | local | no-cache | 4,814.8 | 13.3 |
+| LanceDB | local | cached | 4,431.3 | 14.4 |
+| Video | local | - | 1,330.6 | 48.1 |
+| LanceDB | s3 | no-cache | 3,183.7 | 20.1 |
+| LanceDB | s3 | cached | 3,253.2 | 19.7 |
+| HDF5 | s3 | no-cache | 9.1 | 7,032.5 |
+| HDF5 | s3 | cached | 756.5 | 84.6 |
+
+In that benchmark, local LanceDB reached about **3.4x** the no-cache throughput of local HDF5, while S3-backed LanceDB reached about **350x** the no-cache throughput of S3-backed HDF5. Even with cache enabled, S3-backed LanceDB was about **4.3x** faster than S3-backed HDF5.
+
+These numbers come from the Stable World Model project's own benchmark setup, so they're best read as a reproducible directional baseline that may shift across environments, models, and storage configurations.
+
+## Storage {#storage}
+
+The same README reports these local storage sizes for the benchmark dataset:
+
+| Format | Local size |
+|:---|---:|
+| HDF5 | 43.12 GB |
+| LanceDB | 13.31 GB |
+| Video | 496.29 MB |
+
+LanceDB used about **69% less local storage than HDF5** in the reported benchmark, while preserving a table interface built for fast training reads and append-heavy collection.
+
+## More resources {#more-resources}
+
+<CardGroup cols={2}>
+  <Card title="Stable World Model README" href="https://github.com/galilai-group/stable-worldmodel/blob/main/README.md">
+    Installation, quick start, supported formats, benchmarks, environments, solvers, and citation.
+  </Card>
+  <Card title="Stable World Model docs" href="https://galilai-group.github.io/stable-worldmodel/">
+    Full upstream documentation with tutorials, API references, and guides.
+  </Card>
+</CardGroup>
diff --git a/docs/web/maintenance/background-jobs.mdx b/docs/web/maintenance/background-jobs.mdx
new file mode 100644
index 000000000..2fe5d549c
--- /dev/null
+++ b/docs/web/maintenance/background-jobs.mdx
@@ -0,0 +1,73 @@
+---
+title: "Background jobs"
+sidebarTitle: "Background Jobs"
+description: "Understand the Enterprise background jobs that maintain tables, indexes and caches."
+icon: gears
+keywords: ["background jobs", "job type", "wal", "spfresh", "prewarm", "archive"]
+availability:
+  oss: unavailable
+  enterprise: available
+  summary: >-
+    LanceDB Enterprise schedules background maintenance jobs according to your
+    cluster configuration.
+---
+
+The jobs documented elsewhere in these docs -- [Build & Repair](/indexing/reindexing),
+[Compaction](/maintenance/compaction), [Cleanup](/maintenance/cleanup),
+[Column Refresh](/feature-engineering/refresh-column) -- start from a call you
+make or, for compaction, cleanup and index updates, from the deployment's own
+maintenance. This page is the rest: jobs the deployment runs to keep itself
+correct and fast, which you may see named in [`SHOW JOBS`](/sql/ddl/job#show)
+without having asked for any of them.
+
+These jobs are configured by the deployment's operators rather than through
+the table SDK. If a maintenance job repeatedly fails or stops making progress,
+share its job ID and errors with your Enterprise support team. Operators can
+also start cache-warming jobs through the admin API.
+
+## Write-ahead log maintenance {#wal}
+
+For a table with a [write-ahead log](/sql/ddl/table#properties) enabled, two jobs keep the
+log from growing without bound:
+
+| Job type | What it does |
+| --- | --- |
+| `sstable_compaction` | Folds a table shard's SSTables into the base table by primary key. Distinct from `compact`, which merges base-table fragments -- the two are unrelated. |
+| `wal_index_catchup` | Extends a base-table index over the rows `sstable_compaction` just folded in, and records how far the index has caught up. Nothing else advances that record. |
+
+## Index maintenance {#index}
+
+| Job type | What it does |
+| --- | --- |
+| `index_remap` | Reassigns index entries after row ids shift underneath an index -- the kind of remap a delta compaction can require. |
+| `spfresh_merge` | Merges SPFresh-managed segments for indexes that use it. |
+
+## Cache warming {#cache}
+
+| Job type | What it does |
+| --- | --- |
+| `prewarm_page_cache` | Loads table data into a query node's page cache ahead of a request needing it. |
+| `prewarm_index_cache` | The same, for index structures. |
+| `startup_prewarm_idx` | Runs cache prewarming as part of a plan executor's own startup, rather than on a schedule, when the deployment enables it. |
+
+## Registry housekeeping {#registry}
+
+| Job type | What it does |
+| --- | --- |
+| `job_registry_archive` | Sweeps terminal jobs out of the active registry and into archival storage, on a per-database schedule. |
+| `job_events_export` | Copies terminal jobs' event histories into a curated system table for metering. Registry-scoped, like the archive sweep. |
+
+These two are why an old job you completed weeks ago no longer shows in
+`SHOW JOBS` -- it was archived, not deleted. Where the deployment enables the
+export, its events also survive in the exported table.
+
+## Object teardown {#teardown}
+
+| Job type | What it does |
+| --- | --- |
+| `drop_object` | Deletes a dropped catalog object's content, then clears it from the database's cleanup backlog. Whether a dropped table runs as this job or as [`drop_table`](/management/table#drop), which fences the table's directory with its own marker instead, depends on how the deployment stores its catalog. |
+
+You trigger it indirectly, by dropping a table, a [view](/sql/ddl/view#drop) or
+a [namespace](/management/namespace); the teardown runs as this job rather than
+inline with the call.
+{/* Draft for the Functions release: Dropping a [function](/management/function#drop) starts it the same way. */}
diff --git a/docs/web/maintenance/cleanup.mdx b/docs/web/maintenance/cleanup.mdx
new file mode 100644
index 000000000..60a2f6bb0
--- /dev/null
+++ b/docs/web/maintenance/cleanup.mdx
@@ -0,0 +1,117 @@
+---
+title: "Cleanup"
+sidebarTitle: "Cleanup"
+description: "Pruning old versions and the files only they referenced."
+icon: broom
+keywords: ["cleanup", "retention", "prune", "delete_unverified", "vacuum"]
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Both offerings support cleanup. Embedded `optimize()` uses a retention window of 7 days by default; Enterprise uses configured background cleanup and retention, and does not support `optimize()`.
+---
+
+Every write keeps the version before it, which is what makes
+[time travel](/management/table#versioning) possible. Cleanup is what ends
+that -- pruning versions older than a retention window, and the files that only
+those versions referenced.
+
+<Note>
+**Enterprise job type:** `cleanup`. Distinct from `compact` -- compaction
+merges files, cleanup removes them. `optimize()` in OSS runs both in one call;
+Enterprise schedules them as separate jobs, and runs cleanup only when the
+deployment's configuration enables it.
+</Note>
+
+## Retention {#retention}
+
+```python
+from datetime import timedelta
+
+table.optimize(cleanup_older_than=timedelta(days=30))
+```
+
+`cleanup_older_than` defaults to **7 days**. Versions older than that are
+removed, along with the files only they referenced. In LanceDB Enterprise, the
+retention period comes from its configuration instead.
+
+The latest version is never removed, whatever you pass. `timedelta(0)` makes
+every older version eligible for removal, but cleanup still keeps tagged
+versions and the files branches still read, as the warning below describes.
+
+<Warning>
+Pruning is what makes [time travel](/management/table#versioning) finite: an
+eligible version outside the retention window is gone. Cleanup keeps a version,
+whatever its age, when it is:
+
+- the **latest** version;
+- a **tagged** version.
+
+Cleanup also keeps the files a **branch** still reads, and the version the
+branch was forked from while the branch's remaining versions read the parent's
+files. That does not keep the fork for good: once the branch has replaced the
+data it inherited and its own older versions are pruned, cleanup prunes the
+fork version like any other. To keep a version recoverable, tag it, subject to
+the policy below, or keep the window wide enough to cover it.
+
+Under the default policy, a tag can also stop cleanup. When a tagged version is
+older than the window and is not the latest, cleanup refuses to run: it raises
+an error naming the tag and prunes nothing. To let it proceed, move the tag to a
+version inside the window, delete the tag, or widen the window.
+
+A tag on the latest version does not stop cleanup. But `optimize()` compacts
+before it prunes, and compacting commits a new version whenever it merges
+files, so a tag on what was the latest version can become an older one by the
+time cleanup runs. `optimize()` then raises after compacting, and its index
+update does not run.
+
+A deployment's background cleanup follows the same rules. Its own background
+jobs, such as compaction and index updates, also commit new versions, so a tag
+on what is now the latest version can become an older one without a call from
+you. If you rely on reading old versions, set the window to cover the range you
+need before running this, not after.
+</Warning>
+
+## Disk usage does not drop immediately {#disk}
+
+Compaction writes new, merged files before the old ones are removed, so disk
+usage can rise before it falls. Space is reclaimed only when cleanup prunes the
+versions that referenced the superseded files.
+
+If you need to reclaim space aggressively in the embedded library, shorten the
+retention window rather than waiting for the default:
+
+```python
+table.optimize(cleanup_older_than=timedelta(days=1))
+```
+
+## Files from failed transactions {#unverified}
+
+A write that fails partway can leave files that look like an operation still
+in progress. Cleanup will not remove them until they are at least seven days
+old, because it cannot tell them apart from a live write.
+
+```python
+table.optimize(delete_unverified=True)
+```
+
+This option belongs to `optimize()` in the embedded library. A deployment's
+cleanup jobs never set it.
+
+<Warning>
+`delete_unverified=True` removes them regardless of age. Only set it when you
+can guarantee **no other process is writing to the table** -- otherwise it can
+delete files belonging to a write that is genuinely in progress, and corrupt
+the table.
+</Warning>
+
+## What to read next {#next}
+
+<CardGroup cols={2}>
+  <Card title="Compaction" icon="broom" href="/maintenance/compaction">
+    Merging small files -- the other half of `optimize()`.
+  </Card>
+  <Card title="Background jobs" icon="gears" href="/maintenance/background-jobs">
+    The Enterprise jobs that run without a call from you.
+  </Card>
+</CardGroup>
diff --git a/docs/web/maintenance/compaction.mdx b/docs/web/maintenance/compaction.mdx
new file mode 100644
index 000000000..757a344fc
--- /dev/null
+++ b/docs/web/maintenance/compaction.mdx
@@ -0,0 +1,106 @@
+---
+title: "Compaction and cleanup"
+sidebarTitle: "Compaction"
+description: "Merge small files, prune old versions, and fold new rows into existing indexes."
+icon: broom
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Tables are compacted in the embedded library and in LanceDB Enterprise. In the
+    embedded library you call `optimize()`; a deployment compacts tables in the
+    background, and `optimize()` is not available there.
+---
+
+import { PyStorageRebuildVectorIndex } from '/snippets/doc_storage_examples.mdx';
+
+Every write commits a new table version, and versions accumulate: small files
+from frequent appends, superseded files from updates and deletes, and rows added
+since an index was built. In the embedded library, `optimize()` addresses all
+three; a deployment handles them in the background and does not support
+`optimize()`.
+
+Run optimization as regular table maintenance, particularly after many small
+writes or a large ingest.
+
+## Optimize a table {#what-it-does}
+
+```python
+table.optimize()
+```
+
+Three operations, in one call:
+
+| | |
+| --- | --- |
+| **Compaction** | merges small files into larger ones |
+| **Prune** | removes versions older than the retention window -- see [Cleanup](/maintenance/cleanup) |
+| **Index** | folds newly added rows into existing indexes -- see [Build & Repair](/indexing/reindexing) |
+
+Compaction itself is only the first of the three: merging small fragments back
+into larger ones, which is what keeps a scan from opening more files than it
+needs to.
+
+<Note>
+**Enterprise job type:** `compact`. Runs automatically as data accumulates,
+separately from [`cleanup`](/maintenance/cleanup) -- the two are distinct jobs
+even though `optimize()` bundles both in one OSS call.
+</Note>
+
+## Retraining indexes {#retrain}
+
+Folding new rows into an index preserves the structure the index was trained on.
+When the data distribution has shifted enough that the index's clusters are a
+poor fit, recreate the index with `replace=True` to train it on the current data.
+The deprecated `retrain` argument to `optimize()` does not rebuild indexes.
+
+This example assumes a populated table whose `vector` column has an IVF_FLAT
+index configured for L2 distance and four partitions:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyStorageRebuildVectorIndex}
+    </CodeBlock>
+</CodeGroup>
+
+Use your index's column, type, distance metric, and training settings when
+rebuilding it. Recreating an index is more expensive than a normal optimize,
+and can help after a large ingest that changed the data distribution.
+
+## When to run it {#when}
+
+In the embedded library there is no background compactor: `optimize()` runs
+when you call it. A deployment compacts a table automatically once enough small
+fragments accumulate, unless its operator turns this off.
+
+Frequent small appends are what make it necessary, because each one commits
+files. A table written in a few large batches needs it rarely; one taking
+continuous small writes needs it on a schedule.
+
+<Note>
+Compaction rewrites files, which conflicts with a concurrent
+[computed-column refresh](/feature-engineering/add-computed-column) or
+[view refresh](/feature-engineering/materialized-views) planned against the
+version it replaces. Sequence them rather than running both at once.
+</Note>
+
+See [Cleanup](/maintenance/cleanup) for pruning old versions, and
+[Build & Repair](/indexing/reindexing) for the index-folding half of
+`optimize()`.
+
+## Performance {#performance}
+
+Two things accumulate on a long-lived table as more and more data gets added to it:
+
+- **Many small fragments build up as you write**, slowing down queries that have to scan across more files. **Compaction** merges them back into larger fragments.
+- **Old versions build up as the table changes**, growing disk usage beyond the live data size (LanceDB retains them for time-travel and rollback). **Cleanup** prunes versions older than a retention window.
+
+In OSS, you run them yourself via `optimize()`, which bundles both into a single call:
+
+```python Python icon="python"
+from datetime import timedelta
+table.optimize()                                       # 7-day default retention
+table.optimize(cleanup_older_than=timedelta(days=1))   # reclaim space sooner
+```
+
+The best practice is to run `optimize()` after large writes or on a schedule. It also bundles incremental index updates. See [Build & Repair](/indexing/reindexing) for the breakdown. Updates also move rows out of the vector index, so they remain searchable but unindexed. Update or rebuild the index after large update batches.
diff --git a/docs/web/management/database.mdx b/docs/web/management/database.mdx
new file mode 100644
index 000000000..a4b76f195
--- /dev/null
+++ b/docs/web/management/database.mdx
@@ -0,0 +1,163 @@
+---
+title: "Database"
+sidebarTitle: "Database"
+description: "Connect to LanceDB and configure storage, credentials and read consistency."
+icon: database
+keywords: ["connect", "database", "uri", "api key", "storage options", "consistency"]
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    OSS opens tables in a local directory or object storage. Enterprise connects
+    to remote compute with a database URI and credentials.
+---
+
+import { PyConnectEnterprisePlaceholders, TsConnectEnterprisePlaceholders } from '/snippets/connection.mdx';
+
+A database connection provides access to tables and namespaces in a
+[catalog](/basics/catalog). Use it to create and open tables, list catalog
+objects, and configure how your application reaches the data.
+
+## Connection URIs {#uris}
+
+| URI | Offering | Where queries run |
+| --- | --- | --- |
+| `./.lancedb` | OSS, with local storage | In your application |
+| `s3://bucket/path`, `gs://bucket/path`, `az://container/path` | OSS, with object storage | In your application |
+| `db://my-database` | Enterprise | On remote compute |
+
+[Connect to LanceDB](/basics/connect) has examples for each connection type.
+A connection to object storage still uses the embedded library: your process
+reads and writes the bucket directly. Enterprise runs operations remotely and
+manages the supporting compute and maintenance.
+
+## Connect to Enterprise {#deployment}
+
+Replace the placeholders with the database URI, key, region and endpoint
+provided by your administrator.
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyConnectEnterprisePlaceholders}
+  </CodeBlock>
+  <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    { "import * as lancedb from \"@lancedb/lancedb\";\n\n" }
+    {TsConnectEnterprisePlaceholders}
+  </CodeBlock>
+</CodeGroup>
+
+| Python option | TypeScript option | Purpose |
+| --- | --- | --- |
+| `api_key` | `apiKey` | Authenticate the connection |
+| `region` | `region` | Select the database's region |
+| `host_override` | `hostOverride` | Set the Enterprise endpoint |
+| `client_config` | `clientConfig` | Configure retries, timeouts and connection pooling. A retry can [repeat an append](/ingestion/insert#performance). |
+
+Use your deployment's region rather than relying on the `us-east-1` default.
+For application code, load credentials from environment variables or a secrets
+manager. See [Authentication](/enterprise/authentication) for OAuth and
+[SQL](/basics/sql) for the separate SQL connection settings.
+
+## Storage options {#storage-options}
+
+For OSS tables in object storage, `storage_options` configures credentials,
+endpoints and options for new tables. This example enables stable row IDs for
+tables created through the connection:
+
+<CodeGroup>
+```python Python icon="python"
+import lancedb
+
+db = lancedb.connect(
+    "s3://bucket/path",
+    storage_options={"new_table_enable_stable_row_ids": "true"},
+)
+```
+
+```typescript TypeScript icon="square-js"
+import * as lancedb from "@lancedb/lancedb";
+
+const db = await lancedb.connect("s3://bucket/path", {
+  storageOptions: { new_table_enable_stable_row_ids: "true" },
+});
+```
+</CodeGroup>
+
+Set stable row IDs before creating a table that needs them, such as an ordinary
+materialized-view source in Python SDK 0.40.0. The option does not change tables
+that already exist. Enterprise storage settings are managed on the server.
+
+See [Storage](/deployment/storage) for provider credentials and other options.
+
+## Read consistency {#consistency}
+
+For OSS connections, `read_consistency_interval` controls how often reads check
+for table versions committed by other writers. For example, check at most once
+every five seconds:
+
+<CodeGroup>
+```python Python icon="python"
+from datetime import timedelta
+import lancedb
+
+db = lancedb.connect(
+    "./.lancedb", read_consistency_interval=timedelta(seconds=5)
+)
+```
+
+```typescript TypeScript icon="square-js"
+import * as lancedb from "@lancedb/lancedb";
+
+const db = await lancedb.connect("./.lancedb", { readConsistencyInterval: 5 });
+```
+</CodeGroup>
+
+By default, an OSS table handle does not automatically pick up another
+process's writes. Reopen the table, check out its latest version, or configure
+a consistency interval when you need those updates. A zero interval checks on
+every read, with the cost of a metadata check.
+
+Enterprise manages read freshness on the server. See
+[Consistency](/tables/consistency) for its behavior and the distinction between
+seeing your own writes and seeing another client's writes.
+
+## Database operations {#operations}
+
+The connection provides methods to create, open, list and drop tables.
+
+| Operation | Python | TypeScript |
+| --- | --- | --- |
+| Create a table | `create_table` | `createTable` |
+| Open a table | `open_table` | `openTable` |
+| List tables | `table_names`, `list_tables` | `tableNames`, `listTables` |
+| Drop a table | `drop_table` | `dropTable` |
+
+Use the namespace and pagination options documented in
+[Namespaces](/management/namespace) when working outside the root namespace
+or listing a large catalog. [Tables](/management/table) covers table schemas,
+versions and lifecycle operations.
+
+<Warning>
+Dropping a table removes its history as well as its current data. You cannot
+recover a dropped table through table versioning. The OSS `drop_database()`
+and `drop_all_tables()` operations remove all tables without a confirmation prompt.
+</Warning>
+
+## Async connections {#async}
+
+Python's `connect_async` returns an `AsyncConnection`. Await its operations
+when working in an asynchronous application:
+
+```python
+import lancedb
+
+db = await lancedb.connect_async("./.lancedb")
+table = await db.open_table("documents")
+```
+
+TypeScript's connection and table operations already return promises.
+
+An async connection and a background job serve different purposes. Async APIs
+let other tasks run while your application waits for an operation. A
+[job](/basics/jobs) represents work that can continue on the server after the
+submission call returns.
diff --git a/docs/web/management/function.mdx b/docs/web/management/function.mdx
new file mode 100644
index 000000000..5734d2545
--- /dev/null
+++ b/docs/web/management/function.mdx
@@ -0,0 +1,236 @@
+---
+title: "Functions"
+sidebarTitle: "Function"
+description: "Register a Python function once and apply it as a column, for work SQL cannot express."
+icon: code
+keywords: ["udf", "functions", "create_function", "embedding", "feature engineering"]
+availability:
+  oss: unavailable
+  enterprise: available
+  summary: >-
+    Enterprise supports Functions when enabled for your Enterprise cluster; the embedded library cannot register or run them.
+---
+
+import {
+    PyFunctionDefine as FunctionDefine,
+    PyFunctionLocalCall as FunctionLocalCall,
+    PyFunctionCreateTable as FunctionCreateTable,
+    PyFunctionRegister as FunctionRegister,
+    PyFunctionApply as FunctionApply,
+    PyFunctionRefresh as FunctionRefresh,
+    PyFunctionInspect as FunctionInspect,
+} from '/snippets/function.mdx';
+import { PyConnectEnterprisePlaceholders as FunctionConnect } from '/snippets/connection.mdx';
+
+Some columns cannot be written as a SQL expression: an embedding, a model call,
+an image decode. A function is a Python callable registered with the database,
+versioned, and then applied as a column — the code runs where the data is
+rather than pulling rows to your process.
+
+Registering and running a function needs a LanceDB Enterprise deployment with
+Functions enabled, which they are not by default. On a
+local connection `create_function` raises `NotImplementedError`; to derive a
+column locally, declare a
+[SQL computed column](/feature-engineering/add-computed-column) instead.
+
+<Note>
+In SQL, a function is registered with [`CREATE FUNCTION`](/sql/ddl/function#create) and listed with [`SHOW FUNCTIONS`](/sql/ddl/function#show).
+</Note>
+
+Functions are the way to fill columns with Python in LanceDB Enterprise. If you
+already use [Geneva](/geneva), it remains available; see
+[Geneva and Functions](/geneva#functions) for how its concepts map onto these.
+
+The steps below take a column of numbers to a filled column of their doubles.
+
+## Define a function {#define}
+
+`@udf` packages a scalar Python callable. Input and output types are inferred
+from annotations, so `double` takes one non-nullable `float64` parameter,
+`value`, and returns a non-nullable `float64`:
+
+<CodeBlock filename="Python" language="Python" icon="python">
+{FunctionDefine}
+</CodeBlock>
+
+The decorated object stays callable. Calling it runs the original function in
+your process, without the database, so unit tests stay ordinary tests:
+
+<CodeBlock filename="Python" language="Python" icon="python">
+{FunctionLocalCall}
+</CodeBlock>
+
+Where annotations cannot express the Arrow type precisely, pass `input_schema`
+and `output_schema` together — one without the other is rejected. A scalar
+output must be non-nullable, because a physical NULL is what marks a
+computed-column row as unfilled. A named-struct output may declare nullable
+fields: whether a row has a result at all is the struct's own validity, which
+Enterprise keeps in a separate internal column when it expands the result into
+sibling columns, so a null field is a value rather than a missing row.
+
+## Connect and create a table {#table}
+
+The remaining steps run against your Enterprise deployment. Replace the database
+URI, API key, region and endpoint below with your deployment details. In application
+code, load credentials from environment variables or a secrets manager:
+
+<CodeBlock filename="Python" language="Python" icon="python">
+{FunctionConnect}
+</CodeBlock>
+
+For an ordinary stored input column, the Arrow type must match the parameter.
+A nullable column requires a nullable parameter, such as `Optional[float]`;
+a non-nullable column can bind to either. This example uses `value: float`,
+so the table declares `value` non-nullable.
+
+Computed-column inputs depend on the Enterprise server version. Where supported,
+they bind to non-nullable parameters even though the physical column is
+nullable: refresh skips rows whose computed input is still null. Check your
+deployment's support before using another computed column as an input.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+{FunctionCreateTable}
+</CodeBlock>
+
+## Register it {#register}
+
+Registration returns a job; the immutable version exists once the job succeeds.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+{FunctionRegister}
+</CodeBlock>
+
+`create_function` is the blocking equivalent. A registered version is fetched
+with `db.get_function(name, version=...)`.
+
+<Note>
+**Enterprise job type:** `create_function`. Registration returns a job because
+the function is baked into an image before the version becomes usable.
+</Note>
+
+## Apply it as a column {#apply}
+
+Calling the registered version binds each parameter, by name, to a column with
+`col(...)`. `add_columns` declares the result as a new column:
+
+<CodeBlock filename="Python" language="Python" icon="python">
+{FunctionApply}
+</CodeBlock>
+
+The column is declared empty; the function has not run yet. A mapping with one
+function application keeps its scalar or named-struct result in the named
+column. A bare named-struct application expands its result fields as one atomic
+binding, with aliases from `rename(columns=...)`.
+
+## Fill the column {#refresh}
+
+A refresh runs the function on the cluster as a [job](/basics/jobs). `wait()`
+blocks until the job finishes and returns its result, including how many rows
+it filled:
+
+<CodeBlock filename="Python" language="Python" icon="python">
+{FunctionRefresh}
+</CodeBlock>
+
+The filled values are read like any other column:
+
+<CodeBlock filename="Python" language="Python" icon="python">
+{FunctionInspect}
+</CodeBlock>
+
+Filling then works as it does for a SQL computed column: a later refresh fills
+rows added since the last one and recomputes rows whose inputs changed, and
+leaves filled rows with unchanged inputs alone. See
+[Refresh a column](/feature-engineering/refresh-column).
+
+## Declare the environment {#environment}
+
+The function runs remotely, so its dependencies are part of its definition:
+
+| | |
+| --- | --- |
+| `pip` | requirements for the remote environment |
+| `conda`, `conda_channels` | conda packages instead of pip; channels in priority order |
+| `env` | environment variables |
+| `python_version` | remote Python major/minor, defaulting to yours |
+| `gpu` | whether every execution requires a GPU |
+
+These are part of the **immutable function version**. Changing a dependency
+produces a new version rather than altering the one already registered, so a
+column filled by version 1 is unaffected by version 2.
+
+Declaring them is not optional bookkeeping. If the worker environment differs
+from the one the function was written against, the failures are among the
+hardest to diagnose in the system, because the error rarely names the cause.
+
+### Symptoms of an environment mismatch {#mismatch-symptoms}
+
+| Symptom | Likely cause |
+| --- | --- |
+| `TypeError: Enum.__new__() missing 1 required positional argument` | `attrs` version mismatch |
+| `TypeError: Can't instantiate abstract class` | package structure differs between versions |
+| `ArrowInvalid: cannot cast`, or serialization errors | NumPy 1.x against 2.x |
+| `ModuleNotFoundError` on the worker | package installed locally but not declared |
+| Model loading failures | PyTorch version mismatch |
+| Permission denied | an API key present locally but absent from `env` |
+
+The packages worth pinning explicitly, because a mismatch in them produces the
+errors above rather than a clear one: **numpy, torch, pyarrow, attrs,
+pydantic**.
+
+<Note>
+The client version defaults `python_version`, so a function registered from a
+3.11 environment runs on 3.11 unless you say otherwise. That default is usually
+right and occasionally surprising -- pin it when the function is sensitive to it.
+</Note>
+
+## Failure handling {#failures}
+
+A function that raises fails the job. There is no per-row retry or skip policy
+in the function definition: `@udf` takes no error-handling parameter, so a
+function that calls a flaky external service should handle that itself, inside
+the function body.
+
+Letting the job fail is the other option: fix the cause and refresh again. The
+next refresh [skips rows that are filled and current](/feature-engineering/refresh-column#idempotent),
+but how much of the failed job's work was kept is not guaranteed, so do not
+count on the retry costing only the rows that did not complete.
+
+## What gets packaged {#packaging}
+
+<Warning>
+The artifact is a **snapshot**: the function source, plus exactly the
+module-level names it references — modules as imports, importable classes and
+functions as imports, literals inlined.
+
+Code that reaches the module namespace another way is rejected where it can be
+seen and unsupported where it cannot: `globals()`, `eval`, `sys.modules`,
+`builtins`. Closures and a non-standard `__builtins__` are rejected outright.
+</Warning>
+
+In practice this means a function should be self-contained: take its inputs as
+arguments, import what it needs at module level, and avoid capturing state from
+the surrounding scope.
+
+## Drop it {#drop}
+
+`db.drop_function(name, version=...)` drops the function's name and the object
+it is bound to; `version` must be a version that exists. It returns `False` if
+the name was already gone. Deleting the object's content can finish after the
+call returns, as a [background job](/maintenance/background-jobs).
+`drop_function_async` returns that job with the result, so you can wait for it.
+
+A deployment refuses the drop unless it also allows changes to a function's
+lifecycle, which is a separate setting, off by default even where Functions are
+enabled.
+
+## What to read next {#next}
+
+<CardGroup cols={2}>
+  <Card title="Computed columns" icon="table-columns" href="/feature-engineering/refresh-column">
+    Refresh semantics, and the SQL alternative.
+  </Card>
+  <Card title="Embedding functions" icon="vector-square" href="/embedding">
+    Built-in embedding providers, which need no registration.
+  </Card>
+</CardGroup>
diff --git a/docs/web/management/index.mdx b/docs/web/management/index.mdx
new file mode 100644
index 000000000..b6bc07e88
--- /dev/null
+++ b/docs/web/management/index.mdx
@@ -0,0 +1,160 @@
+---
+title: "Management"
+sidebarTitle: "Overview"
+description: "Create, list, and drop the objects in a catalog, from Python, TypeScript, or SQL."
+icon: folder-tree
+keywords: ["ddl", "create", "drop", "list", "catalog", "management"]
+---
+
+Use the SDKs or SQL to manage [catalog objects](/basics/catalog), including
+tables, namespaces and views. The tables below provide a quick reference to
+the available methods; each object's guide covers its setup and behavior.
+
+Some long-running operations also provide a submission method that returns a
+[job](/basics/jobs). See the operation's guide for how to track completion.
+
+## Management APIs {#surface}
+
+<Note>
+SQL is available through the Enterprise Flight SQL endpoint.
+See [SQL](/basics/sql#sql-is-remote).
+</Note>
+
+### Namespaces {#namespaces}
+
+| | Python | TypeScript | SQL |
+| --- | --- | --- | --- |
+| create | `create_namespace` | `createNamespace` | -- |
+| list | `list_namespaces` | `listNamespaces` | -- |
+| describe | `describe_namespace` | `describeNamespace` | -- |
+| exists | `namespace_exists` | -- | -- |
+| drop | `drop_namespace` | `dropNamespace` | -- |
+
+See [Namespaces](/management/namespace).
+
+### Tables {#tables}
+
+| | Python | TypeScript | SQL |
+| --- | --- | --- | --- |
+| create | `create_table` | `createTable` | `CREATE TABLE` |
+| create empty | `create_table(schema=)` | `createEmptyTable` | `CREATE TABLE` |
+| open | `open_table` | `openTable` | -- |
+| list | `table_names`, `list_tables` | `tableNames`, `listTables` | -- |
+| exists | `table_exists` | -- | -- |
+| rename | `rename_table` | `renameTable` | -- |
+| clone | `clone_table` | `cloneTable` | -- |
+| drop | `drop_table`, `drop_table_async` | `dropTable`, `dropTableAsync` | `DROP TABLE` |
+| drop all | `drop_all_tables` | `dropAllTables` | -- |
+
+See [Tables](/management/table) for creating one and shaping its schema.
+
+### Materialized views {#materialized-views}
+
+| | Python | TypeScript | SQL |
+| --- | --- | --- | --- |
+| create | `create_materialized_view` | `createMaterializedView` | `CREATE MATERIALIZED VIEW` |
+| open | `open_materialized_view` | `openMaterializedView` | -- |
+| list | `list_materialized_views` | `listMaterializedViews` | `SHOW MATERIALIZED VIEWS` |
+| refresh | `refresh` | `refresh` | `REFRESH MATERIALIZED VIEW` |
+| drop | `drop_table` | `dropTable` | `DROP TABLE` |
+
+<Note>
+Local `list_materialized_views` reads table schemas to find views. On
+Enterprise, it requests the server's view listing.
+</Note>
+
+See [Materialized views](/management/materialized-view) for creating one, and
+[Refresh a materialized view](/feature-engineering/materialized-views) for keeping
+it current.
+
+### Views {#views}
+
+| | Python | TypeScript | SQL |
+| --- | --- | --- | --- |
+| create | `create_view` | `createView` | `CREATE VIEW` |
+| list | `list_views` | `listViews` | -- |
+| drop | `drop_view`, `drop_view_async` | `dropView`, `dropViewAsync` | `DROP VIEW` |
+
+<Badge color="red">Enterprise</Badge> Views exist only on an Enterprise cluster that
+enables them; the SDK methods send the view's SQL query to the deployment. See
+[Views](/management/view).
+
+{/*
+Draft for the Functions release.
+
+### Functions {#functions}
+
+| | Python | TypeScript | SQL |
+| --- | --- | --- | --- |
+| create | `create_function`, `create_function_async` | -- | `CREATE FUNCTION` |
+| get | `get_function` | -- | -- |
+| list | `list_functions` | -- | `SHOW FUNCTIONS` |
+| drop | `drop_function` | -- | -- |
+
+<Badge color="red">Enterprise</Badge> A function runs on the server, and only on
+a deployment that enables functions, so there is no local form. The TypeScript
+SDK has no function methods; use Python or SQL.
+See [Functions](/management/function).
+*/}
+
+### Jobs {#jobs}
+
+| | Python | TypeScript | SQL |
+| --- | --- | --- | --- |
+| list | `list_jobs` | `listJobs` | `SHOW JOBS` |
+| open | `open_job` | `openJob` | -- |
+| cancel | `cancel_job` | `cancelJob` | -- |
+| errors | `job.events` | `job.events` | `SHOW ERRORS` |
+
+See [Jobs](/basics/jobs).
+
+### Indexes {#indexes}
+
+| | Python | TypeScript | SQL |
+| --- | --- | --- | --- |
+| create | `create_index`, `create_index_async` | `createIndex`, `createIndexAsync` | `CREATE INDEX` |
+| list | `list_indices` | `listIndices` | -- |
+| stats | `index_stats` | `indexStats` | -- |
+| drop | `drop_index` | `dropIndex` | `DROP INDEX` |
+
+<Note>
+SQL `CREATE INDEX` covers one column and takes no user-chosen name -- the server
+names it. For a multi-column or named index, use the SDK.
+</Note>
+
+Indexes are a performance decision rather than a structural one, so they are
+covered under [Maintenance](/indexing).
+
+## Work within a namespace {#namespace-paths}
+
+Most connection-level operations take a `namespace_path` (`namespacePath`) to
+act inside a namespace rather than at the database root:
+
+<CodeGroup>
+```python Python icon="python"
+db.table_names(namespace_path=["prod", "events"])
+tbl = db.open_table("clicks", namespace_path=["prod", "events"])
+```
+
+```typescript TypeScript icon="square-js"
+await db.tableNames(["prod", "events"]);
+const tbl = await db.openTable("clicks", ["prod", "events"]);
+```
+
+```sql SQL
+SELECT * FROM prod.events.clicks
+```
+</CodeGroup>
+
+In SQL the path is written as dotted identifiers instead.
+
+## Delete catalog objects {#dropping}
+
+<Warning>
+`drop_table` removes the table and every version of it. Table
+[versioning](/management/table#versioning) does not survive the table being dropped, and
+neither `drop_database` nor `drop_all_tables` asks for confirmation.
+</Warning>
+
+Dropping a **view** removes only the saved query. Dropping a **materialized
+view** removes stored rows, because a materialized view is a table.
diff --git a/docs/web/management/materialized-view.mdx b/docs/web/management/materialized-view.mdx
new file mode 100644
index 000000000..1475bd93a
--- /dev/null
+++ b/docs/web/management/materialized-view.mdx
@@ -0,0 +1,96 @@
+---
+title: "Materialized view"
+sidebarTitle: "Materialized View"
+description: "Create, open, list and drop a table whose contents are defined by a query."
+icon: table
+keywords: ["materialized view", "create_materialized_view", "catalog", "ddl"]
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Both offerings support materialized views. See [Enterprise compatibility](#deployment) for connection and server requirements.
+---
+
+A materialized view is a table whose contents are defined by a query over
+another table. The query is recorded in the view's schema, and a refresh
+brings its stored rows up to date with the source.
+
+The result is an ordinary table -- queryable, indexable, searchable, and present
+in `table_names()`. Nothing about reading it is special.
+
+This page covers creating and managing one. Keeping it current is
+[Refresh a materialized view](/feature-engineering/materialized-views).
+
+<Note>
+In SQL, see [`REFRESH MATERIALIZED VIEW`](/sql/dml/refresh#refresh-materialized-view) and [`SHOW MATERIALIZED VIEWS`](/sql/ddl/materialized-view#show).
+</Note>
+
+## Create a materialized view {#create}
+
+<CodeGroup>
+```python Python icon="python"
+view = db.create_materialized_view(
+    "recent_docs",
+    source="documents",
+    select=["id", "title", "embedding"],
+    where="created_at > TIMESTAMP '2026-01-01 00:00:00'",
+)
+```
+
+```typescript TypeScript icon="square-js"
+const view = await db.createMaterializedView("recent_docs", "documents", {
+  select: ["id", "title", "embedding"],
+  where: "created_at > TIMESTAMP '2026-01-01 00:00:00'",
+});
+```
+</CodeGroup>
+
+`source` names the table the view reads. With Python SDK 0.40.0, create that
+table with stable row IDs before creating the view. The
+[complete example](/feature-engineering/materialized-views#create-and-refresh)
+shows the setup. `select`, `where` and `limit` define the query.
+
+## Open, list and drop views {#manage}
+
+| | Python | TypeScript | SQL |
+| --- | --- | --- | --- |
+| create | `create_materialized_view` | `createMaterializedView` | `CREATE MATERIALIZED VIEW` |
+| open | `open_materialized_view` | `openMaterializedView` | -- |
+| list | `list_materialized_views` | `listMaterializedViews` | `SHOW MATERIALIZED VIEWS` |
+| refresh | `refresh` | `refresh` | `REFRESH MATERIALIZED VIEW` |
+| drop | `drop_table` | `dropTable` | `DROP TABLE` |
+
+<Note>
+On a local database, `list_materialized_views` reads table schemas to find
+views. On Enterprise, it asks the server for the view listing, as
+`SHOW MATERIALIZED VIEWS` does.
+</Note>
+
+A view is dropped with `drop_table`, because a materialized view **is** a table.
+That also means dropping one removes stored rows -- unlike dropping a
+[view](/management/view), which removes only a saved query.
+
+## Read the view and its definition {#working}
+
+`view.table` is the underlying table, for querying, indexing, or search.
+`view.definition` contains the query's select list, predicate and limit. Both are properties; on an async view, the definition is read with
+`await view.definition()`.
+
+## Enterprise compatibility {#deployment}
+
+Enterprise supports creating and refreshing materialized views on remote
+compute. Use the SDK version supported by your deployment, or the
+[SQL interface](/sql/ddl/materialized-view). See
+[Enterprise refreshes](/feature-engineering/materialized-views#availability)
+for compatibility considerations when upgrading an existing pipeline.
+
+## Related guides {#next}
+
+<CardGroup cols={2}>
+  <Card title="Refresh a materialized view" icon="arrows-rotate" href="/feature-engineering/materialized-views">
+    How a refresh works, and what it writes.
+  </Card>
+  <Card title="Views" icon="eye" href="/management/view">
+    The non-materialized counterpart, defined in SQL.
+  </Card>
+</CardGroup>
diff --git a/docs/web/management/namespace.mdx b/docs/web/management/namespace.mdx
new file mode 100644
index 000000000..a3485f46d
--- /dev/null
+++ b/docs/web/management/namespace.mdx
@@ -0,0 +1,381 @@
+---
+title: "Namespaces"
+sidebarTitle: "Namespace"
+description: "Grouping tables into a hierarchy, and addressing them through it."
+icon: folder-tree
+keywords: ["namespace", "catalog", "hierarchy", "directory namespace", "rest namespace"]
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Namespaces work in the embedded library, through directory or REST
+    catalogs, and in LanceDB Enterprise.
+---
+
+import {
+    PyNamespaceTableOps,
+    PyNamespaceAdminOps,
+    TsNamespaceTableOps,
+    TsNamespaceAdminOps,
+    RsNamespaceAdminOps,
+    RsNamespaceTableOps,
+} from '/snippets/connection.mdx';
+
+LanceDB organizes Lance tables through a catalog-level abstraction defined by
+the *namespace spec*. LanceDB OSS is an embedded library for accessing those
+tables, and LanceDB Enterprise is a multimodal lakehouse that provides shared
+compute and catalog services on top of the Lance format.
+If you're coming from traditional databases or lakehouses, you can think of a namespace as the catalog path that says where a table name lives.
+
+Lance provides the **file** and **table** formats to store and manage your data and indexes.
+LanceDB operates at the catalog layer (used to organize, discover, and operate on many Lance tables)
+and provides a compute engine on top of the Lance format.
+
+This is why many SDK methods in LanceDB, like `create_table`, `open_table`, `drop_table`, and
+`rename_table`, accept namespace input. The SDK methods expose that input in the idiom of each
+language: Python uses `namespace_path`, Rust uses builder methods like `.namespace(...)`, and
+TypeScript uses `namespacePath` arguments.
+
+<Note>
+In LanceDB Enterprise SQL, a namespaced table is addressed as a [dotted path](/sql/dql/select#namespaces).
+</Note>
+
+## Namespace hierarchy {#namespace-hierarchy}
+
+Namespaces are generalizations of catalog specs that give platform developers a clean way to present Lance tables in the structures users expect. The diagram below shows how the hierarchy can go beyond a single level.
+A namespace can contain a collection of tables, and it can also contain namespaces recursively.
+
+![](/static/assets/images/namespaces/lance-namespace.png)
+
+Before diving into examples, it helps to keep two terms in mind: the **namespace client** is the abstraction that presents a consistent namespace API, while the **namespace implementation** is the concrete backend that resolves namespaces and table locations (for example, a local directory or an external catalog).
+If you want to go deeper, see the Lance format [namespace documentation](https://lance.org/format/namespace/).
+
+## Namespace paths and names {#namespace-paths-and-names}
+
+A namespace path is a list of components. For example, `["prod", "search"]` means the `search`
+namespace inside the `prod` namespace. The empty path, `[]`, means the root namespace.
+
+Each component is a name, not a filesystem path segment, and can't be empty. Keep each one to
+letters, numbers, underscores, hyphens, and periods, so the same identifier is usable everywhere: a
+local directory namespace accepts other characters too, but other namespace implementations and
+deployments can refuse them. [Naming rules](/basics/naming) has the lengths and the other limits.
+
+## Directory namespaces {#directory-namespaces}
+
+The simplest namespace model in LanceDB is a single root namespace, often represented by one
+directory. Namespaces can nest inside it, as in this hierarchy:
+
+```bash
+./local_lancedb (root)
+└── prod
+    └── search
+        └── user   (table)
+            └── data   (table)
+```
+
+The hierarchy is logical, not a tree of folders: a directory namespace records it in a
+`__manifest` table in the root directory, and stores each table in a directory of its own there.
+
+As a user of LanceDB OSS, you might never notice namespaces at first, because a plain connection
+uses only the root namespace, which is implicit. Connecting to this namespace is as simple as
+connecting to the catalog root:
+
+<CodeGroup>
+```python Python icon="python"
+import lancedb
+
+# Connect to the directory namespace root
+db = lancedb.connect("./local_lancedb")
+```
+
+```typescript TypeScript icon="square-js"
+import * as lancedb from "@lancedb/lancedb";
+
+// Connect to the directory namespace root
+const db = await lancedb.connect("./local_lancedb");
+```
+
+```rust Rust icon="rust"
+use lancedb::connect;
+
+// Connect to the directory namespace root
+let db = connect("./local_lancedb").execute().await?;
+```
+</CodeGroup>
+
+A table created through this connection is stored directly under that path, as `<name>.lance`.
+
+You can also explicitly connect to a namespace using `lancedb.connect_namespace(...)` with the directory namespace implementation:
+
+<CodeGroup>
+```python Python icon="python"
+import lancedb
+
+# Local namespace-backed catalog root (DirectoryNamespace)
+# See https://lance.org/format/namespace/dir/catalog-spec/
+db = lancedb.connect_namespace("dir", {"root": "./local_lancedb"})
+
+table_name = "user"
+data = [{"id": 1, "vector": [0.1, 0.2], "name": "alice"}]
+
+table = db.create_table(table_name, data=data, mode="create")
+print(f"Created table: {table.name}")
+# Created table: user
+```
+
+```typescript TypeScript icon="square-js"
+import * as lancedb from "@lancedb/lancedb";
+
+// Local namespace-backed catalog root (DirectoryNamespace)
+// See https://lance.org/format/namespace/dir/catalog-spec/
+const db = await lancedb.connectNamespace("dir", { root: "./local_lancedb" });
+
+const table = await db.createTable(
+  "user",
+  [{ id: 1, vector: [0.1, 0.2], name: "alice" }],
+  { mode: "create" },
+);
+console.log(`Created table: ${table.name}`);
+// Created table: user
+```
+
+```rust Rust icon="rust"
+use std::collections::HashMap;
+use std::sync::Arc;
+use arrow_schema::{DataType, Field, Schema};
+
+// Local namespace-backed catalog root (DirectoryNamespace)
+// See https://lance.org/format/namespace/dir/catalog-spec/
+let mut properties = HashMap::new();
+properties.insert("root".to_string(), "./local_lancedb".to_string());
+let db = lancedb::connect_namespace("dir", properties)
+    .execute()
+    .await?;
+
+let schema = Arc::new(Schema::new(vec![
+    Field::new("id", DataType::Int64, false),
+]));
+let table = db
+    .create_empty_table("user", schema)
+    .execute()
+    .await?;
+println!("Created table: {}", table.name());
+// Created table: user
+```
+</CodeGroup>
+
+<Info>
+- For simple use cases in LanceDB OSS, you don't need to go too deep into namespaces.
+- To integrate LanceDB with external catalogs, understand how each namespace implementation resolves table names and locations in your organization's setup.
+</Info>
+
+## Remote or external catalog namespaces {#remote-or-external-catalog-namespaces}
+
+The example above showed local directory-based namespaces. LanceDB also supports namespaces backed by remote object stores and external catalogs, via the REST namespace implementation.
+
+For remote object stores with central metadata or catalog services (either commercial or open source),
+use the REST namespace implementation. It is backed by REST routes
+(for example `POST /v1/namespace/{id}/create` and `GET /v1/namespace/{id}/list`) and server-provided table locations.
+
+For authentication, any property prefixed with `headers` is forwarded as an HTTP header
+(for example `headers.Authorization` becomes `Authorization`, and `headers.X-API-Key` becomes `X-API-Key`).
+LanceDB Enterprise REST requests use the `x-api-key` header for API-key authentication. Deployments
+that route multiple databases through the same endpoint can also use headers such as
+`x-lancedb-database` or `x-lancedb-database-prefix` for database context.
+
+Replace the catalog URL and credential placeholders below with your values. In
+application code, load credentials from environment variables or a secrets manager.
+
+<CodeGroup>
+```python Python icon="python"
+import lancedb
+
+# Remote namespace-backed catalog root (RestNamespace)
+# See https://lance.org/format/namespace/rest/catalog-spec/
+db = lancedb.connect_namespace(
+    "rest",
+    {
+        "uri": "https://<your_catalog>.internal.<your_org>.com",
+        "headers.x-api-key": "your-api-key",
+        # or:
+        # "headers.Authorization": "Bearer your-access-token",
+    },
+)
+```
+
+```typescript TypeScript icon="square-js"
+import * as lancedb from "@lancedb/lancedb";
+
+// Remote namespace-backed catalog root (RestNamespace)
+// See https://lance.org/format/namespace/rest/catalog-spec/
+const db = await lancedb.connectNamespace("rest", {
+  uri: "https://<your_catalog>.internal.<your_org>.com",
+  headers: {
+    "x-api-key": "your-api-key",
+    // or:
+    // Authorization: "Bearer your-access-token",
+  },
+});
+```
+
+```rust Rust icon="rust"
+use std::collections::HashMap;
+
+// Remote namespace-backed catalog root (RestNamespace)
+// See https://lance.org/format/namespace/rest/catalog-spec/
+let mut properties = HashMap::new();
+properties.insert(
+    "uri".to_string(),
+    "https://<your_catalog>.internal.<your_org>.com".to_string(),
+);
+properties.insert(
+    "headers.x-api-key".to_string(),
+    "your-api-key".to_string(),
+);
+// or:
+// properties.insert(
+//     "headers.Authorization".to_string(),
+//     "Bearer your-access-token".to_string(),
+// );
+let db = lancedb::connect_namespace("rest", properties)
+    .execute()
+    .await?;
+```
+</CodeGroup>
+[LanceDB Enterprise](/enterprise) operates a REST namespace server on top of the Lance format, so any REST client that can speak the REST namespace API
+contract can be used to interact with it. It does not serve the contract's `exists` routes, so checks such as `namespace_exists`
+fail against it. For authentication examples in LanceDB Enterprise, visit
+the [Namespaces in SDKs](#namespaces-in-lancedb-enterprise) page. 
+
+## Best practices {#best-practices}
+
+Below, we list some best practices for working with namespaces:
+- For simple use cases and single, stand-alone applications, the directory-based root namespace is sufficient and requires no special configuration.
+- For remote storage locations, introduce explicit namespaces when multiple teams, environments, or domains share the same catalog.
+- Treat namespace paths as stable identifiers (for example `"prod/search"`, `"staging/recs"`).
+- For maintainability reasons, avoid hard-coding object-store table paths in application code -- instead, prefer catalog identifiers + namespaces.
+
+
+## Table operations with namespace paths {#table-operations-with-namespace-paths}
+
+Let's imagine a scenario where your table management needs have evolved, and you now have the following multi-level structure to organize your tables outside the root namespace.
+```
+./local_lancedb (root)
+└── prod
+    └── search
+        └── user   (table)
+            └── data   (table)
+    └── recommendations
+        └── user   (table)
+            └── data   (data)
+```
+
+Below, we show how you would express table operations within that namespace. Each item in the namespace
+list (`["prod", "search"]`) represents a level in the namespace hierarchy, and the table name is
+specified when you create, open, list, or drop it.
+
+The SDK methods expose the namespace path in the idiom of each language:
+
+- Python: pass `namespace_path=["prod", "search"]` to table operations.
+- Rust: call builder methods such as `.namespace(vec!["prod".to_string(), "search".to_string()])`.
+- TypeScript: pass a `namespacePath` array, for example `await db.openTable("user", ["prod", "search"])`.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyNamespaceTableOps}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsNamespaceTableOps}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsNamespaceTableOps}
+    </CodeBlock>
+</CodeGroup>
+
+<Note>
+Using namespaces is **optional** in LanceDB, and most basic use cases do not require to work with them.
+An empty namespace (`[]`), which is the default, means the root namespace, whose tables are stored
+directly under the specified root path.
+</Note>
+
+## Namespace management APIs {#namespace-management-apis}
+
+You can open/create/drop tables inside a namespace path (like `["prod", "search"]`).
+All three SDKs expose namespace lifecycle operations directly.
+In Python, use `lancedb.connect_namespace(...)` when calling namespace lifecycle methods such as
+`create_namespace`, `list_namespaces`, `describe_namespace`, and `drop_namespace`.
+In TypeScript, use `lancedb.connectNamespace(...)` and call `createNamespace`, `listNamespaces`,
+`describeNamespace`, and `dropNamespace` on the returned `Connection`.
+In Rust, use `lancedb::connect_namespace(...)` and call `create_namespace`, `list_namespaces`,
+and `drop_namespace`.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyNamespaceAdminOps}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsNamespaceAdminOps}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsNamespaceAdminOps}
+    </CodeBlock>
+</CodeGroup>
+
+Namespace creation and deletion have modes that control what happens when the target already exists,
+doesn't exist, or contains data:
+
+- Create mode: `create` fails if the namespace already exists, `exist_ok` keeps the existing namespace, and `overwrite` replaces it.
+- Drop mode: `fail` reports an error when the namespace doesn't exist, and `skip` treats a missing namespace as a successful no-op.
+- Drop behavior: `restrict` keeps non-empty namespaces from being dropped, and `cascade` drops child namespaces and tables first.
+
+Namespace path components can't be empty, and are best kept to letters, numbers, underscores,
+hyphens, and periods, as [above](#namespace-paths-and-names).
+
+Listing APIs return the immediate children of the requested namespace path. Use `limit` with the
+returned `page_token` to page through large catalogs; pass an empty namespace path (`[]`) when you
+want to list from the root namespace. In Python, a listing returns a response: the names are in its
+`namespaces` or `tables`, and `page_token` is set when there is another page.
+
+## Namespaces in LanceDB Enterprise {#namespaces-in-lancedb-enterprise}
+
+In LanceDB Enterprise deployments, configure namespace-backed federated databases in a TOML file under your deployment's `config` directory.
+LanceDB Enterprise supports both directory-based (`ns_impl = "dir"`) and REST-based (`ns_impl = "rest"`) namespace implementations.
+The example below shows how to configure a directory-based namespace implementation in LanceDB Enterprise.
+```toml
+# Federated database configuration for DirectoryNamespace
+# This example uses minio storage
+[federated_dbs.federated_dir_test]
+ns_impl = "dir"
+root = "s3://<your-deployment>/<your-dir>"
+"storage.region" = "us-east-1"
+"storage.endpoint" = "http://localhost:9000"
+"storage.access_key_id" = "minioadmin"
+"storage.secret_access_key" = "minioadmin"
+"storage.allow_http" = "true"
+# Far future expiration (year 2100)
+"storage.expires_at_millis" = "4102444800000"
+```
+
+The example above uses MinIO, but the same approach applies to other cloud object storage platforms based on your deployment.
+
+For REST-based namespace servers, you can specify the namespace implementation as `"rest"` with forwarding prefixed headers
+for authentication and context propagation.
+
+```toml
+[federated_dbs.federated_rest_test]
+ns_impl = "rest"
+uri = "http://<your_org>.internal.catalog.com"
+forward_header_prefixes = ["X-forward"]
+```
+
+With `forward_header_prefixes = ["X-forward"]`, any incoming header starting with `X-forward` is forwarded to
+`http://<your_org>.internal.catalog.com`. This is useful for auth propagation, for example sending
+`X-forward-authorization: Bearer xxxx`.
+
+For the LanceDB REST API itself, requests use `x-api-key` for API-key authentication. If your endpoint
+serves more than one database, LanceDB can also use headers such as `x-lancedb-database` or
+`x-lancedb-database-prefix` to route the request to the right database context.
diff --git a/docs/web/management/table.mdx b/docs/web/management/table.mdx
new file mode 100644
index 000000000..1b8ede070
--- /dev/null
+++ b/docs/web/management/table.mdx
@@ -0,0 +1,1185 @@
+---
+title: "Tables"
+sidebarTitle: "Table"
+description: "The object most operations act on: creating, opening, renaming, cloning and dropping one."
+icon: table
+keywords: ["table", "create_table", "open_table", "clone", "rename", "drop"]
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Tables are created, evolved, versioned and dropped in the embedded library
+    and in LanceDB Enterprise. Renaming, cloning, dropping every table and pruning old
+    versions differ between the two.
+---
+
+import {
+    PySchemaAddSetup,
+    TsSchemaAddSetup as TsSchemaAddSetup,
+    RsSchemaAddSetup as RsSchemaAddSetup,
+    PyAddColumnsDefaultValues as AddColumnsDefaultValues,
+    TsAddColumnsDefaultValues as TsAddColumnsDefaultValues,
+    RsAddColumnsDefaultValues as RsAddColumnsDefaultValues,
+    PyAddColumnsNullable as AddColumnsNullable,
+    TsAddColumnsNullable as TsAddColumnsNullable,
+    RsAddColumnsNullable as RsAddColumnsNullable,
+    PySchemaAlterSetup as SchemaAlterSetup,
+    TsSchemaAlterSetup as TsSchemaAlterSetup,
+    RsSchemaAlterSetup as RsSchemaAlterSetup,
+    PyAlterColumnsRename as AlterColumnsRename,
+    TsAlterColumnsRename as TsAlterColumnsRename,
+    RsAlterColumnsRename as RsAlterColumnsRename,
+    PyAlterColumnsDataType as AlterColumnsDataType,
+    TsAlterColumnsDataType as TsAlterColumnsDataType,
+    RsAlterColumnsDataType as RsAlterColumnsDataType,
+    PyAlterColumnsNullable as AlterColumnsNullable,
+    TsAlterColumnsNullable as TsAlterColumnsNullable,
+    RsAlterColumnsNullable as RsAlterColumnsNullable,
+    PyAlterColumnsMultiple as AlterColumnsMultiple,
+    TsAlterColumnsMultiple as TsAlterColumnsMultiple,
+    PyAlterColumnsWithExpression as AlterColumnsWithExpression,
+    TsAlterColumnsWithExpression as TsAlterColumnsWithExpression,
+    RsAlterColumnsMultiple as RsAlterColumnsMultiple,
+    RsAlterColumnsWithExpression as RsAlterColumnsWithExpression,
+    PySchemaDropSetup as SchemaDropSetup,
+    TsSchemaDropSetup as TsSchemaDropSetup,
+    RsSchemaDropSetup as RsSchemaDropSetup,
+    PyDropColumnsSingle as DropColumnsSingle,
+    TsDropColumnsSingle as TsDropColumnsSingle,
+    RsDropColumnsSingle as RsDropColumnsSingle,
+    PyDropColumnsMultiple as DropColumnsMultiple,
+    TsDropColumnsMultiple as TsDropColumnsMultiple,
+    RsDropColumnsMultiple as RsDropColumnsMultiple,
+    PyAlterVectorColumn as AlterVectorColumn,
+    TsAlterVectorColumn as TsAlterVectorColumn,
+    RsAlterVectorColumn as RsAlterVectorColumn,
+    PySchemaFieldMetadataMerge as SchemaFieldMetadataMerge,
+    TsSchemaFieldMetadataMerge as TsSchemaFieldMetadataMerge,
+    RsSchemaFieldMetadataMerge as RsSchemaFieldMetadataMerge,
+    PySchemaFieldMetadataReplace as SchemaFieldMetadataReplace,
+    TsSchemaFieldMetadataReplace as TsSchemaFieldMetadataReplace,
+    RsSchemaFieldMetadataReplace as RsSchemaFieldMetadataReplace,
+    PyVersioningBasicSetup as VersioningBasicSetup,
+    TsVersioningBasicSetup as TsVersioningBasicSetup,
+    RsVersioningBasicSetup as RsVersioningBasicSetup,
+    PyVersioningCheckInitialVersion as VersioningCheckInitialVersion,
+    TsVersioningCheckInitialVersion as TsVersioningCheckInitialVersion,
+    RsVersioningCheckInitialVersion as RsVersioningCheckInitialVersion,
+    PyVersioningUpdateData as VersioningUpdateData,
+    TsVersioningUpdateData as TsVersioningUpdateData,
+    RsVersioningUpdateData as RsVersioningUpdateData,
+    PyVersioningAddData as VersioningAddData,
+    TsVersioningAddData as TsVersioningAddData,
+    RsVersioningAddData as RsVersioningAddData,
+    PyVersioningCheckVersionsAfterMod as VersioningCheckVersionsAfterMod,
+    TsVersioningCheckVersionsAfterMod as TsVersioningCheckVersionsAfterMod,
+    RsVersioningCheckVersionsAfterMod as RsVersioningCheckVersionsAfterMod,
+    PyVersioningListAllVersions as VersioningListAllVersions,
+    TsVersioningListAllVersions as TsVersioningListAllVersions,
+    RsVersioningListAllVersions as RsVersioningListAllVersions,
+    PyVersioningRollback as VersioningRollback,
+    TsVersioningRollback as TsVersioningRollback,
+    RsVersioningRollback as RsVersioningRollback,
+    PyVersioningCheckoutLatest as VersioningCheckoutLatest,
+    TsVersioningCheckoutLatest as TsVersioningCheckoutLatest,
+    RsVersioningCheckoutLatest as RsVersioningCheckoutLatest,
+    PyVersioningDeleteData as VersioningDeleteData,
+    TsVersioningDeleteData as TsVersioningDeleteData,
+    RsVersioningDeleteData as RsVersioningDeleteData,
+    PyVersioningTags as VersioningTags,
+    TsVersioningTags as TsVersioningTags,
+    RsVersioningTags as RsVersioningTags,
+    RsVersioningMakeQuotesReader as RsVersioningMakeQuotesReader,
+} from '/snippets/tables.mdx';
+import {
+    PyMultimodalImports as MultimodalImports,
+    TsMultimodalImports as TsMultimodalImports,
+    RsMultimodalImports as RsMultimodalImports,
+    PyCreateDummyData as CreateDummyData,
+    TsCreateDummyData as TsCreateDummyData,
+    RsCreateDummyData as RsCreateDummyData,
+    PyDefineSchema as DefineSchema,
+    TsDefineSchema as TsDefineSchema,
+    RsDefineSchema as RsDefineSchema,
+    PyIngestData as IngestData,
+    TsIngestData as TsIngestData,
+    RsIngestData as RsIngestData,
+    PySearchData as SearchData,
+    TsSearchData as TsSearchData,
+    RsSearchData as RsSearchData,
+    PyProcessResults as ProcessResults,
+    TsProcessResults as TsProcessResults,
+    RsProcessResults as RsProcessResults,
+    PyBlobApiSchema as BlobApiSchema,
+    TsBlobApiSchema as TsBlobApiSchema,
+    RsBlobApiSchema as RsBlobApiSchema,
+    PyBlobApiIngest as BlobApiIngest,
+    TsBlobApiIngest as TsBlobApiIngest,
+    RsBlobApiIngest as RsBlobApiIngest,
+    PyBlobApiToPandas as BlobApiToPandas,
+    PyQueryToPandasKwargs as QueryToPandasKwargs,
+} from '/snippets/multimodal.mdx';
+
+A table stores data in rows and columns and keeps a history of committed
+versions. Queries read table data, writes update it, and indexes accelerate
+searches over its columns. Branches let you make changes separately from the
+main table history.
+
+This page covers table management, data types, schema changes and version
+history. For loading records, see [Ingestion](/ingestion). For changes to
+columns, see [Schema evolution](/management/table#schema).
+
+<Note>
+In LanceDB Enterprise, tables are also created and dropped in SQL with [`CREATE TABLE` and `DROP TABLE`](/sql/ddl/table).
+</Note>
+
+## Table structure and metadata {#carries}
+
+| | |
+| --- | --- |
+| **Columns** | including vectors, blobs, and [computed](/feature-engineering/add-computed-column) columns |
+| **Versions** | every write commits one, so reads can [travel back](/management/table#versioning) |
+| **Indexes** | vector, full-text and scalar, built over its columns |
+| **Branches** | isolated lines of change you can [merge or discard](/tables/branching) |
+
+## Create a table {#create}
+
+<CodeGroup>
+```python Python icon="python"
+tbl = db.create_table("documents", data)
+tbl = db.create_table("documents", schema=schema)     # empty, typed
+```
+
+```typescript TypeScript icon="square-js"
+const tbl = await db.createTable("documents", data);
+const tbl = await db.createEmptyTable("documents", schema);
+```
+
+```sql SQL
+CREATE TABLE documents (id BIGINT, title VARCHAR)
+```
+</CodeGroup>
+
+`data` can be a list of objects, an Arrow table or reader, a pandas or polars
+frame, or Pydantic models. [Ingesting data](/ingestion) covers each, plus
+`exist_ok` and `mode="overwrite"` for re-runnable scripts.
+
+<Warning>
+An explicit schema is where nullability is decided. In the embedded library,
+creation is also the only point at which a table can be given **stable row
+ids**; in LanceDB Enterprise, whether a new table gets them is the deployment's
+setting. See [Limits](/basics/limits#schema).
+</Warning>
+
+## Open and list tables {#open}
+
+<CodeGroup>
+```python Python icon="python"
+tbl = db.open_table("documents")
+db.table_names()
+```
+
+```typescript TypeScript icon="square-js"
+const tbl = await db.openTable("documents");
+await db.tableNames();
+```
+
+</CodeGroup>
+
+Each accepts `namespace_path` to act inside a [namespace](/management/namespace)
+rather than at the database root.
+
+To find out whether a table exists, open it and handle the error, or look for
+its name in a listing. A missing table raises `ValueError` on a local directory
+or a deployment, and `lance_namespace.TableNotFoundError` on a connection from
+`connect_namespace`. A listing returns one page of names at a time, so check
+every page. Python's `table_exists` is no substitute: a local directory or a
+deployment raises `NotImplementedError` for it, and on a connection from
+`connect_namespace("dir", ...)` or `connect_namespace("rest", ...)` it fails with
+an `AttributeError`. TypeScript has no `tableExists`.
+
+## Rename and clone tables {#rename-clone}
+
+```python
+db.rename_table("documents", "docs")
+
+db.clone_table(
+    "docs_snapshot",
+    source_uri="s3://bucket/db/documents.lance",
+    source_version=42,       # or source_tag="release-1"
+    is_shallow=True,
+)
+```
+
+Renaming is not supported on a local directory connection. In LanceDB Enterprise,
+whether a table can be renamed or cloned at all depends on how the deployment
+stores its catalog.
+
+A **shallow** clone -- the default -- references the source's data files rather
+than copying them, so it is fast and cheap regardless of table size. It is the
+practical way to branch an experiment off a known version, or to pin a training
+set to the exact version a model saw.
+
+<Warning>
+A shallow clone depends on the source's files continuing to exist. If the source
+is [compacted](/maintenance/compaction) past the retention window, the version
+the clone points at can be pruned. Local OSS connections support shallow clones
+only; `is_shallow=False` is not implemented. If the copy must outlive the
+source's history, copy its data into an independent table instead. A deployment
+that supports cloning also makes shallow clones only, and one whose catalog
+does not support it refuses every clone.
+</Warning>
+
+<Note>
+**Enterprise job type:** on an Enterprise cluster that drops tables in the background,
+the teardown runs as a job: `drop_table`, which fences the table's directory
+with a marker and deletes its content, or `drop_object`, depending on how the
+deployment stores its catalog. See
+[Background jobs](/maintenance/background-jobs#teardown).
+</Note>
+
+## Drop a table {#drop}
+
+<CodeGroup>
+```python Python icon="python"
+db.drop_table("documents")
+job = db.drop_table_async("documents")
+db.drop_all_tables()
+```
+
+```typescript TypeScript icon="square-js"
+await db.dropTable("documents");
+const job = await db.dropTableAsync("documents");
+await db.dropAllTables();
+```
+
+```sql SQL
+DROP TABLE documents
+```
+</CodeGroup>
+
+<Warning>
+Dropping removes the table and **every version of it**.
+[Versioning](/management/table#versioning) does not survive a drop, and neither
+`drop_all_tables` nor `drop_database` asks for confirmation. Both work only on
+a local database; on an Enterprise connection the SDK raises instead.
+</Warning>
+
+On a large table the drop can take a while, so `drop_table_async` returns a
+[job](/basics/jobs) rather than blocking when the deployment drops tables in the
+background. In the embedded library, and on an Enterprise cluster that drops inline, the
+returned job has already finished.
+
+## Table management APIs {#surface}
+
+| | Python | TypeScript | SQL |
+| --- | --- | --- | --- |
+| create | `create_table` | `createTable` | `CREATE TABLE` |
+| create empty | `create_table(schema=)` | `createEmptyTable` | `CREATE TABLE` |
+| open | `open_table` | `openTable` | -- |
+| list | `table_names`, `list_tables` | `tableNames`, `listTables` | -- |
+| exists | `table_exists` | -- | -- |
+| rename | `rename_table` | `renameTable` | -- |
+| clone | `clone_table` | `cloneTable` | -- |
+| drop | `drop_table`, `drop_table_async` | `dropTable`, `dropTableAsync` | `DROP TABLE` |
+| drop all | `drop_all_tables` | `dropAllTables` | -- |
+
+## Data types {#data-types}
+
+A table's schema is an **Arrow schema** -- there is no separate LanceDB type
+system mapped onto storage, so a column is the same object to the query engine
+as it is to the format.
+
+| Category | Types |
+| --- | --- |
+| Numeric | `int8`-`int64` and unsigned, `float16`, `float32`, `float64` |
+| String and binary | `utf8`, `large_utf8`, `binary`, `large_binary`, `fixed_size_binary(n)` |
+| Date and time | `date32`, `date64`, `time32`, `time64`, `timestamp`, `duration` |
+| Nested | `struct`, `list`, `large_list`, `fixed_size_list`, `map` |
+| Lance extensions | vectors, [JSON](/sql/data-types#json), [blobs](#multimodal), bfloat16 |
+
+A vector column is a `fixed_size_list<float32, dim>` -- an ordinary Arrow type,
+which is why it is selectable and joinable but why arithmetic over it is list
+arithmetic rather than scalar.
+
+See [SQL data types](/sql/data-types) for the full reference, including the
+extension types and pseudo-columns.
+
+## Multimodal data {#multimodal}
+
+Binary data -- images, audio, video, model artifacts -- lives in the table
+beside the structured columns and embeddings that describe it, and is governed
+by the same versioning and search.
+
+Small values go in an ordinary `binary` column. Large ones go in a **blob**
+column, stored out of line so a scan over the other columns stays fast. See
+[Data types](/sql/data-types#blobs) for how the two differ to a reader.
+
+### Store binary data {#multimodal-store-binary-data}
+
+To store binary data, define a binary Arrow field in your schema (`pa.binary()` in Python, `Binary` in TypeScript, and `DataType::Binary` in Rust).
+
+#### 1\. Setup and imports {#multimodal-1-setup-and-imports}
+
+First, import the necessary libraries for LanceDB and Arrow in your SDK.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {MultimodalImports}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsMultimodalImports}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsMultimodalImports}
+    </CodeBlock>
+</CodeGroup>
+
+#### 2\. Prepare data {#multimodal-2-prepare-data}
+
+For this example, we'll create some dummy in-memory images. In a real application, you would read these from files or an API. The key is to convert your data (image, audio, etc.) into a raw `bytes` object.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {CreateDummyData}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsCreateDummyData}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsCreateDummyData}
+    </CodeBlock>
+</CodeGroup>
+
+#### 3\. Define the schema {#multimodal-3-define-the-schema}
+
+When creating the table, it is **highly recommended** to define the schema explicitly. This ensures that your binary data is correctly interpreted as a `binary` type by Arrow/LanceDB and not as a generic string or list.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {DefineSchema}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsDefineSchema}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsDefineSchema}
+    </CodeBlock>
+</CodeGroup>
+
+#### 4\. Ingest data {#multimodal-4-ingest-data}
+
+Now, create the table using the data and the defined schema.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {IngestData}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsIngestData}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsIngestData}
+    </CodeBlock>
+</CodeGroup>
+
+### Retrieve and use blobs {#multimodal-retrieve-and-use-blobs}
+
+When you search your LanceDB table, you can retrieve the binary column just like any other metadata.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {SearchData}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsSearchData}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsSearchData}
+    </CodeBlock>
+</CodeGroup>
+
+#### Convert bytes back to objects {#multimodal-convert-bytes-back-to-objects}
+
+Once you have the bytes back from the search result, you can decode them into the original format (for example, an image object or audio buffer).
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {ProcessResults}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsProcessResults}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsProcessResults}
+    </CodeBlock>
+</CodeGroup>
+
+### Large Blobs (Blob API) {#multimodal-large-blobs-blob-api}
+
+For larger files like high-resolution images or videos, Lance provides a specialized **Blob API**. By using a large-binary Arrow type (`pa.large_binary()` in Python, `LargeBinary` in TypeScript, and `DataType::LargeBinary` in Rust) and specific metadata, you enable **lazy loading** and optimized encoding. This allows you to work with massive datasets without loading all binary data into memory upfront.
+
+#### 1\. Define a blob schema {#multimodal-1-define-a-blob-schema}
+
+To use the Blob API, you must mark the column with `{"lance-encoding:blob": "true"}` metadata.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {BlobApiSchema}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsBlobApiSchema}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsBlobApiSchema}
+    </CodeBlock>
+</CodeGroup>
+
+#### 2\. Ingest large blobs {#multimodal-2-ingest-large-blobs}
+
+You can then ingest data normally, and Lance will handle the optimized storage.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {BlobApiIngest}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsBlobApiIngest}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsBlobApiIngest}
+    </CodeBlock>
+</CodeGroup>
+
+<Card>
+For more advanced usage, including random access and file-like reading of blobs, see the
+Lance format's [blob API documentation](https://lance.org/guide/blob/).
+</Card>
+
+#### 3\. Convert blob tables to pandas {#multimodal-3-convert-blob-tables-to-pandas}
+
+When you call `to_pandas()` on a local LanceDB table that contains Blob API columns, the `blob_mode` argument controls how those columns materialize. This is available in the Python SDK on local tables; remote tables raise `NotImplementedError`.
+
+`blob_mode` accepts:
+
+- `"lazy"` (default): returns blob columns as lazy `BlobFile` objects without eagerly materializing their payloads. Use this when you want to stream blob bytes on demand or only inspect a subset of rows. Namespace-backed local tables also use the Lance native blob-aware pandas conversion for lazy blobs; in-memory datasets fall back to the standard PyArrow `to_pandas()` path.
+- `"bytes"`: eagerly materializes each blob as `bytes`. Use this when you need the raw payload in the DataFrame, for example to decode an image or audio clip in-process.
+- `"descriptions"`: returns blob descriptors (offsets, sizes, and positions) instead of the data itself. Use this when you want to plan I/O without paying the cost of loading every blob.
+
+`"bytes"` is not supported on in-memory tables.
+
+Extra keyword arguments are forwarded to the underlying PyArrow / Lance pandas conversion, so you can also pass options like `split_blocks` or `self_destruct`:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {BlobApiToPandas}
+    </CodeBlock>
+</CodeGroup>
+
+Query builders also accept `blob_mode` on their `to_pandas()` method:
+
+- Plain scans support `"lazy"`, `"bytes"`, and `"descriptions"` with filters, projections, aliases, `limit`, and `offset`.
+- Vector, FTS, hybrid, and ordered queries can't materialize blob columns through `to_pandas()`; omit blob columns from the projection for those query shapes.
+- This works on both sync and async query builders. Extra PyArrow kwargs like `split_blocks` and `self_destruct` are still forwarded.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {QueryToPandasKwargs}
+    </CodeBlock>
+</CodeGroup>
+
+### Other modalities {#multimodal-other-modalities}
+
+The `pa.binary()` and `pa.large_binary()` types are universal. You can use this same pattern for other types of multimodal data:
+
+- **Audio:** Read `.wav` or `.mp3` files as bytes.
+- **Video:** Store video transitions or full clips using the Blob API.
+- **PDFs/Documents:** Store the raw file content for document search.
+
+### bfloat16 {#multimodal-bfloat16}
+
+Sixteen-bit floats in the bfloat16 layout, stored as `fixed_size_binary(2)`.
+Useful as a vector element type when halving the memory matters more than the
+precision does.
+
+## Schema evolution {#schema}
+
+Schema evolution enables non-breaking modifications to a database table's structure — such as adding columns, altering data types, or dropping fields — to adapt to evolving data requirements without service interruptions. 
+LanceDB supports ACID-compliant schema evolution through granular operations (add/alter/drop columns), allowing you to:
+
+* Iterate Safely: Modify schemas in production with versioned datasets and backward compatibility
+* Scale Seamlessly: Handle ML model iterations, regulatory changes, or feature additions
+* Optimize Continuously: Remove unused fields or enforce new constraints without downtime
+
+<Note>
+In SQL, schema changes are [`ALTER TABLE`](/sql/ddl/table#alter).
+</Note>
+
+### Schema evolution operations {#schema-schema-evolution-operations}
+
+LanceDB supports four primary schema evolution operations:
+
+1. **Adding new columns**: Extend your table with additional attributes
+2. **Altering existing columns**: Change column names, data types, or nullability
+3. **Updating field metadata**: Attach or change per-column Arrow metadata
+4. **Dropping columns**: Remove unnecessary columns from your schema
+
+
+<Tip title="Schema Evolution Performance">
+Schema evolution operations are applied immediately but do not typically require rewriting all data. However, data type changes may involve more substantial operations.
+</Tip>
+
+Each schema evolution operation commits a new table version and returns status metadata such as
+the committed `version`. Run these operations from a mutable table handle; if you checked out an
+older version for reads, call `checkout_latest` / `checkoutLatest` before modifying the schema.
+
+### Add new columns {#schema-add-new-columns}
+
+You can add new columns to a table with the [`add_columns`](https://lancedb.github.io/lancedb/python/python/#lancedb.table.Table.add_columns) 
+method in Python, [`addColumns`](https://lancedb.github.io/lancedb/js/classes/Table/#addcolumns) in TypeScript/JavaScript, or `add_columns` in Rust. 
+New columns are populated based on SQL expressions you provide.
+
+
+#### Set up the example table {#schema-set-up-the-example-table}
+
+First, let's create a sample table with product data to demonstrate schema evolution:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PySchemaAddSetup}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsSchemaAddSetup}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsSchemaAddSetup}
+    </CodeBlock>
+</CodeGroup>
+
+<Note>
+Columns derived from existing data -- SQL expressions over other columns -- are
+covered in [Data evolution](/feature-engineering/data-evolution), because
+deriving values is a feature-engineering job rather than a schema one.
+</Note>
+
+#### Add columns with default values {#schema-add-columns-with-default-values}
+
+Add boolean columns with default values for status tracking:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {AddColumnsDefaultValues}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsAddColumnsDefaultValues}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsAddColumnsDefaultValues}
+    </CodeBlock>
+</CodeGroup>
+
+#### Add nullable columns {#schema-add-nullable-columns}
+
+Add timestamp columns that can contain NULL values:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {AddColumnsNullable}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsAddColumnsNullable}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsAddColumnsNullable}
+    </CodeBlock>
+</CodeGroup>
+
+<Warning title="NULL Values in New Columns">
+When adding columns that should contain NULL values, be sure to cast the NULL to the appropriate type, e.g., `cast(NULL as timestamp)`.
+</Warning>
+
+### Alter existing columns {#schema-alter-existing-columns}
+
+You can alter columns using the [`alter_columns`](https://lancedb.github.io/lancedb/python/python/#lancedb.table.Table.alter_columns)
+method in Python, [`alterColumns`](https://lancedb.github.io/lancedb/js/classes/Table/#altercolumns) in TypeScript/JavaScript, or `alter_columns` in Rust. This allows you to:
+
+- Rename a column
+- Change a column's data type
+- Modify nullability (whether a column can contain NULL values)
+
+
+#### Set up the example table {#schema-set-up-the-example-table-2}
+
+Create a table with a custom schema to demonstrate column alterations:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {SchemaAlterSetup}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsSchemaAlterSetup}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsSchemaAlterSetup}
+    </CodeBlock>
+</CodeGroup>
+
+#### Rename columns {#schema-rename-columns}
+
+Change column names to better reflect their purpose:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {AlterColumnsRename}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsAlterColumnsRename}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsAlterColumnsRename}
+    </CodeBlock>
+</CodeGroup>
+
+#### Change data types {#schema-change-data-types}
+
+Convert column data types for better performance or compatibility:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {AlterColumnsDataType}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsAlterColumnsDataType}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsAlterColumnsDataType}
+    </CodeBlock>
+</CodeGroup>
+
+#### Make columns nullable {#schema-make-columns-nullable}
+
+You can alter columns to contain NULL values:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {AlterColumnsNullable}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsAlterColumnsNullable}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsAlterColumnsNullable}
+    </CodeBlock>
+</CodeGroup>
+
+Changing a column to nullable affects future writes and merges too: missing values are accepted
+only when the target column is nullable.
+
+#### Multiple changes at once {#schema-multiple-changes-at-once}
+
+Apply several alterations in a single operation:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {AlterColumnsMultiple}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsAlterColumnsMultiple}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsAlterColumnsMultiple}
+    </CodeBlock>
+</CodeGroup>
+
+#### Expression-based type changes {#schema-expression-based-type-changes}
+
+For transformations that are not simple casts (for example, converting `"$100"` to an integer), use a SQL-expression column add, then drop and rename:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {AlterColumnsWithExpression}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsAlterColumnsWithExpression}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsAlterColumnsWithExpression}
+    </CodeBlock>
+</CodeGroup>
+
+#### Alter embedding types and dimensions {#schema-alter-embedding-types-and-dimensions}
+
+It's quite common to need to change an embedding column's schema, in case a new model becomes available with a different embedding dimension.
+- In Python, the example shows an in-place type update when the cast is compatible.
+- In TypeScript and Rust, the example shows a dimension change (`384 -> 1024`), which cannot be cast in-place.
+
+For dimension changes, use this 3-step pattern: add a new column with the target type, drop the old column, then rename the new column to the original name.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {AlterVectorColumn}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsAlterVectorColumn}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsAlterVectorColumn}
+    </CodeBlock>
+</CodeGroup>
+
+<Warning>
+**`FixedSizeList` Dimension Changes in TypeScript and Rust**
+
+`alterColumns` / `alter_columns` can cast between compatible types, but changing `FixedSizeList` dimensions (for example `384 -> 1024`) is not a compatible cast.
+For such cases, use `addColumns` / `add_columns` (with `arrow_cast`), then `dropColumns` / `drop_columns`, then rename the replacement column.
+</Warning>
+
+<Warning title="Data Type Changes">
+Changing data types requires rewriting the column data and may be resource-intensive for large tables. Renaming columns or changing nullability is more efficient as it only updates metadata.
+</Warning>
+
+### Update field metadata {#schema-update-field-metadata}
+
+Each column in a LanceDB table can carry a small key/value map of Arrow field metadata — useful
+for annotating columns with units, provenance, PII flags, embedding model versions, or any other
+schema-level context your application needs.
+
+Use [`update_field_metadata`](https://lancedb.github.io/lancedb/python/python/#lancedb.table.Table.update_field_metadata)
+in Python, [`updateFieldMetadata`](https://lancedb.github.io/lancedb/js/classes/Table/#updatefieldmetadata)
+in TypeScript/JavaScript, or `update_field_metadata` in Rust to add, change, or remove these
+key/value pairs without rewriting the column data. Each call commits a new table version and returns
+the new `version`.
+
+Each update targets one field by **dot-path**: top-level columns are addressed by name (for
+example `"embedding"`), and nested fields by their full path (for example `"address.zip"`). By
+default, the keys you pass are **merged** into the field's existing metadata — keys you do not
+mention are preserved, and passing `None` (Python) or `null` (TypeScript) deletes a key. Set
+`replace: true` to swap the field's entire metadata map instead of merging.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {SchemaFieldMetadataMerge}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsSchemaFieldMetadataMerge}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsSchemaFieldMetadataMerge}
+    </CodeBlock>
+</CodeGroup>
+
+To overwrite a field's metadata entirely instead of merging, set `replace` to `true`:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {SchemaFieldMetadataReplace}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsSchemaFieldMetadataReplace}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsSchemaFieldMetadataReplace}
+    </CodeBlock>
+</CodeGroup>
+
+<Tip>
+You can pass multiple updates in a single call to change metadata on several fields at once —
+each call commits a single new table version.
+</Tip>
+
+#### Canonical metadata keys {#schema-canonical-metadata-keys}
+
+You can use any string as a metadata key. By convention, LanceDB treats keys prefixed with
+`lancedb:` as canonical. LanceDB Enterprise displays these keys in the UI, and agents can rely
+on finding this information in a consistent place. Use them when they fit your use case:
+
+| Key | Purpose | Example |
+| --- | --- | --- |
+| `lancedb:description` | Human-readable description of the field. | `"CLIP embedding of the product image"` |
+| `lancedb:tag:<name>` | User-defined key/value tag, where the suffix names the tag category. | `"lancedb:tag:model": "clip"` |
+| `lancedb:logical-column` | Groups related columns into one logical column. For example, `feature_v1` and `feature_v2` might belong to the same logical column. | `"lancedb:logical-column": "feature"` |
+| `lancedb:status` | Life cycle state of the column: `production`, `candidate`, `deprecated`, or `archived`. | `"lancedb:status": "production"` |
+
+<Note>
+These keys are conventions, not enforced constraints. LanceDB does not validate their values,
+but following the conventions keeps your tables consistent with Enterprise tooling.
+</Note>
+
+### Drop columns {#schema-drop-columns}
+
+You can remove columns using the [`drop_columns`](https://lancedb.github.io/lancedb/python/python/#lancedb.table.Table.drop_columns)
+ method in Python, [`dropColumns`](https://lancedb.github.io/lancedb/js/classes/Table/#dropcolumns) in TypeScript/JavaScript, or `drop_columns` in Rust.
+
+
+#### Set Up the example table {#schema-set-up-the-example-table-3}
+
+Create a table with temporary columns that we'll remove:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {SchemaDropSetup}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsSchemaDropSetup}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsSchemaDropSetup}
+    </CodeBlock>
+</CodeGroup>
+
+#### Drop single columns {#schema-drop-single-columns}
+
+Remove individual columns that are no longer needed:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {DropColumnsSingle}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsDropColumnsSingle}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsDropColumnsSingle}
+    </CodeBlock>
+</CodeGroup>
+
+#### Drop multiple columns {#schema-drop-multiple-columns}
+
+Remove several columns at once for efficiency:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {DropColumnsMultiple}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsDropColumnsMultiple}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsDropColumnsMultiple}
+    </CodeBlock>
+</CodeGroup>
+
+<Warning title="Dropping a column is undone only by restoring a version">
+Dropping a column changes the latest version only. Until
+[cleanup](/maintenance/cleanup) prunes the versions from before the drop,
+[restoring](#versioning) one of them brings the column and its values back --
+along with the rest of the table as it was then. Once those versions are
+pruned, the drop cannot be undone, so check before dropping a column you may
+need.
+</Warning>
+
+## Versioning {#versioning}
+
+This page shows the core table-versioning APIs used in the code snippets for Python, TypeScript, and Rust.
+Each operation below maps directly to methods shown in the examples.
+
+### Basic Versioning Example {#versioning-basic-versioning-example}
+
+Let's create a table with sample data to demonstrate LanceDB's versioning capabilities:
+
+#### Set Up the Table {#versioning-set-up-the-table}
+
+First, let's create a table with some sample data:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VersioningBasicSetup}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsVersioningBasicSetup}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsVersioningBasicSetup}
+    </CodeBlock>
+</CodeGroup>
+
+<Expandable title="Rust helper used in this page">
+<CodeGroup>
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsVersioningMakeQuotesReader}
+    </CodeBlock>
+</CodeGroup>
+</Expandable>
+
+#### Check Initial Version {#versioning-check-initial-version}
+
+After creating the table, let's check the initial version information:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VersioningCheckInitialVersion}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsVersioningCheckInitialVersion}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsVersioningCheckInitialVersion}
+    </CodeBlock>
+</CodeGroup>
+
+### Modify Data {#versioning-modify-data}
+
+When you modify data through operations like update or delete, LanceDB automatically creates new versions.
+
+#### Update Existing Data {#versioning-update-existing-data}
+
+Let's update some existing records to see versioning in action:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VersioningUpdateData}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsVersioningUpdateData}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsVersioningUpdateData}
+    </CodeBlock>
+</CodeGroup>
+
+#### Add New Data {#versioning-add-new-data}
+
+Now let's add more records to the table:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VersioningAddData}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsVersioningAddData}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsVersioningAddData}
+    </CodeBlock>
+</CodeGroup>
+
+#### Check Version Changes {#versioning-check-version-changes}
+
+Let's see how the versions have changed after our modifications:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VersioningCheckVersionsAfterMod}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsVersioningCheckVersionsAfterMod}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsVersioningCheckVersionsAfterMod}
+    </CodeBlock>
+</CodeGroup>
+
+### Rollback to Previous Versions {#versioning-rollback-to-previous-versions}
+
+LanceDB supports fast rollbacks to any previous version without data duplication.
+
+#### View All Versions {#versioning-view-all-versions}
+
+First, let's see all the versions we've created:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VersioningListAllVersions}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsVersioningListAllVersions}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsVersioningListAllVersions}
+    </CodeBlock>
+</CodeGroup>
+
+#### Restore a Version Snapshot {#versioning-restore-a-version-snapshot}
+
+Now let's restore a captured version snapshot:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VersioningRollback}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsVersioningRollback}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsVersioningRollback}
+    </CodeBlock>
+</CodeGroup>
+
+### Tag-Based Versioning {#versioning-tag-based-versioning}
+
+Numeric table versions like `v3` or `v17` are precise but hard to remember. Tags
+let you attach human-readable labels (e.g., `"prod"`, `"baseline"`,
+`"q3-evaluation"`) to specific versions and check those out by name. They are
+conceptually similar to git tags, and unlike numeric versions, **tagged versions
+are never pruned** -- a tagged version that is older than the retention window and
+is not the latest stops cleanup instead (see the cleanup note at the bottom of this
+page).
+
+The tags API supports the standard CRUD operations — create, list, update, delete —
+plus checking out by tag name.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VersioningTags}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsVersioningTags}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsVersioningTags}
+    </CodeBlock>
+</CodeGroup>
+
+<Note>
+Deleting a tag only removes the label, not the version it points to. After
+deletion, the underlying table version becomes eligible for cleanup again.
+</Note>
+
+### Branches {#versioning-branches}
+
+Beyond linear history, LanceDB also supports **branches** — isolated, writable
+lines of history forked from `main` (or a specific version). Whereas tags and
+`checkout` give you read-only views of existing versions, a branch has its own
+writable history, making it ideal for experiments, backfills, and migrations
+that you want to keep separate from production reads on `main`.
+
+Branches are covered in their own guide: see [Branches](/tables/branching).
+
+### Delete Data From the Table {#versioning-delete-data-from-the-table}
+
+Let's demonstrate how deletions also create new versions:
+
+#### Go Back to Latest Version {#versioning-go-back-to-latest-version}
+
+First, let's return to the latest version:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VersioningCheckoutLatest}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsVersioningCheckoutLatest}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsVersioningCheckoutLatest}
+    </CodeBlock>
+</CodeGroup>
+
+#### Delete Data {#versioning-delete-data}
+
+Now let's delete some data to see how it affects versioning:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {VersioningDeleteData}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsVersioningDeleteData}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsVersioningDeleteData}
+    </CodeBlock>
+</CodeGroup>
+
+#### Version History and Operations {#versioning-version-history-and-operations}
+
+On a fresh table, the snippets in this guide produce this version sequence:
+
+1. `v1`: create table (`create_table` / `createTable` / `create_table`)
+2. `v2`: update rows (`update`)
+3. `v3`: add rows (`add`)
+4. `v4`: restore snapshot (`restore`) from `version_after_mod`/`versionAfterMod`
+5. `v5`: delete rows (`delete`)
+
+Read-only and checkout operations shown here (`list_versions`/`listVersions`, `version`, `checkout`, `checkout_latest`/`checkoutLatest`) do not create new versions.
+
+The version metadata fields can differ by backend. Direct table-backed version listing exposes a
+timestamp, while namespace-backed listing may expose fields such as `manifest_path`,
+`manifest_size`, `e_tag`, and `timestamp_millis`. In deployments that use managed versioning,
+prefer the table version APIs exposed by LanceDB Enterprise or the namespace service instead of
+mixing in lower-level Lance file operations.
+
+<Note>
+**System Operations**
+
+System operations like `optimize()`, index updates, and table compaction also increment table version numbers.
+In LanceDB OSS, `optimize()` can prune older versions based on its retention setting (`cleanup_older_than`, 7 days by default),
+which is when old-version files are removed and disk space is reclaimed. A deployment prunes them in the background
+when its configuration enables [cleanup](/maintenance/cleanup); `optimize()` is not available there.
+
+**Tagged versions are never pruned.** A version with a tag pointing at it is
+retained regardless of age. When a tagged version is older than the retention window and
+is not the latest, cleanup refuses to run instead of pruning around it: `optimize()` raises
+an error naming the tag and prunes nothing. A tag on the latest version does not cause
+this, but `optimize()` compacts before it prunes, and compacting commits a new version
+whenever it merges files, so a tag on what was the latest version can become an older
+one by the time cleanup runs; the error then comes after compacting, and the index
+update does not run. To let cleanup proceed, [move the tag](#versioning-tag-based-versioning)
+to a version inside the retention window, delete it, or widen the window.
+</Note>
diff --git a/docs/web/management/view.mdx b/docs/web/management/view.mdx
new file mode 100644
index 000000000..de6cd38a5
--- /dev/null
+++ b/docs/web/management/view.mdx
@@ -0,0 +1,34 @@
+---
+title: "Views"
+sidebarTitle: "View"
+description: "A saved query, evaluated when read. The non-materialized counterpart to a materialized view."
+icon: eye
+keywords: ["view", "create view", "sql", "catalog"]
+availability:
+  oss: unavailable
+  enterprise: available
+  summary: >-
+    Enterprise supports views when enabled for your Enterprise cluster; the embedded library cannot create them.
+---
+
+A view saves a query that you can refer to by name. Reading the view executes
+that query against the source tables, using their read-consistency behavior.
+Views do not store a separate copy of the result.
+
+It is the counterpart to a
+[materialized view](/management/materialized-view), which stores rows and is
+brought up to date by a refresh. Both are [catalog objects](/basics/catalog)
+defined by a query; choose a materialized view when you need to store and reuse the result.
+
+| | View | Materialized view |
+| --- | --- | --- |
+| Stores | the query | the rows |
+| Freshness | follows source-table reads | as of the last refresh |
+| Read cost | executes the saved query | queries the stored result |
+| Write cost | none | a refresh |
+
+Views are created in LanceDB Enterprise, either in SQL over the
+[Flight SQL endpoint](/sql#connecting) or with the SDKs' `create_view` /
+`createView`, which send the query to the deployment. Either way, the deployment
+must have views enabled. See [View](/sql/ddl/view) for `CREATE VIEW`,
+`DROP VIEW`, and listing.
diff --git a/docs/web/quickstart.mdx b/docs/web/quickstart.mdx
new file mode 100644
index 000000000..788dd6585
--- /dev/null
+++ b/docs/web/quickstart.mdx
@@ -0,0 +1,300 @@
+---
+title: Quickstart
+sidebarTitle: "Quickstart"
+description: "Install LanceDB, create a table, and run your first vector search."
+icon: rocket
+---
+import { PyConnect, TsConnect, RsConnect, PyConnectEnterprisePlaceholders, TsConnectEnterprisePlaceholders, RsConnectEnterprisePlaceholders } from '/snippets/connection.mdx';
+import { PyQuickstartData, PyQuickstartCreateTable, PyQuickstartAddData, PyQuickstartVectorSearch1, TsQuickstartData, TsQuickstartCreateTable, TsQuickstartAddData, TsQuickstartVectorSearch1, RsQuickstartDefineStruct, RsQuickstartData, RsQuickstartCreateTable, RsQuickstartAddData, RsQuickstartVectorSearch1 } from '/snippets/quickstart.mdx';
+
+This quickstart uses a local database and works without an account or API key.
+
+Follow the steps below, or jump to the [complete examples](#code) to copy a runnable program.
+
+## 1\. Install LanceDB {#1-install-lancedb}
+
+Choose your language. Python uses Polars to display the search results. TypeScript needs Node.js 22.18 or later to run the `.mts` example directly.
+
+<CodeGroup>
+```bash Python (pip) icon="python"
+pip install lancedb polars
+```
+
+```bash Python (uv) icon="python"
+uv venv
+uv pip install lancedb polars
+```
+
+```bash TypeScript icon="square-js"
+npm install @lancedb/lancedb
+```
+
+```bash Rust icon="rust"
+cargo init --name lancedb_quickstart
+cargo add lancedb@0.31.0 --features polars,remote
+cargo add tokio --features macros,rt-multi-thread
+```
+</CodeGroup>
+
+Run the Rust commands in a new, empty directory. The `polars` feature provides DataFrame output; `remote` also lets this example connect to Enterprise.
+
+<Accordion title="Python pre-release builds and older CPUs">
+### Python pre-release builds {#python-pre-release-builds}
+
+To pick up the latest features and bug fixes
+before the next stable release, install a pre-release from LanceDB's Fury index.
+
+<CodeGroup>
+```bash Python (pip) icon="terminal"
+pip install --pre --extra-index-url https://pypi.fury.io/lancedb/ lancedb
+```
+
+```bash Python (uv) icon="terminal"
+uv venv
+uv pip install --prerelease allow --index https://pypi.fury.io/lancedb/ lancedb
+
+# To add to pyproject.toml, use:
+uv add --prerelease allow --index https://pypi.fury.io/lancedb/ lancedb
+```
+</CodeGroup>
+
+<Note>
+Pre-release builds receive the same level of testing as stable releases, but their availability is not guaranteed
+for more than 6 months after release. For real-world workloads, we recommend you use the latest stable release
+as far as possible.
+</Note>
+
+### Older x86_64 CPUs {#older-x86-cpus}
+The default `lancedb` wheel targets `x86-64-haswell` (AVX2 + FMA + F16C). On older x86_64 CPUs without AVX2
+(Intel Sandy Bridge, Ivy Bridge, Westmere; AMD Bulldozer, Piledriver, Steamroller), `import lancedb` crashes
+with `Illegal instruction`.
+
+Install `lancedb-compat` instead. It exposes the same API (`import lancedb` still works), is built at the
+`x86-64-v2` baseline, and uses runtime SIMD dispatch to still use AVX2, FMA, or AVX-512 when available:
+
+```bash Python (pip) icon="terminal"
+pip install lancedb-compat
+```
+
+The two packages share the same `lancedb/` namespace and conflict at install time, so pick one. To switch,
+uninstall the other first (`pip uninstall lancedb && pip install lancedb-compat`).
+
+
+</Accordion>
+
+## 2\. Connect to a database {#2-connect-to-a-lancedb-database}
+
+### Use a local directory {#connect-via-local-directory-path}
+
+LanceDB creates an `ex_lancedb` directory to store your tables. No server is needed.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyConnect}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {"import * as lancedb from \"@lancedb/lancedb\";\n\n"}
+    {TsConnect}
+    </CodeBlock>
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {"use lancedb::connect;\n\n"}
+    {RsConnect}
+    </CodeBlock>
+</CodeGroup>
+
+The [complete examples](#code) include the imports and entry points for each language. For Python's async API, see [Connecting to a database](/basics/connect).
+
+<Accordion title="Connect to LanceDB Enterprise instead">
+### Connect to LanceDB Enterprise {#connect-to-lancedb-enterprise}
+
+Use the database URI, API key, and endpoint provided by your administrator or the LanceDB team. Replace the placeholders below with those details. Use the deployment's region if provided; otherwise keep `us-east-1`.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyConnectEnterprisePlaceholders}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {"import * as lancedb from \"@lancedb/lancedb\";\n\n"}
+    {TsConnectEnterprisePlaceholders}
+    </CodeBlock>
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {"use lancedb::connect;\n\n"}
+    {RsConnectEnterprisePlaceholders}
+    </CodeBlock>
+</CodeGroup>
+
+<Note>
+In application code, load credentials from environment variables or a secrets manager. See [connection settings](/basics/connect#enterprise) for an example.
+</Note>
+
+Need a deployment? [Contact the LanceDB team](https://www.lancedb.com/contact). For differences between local and server execution, see [Moving to LanceDB Enterprise](/basics/sdk#moving-to-enterprise).
+</Accordion>
+
+<Accordion title="Connect directly to object storage instead">
+### Connect via object storage URIs {#connect-via-object-storage-uris}
+
+Use an `s3://`, `gs://`, or `az://` URI in place of the local path. See [Configuring storage](/deployment/storage) for credentials and provider-specific options.
+</Accordion>
+
+## 3\. Create a table and add data {#3-create-a-new-table}
+
+Each row describes a character and stores a vector alongside their name, description, and stats.
+
+<Info>
+These vectors are made up for this example. In your application, use an embedding model to generate vectors from your data.
+</Info>
+
+<Accordion title="Character records">
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyQuickstartData}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsQuickstartData}
+    </CodeBlock>
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsQuickstartDefineStruct}
+    {RsQuickstartData}
+    </CodeBlock>
+</CodeGroup>
+</Accordion>
+
+Create a `characters` table from those records:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyQuickstartCreateTable}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsQuickstartCreateTable}
+    </CodeBlock>
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsQuickstartCreateTable}
+    </CodeBlock>
+</CodeGroup>
+
+This example uses overwrite mode so you can run it again. It replaces any existing `characters` table in the database you connect to.
+
+Add one more character:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyQuickstartAddData}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsQuickstartAddData}
+    </CodeBlock>
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsQuickstartAddData}
+    </CodeBlock>
+</CodeGroup>
+
+## 4\. Search the table {#4-semantic-search}
+
+Find the two vectors closest to the query vector. This example uses a fixed vector; it does not call an embedding model.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyQuickstartVectorSearch1}
+    </CodeBlock>
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsQuickstartVectorSearch1}
+    </CodeBlock>
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsQuickstartVectorSearch1}
+    </CodeBlock>
+</CodeGroup>
+
+Merlin and Morgana come back first. Python and Rust display a Polars DataFrame; TypeScript displays an array of results. The `_distance` column measures how far each result is from your query vector, with smaller values indicating a closer match.
+
+## Complete examples {#code}
+
+Each example below creates the local database, adds four characters, and prints the two closest matches. Use the dependencies from step 1.
+
+<Accordion title="Python: quickstart.py">
+Save this as `quickstart.py` and run `python quickstart.py`. If you installed with uv, run `uv run quickstart.py`.
+
+<CodeGroup>
+    <CodeBlock filename="quickstart.py" language="Python" icon="python">
+    {PyConnect}
+    {PyQuickstartData}
+    {PyQuickstartCreateTable}
+    {PyQuickstartAddData}
+    {PyQuickstartVectorSearch1}
+    </CodeBlock>
+</CodeGroup>
+</Accordion>
+
+<Accordion title="TypeScript: quickstart.mts">
+Save this as `quickstart.mts` and run `node quickstart.mts` from the directory where you installed LanceDB.
+
+<CodeGroup>
+    <CodeBlock filename="quickstart.mts" language="TypeScript" icon="square-js">
+    {"import * as lancedb from \"@lancedb/lancedb\";\n\n"}
+    {TsConnect}
+    {TsQuickstartData}
+    {TsQuickstartCreateTable}
+    {TsQuickstartAddData}
+    {TsQuickstartVectorSearch1}
+    </CodeBlock>
+</CodeGroup>
+</Accordion>
+
+<Accordion title="Rust: src/main.rs">
+Replace `src/main.rs` with this program, then run `cargo run` from your project directory. The schema and Arrow conversion helpers are included.
+
+<CodeGroup>
+    <CodeBlock filename="src/main.rs" language="Rust" icon="rust">
+    {"use lancedb::connect;\n\n"}
+    {RsQuickstartDefineStruct}
+    {"\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n"}
+    {RsConnect}
+    {RsQuickstartData}
+    {RsQuickstartCreateTable}
+    {RsQuickstartAddData}
+    {RsQuickstartVectorSearch1}
+    {"\nOk(())\n}\n"}
+    </CodeBlock>
+</CodeGroup>
+</Accordion>
+
+## Next steps {#next-steps}
+
+You've created a table, added rows, and searched it. Continue with the guides below.
+
+### Filter results {#5-curation}
+
+Combine vector search with metadata filters, such as `stats.magic >= 4`. See [SQL filtering](/search/filtering).
+
+### Extend your table {#extensions}
+
+#### Add a derived feature {#6-add-a-derived-feature}
+
+Add columns calculated from existing fields. See [Adding columns](/management/table#schema-add-new-columns).
+
+#### Computed columns {#computed-columns}
+
+Declare a column and choose when to fill or refresh it. See [Computed columns](/feature-engineering/add-computed-column).
+
+#### Store multimodal data {#7-store-multimodal-data}
+
+Store images, audio, and other binary data alongside vectors and metadata. See [Multimodal data types](/management/table#multimodal).
+
+#### Train on your data {#8-train}
+
+Read table data into PyTorch for training. See [Data loading](/training/data-loading).
+
+<Columns cols={2}>
+<Card title="Basic table operations" icon="table" href="/management/table">
+Create, update, and manage your tables.
+</Card>
+<Card title="Build a RAG app" icon="search" href="/tutorials/agents/contextual-rag">
+Use retrieved context in a language model application.
+</Card>
+<Card title="Indexing" icon="layer-group" href="/indexing/">
+Speed up queries on larger datasets.
+</Card>
+<Card title="Embedding models" icon="vector-square" href="/embedding/">
+Generate vectors from your own data.
+</Card>
+</Columns>
diff --git a/docs/web/reranking/index.mdx b/docs/web/reranking/index.mdx
new file mode 100644
index 000000000..26db30442
--- /dev/null
+++ b/docs/web/reranking/index.mdx
@@ -0,0 +1,536 @@
+---
+title: "Reranking Search Results"
+sidebarTitle: "Reranking"
+description: "Choose a reranker, apply it to search candidates and evaluate relevance and latency."
+icon: "sort-amount-down"
+keywords: ["re-ranking", "reranker", "rerank"]
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Reranking is available with OSS and Enterprise.
+---
+
+import {
+    PyRerankingLinearCombinationUsage,
+    PyRerankingRrfUsage,
+    PyRerankingMrrUsage,
+    PyRerankingCrossEncoderUsage,
+} from '/snippets/integrations.mdx';
+import { PyRerankingCohereUsage } from '/snippets/provider_examples.mdx';
+
+import { PyHybridSearch, TsHybridSearchBody, TsHybridSearchTail } from '/snippets/search_examples.mdx';
+import { RsExampleBody as RsHybridSearchBody, RsExampleTail as RsHybridSearchTail } from '/snippets/search_hybrid_search.mdx';
+
+Reranking changes the order of records returned by an initial search. It can
+combine several ranked lists or use a model to score each candidate against
+the query. It cannot recover records that the initial searches did not retrieve.
+
+## Choose a reranker
+
+- **Rank fusion:** start with RRF for hybrid search. It combines vector and
+  full-text rankings without a model or a provider API call.
+- **Model-based reranking:** try a cross encoder or hosted reranking model
+  when you need a different relevance judgment over the retrieved text.
+  Compare the relevance gain with added latency and provider cost.
+- **Custom ranking:** implement the reranker interface when your application
+  needs its own scoring policy.
+
+<span id="quickstart"></span>
+
+<Accordion title="Run a complete RRF example in Python, TypeScript or Rust">
+
+These examples use three sample records and hand-written vectors, so no
+embedding service is needed. Each program creates a temporary local table
+and removes it when finished.
+
+- **Python:** Install with `pip install lancedb`, save as `rerank_search.py`, then run `python rerank_search.py`.
+- **TypeScript:** Install with `npm install @lancedb/lancedb`, save as `rerank_search.mts`, then run `node rerank_search.mts` with Node.js 22.18 or later.
+- **Rust:** Download and extract the [complete Rust examples](/static/code/search-rust.zip), then run `cargo run --locked --bin hybrid_search` from the extracted directory.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{PyHybridSearch}
+</CodeBlock>
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsHybridSearchBody + "\n" + TsHybridSearchTail}
+</CodeBlock>
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsHybridSearchBody + "\n" + RsHybridSearchTail}
+</CodeBlock>
+</CodeGroup>
+
+The result includes `_relevance_score` in descending order. RRF is already the
+hybrid-search default; passing it explicitly shows where to substitute another
+reranker. These scores express a ranking, not a probability of correctness.
+
+</Accordion>
+
+## Supported rerankers {#supported-rerankers}
+
+Use rank fusion to combine search results without a model, or a model-based
+reranker to score each document against the query. Reranking runs in your
+application after retrieval, including when the table is in Enterprise.
+
+| Reranker | Method | SDK |
+|:---------|:-------|:----|
+| `RRFReranker` | Reciprocal rank fusion | Python, TypeScript, Rust |
+| `MRRReranker` | Weighted reciprocal rank fusion | Python |
+| `LinearCombinationReranker` (deprecated) | Blend normalized scores | Python |
+| `CrossEncoderReranker` | Score query-document pairs with a local cross encoder | Python |
+| `ColbertReranker`, `AnswerdotaiRerankers` | Local model reranking | Python |
+| Provider rerankers | Call a hosted model, such as Cohere, Jina or Voyage AI | Python |
+
+Local model rerankers need their model runtime packages. Hosted rerankers need
+provider credentials and make API calls. See [reranking integrations](/integrations/reranking)
+for provider setup and model selection; specify a model supported by your provider.
+
+Rerankers add `_relevance_score` and return rows ordered by descending relevance. Python rerankers
+accept a `return_score` option; use `"all"` where supported to keep the
+original vector distance or FTS score columns for debugging. Model-based rerankers read from
+`column="text"` by default, so either return that column in the search results or pass a different
+column.
+
+`refine_factor` expands the vector index's internal candidate pool and rescores
+those candidates using full vectors before keeping the requested limit. It
+can improve the candidates available for reranking, but it does not multiply
+the final result count. Compare retrieval recall and total query latency.
+
+<Note>
+**SDK coverage differs across languages**
+
+The provider-specific rerankers in the table above
+(`CohereReranker`, `CrossEncoderReranker`, `ColbertReranker`, and others under `lancedb.rerankers`)
+are currently **Python-only**. The TypeScript and Rust SDKs currently expose hybrid reranking through
+the generic `Reranker` interface (`rerankHybrid` / `rerank_hybrid`) and the built-in `RRFReranker`.
+In TypeScript, create the built-in RRF reranker with `await RRFReranker.create(k)`. To use a
+model-based reranker from TypeScript or Rust, you must implement the hybrid reranker interface
+yourself.
+</Note>
+
+
+### Multi-vector reranking {#multi-vector-reranking}
+Use `rerank_multivector()` to combine result sets from several vector queries.
+The example below assumes `table` has three vector columns with embedding
+metadata and uses a cross encoder to rerank their candidates.
+
+<CodeGroup>
+```python Python icon="python"
+from lancedb.rerankers import CrossEncoderReranker
+
+reranker = CrossEncoderReranker()
+
+query = "hello"
+
+# `deduplicate=True` requires `_rowid` on every input result set,
+# so call `.with_row_id(True)` on each search before passing it in.
+res1 = table.search(query, vector_column_name="vector").limit(3).with_row_id(True)
+res2 = table.search(query, vector_column_name="text_vector").limit(3).with_row_id(True)
+res3 = table.search(query, vector_column_name="meta_vector").limit(3).with_row_id(True)
+
+reranked = reranker.rerank_multivector([res1, res2, res3], deduplicate=True)
+```
+</CodeGroup>
+
+- Passing `deduplicate=True` to `rerank_multivector(...)` raises a `ValueError` if any of the
+input result sets is missing the `_rowid` column. Therefore, it's recommended to add `.with_row_id(True)` to every
+`table.search(...)` call before reranking, or omit `deduplicate=True` if you don't need it.
+- `RRFReranker.rerank_multivector(...)` always requires `_rowid` on its inputs, regardless of
+the `deduplicate` flag.
+
+## Implement a custom ranking policy {#creating-custom-rerankers}
+
+Python custom rerankers implement `rerank_hybrid()` and can optionally implement
+`rerank_vector()` and `rerank_fts()`. TypeScript and Rust expose hybrid reranking
+interfaces. See [the interface requirements](#custom) before implementing one.
+
+## Algorithm options
+
+The following sections describe the Python constructor options and examples.
+Provider and local-model examples require their additional packages and model
+or credential setup. They are separate programs, not steps to concatenate.
+
+## Reciprocal rank fusion {#rrf}
+
+<Accordion title="RRF options and examples">
+
+
+<span id="reciprocal-rank-fusion-reranker"></span>
+
+**Reciprocal Rank Fusion (RRF)** is a model-free way to merge several ranked result lists into a
+single ordering. Rather than comparing raw similarity scores (which aren't directly comparable
+across, say, a vector search and a full-text search), RRF looks only at each document's *rank
+position* in each list. It scores every document with the formula `1 / (rank + K)`, sums those
+contributions across the lists, and re-sorts by the total. Documents that rank highly in more than
+one search rise to the top. Because there's no model to load or call, it's fast and cheap, which is
+why it's the default reranker for LanceDB hybrid search. The implementation follows the
+[Cormack et al. paper](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf).
+
+> **Supported query types:** hybrid search and [multi-vector reranking](/reranking#multi-vector-reranking).
+> Because RRF fuses two or more ranked lists, it can't rerank a single vector or full-text result set
+> on its own. Calling `rerank_vector` or `rerank_fts` on an `RRFReranker` raises `NotImplementedError`.
+
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyRerankingRrfUsage}
+  </CodeBlock>
+</CodeGroup>
+
+### Python arguments {#rrf-accepted-arguments}
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `K` | `int` | `60` | A constant used in the RRF formula (default is 60). Larger values reduce the difference in contribution between nearby rank positions. |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score`. If "all", will return all scores from the vector and FTS search along with the relevance score. |
+
+`K` must be greater than `0`. In TypeScript, construct the built-in reranker with
+`await RRFReranker.create(k)` before passing it to `.rerank(...)`.
+
+### Multi-vector reranking {#rrf-multi-vector-reranking}
+
+`RRFReranker` can also fuse the results of several vector searches with `rerank_multivector`, applying
+the same rank-fusion algorithm across more than two lists. Every input result set must include the
+`_rowid` column, so add `.with_row_id(True)` to each `table.search(...)` call before reranking,
+otherwise the call raises a `ValueError`. See [multi-vector reranking](/reranking#multi-vector-reranking)
+for a full example.
+
+### Supported Scores for each query type {#rrf-supported-scores-for-each-query-type}
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+#### Hybrid Search {#rrf-hybrid-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Keeps `_relevance_score` as the score column alongside the selected data columns. |
+| `all` | ✅ Supported | Returned rows have vector(`_distance`) and FTS(`_score`) along with Hybrid Search score(`_relevance_score`). |
+
+
+</Accordion>
+
+## Linear combination {#linear-combination}
+
+<Accordion title="Linear combination options (deprecated)">
+
+
+<span id="linear-combination-reranker"></span>
+
+> **Note:** This reranker is deprecated. Use `RRFReranker` to combine vector and full-text rankings.
+
+The Linear Combination Reranker combines the results of semantic and full-text search using a linear combination of the scores. The weights for the linear combination can be specified, and defaults to 0.7, i.e, 70% weight for semantic search and 30% weight for full-text search.
+
+> **Note:** Supported query type: Hybrid search only.
+
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyRerankingLinearCombinationUsage}
+  </CodeBlock>
+</CodeGroup>
+
+### Python arguments {#linear-combination-accepted-arguments}
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `weight` | `float` | `0.7` | The weight to use for the semantic search score. The weight for the full-text search score is `1 - weight`. |
+| `fill` | `float` | `1.0` | Score used when a result is missing from one side of the hybrid query. The default strongly penalizes missing vector or FTS matches. |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", returns only `_relevance_score`. If "all", returns all scores from the vector and FTS search along with the relevance score. |
+
+`weight` must be between `0` and `1`. If either the vector or FTS side returns no rows, the reranker
+returns the non-empty side with `_relevance_score` attached rather than failing.
+
+
+### Supported Scores for each query type {#linear-combination-supported-scores-for-each-query-type}
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+#### Hybrid Search {#linear-combination-hybrid-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Keeps `_relevance_score` alongside the selected data columns |
+| `all` | ✅ Supported | Results have vector(`_distance`) and FTS(`_score`) along with Hybrid Search score(`_relevance_score`) |
+
+
+</Accordion>
+
+## MRR {#mrr}
+
+<Accordion title="MRR options and examples">
+
+
+<span id="mrr-reranker"></span>
+
+This reranker uses the Mean Reciprocal Rank (MRR) algorithm to combine and rerank search results from vector and full-text search. You can use this reranker by passing `MRRReranker()` to the `rerank()` method. The MRR algorithm calculates the average of reciprocal ranks across different search results, providing a balanced way to merge results from multiple ranking systems.
+
+> **Note:** Supported query types: Hybrid and Multivector search.
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyRerankingMrrUsage}
+  </CodeBlock>
+</CodeGroup>
+
+### Python arguments {#mrr-accepted-arguments}
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `weight_vector` | `float` | `0.5` | Weight for vector search results (0.0 to 1.0). |
+| `weight_fts` | `float` | `0.5` | Weight for FTS search results (0.0 to 1.0). |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score`. If "all", will return all scores from the vector and FTS search along with the relevance score. |
+
+**Note:** `weight_vector` + `weight_fts` must equal 1.0.
+
+For multivector reranking, input result sets need `_rowid` so LanceDB can identify the same row
+across the ranked lists. Add `.with_row_id(True)` to each vector search before passing the results
+to the reranker.
+
+### Supported Scores for each query type {#mrr-supported-scores-for-each-query-type}
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+#### Hybrid Search {#mrr-hybrid-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Keeps `_relevance_score` alongside the selected data columns. |
+| `all` | ✅ Supported | Results have vector(`_distance`) and FTS(`_score`) along with Hybrid Search score(`_relevance_score`). |
+
+#### Multivector Search {#mrr-multivector-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Keeps `_relevance_score` alongside the selected data columns. |
+| `all` | ✅ Supported | Results have vector distances from all searches along with `_relevance_score`. |
+
+
+</Accordion>
+
+## Cross encoder {#cross-encoder}
+
+<Accordion title="Cross-encoder options and examples">
+
+
+<span id="cross-encoder-reranker"></span>
+
+This reranker uses Cross Encoder models from sentence-transformers to rerank the search results. You can use this reranker by passing `CrossEncoderReranker()` to the `rerank()` method. 
+> **Note:** Supported query types: Hybrid, Vector, and FTS.
+
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyRerankingCrossEncoderUsage}
+  </CodeBlock>
+</CodeGroup>
+
+### Python arguments {#cross-encoder-accepted-arguments}
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `model_name` | `str` | `"cross-encoder/ms-marco-TinyBERT-L-6"` | The name of the reranker model to use.|
+| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
+| `device` | `str` | `None` | The device to use for the cross encoder model. If None, will use "cuda" if available, otherwise "cpu". |
+| `trust_remote_code` | `bool` | `True` | Passed to Sentence Transformers model loading. Set this to `False` when you only want to load models that do not require custom repository code. |
+| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", returns only `_relevance_score`. If "all" is supported, returns relevance score along with the vector and/or FTS scores depending on query type. |
+
+The reranker loads the model locally through `sentence-transformers`, so install the local model
+runtime dependencies you need, such as PyTorch and any device-specific acceleration packages.
+
+### Supported Scores for each query type {#cross-encoder-supported-scores-for-each-query-type}
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+#### Hybrid Search {#cross-encoder-hybrid-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Keeps `_relevance_score` alongside the selected data columns. |
+| `all` | ✅ Supported | Keeps `_distance`, `_score` and `_relevance_score` alongside the data columns. |
+
+#### Vector Search {#cross-encoder-vector-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Keeps `_relevance_score` alongside the selected data columns. |
+| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). |
+
+#### FTS Search {#cross-encoder-fts-search}
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Keeps `_relevance_score` alongside the selected data columns. |
+| `all` | ✅ Supported | Results have FTS(`_score`) along with Hybrid Search score(`_relevance_score`). |
+
+
+</Accordion>
+
+## Building a custom reranker {#custom}
+
+You can build your own custom reranker in LanceDB by subclassing the base `Reranker` class. At a
+minimum, you need to implement `rerank_hybrid()`, which is the logic that combines vector and
+full-text search results. Beyond that, you can optionally implement `rerank_vector()` and
+`rerank_fts()` if you want your reranker to also handle pure vector or pure full-text searches.
+
+Implement the hooks your query paths call. The base class raises
+`NotImplementedError` for vector-only or FTS-only reranking unless you
+implement those methods.
+
+The Python base class exposes hybrid, vector-only, and FTS-only rerank hooks. TypeScript and Rust
+currently expose the custom reranker interface for hybrid reranking. In Rust, a custom reranker must
+also satisfy the trait bounds `Debug + Send + Sync`.
+
+### Interface {#custom-interface}
+
+The `Reranker` base interface comes with a `merge_results()` method that can be used to combine the
+results of semantic and full-text search. This is a vanilla merging algorithm that simply concatenates
+the results and removes the duplicates without taking the scores into consideration. It only keeps the
+first copy of the row encountered. This works well in cases that don't require the scores of semantic
+and full-text search to combine the results. If you want to use the scores or want to support
+`return_score="all"`, you'll need to implement your own merging algorithm. The base
+`return_score` option accepts only `"relevance"` and `"all"`.
+
+Whichever methods you override, your reranker has one job on the way out: attach a
+`_relevance_score` column with the most relevant rows at the top. LanceDB will reject the result
+if that column is missing, and downstream `.limit(...)` calls trust the order you return, so
+sort descending before handing the table back.
+
+For vector-only reranking in Python, pass a text query to `.rerank(..., query_string="...")`.
+The vector query itself may be numeric, but `rerank_vector(query, vector_results)` still receives
+a string query so your reranker can score each candidate against the user's text intent.
+
+The method contract is:
+
+| Method | Input | Required output |
+|:-------|:------|:----------------|
+| `rerank_hybrid` | Query text, vector results and FTS results | A merged Arrow table ordered by descending `_relevance_score` |
+| `rerank_vector` | Query text and vector results | An Arrow table ordered by descending `_relevance_score` |
+| `rerank_fts` | Query text and FTS results | An Arrow table ordered by descending `_relevance_score` |
+
+Implement only the single-search hooks your application uses. The base class
+raises `NotImplementedError` for those hooks until they are implemented.
+
+### Extend an existing reranker {#custom-example}
+
+This class definition extends `CohereReranker` to exclude text patterns after
+reranking. It requires the Cohere package and credentials when instantiated.
+It can return fewer results because exclusions happen after retrieval; use a
+query predicate instead when a metadata filter expresses the same restriction.
+
+<CodeGroup>
+```python Python icon="python"
+from typing import List, Union
+import pyarrow as pa
+from lancedb.rerankers import CohereReranker
+
+class ModifiedCohereReranker(CohereReranker):
+    def __init__(self, filters: Union[str, List[str]], **kwargs):
+        super().__init__(**kwargs)
+        filters = filters if isinstance(filters, list) else [filters]
+        self.filters = filters
+
+    def rerank_hybrid(self, query: str, vector_results: pa.Table, fts_results: pa.Table)-> pa.Table:
+        combined_result = super().rerank_hybrid(query, vector_results, fts_results)
+        df = combined_result.to_pandas()
+        for filter in self.filters:
+            df = df.query("not text.str.contains(@filter)")
+
+        return pa.Table.from_pandas(df)
+
+    def rerank_vector(self, query: str, vector_results: pa.Table)-> pa.Table:
+        vector_results = super().rerank_vector(query, vector_results)
+        df = vector_results.to_pandas()
+        for filter in self.filters:
+            df = df.query("not text.str.contains(@filter)")
+
+        return pa.Table.from_pandas(df)
+
+    def rerank_fts(self, query: str, fts_results: pa.Table)-> pa.Table:
+        fts_results = super().rerank_fts(query, fts_results)
+        df = fts_results.to_pandas()
+        for filter in self.filters:
+            df = df.query("not text.str.contains(@filter)")
+
+        return pa.Table.from_pandas(df)
+```
+</CodeGroup>
+
+The reranker receives and returns PyArrow tables. Use `to_pandas()` for a
+DataFrame transformation and `pa.Table.from_pandas()` to convert the result
+back, preserving `_relevance_score` and the desired row order.
+
+
+## Evaluate relevance, latency and cost {#eval}
+
+Compare rerankers on the same candidate inputs so you can distinguish ranking
+changes from retrieval changes. For hybrid retrieval, prepare the text index
+and query vectors before collecting those candidates.
+
+### Reranking strategies {#eval-reranking-strategies}
+
+Compare these approaches:
+
+- **Rank fusion:** RRF and MRR combine rank positions from multiple result lists.
+- **Score blending:** linear combination mixes normalized vector and text scores.
+- **Model-based reranking:** a model scores each query-document pair.
+
+Hybrid search uses RRF by default. You do not need to call `rerank()` to select
+it. Pass a reranker when you want a different algorithm or configuration.
+
+By default, rerankers return `_relevance_score`. Pass `return_score="all"` when a reranker
+supports it, and you also need the original vector or FTS scores for debugging.
+Evaluation code can rely on returned rows being ordered by descending `_relevance_score`. Empty
+reranked result sets still include the `_relevance_score` column.
+
+The synchronous Python hybrid `rerank(...)` method also accepts a `normalize` argument that controls how the raw
+vector and FTS scores are made comparable before reranking:
+
+- `normalize="score"` (the default); normalizes the raw vector and FTS scores directly.
+- `normalize="rank"`; converts each result list to ranks first, then normalizes.
+
+Normalization matters for algorithms that use raw scores, such as linear
+combination. RRF uses rank positions instead. Compare normalization settings
+only when the chosen reranker uses those scores.
+
+For score-based evaluation, the main built-in knobs are:
+
+- `RRFReranker(K=60)`: rank fusion with a positive smoothing constant.
+- `MRRReranker(weight_vector=0.5, weight_fts=0.5)`: weighted reciprocal rank fusion where the weights must sum to `1.0`.
+- `LinearCombinationReranker(weight=0.7, fill=1.0)`: blends vector and FTS scores directly; `fill` controls how strongly to penalize a result missing from one side.
+
+A result on one dataset does not establish which reranker will work best on
+another. Include representative queries and measure both retrieval quality
+and ranking quality.
+
+### Historical evaluation example {#eval-example-evaluation}
+
+The tables below preserve an earlier experiment comparing multiple rerankers on
+~800 hybrid search queries. This is a modified version of an evaluation script by
+[LlamaIndex](https://github.com/run-llama/finetune-embedding/blob/main/evaluate.ipynb) that measures
+hit-rate \@ top-k. The recorded results do not establish performance for
+current models, SDK releases or your dataset; treat them as an example of an
+evaluation format.
+
+#### Using OpenAI `text-embedding-ada-002` {#eval-using-openai-text-embedding-ada-002}
+
+Vector Search baseline: **0.64**
+
+| Reranker | Top-3 | Top-5 | Top-10 |
+| --- | --- | --- | --- |
+| Linear Combination | `0.73` | `0.74` | `0.85` |
+| Cross Encoder | `0.71` | `0.70` | `0.77` |
+| Cohere | `0.81` | `0.81` | `0.85` |
+| ColBERT | `0.68` | `0.68` | `0.73` |
+
+<img src="https://github.com/AyushExel/assets/assets/15766192/d57b1780-ef27-414c-a5c3-73bee7808a45" />
+
+
+#### Using OpenAI `text-embedding-3-small` {#eval-using-openai-text-embedding-3-small}
+
+Vector Search baseline: **0.59**
+
+| Reranker | Top-3 | Top-5 | Top-10 |
+| --- | --- | --- | --- |
+| Linear Combination | `0.68` | `0.70` | `0.84` |
+| Cross Encoder | `0.72` | `0.72` | `0.79` |
+| Cohere | `0.79` | `0.79` | `0.84` |
+| ColBERT | `0.70` | `0.70` | `0.76` |
+
+<img src="https://github.com/AyushExel/assets/assets/15766192/259adfd2-6ec6-4df6-a77d-1456598970dd" />
+
+### Compare results on your workload {#eval-conclusion}
+
+Build a set of representative queries with known relevant records. Compare
+retrieval alone with each reranker using the same candidate inputs and result
+limit. Record a relevance metric such as recall or nDCG alongside latency and
+provider cost. Include queries with no useful matches and queries containing
+exact names or identifiers.
+
+If the relevant records never reach the reranker, adjust retrieval first.
+Then change one ranking setting at a time and retain the configuration that
+meets your application's quality and latency requirements.
diff --git a/docs/web/search/explain.mdx b/docs/web/search/explain.mdx
new file mode 100644
index 000000000..383745e5b
--- /dev/null
+++ b/docs/web/search/explain.mdx
@@ -0,0 +1,551 @@
+---
+title: "Explain a query plan"
+sidebarTitle: "Query Plans"
+description: "Inspect query execution, identify expensive work and compare changes."
+icon: "map"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Query plans are available in OSS and Enterprise.
+---
+
+import { PyQueryPlan, TsQueryPlanBody, TsQueryPlanTail } from '/snippets/search_examples.mdx';
+import { RsExampleBody as RsQueryPlanBody, RsExampleTail as RsQueryPlanTail } from '/snippets/search_query_plan.mdx';
+
+Use query plans to understand how LanceDB reads data, applies filters and uses
+indexes. `explain_plan()` shows the planned operations. `analyze_plan()` runs
+the query and adds measurements, including execution time and rows processed.
+
+## Inspect a plan or measure execution {#inspect-a-query-plan}
+
+| Operation | Python / Rust | TypeScript |
+|:----------|:--------------|:-----------|
+| Inspect the plan without executing the query | `explain_plan()` | `explainPlan()` |
+| Execute the query and report runtime measurements | `analyze_plan()` | `analyzePlan()` |
+
+Use the first to inspect query structure and the second to investigate work
+actually performed. Operator names and available measurements can differ
+between OSS and Enterprise. SQL provides [EXPLAIN and EXPLAIN ANALYZE](/sql/dql/explain).
+
+<span id="try-both-methods-on-a-local-table"></span>
+
+<Accordion title="Explain and analyze a query in Python, TypeScript or Rust">
+
+These examples use three sample records and hand-written vectors, so no
+embedding service is needed. Each program creates a temporary local table
+and removes it when finished.
+
+- **Python:** Install with `pip install lancedb`, save as `query_plan.py`, then run `python query_plan.py`.
+- **TypeScript:** Install with `npm install @lancedb/lancedb`, save as `query_plan.mts`, then run `node query_plan.mts` with Node.js 22.18 or later.
+- **Rust:** Download and extract the [complete Rust examples](/static/code/search-rust.zip), then run `cargo run --locked --bin query_plan` from the extracted directory.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{PyQueryPlan}
+</CodeBlock>
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsQueryPlanBody + "\n" + TsQueryPlanTail}
+</CodeBlock>
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsQueryPlanBody + "\n" + RsQueryPlanTail}
+</CodeBlock>
+</CodeGroup>
+
+This tiny table demonstrates the API, not a performance benchmark. The plan
+uses a scan because the example has no vector index. The following larger
+example illustrates how to read scans, filters and index operations.
+
+</Accordion>
+
+## Read scans, filters and result retrieval {#reading-the-execution-plan}
+
+The recorded example below uses 1.2 million rows sampled from the
+[Wikipedia dataset](https://huggingface.co/datasets/wikimedia/wikipedia).
+The counts describe that run. The table initially has no indexes, so the plan
+shows a scan. This Enterprise plan includes `RemoteTake` and hides storage paths
+as `uri=***`. A local plan shows the dataset path instead, and fetches the result
+rows with Lance's `Take` or, depending on the library version, a second
+`LanceRead` whose `source` is `stream(_rowid)`.
+
+Let's examine a vector search query that:
+
+- Filters rows where `identifier` is between 0 and 1,000,000
+- Returns the top 100 matches
+- Projects specific columns: `chunk_index`, `title`, and `identifier`
+
+<CodeGroup>
+```python Python icon="python"
+# explain_plan
+query_explain_plan = (
+    table.search(query_embed)
+    .where("identifier > 0 AND identifier < 1000000")
+    .select(["chunk_index", "title", "identifier"])
+    .limit(100)
+    .explain_plan(True)
+)
+```
+
+```typescript TypeScript icon="square-js"
+// explain_plan
+const explainPlan = await table
+    .search(queryEmbed)
+    .where("identifier > 0 AND identifier < 1000000")
+    .select(["chunk_index", "title", "identifier"])
+    .limit(100)
+    .explainPlan(true);
+```
+</CodeGroup>
+
+### Execution Plan Components {#execution-plan-components}
+
+<Accordion title="Read the recorded vector plan operator by operator">
+
+
+The execution plan reveals the sequence of operations performed to execute your query. Let's examine each component:
+
+```
+ProjectionExec: expr=[chunk_index@4 as chunk_index, title@5 as title, identifier@1 as identifier, _distance@3 as _distance]
+  RemoteTake: columns="vector, identifier, _rowid, _distance, chunk_index, title"
+    CoalesceBatchesExec: target_batch_size=1024
+      GlobalLimitExec: skip=0, fetch=100
+        FilterExec: _distance@3 IS NOT NULL
+          SortExec: TopK(fetch=100), expr=[_distance@3 ASC NULLS LAST], preserve_partitioning=[false]
+            KNNVectorDistance: metric=l2
+              FilterExec: identifier@1 > 0 AND identifier@1 < 1000000
+                LanceScan: uri=***, projection=[vector, identifier], row_id=true, row_addr=false, ordered=false
+```
+
+#### 1\. Base Layer (LanceScan) {#1-base-layer-lancescan}
+
+- Initial data scan loading only specified columns to minimize I/O
+- Unordered scan enabling parallel processing
+
+```
+LanceScan: 
+- projection=[vector, identifier]
+- row_id=true, row_addr=false, ordered=false
+```
+
+#### 2\. First Filter {#2-first-filter}
+
+- Apply requested filter on `identifier` column
+- Reduces the number of vectors that need KNN computation
+
+```
+FilterExec: identifier@1 > 0 AND identifier@1 < 1000000
+```
+
+#### 3\. Vector Search {#3-vector-search}
+
+- Computes squared L2 distances between query vector and all vectors that passed the filter
+
+```
+KNNVectorDistance: metric=l2
+```
+
+#### 4\. Results Processing {#4-results-processing}
+
+- Filters out null distance results
+- Sorts by distance and takes top 100 results
+- Coalesces output into batches targeting 1,024 rows in this plan
+
+```
+SortExec: TopK(fetch=100)
+- expr=[_distance@3 ASC NULLS LAST]
+- preserve_partitioning=[false]
+FilterExec: _distance@3 IS NOT NULL
+GlobalLimitExec: skip=0, fetch=100
+CoalesceBatchesExec: target_batch_size=1024
+```
+
+#### 5\. Data Retrieval {#5-data-retrieval}
+
+- `RemoteTake` is the deployment's operator for this step; a local plan shows Lance's `Take` or a `LanceRead` over the selected row IDs, depending on the library version
+- Handles efficient data retrieval from remote storage locations
+- Fetches specific rows and columns needed for the final output
+- Optimizes network bandwidth by only retrieving required data
+
+```
+RemoteTake: columns="vector, identifier, _rowid, _distance, chunk_index, title"
+```
+
+#### 6\. Final Output {#6-final-output}
+
+- Returns only requested columns and maintains column ordering
+
+```text
+ProjectionExec: expr=[chunk_index@4 as chunk_index, title@5 as title, identifier@1 as identifier, _distance@3 as _distance]
+```
+
+This plan demonstrates a basic search without index optimizations: it performs a full scan and filter before vector search.
+
+
+</Accordion>
+
+## Non-vector queries {#analyzing-non-vector-queries}
+
+Query plans also cover filters and full-text search. Python can show both
+sub-plans of a hybrid query. Check where a filter runs and how many rows flow
+through each operator; a separate `FilterExec` does not by itself establish
+that the plan is inefficient or that an index is missing.
+
+### FTS queries {#fts-queries}
+
+<CodeGroup>
+```python Python icon="python"
+plan = (
+    table.search("puppy", query_type="fts")
+    .where("category = 'animals'", prefilter=True)
+    .limit(10)
+    .explain_plan(True)
+)
+print(plan)
+```
+
+```typescript TypeScript icon="square-js"
+const plan = await table
+    .query()
+    .fullTextSearch("puppy")
+    .where("category = 'animals'")
+    .limit(10)
+    .explainPlan(true);
+```
+</CodeGroup>
+
+Look for the text-index operation and any scan of unindexed rows. A scan can
+be needed to include records written after the index was built. Use index
+coverage and execution measurements to determine whether that extra work
+matters. Check filter placement separately from text-index use.
+
+### Hybrid queries {#hybrid-queries}
+
+For hybrid queries, `explain_plan` returns the reranker label followed by the vector and FTS sub-plans, indented for readability:
+
+<CodeGroup>
+```python Python icon="python"
+plan = (
+    table.search("flower moon", query_type="hybrid")
+    .where("category = 'film'", prefilter=True)
+    .limit(10)
+    .explain_plan(True)
+)
+print(plan)
+# RRFReranker(...)
+#   <vector sub-plan>
+#   <FTS sub-plan>
+```
+</CodeGroup>
+
+`analyze_plan` executes both sub-queries and labels them `Vector Search Plan:`
+and `FTS Search Plan:`. Compare their filter placement and execution measurements.
+
+`analyze_plan` executes the query and returns the physical plan annotated with
+runtime metrics such as elapsed time, rows produced, bytes read and index comparisons.
+Where [`explain_plan`](#inspect-a-query-plan) tells you what a query intends to do,
+analysis shows the work performed.
+
+<Note>
+The SQL equivalent is [`EXPLAIN ANALYZE`](/sql/dql/explain).
+</Note>
+
+## Read execution measurements {#analyze-a-query-plan}
+
+Executes the query and provides detailed runtime metrics, including:
+
+- Operation duration (`_elapsed_compute_`)
+- Data processing statistics (`_output_rows_`, `_bytes_read_`)
+- Index effectiveness (`_index_comparisons_`, `_indices_loaded_`)
+- Resource utilization (`_iops_`, `_requests_`)
+
+## Measure the example query {#performance-analysis}
+
+Let's use `analyze_plan` to run the query and analyze the query performance, which will help us identify potential bottlenecks:
+
+<CodeGroup>
+```python Python icon="python"
+# analyze_plan
+query_analyze_plan = (
+    table.search(query_embed)
+    .where("identifier > 0 AND identifier < 1000000")
+    .select(["chunk_index", "title", "identifier"])
+    .limit(100)
+    .analyze_plan()
+)
+```
+
+```typescript TypeScript icon="square-js"
+// analyze_plan
+const analyzePlan = await table
+    .search(queryEmbed)
+    .where("identifier > 0 AND identifier < 1000000")
+    .select(["chunk_index", "title", "identifier"])
+    .limit(100)
+    .analyzePlan();
+```
+</CodeGroup>
+
+### Performance Metrics Analysis {#performance-metrics-analysis}
+
+<Accordion title="Inspect the recorded execution measurements">
+
+
+```
+ProjectionExec: expr=[chunk_index@4 as chunk_index, title@5 as title, identifier@1 as identifier, _distance@3 as _distance], metrics=[output_rows=100, elapsed_compute=1.424µs]
+    RemoteTake: columns="vector, identifier, _rowid, _distance, chunk_index, title", metrics=[output_rows=100, elapsed_compute=175.53097ms, output_batches=1, remote_takes=100]
+      CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=100, elapsed_compute=2.748µs]
+        GlobalLimitExec: skip=0, fetch=100, metrics=[output_rows=100, elapsed_compute=1.819µs]
+          FilterExec: _distance@3 IS NOT NULL, metrics=[output_rows=100, elapsed_compute=10.275µs]
+            SortExec: TopK(fetch=100), expr=[_distance@3 ASC NULLS LAST], preserve_partitioning=[false], metrics=[output_rows=100, elapsed_compute=39.259451ms, row_replacements=546]
+              KNNVectorDistance: metric=l2, metrics=[output_rows=1099508, elapsed_compute=56.783526ms, output_batches=1076]
+                FilterExec: identifier@1 > 0 AND identifier@1 < 1000000, metrics=[output_rows=1099508, elapsed_compute=17.136819ms]
+                  LanceScan: uri=***, projection=[vector, identifier], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1200000, elapsed_compute=21.348178ms, bytes_read=1852931072, iops=78, requests=78]
+```
+
+#### 1\. Data Loading (LanceScan) {#1-data-loading-lancescan}
+
+- Scanned 1,200,000 rows from the LanceDB table
+- Read 1.86GB of data in 78 I/O operations
+- Only loaded necessary columns (`vector` and `identifier`)
+- Unordered scan for parallel processing
+
+#### 2\. Filtering and Search {#2-filtering-and-search}
+
+- Applied prefilter condition (`identifier > 0 AND identifier < 1000000`)
+- Reduced dataset from 1.2M to 1,099,508 rows
+- KNN search used L2 (Euclidean) distance metric
+- Vector comparisons processed in 1076 batches
+
+#### 3\. Results Processing {#3-results-processing}
+
+- KNN results sorted by distance (TopK with fetch=100)
+- Null distances filtered out
+- Batches coalesced to target size of 1024 rows
+- Additional columns fetched for final results
+- Remote take operation for 100 results
+- Final projection of required columns
+
+
+</Accordion>
+
+### Distributed metrics on remote tables {#distributed-metrics-on-remote-tables}
+
+<Badge color="red">Enterprise</Badge>
+
+When you call `analyze_plan` against a LanceDB Enterprise deployment that distributes its queries across a pool of workers, the query runs on those workers. By default the returned plan aggregates each operator's metrics into a single value, matching the local single-node output. Pass a `distributed_metrics` mode when you need to see how work was split across workers:
+
+| Mode          | What it shows                                                                       | When to use it                                                                                          |
+| :------------ | :---------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------ |
+| `aggregate`   | One summary value per operator (default). Same shape as local `analyze_plan`.       | Everyday performance checks and comparisons against a local plan.                                       |
+| `per_worker`  | Metrics reported separately for each worker that participated in the query.         | Diagnosing stragglers or uneven work distribution across the cluster.                                   |
+| `full`        | Both the aggregate summary and the per-worker breakdown.                            | Deep investigations where you need the totals *and* the per-worker detail in a single output.           |
+
+Python exposes this option through `distributed_metrics`. In TypeScript, pass
+the same mode to `analyzePlan()` as its `distributedMetrics` argument. Local
+Python queries ignore the distributed mode and return local measurements.
+
+<CodeGroup>
+```python Python icon="python"
+# Show a per-worker breakdown for a remote query
+plan = (
+    table.search(query_embed)
+    .where("identifier > 0 AND identifier < 1000000")
+    .limit(100)
+    .analyze_plan(distributed_metrics="per_worker")
+)
+print(plan)
+```
+
+
+</CodeGroup>
+
+<Note>
+Use `per_worker` or `full` sparingly; the plan output grows with the size of your worker pool. Stick with the default `aggregate` mode for routine tuning.
+</Note>
+
+### Identify expensive operations {#key-observations}
+
+- The query computes distances for 1,099,508 vectors; inspect timings to assess its contribution to latency
+- The scan reads 1.86 GB in this recorded run
+- Full table scan due to lack of indices
+- Compare this scan with an indexed plan using the same query
+
+## Compare the query after adding indexes {#optimized-query-execution}
+
+<Accordion title="Compare the recorded indexed and unindexed plans">
+
+
+The recorded query after adding vector and scalar indexes produced this plan:
+
+```
+ProjectionExec: expr=[chunk_index@3 as chunk_index, title@4 as title, identifier@2 as identifier, _distance@0 as _distance]
+  RemoteTake: columns="_distance, _rowid, identifier, chunk_index, title"
+    CoalesceBatchesExec: target_batch_size=1024
+      GlobalLimitExec: skip=0, fetch=100
+        SortExec: TopK(fetch=100), expr=[_distance@0 ASC NULLS LAST], preserve_partitioning=[false]
+          ANNSubIndex: name=vector_idx, k=100, deltas=1
+            ANNIvfPartition: uuid=83916fd5-fc45-4977-bad9-1f0737539bb9, nprobes=20, deltas=1
+            ScalarIndexQuery: query=AND(identifier > 0,identifier < 1000000)
+```
+
+### Optimized Plan Analysis {#optimized-plan-analysis}
+
+#### 1\. Scalar Index Query {#1-scalar-index-query}
+
+```
+ScalarIndexQuery: query=AND(identifier > 0,identifier < 1000000)
+metrics=[
+    output_rows=2
+    index_comparisons=2,301,824
+    indices_loaded=2
+    output_batches=1
+    parts_loaded=562
+    elapsed_compute=86.979354ms
+]
+```
+
+- Range filter using scalar index
+- Only 2 index files and 562 scalar index parts loaded
+- 2.3M index comparisons for matches
+
+#### 2\. Vector Search {#2-vector-search}
+
+```
+ANNSubIndex: name=vector_idx, k=100, deltas=1
+metrics=[
+    output_rows=2,000
+    index_comparisons=25,893
+    indices_loaded=0
+    output_batches=20
+    parts_loaded=20
+    elapsed_compute=111.849043ms
+]
+```
+
+- IVF index with 20 probes
+- Only 20 index parts loaded
+- 25,893 vector comparisons
+- 2,000 matching vectors
+
+#### 3\. Results Processing {#3-results-processing-2}
+
+```
+SortExec: TopK(fetch=100), expr=[_distance@0 ASC NULLS LAST], preserve_partitioning=[false]
+GlobalLimitExec: skip=0, fetch=100
+CoalesceBatchesExec: target_batch_size=1024
+```
+
+- Sorts by distance
+- Limits to top 100 results
+- Batches into groups of 1024
+
+#### 4\. Data Fetching {#4-data-fetching}
+
+```
+RemoteTake: columns="_distance, _rowid, identifier, chunk_index, title"
+metrics=[output_rows=100, elapsed_compute=113.491859ms, output_batches=1, remote_takes=100]
+```
+
+- Single output batch
+- One remote take per row
+
+#### 5\. Final Projection {#5-final-projection}
+
+```
+ProjectionExec: expr=[chunk_index@3 as chunk_index, title@4 as title, identifier@2 as identifier, _distance@0 as _distance]
+```
+
+- Returns specified columns: chunk_index, title, identifier, and distance
+
+### Performance Improvements {#performance-improvements}
+
+#### 1\. Initial Data Access {#1-initial-data-access}
+
+```
+ScalarIndexQuery metrics:
+- indices_loaded=2
+- parts_loaded=562
+- output_batches=1
+```
+
+- Before: Full table scan of 1.2M rows, 1.86GB data
+- After: Only 2 indices and 562 scalar index parts loaded
+- Benefit: Eliminated table scans for prefilter
+
+#### 2\. Vector Search Efficiency {#2-vector-search-efficiency}
+
+```
+ANNSubIndex:
+- index_comparisons=25,893
+- indices_loaded=0
+- parts_loaded=20
+- output_batches=20
+```
+
+- Before: L2 calculations on 1,099,508 vectors
+- After: 
+  - About 97.6% fewer vector comparisons (25,893 versus 1,099,508)
+  - Decreased output batches from 1,076 to 20
+
+#### 3\. Data Retrieval Optimization {#3-data-retrieval-optimization}
+
+```
+RemoteTake:
+- remote_takes=100
+- output_batches=1
+```
+
+- RemoteTake operation remains consistent
+
+
+</Accordion>
+
+## Choose a change to measure {#performance-optimization-guide}
+
+### 1\. Index Implementation {#1-index-implementation}
+
+#### When to Create Indices {#when-to-create-indices}
+
+- Columns used in WHERE clauses
+- Vector columns for similarity searches
+- Join columns used in `merge_insert`
+
+#### Index Type Selection {#index-type-selection}
+
+| Data Type   | Recommended Index  | Use Case                                 |
+| ----------- | ------------------ | ---------------------------------------- |
+| Vector      | IVF_PQ/IVF_HNSW_SQ/IVF_HNSW_FLAT | Approximate nearest neighbor search      |
+| Scalar      | B-Tree             | Equality and range filters               |
+| Categorical | Bitmap             | Multi-value filters and set operations   |
+| `List`      | Label_list         | Multi-label classification and filtering |
+
+<Card title="Index Coverage Monitoring" icon="lightbulb">
+Use `table.index_stats()` to monitor index coverage.
+Unindexed rows can add scan work. Use their count alongside query measurements
+to decide when index maintenance is needed.
+</Card>
+
+### 2\. Query Plan Optimization {#2-query-plan-optimization}
+
+#### Common Patterns and Fixes {#common-patterns-and-fixes}
+
+| Plan Pattern                               | Optimization                                 |
+| ------------------------------------------ | -------------------------------------------- |
+| Scan with high bytes read or I/O count | Check index coverage and whether the query can use an index |
+|                                            | Use `select()` to limit returned columns     |
+|                                            | Check whether the dataset has been compacted |
+| Multiple filters | Check their placement and row counts; compare the plan after changing the query |
+
+<Note>
+Regularly analyze your query plans to identify and address performance
+bottlenecks. The `analyze_plan` output provides detailed metrics to guide
+optimization efforts.
+</Note>
+
+### 3\. Getting Started with Optimization {#3-getting-started-with-optimization}
+
+Keep the query, data and result limit fixed while changing one factor. Compare
+rows processed, bytes read, timings and returned results. For ANN changes,
+measure recall too: doing less work is useful only if retrieval quality meets
+your application's needs. See [vector indexes](/indexing/vector-index) and
+[scalar indexes](/indexing/scalar-index) for index choices.
diff --git a/docs/web/search/filtering.mdx b/docs/web/search/filtering.mdx
new file mode 100644
index 000000000..50d37acc0
--- /dev/null
+++ b/docs/web/search/filtering.mdx
@@ -0,0 +1,317 @@
+---
+title: "Metadata Filtering in LanceDB"
+sidebarTitle: Filtering
+description: "Use predicates to select rows and control when filters apply to search."
+icon: "filter"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Metadata filtering is available in OSS and Enterprise.
+---
+
+import { PyFilterSearch, TsFilterSearchBody, TsFilterSearchTail } from '/snippets/search_examples.mdx';
+import { RsExampleBody as RsFilterSearchBody, RsExampleTail as RsFilterSearchTail } from '/snippets/search_filter_search.mdx';
+
+A filter restricts results to rows that satisfy a condition. Use it on its
+own to select records, or combine it with vector, full-text or hybrid search.
+For example, search for similar documents only within a particular category.
+
+<span id="search-within-matching-rows"></span>
+
+<Accordion title="Run a complete filtering example">
+
+These examples use three sample records and hand-written vectors, so no
+embedding service is needed. Each program creates a temporary local table
+and removes it when finished.
+
+- **Python:** Install with `pip install lancedb`, save as `filtered_search.py`, then run `python filtered_search.py`.
+- **TypeScript:** Install with `npm install @lancedb/lancedb`, save as `filtered_search.mts`, then run `node filtered_search.mts` with Node.js 22.18 or later.
+- **Rust:** Download and extract the [complete Rust examples](/static/code/search-rust.zip), then run `cargo run --locked --bin filter_search` from the extracted directory.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{PyFilterSearch}
+</CodeBlock>
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsFilterSearchBody + "\n" + TsFilterSearchTail}
+</CodeBlock>
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsFilterSearchBody + "\n" + RsFilterSearchTail}
+</CodeBlock>
+</CodeGroup>
+
+The query vector is closest to the gardening record, but the filter excludes
+that category. The results contain only the two animal records.
+
+</Accordion>
+
+## Prefiltering and postfiltering {#choose-when-the-filter-runs}
+
+- **Prefiltering**, the default, limits the rows eligible for retrieval before
+  search chooses its candidates.
+- **Postfiltering** removes nonmatching rows after candidates are selected.
+  It can return fewer than `limit` rows, including zero, even when matching
+  records exist elsewhere in the table.
+
+Both modes exclude rows that fail the predicate. They differ in which matching
+rows the search can find. On hybrid queries, the filter applies to both the
+vector and full-text searches.
+
+SDK filters use SQL expressions without requiring a SQL connection. For
+Enterprise SQL queries, see [SELECT](/sql/dql/select#filtering).
+
+## Filtering examples by language {#example-metadata-filtering}
+
+The following excerpts use a connected `db` handle. Create this sample table
+before running the queries beneath it; each query uses the same four records.
+
+<CodeGroup>
+```python Python icon="python"
+data = [
+    {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+    {"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
+    {"vector": [10.2, 100.8], "item": "baz", "price": 30.0},
+    {"vector": [1.4, 9.5], "item": "fred", "price": 40.0},
+]
+table = db.create_table("metadata_filter_example", data=data, mode="overwrite")
+```
+
+```typescript TypeScript icon="square-js"
+const data = [
+  { vector: [3.1, 4.1], item: "foo", price: 10.0 },
+  { vector: [5.9, 26.5], item: "bar", price: 20.0 },
+  { vector: [10.2, 100.8], item: "baz", price: 30.0 },
+  { vector: [1.4, 9.5], item: "fred", price: 40.0 },
+];
+
+const tableName = "metadata_filter_example";
+const table = await db.createTable(tableName, data, {
+  mode: "overwrite",
+});
+```
+</CodeGroup>
+
+## Combine predicates {#chaining-where-clauses}
+
+Repeated filter calls combine predicates with logical `AND`. For example, `where("a > 0").where("b < 10")` is equivalent to `where("(a > 0) AND (b < 10)")`.
+
+Use a single expression for a fixed filter. Chain calls when your application
+builds the filter incrementally.
+
+<CodeGroup>
+```python Python icon="python"
+# Both filters apply: item is in the list AND price is above 15.
+result = (
+    table.search([100, 102])
+    .where("item IN ('foo', 'bar', 'baz')")
+    .where("price > 15.0")
+    .limit(3)
+    .to_arrow()
+)
+```
+
+```typescript TypeScript icon="square-js"
+// Both filters apply: item is in the list AND price is above 15.
+const result = await table
+  .search([100, 102])
+  .where("item IN ('foo', 'bar', 'baz')")
+  .where("price > 15.0")
+  .limit(3)
+  .toArray();
+```
+</CodeGroup>
+
+<Warning>
+In Python SDK versions before `0.34.0` and Rust/TypeScript SDK versions before `0.31.0`, a second `where(...)` or `only_if(...)` call replaced the first filter, so only the last predicate was applied. If your code needs to run on those older versions, write a single predicate with `AND` instead of chaining calls. When upgrading to the latest SDKs, review any existing chained filters and drop earlier calls you no longer want to apply.
+</Warning>
+
+### Filtering Without Vector Search {#filtering-without-vector-search}
+
+You can always filter your data without search. This is useful when you need to query based on metadata:
+
+<CodeGroup>
+```python Python icon="python"
+filtered_no_search_result = (
+    table.search()
+    .where("(item IN ('foo', 'bar', 'baz')) AND (price > 15.0)")
+    .limit(3)
+    .to_arrow()
+)
+```
+
+```typescript TypeScript icon="square-js"
+const filteredResult = await table
+  .query()
+  .where("(item IN ('foo', 'bar', 'baz')) AND (price > 15.0)")
+  .limit(3)
+  .toArray();
+```
+</CodeGroup>
+
+Set `limit()` explicitly to control how many matching records your application
+receives.
+
+### Pre-Filtering with Vector Search {#pre-filtering-with-vector-search}
+
+<CodeGroup>
+```python Python icon="python"
+filtered_result = (
+    table.search([100, 102])
+    .where("(item IN ('foo', 'bar')) AND (price > 15.0)")
+    .limit(3)
+    .to_arrow()
+)
+```
+
+```typescript TypeScript icon="square-js"
+const results = await table
+  .search([100, 102])
+  .where("(item IN ('foo', 'bar')) AND (price > 15.0)")
+  .limit(3)
+  .toArray();
+```
+</CodeGroup>
+
+### Post-Filtering with Vector Search {#post-filtering-with-vector-search}
+
+<CodeGroup>
+```python Python icon="python"
+post_filtered_result = (
+    table.search([100, 102])
+    .where("(item IN ('foo', 'bar')) AND (price > 15.0)", prefilter=False)
+    .limit(3)
+    .to_arrow()
+)
+```
+
+```typescript TypeScript icon="square-js"
+const postFilteredResult = await table.vectorSearch([100, 102])
+  .where("(item IN ('foo', 'bar')) AND (price > 15.0)")
+  .postfilter()
+  .limit(3)
+  .toArray();
+```
+</CodeGroup>
+
+Postfiltering can return fewer than the requested number of records because
+the predicate removes candidates after search.
+
+## Write SQL predicates {#filtering-with-sql}
+
+Pass a SQL expression as the filter string. These expressions also select rows
+for SDK update and delete operations; they are not standalone SQL statements.
+
+LanceDB supports a growing list of SQL expressions:
+
+| SQL Expression | Description |
+|:---------------|:-------------|
+| `>, >=, <, <=, =` | Comparison operators |
+| `AND`, `OR`, `NOT` | Logical operators |
+| `IS NULL`, `IS NOT NULL` | Null checks |
+| `IS TRUE`, `IS NOT TRUE`, `IS FALSE`, `IS NOT FALSE` | Boolean checks |
+| `IN` | Value matching from a set |
+| `LIKE`, `NOT LIKE` | Pattern matching |
+| `CAST` | Type conversion |
+| `regexp_match(column, pattern)` | Regular expression matching |
+| [DataFusion Functions](https://datafusion.apache.org/user-guide/sql/scalar_functions.html) | Additional SQL functions |
+
+### Simple SQL Filters {#simple-sql-filters}
+
+For example, the following filter string is acceptable:
+
+<CodeGroup>
+```python Python icon="python"
+table.search([100, 102]).where(
+    "(item IN ('foo', 'baz')) AND (price > 20.0)"
+).to_arrow()
+```
+
+```typescript TypeScript icon="square-js"
+await table
+  .search([100, 102])
+  .where("(item IN ('foo', 'baz')) AND (price > 20.0)")
+  .toArray();
+```
+</CodeGroup>
+
+### Advanced SQL Filters {#advanced-sql-filters}
+
+If your column name contains special characters, upper-case characters, or is a [SQL Keyword](https://docs.rs/sqlparser/latest/sqlparser/keywords/index.html),
+you can use backtick (`` ` ``) to escape it. For nested fields, each segment of the
+path must be wrapped in backticks.
+
+```sql
+`CUBE` = 10 AND `UpperCaseName` = '3' AND `column name with space` IS NOT NULL
+AND `nested with space`.`inner with space` < 2
+```
+
+<Note>
+Periods separate nested fields. LanceDB does not allow a literal period in a
+top-level column name; backticks do not remove that restriction.
+</Note>
+
+### Dates, Timestamps, Decimals {#dates-timestamps-decimals}
+
+Literals for dates, timestamps, and decimals can be written by writing the string
+value after the type name. For example:
+
+<CodeGroup>
+```sql SQL icon="SQL"
+date_col = date '2021-01-01'
+and timestamp_col = timestamp '2021-01-01 00:00:00'
+and decimal_col = decimal(8,3) '1.000'
+```
+</CodeGroup>
+
+For timestamp columns, the precision can be specified as a number in the type
+parameter. Microsecond precision (6) is the default.
+
+| SQL            | Time unit    |
+|:-------------- |:------------ |
+| `timestamp(0)` | Seconds      |
+| `timestamp(3)` | Milliseconds |
+| `timestamp(6)` | Microseconds |
+| `timestamp(9)` | Nanoseconds  |
+
+## Apache Arrow Mapping {#apache-arrow-mapping}
+
+LanceDB uses Arrow types for schemas and in-memory data. On disk, tables use
+the [Lance format](/basics/lance).
+The mapping from SQL types to Arrow types is:
+
+| SQL type                                                  | Arrow type         |
+|:--------------------------------------------------------- |:------------------ |
+| `boolean`                                                 | `Boolean`          |
+| `tinyint` / `tinyint unsigned`                            | `Int8` / `UInt8`   |
+| `smallint` / `smallint unsigned`                          | `Int16` / `UInt16` |
+| `int` or `integer` / `int unsigned` or `integer unsigned` | `Int32` / `UInt32` |
+| `bigint` / `bigint unsigned`                              | `Int64` / `UInt64` |
+| `float`                                                   | `Float32`          |
+| `double`                                                  | `Float64`          |
+| `decimal(precision, scale)`                               | `Decimal128`       |
+| `date`                                                    | `Date32`           |
+| `timestamp`                                               | `Timestamp`        |
+| `string`                                                  | `Utf8`             |
+| `binary`                                                  | `Binary`           |
+
+
+## Speed up frequently used filters {#best-practices}
+
+Consider a [scalar index](/indexing/scalar-index) on frequently filtered columns.
+Use the query plan to check whether a predicate uses that index. Functions or
+casts applied to a column can change which optimizations are available.
+For list columns, a label-list index supports membership filters such as
+`array_has_any` and `array_has_all`.
+
+## Understand missing matches {#limitations}
+
+A restrictive predicate can legitimately leave fewer rows than the requested
+limit. Approximate vector search can also miss qualifying neighbors, and
+postfiltering can discard candidates without retrieving replacements.
+
+For a recall comparison, run the same query and predicate while bypassing the
+vector index on a suitably sized test table. Then compare the indexed query's
+results and latency while changing probe or refinement settings. Increasing
+those settings does not guarantee a full result set. See
+[vector search](/search/vector-search) and [query plans](/search/explain).
diff --git a/docs/web/search/full-text-search.mdx b/docs/web/search/full-text-search.mdx
new file mode 100644
index 000000000..0dc0096f7
--- /dev/null
+++ b/docs/web/search/full-text-search.mdx
@@ -0,0 +1,1194 @@
+---
+title: Full-Text Search (FTS)
+sidebarTitle: Full-Text Search (FTS)
+description: "Find records by words, phrases or partial matches using a full-text index."
+icon: "book"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Full-text search is available in OSS and Enterprise.
+---
+
+import {
+    PyFtsPrefiltering,
+    PyFtsPostfiltering,
+    PyFtsIncrementalIndex,
+} from '/snippets/search.mdx'
+import { PyFtsBooleanQueries } from '/snippets/doc_function_examples.mdx'
+
+import { PyTextSearch, TsTextSearchBody, TsTextSearchTail } from '/snippets/search_examples.mdx';
+import { RsExampleBody as RsTextSearchBody, RsExampleTail as RsTextSearchTail } from '/snippets/search_text_search.mdx';
+
+Full-text search finds documents by matching words and ranks them with BM25.
+Use it to search for names, product codes, technical terms or phrases in your
+text columns. Create an FTS index on the column before running a query.
+
+You can also combine keyword matches with vector similarity using
+[hybrid search](/search/hybrid-search).
+
+<span id="create-an-index-and-run-a-text-query"></span>
+
+<Accordion title="Run a complete full-text search example">
+
+Each example creates a temporary local table, runs the query and removes the
+table when finished. No embedding model is needed for a text query.
+
+- **Python:** Install with `pip install lancedb`, save as `text_search.py`, then run `python text_search.py`.
+- **TypeScript:** Install with `npm install @lancedb/lancedb`, save as `text_search.mts`, then run `node text_search.mts` with Node.js 22.18 or later.
+- **Rust:** Download and extract the [complete Rust examples](/static/code/search-rust.zip), then run `cargo run --locked --bin text_search` from the extracted directory.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{PyTextSearch}
+</CodeBlock>
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsTextSearchBody + "\n" + TsTextSearchTail}
+</CodeBlock>
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsTextSearchBody + "\n" + RsTextSearchTail}
+</CodeBlock>
+</CodeGroup>
+
+Each example searches the indexed `text` column and returns the puppy record.
+Higher `_score` values rank first; they are relevance scores, not probabilities.
+
+</Accordion>
+
+## Basic usage {#basic-usage}
+
+The steps below create a table, index its `text` column and search for a word.
+For a standalone program with imports and sample data, expand the complete
+example above.
+
+### Create the sample table {#table-setup}
+
+First, open or create the table you want to search:
+
+<CodeGroup>
+```python Python icon="python"
+import lancedb
+from lancedb.index import FTS
+
+uri = "data/sample-lancedb"
+db = lancedb.connect(uri)
+
+table = db.create_table(
+    "my_table_fts",
+    data=[
+        {"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"},
+        {"vector": [5.9, 26.5], "text": "There are several kittens playing"},
+    ],
+)
+```
+
+```ts TypeScript icon="square-js"
+import * as lancedb from "@lancedb/lancedb";
+const uri = "data/sample-lancedb"
+const db = await lancedb.connect(uri);
+
+const data = [
+    { vector: [3.1, 4.1], text: "Frodo was a happy puppy" },
+    { vector: [5.9, 26.5], text: "There are several kittens playing" },
+];
+const tbl = await db.createTable("my_table", data, { mode: "overwrite" });
+```
+
+```rust Rust icon="rust"
+let uri = "data/sample-lancedb";
+let db = connect(uri).execute().await?;
+let initial_data: Box<dyn RecordBatchReader + Send> = create_some_records()?;
+let tbl = db
+    .create_table("my_table", initial_data)
+    .execute()
+    .await?;
+```
+</CodeGroup>
+
+### Build the text index {#construct-fts-index}
+
+Create a full-text search index on your text column:
+
+For asynchronous Python index creation, use
+`await table.create_index("text", config=FTS())`. See [FTS index](/indexing/fts-index).
+
+<CodeGroup>
+```python Python icon="python"
+table.create_index("text", config=FTS())
+```
+
+```typescript TypeScript icon="square-js"
+await tbl.createIndex("text", {
+    config: lancedb.Index.fts(),
+});
+```
+
+```rust Rust icon="rust"
+tbl
+    .create_index(&["text"], Index::FTS(FtsIndexBuilder::default()))
+    .execute()
+    .await?;
+```
+</CodeGroup>
+
+OSS builds the index before the call returns. Enterprise builds it in the
+background. Wait for completion with Python’s `table.wait_for_index(["text_idx"])`
+or TypeScript’s `await tbl.waitForIndex(["text_idx"], 300)` before querying it.
+Use your actual index name if you chose a different one.
+
+### Run the query {#full-text-search}
+
+Perform full-text search and retrieve results:
+
+<CodeGroup>
+```python Python icon="python"
+results = (
+    table.search("puppy", query_type="fts", fts_columns="text")
+    .limit(10)
+    .select(["text", "_score"])
+    .to_list()
+)
+# Returns the puppy record with its BM25 score.
+```
+
+```typescript TypeScript icon="square-js"
+const results = await tbl
+    .search("puppy", "fts")
+    .select(["text"])
+    .limit(10)
+    .toArray();
+```
+
+```rust Rust icon="Rust"
+let results = tbl
+    .query()
+    .full_text_search(FullTextSearchQuery::new("puppy".to_owned()))
+    .select(lancedb::query::Select::Columns(vec!["text".to_owned()]))
+    .limit(10)
+    .execute()
+    .await?;
+```
+</CodeGroup>
+
+Specify the text column with `fts_columns="text"` in synchronous Python,
+especially when the table contains multiple text indexes.
+
+<Note>
+A string query can also invoke an embedding function when the table has
+embedding metadata. Use `query_type="fts"` in Python when you specifically
+want full-text search.
+</Note>
+
+If a table has more than one FTS index, specify the indexed text column in the query. In Python you can use `fts_columns` or the query builder's `nearest_to_text(..., columns=...)`; in TypeScript, use `query().nearestToText(..., columns)`. The newer Lance-native FTS does not accept legacy Tantivy-only index parameters.
+
+BM25 scores depend on the indexed corpus and its term statistics. Compare
+rankings on the dataset you will search; treat scores as relative relevance
+values rather than probabilities.
+
+### Keeping the index up to date {#keeping-the-index-up-to-date}
+
+In OSS, new rows can remain outside the FTS index until you optimize the table.
+Search includes those rows by scanning them, but that work can increase query
+latency. Call `optimize()` to update the index:
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{PyFtsIncrementalIndex}
+</CodeBlock>
+
+```typescript TypeScript icon="square-js"
+await tbl.add([{ vector: [3.1, 4.1], text: "Frodo was a happy puppy" }]);
+await tbl.optimize();
+```
+
+```rust Rust icon="rust"
+tbl.add(new_data).execute().await?;
+tbl.optimize(OptimizeAction::All).await?;
+```
+</CodeGroup>
+
+Choose an OSS maintenance schedule based on write volume and query latency.
+Inspect index coverage with `table.index_stats(...)` and optimize when the
+unindexed data adds too much work. `.fast_search()` skips unindexed rows, so
+use it only when omitting recent records is acceptable.
+
+Enterprise manages index updates automatically. You do not call `optimize()`
+on an Enterprise table.
+
+## Choose the matching behavior
+
+| You need | Use |
+|:---------|:----|
+| Words in any order | A terms query |
+| Words in a particular sequence | A [phrase query](#phrase-vs-terms-queries) and an index with token positions |
+| Tolerance for typing errors | [Fuzzy search](#fuzzy-search) |
+| Parts of words | An [n-gram index](#search-for-substring) |
+| Required, optional or excluded terms | [Boolean queries](#boolean-queries) |
+
+Use a metadata predicate to restrict qualifying records. See
+[filtering options](#filtering-options) for prefiltering and postfiltering.
+Enterprise SQL provides [`fts()`](/sql/functions#fts) for scored text retrieval.
+
+## Configure tokenization and matching {#advanced-usage}
+
+### Tokenize Table Data {#tokenize-table-data}
+
+The default tokenizer splits text at whitespace and punctuation, lowercases
+tokens and removes tokens longer than 40 characters. Stemming, stop-word
+removal and accent folding are also enabled by default.
+
+Stemming is useful for improving search results by reducing words to their root form, e.g. "running" to "run". LanceDB supports stemming for Arabic, Danish, Dutch, English, Finnish, French, German, Greek, Hungarian, Italian, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish, Tamil, and Turkish. You should set the `base_tokenizer` parameter rather than `tokenizer_name` because you cannot customize the tokenizer if `tokenizer_name` is specified.
+
+Tokenization and language filters are separate settings. `base_tokenizer` controls how text is split into searchable tokens. `language` controls stemming and stop-word removal when `stem=True` or `remove_stop_words=True`; choose the tokenizer for CJK or mixed-language segmentation.
+
+For example, to enable stemming for English:
+
+<CodeGroup>
+```python Python icon="python"
+table.create_fts_index("text", language="English", replace=True)
+```
+</CodeGroup>
+
+Configure tokenization when you create the index. Rebuild the index to change
+how existing text is tokenized.
+
+**Default index parameters:**
+- `base_tokenizer`: `"simple"`
+- `language`: English
+- `with_position`: false
+- `max_token_length`: 40
+- `lower_case`: true
+- `stem`: true
+- `remove_stop_words`: true
+- `ascii_folding`: true
+- `custom_stop_words`: `None`; pass a `list[str]` to drop additional words beyond the language defaults. Requires `remove_stop_words=True`.
+
+For multilingual use cases, use `base_tokenizer="icu"` for unicode-aware word segmentation on mixed-language text. ICU stands for [International Components for Unicode](https://icu.unicode.org/). The ICU tokenizer uses bundled ICU4X segmenter data, so it does not require external tokenizer model files. It is a good default when documents mix languages or include scripts where the simple tokenizer would keep an unspaced span as one large token.
+
+The Python API also supports tokenizer implementations that load language model files. Use `base_tokenizer="jieba/default"` for Jieba tokenization, which segments Chinese text into searchable word tokens when the text is written without spaces between words. Use Lindera-backed tokenizers for dictionary-based East Asian morphological segmentation, such as `base_tokenizer="lindera/ipadic"` for Japanese or `base_tokenizer="lindera/ko-dic"` for Korean when you have installed and compiled that Lindera model. These are language-specific tokenizers; ICU is the broader mixed-language option.
+
+<CodeGroup>
+```python Python icon="python"
+table.create_fts_index(
+    "text",
+    base_tokenizer="jieba/default",
+    stem=False,
+    remove_stop_words=False,
+    ascii_folding=False,
+    replace=True,
+)
+```
+</CodeGroup>
+
+Model-backed tokenizers require tokenizer model files in Lance's language model home. Lance looks under the default platform data directory for `lance/language_models`, or you can set `LANCE_LANGUAGE_MODEL_HOME` to point to a different model root:
+
+```bash
+export LANCE_LANGUAGE_MODEL_HOME=/path/to/lance/language_models
+```
+
+For example, `jieba/default` is resolved under `<model-home>/jieba/default/...`, `lindera/ipadic` under `<model-home>/lindera/ipadic/...`, and `lindera/ko-dic` under `<model-home>/lindera/ko-dic/...`.
+
+Model files must be installed where indexing and queries run: on your machine
+for OSS, or on the Enterprise cluster for a remote table.
+
+<Note>
+Built-in stop-word removal supports Danish, Dutch, English, Finnish, French, German, Hungarian, Italian, Norwegian, Portuguese, Russian, Spanish, and Swedish. If you use another stemming language, such as Arabic, Greek, Romanian, Tamil, or Turkish, set `remove_stop_words=False` or pass `custom_stop_words`.
+</Note>
+
+Use `ascii_folding=True` to match accented and unaccented forms, such as
+“é” and “e”:
+
+<CodeGroup>
+```python Python icon="python"
+table.create_fts_index(
+        "text",
+        language="French",
+        stem=True,
+        ascii_folding=True,
+        replace=True,
+    )
+```
+</CodeGroup>
+
+### Filtering Options {#filtering-options}
+
+Use `where(...)` to restrict text results to rows that match a predicate.
+Prefiltering applies the restriction before retrieval; postfiltering removes
+nonmatching candidates afterward and can leave fewer results.
+ 
+With pre-filtering:
+
+<CodeGroup>
+
+<CodeBlock filename="Python" language="python" icon="python">
+{PyFtsPrefiltering}
+</CodeBlock>
+
+```typescript TypeScript icon="square-js"
+await tbl
+.search("puppy")
+.select(["id", "doc"])
+.limit(10)
+.where("meta='foo'")
+.prefilter(true)
+.toArray();
+```
+
+```rust Rust icon="Rust"
+table
+    .query()
+    .full_text_search(FullTextSearchQuery::new("puppy".to_owned()))
+    .select(lancedb::query::Select::Columns(vec!["doc".to_owned()]))
+    .limit(10)
+    .only_if("meta='foo'")
+    .execute()
+    .await?;
+```
+</CodeGroup>
+
+With post-filtering:
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{PyFtsPostfiltering}
+</CodeBlock>
+
+```typescript TypeScript icon="square-js"
+await tbl
+.search("apple")
+.select(["id", "doc"])
+.limit(10)
+.where("meta='foo'")
+.prefilter(false)
+.toArray();
+```
+
+```rust Rust icon="Rust"
+table
+    .query()
+    .full_text_search(FullTextSearchQuery::new(words[0].to_owned()))
+    .select(lancedb::query::Select::Columns(vec!["doc".to_owned()]))
+    .postfilter()
+    .limit(10)
+    .only_if("meta='foo'")
+    .execute()
+    .await?;
+```
+</CodeGroup>
+
+### Phrase vs. Terms Queries {#phrase-vs-terms-queries}
+
+<Warning>
+Lance-based FTS doesn't support queries using boolean operators `OR`, `AND` in the search string.
+</Warning>
+
+For full-text search you can specify either a **phrase** query like `"the old man and the sea"`,
+or a **terms** search query like `old man sea`. 
+
+Enable token positions to search for phrases. Set `remove_stop_words=False` if you also need to match common words such as “the” and “and”:
+
+<CodeGroup>
+```python Python icon="python"
+from lancedb.index import FTS
+
+table.create_index("text", config=FTS(with_position=True, remove_stop_words=False), replace=True)
+```
+</CodeGroup>
+
+Storing token positions increases the index size and adds indexing work.
+
+### Fuzzy Search {#fuzzy-search}
+
+Fuzzy search allows you to find matches even when the search terms contain typos or slight variations. 
+LanceDB uses the classic [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance) 
+to find similar terms within a specified edit distance.
+
+| Parameter      | Type | Default | Description                                                                                                                                                 |
+| -------------- | ---- | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| fuzziness      | int  | 0       | Maximum edit distance allowed for each term. The default, `0`, requires an exact match; set `1` or `2` to allow typos. |
+| max_expansions | int  | 50      | Maximum number of terms to consider for fuzzy matching. Higher values may improve recall but increase search time                                           |
+
+For a complete walkthrough that creates a sample table and demonstrates fuzzy search and relevance boosting, see [the worked example below](#fuzzy-search-and-boosting-example).
+
+### Search for Substring {#search-for-substring}
+
+LanceDB supports searching for substrings in the text column, you can set the `base_tokenizer` parameter to `"ngram"` to enable this feature, and use the parameters `ngram_min_length` and `ngram_max_length` to control the length of the substrings:
+
+| Parameter        | Type | Default | Description                                        |
+| ---------------- | ---- | ------- | -------------------------------------------------- |
+| ngram_min_length | int  | 3       | Minimum length of the n-grams to search for        |
+| ngram_max_length | int  | 3       | Maximum length of the n-grams to search for        |
+| prefix_only      | bool | false   | Whether to only search for prefixes of the n-grams |
+
+
+## Fuzzy Search and Boosting Example {#fuzzy-search-and-boosting-example}
+
+<Accordion title="Worked examples: fuzzy, phrase, boosted and Boolean queries">
+
+
+### Generate Data {#generate-data}
+
+Run the following steps in order. They use a temporary local database with four records, so the same data is available for each query.
+
+<CodeGroup>
+```python Python icon="python"
+from tempfile import TemporaryDirectory
+
+import lancedb
+import pandas as pd
+from lancedb.index import FTS
+from lancedb.query import MatchQuery, PhraseQuery, BoostQuery, MultiMatchQuery
+
+directory = TemporaryDirectory()
+db = lancedb.connect(directory.name)
+table_name = "fts_examples"
+text = ["puppy runs merrily", "car drives crazily", "puppy jumps crazily", "car runs slowly"]
+text2 = ["rabbit jumps crazily", "girl runs merrily", "monkey jumps slowly", "rabbit runs merrily"]
+```
+
+```typescript TypeScript icon="square-js"
+import * as lancedb from "@lancedb/lancedb";
+import { Index, MatchQuery, PhraseQuery, BoostQuery, MultiMatchQuery, BooleanQuery, Occur } from "@lancedb/lancedb";
+import { mkdtemp, rm } from "node:fs/promises";
+import { tmpdir } from "node:os";
+import { join } from "node:path";
+
+const directory = await mkdtemp(join(tmpdir(), "lancedb-fts-"));
+const db = await lancedb.connect(directory);
+const tableName = "fts_examples";
+const text = ["puppy runs merrily", "car drives crazily", "puppy jumps crazily", "car runs slowly"];
+const text2 = ["rabbit jumps crazily", "girl runs merrily", "monkey jumps slowly", "rabbit runs merrily"];
+```
+</CodeGroup>
+
+### Create Table {#create-table}
+
+<CodeGroup>
+```python Python icon="python"
+# Create table with sample data
+table = db.create_table(
+    table_name,
+    data=pd.DataFrame({
+        "id": range(len(text)),
+        "text": text,
+        "text2": text2,
+    }),
+    mode="overwrite"
+)
+```
+
+```typescript TypeScript icon="square-js"
+// Create table with sample data
+const data = text.map((value, i) => ({ id: i, text: value, text2: text2[i] }));
+
+const table = await db.createTable(tableName, data, { mode: "overwrite" });
+```
+</CodeGroup>
+
+### Construct the index for these examples {#example-construct-fts-index}
+
+Create a full-text search index on the first text column:
+
+<CodeGroup>
+```python Python icon="python"
+# Create FTS index on first text column
+table.create_index("text", config=FTS(with_position=True, remove_stop_words=False))
+table.wait_for_index(["text_idx"])
+```
+
+```typescript TypeScript icon="square-js"
+// Create FTS index on first text column
+await table.createIndex("text", { config: Index.fts({ withPosition: true, removeStopWords: false }) });
+await table.waitForIndex(["text_idx"], 300);
+```
+</CodeGroup>
+
+Then, create an index on the second text column:
+
+<CodeGroup>
+```python Python icon="python"
+# Create FTS index on second text column
+table.create_index("text2", config=FTS(with_position=True, remove_stop_words=False))
+table.wait_for_index(["text2_idx"])
+```
+
+```typescript TypeScript icon="square-js"
+// Create FTS index on second text column
+await table.createIndex("text2", { config: Index.fts({ withPosition: true, removeStopWords: false }) });
+await table.waitForIndex(["text2_idx"], 300);
+```
+</CodeGroup>
+
+### Basic and Fuzzy Search {#basic-and-fuzzy-search}
+
+Now we can perform basic, fuzzy, and prefix match searches:
+
+#### Basic Exact Search {#basic-exact-search}
+
+<CodeGroup>
+```python Python icon="python"
+from lancedb.query import MatchQuery
+
+# Basic match (exact search)
+basic_match_results = (
+    table.search(MatchQuery("crazily", "text"))
+    .select(["id", "text", "_score"])
+    .limit(100)
+    .to_arrow().to_pandas()
+)
+```
+
+```typescript TypeScript icon="square-js"
+// Basic match (exact search)
+const basicMatchResults = await table.query()
+    .fullTextSearch(new MatchQuery("crazily", "text"))
+    .select(["id", "text", "_score"])
+    .limit(100)
+    .toArray();
+```
+</CodeGroup>
+
+#### Fuzzy Search with Typos {#fuzzy-search-with-typos}
+
+<CodeGroup>
+```python Python icon="python"
+# Fuzzy match (allows typos)
+fuzzy_results = (
+    table.search(MatchQuery("craziou", "text", fuzziness=2))
+    .select(["id", "text", "_score"])
+    .limit(100)
+    .to_arrow().to_pandas()
+)
+```
+
+```typescript TypeScript icon="square-js"
+// Fuzzy match (allows typos)
+const fuzzyResults = await table.query()
+    .fullTextSearch(new MatchQuery("craziou", "text", {
+        fuzziness: 2,
+    }))
+    .select(["id", "text", "_score"])
+    .limit(100)
+    .toArray();
+```
+</CodeGroup>
+
+#### Require an exact prefix in fuzzy matching {#prefix-based-match}
+
+`prefix_length` keeps the first characters unchanged when matching a misspelled term. It does not turn a short query into a prefix-completion search.
+
+<CodeGroup>
+```python Python icon="python"
+# Fuzzy match (allows typos)
+prefix_results = (
+    table.search(MatchQuery("craziou", "text", fuzziness=2, prefix_length=3))
+    .select(["id", "text", "_score"])
+    .limit(100)
+    .to_arrow().to_pandas()
+)
+```
+
+```typescript TypeScript icon="square-js"
+// Fuzzy match (allows typos)
+const prefixResults = await table.query()
+    .fullTextSearch(new MatchQuery("craziou", "text", {
+        fuzziness: 2,
+        prefixLength: 3,
+    }))
+    .select(["id", "text", "_score"])
+    .limit(100)
+    .toArray();
+```
+</CodeGroup>
+
+### Phrase Match {#phrase-match}
+
+Phrase matching enables you to search for exact sequences of words. Unlike regular text search 
+which matches individual terms independently, phrase matching requires words to appear in the 
+specified order with no intervening terms. 
+
+<Note>
+Phrase queries are supported but only for a single column; providing multiple columns with a quoted phrase raises an error.
+</Note>
+
+Phrase matching is particularly useful for:
+
+- Searching for specific multi-word expressions
+- Matching exact titles or quotes  
+- Finding precise word combinations in a specific order
+
+<CodeGroup>
+```python Python icon="python"
+# Exact phrase match
+from lancedb.query import PhraseQuery
+
+phrase_results = (
+    table.search(PhraseQuery("puppy runs", "text"))
+    .select(["id", "text", "_score"])
+    .limit(100)
+    .to_arrow().to_pandas()
+)
+```
+
+```typescript TypeScript icon="square-js"
+// Exact phrase match
+const phraseResults = await table.query()
+  .fullTextSearch(new PhraseQuery("puppy runs", "text"))
+  .select(["id", "text", "_score"])
+  .limit(100)
+  .toArray();
+```
+</CodeGroup>
+
+#### Flexible Phrase Match {#flexible-phrase-match}
+To provide more flexible phrase matching, LanceDB supports the `slop` parameter. This allows you to match phrases where the terms appear close to each other, with gaps between them. The `slop` value limits the extra positions allowed in the phrase.
+
+For example, the phrase query "puppy merrily" would not return any results by default. However, if you set `slop=1`, it will match phrases like "puppy jumps merrily", "puppy runs merrily", and similar variations where one word appears between "puppy" and "merrily".
+
+<CodeGroup>
+```python Python icon="python"
+# Flexible phrase match with slop=1 for 'puppy merrily'
+from lancedb.query import PhraseQuery
+
+slop_results = (
+    table.search(PhraseQuery("puppy merrily", "text", slop=1))
+    .select(["id", "text", "_score"])
+    .limit(100)
+    .to_arrow().to_pandas()
+)
+```
+
+```typescript TypeScript icon="square-js"
+// Flexible phrase match with slop=1 for 'puppy merrily'
+const slopResults = await table.query()
+  .fullTextSearch(new PhraseQuery("puppy merrily", "text", { slop: 1 }))
+  .select(["id", "text", "_score"])
+  .limit(100)
+  .toArray();
+```
+</CodeGroup>
+
+### Search with Boosting {#search-with-boosting}
+
+Boosting allows you to control the relative importance of different search terms or fields 
+in your queries. This feature is particularly useful when you need to:
+
+* Prioritize matches in certain columns
+* Promote specific terms while demoting others 
+* Fine-tune relevance scoring for better search results
+
+| Parameter      | Type  | Default  | Description                                                        |
+| -------------- | ----- | -------- | ------------------------------------------------------------------ |
+| positive       | Query | required | The primary query terms to match and promote in results            |
+| negative       | Query | required | Terms to demote in the search results                              |
+| negative_boost | float | 0.5      | Multiplier for negative matches (lower values = stronger demotion) |
+
+<CodeGroup>
+```python Python icon="python"
+from lancedb.query import MatchQuery, BoostQuery, MultiMatchQuery
+
+# Boost data with 'runs' in text more than 'puppy' in text
+boosting_results = (
+  table.search(
+      BoostQuery(
+          MatchQuery("runs", "text"),
+          MatchQuery("puppy", "text"),
+          negative_boost=0.2,
+      ),
+  )
+  .select(["id", "text", "_score"])
+  .limit(100)
+  .to_arrow().to_pandas()
+)
+
+# Search across both text and text2
+multi_match_results = (
+    table.search(MultiMatchQuery("crazily", ["text", "text2"]))
+    .select(["id", "text", "text2", "_score"])
+    .limit(100)
+    .to_arrow().to_pandas()
+)
+
+# Search with field boosting
+multi_match_boosting_results = (
+    table.search(
+        MultiMatchQuery("crazily", ["text", "text2"], boosts=[1.0, 2.0]),
+    )
+    .select(["id", "text", "text2", "_score"])
+    .limit(100)
+    .to_arrow().to_pandas()
+)
+```
+
+```typescript TypeScript icon="square-js"
+// Boosting Example
+const boostingResults = await table.query()
+  .fullTextSearch(new BoostQuery(new MatchQuery("runs", "text"), new MatchQuery("puppy", "text"), {
+    negativeBoost: 0.2,
+  }))
+  .select(["id", "text", "_score"])
+  .limit(100)
+  .toArray();
+
+// Search across both text fields
+const multiMatchResults = await table.query()
+  .fullTextSearch(new MultiMatchQuery("crazily", ["text", "text2"]))
+  .select(["id", "text", "text2", "_score"])
+  .limit(100)
+  .toArray();
+
+// Search with field boosting
+const multiMatchBoostingResults = await table.query()
+  .fullTextSearch(new MultiMatchQuery("crazily", ["text", "text2"], {
+    boosts: [1.0, 2.0],
+  }))
+  .select(["id", "text", "text2", "_score"])
+  .limit(100)
+  .toArray();
+```
+</CodeGroup>
+
+
+### Boolean Queries {#boolean-queries}
+LanceDB supports boolean logic in full-text search, allowing you to combine multiple queries using `and` and `or` operators. This is useful when you want to match documents that satisfy multiple conditions (intersection) or at least one of several conditions (union).
+
+#### Combining Two Match Queries {#combining-two-match-queries}
+
+In Python, combine two `MatchQuery` objects with `&` for intersection or `|` for union. Python's `and` and `or` operators return one operand and do not construct a boolean query.
+
+<CodeBlock filename="Python" language="Python" icon="python">
+{PyFtsBooleanQueries}
+</CodeBlock>
+
+In TypeScript, pass `[Occur, subquery]` pairs to `BooleanQuery`. Use `Must` for required matches, `Should` for alternatives, and `MustNot` to exclude matches.
+
+<Info>
+**Which queries are allowed?**
+
+A boolean query must include at least one `SHOULD` or `MUST` clause. Queries that contain only a `MUST_NOT` clause are not allowed.
+</Info>
+
+<CodeGroup>
+```python Python icon="python"
+from lancedb.query import MatchQuery
+
+# Example: Find documents containing both "puppy" and "merrily"
+and_query = MatchQuery("puppy", "text") & MatchQuery("merrily", "text")
+and_results = (
+    table.search(and_query)
+    .select(["id", "text", "_score"])
+    .limit(100)
+    .to_arrow().to_pandas()
+)
+
+# Example: Find documents containing either "puppy" or "merrily"
+or_query = MatchQuery("puppy", "text") | MatchQuery("merrily", "text")
+or_results = (
+    table.search(or_query)
+    .select(["id", "text", "_score"])
+    .limit(100)
+    .to_arrow().to_pandas()
+)
+```
+
+```typescript TypeScript icon="square-js" expandable=true
+// Flexible boolean queries with MatchQuery
+
+// Find documents containing both "puppy" and "merrily"
+const mustResults = await table
+    .search(
+      new BooleanQuery([
+        [Occur.Must, new MatchQuery("puppy", "text")],
+        [Occur.Must, new MatchQuery("merrily", "text")],
+      ]),
+    )
+    .select(["id", "text", "_score"])
+    .limit(100)
+    .toArray();
+
+// Find documents containing either "puppy" or "merrily"
+const shouldResults = await table
+    .search(
+      new BooleanQuery([
+        [Occur.Should, new MatchQuery("puppy", "text")],
+        [Occur.Should, new MatchQuery("merrily", "text")],
+      ]),
+    )
+    .select(["id", "text", "_score"])
+    .limit(100)
+    .toArray();
+```
+</CodeGroup>
+
+<Info>
+**How to use booleans?**
+
+- Use `&` (Python), `Occur.Must`(Typescript) for intersection (documents must match all queries).
+- Use `|` (Python), `Occur.Should`(Typescript) for union (documents must match at least one query).
+</Info>
+
+
+### Clean up the example
+
+Remove the temporary database when you have finished running the queries.
+
+<CodeGroup>
+```python Python icon="python"
+directory.cleanup()
+```
+```typescript TypeScript icon="square-js"
+table.close();
+db.close();
+await rm(directory, { recursive: true, force: true });
+```
+</CodeGroup>
+
+</Accordion>
+
+## Substring Search Example {#substring-search-example}
+
+<Accordion title="Worked examples: substring search">
+
+
+LanceDB supports searching for substrings in text columns using n-gram tokenization. This is useful for finding partial matches within text content.
+
+### Setting Up the Table {#setting-up-the-table}
+
+First, create a table with sample text data and configure n-gram tokenization:
+
+<CodeGroup>
+```python Python icon="python"
+import pyarrow as pa
+import lancedb
+
+from tempfile import TemporaryDirectory
+from lancedb.index import FTS
+
+directory = TemporaryDirectory()
+db = lancedb.connect(directory.name)
+
+data = pa.table({"text": ["hello world", "lance database", "lance is cool"]})
+table = db.create_table("test", data=data)
+table.create_index("text", config=FTS(base_tokenizer="ngram"))
+```
+</CodeGroup>
+
+### Basic Substring Search {#basic-substring-search}
+
+With the default n-gram settings (minimum length of 3), you can search for substrings of length 3 or more:
+
+<CodeGroup>
+```python Python icon="python"
+results = table.search("lan", query_type="fts").limit(10).to_list()
+assert len(results) == 2
+assert set(r["text"] for r in results) == {"lance database", "lance is cool"}
+
+results = (
+    table.search("nce", query_type="fts").limit(10).to_list()
+)  # spellchecker:disable-line
+assert len(results) == 2
+assert set(r["text"] for r in results) == {"lance database", "lance is cool"}
+```
+</CodeGroup>
+
+### Handling Short Substrings {#handling-short-substrings}
+
+By default, the minimum n-gram length is 3, so shorter substrings like "la" won't match:
+
+<CodeGroup>
+```python Python icon="python"
+results = table.search("la", query_type="fts").limit(10).to_list()
+assert len(results) == 0
+```
+</CodeGroup>
+
+### Customizing N-gram Parameters {#customizing-n-gram-parameters}
+
+You can customize the n-gram behavior by adjusting the minimum length and using prefix-only matching:
+
+<CodeGroup>
+```python Python icon="python"
+table.create_index(
+    "text",
+    config=FTS(base_tokenizer="ngram", ngram_min_length=2, prefix_only=True),
+    replace=True,
+)
+```
+</CodeGroup>
+
+### Testing Custom N-gram Settings {#testing-custom-n-gram-settings}
+
+With the new settings, you can now search for shorter substrings and use prefix-only matching:
+
+<CodeGroup>
+```python Python icon="python"
+results = table.search("lan", query_type="fts").limit(10).to_list()
+assert len(results) == 2
+assert set(r["text"] for r in results) == {"lance database", "lance is cool"}
+
+results = (
+    table.search("nce", query_type="fts").limit(10).to_list()
+)  # spellchecker:disable-line
+assert len(results) == 0
+
+results = table.search("la", query_type="fts").limit(10).to_list()
+assert len(results) == 2
+assert set(r["text"] for r in results) == {"lance database", "lance is cool"}
+```
+</CodeGroup>
+
+
+```python Python icon="python"
+directory.cleanup()
+```
+
+</Accordion>
+
+## Full-Text Search on Array Fields {#full-text-search-on-array-fields}
+
+<Accordion title="Worked examples: searching string arrays">
+
+
+LanceDB supports full-text search on string array columns, enabling efficient keyword-based search across multiple values within a single field (e.g., tags, keywords).
+
+### Setting Up the Connection {#setting-up-the-connection}
+
+This example uses a temporary local database. Run the steps in order.
+
+<CodeGroup>
+```python Python icon="python"
+from tempfile import TemporaryDirectory
+
+import lancedb
+import pyarrow as pa
+from lancedb.index import FTS
+from lancedb.query import MatchQuery, PhraseQuery
+
+directory = TemporaryDirectory()
+db = lancedb.connect(directory.name)
+```
+
+```typescript TypeScript icon="square-js"
+import * as lancedb from "@lancedb/lancedb";
+import { Index, MatchQuery, PhraseQuery, makeArrowTable } from "@lancedb/lancedb";
+import { Schema, Field, List, Utf8 } from "apache-arrow";
+import { mkdtemp, rm } from "node:fs/promises";
+import { tmpdir } from "node:os";
+import { join } from "node:path";
+
+const directory = await mkdtemp(join(tmpdir(), "lancedb-array-fts-"));
+const db = await lancedb.connect(directory);
+```
+</CodeGroup>
+
+### Defining the Schema {#defining-the-schema}
+
+Create a schema that includes an array field for tags:
+
+<CodeGroup>
+```python Python icon="python"
+table_name = "fts-array-field-test"
+schema = pa.schema([
+    pa.field("id", pa.string()),
+    pa.field("tags", pa.list_(pa.string())),
+    pa.field("description", pa.string())
+])
+```
+
+```typescript TypeScript icon="square-js" expandable=true
+const tableName = "fts-array-field-test-ts";
+
+// Create schema
+const schema = new Schema([
+  new Field("id", new Utf8(), false),
+  new Field("tags", new List(new Field("item", new Utf8()))),
+  new Field("description", new Utf8(), false)
+]);
+```
+</CodeGroup>
+
+### Creating Sample Data {#creating-sample-data}
+
+Generate sample data with array fields containing tags:
+
+<CodeGroup>
+```python Python icon="python" expandable=true
+# Generate sample data
+data = {
+    "id": [f"doc_{i}" for i in range(10)],
+    "tags": [
+        ["python", "machine learning", "data science"],
+        ["deep learning", "neural networks", "AI"],
+        ["database", "indexing", "search"],
+        ["vector search", "embeddings", "AI"],
+        ["full text search", "indexing", "database"],
+        ["python", "web development", "flask"],
+        ["machine learning", "deep learning", "pytorch"],
+        ["database", "SQL", "postgresql"],
+        ["search engine", "elasticsearch", "indexing"],
+        ["AI", "transformers", "NLP"]
+    ],
+    "description": [
+        "Python for data science projects",
+        "Deep learning fundamentals",
+        "Database indexing techniques",
+        "Vector search implementations",
+        "Full-text search guide",
+        "Web development with Python",
+        "Machine learning with PyTorch",
+        "Database management systems",
+        "Search engine optimization",
+        "AI and NLP applications"
+    ]
+}
+```
+
+```typescript TypeScript icon="square-js" expandable=true
+// Generate sample data
+const data = makeArrowTable(
+  Array(10).fill(0).map((_, i) => ({
+    id: `doc_${i}`,
+    tags: [
+      ["python", "machine learning", "data science"],
+      ["deep learning", "neural networks", "AI"],
+      ["database", "indexing", "search"],
+      ["vector search", "embeddings", "AI"],
+      ["full text search", "indexing", "database"],
+      ["python", "web development", "flask"],
+      ["machine learning", "deep learning", "pytorch"],
+      ["database", "SQL", "postgresql"],
+      ["search engine", "elasticsearch", "indexing"],
+      ["AI", "transformers", "NLP"]
+    ][i],
+    description: [
+      "Python for data science projects",
+      "Deep learning fundamentals",
+      "Database indexing techniques",
+      "Vector search implementations",
+      "Full-text search guide",
+      "Web development with Python",
+      "Machine learning with PyTorch",
+      "Database management systems",
+      "Search engine optimization",
+      "AI and NLP applications"
+    ][i]
+  })),
+  { schema }
+);
+```
+</CodeGroup>
+
+### Creating the Table and Adding Data {#creating-the-table-and-adding-data}
+
+Create the table and populate it with the sample data:
+
+<CodeGroup>
+```python Python icon="python"
+# Create table and add data
+table = db.create_table(table_name, schema=schema, mode="overwrite")
+table_data = pa.Table.from_pydict(data, schema=schema)
+table.add(table_data)
+```
+
+```typescript TypeScript icon="square-js"
+// Create table
+const table = await db.createTable(tableName, data, { mode: "overwrite" });
+console.log(`Created table: ${tableName}`);
+```
+</CodeGroup>
+
+### Building the Full-Text Search Index {#building-the-full-text-search-index}
+
+Store token positions for phrase queries. This example disables stemming so fuzzy matching compares the original word forms:
+
+<CodeGroup>
+```python Python icon="python"
+# Create FTS index
+table.create_index("tags", config=FTS(with_position=True, stem=False))
+table.wait_for_index(["tags_idx"])
+```
+
+```typescript TypeScript icon="square-js"
+// Create FTS index
+console.log("Creating FTS index on 'tags' column...");
+await table.createIndex("tags", {
+  config: Index.fts({ withPosition: true, stem: false })
+});
+
+// Wait for index
+const ftsIndexName = "tags_idx";
+await table.waitForIndex([ftsIndexName], 300);
+```
+</CodeGroup>
+
+### Performing Fuzzy Search {#performing-fuzzy-search}
+
+Search for terms with typos using fuzzy matching:
+
+<CodeGroup>
+```python Python icon="python"
+# Search examples
+print("\nSearching for 'learning' in tags with a typo:")
+result = (
+    table.search(MatchQuery("learnin", column="tags", fuzziness=1))
+    .select(["id", "tags", "description", "_score"])
+    .to_arrow()
+)
+```
+
+```typescript TypeScript icon="square-js"
+// Search examples
+console.log("\nSearching for 'learning' in tags with a typo:");
+const fuzzyResults = await table.query()
+  .fullTextSearch(new MatchQuery("learnin", "tags", {
+    fuzziness: 1,
+  }))
+  .select(["id", "tags", "description", "_score"])
+  .toArray();
+console.log(fuzzyResults);
+```
+</CodeGroup>
+
+A string-array query can return a row more than once when several elements match.
+Deduplicate by your record ID if your application needs one result per row.
+
+### Performing Phrase Search {#performing-phrase-search}
+
+Search for exact phrases within the array fields:
+
+<CodeGroup>
+```python Python icon="python"
+print("\nSearching for 'machine learning' in tags:")
+result = (
+    table.search(PhraseQuery("machine learning", column="tags"))
+    .select(["id", "tags", "description", "_score"])
+    .to_arrow()
+)
+```
+
+```typescript TypeScript icon="square-js"
+console.log("\nSearching for 'machine learning' in tags:");
+const phraseResults = await table.query()
+  .fullTextSearch(new PhraseQuery("machine learning", "tags"))
+  .select(["id", "tags", "description", "_score"])
+  .toArray();
+console.log(phraseResults);
+```
+</CodeGroup>
+
+### Clean up the array example
+
+Remove the temporary database when you have finished running the queries.
+
+<CodeGroup>
+```python Python icon="python"
+directory.cleanup()
+```
+```typescript TypeScript icon="square-js"
+table.close();
+db.close();
+await rm(directory, { recursive: true, force: true });
+```
+</CodeGroup>
+
+</Accordion>
diff --git a/docs/web/search/hybrid-search.mdx b/docs/web/search/hybrid-search.mdx
new file mode 100644
index 000000000..c3e453145
--- /dev/null
+++ b/docs/web/search/hybrid-search.mdx
@@ -0,0 +1,399 @@
+---
+title: Hybrid Search
+sidebarTitle: "Hybrid Search"
+description: "Combine vector similarity and keyword matches in a single ranked result set."
+icon: "search"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Hybrid search is available in OSS and Enterprise.
+---
+
+import { PyConnectEnterprisePlaceholders, TsConnectEnterprisePlaceholders } from '/snippets/connection.mdx';
+
+import { PyHybridSearch, TsHybridSearchBody, TsHybridSearchTail } from '/snippets/search_examples.mdx';
+import { RsExampleBody as RsHybridSearchBody, RsExampleTail as RsHybridSearchTail } from '/snippets/search_hybrid_search.mdx';
+
+Hybrid search combines vector similarity with keyword matching. It is useful
+when a query needs both: for example, finding products with a particular model
+number whose descriptions match what the customer is looking for.
+
+LanceDB runs a vector search and a full-text search, then merges their results
+with a reranker. Reciprocal Rank Fusion (RRF) is the default. It combines the
+positions of records in the two ranked lists.
+
+Before you query, store vectors and create an FTS index on the text column.
+
+<span id="run-a-hybrid-query-with-explicit-vectors"></span>
+
+<Accordion title="Run a complete hybrid search example">
+
+These examples use three sample records and hand-written vectors, so no
+embedding service is needed. Each program creates a temporary local table
+and removes it when finished.
+
+- **Python:** Install with `pip install lancedb`, save as `hybrid_search.py`, then run `python hybrid_search.py`.
+- **TypeScript:** Install with `npm install @lancedb/lancedb`, save as `hybrid_search.mts`, then run `node hybrid_search.mts` with Node.js 22.18 or later.
+- **Rust:** Download and extract the [complete Rust examples](/static/code/search-rust.zip), then run `cargo run --locked --bin hybrid_search` from the extracted directory.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{PyHybridSearch}
+</CodeBlock>
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsHybridSearchBody + "\n" + TsHybridSearchTail}
+</CodeBlock>
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsHybridSearchBody + "\n" + RsHybridSearchTail}
+</CodeBlock>
+</CodeGroup>
+
+The puppy record ranks first because both searches place it first. The query
+returns `_relevance_score`, with higher values first. This small example shows
+the mechanics; evaluate retrieval quality with embeddings and queries from
+your own application.
+
+</Accordion>
+
+## Generate query embeddings automatically {#example-hybrid-search}
+
+### 1\. Setup {#1-setup}
+The following walkthrough uses Sentence Transformers in Python and OpenAI in
+TypeScript. Install the corresponding provider packages before starting.
+Python downloads a local model; TypeScript calls OpenAI. Install
+`lancedb sentence-transformers pandas` for Python, or
+`@lancedb/lancedb apache-arrow openai` for TypeScript. Set your OpenAI API key
+as described in the [OpenAI embedding setup](/integrations/embedding/openai).
+Run the steps for your chosen language in order, choosing one connection option.
+
+<CodeGroup>
+```python Python icon="python"
+import lancedb
+from lancedb.index import FTS
+from lancedb.rerankers import RRFReranker
+from lancedb.embeddings import get_registry
+from lancedb.pydantic import LanceModel, Vector
+```
+
+```typescript TypeScript icon="square-js"
+import * as lancedb from "@lancedb/lancedb";
+import "@lancedb/lancedb/embedding/openai";
+import { Utf8 } from "apache-arrow";
+```
+</CodeGroup>
+
+### 2\. Connect to LanceDB {#2-connect-to-lancedb}
+Choose a local OSS connection or use your Enterprise connection details.
+
+<Badge color="green">OSS</Badge>
+
+<CodeGroup>
+```python Python icon="python"
+uri = "data/sample-lancedb"
+db = lancedb.connect(uri)
+```
+
+```typescript TypeScript icon="square-js"
+const databaseDir = "data/sample-lancedb";
+const db = await lancedb.connect(databaseDir);
+```
+</CodeGroup>
+
+<Badge color="red">Enterprise</Badge>
+
+Replace the placeholders with your Enterprise database URI, API key, region and
+endpoint. In application code, load credentials from environment variables or a
+secrets manager.
+
+<CodeGroup>
+  <CodeBlock filename="Python" language="Python" icon="python">
+    {PyConnectEnterprisePlaceholders}
+  </CodeBlock>
+  <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsConnectEnterprisePlaceholders}
+  </CodeBlock>
+</CodeGroup>
+
+### 3\. Configure Embedding Model {#3-configure-embedding-model}
+Configure the model used for both document and query embeddings.
+
+<CodeGroup>
+```python Python icon="python"
+embeddings = get_registry().get("sentence-transformers").create()
+```
+
+```typescript TypeScript icon="square-js"
+const embedFunc = lancedb.embedding.getRegistry().get("openai")?.create({
+  model: "text-embedding-ada-002",
+}) as lancedb.embedding.EmbeddingFunction;
+```
+</CodeGroup>
+
+### 4\. Create Table and Schema {#4-create-table-and-schema}
+Define the data structure for your documents, including both the text content and its vector representation.
+
+<CodeGroup>
+```python Python icon="python"
+class Documents(LanceModel):
+    category: str
+    text: str = embeddings.SourceField()
+    vector: Vector(embeddings.ndims()) = embeddings.VectorField()
+
+table_name = "hybrid_search_example"
+table = db.create_table(table_name, schema=Documents, mode="overwrite")
+```
+
+```typescript TypeScript icon="square-js"
+const documentSchema = lancedb.embedding.LanceSchema({
+  category: new Utf8(),
+  text: embedFunc.sourceField(new Utf8()),
+  vector: embedFunc.vectorField(),
+});
+
+const tableName = "hybrid_search_example";
+const table = await db.createEmptyTable(tableName, documentSchema, {
+  mode: "overwrite",
+});
+```
+</CodeGroup>
+
+### 5\. Add Data {#5-add-data}
+Insert sample documents into your table, which will be used for both semantic and keyword search.
+
+<CodeGroup>
+```python Python icon="python"
+data = [
+    {"text": "rebel spaceships striking from a hidden base", "category": "film"},
+    {"text": "have won their first victory against the evil Galactic Empire", "category": "film"},
+    {"text": "during the battle rebel spies managed to steal secret plans", "category": "film"},
+    {"text": "to the Empire's ultimate weapon the Death Star", "category": "film"},
+]
+table.add(data=data)
+```
+
+```typescript TypeScript icon="square-js"
+const data = [
+  { text: "rebel spaceships striking from a hidden base", category: "film" },
+  { text: "have won their first victory against the evil Galactic Empire", category: "film" },
+  { text: "during the battle rebel spies managed to steal secret plans", category: "film" },
+  { text: "to the Empire's ultimate weapon the Death Star", category: "film" },
+];
+await table.add(data);
+console.log(`Created table: ${tableName} with ${data.length} rows`);
+```
+</CodeGroup>
+
+### 6\. Build Full Text Index {#6-build-full-text-index}
+Create a full-text search index on the text column to enable keyword-based search capabilities.
+
+<CodeGroup>
+```python Python icon="python"
+table.create_index("text", config=FTS())
+table.wait_for_index(["text_idx"])
+```
+
+```typescript TypeScript icon="square-js"
+console.log("Creating full-text search index...");
+await table.createIndex("text", {
+  config: lancedb.Index.fts(),
+});
+await table.waitForIndex(["text_idx"], 300);
+```
+</CodeGroup>
+
+### 7\. Set Reranker [Optional] {#7-set-reranker-optional}
+RRF is the default. The examples below configure it explicitly. Other
+rerankers and their language support are described in [Reranking](/reranking).
+
+<CodeGroup>
+```python Python icon="python"
+reranker = RRFReranker()
+```
+
+```typescript TypeScript icon="square-js"
+const reranker = await lancedb.rerankers.RRFReranker.create();
+```
+</CodeGroup>
+
+### 8\. Hybrid Search {#8-hybrid-search}
+Perform a hybrid search query that combines semantic similarity with keyword matching, using the specified reranker to merge and rank the results.
+
+<CodeGroup>
+```python Python icon="python"
+results = (
+    table.search(
+        "rebel spaceships",
+        query_type="hybrid",
+        vector_column_name="vector",
+        fts_columns="text",
+    )
+    .rerank(reranker)
+    .limit(10)
+    .to_pandas()
+)
+
+print("Hybrid search results:")
+print(results)
+```
+
+```typescript TypeScript icon="square-js"
+console.log("Performing hybrid search...");
+const queryVector = await embedFunc.computeQueryEmbeddings("rebel spaceships");
+const hybridResults = await table
+  .query()
+  .fullTextSearch("rebel spaceships")
+  .nearestTo(queryVector)
+  .rerank(reranker)
+  .select(["text"])
+  .limit(10)
+  .toArray();
+
+console.log("Hybrid search results:");
+console.log(hybridResults);
+```
+</CodeGroup>
+
+### 9\. Supply a vector and text separately {#9-hybrid-search-explicit-vector-and-text-query-pattern}
+You can supply the vector and text separately. The vector must use the same
+model and dimensions as the stored embeddings. This example reuses the model
+configured above; the complete example at the top also demonstrates this pattern
+in TypeScript and Rust.
+
+<CodeGroup>
+```python Python icon="python"
+text_query = "rebel spaceships"
+vector_query = embeddings.compute_query_embeddings(text_query)[0]
+(
+    table.search(query_type="hybrid")
+    .vector(vector_query)
+    .text(text_query)
+    .limit(5)
+    .to_pandas()
+)
+```
+</CodeGroup>
+
+## Control the candidate results {#query-controls}
+
+Use a result limit, metadata filters and vector distance bounds to control
+which candidates are available to the reranker. The examples below show each
+option on the query builder.
+
+<Info>
+Set `limit()` to the number of results your application needs. The default is 10.
+A larger candidate set can help a reranker find relevant records, but adds work.
+</Info>
+
+### Returning row IDs {#returning-row-ids}
+
+Pass `with_row_id(True)` (Python) or `withRowId()` (TypeScript) to include the internal `_rowid` column in the results. This is useful for joining hybrid results back to a primary table, or for deduping across multiple queries:
+
+<CodeGroup>
+```python Python icon="python"
+results = (
+    table.search("rebel spaceships", query_type="hybrid")
+    .with_row_id(True)
+    .limit(10)
+    .to_pandas()
+)
+# results now contains a `_rowid` column alongside `_relevance_score`
+```
+
+```typescript TypeScript icon="square-js"
+const results = await table
+    .query()
+    .fullTextSearch("rebel spaceships")
+    .nearestTo(queryVector)
+    .withRowId()
+    .limit(10)
+    .toArray();
+```
+</CodeGroup>
+
+### Bounding vector distance {#bounding-vector-distance}
+
+`distance_range(lower, upper)` (Python) and `distanceRange(lower, upper)` (TypeScript) constrain the vector half of the hybrid query to the half-open interval `[lower, upper)`. This is helpful when you want to cap how far semantic candidates can drift from the query vector before reranking:
+
+<CodeGroup>
+```python Python icon="python"
+results = (
+    table.search("rebel spaceships", query_type="hybrid")
+    .distance_range(lower_bound=0.0, upper_bound=0.4)
+    .limit(10)
+    .to_pandas()
+)
+```
+
+```typescript TypeScript icon="square-js"
+const results = await table
+    .query()
+    .fullTextSearch("rebel spaceships")
+    .nearestTo(queryVector)
+    .distanceRange(0.0, 0.4)
+    .limit(10)
+    .toArray();
+```
+</CodeGroup>
+
+Either bound can be omitted to leave that side unbounded.
+
+### Prefilter vs. postfilter {#prefilter-vs-postfilter}
+
+A hybrid query applies its metadata filter to both the vector and full-text
+searches. Prefiltering, the default, restricts eligible rows before retrieval.
+Postfiltering removes candidates after retrieval and can leave fewer results
+than requested. See [Filtering](/search/filtering).
+
+<CodeGroup>
+```python Python icon="python"
+# Prefilter (default): filter applied before scoring
+table.search("rebel spaceships", query_type="hybrid") \
+    .where("category = 'film'", prefilter=True) \
+    .limit(10) \
+    .to_pandas()
+
+# Postfilter: filter applied after the sub-queries return top-k
+table.search("rebel spaceships", query_type="hybrid") \
+    .where("category = 'film'", prefilter=False) \
+    .limit(10) \
+    .to_pandas()
+```
+
+```typescript TypeScript icon="square-js"
+// Prefilter (default): just call .where(...)
+await table.query()
+    .fullTextSearch("rebel spaceships")
+    .nearestTo(queryVector)
+    .where("category = 'film'")
+    .limit(10)
+    .toArray();
+
+// Postfilter: chain .postfilter() after .where(...)
+await table.query()
+    .fullTextSearch("rebel spaceships")
+    .nearestTo(queryVector)
+    .where("category = 'film'")
+    .postfilter()
+    .limit(10)
+    .toArray();
+```
+</CodeGroup>
+
+In Python, use [`explain_plan`](/search/explain#analyzing-non-vector-queries) on a hybrid query to see whether the filter pushed into the scan or ran as a separate `FilterExec` step.
+
+## Choose and evaluate a reranker {#more-on-reranking}
+
+Start with RRF, then compare alternatives on representative queries. Review
+which relevant rows reach the candidate set before evaluating how a reranker
+orders them. Record latency as well as relevance: increasing candidate counts
+or applying a model-based reranker adds work.
+
+Reranking runs in the application for both OSS and Enterprise connections.
+See [Reranking](/reranking) for model-free fusion, model-based rerankers,
+custom implementations and score interpretation.
+
+In synchronous Python, `.rerank(...)` accepts a reranker and a `normalize`
+option (`"score"` or `"rank"`). Normalization matters for methods that blend
+raw scores; RRF uses rank positions. These are Python query options, not a
+shared signature across every SDK.
diff --git a/docs/web/search/index.mdx b/docs/web/search/index.mdx
new file mode 100644
index 000000000..9d439b204
--- /dev/null
+++ b/docs/web/search/index.mdx
@@ -0,0 +1,118 @@
+---
+title: Search
+sidebarTitle: Overview
+description: "Choose vector, full-text or hybrid search and return the records your application needs."
+icon: "list"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Vector, full-text and hybrid search are available in OSS and Enterprise.
+---
+
+import { PyVectorSearch, TsVectorSearchBody, TsVectorSearchTail } from '/snippets/search_examples.mdx';
+import { RsExampleBody as RsVectorSearchBody, RsExampleTail as RsVectorSearchTail } from '/snippets/search_vector_search.mdx';
+
+LanceDB supports vector, full-text and hybrid search. Use vector search to find
+similar content, full-text search to match words, or hybrid search to combine
+both. Filters narrow the results to records that match your criteria.
+
+## Choose a search method
+
+| You want to find | Use | Prepare |
+|:----------------|:----|:--------|
+| Items similar to a query, such as related passages or images | [Vector search](/search/vector-search) | Stored vectors and a compatible query vector |
+| Documents represented by several embeddings | [Multivector search](/search/vector-search#multivector) | A multivector column and compatible query vectors |
+| Records containing particular words or phrases | [Full-text search](/search/full-text-search) | An FTS index on the text column |
+| Relevant meaning alongside keyword matches | [Hybrid search](/search/hybrid-search) | Vectors and an FTS index |
+| Rows matching a condition, without similarity ranking | [Filtering](/search/filtering) | A predicate over table columns |
+
+Vector search can run without an index. An approximate nearest-neighbor index
+reduces the work needed for larger tables, with a tradeoff between recall and
+latency. Full-text search needs an FTS index before you query it.
+
+## Before you search {#before-you-search}
+
+For vector search, generate stored and query vectors with the same embedding
+model and compatible settings. Their dimensions must match. Keep the fields
+you want to return, such as document text or an item ID, in the same table.
+
+For full-text search, choose the text column and build its index. Enterprise
+builds indexes in the background, so wait for the index to be ready before
+querying it. Phrase queries also need token positions in the index. See
+[FTS indexes](/indexing/fts-index).
+
+<span id="run-a-first-query"></span>
+
+<Accordion title="Run a complete search example in Python, TypeScript or Rust">
+
+These examples use three sample records and hand-written vectors, so no
+embedding service is needed. Each program creates a temporary local table
+and removes it when finished.
+
+- **Python:** Install with `pip install lancedb`, save as `search.py`, then run `python search.py`.
+- **TypeScript:** Install with `npm install @lancedb/lancedb`, save as `search.mts`, then run `node search.mts` with Node.js 22.18 or later.
+- **Rust:** Download and extract the [complete Rust examples](/static/code/search-rust.zip), then run `cargo run --locked --bin vector_search` from the extracted directory.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{PyVectorSearch}
+</CodeBlock>
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsVectorSearchBody + "\n" + TsVectorSearchTail}
+</CodeBlock>
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsVectorSearchBody + "\n" + RsVectorSearchTail}
+</CodeBlock>
+</CodeGroup>
+
+This query returns the puppy and dog records, ordered by vector distance.
+It searches the three-row table directly, without an ANN index or an embedding
+service. For connection options, see [Connect](/basics/connect).
+
+</Accordion>
+
+## Read the results
+
+Use `select()` to choose the columns your application needs and `limit()` to
+set the maximum number of results. The search also returns a ranking value:
+
+| Search method | Ranking column | Order |
+|:--------------|:---------------|:------|
+| Vector | `_distance` | Smaller distance first |
+| Full-text | `_score` | Higher text relevance first |
+| Hybrid | `_relevance_score` | Higher combined relevance first |
+
+These values have different meanings. Do not compare a vector distance directly
+with a full-text score or treat a reranker score as a probability.
+
+## Add filters and reranking {#performance}
+
+[Filters](/search/filtering) restrict which rows qualify, such as documents
+from a particular collection. Prefiltering searches within those rows.
+Postfiltering removes nonmatching rows from the candidates already retrieved,
+so it can leave fewer results than requested.
+
+[Reranking](/reranking) reorders the candidates returned by retrieval. Start
+with representative queries and expected results, then compare changes to
+relevance and latency. A reranker cannot recover a relevant row that retrieval
+never returned.
+
+## Read large result sets in batches {#avoid-materializing-the-whole-table}
+
+Materializing all rows at once can exhaust application memory. For exports and
+training reads, use bounded queries or the batch-reading interfaces described
+in [Data loading](/training/data-loading). The SDK buffers Enterprise
+query responses before exposing batches. Set a query limit rather than relying
+on batch iteration alone to bound memory.
+
+Direct Lance dataset access through Python's `table.to_lance()` is an OSS
+interface. Use the SDK query builders for reads that need to work with both
+OSS and Enterprise.
+
+## Investigate a slow query {#diagnostics}
+
+Use [query plans](/search/explain) to see the scans, filters and indexes selected
+for a query. `explain_plan()` inspects the plan; `analyze_plan()` executes the
+query and reports measurements. Compare one change at a time, such as adding
+an index, returning fewer columns, or adjusting search parameters.
diff --git a/docs/web/search/vector-search.mdx b/docs/web/search/vector-search.mdx
new file mode 100644
index 000000000..3519df553
--- /dev/null
+++ b/docs/web/search/vector-search.mdx
@@ -0,0 +1,740 @@
+---
+title: "Vector Search"
+sidebarTitle: "Vector Search"
+description: "Search by vector similarity, choose a distance metric and tune your queries."
+icon: "arrow-up-right-dots"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Vector search is available in OSS and Enterprise.
+---
+
+import {
+    PyConfigureDistanceMetric as ConfigureDistanceMetric,
+    PySelectVectorColumn as SelectVectorColumn,
+    PyIndexNestedColumn as IndexNestedColumn,
+    PyExactVsApproximateDistances as ExactVsApproximateDistances,
+    PyVectorSearchPrefilter as VectorSearchPrefilter,
+    PyVectorSearchPostfilter as VectorSearchPostfilter,
+    PyMultivectorSearch as MultivectorSearch,
+    PySearchDistanceRange as SearchDistanceRange,
+    PySearchBinaryVectors as SearchBinaryVectors,
+    PyBatchSearch as BatchSearch,
+    PyFastSearch as FastSearch,
+    PyBruteForceSearch as BruteForceSearch,
+    PyBypassVectorIndex as BypassVectorIndex,
+    TsSearch2,
+    TsSelectVectorColumn,
+    TsIndexNestedColumn,
+    TsExactVsApproximate,
+    TsVectorSearchPrefilter,
+    TsVectorSearchPostfilter,
+    TsDistanceRange,
+    TsBinarySearch,
+    TsBatchSearch,
+    TsFastSearch,
+    TsBruteForceSearch,
+    TsBypassVectorIndex,
+    RsConfigureDistanceMetric,
+    RsExactVsApproximate,
+    RsVectorSearchPrefilter,
+    RsVectorSearchPostfilter,
+    RsSearchDistanceRange,
+    RsFastSearch,
+    RsBruteForceSearch,
+    RsBypassVectorIndex,
+    RsBatchSearch,
+    RsBinarySearch,
+} from '/snippets/search.mdx';
+
+import { PyVectorSearch, TsVectorSearchBody, TsVectorSearchTail } from '/snippets/search_examples.mdx';
+import { RsExampleBody as RsVectorSearchBody, RsExampleTail as RsVectorSearchTail } from '/snippets/search_vector_search.mdx';
+
+Vector search finds similar items by comparing their embeddings: numerical
+representations of text, images, audio or other data. An embedding model maps
+related items to nearby points in vector space, so you can search by meaning
+or similarity instead of matching exact words.
+
+![An embedding model maps data into vectors for similarity search.](/static/assets/images/search/vector-db-basics.png)
+
+To run a search, pass a query vector to LanceDB and specify how many matches
+to return. Generate the query vector with the same model and settings used for
+your stored embeddings. LanceDB ranks the results by distance from the query.
+
+For small tables, you can search the vectors directly. A [vector index](/indexing/vector-index)
+speeds up searches on larger tables by narrowing the candidates to examine.
+
+<span id="run-a-nearest-neighbor-query"></span>
+
+<Accordion title="Run a complete example in Python, TypeScript or Rust">
+
+These examples create a three-row table and find the two closest matches to a
+query vector. They use hand-written vectors, so you can try the query without
+an embedding model. The temporary table is removed when the program finishes.
+
+- **Python:** Install with `pip install lancedb`, save as `vector_search.py`, then run `python vector_search.py`.
+- **TypeScript:** Install with `npm install @lancedb/lancedb`, save as `vector_search.mts`, then run `node vector_search.mts` with Node.js 22.18 or later.
+- **Rust:** Download and extract the [complete Rust examples](/static/code/search-rust.zip), then run `cargo run --locked --bin vector_search` from the extracted directory.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{PyVectorSearch}
+</CodeBlock>
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsVectorSearchBody + "\n" + TsVectorSearchTail}
+</CodeBlock>
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsVectorSearchBody + "\n" + RsVectorSearchTail}
+</CodeBlock>
+</CodeGroup>
+
+The first result is the puppy record, whose vector exactly matches the query.
+The dog record is next. `_distance` is smaller for closer matches. This example
+uses an exhaustive scan; the indexing section explains how to search larger tables.
+
+</Accordion>
+
+## Supported distance metrics {#supported-distance-metrics}
+
+A distance metric determines how LanceDB compares vectors. Choose the metric
+recommended by your embedding model. Use the same metric when building an index
+and querying it.
+
+LanceDB supports `l2`, `cosine`, `dot` and `hamming`. An unindexed search uses
+`l2` by default; Hamming distance is for binary vectors. The table below shows
+the value returned in the `_distance` column. Smaller values rank first.
+
+| Distance metric | Mathematical form | Notes |
+|---|---|---|
+| `l2` | $\sum_i (x_i-y_i)^2$ | Squared Euclidean distance returned in `_distance`. |
+| `cosine` | $1-\frac{x\cdot y}{\|x\|_2\|y\|_2}$ | Measures directional difference between vectors. Insensitive to nonzero scaling of vectors. |
+| `dot` | $1-\sum_i x_i y_i$ | Dot-product distance. For unit-normalized vectors, this equals cosine distance. |
+| `hamming` | $\underset{i}{\sum} \,\: \mathbf{1}_{\, [x_i\neq y_i]}$ | Counts the number of bitwise-differing bits between binary vectors. |
+
+For indexed search, supported distance metrics vary by index type:
+
+| Index type | Supported distance metrics |
+|---|---|
+| `IVF_FLAT` | `["l2", "cosine", "dot", "hamming"]` |
+| `IVF_PQ` | `["l2", "cosine", "dot"]` |
+| `IVF_SQ` | `["l2", "cosine", "dot"]` |
+| `IVF_RQ` | `["l2", "cosine", "dot"]` |
+| `IVF_HNSW_FLAT` | `["l2", "cosine", "dot"]` |
+| `IVF_HNSW_PQ` | `["l2", "cosine", "dot"]` |
+| `IVF_HNSW_SQ` | `["l2", "cosine", "dot"]` |
+
+### Configure Distance Metric
+
+Without a vector index, searches use `l2` by default. You can specify `cosine` or
+`dot` instead (`hamming` is supported for `IVF_FLAT` indexes only).
+
+When a vector index exists, the search metric must match the metric used to build
+the index. If you explicitly request a different metric, LanceDB cannot use that
+index and falls back to an exhaustive (brute-force) search. On LanceDB Cloud, the
+brute-force KNN safety limit can reject that search on a large table instead of
+executing it. LanceDB Enterprise doesn't have the limit.
+
+If you omit the search metric, current SDKs use the index metric when an index is
+available. Some older SDK versions sent `l2` when the metric was omitted; with a
+non-`l2` index, explicitly set the matching metric or upgrade the SDK.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{ConfigureDistanceMetric}
+</CodeBlock>
+
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsSearch2}
+</CodeBlock>
+
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsConfigureDistanceMetric}
+</CodeBlock>
+</CodeGroup>
+
+These examples use cosine distance and return up to ten matches. If the table
+has a vector index, build it with the same metric.
+
+Set `.limit(...)` on vector searches you run in applications. You can page through results with `.offset(...)` and include LanceDB's internal row id with `.with_row_id()` / `.withRowId()` when an operation requires internal row IDs. Use your own ID column for application identifiers.
+
+## Selecting the vector column
+
+If your table has exactly one vector column, you can omit the column name and LanceDB will pick it for you. This works for both top-level columns (such as `vector`) and vector fields nested inside a struct (such as `image.embedding`).
+
+When LanceDB can't infer a single column, it raises a `ValueError` (Python) or rejects the query (Node/Rust). Two cases trigger this:
+
+- No vector column: the schema has no `fixed_size_list` or `list` of floats.
+- Multiple candidates: more than one column matches the query's dimension. The error lists every candidate path so you can pick one explicitly.
+
+To disambiguate, pass the field path with dot notation. Wrap any segment that contains characters outside `[A-Za-z0-9_]` in backticks (for example, `` `image-meta`.`embedding.v1` ``).
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{SelectVectorColumn}
+</CodeBlock>
+
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsSelectVectorColumn}
+</CodeBlock>
+</CodeGroup>
+
+The same field-path syntax works when creating an index on a nested vector column:
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{IndexNestedColumn}
+</CodeBlock>
+
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsIndexNestedColumn}
+</CodeBlock>
+</CodeGroup>
+
+When several columns share a name across structs (for example, `image.embedding` and `text.embedding`), LanceDB still picks the one whose dimension matches your query vector. If two candidates have the same dimension, you must pass the column name explicitly.
+
+## Search with a vector index {#vector-search-with-ann-index}
+
+An approximate nearest-neighbor (ANN) index narrows the set of vectors examined
+for a query. It can reduce query latency, but may miss neighbors that an
+exhaustive search would find. Compare recall and latency on queries that
+represent your application before choosing an index and its search settings.
+
+<Warning>
+When a vector index is used, `_distance` is not always the true distance between full vectors. On quantized ANN indexes, LanceDB may compute `_distance` from the compressed representation for speed. Use `refine_factor` when you want reranking on full vectors.
+</Warning>
+
+### Exact vs Approximate Distances
+
+When doing vector search, the meaning of "distance" depends on whether you are using an index and whether `refine_factor` is specified as part of your query.
+`nprobes` controls how many partitions are searched to find candidates, `approx_mode` controls the query-time speed/recall trade-off for RQ-quantized indexes, and `refine_factor` controls how many candidates are rescored on full vectors for better distance fidelity and reranking quality.
+
+The table below summarizes the behavior of `_distance` in search results based on your query configuration:
+
+| Query mode | Neighbor quality | `_distance` in results |
+| :--- | :--- | :--- |
+| No index or `.bypass_vector_index()` | Exact kNN (100% recall) | True distance on full vectors |
+| Indexed ANN, no `refine_factor` | Approximate neighbors | Distance on the index representation: exact for flat indexes, approximate for quantized indexes |
+| Indexed ANN + `refine_factor(1)` | Approximate neighbors (same candidate set) | Distances recomputed on full vectors for reranked candidates |
+| Indexed ANN + `refine_factor(>1)` | Better recall than no refine (usually) | Distances recomputed on full vectors for reranked candidates |
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{ExactVsApproximateDistances}
+</CodeBlock>
+
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsExactVsApproximate}
+</CodeBlock>
+
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsExactVsApproximate}
+</CodeBlock>
+</CodeGroup>
+
+See [vector indexes](/indexing/vector-index) for index configuration. The
+following sections cover query-time settings.
+
+### Tuning `approx_mode`
+
+Use `approx_mode` when you want to adjust the speed/recall trade-off for approximate vector search at query time. This setting currently applies only to RQ-quantized indexes, such as `IVF_RQ`; other index types ignore it.
+
+The supported values are:
+
+| Value | Behavior |
+| :--- | :--- |
+| `fast` | Prefer lower query latency, which can reduce recall. |
+| `normal` | Use the default balance between query latency and recall. This only has an effect for RQ indexes built with `num_bits > 1`. |
+| `accurate` | Prefer higher recall, which can increase query latency. |
+
+Only the Rust SDK sets `approx_mode`; the Python and TypeScript SDKs do not expose it. You can change `approx_mode` per query without rebuilding the index. For RQ indexes built with `num_bits=1`, `normal` uses the same one-bit scoring path as `fast`. If you also set `refine_factor`, LanceDB first uses `approx_mode` while finding candidates, then reranks the selected candidates on full vectors.
+
+### Tuning `nprobes`
+
+- `nprobes` controls how many partitions are searched at query time.
+- `nprobes` improves candidate recall, but does not by itself make `_distance` exact.
+- The default probe selection is a starting point. Measure recall and latency for your data before overriding it.
+- In most cases, leave `nprobes` unset and use the auto-tuned value.
+- Only tune `nprobes` manually when recall is below your target, or when you need even higher performance for your workload.
+- If recall is too low, increase `nprobes` gradually, but after a certain threshold, increasing `nprobes` yields only marginal accuracy gains.
+- If you need higher performance and have recall headroom, decrease `nprobes` gradually.
+
+For filtered ANN searches, you can also set `minimum_nprobes` and `maximum_nprobes`. LanceDB starts
+with the minimum and can scan more partitions up to the maximum if the filter leaves too few
+candidates. Calling `nprobes(n)` fixes both values to `n`, which disables that adaptive behavior.
+Whether a deployment honors this range depends on its version; one that does not searches exactly
+`minimum_nprobes` partitions.
+
+### Vector Search with Prefiltering
+
+This is the default vector search setting. You can use prefiltering to boost query performance by reducing the search space before vector calculations begin. The system first applies your filter criteria to the dataset, then conducts vector search operations only on the remaining relevant subset.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{VectorSearchPrefilter}
+</CodeBlock>
+
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsVectorSearchPrefilter}
+</CodeBlock>
+
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsVectorSearchPrefilter}
+</CodeBlock>
+</CodeGroup>
+
+The filter limits which rows can match. Use `select()` to choose the returned
+columns and `limit()` to set the maximum number of results.
+
+### Vector Search with Postfiltering
+
+Postfiltering selects vector-search candidates before applying the metadata
+predicate. It can return fewer matches than requested, even when other rows in
+the table satisfy the predicate. Use prefiltering when you want the nearest
+records within the matching subset.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{VectorSearchPostfilter}
+</CodeBlock>
+
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsVectorSearchPostfilter}
+</CodeBlock>
+
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsVectorSearchPostfilter}
+</CodeBlock>
+</CodeGroup>
+
+Only retrieved candidates that satisfy the predicate remain in the result.
+Increase the candidate limit or use prefiltering if too few records are returned.
+
+<Card icon="book">
+[Post-filtering](/search/filtering/#post-filtering-with-vector-search) in LanceDB applies
+the filter condition after obtaining the nearest neighbors based on vector similarity.
+</Card>
+
+## Multivector Search
+
+Use multivector search when your documents contain multiple embeddings and you need sophisticated matching between query and document vector pairs. The late interaction approach finds the most relevant combinations across all available embeddings and provides nuanced similarity scoring.
+
+Only `cosine` similarity is supported as the distance metric for multivector search operations.
+Every query vector must match the inner dimension of the multivector column.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{MultivectorSearch}
+</CodeBlock>
+</CodeGroup>
+
+Here you can see how to take 2 query vectors and find the best matching pairs between them and document vectors using late interaction. The `np.random.random(size=(2, 256))` creates a 2×256 array with two random query vectors, `.limit(5)` returns the top 5 best document-query combinations, and `.to_pandas()` provides results in a DataFrame format. 
+
+**Read more:** [Multivector search walkthrough](#multivector)
+
+## Advanced Search Scenarios
+
+### Search With Distance Range
+
+Use `distance_range` search when you need vectors within particular similarity bounds rather than just the closest neighbors. The system filters results to only include vectors that fall within your specified distance thresholds from the query.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{SearchDistanceRange}
+</CodeBlock>
+
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsDistanceRange}
+</CodeBlock>
+
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsSearchDistanceRange}
+</CodeBlock>
+</CodeGroup>
+
+This shows three ways to search within distance ranges: bounded, upper bound only, and lower bound only. 
+
+The `distance_range()` method filters results by similarity thresholds - the first example finds vectors with distance between `0.1` and `0.5`, the second finds vectors closer than `0.5`, and the third finds vectors farther than `0.1`. 
+
+  Each approach returns Arrow tables with vectors that fall within your specified distance thresholds.
+
+### Search With Binary Vectors
+
+Use binary vector search for scenarios involving binary embeddings, such as those produced by hashing algorithms. The system stores these efficiently as packed uint8 arrays and uses Hamming distance calculations to determine vector similarity.
+
+<Tip>
+The number of dimensions of the binary vector must be a multiple of 8. A vector of dimensionality 128 will be stored as a `uint8` array of size 16.
+</Tip>
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{SearchBinaryVectors}
+</CodeBlock>
+
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsBinarySearch}
+</CodeBlock>
+
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsBinarySearch}
+</CodeBlock>
+</CodeGroup>
+
+Here you can see how to set up a table for binary vectors, pack them efficiently into bytes, and search using Hamming distance. 
+
+The schema defines a 32-byte vector field (256 bits ÷ 8), `np.random.randint(0, 2, size=256)` creates binary vectors, `np.packbits()` compresses them to bytes, and `.distance_type("hamming")` specifies `hamming` distance for similarity calculation. 
+
+The search produces an Arrow table with binary vectors ranked by how many bits differ from the query.
+
+## Scaling Vector Search
+
+### Query multiple vectors in a batch {#batch-search}
+
+Batch search accepts multiple query vectors in one request. The `query_index`
+column identifies which query produced each result. Measure throughput and
+latency with your workload when choosing a batch size.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{BatchSearch}
+</CodeBlock>
+
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsBatchSearch}
+</CodeBlock>
+
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsBatchSearch}
+</CodeBlock>
+</CodeGroup>
+
+Each input vector has its own nearest-neighbor results. Use `query_index` to
+associate each row with the corresponding input vector.
+
+### Include or skip unindexed rows {#search-with-asynchronous-indexing}
+
+To optimize for speed over completeness, enable the `fast_search` flag in your query to skip searching unindexed data.
+
+By default, search includes both indexed vectors and unindexed rows visible to
+the query. LanceDB scans the unindexed rows directly. `fast_search` skips that
+scan, which can reduce query time but omit recent records.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{FastSearch}
+</CodeBlock>
+
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsFastSearch}
+</CodeBlock>
+
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsFastSearch}
+</CodeBlock>
+</CodeGroup>
+
+Use this option only when results from indexed data are sufficient.
+
+## Search without a vector index {#brute-force-search}
+
+### Search With No Index
+
+The simplest way to perform vector search is to perform a brute force search, without an index, where the distance between the query vector and all the vectors in the database are computed, with the top-k closest vectors returned. 
+
+This is equivalent to a k-nearest neighbours (kNN) search in vector space.
+
+Choose brute force search when you need guaranteed 100% recall, typically with smaller datasets where query speed isn't the primary concern. The system scans every vector in the table and calculates precise distances to find the exact nearest neighbors.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{BruteForceSearch}
+</CodeBlock>
+
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsBruteForceSearch}
+</CodeBlock>
+
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsBruteForceSearch}
+</CodeBlock>
+</CodeGroup>
+
+These queries return the three nearest matches. Without a vector index, LanceDB
+calculates distances directly from the stored vectors.
+
+![](/static/assets/images/search/knn_search.png)
+
+Exhaustive search does more work as the table grows. When query latency becomes
+too high, add an ANN index and compare its results with the exhaustive baseline.
+
+### Bypass the Vector Index
+
+Use `bypass_vector_index` to get exact, ground-truth results by performing exhaustive searches across all vectors. Instead of relying on approximate methods, the system directly compares your query against every vector in the table, ensuring 100% recall at the cost of increased query time.
+
+<CodeGroup>
+<CodeBlock filename="Python" language="python" icon="python">
+{BypassVectorIndex}
+</CodeBlock>
+
+<CodeBlock filename="TypeScript" language="typescript" icon="square-js">
+{TsBypassVectorIndex}
+</CodeBlock>
+
+<CodeBlock filename="Rust" language="rust" icon="rust">
+{RsBypassVectorIndex}
+</CodeBlock>
+</CodeGroup>
+
+This skips the approximate index and checks every single vector for exact, ground-truth results. 
+
+The `.bypass_vector_index()` method forces LanceDB to perform an exhaustive search through all vectors instead of using the approximate nearest neighbor index, ensuring exact results but at the cost of slower performance. 
+
+The query returns the nearest five rows within its filters and distance bounds.
+Use an exhaustive scan as a baseline when measuring ANN recall.
+
+This approach is particularly useful when:
+- Evaluating ANN index quality
+- Calculating recall metrics to tune index parameters
+- Ensuring exact results for critical applications
+
+## Multi-vector search {#multivector}
+
+LanceDB's multivector support enables you to store and search multiple vector embeddings for a single item.
+
+This capability is particularly valuable when working with late-interaction models like ColBERT and ColPaLi, which generate multiple embeddings per document.
+
+In this tutorial, you'll create a table with multiple vector embeddings per document and learn how to perform multivector search. For more end-to-end examples, see the [VectorDB recipes repository](https://github.com/lancedb/vectordb-recipes/tree/main/examples).
+
+### Multivector Support
+
+Each item in your dataset can have a column containing multiple vectors, which LanceDB can efficiently index and search. When performing a search, you can query with either a single vector embedding or multiple vector embeddings.
+
+<Warning>
+Currently, only the `cosine` metric is supported for multivector search. The vector value type can be `float16`, `float32`, or `float64`.
+</Warning>
+
+Each query vector must match the inner vector dimension in the multivector column. This applies to both single-vector queries and multi-vector query matrices.
+
+### Computing Similarity
+
+MaxSim (Maximum Similarity) is a key concept in late-interaction models that:
+
+- Computes the maximum similarity between each query embedding and all document embeddings
+- Sums these maximum similarities to get the final relevance score
+- Effectively captures fine-grained semantic matches between query and document tokens
+
+The MaxSim calculation can be expressed as:
+
+$$
+\text{MaxSim}(Q, D) = \sum_{i=1}^{|Q|} \max_{j=1}^{|D|} \text{sim}(q_i, d_j)
+$$
+
+Where $sim$ is the similarity function (e.g., cosine similarity).
+
+$$
+Q = \{q_1, q_2, ..., q_{|Q|}\}
+$$
+
+$Q$ represents the query embeddings, and $D = \{d_1, d_2, ..., d_{|D|}\}$ represents the document embeddings.
+
+### Using Multivector Search
+
+#### 1. Setup
+
+Connect to LanceDB and import the required libraries.
+
+<CodeGroup>
+```python Python icon="python"
+import lancedb
+import numpy as np
+import pyarrow as pa
+
+db = lancedb.connect(
+    uri="db://your-project-slug",
+    api_key="your-api-key",
+    region="your-region"
+)
+```
+</CodeGroup>
+
+#### 2. Define Schema
+
+Define a schema that specifies a multivector field. A multivector field is a nested list structure in which each document contains multiple vectors. In this case, we'll create a schema with:
+
+1. An ID field as an integer (int64)
+2. A vector field that is a list of lists of float32 values
+   - The outer list represents multiple vectors per document
+   - Each inner list is a 256-dimensional vector
+   - Using float32 for memory efficiency while maintaining precision
+
+<CodeGroup>
+```python Python icon="python"
+db = lancedb.connect("data/multivector_demo")
+schema = pa.schema(
+    [
+        pa.field("id", pa.int64()),
+        # float16, float32, and float64 are supported
+        pa.field("vector", pa.list_(pa.list_(pa.float32(), 256))),
+    ]
+)
+```
+</CodeGroup>
+
+#### 3. Generate Multivectors
+
+Generate sample data where each document contains multiple vector embeddings, which can represent different aspects or views of the same document.
+
+In this example, we create **1024 documents** where each document has **2 random vectors** of **dimension 256**, simulating a real-world scenario where you might have multiple embeddings per item.
+
+<CodeGroup>
+```python Python icon="python"
+data = [
+    {
+        "id": i,
+        "vector": np.random.random(size=(2, 256)).tolist(),  # Each document has 2 vectors
+    }
+    for i in range(1024)
+]
+```
+</CodeGroup>
+
+#### 4. Create a Table
+
+Create a table with the defined schema and sample data, which will store multiple vectors per document for similarity search.
+
+<CodeGroup>
+```python Python icon="python"
+tbl = db.create_table("multivector_example", data=data, schema=schema)
+```
+</CodeGroup>
+
+#### 5. Build an Index
+
+Only cosine similarity is supported for multivector search operations.
+For faster search, build the standard `IVF_PQ` index over your vectors:
+
+<CodeGroup>
+```python Python icon="python"
+tbl.create_index(metric="cosine", vector_column_name="vector")
+```
+</CodeGroup>
+
+<Info>
+**Indexing matters more for multivector tables than for single-vector ones.**
+
+A brute-force scan over a multivector column has to compare every query vector to every document vector in every row, so the cost grows with both the row count and the number of vectors per row. 
+In LanceDB OSS, the query will just run, so a large unindexed multivector table can stall a process for a long time before returning results.
+
+On LanceDB Cloud, the brute-force KNN safety check applies a stricter row threshold to multivector columns; roughly 10× lower than for single-vector columns. So an unindexed multivector table can be rejected with a "vector search would use brute-force KNN" error well before a comparable single-vector table would. LanceDB Enterprise doesn't have the limit. Build the index before you start hitting it from production traffic, even if the dataset is small enough that you'd skip indexing for a single-vector workload.
+</Info>
+
+#### 6. Query a Single Vector
+
+When searching with a single query vector, it will be compared against all vectors in each document, and the similarity scores will be aggregated to find the most relevant documents.
+
+<CodeGroup>
+```python Python icon="python"
+query = np.random.random(256)
+results_single = tbl.search(query).limit(5).to_pandas()
+```
+</CodeGroup>
+
+#### 7. Query Multiple Vectors
+
+With multiple query vectors, LanceDB calculates similarity using late interaction, a late-interaction technique that computes relevance by finding the best-matching pairs between query and document vectors. This approach provides more nuanced matching while maintaining fast retrieval speeds.
+
+<CodeGroup>
+```python Python icon="python"
+query_multi = np.random.random(size=(2, 256))
+results_multi = tbl.search(query_multi).limit(5).to_pandas()
+```
+</CodeGroup>
+
+
+Visit the [Hugging Face embedding integration](/integrations/embedding/huggingface/) page for information on embedding models.
+
+### Simple Example: ColBERT Embeddings
+
+[ColBERT](https://arxiv.org/abs/2004.12832) is the most well-known late-interaction retrieval model that
+represents each document and query as multiple token embeddings and scores matches by taking the best
+token-to-token similarities (MaxSim) across them.
+
+Install the dependencies before running this example:
+
+```bash
+pip install pylate lancedb pandas
+```
+
+<CodeGroup>
+```python Python icon="python"
+import numpy as np
+import pyarrow as pa
+import lancedb
+from pylate import models
+
+## 1) Load a late-interaction model via PyLate
+## PyLate docs show ColBERT() + encode(..., is_query=...)
+model = models.ColBERT(model_name_or_path="lightonai/GTE-ModernColBERT-v1")
+
+## You can discover dim from one embedding (avoid guessing)
+dim = model.encode(["hello"], is_query=True)[0].shape[1]
+
+## 2) Create a LanceDB table with a multivector column
+db = lancedb.connect("./pylate_lancedb")
+schema = pa.schema([
+    pa.field("doc_id", pa.string()),
+    pa.field("text", pa.string()),
+    # multivector: list<list<float32, dim>>
+    pa.field("mv", pa.list_(pa.list_(pa.float32(), dim))),
+])
+
+docs = [
+    {"doc_id": "1", "text": "The train to Tokyo leaves at 5pm."},
+    {"doc_id": "2", "text": "That Pho restaurant in Hanoi is highly rated."},
+    {"doc_id": "3", "text": "This is a noodle bar in Osaka, Japan."},
+]
+
+## 3) Encode documents with PyLate (token vectors per doc)
+doc_texts = [d["text"] for d in docs]
+doc_embs = model.encode(doc_texts, is_query=False)  # list/array of (T, dim) per doc
+
+rows = []
+for d, emb in zip(docs, doc_embs):
+    emb = np.asarray(emb, dtype=np.float32)
+    rows.append({**d, "mv": emb.tolist()})
+
+tbl = db.create_table("docs", data=rows, schema=schema, mode="overwrite")
+
+## 4) Build an index + query using a query matrix.
+## Multivector brute-force scales with rows × vectors-per-row, so build the index
+## at much smaller dataset sizes than you would for single-vector search; and
+## always before exposing the table to remote traffic.
+tbl.create_index(vector_column_name="mv", metric="cosine")
+
+query = "Tell me about ramen in Japan"
+q_emb = np.asarray(model.encode([query], is_query=True)[0], dtype=np.float32)  # (Tq, dim)
+
+out = tbl.search(q_emb).limit(5).to_pandas()  # multivector search accepts a matrix
+print(out[["doc_id", "text"]])
+```
+</CodeGroup>
+
+Late-interaction model implementations evolve rapidly, so it's a good idea to check the latest popular models
+when trying multivector search.
+
+### Advanced Example: XTR Embeddings
+
+[ConteXtualized Token Retriever (XTR)](https://arxiv.org/abs/2304.01982) is a late-interaction retrieval model that represents text as token-level vectors instead of a single embedding.
+This lets search score token-to-token matches (MaxSim), which can improve fine-grained relevance.
+
+The notebook linked below shows how to integrate XTR, which prioritizes critical document
+tokens during the initial retrieval stage and removes the gathering stage to improve performance significantly.
+By focusing on the most semantically salient tokens early in the process, XTR reduces computational complexity
+while improving recall and ensuring rapid identification of candidate documents.
+
+<Card
+    icon="book"
+    title="Multivector search with ColPali + XTR embeddings and LanceDB"
+    href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multivector_xtr/main.ipynb"
+>
+</Card>
diff --git a/docs/web/snippets/ann_indexes.mdx b/docs/web/snippets/ann_indexes.mdx
new file mode 100644
index 000000000..ef4021e16
--- /dev/null
+++ b/docs/web/snippets/ann_indexes.mdx
@@ -0,0 +1,12 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const TsImport = "import * as lancedb from \"@lancedb/lancedb\";\nimport type { VectorQuery } from \"@lancedb/lancedb\";\n";
+
+export const TsIngest = "const db = await lancedb.connect(databaseDir);\n\nconst data = Array.from({ length: 5_000 }, (_, i) => ({\n  vector: Array(128).fill(i),\n  id: `${i}`,\n  content: \"\",\n  longId: `${i}`,\n}));\n\nconst table = await db.createTable(\"my_vectors\", data, {\n  mode: \"overwrite\",\n});\nawait table.createIndex(\"vector\", {\n  config: lancedb.Index.ivfPq({\n    numPartitions: 10,\n    numSubVectors: 16,\n  }),\n});\n";
+
+export const TsSearch1 = "const search = table.search(Array(128).fill(1.2)).limit(2) as VectorQuery;\nconst results1 = await search.nprobes(20).refineFactor(10).toArray();\n";
+
+export const TsSearch2 = "const results2 = await table\n  .search(Array(128).fill(1.2))\n  .where(\"id != '1141'\")\n  .limit(2)\n  .toArray();\n";
+
+export const TsSearch3 = "const results3 = await table\n  .search(Array(128).fill(1.2))\n  .select([\"id\"])\n  .limit(2)\n  .toArray();\n";
+
diff --git a/docs/web/snippets/basic_usage.mdx b/docs/web/snippets/basic_usage.mdx
new file mode 100644
index 000000000..d377efa9c
--- /dev/null
+++ b/docs/web/snippets/basic_usage.mdx
@@ -0,0 +1,100 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyBasicAddColumns = "table.add_columns(\n    {\n        \"power\": \"cast(((stats.strength + stats.courage + stats.magic + stats.wisdom) / 4.0) as float)\"\n    }\n)\n";
+
+export const PyBasicAddData = "magical_characters = [\n    {\n        \"id\": 9,\n        \"name\": \"Morgan le Fay\",\n        \"role\": \"Sorceress\",\n        \"description\": \"A powerful enchantress, Arthur's half-sister, and a complex figure who oscillates between aiding and opposing Camelot.\",\n        \"vector\": [0.10, 0.84, 0.25, 0.70],\n        \"stats\": { \"strength\": 2, \"courage\": 3, \"magic\": 5, \"wisdom\": 4 }\n    },\n    {\n        \"id\": 10,\n        \"name\": \"The Lady of the Lake\",\n        \"role\": \"Mystical Guardian\",\n        \"description\": \"A mysterious supernatural figure associated with Avalon, known for giving Arthur the sword Excalibur.\",\n        \"vector\": [0.00, 0.90, 0.58, 0.88],\n        \"stats\": { \"strength\": 2, \"courage\": 3, \"magic\": 5, \"wisdom\": 5 }\n    }\n]\ntable.add(magical_characters)\n";
+
+export const PyBasicAsyncApi = "import lancedb\n\nasync_db = await lancedb.connect_async(uri)\nasync_table = await async_db.create_table(\n    \"camelot_async\",\n    data=data,\n    mode=\"overwrite\",\n)\n\nquery_vector = [0.03, 0.85, 0.61, 0.90]\nasync_results = await (\n    await async_table.search(query_vector)\n).limit(5).select([\"name\", \"role\", \"description\"]).to_polars()\nprint(async_results)\n";
+
+export const PyBasicCreateEmptyTable = "schema = pa.schema(\n    [\n        pa.field(\"id\", pa.uint16()),\n        pa.field(\"name\", pa.string()),\n        pa.field(\"role\", pa.string()),\n        pa.field(\"description\", pa.string()),\n        pa.field(\"vector\", pa.list_(pa.float32(), 4)),\n        pa.field(\n            \"stats\",\n            pa.struct(\n                [\n                    pa.field(\"strength\", pa.int8()),\n                    pa.field(\"courage\", pa.int8()),\n                    pa.field(\"magic\", pa.int8()),\n                    pa.field(\"wisdom\", pa.int8()),\n                ]\n            ),\n        ),\n    ]\n)\ndb.create_table(\"camelot_pa\", schema=schema, mode=\"overwrite\")\n";
+
+export const PyBasicCreateTable = "table = db.create_table(\"camelot\", data=data, mode=\"overwrite\")\n";
+
+export const PyBasicCreateTablePandas = "pandas_df = pd.DataFrame(data)\ntable_pd = db.create_table(\"camelot_pd\", data=pandas_df, mode=\"overwrite\")\n";
+
+export const PyBasicCreateTablePolars = "polars_df = pl.DataFrame(data)\ntable_pl = db.create_table(\"camelot_pl\", data=polars_df, mode=\"overwrite\")\n";
+
+export const PyBasicDeleteRows = "table.delete('role = \"Traitor Knight\"')\n";
+
+export const PyBasicDropColumns = "table.drop_columns([\"power\"])\n";
+
+export const PyBasicDropTable = "db.drop_table(\"camelot\")\n";
+
+export const PyBasicImports = "import json\n\nimport lancedb\nimport pandas as pd\nimport polars as pl\nimport pyarrow as pa\n";
+
+export const PyBasicOpenTable = "table = db.open_table(\"camelot\")\n";
+
+export const PyBasicSortPolars = "# Sort Polars DataFrame by power in descending order\nprint(r4.sort(\"power\", descending=True).limit(5))\n";
+
+export const PyBasicVectorSearch = "query_vector = [0.03, 0.85, 0.61, 0.90]\nresult = table.search(query_vector).limit(5).to_polars()\nprint(result)\n";
+
+export const PyBasicVectorSearchQ1 = "# Who are the characters similar to  \"wizard\"?\nquery_vector_1 = [0.03, 0.85, 0.61, 0.90]\nr1 = (\n    table.search(query_vector_1)\n    .limit(5)\n    .select([\"name\", \"role\", \"description\"])\n    .to_polars()\n)\nprint(r1)\n";
+
+export const PyBasicVectorSearchQ2 = "# Who are the characters with high magic stats?\nquery_vector_2 = [0.03, 0.85, 0.61, 0.90]\nr2 = (\n    table.search(query_vector_2)\n    .where(\"stats.magic > 3\")\n    .select([\"name\", \"role\", \"description\"])\n    .limit(5)\n    .to_polars()\n)\nprint(r2)\n";
+
+export const PyBasicVectorSearchQ3 = "# Who are the strongest characters?\nr3 = (\n    table.search()\n    .where(\"stats.strength > 3\")\n    .select([\"name\", \"role\", \"description\"])\n    .limit(5)\n    .to_polars()\n)\nprint(r3)\n";
+
+export const PyBasicVectorSearchQ4 = "# Who are the strongest characters?\nr4 = (\n    table.search()\n    .select([\"name\", \"role\", \"description\", \"power\"])\n    .to_polars()\n)\nprint(r4)\n";
+
+export const PyDataLoad = "with open(data_path, \"r\") as f:\n    data = json.load(f)\n";
+
+export const TsBasicAddColumns = "await table.addColumns([\n  {\n    name: \"power\",\n    valueSql:\n      \"cast(((stats.strength + stats.courage + stats.magic + stats.wisdom) / 4.0) as float)\",\n  },\n]);\n";
+
+export const TsBasicAddData = "const magicalCharacters = [\n  {\n    id: 9,\n    name: \"Morgan le Fay\",\n    role: \"Sorceress\",\n    description:\n      \"A powerful enchantress, Arthur's half-sister, and a complex figure who oscillates between aiding and opposing Camelot.\",\n    vector: [0.1, 0.84, 0.25, 0.7],\n    stats: { strength: 2, courage: 3, magic: 5, wisdom: 4 },\n  },\n  {\n    id: 10,\n    name: \"The Lady of the Lake\",\n    role: \"Mystical Guardian\",\n    description:\n      \"A mysterious supernatural figure associated with Avalon, known for giving Arthur the sword Excalibur.\",\n    vector: [0.0, 0.9, 0.58, 0.88],\n    stats: { strength: 2, courage: 3, magic: 5, wisdom: 5 },\n  },\n];\nawait table.add(magicalCharacters);\n";
+
+export const TsBasicCreateEmptyTable = "const schema = new arrow.Schema([\n  new arrow.Field(\"id\", new arrow.Int16()),\n  new arrow.Field(\"name\", new arrow.Utf8()),\n  new arrow.Field(\"role\", new arrow.Utf8()),\n  new arrow.Field(\"description\", new arrow.Utf8()),\n  new arrow.Field(\n    \"vector\",\n    new arrow.FixedSizeList(\n      4,\n      new arrow.Field(\"item\", new arrow.Float32(), true),\n    ),\n  ),\n  new arrow.Field(\n    \"stats\",\n    new arrow.Struct([\n      new arrow.Field(\"strength\", new arrow.Int8()),\n      new arrow.Field(\"courage\", new arrow.Int8()),\n      new arrow.Field(\"magic\", new arrow.Int8()),\n      new arrow.Field(\"wisdom\", new arrow.Int8()),\n    ]),\n  ),\n]);\nawait db.createEmptyTable(\"camelot_empty\", schema, { mode: \"overwrite\" });\n";
+
+export const TsBasicCreateTable = "let table = await db.createTable(\"camelot\", data, {\n   mode: \"overwrite\",\n});\n";
+
+export const TsBasicDeleteRows = "await table.delete('role = \"Traitor Knight\"');\n";
+
+export const TsBasicDropColumns = "await table.dropColumns([\"power\"]);\n";
+
+export const TsBasicDropTable = "await db.dropTable(\"camelot\");\n";
+
+export const TsBasicImports = "import * as lancedb from \"@lancedb/lancedb\";\nimport * as arrow from \"apache-arrow\";\n";
+
+export const TsBasicOpenTable = "table = await db.openTable(\"camelot\");\n";
+
+export const TsBasicVectorSearch = "const queryVector = [0.03, 0.85, 0.61, 0.9];\nconst result = await table.search(queryVector).limit(5).toArray();\nconsole.log(result);\n";
+
+export const TsBasicVectorSearchQ1 = "// Who are the characters similar to  \"wizard\"?\nconst queryVector1 = [0.03, 0.85, 0.61, 0.9];\nconst r1 = await table\n  .search(queryVector1)\n  .limit(5)\n  .select([\"name\", \"role\", \"description\"])\n  .toArray();\nconsole.log(r1);\n";
+
+export const TsBasicVectorSearchQ2 = "// Who are the characters similar to \"wizard\" with high magic stats?\nconst queryVector2 = [0.03, 0.85, 0.61, 0.9];\nconst r2 = await table\n  .search(queryVector2)\n  .where(\"stats.magic > 3\")\n  .select([\"name\", \"role\", \"description\"])\n  .limit(5)\n  .toArray();\nconsole.log(r2);\n";
+
+export const TsBasicVectorSearchQ3 = "// Who are the strongest characters?\nconst r3 = await table\n  .query()\n  .where(\"stats.strength > 3\")\n  .select([\"name\", \"role\", \"description\"])\n  .limit(5)\n  .toArray();\nconsole.log(r3);\n";
+
+export const TsBasicVectorSearchQ4 = "// Who are the strongest characters?\nconst r4 = await table\n  .query()\n  .select([\"name\", \"role\", \"description\", \"power\"])\n  .toArray();\nconsole.log(r4);\n";
+
+export const TsDataLoad = "const data = JSON.parse(fs.readFileSync(dataPath, \"utf-8\"));\n";
+
+export const RsBasicAddColumns = "table\n    .add_columns(\n        NewColumnTransform::SqlExpressions(vec![(\n            \"power\".to_string(),\n            \"cast(((stats.strength + stats.courage + stats.magic + stats.wisdom) / 4.0) as float)\"\n                .to_string(),\n        )]),\n        None,\n    )\n    .await\n    .unwrap();\n";
+
+export const RsBasicAddData = "let magical_characters = vec![\n    Character {\n        id: 9,\n        name: \"Morgan le Fay\".to_string(),\n        role: \"Sorceress\".to_string(),\n        description: \"A powerful enchantress, Arthur's half-sister, and a complex figure who oscillates between aiding and opposing Camelot.\".to_string(),\n        vector: [0.10, 0.84, 0.25, 0.70],\n        stats: Stats {\n            strength: 2,\n            courage: 3,\n            magic: 5,\n            wisdom: 4,\n        },\n    },\n    Character {\n        id: 10,\n        name: \"The Lady of the Lake\".to_string(),\n        role: \"Mystical Guardian\".to_string(),\n        description: \"A mysterious supernatural figure associated with Avalon, known for giving Arthur the sword Excalibur.\".to_string(),\n        vector: [0.00, 0.90, 0.58, 0.88],\n        stats: Stats {\n            strength: 2,\n            courage: 3,\n            magic: 5,\n            wisdom: 5,\n        },\n    },\n];\ntable\n    .add(characters_to_reader(camelot_schema(), &magical_characters))\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsBasicCreateEmptyTable = "let schema = Arc::new(Schema::new(vec![\n    Field::new(\"id\", DataType::Int16, false),\n    Field::new(\"name\", DataType::Utf8, false),\n    Field::new(\"role\", DataType::Utf8, false),\n    Field::new(\"description\", DataType::Utf8, false),\n    Field::new(\n        \"vector\",\n        DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 4),\n        false,\n    ),\n    Field::new(\n        \"stats\",\n        DataType::Struct(arrow_schema::Fields::from(vec![\n            Arc::new(Field::new(\"strength\", DataType::Int8, false)),\n            Arc::new(Field::new(\"courage\", DataType::Int8, false)),\n            Arc::new(Field::new(\"magic\", DataType::Int8, false)),\n            Arc::new(Field::new(\"wisdom\", DataType::Int8, false)),\n        ])),\n        false,\n    ),\n]));\ndb.create_empty_table(\"camelot_empty\", schema)\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsBasicCreateTable = "let mut table = db\n    .create_table(\"camelot\", characters_to_reader(schema.clone(), &data))\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsBasicDeleteRows = "table.delete(\"role = 'Traitor Knight'\").await.unwrap();\n";
+
+export const RsBasicDropColumns = "table.drop_columns(&[\"power\"]).await.unwrap();\n";
+
+export const RsBasicDropTable = "db.drop_table(\"camelot\", &[]).await.unwrap();\n";
+
+export const RsBasicImports = "use arrow_array::types::Float32Type;\nuse arrow_array::{\n    FixedSizeListArray, Int8Array, Int16Array, RecordBatch, RecordBatchIterator, StringArray,\n    StructArray,\n};\nuse arrow_schema::{DataType, Field, FieldRef, Schema};\nuse futures_util::TryStreamExt;\nuse lancedb::database::CreateTableMode;\nuse lancedb::query::{ExecutableQuery, QueryBase, Select};\nuse lancedb::{connect, table::NewColumnTransform};\n";
+
+export const RsBasicOpenTable = "table = db.open_table(\"camelot\").execute().await.unwrap();\n";
+
+export const RsBasicVectorSearch = "let query_vector = [0.03, 0.85, 0.61, 0.90];\nlet result = table\n    .query()\n    .nearest_to(&query_vector)\n    .unwrap()\n    .limit(5)\n    .execute()\n    .await\n    .unwrap()\n    .try_collect::<Vec<_>>()\n    .await\n    .unwrap();\nprintln!(\"{result:?}\");\n";
+
+export const RsBasicVectorSearchQ1 = "// Who are the characters similar to  \"wizard\"?\nlet query_vector_1 = [0.03, 0.85, 0.61, 0.90];\nlet r1 = table\n    .query()\n    .nearest_to(&query_vector_1)\n    .unwrap()\n    .limit(5)\n    .select(Select::Columns(vec![\n        \"name\".to_string(),\n        \"role\".to_string(),\n        \"description\".to_string(),\n    ]))\n    .execute()\n    .await\n    .unwrap()\n    .try_collect::<Vec<_>>()\n    .await\n    .unwrap();\nprintln!(\"{r1:?}\");\n";
+
+export const RsBasicVectorSearchQ2 = "// Who are the characters similar to \"wizard\" with high magic stats?\nlet query_vector_2 = [0.03, 0.85, 0.61, 0.90];\nlet r2 = table\n    .query()\n    .nearest_to(&query_vector_2)\n    .unwrap()\n    .only_if(\"stats.magic > 3\")\n    .select(Select::Columns(vec![\n        \"name\".to_string(),\n        \"role\".to_string(),\n        \"description\".to_string(),\n    ]))\n    .limit(5)\n    .execute()\n    .await\n    .unwrap()\n    .try_collect::<Vec<_>>()\n    .await\n    .unwrap();\nprintln!(\"{r2:?}\");\n";
+
+export const RsBasicVectorSearchQ3 = "// Who are the strongest characters?\nlet r3 = table\n    .query()\n    .only_if(\"stats.strength > 3\")\n    .select(Select::Columns(vec![\n        \"name\".to_string(),\n        \"role\".to_string(),\n        \"description\".to_string(),\n    ]))\n    .limit(5)\n    .execute()\n    .await\n    .unwrap()\n    .try_collect::<Vec<_>>()\n    .await\n    .unwrap();\nprintln!(\"{r3:?}\");\n";
+
+export const RsBasicVectorSearchQ4 = "// Who are the strongest characters?\nlet r4 = table\n    .query()\n    .select(Select::Columns(vec![\n        \"name\".to_string(),\n        \"role\".to_string(),\n        \"description\".to_string(),\n        \"power\".to_string(),\n    ]))\n    .execute()\n    .await\n    .unwrap()\n    .try_collect::<Vec<_>>()\n    .await\n    .unwrap();\nprintln!(\"{r4:?}\");\n";
+
+export const RsDataLoad = "let data: Vec<Character> =\n    serde_json::from_str(&fs::read_to_string(camelot_json_path()).unwrap()).unwrap();\n";
+
diff --git a/docs/web/snippets/build_with_ai_agents.mdx b/docs/web/snippets/build_with_ai_agents.mdx
new file mode 100644
index 000000000..1a82d6fb6
--- /dev/null
+++ b/docs/web/snippets/build_with_ai_agents.mdx
@@ -0,0 +1,7 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyCamelotBatches = "import json\nfrom collections.abc import Iterator\nfrom pathlib import Path\n\n\ndef validated_batches(\n    input_path: Path, batch_size: int\n) -> Iterator[list[dict]]:\n    raw_records = json.loads(input_path.read_text())\n    asset_root = input_path.parent.parent\n    batch: list[dict] = []\n\n    for raw in raw_records:\n        payload = dict(raw)\n        image_path = asset_root / payload.pop(\"img\")\n        payload[\"image_filename\"] = image_path.name\n        payload[\"image\"] = image_path.read_bytes()\n\n        character = Character.model_validate(payload)\n        batch.append(character.model_dump(mode=\"python\"))\n\n        if len(batch) == batch_size:\n            yield batch\n            batch = []\n\n    if batch:\n        yield batch\n";
+
+export const PyCamelotOssIngestion = "import lancedb\n\n\ndef ingest_oss(\n    input_path: Path,\n    uri: str = \"data/camelot.lancedb\",\n    table_name: str = \"camelot_multimodal\",\n    batch_size: int = 64,\n):\n    db = lancedb.connect(uri)\n    if table_name in db.list_tables():\n        raise ValueError(\n            f\"Table {table_name!r} already exists. Choose a fresh table name.\"\n        )\n\n    table = db.create_table(table_name, schema=Character)\n    for batch in validated_batches(input_path, batch_size):\n        table.add(batch)\n\n    table.optimize()\n    return table\n\n\nif __name__ == \"__main__\":\n    ingest_oss(Path(\"data/camelot.json\"))\n";
+
+export const PyCamelotSchema = "from lancedb.pydantic import LanceModel\nfrom pydantic import ConfigDict\n\n\nclass Stats(LanceModel):\n    model_config = ConfigDict(strict=True, extra=\"forbid\")\n\n    strength: int\n    courage: int\n    magic: int\n    wisdom: int\n\n\nclass Character(LanceModel):\n    model_config = ConfigDict(strict=True, extra=\"forbid\")\n\n    id: int\n    name: str\n    role: str\n    description: str\n    stats: Stats\n    image_filename: str\n    image: bytes\n";
diff --git a/docs/web/snippets/connection.mdx b/docs/web/snippets/connection.mdx
new file mode 100644
index 000000000..c145c1fb6
--- /dev/null
+++ b/docs/web/snippets/connection.mdx
@@ -0,0 +1,42 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyConnect = "import lancedb\n\nuri = \"ex_lancedb\"\ndb = lancedb.connect(uri)\n";
+
+export const PyConnectAsync = "import lancedb\n\nuri = \"ex_lancedb\"\nasync_db = await lancedb.connect_async(uri)\n";
+
+export const PyConnectEnterprisePlaceholders = "import lancedb\n\ndb = lancedb.connect(\n    uri=\"db://your-database\",\n    api_key=\"your-api-key\",\n    region=\"us-east-1\",\n    host_override=\"https://your-endpoint\",\n)\n";
+
+export const PyConnectEnterpriseQuickstart = "import os\n\nimport lancedb\n\ndb = lancedb.connect(\n    uri=os.environ[\"LANCEDB_URI\"],  # db://your-database\n    api_key=os.environ[\"LANCEDB_API_KEY\"],\n    region=os.environ.get(\"LANCEDB_REGION\", \"us-east-1\"),\n    host_override=os.environ[\"LANCEDB_HOST_OVERRIDE\"],  # https://your-endpoint\n)\n";
+
+export const PyConnectObjectStorage = "import lancedb\n\nuri = \"s3://your-bucket/path\"\n# You can also use \"gs://your-bucket/path\" or \"az://your-container/path\".\ndb = lancedb.connect(uri)\n";
+
+export const PyConnectObjectStorageAsync = "import lancedb\n\nuri = \"s3://your-bucket/path\"\n# You can also use \"gs://your-bucket/path\" or \"az://your-container/path\".\nasync_db = await lancedb.connect_async(uri)\n";
+
+export const PyNamespaceAdminOps = "import lancedb\n\ndb = lancedb.connect_namespace(\"dir\", {\"root\": \"./local_lancedb\"})\nnamespace = [\"prod\", \"search\"]\n\ndb.create_namespace([\"prod\"])\ndb.create_namespace([\"prod\", \"search\"])\n\nchild_namespaces = db.list_namespaces(namespace_path=[\"prod\"]).namespaces\nprint(f\"Child namespaces under ['prod']: {child_namespaces}\")\n# Child namespaces under ['prod']: ['search']\n\nmetadata = db.describe_namespace([\"prod\", \"search\"])\nprint(f\"Metadata for namespace {namespace}: {metadata}\")\n# Metadata for namespace ['prod', 'search']: properties=None\n\ndb.drop_namespace([\"prod\", \"search\"], mode=\"skip\")\ndb.drop_namespace([\"prod\"], mode=\"skip\")\n";
+
+export const PyNamespaceTableOps = "import lancedb\n\ndb = lancedb.connect_namespace(\"dir\", {\"root\": \"./local_lancedb\"})\n\n# Create namespace tree: prod/search\ndb.create_namespace([\"prod\"], mode=\"exist_ok\")\ndb.create_namespace([\"prod\", \"search\"], mode=\"exist_ok\")\ndb.create_namespace([\"prod\", \"recommendations\"], mode=\"exist_ok\")\n\ndb.create_table(\n    \"user\",\n    data=[{\"id\": 1, \"vector\": [0.1, 0.2], \"name\": \"alice\"}],\n    namespace_path=[\"prod\", \"search\"],\n    mode=\"create\",  # use \"overwrite\" only if you want to replace existing table\n)\n\ndb.create_table(\n    \"user\",\n    data=[{\"id\": 2, \"vector\": [0.3, 0.4], \"name\": \"bob\"}],\n    namespace_path=[\"prod\", \"recommendations\"],\n    mode=\"create\",  # use \"overwrite\" only if you want to replace existing table\n)\n\n# Verify: listings return a response with the names and a page token\nprint(db.list_namespaces().namespaces)  # ['prod']\nprint(db.list_namespaces(namespace_path=[\"prod\"]).namespaces)\n# ['recommendations', 'search']\nprint(db.list_tables(namespace_path=[\"prod\", \"search\"]).tables)  # ['user']\nprint(db.list_tables(namespace_path=[\"prod\", \"recommendations\"]).tables)  # ['user']\n";
+
+export const TsConnect = "const db = await lancedb.connect(\"ex_lancedb\");\n";
+
+export const TsConnectEnterprisePlaceholders = "const db = await lancedb.connect(\"db://your-database\", {\n  apiKey: \"your-api-key\",\n  region: \"us-east-1\",\n  hostOverride: \"https://your-endpoint\",\n});\n";
+
+export const TsConnectEnterpriseQuickstart = "const setting = (name: string) => {\n  const value = process.env[name];\n  if (!value) throw new Error(`Set ${name}`);\n  return value;\n};\n\nconst db = await lancedb.connect(setting(\"LANCEDB_URI\"), {\n  apiKey: setting(\"LANCEDB_API_KEY\"),\n  region: process.env.LANCEDB_REGION ?? \"us-east-1\",\n  hostOverride: setting(\"LANCEDB_HOST_OVERRIDE\"),\n});\n";
+
+export const TsConnectObjectStorage = "const uri = \"s3://your-bucket/path\";\n// You can also use \"gs://your-bucket/path\" or \"az://your-container/path\".\nconst db = await lancedb.connect(uri);\n";
+
+export const TsNamespaceAdminOps = "const db = await lancedb.connectNamespace(\"dir\", { root: \"./local_lancedb\" });\nconst namespace = [\"prod\", \"search\"];\n\nawait db.createNamespace([\"prod\"]);\nawait db.createNamespace([\"prod\", \"search\"]);\n\nconst childNamespaces = (await db.listNamespaces([\"prod\"])).namespaces;\nconsole.log(`Child namespaces under ${JSON.stringify(namespace)}:`, childNamespaces);\n// Child namespaces under [\"prod\",\"search\"]: [ 'search' ]\n\nconst metadata = await db.describeNamespace([\"prod\", \"search\"]);\nconsole.log(`Metadata for namespace ${JSON.stringify(namespace)}:`, metadata);\n\nawait db.dropNamespace([\"prod\", \"search\"], { mode: \"skip\" });\nawait db.dropNamespace([\"prod\"], { mode: \"skip\" });\n";
+
+export const TsNamespaceTableOps = "const db = await lancedb.connectNamespace(\"dir\", { root: \"./local_lancedb\" });\n\n// Create namespace tree: prod/search and prod/recommendations\nawait db.createNamespace([\"prod\"], { mode: \"exist_ok\" });\nawait db.createNamespace([\"prod\", \"search\"], { mode: \"exist_ok\" });\nawait db.createNamespace([\"prod\", \"recommendations\"], { mode: \"exist_ok\" });\n\nawait db.createTable(\n  \"user\",\n  [{ id: 1, vector: [0.1, 0.2], name: \"alice\" }],\n  [\"prod\", \"search\"],\n  { mode: \"create\" }, // use \"overwrite\" only if you want to replace existing table\n);\n\nawait db.createTable(\n  \"user\",\n  [{ id: 2, vector: [0.3, 0.4], name: \"bob\" }],\n  [\"prod\", \"recommendations\"],\n  { mode: \"create\" },\n);\n\n// Verify\nconsole.log((await db.listNamespaces()).namespaces); // [\"prod\"]\nconsole.log((await db.listNamespaces([\"prod\"])).namespaces); // [\"recommendations\", \"search\"]\nconsole.log(await db.tableNames([\"prod\", \"search\"])); // [\"user\"]\nconsole.log(await db.tableNames([\"prod\", \"recommendations\"])); // [\"user\"]\n";
+
+export const RsConnect = "let db = connect(\"ex_lancedb\").execute().await?;\n";
+
+export const RsConnectEnterprisePlaceholders = "let db = connect(\"db://your-database\")\n    .api_key(\"your-api-key\")\n    .region(\"us-east-1\")\n    .host_override(\"https://your-endpoint\")\n    .execute()\n    .await?;\n";
+
+export const RsConnectEnterpriseQuickstart = "let uri = std::env::var(\"LANCEDB_URI\")?; // db://your-database\nlet api_key = std::env::var(\"LANCEDB_API_KEY\")?;\nlet region = std::env::var(\"LANCEDB_REGION\").unwrap_or_else(|_| \"us-east-1\".to_string());\nlet host_override = std::env::var(\"LANCEDB_HOST_OVERRIDE\")?; // https://your-endpoint\n\nlet db = connect(&uri)\n    .api_key(&api_key)\n    .region(&region)\n    .host_override(&host_override)\n    .execute()\n    .await?;\n";
+
+export const RsConnectObjectStorage = "let uri = \"s3://your-bucket/path\";\n// You can also use \"gs://your-bucket/path\" or \"az://your-container/path\".\nlet db = connect(uri).execute().await?;\n";
+
+export const RsNamespaceAdminOps = "let mut properties = std::collections::HashMap::new();\nproperties.insert(\"root\".to_string(), \"./local_lancedb\".to_string());\nlet db = lancedb::connect_namespace(\"dir\", properties).execute().await?;\nlet namespace = vec![\"prod\".to_string(), \"search\".to_string()];\n\ndb.create_namespace(lance_namespace::models::CreateNamespaceRequest {\n    id: Some(vec![\"prod\".to_string()]),\n    ..Default::default()\n})\n.await?;\ndb.create_namespace(lance_namespace::models::CreateNamespaceRequest {\n    id: Some(namespace.clone()),\n    ..Default::default()\n})\n.await?;\n\nlet child_namespaces = db\n    .list_namespaces(lance_namespace::models::ListNamespacesRequest {\n        id: Some(vec![\"prod\".to_string()]),\n        ..Default::default()\n    })\n    .await?;\nprintln!(\n    \"Child namespaces under {:?}: {:?}\",\n    namespace, child_namespaces\n);\n// Child namespaces under [\"prod\", \"search\"]: [\"search\"]\n\ndb.drop_namespace(lance_namespace::models::DropNamespaceRequest {\n    id: Some(namespace.clone()),\n    ..Default::default()\n})\n.await?;\ndb.drop_namespace(lance_namespace::models::DropNamespaceRequest {\n    id: Some(vec![\"prod\".to_string()]),\n    ..Default::default()\n})\n.await?;\n";
+
+export const RsNamespaceTableOps = "let conn = connect(uri).execute().await?;\nlet search_namespace = vec![\"prod\".to_string(), \"search\".to_string()];\nlet recommendations_namespace = vec![\"prod\".to_string(), \"recommendations\".to_string()];\n\nlet schema = std::sync::Arc::new(arrow_schema::Schema::new(vec![\n    arrow_schema::Field::new(\"id\", arrow_schema::DataType::Int64, false),\n]));\n\nconn.create_empty_table(\"user\", schema.clone())\n    .namespace(search_namespace.clone())\n    .execute()\n    .await?;\n\nconn.create_empty_table(\"user\", schema)\n    .namespace(recommendations_namespace.clone())\n    .execute()\n    .await?;\n\nlet search_table_names = conn\n    .table_names()\n    .namespace(search_namespace)\n    .execute()\n    .await?;\nlet recommendation_table_names = conn\n    .table_names()\n    .namespace(recommendations_namespace)\n    .execute()\n    .await?;\n\nprintln!(\"{search_table_names:?}\"); // [\"user\"]\nprintln!(\"{recommendation_table_names:?}\"); // [\"user\"]\n";
+
diff --git a/docs/web/snippets/curation_sampling.mdx b/docs/web/snippets/curation_sampling.mdx
new file mode 100644
index 000000000..9c3d482b8
--- /dev/null
+++ b/docs/web/snippets/curation_sampling.mdx
@@ -0,0 +1,4 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PySampleDataset = "from tempfile import TemporaryDirectory\n\nimport lancedb\nfrom lancedb.permutation import Permutation, permutation_builder\n\nwith TemporaryDirectory(prefix=\"lancedb-sampling-\") as uri:\n    db = lancedb.connect(uri)\n    table = db.create_table(\"records\", [{\"id\": i} for i in range(10)])\n    splits = (\n        permutation_builder(table).split_random(counts=[3, 7], seed=42).execute()\n    )\n    sample = Permutation.from_tables(table, splits, split=0).with_format(\"arrow\")\n    selected_ids = []\n    for batch in sample.iter(batch_size=3):\n        selected_ids.extend(batch.column(\"id\").to_pylist())\n    print(f\"Selected {len(selected_ids)} rows: {selected_ids}\")\n";
+
diff --git a/docs/web/snippets/custom_embedding_function.mdx b/docs/web/snippets/custom_embedding_function.mdx
new file mode 100644
index 000000000..bdbb591d8
--- /dev/null
+++ b/docs/web/snippets/custom_embedding_function.mdx
@@ -0,0 +1,8 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const TsCallCustomFunction = "const registry = getRegistry();\n\nconst sentenceTransformer = await registry\n  .get<SentenceTransformersEmbeddings>(\"sentence-transformers\")!\n  .create();\n\nconst schema = LanceSchema({\n  vector: sentenceTransformer.vectorField(),\n  text: sentenceTransformer.sourceField(),\n});\n\nconst db = await lancedb.connect(databaseDir);\nconst table = await db.createEmptyTable(\"table\", schema, {\n  mode: \"overwrite\",\n});\n\nawait table.add([{ text: \"hello\" }, { text: \"world\" }]);\n\nconst results = await table.search(\"greeting\").limit(1).toArray();\n";
+
+export const TsEmbeddingImpl = "@register(\"sentence-transformers\")\nclass SentenceTransformersEmbeddings extends TextEmbeddingFunction {\n  name = \"Xenova/all-miniLM-L6-v2\";\n  #ndims!: number;\n  extractor!: FeatureExtractionPipeline;\n\n  async init() {\n    this.extractor = await pipeline(\"feature-extraction\", this.name, {\n      dtype: \"fp32\",\n    });\n    this.#ndims = await this.generateEmbeddings([\"hello\"]).then(\n      (e) => e[0].length,\n    );\n  }\n\n  ndims() {\n    return this.#ndims;\n  }\n\n  toJSON() {\n    return {\n      name: this.name,\n    };\n  }\n  async generateEmbeddings(texts: string[]) {\n    const output = await this.extractor(texts, {\n      pooling: \"mean\",\n      normalize: true,\n    });\n    return output.tolist();\n  }\n}\n";
+
+export const TsImports = "import * as lancedb from \"@lancedb/lancedb\";\nimport {\n  LanceSchema,\n  TextEmbeddingFunction,\n  getRegistry,\n  register,\n} from \"@lancedb/lancedb/embedding\";\n";
+
diff --git a/docs/web/snippets/doc_function_examples.mdx b/docs/web/snippets/doc_function_examples.mdx
new file mode 100644
index 000000000..afc637e03
--- /dev/null
+++ b/docs/web/snippets/doc_function_examples.mdx
@@ -0,0 +1,4 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyFtsBooleanQueries = "from lancedb.query import MatchQuery\n\nintersection = MatchQuery(\"puppy\", \"text\") & MatchQuery(\"merrily\", \"text\")\nunion = MatchQuery(\"puppy\", \"text\") | MatchQuery(\"merrily\", \"text\")\n";
+
diff --git a/docs/web/snippets/doc_storage_examples.mdx b/docs/web/snippets/doc_storage_examples.mdx
new file mode 100644
index 000000000..47e2da787
--- /dev/null
+++ b/docs/web/snippets/doc_storage_examples.mdx
@@ -0,0 +1,4 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyStorageRebuildVectorIndex = "from lancedb.index import IvfFlat\n\ntable.create_index(\n    \"vector\",\n    config=IvfFlat(distance_type=\"l2\", num_partitions=4),\n    replace=True,\n)\n";
+
diff --git a/docs/web/snippets/embedding.mdx b/docs/web/snippets/embedding.mdx
new file mode 100644
index 000000000..da3388ea7
--- /dev/null
+++ b/docs/web/snippets/embedding.mdx
@@ -0,0 +1,46 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyAsyncOpenaiEmbeddings = "db = await lancedb.connect_async(uri)\nfunc = get_registry().get(\"openai\").create(name=\"text-embedding-ada-002\")\n\nclass Words(LanceModel):\n    text: str = func.SourceField()\n    vector: Vector(func.ndims()) = func.VectorField()\n\ntable = await db.create_table(\"words\", schema=Words, mode=\"overwrite\")\nawait table.add([{\"text\": \"hello world\"}, {\"text\": \"goodbye world\"}])\n\nquery = \"greetings\"\nactual = await (await table.search(query)).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n";
+
+export const PyCreateEmbeddingFunction = "func = get_registry().get(\"openai\").create(\n    name=\"text-embedding-3-small\",\n    max_retries=7,\n)\n";
+
+export const PyEmbeddingFunction = "from functools import cached_property\n\nfrom lancedb.embeddings import TextEmbeddingFunction, register\n\nclass MyEmbeddingModel:\n    def __init__(self, model_name: str):\n        self.model_name = model_name\n\n    def encode(self, texts: list[str]) -> list[list[float]]:\n        return [[1.0, 2.0, 3.0] for _ in texts]\n\n@register(\"my-embedder\")\nclass MyTextEmbedder(TextEmbeddingFunction):\n    model_name: str = \"my-model\"\n\n    def generate_embeddings(self, texts: list[str]) -> list[list[float]]:\n        # Your embedding logic here\n        return self._model.encode(texts)\n\n    def ndims(self) -> int:\n        # Return the dimensionality of the embeddings\n        return len(self.generate_embeddings([\"test\"])[0])\n\n    @cached_property\n    def _model(self) -> MyEmbeddingModel:\n        # Initialize your model once\n        return MyEmbeddingModel(self.model_name)\n";
+
+export const PyImports = "from lancedb.pydantic import LanceModel, Vector\nfrom lancedb.embeddings import get_registry\n";
+
+export const PyManualQueryEmbeddings = "db = lancedb.connect(\"/tmp/db\")\nfunc = get_registry().get(\"openai\").create(name=\"text-embedding-ada-002\")\n\nclass Words(LanceModel):\n    text: str = func.SourceField()\n    vector: Vector(func.ndims()) = func.VectorField()\n\ntable = db.create_table(\"words\", schema=Words, mode=\"overwrite\")\ntable.add([{\"text\": \"hello world\"}, {\"text\": \"goodbye world\"}])\n\nquery_vector = func.generate_embeddings([\"greetings\"])[0]\n# --8<-- [start:manual_query_search]\n# query_vector is assumed to already be generated by your embedding function\nactual = table.search(query_vector).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n# --8<-- [end:manual_query_search]\n";
+
+export const PyManualQuerySearch = "# query_vector is assumed to already be generated by your embedding function\nactual = table.search(query_vector).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n";
+
+export const PyOpenaiEmbeddings = "db = lancedb.connect(\"/tmp/db\")\nfunc = get_registry().get(\"openai\").create(name=\"text-embedding-ada-002\")\n\nclass Words(LanceModel):\n    text: str = func.SourceField()\n    vector: Vector(func.ndims()) = func.VectorField()\n\ntable = db.create_table(\"words\", schema=Words, mode=\"overwrite\")\ntable.add([{\"text\": \"hello world\"}, {\"text\": \"goodbye world\"}])\n\nquery = \"greetings\"\nactual = table.search(query).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n";
+
+export const PyRegisterDevice = "import torch\n\nregistry = get_registry()\nif torch.cuda.is_available():\n    registry.set_var(\"device\", \"cuda\")\n\nfunc = registry.get(\"huggingface\").create(device=\"$var:device:cpu\")\n";
+
+export const PyRegisterSecret = "registry = get_registry()\nregistry.set_var(\"api_key\", \"sk-...\")\n\nfunc = registry.get(\"openai\").create(api_key=\"$var:api_key\")\n";
+
+export const TsCreateEmbeddingFunction = "const func = getRegistry().get(\"openai\")!.create({\n  model: \"text-embedding-3-small\",\n});\n";
+
+export const TsEmbeddingFunction = "const db = await lancedb.connect(databaseDir);\n\n@register(\"my_embedding\")\nclass MyEmbeddingFunction extends EmbeddingFunction<string> {\n  constructor(optionsRaw = {}) {\n    super();\n    const options = this.resolveVariables(optionsRaw);\n    // Initialize using options\n  }\n  ndims() {\n    return 3;\n  }\n  protected getSensitiveKeys(): string[] {\n    return [];\n  }\n  embeddingDataType(): Float {\n    return new Float32();\n  }\n  async computeQueryEmbeddings(_data: string) {\n    // This is a placeholder for a real embedding function\n    return [1, 2, 3];\n  }\n  async computeSourceEmbeddings(data: string[]) {\n    // This is a placeholder for a real embedding function\n    return Array.from({ length: data.length }).fill([\n      1, 2, 3,\n    ]) as number[][];\n  }\n}\n\nconst func = new MyEmbeddingFunction();\n\nconst data = [{ text: \"pepperoni\" }, { text: \"pineapple\" }];\n\n// Option 1: manually specify the embedding function\nconst table = await db.createTable(\"vectors\", data, {\n  embeddingFunction: {\n    function: func,\n    sourceColumn: \"text\",\n    vectorColumn: \"vector\",\n  },\n  mode: \"overwrite\",\n});\n\n// Option 2: provide the embedding function through a schema\n\nconst schema = LanceSchema({\n  text: func.sourceField(new Utf8()),\n  vector: func.vectorField(),\n});\n\nconst table2 = await db.createTable(\"vectors2\", data, {\n  schema,\n  mode: \"overwrite\",\n});\n";
+
+export const TsImports = "import * as lancedb from \"@lancedb/lancedb\";\nimport \"@lancedb/lancedb/embedding/openai\";\nimport { LanceSchema, getRegistry, register } from \"@lancedb/lancedb/embedding\";\nimport { EmbeddingFunction } from \"@lancedb/lancedb/embedding\";\nimport { type Float, Float32, Utf8 } from \"apache-arrow\";\n";
+
+export const TsManualQueryEmbeddings = "const db = await lancedb.connect(databaseDir);\nconst func = getRegistry()\n  .get(\"openai\")\n  ?.create({ model: \"text-embedding-ada-002\" }) as EmbeddingFunction;\n\nconst wordsSchema = LanceSchema({\n  text: func.sourceField(new Utf8()),\n  vector: func.vectorField(),\n});\nconst tbl = await db.createEmptyTable(\"words\", wordsSchema, {\n  mode: \"overwrite\",\n});\nawait tbl.add([{ text: \"hello world\" }, { text: \"goodbye world\" }]);\n\nconst queryVector = await func.computeQueryEmbeddings(\"greetings\");\n// --8<-- [start:manual_query_search]\n// queryVector is assumed to already be generated by your embedding function\nconst actual = (await tbl.search(queryVector).limit(1).toArray())[0];\n// --8<-- [end:manual_query_search]\n";
+
+export const TsManualQuerySearch = "// queryVector is assumed to already be generated by your embedding function\nconst actual = (await tbl.search(queryVector).limit(1).toArray())[0];\n";
+
+export const TsOpenaiEmbeddings = "const db = await lancedb.connect(databaseDir);\nconst func = getRegistry()\n  .get(\"openai\")\n  ?.create({ model: \"text-embedding-ada-002\" }) as EmbeddingFunction;\n\nconst wordsSchema = LanceSchema({\n  text: func.sourceField(new Utf8()),\n  vector: func.vectorField(),\n});\nconst tbl = await db.createEmptyTable(\"words\", wordsSchema, {\n  mode: \"overwrite\",\n});\nawait tbl.add([{ text: \"hello world\" }, { text: \"goodbye world\" }]);\n\nconst query = \"greetings\";\nconst actual = (await tbl.search(query).limit(1).toArray())[0];\n";
+
+export const TsRegisterModelFallback = "const registry = getRegistry();\nregistry.setVar(\"openai_model\", \"text-embedding-3-large\");\n\nconst func = registry.get(\"openai\")!.create({\n  model: \"$var:openai_model:text-embedding-3-small\",\n});\n";
+
+export const TsRegisterSecret = "const registry = getRegistry();\nregistry.setVar(\"api_key\", \"sk-...\");\n\nconst func = registry.get(\"openai\")!.create({\n  apiKey: \"$var:api_key\",\n});\n";
+
+export const RsCreateEmbeddingFunction = "use std::sync::Arc;\n\nuse lancedb::embeddings::openai::OpenAIEmbeddingFunction;\n\nlet api_key = \"your-openai-api-key\".to_string();\nlet embedding = Arc::new(\n    OpenAIEmbeddingFunction::new_with_model(api_key, \"text-embedding-3-small\")\n        .expect(\"failed to create OpenAI embedding function\"),\n);\n";
+
+export const RsEmbeddingFunction = "use std::{borrow::Cow, sync::Arc};\n\nuse arrow_array::{Array, FixedSizeListArray, Float32Array};\nuse arrow_schema::{DataType, Field, Schema};\nuse lancedb::{\n    connect,\n    embeddings::{EmbeddingDefinition, EmbeddingFunction},\n    Result,\n};\n\n#[derive(Debug, Clone)]\nstruct MyTextEmbedder {\n    dim: usize,\n}\n\nimpl EmbeddingFunction for MyTextEmbedder {\n    fn name(&self) -> &str {\n        \"my-embedder\"\n    }\n\n    fn source_type(&self) -> Result<Cow<'_, DataType>> {\n        Ok(Cow::Owned(DataType::Utf8))\n    }\n\n    fn dest_type(&self) -> Result<Cow<'_, DataType>> {\n        Ok(Cow::Owned(DataType::new_fixed_size_list(\n            DataType::Float32,\n            self.dim as i32,\n            true,\n        )))\n    }\n\n    fn compute_source_embeddings(&self, source: Arc<dyn Array>) -> Result<Arc<dyn Array>> {\n        let values = Arc::new(Float32Array::from(vec![1.0f32; source.len() * self.dim]));\n        let field = Arc::new(Field::new(\"item\", DataType::Float32, true));\n        Ok(Arc::new(FixedSizeListArray::new(\n            field,\n            self.dim as i32,\n            values,\n            None,\n        )))\n    }\n\n    fn compute_query_embeddings(&self, _input: Arc<dyn Array>) -> Result<Arc<dyn Array>> {\n        unimplemented!()\n    }\n}\n\n#[tokio::main]\nasync fn main() -> Result<()> {\n    let db = connect(\"./mydb\").execute().await?;\n    db.embedding_registry()\n        .register(\"my-embedder\", Arc::new(MyTextEmbedder { dim: 3 }))?;\n\n    let schema = Arc::new(Schema::new(vec![Field::new(\"text\", DataType::Utf8, false)]));\n    db.create_empty_table(\"mytable\", schema)\n        .add_embedding(EmbeddingDefinition::new(\n            \"text\",\n            \"my-embedder\",\n            Some(\"vector\"),\n        ))?\n        .execute()\n        .await?;\n\n    Ok(())\n}\n";
+
+export const RsManualQueryEmbeddings = "use std::{iter::once, sync::Arc};\n\nuse arrow_array::{record_batch, StringArray};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures::StreamExt;\nuse lancedb::{\n    connect,\n    embeddings::{openai::OpenAIEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction},\n    query::{ExecutableQuery, QueryBase},\n    Result,\n};\n\n#[tokio::main]\nasync fn main() -> Result<()> {\n    let db = connect(\"./mydb\").execute().await?;\n    let api_key = \"your-openai-api-key\".to_string();\n    let embedding = Arc::new(OpenAIEmbeddingFunction::new_with_model(\n        api_key,\n        \"text-embedding-3-large\",\n    )?);\n    db.embedding_registry().register(\"openai\", embedding.clone())?;\n\n    let schema = Arc::new(Schema::new(vec![Field::new(\"text\", DataType::Utf8, false)]));\n    let table = db\n        .create_empty_table(\"mytable\", schema)\n        .add_embedding(EmbeddingDefinition::new(\"text\", \"openai\", Some(\"vector\")))?\n        .execute()\n        .await?;\n\n    table\n        .add(record_batch!((\"text\", Utf8, [\"This is a test.\", \"Another example.\"]))?)\n        .execute()\n        .await?;\n\n    // Manually generate embeddings for the query (Enterprise path)\n    let query = Arc::new(StringArray::from_iter_values(once(\"test example\")));\n    let query_vector = embedding.compute_query_embeddings(query)?;\n    // --8<-- [start:manual_query_search]\n    // query_vector is assumed to already be generated by your embedding function\n    let mut results = table.vector_search(query_vector)?.limit(5).execute().await?;\n\n    while let Some(batch) = results.next().await {\n        println!(\"{:?}\", batch?);\n    }\n    // --8<-- [end:manual_query_search]\n\n    Ok(())\n}\n";
+
+export const RsManualQuerySearch = "// query_vector is assumed to already be generated by your embedding function\nlet mut results = table.vector_search(query_vector)?.limit(5).execute().await?;\n\nwhile let Some(batch) = results.next().await {\n    println!(\"{:?}\", batch?);\n}\n";
+
+export const RsOpenaiEmbeddings = "use std::{iter::once, sync::Arc};\n\nuse arrow_array::{record_batch, StringArray};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures::StreamExt;\nuse lancedb::{\n    connect,\n    embeddings::{openai::OpenAIEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction},\n    query::{ExecutableQuery, QueryBase},\n    Result,\n};\n\n#[tokio::main]\nasync fn main() -> Result<()> {\n    let db = connect(\"./mydb\").execute().await?;\n    let api_key = \"your-openai-api-key\".to_string();\n    let embedding = Arc::new(OpenAIEmbeddingFunction::new_with_model(\n        api_key,\n        \"text-embedding-3-large\",\n    )?);\n\n    db.embedding_registry().register(\"openai\", embedding.clone())?;\n\n    let schema = Arc::new(Schema::new(vec![Field::new(\"text\", DataType::Utf8, false)]));\n    let table = db\n        .create_empty_table(\"mytable\", schema)\n        .add_embedding(EmbeddingDefinition::new(\"text\", \"openai\", Some(\"vector\")))?\n        .execute()\n        .await?;\n\n    table\n        .add(record_batch!((\"text\", Utf8, [\"This is a test.\", \"Another example.\"]))?)\n        .execute()\n        .await?;\n\n    let query = Arc::new(StringArray::from_iter_values(once(\"test example\")));\n    let query_vector = embedding.compute_query_embeddings(query)?;\n    let mut results = table.vector_search(query_vector)?.limit(5).execute().await?;\n\n    while let Some(batch) = results.next().await {\n        println!(\"{:?}\", batch?);\n    }\n\n    Ok(())\n}\n";
+
diff --git a/docs/web/snippets/filtering.mdx b/docs/web/snippets/filtering.mdx
new file mode 100644
index 000000000..f4fc0cae8
--- /dev/null
+++ b/docs/web/snippets/filtering.mdx
@@ -0,0 +1,8 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const TsSearch = "const _result = await tbl\n  .search(Array(1536).fill(0.5))\n  .limit(1)\n  .where(\"id = 10\")\n  .toArray();\n";
+
+export const TsSqlSearch = "await tbl.query().where(\"id = 10\").limit(10).toArray();\n";
+
+export const TsVecSearch = "const result = await (\n  tbl.search(Array(1536).fill(0)) as lancedb.VectorQuery\n)\n  .where(\"(item IN ('item 0', 'item 2')) AND (id > 10)\")\n  .postfilter()\n  .toArray();\n";
+
diff --git a/docs/web/snippets/full_text_search.mdx b/docs/web/snippets/full_text_search.mdx
new file mode 100644
index 000000000..7d71409ee
--- /dev/null
+++ b/docs/web/snippets/full_text_search.mdx
@@ -0,0 +1,4 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const TsFullTextSearch = "const result = await tbl\n  .query()\n  .nearestToText(\"apple\")\n  .select([\"id\", \"doc\"])\n  .limit(10)\n  .toArray();\nexpect(result.length).toBe(10);\n";
+
diff --git a/docs/web/snippets/function.mdx b/docs/web/snippets/function.mdx
new file mode 100644
index 000000000..728810e2a
--- /dev/null
+++ b/docs/web/snippets/function.mdx
@@ -0,0 +1,18 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyFunctionApply = "from lancedb import col\n\ntable.add_columns({\"doubled\": version(value=col(\"value\"))})\n";
+
+export const PyFunctionConnect = "import os\n\nimport lancedb\n\ndb = lancedb.connect(\n    uri=os.environ[\"LANCEDB_URI\"],  # db://your-database\n    api_key=os.environ[\"LANCEDB_API_KEY\"],\n    region=os.environ.get(\"LANCEDB_REGION\", \"us-east-1\"),\n    host_override=os.environ[\"LANCEDB_HOST_OVERRIDE\"],\n)\n";
+
+export const PyFunctionCreateTable = "import pyarrow as pa\n\nschema = pa.schema([pa.field(\"value\", pa.float64(), nullable=False)])\ntable = db.create_table(\n    \"function_demo\",\n    [{\"value\": 1.0}, {\"value\": 2.0}, {\"value\": 3.0}],\n    schema=schema,\n)\n";
+
+export const PyFunctionDefine = "from lancedb import udf\n\n\n@udf\ndef double(value: float) -> float:\n    return value * 2\n";
+
+export const PyFunctionInspect = "rows = table.search().select([\"value\", \"doubled\"]).to_list()\nprint(sorted((row[\"value\"], row[\"doubled\"]) for row in rows))\n# [(1.0, 2.0), (2.0, 4.0), (3.0, 6.0)]\n";
+
+export const PyFunctionLocalCall = "print(double(1.5))\n# 3.0\n";
+
+export const PyFunctionRefresh = "refresh = table.refresh_column_async(\"doubled\")\nresult = refresh.wait()\nprint(result.rows_assigned)\n# 3\n";
+
+export const PyFunctionRegister = "job = db.create_function_async(double)\nversion = job.wait()\n";
+
diff --git a/docs/web/snippets/indexing.mdx b/docs/web/snippets/indexing.mdx
new file mode 100644
index 000000000..34e9b72f7
--- /dev/null
+++ b/docs/web/snippets/indexing.mdx
@@ -0,0 +1,74 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyFtsIndexAsync = "import asyncio\n\nimport lancedb\nimport polars as pl\nfrom lancedb.index import FTS\n\ndata = pl.DataFrame(\n    {\n        \"id\": [1, 2],\n        \"text\": [\n            \"His first language is spanish\",\n            \"Her first language is english\",\n        ],\n    }\n)\n\nasync def main(data: pl.DataFrame):\n    uri = \"ex_lancedb\"\n    db = await lancedb.connect_async(uri)\n    tbl = await db.create_table(\"my_text\", data=data, mode=\"overwrite\")\n\n    await tbl.create_index(\"text\", config=FTS(language=\"English\"))\n\n    response = await tbl.search(\"spanish\", query_type=\"fts\")\n    result = await response.limit(1).to_polars()\n    print(result)\n    return result\n\nif __name__ == \"__main__\":\n    asyncio.run(main(data))\n";
+
+export const PyFtsIndexCreate = "table_name = \"fts-index-create\"\ntable = db.open_table(table_name)\ntable.create_fts_index(\"text\")\n";
+
+export const PyFtsIndexNested = "from lancedb.query import MatchQuery, PhraseQuery\n\ntable = db.open_table(\"fts-index-nested\")\n\n# Index a text leaf inside a struct column using a dotted path.\ntable.create_fts_index(\"payload.text\", with_position=True)\n\n# The same dotted path works in MatchQuery and PhraseQuery.\nmatches = (\n    table.search(MatchQuery(\"puppy\", \"payload.text\")).limit(5).to_list()\n)\nphrases = (\n    table.search(PhraseQuery(\"puppy runs\", \"payload.text\"))\n    .limit(5)\n    .to_list()\n)\n";
+
+export const PyFtsIndexWait = "table_name = \"fts-index-wait\"\n\ntable = db.open_table(table_name)\ntable.create_fts_index(\"text\")\n\nindex_name = \"text_idx\"\ntable.wait_for_index([index_name])\n";
+
+export const PyGpuIndexCuda = "table.create_index(\n    num_partitions=256,\n    num_sub_vectors=96,\n    accelerator=\"cuda\",\n)\n";
+
+export const PyGpuIndexMps = "table.create_index(\n    num_partitions=256,\n    num_sub_vectors=96,\n    accelerator=\"mps\",\n)\n";
+
+export const PyQuantizationCustomParams = "table.create_index(\n    index_type=\"IVF_RQ\",\n    num_bits=2,\n    max_iterations=100,\n    sample_rate=512,\n)\n";
+
+export const PyReindexingIncremental = "table = db.open_table(\"reindexing_incremental\")\ntable.add([{\"vector\": [3.1, 4.1], \"text\": \"Frodo was a happy puppy\"}])\ntable.optimize()\n";
+
+export const PyScalarIndexBuild = "tbl = db.open_table(\"scalar_index_build\")\ntbl.create_scalar_index(\"book_id\")\ntbl.create_scalar_index(\"publisher\", index_type=\"BITMAP\")\n";
+
+export const PyScalarIndexFilter = "table = db.open_table(\"books\")\nresult = table.search().where(\"book_id = 2\").limit(10).to_pandas()\n";
+
+export const PyScalarIndexNestedFields = "import pyarrow as pa\nfrom lancedb.index import BTree\n\nmetadata_type = pa.struct(\n    [\n        pa.field(\"user_id\", pa.int32()),\n        pa.field(\"user.id\", pa.int32()),\n    ]\n)\ndata = pa.Table.from_arrays(\n    [\n        pa.array([1, 2, 3], type=pa.int32()),\n        pa.array(\n            [\n                {\"user_id\": 10, \"user.id\": 100},\n                {\"user_id\": 20, \"user.id\": 200},\n                {\"user_id\": 30, \"user.id\": 300},\n            ],\n            type=metadata_type,\n        ),\n    ],\n    names=[\"user_id\", \"metadata\"],\n)\ntable = await db.create_table(\"nested_scalar_index\", data)\n\n# Index a nested struct field.\nawait table.create_index(\n    \"metadata.user_id\", config=BTree(), name=\"nested_user_id_idx\"\n)\n\n# Escape literal dots inside a segment with backticks.\nawait table.create_index(\n    \"metadata.`user.id`\", config=BTree(), name=\"escaped_user_id_idx\"\n)\n\n# `columns` is returned as the canonical path you passed in.\nfor index in await table.list_indices():\n    print(index.name, index.columns)\n# nested_user_id_idx  ['metadata.user_id']\n# escaped_user_id_idx ['metadata.`user.id`']\n";
+
+export const PyScalarIndexOptimize = "table.add([{\"vector\": [7, 8], \"book_id\": 4}])\ntable.optimize()\n";
+
+export const PyScalarIndexPrefilter = "table = db.open_table(\"book_with_embeddings\")\ntable.search([1.2] * 2).where(\"book_id != 3\").limit(10).to_pandas()\n";
+
+export const PyScalarIndexUuidData = "def generate_random_names():\n    base_names = [\"Alice\", \"Bob\", \"Carla\", \"David\", \"Eve\", \"Frank\", \"Grace\"]\n    letter = random.choice(string.ascii_uppercase)\n    return f\"{random.choice(base_names)} {letter}.\"\n\ndef generate_uuids(num_items):\n    return [uuid.uuid4().bytes for _ in range(num_items)]\n\n# Generate some UUIDs and random names\nn = 7\nuuids = generate_uuids(n)\nnames = [generate_random_names() for _ in range(n)]\n";
+
+export const PyScalarIndexUuidTable = "table_name = \"index-on-uuid\"\n\nuuid_array = pa.array(uuids, pa.uuid())\nname_array = pa.array(names, pa.string())\nschema = pa.schema(\n    [\n        pa.field(\"id\", pa.uuid()),\n        pa.field(\"name\", pa.string()),\n    ]\n)\ndata_table = pa.Table.from_arrays([uuid_array, name_array], schema=schema)\ntable = db.create_table(table_name, data=data_table, mode=\"overwrite\")\n";
+
+export const PyScalarIndexUuidType = "import pyarrow as pa\n";
+
+export const PyScalarIndexUuidUpsert = "new_users = [\n    {\"id\": uuid.uuid4().bytes, \"name\": \"Hannah D.\"},\n    {\"id\": uuid.uuid4().bytes, \"name\": \"Ian B.\"},\n]\n# Insert or update using the UUID index\ntable.merge_insert(\n    \"id\"\n).when_matched_update_all().when_not_matched_insert_all().execute(new_users)\n";
+
+export const PyScalarIndexUuidWait = "index_name = \"id_idx\"\ntable.create_scalar_index(\"id\")\ntable.wait_for_index([index_name])\n";
+
+export const PyScalarIndexWait = "index_name = \"label_idx\"\ntable.wait_for_index([index_name])\n";
+
+export const PyVectorIndexAsyncConfig = "import lancedb\nimport numpy as np\nfrom lancedb.index import IvfPq\n\nasync def main():\n    data = [\n        {\"id\": i, \"vector\": np.random.random(8).astype(np.float32).tolist()}\n        for i in range(512)\n    ]\n\n    db = await lancedb.connect_async(\"ex_lancedb\")\n    table = await db.create_table(\n        \"vector_index_async\", data=data, mode=\"overwrite\"\n    )\n\n    await table.create_index(\n        \"vector\",\n        config=IvfPq(\n            distance_type=\"cosine\",\n            num_partitions=16,\n            num_sub_vectors=4,\n        ),\n    )\n    return await table.list_indices()\n";
+
+export const PyVectorIndexBinaryAddData = "table.add(data)\n";
+
+export const PyVectorIndexBinaryBuildIndex = "table.create_index(\n    metric=\"hamming\",\n    vector_column_name=\"vector\",\n    index_type=\"IVF_FLAT\",\n)\n";
+
+export const PyVectorIndexBinarySchema = "table = tmp_db.create_table(table_name, schema=schema, mode=\"overwrite\")\n";
+
+export const PyVectorIndexBinarySearch = "query = np.random.randint(0, 2, size=ndim)\nquery = np.packbits(query)\ndf = table.search(query).metric(\"hamming\").limit(10).to_pandas()\ndf.vector = df.vector.apply(np.unpackbits)\n";
+
+export const PyVectorIndexBuildHnsw = "table.create_index(index_type=\"IVF_HNSW_SQ\")\n";
+
+export const PyVectorIndexBuildIvf = "table_name = \"vector-index-build-ivf\"\ntable = db.open_table(table_name)\ntable.create_index(\n    metric=\"cosine\",\n    vector_column_name=\"keywords_embeddings\",\n)\n";
+
+export const PyVectorIndexBypassRecall = "query = np.random.random(128)\nk = 10\n\n# Ground truth: flat (exhaustive) scan, ignoring the ANN index.\ntruth = set(table.search(query).bypass_vector_index().limit(k).to_pandas()[\"id\"])\n\n# ANN results with the current nprobes setting.\nann = set(table.search(query).nprobes(20).limit(k).to_pandas()[\"id\"])\n\nrecall_at_k = len(truth & ann) / k\n";
+
+export const PyVectorIndexCheckStatus = "index_name = \"keywords_embeddings_idx\"\ntable.wait_for_index([index_name])\nprint(table.index_stats(index_name))\n";
+
+export const PyVectorIndexConfigureIvf = "table.create_index(metric=\"l2\", num_partitions=16, num_sub_vectors=4)\n";
+
+export const PyVectorIndexCustomName = "# Override the default `{column}_idx` convention by passing `name=...`.\ntable.create_index(\n    metric=\"cosine\",\n    vector_column_name=\"keywords_embeddings\",\n    name=\"my_custom_index\",\n)\ntable.wait_for_index([\"my_custom_index\"])\nprint(table.index_stats(\"my_custom_index\"))\n";
+
+export const PyVectorIndexDistanceRange = "# Only return results whose distance falls within [0.0, 0.5).\n# Useful for near-duplicate detection or thresholded similarity search.\n(\n    table.search(np.random.random(128))\n    .distance_range(lower_bound=0.0, upper_bound=0.5)\n    .limit(10)\n    .to_pandas()\n)\n";
+
+export const PyVectorIndexNestedField = "# The vector column `embedding` is nested inside the `image` struct.\n# Pass its full dotted path as `vector_column_name`; the same path is used\n# at query time and is what `list_indices()` reports under `columns`.\ntable.create_index(\n    vector_column_name=\"image.embedding\",\n    num_partitions=1,\n    num_sub_vectors=1,\n    name=\"image_embedding_idx\",\n)\n\nresults = (\n    table.search([0.0, 1.0], vector_column_name=\"image.embedding\")\n    .limit(1)\n    .to_list()\n)\n";
+
+export const PyVectorIndexNprobes = "# Always scan 10 partitions; scan up to 50 only if the initial pass\n# returns fewer than `limit` results (common with narrow filters).\n(\n    table.search(np.random.random(128))\n    .minimum_nprobes(10)\n    .maximum_nprobes(50)\n    .where(\"id > 100\")\n    .limit(5)\n    .to_pandas()\n)\n";
+
+export const PyVectorIndexQueryHnsw = "tbl = table\ntbl.search(np.random.random((16))).limit(2).to_pandas()\n";
+
+export const PyVectorIndexQueryIvf = "tbl = table\ntbl.search(np.random.random((1536))).limit(2).nprobes(20).refine_factor(\n    10\n).to_pandas()\n";
+
+export const PyVectorIndexSetup = "table_name = \"vector-index-tbl\"\ntable = db.open_table(table_name)\n";
+
diff --git a/docs/web/snippets/ingestion_delete.mdx b/docs/web/snippets/ingestion_delete.mdx
new file mode 100644
index 000000000..f6f6222ae
--- /dev/null
+++ b/docs/web/snippets/ingestion_delete.mdx
@@ -0,0 +1,4 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const RsExample = "use std::sync::Arc;\n\nuse arrow_array::{Int64Array, RecordBatch, RecordBatchIterator, RecordBatchReader, StringArray};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures::TryStreamExt;\nuse lancedb::query::{ExecutableQuery, QueryBase};\n\nfn make_users_reader(rows: &[(i64, &str)]) -> Box<dyn RecordBatchReader + Send> {\n    let schema = Arc::new(Schema::new(vec![\n        Field::new(\"id\", DataType::Int64, false),\n        Field::new(\"name\", DataType::Utf8, false),\n    ]));\n    let batch = RecordBatch::try_new(\n        schema.clone(),\n        vec![\n            Arc::new(Int64Array::from_iter_values(rows.iter().map(|r| r.0))),\n            Arc::new(StringArray::from_iter_values(rows.iter().map(|r| r.1))),\n        ],\n    )\n    .unwrap();\n    Box::new(RecordBatchIterator::new(\n        vec![Ok(batch)].into_iter(),\n        schema,\n    ))\n}\n\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n    let directory = tempfile::tempdir()?;\n    let db = lancedb::connect(directory.path().to_str().unwrap())\n        .execute()\n        .await?;\n    let table = db\n        .create_table(\n            \"users\",\n            make_users_reader(&[(1, \"Alice\"), (2, \"Bob\"), (3, \"Charlie\")]),\n        )\n        .execute()\n        .await?;\n    let predicate = \"id = 3\";\n    let selected: Vec<RecordBatch> = table\n        .query()\n        .only_if(predicate)\n        .execute()\n        .await?\n        .try_collect()\n        .await?;\n    println!(\"Selected: {selected:?}\");\n    table.delete(predicate).await?;\n    let rows: Vec<RecordBatch> = table.query().execute().await?.try_collect().await?;\n    println!(\"Remaining: {rows:?}\");\n    assert_eq!(table.count_rows(None).await?, 2);\n    assert_eq!(\n        table\n            .count_rows(Some(\"id = 1 AND name = 'Alice'\".into()))\n            .await?,\n        1\n    );\n    assert_eq!(\n        table\n            .count_rows(Some(\"id = 2 AND name = 'Bob'\".into()))\n            .await?,\n        1\n    );\n    assert_eq!(table.count_rows(Some(\"id = 3\".into())).await?, 0);\n    Ok(())\n}\n";
+
diff --git a/docs/web/snippets/ingestion_examples.mdx b/docs/web/snippets/ingestion_examples.mdx
new file mode 100644
index 000000000..0268a906a
--- /dev/null
+++ b/docs/web/snippets/ingestion_examples.mdx
@@ -0,0 +1,66 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyAddFromDataset = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    from pathlib import Path\n\n    import pyarrow as pa\n    import pyarrow.parquet as pq\n\n    data_path = str(Path(uri) / \"input.parquet\")\n    pq.write_table(pa.table({\"id\": [1, 2], \"name\": [\"Alice\", \"Bob\"]}), data_path)\n    import pyarrow.dataset as ds\n\n    dataset = ds.dataset(data_path, format=\"parquet\")\n    table = db.create_table(\"my_table\", schema=dataset.schema, mode=\"overwrite\")\n    table.add(dataset)\n\n    print(\"Rows:\", table.count_rows())\n";
+
+export const PyAddProgress = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\ndef report_progress(progress):\n    print(f\"Rows written: {progress['output_rows']}\")\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    table = db.create_table(\"users\", [{\"id\": 1, \"name\": \"Alice\"}])\n    table.add(\n        [{\"id\": 2, \"name\": \"Bob\"}, {\"id\": 3, \"name\": \"Charlie\"}],\n        progress=report_progress,\n    )\n    rows = sorted(table.search().to_list(), key=lambda row: row[\"id\"])\n    print(\"After insertion:\", rows)\n";
+
+export const PyAppendRows = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    table = db.create_table(\"users\", [{\"id\": 1, \"name\": \"Alice\"}])\n    table.add([{\"id\": 2, \"name\": \"Bob\"}, {\"id\": 3, \"name\": \"Charlie\"}])\n    rows = sorted(table.search().to_list(), key=lambda row: row[\"id\"])\n    print(rows)\n";
+
+export const PyCreateEmptyTable = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    import pyarrow as pa\n\n    schema = pa.schema(\n        [\n            pa.field(\"vector\", pa.list_(pa.float32(), 2)),\n            pa.field(\"item\", pa.string()),\n            pa.field(\"price\", pa.float32()),\n        ]\n    )\n    tbl = db.create_table(\"test_empty_table\", schema=schema, mode=\"overwrite\")\n\n    print(\"Rows:\", tbl.count_rows())\n";
+
+export const PyCreateEmptyTablePydantic = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    from lancedb.pydantic import LanceModel, Vector\n\n    class Item(LanceModel):\n        vector: Vector(2)\n        item: str\n        price: float\n\n    tbl = db.create_table(\n        \"test_empty_table_new\", schema=Item.to_arrow_schema(), mode=\"overwrite\"\n    )\n\n    print(\"Rows:\", tbl.count_rows())\n";
+
+export const PyCreateTableConflictHandling = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    data = [{\"vector\": [1.1, 1.2], \"lat\": 45.5, \"long\": -122.7}]\n    db.create_table(\"conflict_table\", data)\n    # Idempotent open: reuse the existing table if it exists.\n    # The provided data is ignored; the schema is validated against the\n    # existing table and a mismatch raises an error.\n    tbl = db.create_table(\"conflict_table\", data, exist_ok=True)\n\n    # Overwrite: replace the contents with the provided data.\n    tbl = db.create_table(\"conflict_table\", data, mode=\"overwrite\")\n    print(\"Rows:\", tbl.count_rows())\n";
+
+export const PyCreateTableCustomSchema = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    import pyarrow as pa\n\n    custom_schema = pa.schema(\n        [\n            pa.field(\"vector\", pa.list_(pa.float32(), 4)),\n            pa.field(\"lat\", pa.float32()),\n            pa.field(\"long\", pa.float32()),\n        ]\n    )\n\n    data = [\n        {\"vector\": [1.1, 1.2, 1.3, 1.4], \"lat\": 45.5, \"long\": -122.7},\n        {\"vector\": [0.2, 1.8, 0.4, 3.6], \"lat\": 40.1, \"long\": -74.1},\n    ]\n    tbl = db.create_table(\n        \"my_table_custom_schema\", data, schema=custom_schema, mode=\"overwrite\"\n    )\n\n    print(\"Rows:\", tbl.count_rows())\n";
+
+export const PyCreateTableFromArrow = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    import numpy as np\n    import pyarrow as pa\n\n    dim = 16\n    total = 2\n    schema = pa.schema(\n        [\n            pa.field(\"vector\", pa.list_(pa.float16(), dim)),\n            pa.field(\"text\", pa.string()),\n        ]\n    )\n    data = pa.Table.from_arrays(\n        [\n            pa.array(\n                [np.random.randn(dim).astype(np.float16) for _ in range(total)],\n                pa.list_(pa.float16(), dim),\n            ),\n            pa.array([\"foo\", \"bar\"]),\n        ],\n        [\"vector\", \"text\"],\n    )\n    tbl = db.create_table(\"f16_tbl\", data, schema=schema, mode=\"overwrite\")\n\n    print(\"Rows:\", tbl.count_rows())\n";
+
+export const PyCreateTableFromDicts = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    data = [\n        {\"vector\": [1.1, 1.2], \"lat\": 45.5, \"long\": -122.7},\n        {\"vector\": [0.2, 1.8], \"lat\": 40.1, \"long\": -74.1},\n    ]\n    db.create_table(\"test_table\", data, mode=\"overwrite\")\n    tbl = db[\"test_table\"]\n    tbl.head()\n\n    print(\"Rows:\", tbl.count_rows())\n";
+
+export const PyCreateTableFromIterator = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    import pyarrow as pa\n\n    schema = pa.schema(\n        [\n            pa.field(\"vector\", pa.list_(pa.float32(), 4)),\n            pa.field(\"item\", pa.utf8()),\n            pa.field(\"price\", pa.float32()),\n        ]\n    )\n\n    def make_batches():\n        for i in range(5):\n            yield pa.RecordBatch.from_arrays(\n                [\n                    pa.array(\n                        [[3.1, 4.1, 5.1, 6.1], [5.9, 26.5, 4.7, 32.8]],\n                        pa.list_(pa.float32(), 4),\n                    ),\n                    pa.array([\"foo\", \"bar\"]),\n                    pa.array([10.0, 20.0]),\n                ],\n                [\"vector\", \"item\", \"price\"],\n            )\n\n    table = db.create_table(\"batched_table\", schema=schema)\n    table.add(make_batches())\n\n    print(\"Rows:\", table.count_rows())\n";
+
+export const PyCreateTableFromPandas = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    import pandas as pd\n\n    data = pd.DataFrame(\n        {\n            \"vector\": [[1.1, 1.2, 1.3, 1.4], [0.2, 1.8, 0.4, 3.6]],\n            \"lat\": [45.5, 40.1],\n            \"long\": [-122.7, -74.1],\n        }\n    )\n    db.create_table(\"my_table_pandas\", data, mode=\"overwrite\")\n    db[\"my_table_pandas\"].head()\n";
+
+export const PyCreateTableFromPolars = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    import polars as pl\n\n    data = pl.DataFrame(\n        {\n            \"vector\": [[3.1, 4.1], [5.9, 26.5]],\n            \"item\": [\"foo\", \"bar\"],\n            \"price\": [10.0, 20.0],\n        }\n    )\n    tbl = db.create_table(\"my_table_pl\", data, mode=\"overwrite\")\n\n    print(\"Rows:\", tbl.count_rows())\n";
+
+export const PyCreateTableFromPydantic = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    from lancedb.pydantic import LanceModel, Vector\n\n    class Content(LanceModel):\n        movie_id: int\n        vector: Vector(128)\n        genres: str\n        title: str\n        imdb_id: int\n\n        @property\n        def imdb_url(self) -> str:\n            return f\"https://www.imdb.com/title/tt{self.imdb_id}\"\n\n    tbl = db.create_table(\"movielens_small\", schema=Content, mode=\"overwrite\")\n\n    print(\"Rows:\", tbl.count_rows())\n";
+
+export const PyCreateTableNestedSchema = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    from lancedb.pydantic import LanceModel, Vector\n    from pydantic import BaseModel\n\n    class Document(BaseModel):\n        content: str\n        source: str\n\n    class NestedSchema(LanceModel):\n        id: str\n        vector: Vector(1536)\n        document: Document\n\n    tbl = db.create_table(\"nested_table\", schema=NestedSchema, mode=\"overwrite\")\n\n    print(\"Rows:\", tbl.count_rows())\n";
+
+export const PyDeleteRows = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    table = db.create_table(\n        \"users\",\n        [\n            {\"id\": 1, \"name\": \"Alice\"},\n            {\"id\": 2, \"name\": \"Bob\"},\n            {\"id\": 3, \"name\": \"Charlie\"},\n        ],\n    )\n    predicate = \"id = 3\"\n    print(\"Selected:\", table.search().where(predicate).to_list())\n    table.delete(predicate)\n    rows = sorted(table.search().to_list(), key=lambda row: row[\"id\"])\n    print(\"Remaining:\", rows)\n";
+
+export const PyDropTable = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    # Create a table first\n    data = [{\"vector\": [1.1, 1.2], \"lat\": 45.5}]\n    db.create_table(\"my_table\", data, mode=\"overwrite\")\n\n    # Drop the table\n    db.drop_table(\"my_table\")\n";
+
+export const PyInsertIfNotExists = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    import pyarrow as pa\n\n    table = db.create_table(\n        \"users_example\",\n        data=pa.table(\n            {\n                \"id\": [1, 2],\n                \"name\": [\"Alice\", \"Bob\"],\n                \"login_count\": [10, 20],\n            }\n        ),\n        mode=\"overwrite\",\n    )\n\n    incoming_users = pa.table(\n        {\n            \"id\": [2, 3],\n            \"name\": [\"Bobby\", \"Charlie\"],\n            \"login_count\": [21, 5],\n        }\n    )\n\n    (table.merge_insert(\"id\").when_not_matched_insert_all().execute(incoming_users))\n\n    print(\"Rows:\", table.count_rows())\n";
+
+export const PyMergeDeleteMissingBySource = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    import pyarrow as pa\n\n    table = db.create_table(\n        \"users_example\",\n        data=pa.table(\n            {\n                \"id\": [1, 2, 3],\n                \"name\": [\"Alice\", \"Bob\", \"Charlie\"],\n                \"login_count\": [10, 20, 5],\n            }\n        ),\n        mode=\"overwrite\",\n    )\n\n    incoming_users = pa.table(\n        {\n            \"id\": [2, 3],\n            \"name\": [\"Bobby\", \"Charlie\"],\n            \"login_count\": [21, 5],\n        }\n    )\n\n    (\n        table.merge_insert(\"id\")\n        .when_matched_update_all()\n        .when_not_matched_insert_all()\n        .when_not_matched_by_source_delete()\n        .execute(incoming_users)\n    )\n\n    print(\"Rows:\", table.count_rows())\n";
+
+export const PyMergeMatchedUpdateOnly = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    import pyarrow as pa\n\n    table = db.create_table(\n        \"users_example\",\n        data=pa.table(\n            {\n                \"id\": [1, 2],\n                \"name\": [\"Alice\", \"Bob\"],\n                \"login_count\": [10, 20],\n            }\n        ),\n        mode=\"overwrite\",\n    )\n\n    incoming_users = pa.table(\n        {\n            \"id\": [2, 3],\n            \"name\": [\"Bobby\", \"Charlie\"],\n            \"login_count\": [21, 5],\n        }\n    )\n\n    (table.merge_insert(\"id\").when_matched_update_all().execute(incoming_users))\n\n    print(\"Rows:\", table.count_rows())\n";
+
+export const PyMergePartialColumns = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    import pyarrow as pa\n\n    table = db.create_table(\n        \"users_example\",\n        data=pa.table(\n            {\n                \"id\": [1, 2],\n                \"name\": [\"Alice\", \"Bob\"],\n                \"login_count\": [10, 20],\n            }\n        ),\n        mode=\"overwrite\",\n    )\n\n    incoming_users = pa.table(\n        {\n            \"id\": [2, 3],\n            \"name\": [\"Bobby\", \"Charlie\"],\n        }\n    )\n\n    (\n        table.merge_insert(\"id\")\n        .when_matched_update_all()\n        .when_not_matched_insert_all()\n        .execute(incoming_users)\n    )\n\n    print(\"Rows:\", table.count_rows())\n";
+
+export const PyMergeUpdateInsert = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    import pyarrow as pa\n\n    table = db.create_table(\n        \"users_example\",\n        data=pa.table(\n            {\n                \"id\": [1, 2],\n                \"name\": [\"Alice\", \"Bob\"],\n                \"login_count\": [10, 20],\n            }\n        ),\n        mode=\"overwrite\",\n    )\n\n    incoming_users = pa.table(\n        {\n            \"id\": [2, 3],\n            \"name\": [\"Bobby\", \"Charlie\"],\n            \"login_count\": [21, 5],\n        }\n    )\n\n    (\n        table.merge_insert(\"id\")\n        .when_matched_update_all()\n        .when_not_matched_insert_all()\n        .execute(incoming_users)\n    )\n\n    print(\"Rows:\", table.count_rows())\n";
+
+export const PyOpenExistingTable = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    # Create a table first\n    data = [{\"vector\": [1.1, 1.2], \"lat\": 45.5, \"long\": -122.7}]\n    db.create_table(\"test_table\", data, mode=\"overwrite\")\n\n    # List table names\n    print(db.list_tables().tables)\n\n    # Open existing table\n    tbl = db.open_table(\"test_table\")\n\n    print(\"Rows:\", tbl.count_rows())\n";
+
+export const PyUpdateOperation = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    import pyarrow as pa\n\n    table = db.create_table(\n        \"users_example\",\n        data=pa.table(\n            {\n                \"id\": [1, 2],\n                \"name\": [\"Alice\", \"Bob\"],\n                \"login_count\": [10, 20],\n            }\n        ),\n        mode=\"overwrite\",\n    )\n    table.update(where=\"id = 2\", values={\"name\": \"Bobby\"})\n\n    print(\"Rows:\", table.count_rows())\n";
+
+export const PyUpdateUsingSql = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    import pyarrow as pa\n\n    table = db.create_table(\n        \"users_example\",\n        data=pa.table(\n            {\n                \"id\": [1, 2],\n                \"name\": [\"Alice\", \"Bob\"],\n                \"login_count\": [10, 20],\n            }\n        ),\n        mode=\"overwrite\",\n    )\n    table.update(where=\"id = 2\", values_sql={\"login_count\": \"login_count + 1\"})\n\n    print(\"Rows:\", table.count_rows())\n";
+
+export const PyWriteWorkflow = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-ingestion-\") as uri:\n    db = lancedb.connect(uri)\n    table = db.create_table(\n        \"users\",\n        [\n            {\"id\": 1, \"name\": \"Alice\", \"login_count\": 10},\n            {\"id\": 2, \"name\": \"Bob\", \"login_count\": 20},\n        ],\n    )\n    table.add([{\"id\": 3, \"name\": \"Charlie\", \"login_count\": 5}])\n    table.update(where=\"id = 2\", values={\"name\": \"Bobby\"})\n    table.update(where=\"id = 2\", values_sql={\"login_count\": \"login_count + 1\"})\n    (\n        table.merge_insert(\"id\")\n        .when_matched_update_all()\n        .when_not_matched_insert_all()\n        .execute(\n            [\n                {\"id\": 3, \"name\": \"Charlie\", \"login_count\": 6},\n                {\"id\": 4, \"name\": \"Dana\", \"login_count\": 1},\n            ]\n        )\n    )\n    print(\"Rows selected for deletion:\", table.search().where(\"id = 4\").to_list())\n    table.delete(\"id = 4\")\n    rows = sorted(table.search().to_list(), key=lambda row: row[\"id\"])\n    print(rows)\n";
+
+export const TsAddProgressBody = "const lancedb = await import(\"@lancedb/lancedb\");\nconst { mkdtemp, rm } = await import(\"node:fs/promises\");\nconst { tmpdir } = await import(\"node:os\");\nconst { join } = await import(\"node:path\");\nconst uri = await mkdtemp(join(tmpdir(), \"lancedb-ingestion-\"));\ntry {\n  const db = await lancedb.connect(uri);\n  const table = await db.createTable(\"users\", [{ id: 1, name: \"Alice\" }]);\n  await table.add(\n    [\n      { id: 2, name: \"Bob\" },\n      { id: 3, name: \"Charlie\" },\n    ],\n    {\n      progress: (progress) =>\n        console.log(`Rows written: ${progress.outputRows}`),\n    },\n  );\n  const rows = (await table.query().toArray()).sort((a, b) => a.id - b.id);\n  console.log(\"After insertion:\", rows);\n";
+
+export const TsAddProgressTail = "} finally {\n  await rm(uri, { recursive: true, force: true });\n}\n";
+
+export const TsDeleteRowsBody = "const lancedb = await import(\"@lancedb/lancedb\");\nconst { mkdtemp, rm } = await import(\"node:fs/promises\");\nconst { tmpdir } = await import(\"node:os\");\nconst { join } = await import(\"node:path\");\nconst uri = await mkdtemp(join(tmpdir(), \"lancedb-ingestion-\"));\ntry {\n  const db = await lancedb.connect(uri);\n  const table = await db.createTable(\"users\", [\n    { id: 1, name: \"Alice\" },\n    { id: 2, name: \"Bob\" },\n    { id: 3, name: \"Charlie\" },\n  ]);\n  const predicate = \"id = 3\";\n  console.log(\"Selected:\", await table.query().where(predicate).toArray());\n  await table.delete(predicate);\n  const rows = (await table.query().toArray()).sort((a, b) => a.id - b.id);\n  console.log(\"Remaining:\", rows);\n";
+
+export const TsDeleteRowsTail = "} finally {\n  await rm(uri, { recursive: true, force: true });\n}\n";
+
+export const TsWriteWorkflowBody = "const lancedb = await import(\"@lancedb/lancedb\");\nconst { mkdtemp, rm } = await import(\"node:fs/promises\");\nconst { tmpdir } = await import(\"node:os\");\nconst { join } = await import(\"node:path\");\n\nconst uri = await mkdtemp(join(tmpdir(), \"lancedb-ingestion-\"));\ntry {\n  const db = await lancedb.connect(uri);\n  const table = await db.createTable(\"users\", [\n    { id: 1, name: \"Alice\", login_count: 10 },\n    { id: 2, name: \"Bob\", login_count: 20 },\n  ]);\n  await table.add([{ id: 3, name: \"Charlie\", login_count: 5 }]);\n  await table.update({ where: \"id = 2\", values: { name: \"Bobby\" } });\n  await table.update({\n    where: \"id = 2\",\n    valuesSql: { login_count: \"login_count + 1\" },\n  });\n  await table\n    .mergeInsert(\"id\")\n    .whenMatchedUpdateAll()\n    .whenNotMatchedInsertAll()\n    .execute([\n      { id: 3, name: \"Charlie\", login_count: 6 },\n      { id: 4, name: \"Dana\", login_count: 1 },\n    ]);\n  console.log(\n    \"Rows selected for deletion:\",\n    await table.query().where(\"id = 4\").toArray(),\n  );\n  await table.delete(\"id = 4\");\n  const rows = (await table.query().toArray()).sort((a, b) => a.id - b.id);\n  console.log(rows);\n";
+
+export const TsWriteWorkflowTail = "} finally {\n  await rm(uri, { recursive: true, force: true });\n}\n";
+
+export const RsWriteWorkflow = "use std::sync::Arc;\n\nuse arrow_array::{Int64Array, RecordBatch, RecordBatchIterator, RecordBatchReader, StringArray};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures::TryStreamExt;\nuse lancedb::query::{ExecutableQuery, QueryBase};\n\nfn make_users_reader(rows: &[(i64, &str, i64)]) -> Box<dyn RecordBatchReader + Send> {\n    let schema = Arc::new(Schema::new(vec![\n        Field::new(\"id\", DataType::Int64, false),\n        Field::new(\"name\", DataType::Utf8, false),\n        Field::new(\"login_count\", DataType::Int64, false),\n    ]));\n    let batch = RecordBatch::try_new(\n        schema.clone(),\n        vec![\n            Arc::new(Int64Array::from_iter_values(rows.iter().map(|r| r.0))),\n            Arc::new(StringArray::from_iter_values(rows.iter().map(|r| r.1))),\n            Arc::new(Int64Array::from_iter_values(rows.iter().map(|r| r.2))),\n        ],\n    )\n    .unwrap();\n    Box::new(RecordBatchIterator::new(\n        vec![Ok(batch)].into_iter(),\n        schema,\n    ))\n}\n\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n    let directory = tempfile::tempdir()?;\n    let db = lancedb::connect(directory.path().to_str().unwrap())\n        .execute()\n        .await?;\n    let table = db\n        .create_table(\n            \"users\",\n            make_users_reader(&[(1, \"Alice\", 10), (2, \"Bob\", 20)]),\n        )\n        .execute()\n        .await?;\n    table\n        .add(make_users_reader(&[(3, \"Charlie\", 5)]))\n        .execute()\n        .await?;\n    table\n        .update()\n        .only_if(\"id = 2\")\n        .column(\"name\", \"'Bobby'\")\n        .execute()\n        .await?;\n    table\n        .update()\n        .only_if(\"id = 2\")\n        .column(\"login_count\", \"login_count + 1\")\n        .execute()\n        .await?;\n    let mut merge = table.merge_insert(&[\"id\"]);\n    merge\n        .when_matched_update_all(None)\n        .when_not_matched_insert_all();\n    merge\n        .execute(make_users_reader(&[(3, \"Charlie\", 6), (4, \"Dana\", 1)]))\n        .await?;\n    let selected: Vec<RecordBatch> = table\n        .query()\n        .only_if(\"id = 4\")\n        .execute()\n        .await?\n        .try_collect()\n        .await?;\n    println!(\"Rows selected for deletion: {selected:?}\");\n    table.delete(\"id = 4\").await?;\n    let rows: Vec<RecordBatch> = table.query().execute().await?.try_collect().await?;\n    println!(\"Remaining rows: {rows:?}\");\n    assert_eq!(table.count_rows(None).await?, 3);\n    assert_eq!(\n        table\n            .count_rows(Some(\n                \"id = 2 AND name = 'Bobby' AND login_count = 21\".into()\n            ))\n            .await?,\n        1\n    );\n    assert_eq!(\n        table\n            .count_rows(Some(\"id = 3 AND login_count = 6\".into()))\n            .await?,\n        1\n    );\n    Ok(())\n}\n";
+
diff --git a/docs/web/snippets/ingestion_progress.mdx b/docs/web/snippets/ingestion_progress.mdx
new file mode 100644
index 000000000..d910468f6
--- /dev/null
+++ b/docs/web/snippets/ingestion_progress.mdx
@@ -0,0 +1,4 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const RsExample = "use std::sync::Arc;\n\nuse arrow_array::{Int64Array, RecordBatch, RecordBatchIterator, RecordBatchReader, StringArray};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures::TryStreamExt;\nuse lancedb::query::ExecutableQuery;\n\nfn make_users_reader(rows: &[(i64, &str)]) -> Box<dyn RecordBatchReader + Send> {\n    let schema = Arc::new(Schema::new(vec![\n        Field::new(\"id\", DataType::Int64, false),\n        Field::new(\"name\", DataType::Utf8, false),\n    ]));\n    let batch = RecordBatch::try_new(\n        schema.clone(),\n        vec![\n            Arc::new(Int64Array::from_iter_values(rows.iter().map(|r| r.0))),\n            Arc::new(StringArray::from_iter_values(rows.iter().map(|r| r.1))),\n        ],\n    )\n    .unwrap();\n    Box::new(RecordBatchIterator::new(\n        vec![Ok(batch)].into_iter(),\n        schema,\n    ))\n}\n\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n    let directory = tempfile::tempdir()?;\n    let db = lancedb::connect(directory.path().to_str().unwrap())\n        .execute()\n        .await?;\n    let table = db\n        .create_table(\"users\", make_users_reader(&[(1, \"Alice\")]))\n        .execute()\n        .await?;\n    table\n        .add(make_users_reader(&[(2, \"Bob\"), (3, \"Charlie\")]))\n        .progress(|progress| println!(\"Rows written: {}\", progress.output_rows()))\n        .execute()\n        .await?;\n    let rows: Vec<RecordBatch> = table.query().execute().await?.try_collect().await?;\n    println!(\"After insertion: {rows:?}\");\n    assert_eq!(table.count_rows(None).await?, 3);\n    assert_eq!(\n        table\n            .count_rows(Some(\"id = 1 AND name = 'Alice'\".into()))\n            .await?,\n        1\n    );\n    assert_eq!(\n        table\n            .count_rows(Some(\"id = 2 AND name = 'Bob'\".into()))\n            .await?,\n        1\n    );\n    assert_eq!(table.count_rows(Some(\"id = 3\".into())).await?, 1);\n    Ok(())\n}\n";
+
diff --git a/docs/web/snippets/integrations.mdx b/docs/web/snippets/integrations.mdx
new file mode 100644
index 000000000..67bb654cf
--- /dev/null
+++ b/docs/web/snippets/integrations.mdx
@@ -0,0 +1,182 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyEmbeddingAwsUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nimport pandas as pd\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\nmodel = get_registry().get(\"bedrock-text\").create()\n\nclass TextModel(LanceModel):\n    text: str = model.SourceField()\n    vector: Vector(model.ndims()) = model.VectorField()\n\ndf = pd.DataFrame({\"text\": [\"hello world\", \"goodbye world\"]})\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"bedrock-demo\"))\ntbl = db.create_table(\"test\", schema=TextModel, mode=\"overwrite\")\n\ntbl.add(df)\nrs = tbl.search(\"hello\").limit(1).to_pandas()\nprint(rs.head())\n";
+
+export const PyEmbeddingCohereUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import EmbeddingFunctionRegistry\nfrom lancedb.pydantic import LanceModel, Vector\n\ncohere = (\n    EmbeddingFunctionRegistry.get_instance()\n    .get(\"cohere\")\n    .create(name=\"embed-multilingual-v2.0\")\n)\n\nclass TextModel(LanceModel):\n    text: str = cohere.SourceField()\n    vector: Vector(cohere.ndims()) = cohere.VectorField()\n\ndata = [{\"text\": \"hello world\"}, {\"text\": \"goodbye world\"}]\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"cohere-demo\"))\ntbl = db.create_table(\"test\", schema=TextModel, mode=\"overwrite\")\ntbl.add(data)\n";
+
+export const PyEmbeddingColpaliSetup = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nimport pandas as pd\nimport requests\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, MultiVector\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"colpali-demo\"))\nfunc = get_registry().get(\"colpali\").create()\n\nclass Images(LanceModel):\n    label: str\n    image_uri: str = func.SourceField()\n    image_bytes: bytes = func.SourceField()\n    vector: MultiVector(func.ndims()) = func.VectorField()\n    vec_from_bytes: MultiVector(func.ndims()) = func.VectorField()\n\ntable = db.create_table(\"images\", schema=Images)\nlabels = [\"cat\", \"dog\", \"horse\"]\nuris = [\n    \"http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg\",\n    \"http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg\",\n    \"http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg\",\n]\nimage_bytes = [requests.get(uri).content for uri in uris]\ntable.add(\n    pd.DataFrame({\"label\": labels, \"image_uri\": uris, \"image_bytes\": image_bytes})\n)\n";
+
+export const PyEmbeddingColpaliTextSearch = "actual = (\n    table.search(\"a furry pet\", vector_column_name=\"vector\")\n    .limit(1)\n    .to_pydantic(Images)[0]\n)\nprint(actual.label)\n";
+
+export const PyEmbeddingGeminiUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nimport pandas as pd\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\nmodel = get_registry().get(\"gemini-text\").create()\n\nclass TextModel(LanceModel):\n    text: str = model.SourceField()\n    vector: Vector(model.ndims()) = model.VectorField()\n\ndf = pd.DataFrame({\"text\": [\"hello world\", \"goodbye world\"]})\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"gemini-demo\"))\ntbl = db.create_table(\"test\", schema=TextModel, mode=\"overwrite\")\n\ntbl.add(df)\nrs = tbl.search(\"hello\").limit(1).to_pandas()\nprint(rs.head())\n";
+
+export const PyEmbeddingHuggingfaceUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nimport pandas as pd\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"huggingface-demo\"))\nmodel = get_registry().get(\"huggingface\").create(name=\"facebook/bart-base\")\n\nclass Words(LanceModel):\n    text: str = model.SourceField()\n    vector: Vector(model.ndims()) = model.VectorField()\n\ndf = pd.DataFrame({\"text\": [\"hi hello sayonara\", \"goodbye world\"]})\ntable = db.create_table(\"greets\", schema=Words)\ntable.add(df)\nquery = \"old greeting\"\nactual = table.search(query).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n";
+
+export const PyEmbeddingIbmUsage = "import os\nimport tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import EmbeddingFunctionRegistry\nfrom lancedb.pydantic import LanceModel, Vector\n\nwatsonx_embed = (\n    EmbeddingFunctionRegistry.get_instance()\n    .get(\"watsonx\")\n    .create(\n        name=\"ibm/slate-125m-english-rtrvr\",\n        api_key=os.environ.get(\"WATSONX_API_KEY\"),\n        project_id=os.environ.get(\"WATSONX_PROJECT_ID\"),\n    )\n)\n\nclass TextModel(LanceModel):\n    text: str = watsonx_embed.SourceField()\n    vector: Vector(watsonx_embed.ndims()) = watsonx_embed.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"watsonx-demo\"))\ntbl = db.create_table(\"watsonx_test\", schema=TextModel, mode=\"overwrite\")\ntbl.add(data)\n\nrs = tbl.search(\"hello\").limit(1).to_pandas()\nprint(rs.head())\n";
+
+export const PyEmbeddingImagebindAudioSearch = "query_audio = \"./assets/car_audio2.wav\"\nactual = table.search(query_audio).limit(1).to_pydantic(ImageBindModel)[0]\nprint(actual.text == \"car\")\n";
+
+export const PyEmbeddingImagebindImageSearch = "query_image = \"./assets/dog_image2.jpg\"\nactual = table.search(query_image).limit(1).to_pydantic(ImageBindModel)[0]\nprint(actual.text == \"dog\")\n";
+
+export const PyEmbeddingImagebindSetup = "import lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\ndb = lancedb.connect(\"/tmp/imagebind-db\")\nfunc = get_registry().get(\"imagebind\").create()\n\nclass ImageBindModel(LanceModel):\n    text: str\n    image_uri: str = func.SourceField()\n    audio_path: str\n    vector: Vector(func.ndims()) = func.VectorField()\n\ntext_list = [\"A dog.\", \"A car\", \"A bird\"]\nimage_paths = [\n    \"./assets/dog_image.jpg\",\n    \"./assets/car_image.jpg\",\n    \"./assets/bird_image.jpg\",\n]\naudio_paths = [\n    \"./assets/dog_audio.wav\",\n    \"./assets/car_audio.wav\",\n    \"./assets/bird_audio.wav\",\n]\n\ninputs = [\n    {\"text\": a, \"audio_path\": b, \"image_uri\": c}\n    for a, b, c in zip(text_list, audio_paths, image_paths)\n]\n\ntable = db.create_table(\"img_bind\", schema=ImageBindModel)\ntable.add(inputs)\n";
+
+export const PyEmbeddingImagebindTextSearch = "query = \"an animal which flies and tweets\"\nactual = table.search(query).limit(1).to_pydantic(ImageBindModel)[0]\nprint(actual.text == \"bird\")\n";
+
+export const PyEmbeddingInstructorUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\ninstructor = (\n    get_registry()\n    .get(\"instructor\")\n    .create(\n        source_instruction=\"represent the document for retrieval\",\n        query_instruction=\"represent the document for retrieving the most similar documents\",\n    )\n)\n\nclass Schema(LanceModel):\n    vector: Vector(instructor.ndims()) = instructor.VectorField()\n    text: str = instructor.SourceField()\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"instructor-demo\"))\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\n\ntexts = [\n    {\n        \"text\": \"Capitalism has been dominant in the Western world since the end of feudalism.\"\n    },\n    {\n        \"text\": \"The disparate impact theory is especially controversial under the Fair Housing Act.\"\n    },\n    {\n        \"text\": \"Disparate impact in United States labor law refers to practices in employment.\"\n    },\n]\n\ntbl.add(texts)\n";
+
+export const PyEmbeddingJinaMultimodal = "import os\nimport tempfile\nfrom pathlib import Path\n\nimport lancedb\nimport pandas as pd\nimport requests\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\nos.environ[\"JINA_API_KEY\"] = os.environ.get(\"JINA_API_KEY\", \"jina_*\")\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"jina-images\"))\nfunc = get_registry().get(\"jina\").create()\n\nclass Images(LanceModel):\n    label: str\n    image_uri: str = func.SourceField()\n    image_bytes: bytes = func.SourceField()\n    vector: Vector(func.ndims()) = func.VectorField()\n    vec_from_bytes: Vector(func.ndims()) = func.VectorField()\n\ntable = db.create_table(\"images\", schema=Images)\nlabels = [\"cat\", \"cat\", \"dog\", \"dog\", \"horse\", \"horse\"]\nuris = [\n    \"http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg\",\n    \"http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg\",\n    \"http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg\",\n    \"http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg\",\n    \"http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg\",\n    \"http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg\",\n]\nimage_bytes = [requests.get(uri).content for uri in uris]\ntable.add(\n    pd.DataFrame({\"label\": labels, \"image_uri\": uris, \"image_bytes\": image_bytes})\n)\n";
+
+export const PyEmbeddingJinaText = "import os\nimport tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import EmbeddingFunctionRegistry\nfrom lancedb.pydantic import LanceModel, Vector\n\nos.environ[\"JINA_API_KEY\"] = os.environ[\"JINA_API_KEY\"]\n\njina_embed = (\n    EmbeddingFunctionRegistry.get_instance()\n    .get(\"jina\")\n    .create(name=\"jina-embeddings-v2-base-en\")\n)\n\nclass TextModel(LanceModel):\n    text: str = jina_embed.SourceField()\n    vector: Vector(jina_embed.ndims()) = jina_embed.VectorField()\n\ndata = [{\"text\": \"hello world\"}, {\"text\": \"goodbye world\"}]\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"jina-text\"))\ntbl = db.create_table(\"test\", schema=TextModel, mode=\"overwrite\")\n\ntbl.add(data)\n";
+
+export const PyEmbeddingOllamaUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"ollama-demo\"))\nfunc = get_registry().get(\"ollama\").create(name=\"nomic-embed-text\")\n\nclass Words(LanceModel):\n    text: str = func.SourceField()\n    vector: Vector(func.ndims()) = func.VectorField()\n\ntable = db.create_table(\"words\", schema=Words, mode=\"overwrite\")\ntable.add(\n    [\n        {\"text\": \"hello world\"},\n        {\"text\": \"goodbye world\"},\n    ]\n)\n\nquery = \"greetings\"\nactual = table.search(query).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n";
+
+export const PyEmbeddingOpenaiBasic = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\ndb_path = Path(tempfile.mkdtemp()) / \"openai-embeddings\"\ndb = lancedb.connect(str(db_path))\nfunc = get_registry().get(\"openai\").create(name=\"text-embedding-ada-002\")\n\nclass Words(LanceModel):\n    text: str = func.SourceField()\n    vector: Vector(func.ndims()) = func.VectorField()\n\ntable = db.create_table(\"words\", schema=Words, mode=\"overwrite\")\ntable.add(\n    [\n        {\"text\": \"hello world\"},\n        {\"text\": \"goodbye world\"},\n    ]\n)\n\nquery = \"greetings\"\nactual = table.search(query).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n";
+
+export const PyEmbeddingOpenclipImageSearch = "import io\n\nfrom PIL import Image\n\nquery_image_uri = \"http://farm1.staticflickr.com/200/467715466_ed4a31801f_z.jpg\"\nimage_bytes = requests.get(query_image_uri).content\nquery_image = Image.open(io.BytesIO(image_bytes))\nactual = table.search(query_image).limit(1).to_pydantic(Images)[0]\nprint(actual.label == \"dog\")\n\nother = (\n    table.search(query_image, vector_column_name=\"vec_from_bytes\")\n    .limit(1)\n    .to_pydantic(Images)[0]\n)\nprint(other.label)\n";
+
+export const PyEmbeddingOpenclipSetup = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nimport pandas as pd\nimport requests\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"openclip-demo\"))\nfunc = get_registry().get(\"open-clip\").create()\n\nclass Images(LanceModel):\n    label: str\n    image_uri: str = func.SourceField()\n    image_bytes: bytes = func.SourceField()\n    vector: Vector(func.ndims()) = func.VectorField()\n    vec_from_bytes: Vector(func.ndims()) = func.VectorField()\n\ntable = db.create_table(\"images\", schema=Images)\nlabels = [\"cat\", \"cat\", \"dog\", \"dog\", \"horse\", \"horse\"]\nuris = [\n    \"http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg\",\n    \"http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg\",\n    \"http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg\",\n    \"http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg\",\n    \"http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg\",\n    \"http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg\",\n]\nimage_bytes = [requests.get(uri).content for uri in uris]\ntable.add(\n    pd.DataFrame({\"label\": labels, \"image_uri\": uris, \"image_bytes\": image_bytes})\n)\n";
+
+export const PyEmbeddingOpenclipTextSearch = "actual = table.search(\"man's best friend\").limit(1).to_pydantic(Images)[0]\nprint(actual.label)\n\nfrombytes = (\n    table.search(\"man's best friend\", vector_column_name=\"vec_from_bytes\")\n    .limit(1)\n    .to_pydantic(Images)[0]\n)\nprint(frombytes.label)\n";
+
+export const PyEmbeddingSentenceTransformersBaai = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"sentence-transformers\"))\nmodel = (\n    get_registry()\n    .get(\"sentence-transformers\")\n    .create(name=\"BAAI/bge-small-en-v1.5\", device=\"cpu\")\n)\n\nclass Words(LanceModel):\n    text: str = model.SourceField()\n    vector: Vector(model.ndims()) = model.VectorField()\n\ntable = db.create_table(\"words\", schema=Words)\ntable.add(\n    [\n        {\"text\": \"hello world\"},\n        {\"text\": \"goodbye world\"},\n    ]\n)\n\nquery = \"greetings\"\nactual = table.search(query).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n";
+
+export const PyEmbeddingVoyageaiMultimodal = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import EmbeddingFunctionRegistry\nfrom lancedb.pydantic import LanceModel, Vector\n\n# Create multimodal embedding function with custom dimension\nvoyageai = (\n    EmbeddingFunctionRegistry.get_instance()\n    .get(\"voyageai\")\n    .create(name=\"voyage-multimodal-3.5\", output_dimension=512)\n)\n\nclass ImageModel(LanceModel):\n    image_uri: str = voyageai.SourceField()\n    vector: Vector(voyageai.ndims()) = voyageai.VectorField()\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"voyageai-multimodal\"))\ntbl = db.create_table(\"images\", schema=ImageModel, mode=\"overwrite\")\n\n# Add images using URLs\ntbl.add(\n    [\n        {\"image_uri\": \"https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/300px-PNG_transparency_demonstration_1.png\"},\n    ]\n)\n\n# Search with text query\nresults = tbl.search(\"dice\").limit(1).to_list()\nprint(results)\n";
+
+export const PyEmbeddingVoyageaiUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import EmbeddingFunctionRegistry\nfrom lancedb.pydantic import LanceModel, Vector\n\nvoyageai = (\n    EmbeddingFunctionRegistry.get_instance().get(\"voyageai\").create(name=\"voyage-3\")\n)\n\nclass TextModel(LanceModel):\n    text: str = voyageai.SourceField()\n    vector: Vector(voyageai.ndims()) = voyageai.VectorField()\n\ndata = [{\"text\": \"hello world\"}, {\"text\": \"goodbye world\"}]\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"voyageai-demo\"))\ntbl = db.create_table(\"test\", schema=TextModel, mode=\"overwrite\")\n\ntbl.add(data)\n";
+
+export const PyFrameworksAgnoAgent = "agent = Agent(\n    model=OpenAIResponses(id=\"gpt-5-mini\"),\n    knowledge=knowledge,\n    search_knowledge=True,\n    instructions=\"Search the transcript and answer only from retrieved context.\",\n    markdown=True,\n)\n";
+
+export const PyFrameworksAgnoCliChat = "agent.print_response(\n    \"Summarize the loaded video transcript in 5 concise bullet points.\",\n    stream=True,\n)\nwhile True:\n    question = input(\"You: \").strip()\n    if question.lower() in {\"exit\", \"quit\", \"bye\"}:\n        break\n    agent.print_response(question, stream=True)\n";
+
+export const PyFrameworksAgnoIngestYoutube = "youtube_url = \"https://www.youtube.com/watch?v=wl6mFyXoxos\"\nvideo_id = extract_video_id(youtube_url)\nytt = YouTubeTranscriptApi()\ntranscript_segments = ytt.fetch(video_id, languages=[\"en\", \"en-US\"]).to_raw_data()\ntranscript_text = \" \".join(segment[\"text\"] for segment in transcript_segments)\n\nknowledge.insert(\n    name=f\"YouTube Transcript ({video_id})\",\n    text_content=transcript_text,\n    metadata={\"source\": \"youtube\", \"video_id\": video_id, \"video_url\": youtube_url},\n)\n";
+
+export const PyFrameworksAgnoSetup = "import os\nimport re\n\nfrom agno.agent import Agent\nfrom agno.knowledge.embedder.openai import OpenAIEmbedder\nfrom agno.knowledge.knowledge import Knowledge\nfrom agno.models.openai import OpenAIResponses\nfrom agno.vectordb.lancedb import LanceDb, SearchType\nfrom youtube_transcript_api import YouTubeTranscriptApi\n\nif \"OPENAI_API_KEY\" not in os.environ:\n    os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n\ndef extract_video_id(youtube_url: str) -> str:\n    match = re.search(r\"(?<=v=)[\\w-]+\", youtube_url) or re.search(\n        r\"(?<=be/)[\\w-]+\", youtube_url\n    )\n    if not match:\n        raise ValueError(\"Could not parse YouTube video ID from URL\")\n    return match.group(0)\n\nknowledge = Knowledge(\n    vector_db=LanceDb(\n        uri=\"./tmp/lancedb\",\n        table_name=\"youtube_transcripts\",\n        search_type=SearchType.hybrid,\n        embedder=OpenAIEmbedder(id=\"text-embedding-3-small\"),\n    ),\n)\n";
+
+export const PyFrameworksLangchainAddImages = "image_uris = [\"./assets/image-1.png\", \"./assets/image-2.png\"]\nvector_store.add_images(uris=image_uris)\n# here image_uris are local fs paths to the images.\n";
+
+export const PyFrameworksLangchainAddTexts = "vector_store.add_texts(texts=[\"test_123\"], metadatas=[{\"source\": \"wiki\"}])\n\n# Additionally, to explore the table you can load it into a df or save it in a csv file:\n\ntbl = vector_store.get_table()\nprint(\"tbl:\", tbl)\npd_df = tbl.to_pandas()\npd_df.to_csv(\"docsearch.csv\", index=False)\n\n# you can also create a new vector store object using an older connection object:\nvector_store = LanceDB(connection=tbl, embedding=embeddings)\n";
+
+export const PyFrameworksLangchainCreateIndex = "# for creating vector index\nvector_store.create_index(vector_col=\"vector\", metric=\"cosine\")\n\n# for creating scalar index(for non-vector columns)\nvector_store.create_index(col_name=\"text\")\n";
+
+export const PyFrameworksLangchainMaxMarginalRelevance = "result = docsearch.max_marginal_relevance_search(query=\"text\")\nresult_texts = [doc.page_content for doc in result]\nprint(result_texts)\n\n# search by vector :\nresult = docsearch.max_marginal_relevance_search_by_vector(\n    embeddings.embed_query(\"text\")\n)\nresult_texts = [doc.page_content for doc in result]\nprint(result_texts)\n";
+
+export const PyFrameworksLangchainQuickStart = "import os\n\nfrom langchain.document_loaders import TextLoader\nfrom langchain.vectorstores import LanceDB\nfrom langchain_openai import OpenAIEmbeddings\nfrom langchain_text_splitters import CharacterTextSplitter\n\nos.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n\nloader = TextLoader(\n    \"../../modules/state_of_the_union.txt\"\n)  # Replace with your data path\ndocuments = loader.load()\n\ndocuments = CharacterTextSplitter().split_documents(documents)\nembeddings = OpenAIEmbeddings()\n\ndocsearch = LanceDB.from_documents(documents, embeddings)\nquery = \"What did the president say about Ketanji Brown Jackson\"\ndocs = docsearch.similarity_search(query)\nprint(docs[0].page_content)\n";
+
+export const PyFrameworksLangchainSimilaritySearch = "docs = docsearch.similarity_search(query)\nprint(docs[0].page_content)\n";
+
+export const PyFrameworksLangchainSimilaritySearchByVector = "docs = docsearch.similarity_search_by_vector(query)\nprint(docs[0].page_content)\n";
+
+export const PyFrameworksLangchainSimilaritySearchByVectorWithScores = "query_embedding = embeddings.embed_query(\"text\")\ndocs = docsearch.similarity_search_by_vector_with_relevance_scores(query_embedding)\nprint(\"relevance score - \", docs[0][1])\nprint(\"text- \", docs[0][0].page_content[:1000])\n";
+
+export const PyFrameworksLangchainSimilaritySearchWithScores = "docs = docsearch.similarity_search_with_relevance_scores(query)\nprint(\"relevance score - \", docs[0][1])\nprint(\"text- \", docs[0][0].page_content[:1000])\n";
+
+export const PyFrameworksLangchainVectorStoreConfig = "db_url = \"db://lang_test\"  # url of db you created\napi_key = \"xxxxx\"  # your API key\nregion = \"us-east-1-dev\"  # your selected region\n\nvector_store = LanceDB(\n    uri=db_url,\n    api_key=api_key,  # (dont include for local API)\n    region=region,  # (dont include for local API)\n    embedding=embeddings,\n    table_name=\"langchain_test\",  # Optional\n)\n";
+
+export const PyFrameworksLerobotFilterFrames = "frame_rows = (\n    frames.search()\n    .where(\"episode_index = 0 AND frame_index < 10\", prefilter=True)\n    .select([\"episode_index\", \"frame_index\", \"timestamp\", \"action\"])\n    .limit(10)\n    .to_list()\n)\n\nfor row in frame_rows:\n    print(row[\"episode_index\"], row[\"frame_index\"], row[\"timestamp\"])\n";
+
+export const PyFrameworksLerobotLoad = "from lerobot.datasets import LeRobotDataset\n\n# Hub dataset repo: storage_format in meta/info.json routes to the Lance reader\ndataset = LeRobotDataset(\"lance-format/pusht-lance\")\n\n# HF Storage Bucket, read in place, nothing downloaded\ndataset = LeRobotDataset(\"lance-format/lerobot-tests/pusht-lance\", repo_type=\"bucket\")\n\nsample = dataset[100]\nprint(sample[\"observation.state\"].shape)\nprint(sample[\"observation.image\"].shape)\n";
+
+export const PyFrameworksLerobotOpenLanceTables = "import lancedb\n\ndb = lancedb.connect(\"hf://datasets/lance-format/pusht-lance\")\nframes = db.open_table(\"frames\")\nvideos = db.open_table(\"videos\")\n\nprint(frames.count_rows(), videos.count_rows())\nprint(frames.schema)\n";
+
+export const PyFrameworksLerobotTrainReady = "import torch\n\ndataset = LeRobotDataset(\n    \"lance-format/pusht-lance\",\n    delta_timestamps={\"observation.image\": [-0.2, -0.1, 0.0]},\n    return_uint8=True,\n)\nloader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True)\nbatch = next(iter(loader))\nprint(batch[\"observation.image\"].shape)  # [32, 3, C, H, W]\n";
+
+export const PyFrameworksLlamaindexAddReranker = "from lancedb.rerankers import ColbertReranker\n\nreranker = ColbertReranker()\nvector_store._add_reranker(reranker)\n";
+
+export const PyFrameworksLlamaindexFiltering = "from llama_index.core.vector_stores import (\n    FilterCondition,\n    FilterOperator,\n    MetadataFilter,\n    MetadataFilters,\n)\n\nquery_filters = MetadataFilters(\n    filters=[\n        MetadataFilter(\n            key=\"creation_date\", operator=FilterOperator.EQ, value=\"2024-05-23\"\n        ),\n        MetadataFilter(key=\"file_size\", value=75040, operator=FilterOperator.GT),\n    ],\n    condition=FilterCondition.AND,\n)\n";
+
+export const PyFrameworksLlamaindexHybridSearch = "from lancedb.rerankers import ColbertReranker\n\nreranker = ColbertReranker()\nvector_store._add_reranker(reranker)\n\nquery_engine = index.as_query_engine(\n    filters=query_filters,\n    vector_store_kwargs={\n        \"query_type\": \"hybrid\",\n    },\n)\n\nresponse = query_engine.query(\"How much did Viaweb charge per month?\")\n";
+
+export const PyFrameworksLlamaindexQuickStart = "import logging\nimport sys\nimport textwrap\n\nimport openai\n\n# Uncomment to see debug logs\n# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\nfrom llama_index.core import (\n    Document,\n    SimpleDirectoryReader,\n    StorageContext,\n    VectorStoreIndex,\n)\nfrom llama_index.vector_stores.lancedb import LanceDBVectorStore\n\nopenai.api_key = \"sk-...\"\n\ndocuments = SimpleDirectoryReader(\"./data/your-data-dir/\").load_data()\nprint(\"Document ID:\", documents[0].doc_id, \"Document Hash:\", documents[0].hash)\n\n## For LanceDB Enterprise :\n# vector_store = LanceDBVectorStore(\n#     uri=\"db://db_name\", # your remote DB URI\n#     api_key=\"sk_..\", # lancedb enterprise api key\n#     region=\"your-region\" # the region you configured\n#     host_override=\"https://your-host.com\" # if you have a custom host, otherwise omit this\n# )\n\nvector_store = LanceDBVectorStore(\n    uri=\"./lancedb\", mode=\"overwrite\", query_type=\"vector\"\n)\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\n\nindex = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\nlance_filter = \"metadata.file_name = 'paul_graham_essay.txt' \"\nretriever = index.as_retriever(vector_store_kwargs={\"where\": lance_filter})\nresponse = retriever.retrieve(\"What did the author do growing up?\")\n";
+
+export const PyFrameworksPydanticBaseExample = "table = db.create_table(\"docs\", schema=LanceDocs, mode=\"overwrite\")\ntable.add(\n    [\n        {\"text\": \"hello world\", \"vector\": [1.0, 0.0]},\n        {\"text\": \"goodbye world\", \"vector\": [0.0, 1.0]},\n    ]\n)\nresults = table.search([1.0, 0.0]).limit(1).to_pydantic(LanceDocs)\nprint(results[0].text)\n# hello world\n";
+
+export const PyFrameworksPydanticBaseModel = "class LanceDocs(LanceModel):\n    text: str\n    vector: Vector(2)\n";
+
+export const PyFrameworksPydanticImports = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.pydantic import LanceModel, Vector\n";
+
+export const PyFrameworksPydanticSetUrl = "db = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"pydantic-docs\"))\n";
+
+export const PyFrameworksPydanticTypeConversion = "from typing import List, Optional\n\nimport pyarrow as pa\nimport pydantic\nfrom lancedb.pydantic import Vector, pydantic_to_schema\n\nclass FooModel(pydantic.BaseModel):\n    id: int\n    s: str\n    vec: Vector(1536)  # fixed_size_list<item: float32>[1536]\n    li: List[int]\n\nschema = pydantic_to_schema(FooModel)\nassert schema == pa.schema(\n    [\n        pa.field(\"id\", pa.int64(), False),\n        pa.field(\"s\", pa.utf8(), False),\n        pa.field(\"vec\", pa.list_(pa.float32(), 1536)),\n        pa.field(\"li\", pa.list_(pa.int64()), False),\n    ]\n)\n";
+
+export const PyFrameworksPydanticVectorField = "import pyarrow as pa\nimport pydantic\nfrom lancedb.pydantic import Vector, pydantic_to_schema\n\nclass MyModel(pydantic.BaseModel):\n    id: int\n    url: str\n    embeddings: Vector(768)\n\nschema = pydantic_to_schema(MyModel)\nassert schema == pa.schema(\n    [\n        pa.field(\"id\", pa.int64(), False),\n        pa.field(\"url\", pa.utf8(), False),\n        pa.field(\"embeddings\", pa.list_(pa.float32(), 768)),\n    ]\n)\n";
+
+export const PyFrameworksStableWorldmodelCollectLance = "import stable_worldmodel as swm\n\nworld = swm.World(\"swm/PushT-v1\", num_envs=8)\nworld.set_policy(your_expert_policy)\nworld.collect(\"data/pusht_demo.lance\", episodes=100, seed=0)\n";
+
+export const PyFrameworksStableWorldmodelConvert = "swm.data.convert(\n    \"data/pusht_demo.lance\",\n    \"data/pusht_video\",\n    dest_format=\"video\",\n    fps=30,\n)\n";
+
+export const PyFrameworksStableWorldmodelEvaluate = "from stable_worldmodel.policy import PlanConfig, WorldModelPolicy\nfrom stable_worldmodel.solver import CEMSolver\n\nsolver = CEMSolver(model=world_model, num_samples=300)\npolicy = WorldModelPolicy(solver=solver, config=PlanConfig(horizon=10))\n\nworld.set_policy(policy)\nresults = world.evaluate(episodes=50)\nprint(f\"Success Rate: {results['success_rate']:.1f}%\")\n";
+
+export const PyFrameworksStableWorldmodelLoadLance = "dataset = swm.data.load_dataset(\"data/pusht_demo.lance\", num_steps=16)\n\nbatch = dataset[0]\nprint(batch.keys())\n";
+
+export const PyPlatformsDltAdapterImport = "from dlt.destinations.adapters import lancedb_adapter\n";
+
+export const PyPlatformsDltAdapterUsage = "load_info = pipeline.run(\n    lancedb_adapter(\n        movies_source,\n        embed=\"Title\",\n    )\n)\n";
+
+export const PyPlatformsDltPipeline = "# Import necessary modules\nimport dlt\nfrom rest_api import rest_api_source\n\n# Configure the REST API source\nmovies_source = rest_api_source(\n    {\n        \"client\": {\n            \"base_url\": \"https://www.omdbapi.com/\",\n            \"auth\": {  # authentication strategy for the OMDb API\n                \"type\": \"api_key\",\n                \"name\": \"apikey\",\n                \"api_key\": dlt.secrets[\n                    \"sources.rest_api.api_token\"\n                ],  # read API credentials directly from secrets.toml\n                \"location\": \"query\",\n            },\n            \"paginator\": {  # pagination strategy for the OMDb API\n                \"type\": \"page_number\",\n                \"base_page\": 1,\n                \"total_path\": \"totalResults\",\n                \"maximum_page\": 5,\n            },\n        },\n        \"resources\": [  # list of API endpoints to request\n            {\n                \"name\": \"movie_search\",\n                \"endpoint\": {\n                    \"path\": \"/\",\n                    \"params\": {\n                        \"s\": \"godzilla\",\n                        \"type\": \"movie\",\n                    },\n                },\n            }\n        ],\n    }\n)\n\nif __name__ == \"__main__\":\n    # Create a pipeline object\n    pipeline = dlt.pipeline(\n        pipeline_name=\"movies_pipeline\",\n        destination=\"lancedb\",  # this tells dlt to load the data into LanceDB\n        dataset_name=\"movies_data_pipeline\",\n    )\n\n    # Run the pipeline\n    load_info = pipeline.run(movies_source)\n\n    # pretty print the information on data that was loaded\n    print(load_info)\n";
+
+export const PyPlatformsDuckdbCreateTable = "import lancedb\n\ndb = lancedb.connect(\"data/sample-lancedb\")\ndata = [\n    {\"vector\": [3.1, 4.1], \"item\": \"foo\", \"price\": 10.0},\n    {\"vector\": [5.9, 26.5], \"item\": \"bar\", \"price\": 20.0},\n]\ntable = db.create_table(\"pd_table\", data=data)\n";
+
+export const PyPlatformsDuckdbMeanPrice = "duckdb.query(\"SELECT mean(price) FROM arrow_table\")\n";
+
+export const PyPlatformsDuckdbQueryTable = "import duckdb\n\narrow_table = table.to_lance()\n\nduckdb.query(\"SELECT * FROM arrow_table\")\n";
+
+export const PyPlatformsPandasAsyncExample = "async def run_pandas_async_example() -> pd.DataFrame:\n    async_db = await lancedb.connect_async(\n        str(Path(tempfile.mkdtemp()) / \"pandas-async\")\n    )\n    async_df = pd.DataFrame(\n        [\n            {\"id\": \"10\", \"text\": \"sage\", \"vector\": [0.6, 0.4, 0.8]},\n            {\"id\": \"11\", \"text\": \"bard\", \"vector\": [0.2, 0.7, 0.3]},\n        ]\n    )\n    async_table = await async_db.create_table(\n        \"creatures_async\", data=async_df, mode=\"overwrite\"\n    )\n    # An async search returns its query builder from an await\n    query = await async_table.search([0.6, 0.4, 0.8])\n    async_results = await query.select([\"text\", \"_distance\"]).limit(1).to_pandas()\n    print(async_results)\n    return async_results\n\nasync_results = asyncio.run(run_pandas_async_example())\n";
+
+export const PyPlatformsPandasCreateTable = "pandas_df = pd.DataFrame(\n    [\n        {\"id\": \"1\", \"text\": \"dragon\", \"vector\": [0.9, 0.1, 0.3]},\n        {\"id\": \"2\", \"text\": \"griffin\", \"vector\": [0.4, 0.5, 0.2]},\n        {\"id\": \"3\", \"text\": \"phoenix\", \"vector\": [0.7, 0.3, 0.6]},\n    ]\n)\npandas_db = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"pandas-demo\"))\npandas_table = pandas_db.create_table(\"creatures\", data=pandas_df, mode=\"overwrite\")\n";
+
+export const PyPlatformsPandasImports = "import asyncio\nimport tempfile\nfrom pathlib import Path\n\nimport lancedb\nimport pandas as pd\n";
+
+export const PyPlatformsPandasVectorSearch = "pandas_results = (\n    pandas_table.search([0.9, 0.1, 0.3])\n    .select([\"text\", \"_distance\"])\n    .limit(1)\n    .to_pandas()\n)\nprint(pandas_results)\n";
+
+export const PyPlatformsPolarsCreateTable = "birds = pl.DataFrame(\n    {\n        \"text\": [\"phoenix\", \"sparrow\"],\n        \"vector\": [\n            [0.1, 0.2, 0.3],\n            [0.8, 0.6, 0.5],\n        ],\n    }\n)\npolars_db = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"polars-demo\"))\npolars_table = polars_db.create_table(\n    \"birds\", data=birds.to_arrow(), mode=\"overwrite\"\n)\n";
+
+export const PyPlatformsPolarsImports = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nimport polars as pl\nfrom lancedb.pydantic import LanceModel, Vector\n";
+
+export const PyPlatformsPolarsLazyframe = "lazy_frame = polars_table.to_polars().lazy()\nprint(lazy_frame.select([\"text\"]).collect())\n";
+
+export const PyPlatformsPolarsPydantic = "class BirdModel(LanceModel):\n    text: str\n    vector: Vector(3)\n\nschema_table = polars_db.create_table(\n    \"birds_schema\", schema=BirdModel, mode=\"overwrite\"\n)\nschema_table.add(birds.to_dicts())\n";
+
+export const PyPlatformsPolarsVectorSearch = "polars_results = (\n    polars_table.search([0.1, 0.2, 0.3])\n    .select([\"text\", \"_distance\"])\n    .limit(1)\n    .to_polars()\n)\nprint(polars_results)\n";
+
+export const PyPlatformsVoxel51BackendFlag = "import fiftyone.brain as fob\n\n# Re-run similarity creation using the LanceDB backend explicitly\nfob.compute_similarity(\n    dataset,\n    model=\"clip-vit-base32-torch\",\n    brain_key=\"lancedb_index\",\n    backend=\"lancedb\",\n)\n";
+
+export const PyPlatformsVoxel51BackendParams = "lancedb_index = fob.compute_similarity(\n    dataset,\n    model=\"clip-vit-base32-torch\",\n    backend=\"lancedb\",\n    brain_key=\"lancedb_index\",\n    table_name=\"your-table\",\n    metric=\"euclidean\",\n    uri=\"/tmp/lancedb\",\n)\n";
+
+export const PyPlatformsVoxel51BrainConfig = "import fiftyone.brain as fob\n\n# Print your current brain config\nprint(fob.brain_config)\n";
+
+export const PyPlatformsVoxel51Cleanup = "# Step 5 (optional): Cleanup\n\n# Delete the LanceDB table\nlancedb_index.cleanup()\n\n# Delete run record from FiftyOne\ndataset.delete_brain_run(\"lancedb_index\")\n";
+
+export const PyPlatformsVoxel51ComputeSimilarity = "# Steps 2 and 3: Compute embeddings and create a similarity index\nlancedb_index = fob.compute_similarity(\n    dataset,\n    model=\"clip-vit-base32-torch\",\n    brain_key=\"lancedb_index\",\n    backend=\"lancedb\",\n)\n";
+
+export const PyPlatformsVoxel51LoadDataset = "import fiftyone as fo\nimport fiftyone.brain as fob\nimport fiftyone.zoo as foz\n\n# Step 1: Load your data into FiftyOne\ndataset = foz.load_zoo_dataset(\"quickstart\")\n";
+
+export const PyPlatformsVoxel51SortBySimilarity = "# Step 4: Query your data\nquery = dataset.first().id  # query by sample ID\nview = dataset.sort_by_similarity(\n    query,\n    brain_key=\"lancedb_index\",\n    k=10,  # limit to 10 most similar samples\n)\n";
+
+export const PyRerankingAnswerdotaiUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import AnswerdotaiRerankers\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"answerdotai-rerank\"))\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema)\ntbl.add(data)\nreranker = AnswerdotaiRerankers()\n\n# Run vector search with a reranker\nresult = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\n\n# Build the full-text index before either FTS or hybrid search.\ntbl.create_fts_index(\"text\", replace=True)\n\n# Run FTS search with a reranker\nresult = tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n\n# Run hybrid search with a reranker\nresult = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n";
+
+export const PyRerankingCohereUsage = "import os\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import CohereReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\nreranker = CohereReranker(api_key=os.environ[\"COHERE_API_KEY\"])\n\n# Run vector search with a reranker\nresult = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\n\n# Run FTS search with a reranker\nresult = tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n";
+
+export const PyRerankingColbertUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import ColbertReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"colbert-rerank\"))\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema)\ntbl.add(data)\nreranker = ColbertReranker()\n\n# Run vector search with a reranker\nresult = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\n\n# Build the full-text index before either FTS or hybrid search.\ntbl.create_fts_index(\"text\", replace=True)\n\n# Run FTS search with a reranker\nresult = tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n\n# Run hybrid search with a reranker\nresult = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n";
+
+export const PyRerankingCrossEncoderUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import CrossEncoderReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"cross-encoder-rerank\"))\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema)\ntbl.add(data)\nreranker = CrossEncoderReranker()\n\n# Run vector search with a reranker\nresult = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\n\n# Build the full-text index before either FTS or hybrid search.\ntbl.create_fts_index(\"text\", replace=True)\n\n# Run FTS search with a reranker\nresult = tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n\n# Run hybrid search with a reranker\nresult = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n";
+
+export const PyRerankingJinaUsage = "import os\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import JinaReranker\n\nembedder = get_registry().get(\"jina\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\nreranker = JinaReranker(api_key=os.environ[\"JINA_API_KEY\"])\n\n# Run vector search with a reranker\nresult = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\n\n# Run FTS search with a reranker\nresult = tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n";
+
+export const PyRerankingLinearCombinationUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import LinearCombinationReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"linear-combination-rerank\"))\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema)\ntbl.add(data)\nreranker = LinearCombinationReranker()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n";
+
+export const PyRerankingMrrUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import MRRReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"mrr-rerank\"))\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema)\ntbl.add(data)\nreranker = MRRReranker(weight_vector=0.7, weight_fts=0.3)\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n\n# Run multivector search across multiple vector columns\nrs1 = tbl.search(\"hello\").limit(10).with_row_id(True).to_arrow()\nrs2 = tbl.search(\"greeting\").limit(10).with_row_id(True).to_arrow()\ncombined = MRRReranker().rerank_multivector([rs1, rs2])\n";
+
+export const PyRerankingOpenaiUsage = "import lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import OpenaiReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\nreranker = OpenaiReranker()\n\n# Run vector search with a reranker\nresult = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\n\n# Run FTS search with a reranker\nresult = tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n";
+
+export const PyRerankingRrfUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import RRFReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"rrf-rerank\"))\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema)\ntbl.add(data)\nreranker = RRFReranker()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n";
+
+export const PyRerankingVoyageaiUsage = "import os\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import VoyageAIReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\nreranker = VoyageAIReranker(model_name=\"rerank-2\")\n\n# Run vector search with a reranker\nresult = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\n\n# Run FTS search with a reranker\nresult = tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n";
+
+export const PyRerankingWatsonxUsage = "import os\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import WatsonxReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\n\n# Credentials pulled from WATSONX_API_KEY and WATSONX_PROJECT_ID (or WATSONX_SPACE_ID)\nreranker = WatsonxReranker(\n    api_key=os.environ[\"WATSONX_API_KEY\"],\n    project_id=os.environ[\"WATSONX_PROJECT_ID\"],\n)\n\n# Run vector search with a reranker\nresult = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\n\n# Run FTS search with a reranker\nresult = tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n";
+
+export const TsFrameworksGenkitCustomIndexer = "export const menuPdfIndexer = lancedbIndexerRef({\n  // Using all defaults, for dbUri, tableName, and embedder, etc\n});\n\nconst chunkingConfig = {\n  minLength: 1000,\n  maxLength: 2000,\n  splitter: \"sentence\",\n  overlap: 100,\n  delimiters: \"\",\n} as any;\n\nasync function extractTextFromPdf(filePath: string) {\n  const pdfFile = path.resolve(filePath);\n  const dataBuffer = await readFile(pdfFile);\n  const data = await pdf(dataBuffer);\n  return data.text;\n}\n\nexport const indexMenu = ai.defineFlow(\n  {\n    name: \"indexMenu\",\n    inputSchema: z.string().describe(\"PDF file path\"),\n    outputSchema: z.void(),\n  },\n  async (filePath: string) => {\n    filePath = path.resolve(filePath);\n\n    // Read the pdf.\n    const pdfTxt = await ai.run(\"extract-text\", () => extractTextFromPdf(filePath));\n\n    // Divide the pdf text into segments.\n    const chunks = await ai.run(\"chunk-it\", async () => chunk(pdfTxt, chunkingConfig));\n\n    // Convert chunks of text into documents to store in the index.\n    const documents = chunks.map((text) => {\n      return Document.fromText(text, { filePath });\n    });\n\n    // Add documents to the index.\n    await ai.index({\n      indexer: menuPdfIndexer,\n      documents,\n      options: {\n        writeMode: WriteMode.Overwrite,\n      } as any,\n    });\n  },\n);\n";
+
+export const TsFrameworksGenkitCustomRetriever = "export const menuRetriever = lancedbRetrieverRef({\n  tableName: \"table\", // Use the same table name as the indexer.\n  displayName: \"Menu\", // Use a custom display name.\n});\n\nexport const menuQAFlow = ai.defineFlow(\n  { name: \"Menu\", inputSchema: z.string(), outputSchema: z.string() },\n  async (input: string) => {\n    // retrieve relevant documents\n    const docs = await ai.retrieve({\n      retriever: menuRetriever,\n      query: input,\n      options: {\n        k: 3,\n      },\n    });\n\n    const extractedContent = docs.map((doc) => {\n      if (doc.content && Array.isArray(doc.content) && doc.content.length > 0) {\n        if (doc.content[0].media && doc.content[0].media.url) {\n          return doc.content[0].media.url;\n        }\n      }\n      return \"No content found\";\n    });\n\n    console.log(\"Extracted content:\", extractedContent);\n\n    const { text } = await ai.generate({\n      model: gemini(\"gemini-2.0-flash\"),\n      prompt: `\nYou are acting as a helpful AI assistant that can answer \nquestions about the food available on the menu at Genkit Grub Pub.\n\nUse only the context provided to answer the question.\nIf you don't know, do not make up an answer.\nDo not add or change items on the menu.\n\nContext:\n${extractedContent.join(\"\\n\\n\")}\n\nQuestion: ${input}`,\n      docs,\n    });\n\n    return text;\n  },\n);\n";
+
+export const TsFrameworksGenkitUsage = "import { lancedbIndexerRef, lancedb, lancedbRetrieverRef, WriteMode } from \"genkitx-lancedb\";\nimport { textEmbedding004, vertexAI } from \"@genkit-ai/vertexai\";\nimport { gemini } from \"@genkit-ai/vertexai\";\nimport { z, genkit } from \"genkit\";\nimport { Document } from \"genkit/retriever\";\nimport { chunk } from \"llm-chunk\";\nimport { readFile } from \"fs/promises\";\nimport path from \"path\";\nimport pdf from \"pdf-parse/lib/pdf-parse\";\n\nconst ai = genkit({\n  plugins: [\n    // vertexAI provides the textEmbedding004 embedder\n    vertexAI(),\n\n    // the local vector store requires an embedder to translate from text to vector\n    lancedb([\n      {\n        dbUri: \".db\", // optional lancedb uri, default to .db\n        tableName: \"table\", // optional table name, default to table\n        embedder: textEmbedding004,\n      },\n    ]),\n  ],\n});\n";
+
diff --git a/docs/web/snippets/ivf_pq.mdx b/docs/web/snippets/ivf_pq.mdx
new file mode 100644
index 000000000..fd1c6360b
--- /dev/null
+++ b/docs/web/snippets/ivf_pq.mdx
@@ -0,0 +1,6 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const RsCreateIndex = "// For this example, `table` is a lancedb::Table with a column named\n// \"vector\" that is a vector column with dimension 128.\n\n// By default, if the column \"vector\" appears to be a vector column,\n// then an IVF_PQ index with reasonable defaults is created.\ntable\n    .create_index(&[\"vector\"], Index::Auto)\n    .execute()\n    .await?;\n// For advanced cases, it is also possible to specifically request an\n// IVF_PQ index and provide custom parameters.\ntable\n    .create_index(\n        &[\"vector\"],\n        Index::IvfPq(\n            // Here we specify advanced indexing parameters.  In this case\n            // we are creating an index that my have better recall than the\n            // default but is also larger and slower.\n            IvfPqIndexBuilder::default()\n                // This overrides the default distance type of l2\n                .distance_type(DistanceType::Cosine)\n                // With 1000 rows this have been ~31 by default\n                .num_partitions(50)\n                // With dimension 128 this would have been 8 by default\n                .num_sub_vectors(16),\n        ),\n    )\n    .execute()\n    .await?;\n";
+
+export const RsSearch1 = "let query_vector = [1.0; 128];\n// By default the index will find the 10 closest results using default\n// search parameters that give a reasonable tradeoff between accuracy\n// and search latency\nlet mut results = table\n    .vector_search(&query_vector)?\n    // Note: you should always set the distance_type to match the value used\n    // to train the index\n    .distance_type(DistanceType::Cosine)\n    .execute()\n    .await?;\nwhile let Some(batch) = results.try_next().await? {\n    println!(\"{:?}\", batch);\n}\n// We can also provide custom search parameters.  Here we perform a\n// slower but more accurate search\nlet mut results = table\n    .vector_search(&query_vector)?\n    .distance_type(DistanceType::Cosine)\n    // Override the default of 10 to get more rows\n    .limit(15)\n    // Override the default of 20 to search more partitions\n    .nprobes(30)\n    // Override the default of None to apply a refine step\n    .refine_factor(1)\n    .execute()\n    .await?;\nwhile let Some(batch) = results.try_next().await? {\n    println!(\"{:?}\", batch);\n}\nOk(())\n";
+
diff --git a/docs/web/snippets/materialized_view.mdx b/docs/web/snippets/materialized_view.mdx
new file mode 100644
index 000000000..975f7635b
--- /dev/null
+++ b/docs/web/snippets/materialized_view.mdx
@@ -0,0 +1,6 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyMaterializedViewCreate = "import lancedb\n\ndb = lancedb.connect(\"./.lancedb\")\ntable = db.create_table(\n    \"people\",\n    [{\"name\": \"ada\", \"age\": 36}, {\"name\": \"kid\", \"age\": 7}],\n    storage_options={\"new_table_enable_stable_row_ids\": \"true\"},\n)\n\nview = db.create_materialized_view(\n    \"adults\",\n    \"people\",\n    select=[\"name\", (\"shout\", \"upper(name)\")],\n    where=\"age >= 18\",\n)\nprint(view.table.count_rows())\n# 1\n";
+
+export const PyMaterializedViewRefresh = "table.add([{\"name\": \"bea\", \"age\": 41}])\n\nview.refresh()\nprint(view.table.count_rows())\n# 2\n";
+
diff --git a/docs/web/snippets/merge_insert.mdx b/docs/web/snippets/merge_insert.mdx
new file mode 100644
index 000000000..bdd55a631
--- /dev/null
+++ b/docs/web/snippets/merge_insert.mdx
@@ -0,0 +1,8 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const TsInsertIfNotExists = "const table2 = await db.createTable(\"domains\", [\n  { domain: \"google.com\", name: \"Google\" },\n  { domain: \"github.com\", name: \"GitHub\" },\n]);\n\nconst newDomains = [\n  { domain: \"google.com\", name: \"Google\" },\n  { domain: \"facebook.com\", name: \"Facebook\" },\n];\nawait table2\n  .mergeInsert(\"domain\")\n  .whenNotMatchedInsertAll()\n  .execute(newDomains);\nawait table2.countRows(); // 3\n";
+
+export const TsReplaceRange = "const table3 = await db.createTable(\"chunks\", [\n  { doc_id: 0, chunk_id: 0, text: \"Hello\" },\n  { doc_id: 0, chunk_id: 1, text: \"World\" },\n  { doc_id: 1, chunk_id: 0, text: \"Foo\" },\n  { doc_id: 1, chunk_id: 1, text: \"Bar\" },\n]);\n\nconst newChunks = [{ doc_id: 1, chunk_id: 0, text: \"Baz\" }];\n\nawait table3\n  .mergeInsert([\"doc_id\", \"chunk_id\"])\n  .whenMatchedUpdateAll()\n  .whenNotMatchedInsertAll()\n  .whenNotMatchedBySourceDelete({ where: \"doc_id = 1\" })\n  .execute(newChunks);\n\nawait table3.countRows(\"doc_id = 1\"); // 1\n";
+
+export const TsUpsertBasic = "const table = await db.createTable(\"users\", [\n  { id: 0, name: \"Alice\" },\n  { id: 1, name: \"Bob\" },\n]);\n\nconst newUsers = [\n  { id: 1, name: \"Bobby\" },\n  { id: 2, name: \"Charlie\" },\n];\nawait table\n  .mergeInsert(\"id\")\n  .whenMatchedUpdateAll()\n  .whenNotMatchedInsertAll()\n  .execute(newUsers);\n\nawait table.countRows(); // 3\n";
+
diff --git a/docs/web/snippets/multimodal.mdx b/docs/web/snippets/multimodal.mdx
new file mode 100644
index 000000000..f41af6629
--- /dev/null
+++ b/docs/web/snippets/multimodal.mdx
@@ -0,0 +1,54 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyBlobApiIngest = "import lancedb\n\ndb = lancedb.connect(db_path_factory(\"blob_db\"))\n    \n# Create sample data\ndata = [\n    {\"id\": 1, \"video\": b\"fake_video_bytes_1\"},\n    {\"id\": 2, \"video\": b\"fake_video_bytes_2\"}\n]\n    \n# Create the table\ntbl = db.create_table(\"videos\", data=data, schema=schema)\n";
+
+export const PyBlobApiSchema = "import pyarrow as pa\n\n# Define schema with Blob API metadata for lazy loading\nschema = pa.schema([\n    pa.field(\"id\", pa.int64()),\n    pa.field(\n        \"video\", \n        pa.large_binary(), \n        metadata={\"lance-encoding:blob\": \"true\"} # Enable Blob API\n    ),\n])\n";
+
+export const PyBlobApiToPandas = "# Default: blob columns come back lazily\ndf_lazy = tbl.to_pandas()\n\n# Materialize blob bytes eagerly\ndf_bytes = tbl.to_pandas(blob_mode=\"bytes\")\n\n# Return descriptors instead of payloads\ndf_desc = tbl.to_pandas(blob_mode=\"descriptions\")\n\n# Forward extra kwargs to PyArrow's to_pandas\ndf_typed = tbl.to_pandas(split_blocks=True, self_destruct=True)\n";
+
+export const PyCreateDummyData = "# Create some dummy images\ndef create_dummy_image(color):\n    img = Image.new('RGB', (100, 100), color=color)\n    buf = io.BytesIO()\n    img.save(buf, format='PNG')\n    return buf.getvalue()\n\n# Create dataset with metadata, vectors, and image blobs\ndata = [\n    {\n        \"id\": 1,\n        \"filename\": \"red_square.png\",\n        \"vector\": np.random.rand(128).astype(np.float32),\n        \"image_blob\": create_dummy_image('red'),\n        \"label\": \"red\"\n    },\n    {\n        \"id\": 2,\n        \"filename\": \"blue_square.png\",\n        \"vector\": np.random.rand(128).astype(np.float32),\n        \"image_blob\": create_dummy_image('blue'),\n        \"label\": \"blue\"\n    }\n]\n";
+
+export const PyDefineSchema = "# Define schema explicitly to ensure image_blob is treated as binary\nschema = pa.schema([\n    pa.field(\"id\", pa.int32()),\n    pa.field(\"filename\", pa.string()),\n    pa.field(\"vector\", pa.list_(pa.float32(), 128)),\n    pa.field(\"image_blob\", pa.binary()), # Important: Use pa.binary() for blobs\n    pa.field(\"label\", pa.string())\n])\n";
+
+export const PyIngestData = "tbl = db.create_table(\"images\", data=data, schema=schema, mode=\"overwrite\")\n";
+
+export const PyMultimodalImports = "import lancedb\nimport pyarrow as pa\nimport pandas as pd\nimport numpy as np\nimport io\nfrom PIL import Image\n";
+
+export const PyProcessResults = "# Convert back to PIL Image\nfor _, row in results.iterrows():\n    image_bytes = row['image_blob']\n    image = Image.open(io.BytesIO(image_bytes))\n    print(f\"Retrieved image: {row['filename']}, Size: {image.size}\")\n    # You can now use 'image' with other libraries or display it\n";
+
+export const PyQueryToPandasKwargs = "# Plain scan query: blob_mode is supported end to end\ndf_lazy = (\n    tbl.search()\n    .where(\"id = 1\")\n    .select([\"id\", \"video\"])\n    .to_pandas(blob_mode=\"lazy\")\n)\n\n# Same call shape works on async query builders\ndf_bytes = await (\n    tbl_async.query()\n    .where(\"id = 1\")\n    .select([\"id\", \"video\"])\n    .to_pandas(blob_mode=\"bytes\")\n)\n\n# Vector / FTS / hybrid queries can't materialize blob columns,\n# so omit them from the projection\ndf_vec = (\n    tbl.search(query_vector)\n    .limit(10)\n    .select([\"id\", \"vector\"])\n    .to_pandas(split_blocks=True, self_destruct=True)\n)\n";
+
+export const PySearchData = "# Search for similar images\nquery_vector = np.random.rand(128).astype(np.float32)\nresults = tbl.search(query_vector).limit(1).to_pandas()\n";
+
+export const TsBlobApiIngest = "const blobData = lancedb.makeArrowTable(\n  [\n    { id: 1, video: Buffer.from(\"fake_video_bytes_1\") },\n    { id: 2, video: Buffer.from(\"fake_video_bytes_2\") },\n  ],\n  { schema: blobSchema },\n);\nconst blobTable = await db.createTable(\"videos\", blobData, {\n  mode: \"overwrite\",\n});\n";
+
+export const TsBlobApiSchema = "const blobSchema = new arrow.Schema([\n  new arrow.Field(\"id\", new arrow.Int64()),\n  new arrow.Field(\n    \"video\",\n    new arrow.LargeBinary(),\n    true,\n    new Map([[\"lance-encoding:blob\", \"true\"]]),\n  ),\n]);\n";
+
+export const TsCreateDummyData = "const createDummyImage = (color: string): Uint8Array => {\n  const pngHeader = Uint8Array.from([137, 80, 78, 71, 13, 10, 26, 10]);\n  return Buffer.concat([Buffer.from(pngHeader), Buffer.from(color, \"utf8\")]);\n};\n\nconst data = [\n  {\n    id: 1,\n    filename: \"red_square.png\",\n    vector: Array.from({ length: 128 }, (_, i) => (i % 16) / 16),\n    image_blob: createDummyImage(\"red\"),\n    label: \"red\",\n  },\n  {\n    id: 2,\n    filename: \"blue_square.png\",\n    vector: Array.from({ length: 128 }, (_, i) => ((i + 8) % 16) / 16),\n    image_blob: createDummyImage(\"blue\"),\n    label: \"blue\",\n  },\n];\n";
+
+export const TsDefineSchema = "const schema = new arrow.Schema([\n  new arrow.Field(\"id\", new arrow.Int32()),\n  new arrow.Field(\"filename\", new arrow.Utf8()),\n  new arrow.Field(\n    \"vector\",\n    new arrow.FixedSizeList(\n      128,\n      new arrow.Field(\"item\", new arrow.Float32(), true),\n    ),\n  ),\n  new arrow.Field(\"image_blob\", new arrow.Binary()),\n  new arrow.Field(\"label\", new arrow.Utf8()),\n]);\n";
+
+export const TsIngestData = "const multimodalData = lancedb.makeArrowTable(data, { schema });\nconst tbl = await db.createTable(\"images\", multimodalData, {\n  mode: \"overwrite\",\n});\n";
+
+export const TsMultimodalImports = "import * as arrow from \"apache-arrow\";\nimport { Buffer } from \"node:buffer\";\nimport * as lancedb from \"@lancedb/lancedb\";\n";
+
+export const TsProcessResults = "for (const row of results) {\n  const imageBytes = row.image_blob as Uint8Array;\n  console.log(\n    `Retrieved image: ${row.filename}, Byte length: ${imageBytes.length}`,\n  );\n}\n";
+
+export const TsSearchData = "const queryVector = Array.from({ length: 128 }, (_, i) => (i % 16) / 16);\nconst results = await tbl.search(queryVector).limit(1).toArray();\n";
+
+export const RsBlobApiIngest = "let blob_rows = vec![\n    (1_i64, b\"fake_video_bytes_1\".to_vec()),\n    (2_i64, b\"fake_video_bytes_2\".to_vec()),\n];\n\nlet blob_schema = Arc::new(blob_schema);\nlet blob_batch = RecordBatch::try_new(\n    blob_schema.clone(),\n    vec![\n        Arc::new(Int64Array::from_iter_values(blob_rows.iter().map(|row| row.0))),\n        Arc::new(LargeBinaryArray::from_iter_values(\n            blob_rows.iter().map(|row| row.1.as_slice()),\n        )),\n    ],\n)\n.unwrap();\nlet blob_reader: Box<dyn arrow_array::RecordBatchReader + Send> =\n    Box::new(RecordBatchIterator::new(vec![Ok(blob_batch)].into_iter(), blob_schema));\nlet blob_table = db\n    .create_table(\"videos\", blob_reader)\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsBlobApiSchema = "let blob_metadata = HashMap::from([(\n    \"lance-encoding:blob\".to_string(),\n    \"true\".to_string(),\n)]);\nlet blob_schema = Schema::new(vec![\n    Field::new(\"id\", DataType::Int64, false),\n    Field::new(\"video\", DataType::LargeBinary, true).with_metadata(blob_metadata),\n]);\n";
+
+export const RsCreateDummyData = "let create_dummy_image = |color: u8| -> Vec<u8> {\n    let mut png_like = vec![137, 80, 78, 71, 13, 10, 26, 10];\n    png_like.push(color);\n    png_like\n};\n\nlet data = vec![\n    (\n        1_i32,\n        \"red_square.png\",\n        vec![0.1_f32; 128],\n        create_dummy_image(1),\n        \"red\",\n    ),\n    (\n        2_i32,\n        \"blue_square.png\",\n        vec![0.2_f32; 128],\n        create_dummy_image(2),\n        \"blue\",\n    ),\n];\n";
+
+export const RsDefineSchema = "let schema = Schema::new(vec![\n    Field::new(\"id\", DataType::Int32, false),\n    Field::new(\"filename\", DataType::Utf8, false),\n    Field::new(\n        \"vector\",\n        DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 128),\n        false,\n    ),\n    Field::new(\"image_blob\", DataType::Binary, false),\n    Field::new(\"label\", DataType::Utf8, false),\n]);\n";
+
+export const RsIngestData = "let schema = Arc::new(schema);\nlet image_batch = RecordBatch::try_new(\n    schema.clone(),\n    vec![\n        Arc::new(Int32Array::from_iter_values(data.iter().map(|row| row.0))),\n        Arc::new(StringArray::from_iter_values(data.iter().map(|row| row.1))),\n        Arc::new(\n            FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n                data.iter()\n                    .map(|row| Some(row.2.iter().copied().map(Some).collect::<Vec<_>>())),\n                128,\n            ),\n        ),\n        Arc::new(BinaryArray::from_iter_values(\n            data.iter().map(|row| row.3.as_slice()),\n        )),\n        Arc::new(StringArray::from_iter_values(data.iter().map(|row| row.4))),\n    ],\n)\n.unwrap();\nlet image_reader: Box<dyn arrow_array::RecordBatchReader + Send> =\n    Box::new(RecordBatchIterator::new(vec![Ok(image_batch)].into_iter(), schema.clone()));\nlet table = db\n    .create_table(\"images\", image_reader)\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsMultimodalImports = "use std::collections::HashMap;\nuse std::sync::Arc;\n\nuse arrow_array::types::Float32Type;\nuse arrow_array::{\n    BinaryArray, FixedSizeListArray, Int32Array, Int64Array, LargeBinaryArray, RecordBatch,\n    RecordBatchIterator, StringArray,\n};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures_util::TryStreamExt;\nuse lancedb::connect;\nuse lancedb::database::CreateTableMode;\nuse lancedb::query::{ExecutableQuery, QueryBase};\n";
+
+export const RsProcessResults = "for batch in &results {\n    let filenames = batch\n        .column_by_name(\"filename\")\n        .unwrap()\n        .as_any()\n        .downcast_ref::<StringArray>()\n        .unwrap();\n    let images = batch\n        .column_by_name(\"image_blob\")\n        .unwrap()\n        .as_any()\n        .downcast_ref::<BinaryArray>()\n        .unwrap();\n\n    for row in 0..batch.num_rows() {\n        let image_bytes = images.value(row);\n        println!(\n            \"Retrieved image: {}, Byte length: {}\",\n            filenames.value(row),\n            image_bytes.len()\n        );\n    }\n}\n";
+
+export const RsSearchData = "let query_vector = vec![0.1_f32; 128];\nlet results = table\n    .query()\n    .nearest_to(query_vector)\n    .unwrap()\n    .limit(1)\n    .execute()\n    .await\n    .unwrap()\n    .try_collect::<Vec<_>>()\n    .await\n    .unwrap();\n";
+
diff --git a/docs/web/snippets/openai.mdx b/docs/web/snippets/openai.mdx
new file mode 100644
index 000000000..f279e4797
--- /dev/null
+++ b/docs/web/snippets/openai.mdx
@@ -0,0 +1,6 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const RsImports = "\nuse std::{iter::once, sync::Arc};\n\nuse arrow_array::{Float64Array, Int32Array, RecordBatch, RecordBatchIterator, StringArray};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures::StreamExt;\nuse lancedb::{\n    arrow::IntoArrow,\n    connect,\n    embeddings::{openai::OpenAIEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction},\n    query::{ExecutableQuery, QueryBase},\n    Result,\n};\n";
+
+export const RsOpenaiEmbeddings = "#[tokio::main]\nasync fn main() -> Result<()> {\n    let tempdir = tempfile::tempdir().unwrap();\n    let tempdir = tempdir.path().to_str().unwrap();\n    let api_key = std::env::var(\"OPENAI_API_KEY\").expect(\"OPENAI_API_KEY is not set\");\n    let embedding = Arc::new(OpenAIEmbeddingFunction::new_with_model(\n        api_key,\n        \"text-embedding-3-large\",\n    )?);\n\n    let db = connect(tempdir).execute().await?;\n    db.embedding_registry()\n        .register(\"openai\", embedding.clone())?;\n\n    let table = db\n        .create_table(\"vectors\", make_data())\n        .add_embedding(EmbeddingDefinition::new(\n            \"text\",\n            \"openai\",\n            Some(\"embeddings\"),\n        ))?\n        .execute()\n        .await?;\n\n    let query = Arc::new(StringArray::from_iter_values(once(\"something warm\")));\n    let query_vector = embedding.compute_query_embeddings(query)?;\n    let mut results = table\n        .vector_search(query_vector)?\n        .limit(1)\n        .execute()\n        .await?;\n\n    let rb = results.next().await.unwrap()?;\n    let out = rb\n        .column_by_name(\"text\")\n        .unwrap()\n        .as_any()\n        .downcast_ref::<StringArray>()\n        .unwrap();\n    let text = out.iter().next().unwrap().unwrap();\n    println!(\"Closest match: {}\", text);\n    Ok(())\n}\n";
+
diff --git a/docs/web/snippets/provider_examples.mdx b/docs/web/snippets/provider_examples.mdx
new file mode 100644
index 000000000..13768e265
--- /dev/null
+++ b/docs/web/snippets/provider_examples.mdx
@@ -0,0 +1,18 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyEmbeddingIbmUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import EmbeddingFunctionRegistry\nfrom lancedb.pydantic import LanceModel, Vector\n\nregistry = EmbeddingFunctionRegistry.get_instance()\nregistry.set_var(\"watsonx-api-key\", \"your-api-key\")\nwatsonx_embed = registry.get(\"watsonx\").create(\n    name=\"ibm/slate-125m-english-rtrvr\",\n    api_key=\"$var:watsonx-api-key\",\n    project_id=\"your-project-id\",\n)\n\nclass TextModel(LanceModel):\n    text: str = watsonx_embed.SourceField()\n    vector: Vector(watsonx_embed.ndims()) = watsonx_embed.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"watsonx-demo\"))\ntbl = db.create_table(\"watsonx_test\", schema=TextModel, mode=\"overwrite\")\ntbl.add(data)\n\nrs = tbl.search(\"hello\").limit(1).to_pandas()\nprint(rs.head())\n";
+
+export const PyEmbeddingJinaMultimodal = "import os\nimport tempfile\nfrom pathlib import Path\n\nimport lancedb\nimport pandas as pd\nimport requests\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\nos.environ[\"JINA_API_KEY\"] = \"your-api-key\"\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"jina-images\"))\nfunc = get_registry().get(\"jina\").create()\n\nclass Images(LanceModel):\n    label: str\n    image_uri: str = func.SourceField()\n    image_bytes: bytes = func.SourceField()\n    vector: Vector(func.ndims()) = func.VectorField()\n    vec_from_bytes: Vector(func.ndims()) = func.VectorField()\n\ntable = db.create_table(\"images\", schema=Images)\nlabels = [\"cat\", \"cat\", \"dog\", \"dog\", \"horse\", \"horse\"]\nuris = [\n    \"http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg\",\n    \"http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg\",\n    \"http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg\",\n    \"http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg\",\n    \"http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg\",\n    \"http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg\",\n]\nimage_bytes = [requests.get(uri).content for uri in uris]\ntable.add(\n    pd.DataFrame({\"label\": labels, \"image_uri\": uris, \"image_bytes\": image_bytes})\n)\n";
+
+export const PyEmbeddingJinaText = "import os\nimport tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import EmbeddingFunctionRegistry\nfrom lancedb.pydantic import LanceModel, Vector\n\nos.environ[\"JINA_API_KEY\"] = \"your-api-key\"\n\njina_embed = (\n    EmbeddingFunctionRegistry.get_instance()\n    .get(\"jina\")\n    .create(name=\"jina-embeddings-v2-base-en\")\n)\n\nclass TextModel(LanceModel):\n    text: str = jina_embed.SourceField()\n    vector: Vector(jina_embed.ndims()) = jina_embed.VectorField()\n\ndata = [{\"text\": \"hello world\"}, {\"text\": \"goodbye world\"}]\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"jina-text\"))\ntbl = db.create_table(\"test\", schema=TextModel, mode=\"overwrite\")\n\ntbl.add(data)\n";
+
+export const PyRerankingCohereUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import CohereReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"cohere-rerank\"))\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema)\ntbl.add(data)\nreranker = CohereReranker(api_key=\"your-api-key\")\n\n# Run vector search with a reranker\nvector_results = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\nprint(vector_results)\n\n# Build the full-text index before either FTS or hybrid search.\ntbl.create_fts_index(\"text\", replace=True)\n\n# Run FTS search with a reranker\nfts_results = (\n    tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n)\nprint(fts_results)\n\n# Run hybrid search with a reranker\nhybrid_results = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\nprint(hybrid_results)\n";
+
+export const PyRerankingJinaUsage = "import os\nimport tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import JinaReranker\n\nos.environ[\"JINA_API_KEY\"] = \"your-api-key\"\nembedder = get_registry().get(\"jina\").create()\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"jina-rerank\"))\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema)\ntbl.add(data)\nreranker = JinaReranker(api_key=\"your-api-key\")\n\n# Run vector search with a reranker\nvector_results = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\nprint(vector_results)\n\n# Build the full-text index before either FTS or hybrid search.\ntbl.create_fts_index(\"text\", replace=True)\n\n# Run FTS search with a reranker\nfts_results = (\n    tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n)\nprint(fts_results)\n\n# Run hybrid search with a reranker\nhybrid_results = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\nprint(hybrid_results)\n";
+
+export const PyRerankingOpenaiUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import OpenaiReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"openai-rerank\"))\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema)\ntbl.add(data)\nreranker = OpenaiReranker(api_key=\"your-api-key\")\n\n# Run vector search with a reranker\nvector_results = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\nprint(vector_results)\n\n# Build the full-text index before either FTS or hybrid search.\ntbl.create_fts_index(\"text\", replace=True)\n\n# Run FTS search with a reranker\nfts_results = (\n    tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n)\nprint(fts_results)\n\n# Run hybrid search with a reranker\nhybrid_results = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\nprint(hybrid_results)\n";
+
+export const PyRerankingVoyageaiUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import VoyageAIReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"voyageai-rerank\"))\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema)\ntbl.add(data)\nreranker = VoyageAIReranker(api_key=\"your-api-key\", model_name=\"rerank-2\")\n\n# Run vector search with a reranker\nvector_results = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\nprint(vector_results)\n\n# Build the full-text index before either FTS or hybrid search.\ntbl.create_fts_index(\"text\", replace=True)\n\n# Run FTS search with a reranker\nfts_results = (\n    tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n)\nprint(fts_results)\n\n# Run hybrid search with a reranker\nhybrid_results = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\nprint(hybrid_results)\n";
+
+export const PyRerankingWatsonxUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import WatsonxReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"watsonx-rerank\"))\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema)\ntbl.add(data)\n\n# Supply your API key and project ID (or use space_id instead of project_id).\nreranker = WatsonxReranker(\n    api_key=\"your-api-key\",\n    project_id=\"your-project-id\",\n)\n\n# Run vector search with a reranker\nvector_results = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\nprint(vector_results)\n\n# Build the full-text index before either FTS or hybrid search.\ntbl.create_fts_index(\"text\", replace=True)\n\n# Run FTS search with a reranker\nfts_results = (\n    tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n)\nprint(fts_results)\n\n# Run hybrid search with a reranker\nhybrid_results = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\nprint(hybrid_results)\n";
+
diff --git a/docs/web/snippets/quickstart.mdx b/docs/web/snippets/quickstart.mdx
new file mode 100644
index 000000000..c7cf98e32
--- /dev/null
+++ b/docs/web/snippets/quickstart.mdx
@@ -0,0 +1,66 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyQuickstartAddData = "table.add(\n    [\n        {\n            \"id\": \"4\",\n            \"name\": \"Morgana\",\n            \"role\": \"Sorceress\",\n            \"description\": \"Powerful sorceress of Avalon.\",\n            \"stats\": {\"strength\": 2, \"magic\": 5, \"leadership\": 4, \"wisdom\": 4},\n            \"vector\": [0.3, 0.9, 0.6, 0.8],\n        }\n    ]\n)\n";
+
+export const PyQuickstartAddFeature = "table.add_columns(\n    {\n        \"power_score\": \"cast(((stats.strength + stats.magic + stats.leadership + stats.wisdom) / 4.0) as float)\"\n    }\n)\n";
+
+export const PyQuickstartComputedFeature = "table.add_columns(computed={\"description_length\": \"length(description)\"})\ntable.refresh_column(\"description_length\")\n";
+
+export const PyQuickstartComputedFeatureEnterprise = "table.add_columns(computed={\"description_length\": \"length(description)\"})\njob = table.refresh_column_async(\"description_length\")\njob.wait()\n";
+
+export const PyQuickstartCreateTable = "table = db.create_table(\"characters\", data=data, mode=\"overwrite\")\n";
+
+export const PyQuickstartCreateTableAsync = "async_table = await async_db.create_table(\n    \"characters\",\n    data=data,\n    mode=\"overwrite\",\n)\n";
+
+export const PyQuickstartCurateWithMetadata = "curated = (\n    table.search(query_vector)\n    .where(\"stats.magic >= 4\")\n    .select([\"name\", \"role\", \"description\", \"_distance\"])\n    .limit(2)\n    .to_polars()\n)\nprint(curated)\n";
+
+export const PyQuickstartData = "data = [\n    {\n        \"id\": \"1\",\n        \"name\": \"King Arthur\",\n        \"role\": \"King\",\n        \"description\": \"Leader of Camelot and wielder of Excalibur.\",\n        \"stats\": {\"strength\": 4, \"magic\": 1, \"leadership\": 5, \"wisdom\": 4},\n        \"vector\": [0.7, 0.1, 0.9, 0.7],\n    },\n    {\n        \"id\": \"2\",\n        \"name\": \"Merlin\",\n        \"role\": \"Wizard\",\n        \"description\": \"Advisor and prophet with deep magical knowledge.\",\n        \"stats\": {\"strength\": 2, \"magic\": 5, \"leadership\": 4, \"wisdom\": 5},\n        \"vector\": [0.2, 0.9, 0.4, 0.9],\n    },\n    {\n        \"id\": \"3\",\n        \"name\": \"Sir Lancelot\",\n        \"role\": \"Knight\",\n        \"description\": \"Legendary knight known for courage and combat skill.\",\n        \"stats\": {\"strength\": 5, \"magic\": 1, \"leadership\": 3, \"wisdom\": 3},\n        \"vector\": [0.9, 0.1, 0.5, 0.4],\n    },\n]\n";
+
+export const PyQuickstartMultimodalBytes = "from pathlib import Path\n\nimage_bytes = Path(\"data/sir-lancelot.jpg\").read_bytes()\n\nmultimodal_table = db.create_table(\n    \"character_images\",\n    data=[\n        {\n            \"id\": \"lancelot\",\n            \"description\": \"Portrait of Sir Lancelot\",\n            \"image\": image_bytes,\n            \"vector\": [0.9, 0.1, 0.5, 0.4],\n        }\n    ],\n    mode=\"overwrite\",\n)\n";
+
+export const PyQuickstartOutputPandas = "# Ensure you run `pip install pandas` beforehand\nresult = table.search(query_vector).limit(2).to_pandas()\nprint(result)\n";
+
+export const PyQuickstartQueryFeature = "features = table.search().select([\"name\", \"role\", \"power_score\"]).to_polars()\nprint(features)\n";
+
+export const PyQuickstartTraining = "import torch\nfrom lancedb.permutation import Permutation\n\ntable.add_columns(\n    {\n        \"magic\": \"cast(stats.magic as float)\",\n        \"strength\": \"cast(stats.strength as float)\",\n    }\n)\ndataset = Permutation.identity(table).select_columns([\"magic\", \"strength\"])\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=1024, shuffle=True)\n";
+
+export const PyQuickstartVectorSearch1 = "# Search for examples similar to a \"wise magical advisor\"\nquery_vector = [0.2, 0.8, 0.4, 0.9]\n\n# Ensure you run `pip install polars` beforehand\nresult = (\n    table.search(query_vector)\n    .select([\"name\", \"role\", \"description\", \"_distance\"])\n    .limit(2)\n    .to_polars()\n)\nprint(result)\n";
+
+export const PyQuickstartVectorSearch1Async = "# Search for examples similar to a \"wise magical advisor\"\nquery_vector = [0.2, 0.8, 0.4, 0.9]\n\n# Ensure you run `pip install polars` beforehand\nasync_result = await (\n    await async_table.search(query_vector)\n).select([\"name\", \"role\", \"description\", \"_distance\"]).limit(2).to_polars()\nprint(async_result)\n";
+
+export const TsQuickstartAddData = "await table.add([\n  {\n    id: \"4\",\n    name: \"Morgana\",\n    role: \"Sorceress\",\n    description: \"Powerful sorceress of Avalon.\",\n    stats: { strength: 2, magic: 5, leadership: 4, wisdom: 4 },\n    vector: [0.3, 0.9, 0.6, 0.8],\n  },\n]);\n";
+
+export const TsQuickstartAddFeature = "await table.addColumns([\n  {\n    name: \"power_score\",\n    valueSql:\n      \"cast(((stats.strength + stats.magic + stats.leadership + stats.wisdom) / 4.0) as float)\",\n  },\n]);\n";
+
+export const TsQuickstartCreateTable = "const table = await db.createTable(\"characters\", data, { mode: \"overwrite\" });\n";
+
+export const TsQuickstartCurateWithMetadata = "const curated = await table\n  .search(queryVector)\n  .where(\"stats.magic >= 4\")\n  .select([\"name\", \"role\", \"description\", \"_distance\"])\n  .limit(2)\n  .toArray();\nconsole.table(curated);\n";
+
+export const TsQuickstartData = "const data = [\n  {\n    id: \"1\",\n    name: \"King Arthur\",\n    role: \"King\",\n    description: \"Leader of Camelot and wielder of Excalibur.\",\n    stats: { strength: 4, magic: 1, leadership: 5, wisdom: 4 },\n    vector: [0.7, 0.1, 0.9, 0.7],\n  },\n  {\n    id: \"2\",\n    name: \"Merlin\",\n    role: \"Wizard\",\n    description: \"Advisor and prophet with deep magical knowledge.\",\n    stats: { strength: 2, magic: 5, leadership: 4, wisdom: 5 },\n    vector: [0.2, 0.9, 0.4, 0.9],\n  },\n  {\n    id: \"3\",\n    name: \"Sir Lancelot\",\n    role: \"Knight\",\n    description: \"Legendary knight known for courage and combat skill.\",\n    stats: { strength: 5, magic: 1, leadership: 3, wisdom: 3 },\n    vector: [0.9, 0.1, 0.5, 0.4],\n  },\n];\n";
+
+export const TsQuickstartMultimodalBytes = "const arrow = await import(\"apache-arrow\");\nconst { readFile } = await import(\"node:fs/promises\");\n\nconst imageBytes = await readFile(\"data/sir-lancelot.jpg\");\nconst imageSchema = new arrow.Schema([\n  new arrow.Field(\"id\", new arrow.Utf8()),\n  new arrow.Field(\"description\", new arrow.Utf8()),\n  new arrow.Field(\"image\", new arrow.Binary()),\n  new arrow.Field(\n    \"vector\",\n    new arrow.FixedSizeList(\n      4,\n      new arrow.Field(\"item\", new arrow.Float32(), true),\n    ),\n  ),\n]);\nconst imageData = lancedb.makeArrowTable(\n  [\n    {\n      id: \"lancelot\",\n      description: \"Portrait of Sir Lancelot\",\n      image: imageBytes,\n      vector: [0.9, 0.1, 0.5, 0.4],\n    },\n  ],\n  { schema: imageSchema },\n);\nconst multimodalTable = await db.createTable(\n  \"character_images\",\n  imageData,\n  { mode: \"overwrite\" },\n);\n";
+
+export const TsQuickstartQueryFeature = "const features = await table\n  .query()\n  .select([\"name\", \"role\", \"power_score\"])\n  .toArray();\nconsole.table(features);\n";
+
+export const TsQuickstartVectorSearch1 = "// Search for examples similar to a \"wise magical advisor\"\nconst queryVector = [0.2, 0.8, 0.4, 0.9];\n\nconst result = await table\n  .search(queryVector)\n  .select([\"name\", \"role\", \"description\", \"_distance\"])\n  .limit(2)\n  .toArray();\nconsole.table(result);\n";
+
+export const RsQuickstartAddData = "let more_data = vec![Character {\n    id: \"4\".to_string(),\n    name: \"Morgana\".to_string(),\n    role: \"Sorceress\".to_string(),\n    description: \"Powerful sorceress of Avalon.\".to_string(),\n    stats: Stats {\n        strength: 2,\n        magic: 5,\n        leadership: 4,\n        wisdom: 4,\n    },\n    vector: [0.3, 0.9, 0.6, 0.8],\n}];\ntable\n    .add(characters_to_reader(schema.clone(), &more_data))\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsQuickstartAddFeature = "table\n    .add_columns(\n        NewColumnTransform::SqlExpressions(vec![(\n            \"power_score\".to_string(),\n            \"cast(((stats.strength + stats.magic + stats.leadership + stats.wisdom) / 4.0) as float)\"\n                .to_string(),\n        )]),\n        None,\n    )\n    .await\n    .unwrap();\n";
+
+export const RsQuickstartCreateTable = "use lancedb::database::CreateTableMode;\n\nlet schema = characters_schema();\nlet table = db\n    .create_table(\"characters\", characters_to_reader(schema.clone(), &data))\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsQuickstartCurateWithMetadata = "let curated = table\n    .query()\n    .nearest_to(&query_vector)\n    .unwrap()\n    .only_if(\"stats.magic >= 4\")\n    .select(Select::Columns(vec![\n        \"name\".to_string(),\n        \"role\".to_string(),\n        \"description\".to_string(),\n        \"_distance\".to_string(),\n    ]))\n    .limit(2)\n    .execute()\n    .await\n    .unwrap()\n    .into_polars()\n    .await\n    .unwrap();\nprintln!(\"{curated:?}\");\n";
+
+export const RsQuickstartData = "let data = vec![\n    Character {\n        id: \"1\".to_string(),\n        name: \"King Arthur\".to_string(),\n        role: \"King\".to_string(),\n        description: \"Leader of Camelot and wielder of Excalibur.\".to_string(),\n        stats: Stats {\n            strength: 4,\n            magic: 1,\n            leadership: 5,\n            wisdom: 4,\n        },\n        vector: [0.7, 0.1, 0.9, 0.7],\n    },\n    Character {\n        id: \"2\".to_string(),\n        name: \"Merlin\".to_string(),\n        role: \"Wizard\".to_string(),\n        description: \"Advisor and prophet with deep magical knowledge.\".to_string(),\n        stats: Stats {\n            strength: 2,\n            magic: 5,\n            leadership: 4,\n            wisdom: 5,\n        },\n        vector: [0.2, 0.9, 0.4, 0.9],\n    },\n    Character {\n        id: \"3\".to_string(),\n        name: \"Sir Lancelot\".to_string(),\n        role: \"Knight\".to_string(),\n        description: \"Legendary knight known for courage and combat skill.\".to_string(),\n        stats: Stats {\n            strength: 5,\n            magic: 1,\n            leadership: 3,\n            wisdom: 3,\n        },\n        vector: [0.9, 0.1, 0.5, 0.4],\n    },\n];\n";
+
+export const RsQuickstartDefineStruct = "use std::sync::Arc;\n\nuse lancedb::arrow::arrow_array::types::Float32Type;\nuse lancedb::arrow::arrow_array::{\n    FixedSizeListArray, Int8Array, LargeStringArray, RecordBatch, RecordBatchIterator,\n    RecordBatchReader, StructArray,\n};\nuse lancedb::arrow::arrow_schema::{DataType, Field, FieldRef, Fields, Schema};\n\n// Define structs representing the data schema\n#[derive(Debug, Clone)]\nstruct Stats {\n    strength: i8,\n    magic: i8,\n    leadership: i8,\n    wisdom: i8,\n}\n\n#[derive(Debug, Clone)]\nstruct Character {\n    id: String,\n    name: String,\n    role: String,\n    description: String,\n    stats: Stats,\n    vector: [f32; 4],\n}\n\nfn characters_schema() -> Arc<Schema> {\n    Arc::new(Schema::new(vec![\n        Field::new(\"id\", DataType::LargeUtf8, false),\n        Field::new(\"name\", DataType::LargeUtf8, false),\n        Field::new(\"role\", DataType::LargeUtf8, false),\n        Field::new(\"description\", DataType::LargeUtf8, false),\n        Field::new(\n            \"stats\",\n            DataType::Struct(Fields::from(vec![\n                Arc::new(Field::new(\"strength\", DataType::Int8, false)),\n                Arc::new(Field::new(\"magic\", DataType::Int8, false)),\n                Arc::new(Field::new(\"leadership\", DataType::Int8, false)),\n                Arc::new(Field::new(\"wisdom\", DataType::Int8, false)),\n            ])),\n            false,\n        ),\n        Field::new(\n            \"vector\",\n            DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 4),\n            false,\n        ),\n    ]))\n}\n\nfn characters_to_reader(\n    schema: Arc<Schema>,\n    rows: &[Character],\n) -> Box<dyn RecordBatchReader + Send> {\n    let ids = LargeStringArray::from_iter_values(rows.iter().map(|row| row.id.as_str()));\n    let names = LargeStringArray::from_iter_values(rows.iter().map(|row| row.name.as_str()));\n    let roles = LargeStringArray::from_iter_values(rows.iter().map(|row| row.role.as_str()));\n    let descriptions =\n        LargeStringArray::from_iter_values(rows.iter().map(|row| row.description.as_str()));\n\n    let strength = Int8Array::from_iter_values(rows.iter().map(|row| row.stats.strength));\n    let magic = Int8Array::from_iter_values(rows.iter().map(|row| row.stats.magic));\n    let leadership = Int8Array::from_iter_values(rows.iter().map(|row| row.stats.leadership));\n    let wisdom = Int8Array::from_iter_values(rows.iter().map(|row| row.stats.wisdom));\n    let stats_fields: Vec<FieldRef> = vec![\n        Arc::new(Field::new(\"strength\", DataType::Int8, false)),\n        Arc::new(Field::new(\"magic\", DataType::Int8, false)),\n        Arc::new(Field::new(\"leadership\", DataType::Int8, false)),\n        Arc::new(Field::new(\"wisdom\", DataType::Int8, false)),\n    ];\n    let stats = StructArray::new(\n        stats_fields.into(),\n        vec![\n            Arc::new(strength),\n            Arc::new(magic),\n            Arc::new(leadership),\n            Arc::new(wisdom),\n        ],\n        None,\n    );\n\n    let vectors = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n        rows.iter()\n            .map(|row| Some(row.vector.iter().copied().map(Some).collect::<Vec<_>>())),\n        4,\n    );\n\n    let batch = RecordBatch::try_new(\n        schema.clone(),\n        vec![\n            Arc::new(ids),\n            Arc::new(names),\n            Arc::new(roles),\n            Arc::new(descriptions),\n            Arc::new(stats),\n            Arc::new(vectors),\n        ],\n    )\n    .unwrap();\n\n    Box::new(RecordBatchIterator::new(\n        vec![Ok(batch)].into_iter(),\n        schema,\n    ))\n}\n";
+
+export const RsQuickstartMultimodalBytes = "use lancedb::arrow::arrow_array::BinaryArray;\n\nlet image_bytes = std::fs::read(\"data/sir-lancelot.jpg\").unwrap();\n\nlet image_schema = Arc::new(Schema::new(vec![\n    Field::new(\"id\", DataType::LargeUtf8, false),\n    Field::new(\"description\", DataType::LargeUtf8, false),\n    Field::new(\"image\", DataType::Binary, false),\n    Field::new(\n        \"vector\",\n        DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 4),\n        false,\n    ),\n]));\nlet image_vectors = [[0.9_f32, 0.1, 0.5, 0.4]];\nlet image_batch = RecordBatch::try_new(\n    image_schema.clone(),\n    vec![\n        Arc::new(LargeStringArray::from_iter_values([\"lancelot\"])),\n        Arc::new(LargeStringArray::from_iter_values([\n            \"Portrait of Sir Lancelot\",\n        ])),\n        Arc::new(BinaryArray::from_iter_values([image_bytes.as_slice()])),\n        Arc::new(\n            FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n                image_vectors\n                    .iter()\n                    .map(|vector| Some(vector.iter().copied().map(Some).collect::<Vec<_>>())),\n                4,\n            ),\n        ),\n    ],\n)\n.unwrap();\nlet image_reader: Box<dyn RecordBatchReader + Send> = Box::new(RecordBatchIterator::new(\n    vec![Ok(image_batch)].into_iter(),\n    image_schema,\n));\nlet multimodal_table = db\n    .create_table(\"character_images\", image_reader)\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsQuickstartOutputArray = "let result = table\n    .query()\n    .nearest_to(&query_vector)\n    .unwrap()\n    .select(Select::Columns(vec![\n        \"name\".to_string(),\n        \"role\".to_string(),\n        \"description\".to_string(),\n        \"_distance\".to_string(),\n    ]))\n    .limit(2)\n    .execute()\n    .await\n    .unwrap()\n    .into_polars()\n    .await\n    .unwrap();\nprintln!(\"{result:?}\");\n";
+
+export const RsQuickstartQueryFeature = "let features = table\n    .query()\n    .select(Select::Columns(vec![\n        \"name\".to_string(),\n        \"role\".to_string(),\n        \"power_score\".to_string(),\n    ]))\n    .execute()\n    .await\n    .unwrap()\n    .into_polars()\n    .await\n    .unwrap();\nprintln!(\"{features:?}\");\n";
+
+export const RsQuickstartVectorSearch1 = "use lancedb::arrow::IntoPolars;\nuse lancedb::query::{ExecutableQuery, QueryBase, Select};\n\n// Search for examples similar to a \"wise magical advisor\"\nlet query_vector = [0.2, 0.8, 0.4, 0.9];\n\nlet result = table\n    .query()\n    .nearest_to(&query_vector)\n    .unwrap()\n    .select(Select::Columns(vec![\n        \"name\".to_string(),\n        \"role\".to_string(),\n        \"description\".to_string(),\n        \"_distance\".to_string(),\n    ]))\n    .limit(2)\n    .execute()\n    .await\n    .unwrap()\n    .into_polars()\n    .await\n    .unwrap();\nprintln!(\"{result:?}\");\n";
+
diff --git a/docs/web/snippets/read_consistency.mdx b/docs/web/snippets/read_consistency.mdx
new file mode 100644
index 000000000..f99521a4d
--- /dev/null
+++ b/docs/web/snippets/read_consistency.mdx
@@ -0,0 +1,4 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyReadConsistency = "from datetime import timedelta\nfrom tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-consistency-\") as uri:\n    writer = lancedb.connect(uri)\n    table = writer.create_table(\"documents\", [{\"id\": 1, \"text\": \"first\"}])\n\n    default_reader = lancedb.connect(uri).open_table(\"documents\")\n    latest_reader = lancedb.connect(\n        uri, read_consistency_interval=timedelta(0)\n    ).open_table(\"documents\")\n\n    table.add([{\"id\": 2, \"text\": \"second\"}])\n\n    default_count = default_reader.count_rows()\n    latest_count = latest_reader.count_rows()\n    print(f\"Default reader: {default_count} rows\")  # 1\n    print(f\"Zero-interval reader: {latest_count} rows\")  # 2\n\n    default_reader.checkout_latest()\n    refreshed_count = default_reader.count_rows()\n    print(f\"After explicit refresh: {refreshed_count} rows\")  # 2\n";
+
diff --git a/docs/web/snippets/search.mdx b/docs/web/snippets/search.mdx
new file mode 100644
index 000000000..4d9f0eccb
--- /dev/null
+++ b/docs/web/snippets/search.mdx
@@ -0,0 +1,172 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyBasicFts = "uri = \"data/sample-lancedb\"\ndb = lancedb.connect(uri)\n\ntable = db.create_table(\n    \"my_table_fts\",\n    data=[\n        {\"vector\": [3.1, 4.1], \"text\": \"Frodo was a happy puppy\"},\n        {\"vector\": [5.9, 26.5], \"text\": \"There are several kittens playing\"},\n    ],\n    mode=\"overwrite\",\n)\n\ntable.create_fts_index(\"text\")\ntable.search(\"puppy\").limit(10).select([\"text\"]).to_list()\n# [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}]\n# ...\n";
+
+export const PyBasicFtsAsync = "uri = \"data/sample-lancedb\"\nasync_db = await lancedb.connect_async(uri)\n\nasync_tbl = await async_db.create_table(\n    \"my_table_fts_async\",\n    data=[\n        {\"vector\": [3.1, 4.1], \"text\": \"Frodo was a happy puppy\"},\n        {\"vector\": [5.9, 26.5], \"text\": \"There are several kittens playing\"},\n    ],\n    mode=\"overwrite\",\n)\n\n# async API uses our native FTS algorithm\nawait async_tbl.create_index(\"text\", config=FTS())\nawait (await async_tbl.search(\"puppy\")).select([\"text\"]).limit(10).to_list()\n# [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}]\n# ...\n";
+
+export const PyBasicHybridSearch = "data = [\n    {\"text\": \"rebel spaceships striking from a hidden base\"},\n    {\"text\": \"have won their first victory against the evil Galactic Empire\"},\n    {\"text\": \"during the battle rebel spies managed to steal secret plans\"},\n    {\"text\": \"to the Empire's ultimate weapon the Death Star\"},\n]\nuri = \"data/sample-lancedb\"\ndb = lancedb.connect(uri)\ntable = db.create_table(\"documents\", schema=Documents)\n# ingest docs with auto-vectorization\ntable.add(data)\n# Create a fts index before the hybrid search\ntable.create_fts_index(\"text\")\n# hybrid search with default re-ranker\ntable.search(\"flower moon\", query_type=\"hybrid\").to_pandas()\n";
+
+export const PyBasicHybridSearchAsync = "uri = \"data/sample-lancedb\"\nasync_db = await lancedb.connect_async(uri)\ndata = [\n    {\"text\": \"rebel spaceships striking from a hidden base\"},\n    {\"text\": \"have won their first victory against the evil Galactic Empire\"},\n    {\"text\": \"during the battle rebel spies managed to steal secret plans\"},\n    {\"text\": \"to the Empire's ultimate weapon the Death Star\"},\n]\nasync_tbl = await async_db.create_table(\"documents_async\", schema=Documents)\n# ingest docs with auto-vectorization\nawait async_tbl.add(data)\n# Create a fts index before the hybrid search\nawait async_tbl.create_index(\"text\", config=FTS())\ntext_query = \"flower moon\"\n# hybrid search with default re-ranker\nawait (await async_tbl.search(\"flower moon\", query_type=\"hybrid\")).to_pandas()\n";
+
+export const PyBatchSearch = "# Load a batch of query embeddings\nquery_dataset = load_dataset(\n    \"sunhaozhepy/ag_news_sbert_keywords_embeddings\", split=\"test[5000:5005]\"\n)\nquery_embeds = query_dataset[\"keywords_embeddings\"]\nbatch_results = table.search(query_embeds).limit(5).to_pandas()\nprint(batch_results)\n";
+
+export const PyBruteForceSearch = "tbl.search(np.random.random((1536))).limit(3).to_list()\n";
+
+export const PyBypassVectorIndex = "table.search(embedding).bypass_vector_index().limit(5).to_pandas()\n";
+
+export const PyClassDefinition = "class Metadata(BaseModel):\n    source: str\n    timestamp: datetime\n\n\nclass Document(BaseModel):\n    content: str\n    meta: Metadata\n\n\nclass LanceSchema(LanceModel):\n    id: str\n    vector: Vector(1536)\n    payload: Document\n";
+
+export const PyClassDocuments = "class Documents(LanceModel):\n    vector: Vector(embeddings.ndims()) = embeddings.VectorField()\n    text: str = embeddings.SourceField()\n";
+
+export const PyConfigureDistanceMetric = "tbl.search(np.random.random((1536))).distance_type(\"cosine\").limit(10).to_list()\n";
+
+export const PyCreateTableAsyncWithNestedSchema = "# Let's add 100 sample rows to our dataset\ndata = [\n    LanceSchema(\n        id=f\"id{i}\",\n        vector=np.random.randn(1536),\n        payload=Document(\n            content=f\"document{i}\",\n            meta=Metadata(source=f\"source{i % 10}\", timestamp=datetime.now()),\n        ),\n    )\n    for i in range(100)\n]\n\nasync_tbl = await async_db.create_table(\n    \"documents_async\", data=data, mode=\"overwrite\"\n)\n";
+
+export const PyCreateTableWithNestedSchema = "# Let's add 100 sample rows to our dataset\ndata = [\n    LanceSchema(\n        id=f\"id{i}\",\n        vector=np.random.randn(1536),\n        payload=Document(\n            content=f\"document{i}\",\n            meta=Metadata(source=f\"source{i % 10}\", timestamp=datetime.now()),\n        ),\n    )\n    for i in range(100)\n]\n\n# Synchronous client\ntbl = db.create_table(\"documents\", data=data, mode=\"overwrite\")\n";
+
+export const PyExactVsApproximateDistances = "# Indexed ANN search without refinement (fast, approximate `_distance`)\nfast_results = (\n    table.search(embedding)\n    .limit(10)\n    .to_pandas()\n)\n\n# Recompute distances on full vectors for reranked candidates\nexact_distance_results = (\n    table.search(embedding)\n    .limit(10)\n    .refine_factor(1)\n    .to_pandas()\n)\n\n# Rerank a larger candidate set for better recall (higher latency)\nhigher_recall_results = (\n    table.search(embedding)\n    .limit(10)\n    .refine_factor(20)\n    .to_pandas()\n)\n";
+
+export const PyExhaustiveSearch = "uri = \"data/sample-lancedb\"\ndb = lancedb.connect(uri)\ndata = [\n    {\"vector\": row, \"item\": f\"item {i}\"}\n    for i, row in enumerate(np.random.random((10_000, 1536)).astype(\"float32\"))\n]\ntbl = db.create_table(\"vector_search\", data=data, mode=\"overwrite\")\ntbl.search(np.random.random((1536))).limit(10).to_list()\n";
+
+export const PyExhaustiveSearchAsync = "uri = \"data/sample-lancedb\"\nasync_db = await lancedb.connect_async(uri)\ndata = [\n    {\"vector\": row, \"item\": f\"item {i}\"}\n    for i, row in enumerate(np.random.random((10_000, 1536)).astype(\"float32\"))\n]\nasync_tbl = await async_db.create_table(\n    \"vector_search_async\", data=data, mode=\"overwrite\"\n)\n(await (await async_tbl.search(np.random.random((1536)))).limit(10).to_list())\n";
+
+export const PyExhaustiveSearchAsyncCosine = "(\n    await (await async_tbl.search(np.random.random((1536))))\n    .distance_type(\"cosine\")\n    .limit(10)\n    .to_list()\n)\n";
+
+export const PyExhaustiveSearchCosine = "tbl.search(np.random.random((1536))).distance_type(\"cosine\").limit(10).to_list()\n";
+
+export const PyFastSearch = "table.search(embedding, fast_search=True).limit(5).to_pandas()\n";
+
+export const PyFtsConfigFolding = "table.create_fts_index(\n    \"text\",\n    language=\"French\",\n    stem=True,\n    ascii_folding=True,\n    replace=True,\n)\n";
+
+export const PyFtsConfigFoldingAsync = "await async_tbl.create_index(\n    \"text\", config=FTS(language=\"French\", stem=True, ascii_folding=True)\n)\n";
+
+export const PyFtsConfigStem = "table.create_fts_index(\"text\", tokenizer_name=\"en_stem\", replace=True)\n";
+
+export const PyFtsConfigStemAsync = "await async_tbl.create_index(\n    \"text\", config=FTS(language=\"English\", stem=True, remove_stop_words=True)\n)\n";
+
+export const PyFtsIncrementalIndex = "table.add([{\"vector\": [3.1, 4.1], \"text\": \"Frodo was a happy puppy\"}])\ntable.optimize()\n";
+
+export const PyFtsIncrementalIndexAsync = "await async_tbl.add([{\"vector\": [3.1, 4.1], \"text\": \"Frodo was a happy puppy\"}])\nawait async_tbl.optimize()\n";
+
+export const PyFtsPostfiltering = "table.search(\"puppy\").limit(10).where(\"text='foo'\", prefilter=False).to_list()\n";
+
+export const PyFtsPostfilteringAsync = "await (\n    (await async_tbl.search(\"puppy\"))\n    .limit(10)\n    .where(\"text='foo'\")\n    .postfilter()\n    .to_list()\n)\n";
+
+export const PyFtsPrefiltering = "table.search(\"puppy\").limit(10).where(\"text='foo'\", prefilter=True).to_list()\n";
+
+export const PyFtsPrefilteringAsync = "await (await async_tbl.search(\"puppy\")).limit(10).where(\"text='foo'\").to_list()\n";
+
+export const PyFtsWithPosition = "table.create_fts_index(\"text\", with_position=True, replace=True)\n";
+
+export const PyFtsWithPositionAsync = "await async_tbl.create_index(\"text\", config=FTS(with_position=True))\n";
+
+export const PyHybridSearchPassVectorText = "vector_query = [0.1, 0.2, 0.3, 0.4, 0.5]\ntext_query = \"flower moon\"\n(\n    table.search(query_type=\"hybrid\")\n    .vector(vector_query)\n    .text(text_query)\n    .limit(5)\n    .to_pandas()\n)\n";
+
+export const PyHybridSearchPassVectorTextAsync = "vector_query = [0.1, 0.2, 0.3, 0.4, 0.5]\ntext_query = \"flower moon\"\nawait (\n    async_tbl.query()\n    .nearest_to(vector_query)\n    .nearest_to_text(text_query)\n    .limit(5)\n    .to_pandas()\n)\n";
+
+export const PyImportDatetime = "from datetime import datetime\n";
+
+export const PyImportEmbeddings = "from lancedb.embeddings import get_registry\n";
+
+export const PyImportLancedb = "import lancedb\n";
+
+export const PyImportLancedbFts = "from lancedb.index import FTS\n";
+
+export const PyImportLancedbPydantic = "from lancedb.pydantic import Vector, LanceModel\n";
+
+export const PyImportNumpy = "from lancedb.query import BoostQuery, MatchQuery\nimport numpy as np\nimport pyarrow as pa\n";
+
+export const PyImportOpenai = "import openai\n";
+
+export const PyImportOs = "import os\n";
+
+export const PyImportPydanticBaseModel = "from pydantic import BaseModel\n";
+
+export const PyIndexNestedColumn = "table.create_index(vector_column_name=\"image.embedding\")\n";
+
+export const PyMultivectorSearch = "query_multi = np.random.random(size=(2, 256))\nresults_multi = tbl.search(query_multi).limit(5).to_pandas()\n";
+
+export const PyOpenaiEmbeddings = "# Ingest embedding function in LanceDB table\n# Configuring the environment variable OPENAI_API_KEY\nif \"OPENAI_API_KEY\" not in os.environ:\n    # OR set the key here as a variable\n    openai.api_key = \"sk-...\"\nembeddings = get_registry().get(\"openai\").create()\n";
+
+export const PySearchBinaryVectors = "import numpy as np\nimport pyarrow as pa\n\nschema = pa.schema(\n    [\n        pa.field(\"id\", pa.int64()),\n        # for dim=256, lance stores every 8 bits in a byte\n        # so the vector field should be a list of 256 / 8 = 32 bytes\n        pa.field(\"vector\", pa.list_(pa.uint8(), 32)),\n    ]\n)\ntbl = db.create_table(\"my_binary_vectors\", schema=schema)\n\ndata = []\nfor i in range(1024):\n    vector = np.random.randint(0, 2, size=256)\n    # pack the binary vector into bytes to save space\n    packed_vector = np.packbits(vector)\n    data.append(\n        {\n            \"id\": i,\n            \"vector\": packed_vector,\n        }\n    )\ntbl.add(data)\n\nquery = np.random.randint(0, 2, size=256)\npacked_query = np.packbits(query)\ntbl.search(packed_query).distance_type(\"hamming\").to_arrow()\n";
+
+export const PySearchDistanceRange = "query = np.random.random(256)\n\n# Search for the vectors within the range of [0.1, 0.5)\ntbl.search(query).distance_range(0.1, 0.5).to_arrow()\n\n# Search for the vectors with the distance less than 0.5\ntbl.search(query).distance_range(upper_bound=0.5).to_arrow()\n\n# Search for the vectors with the distance greater or equal to 0.1\ntbl.search(query).distance_range(lower_bound=0.1).to_arrow()\n";
+
+export const PySearchResultAsList = "tbl.search(np.random.randn(1536)).to_list()\n";
+
+export const PySearchResultAsPandas = "tbl.search(np.random.randn(1536)).to_pandas()\n";
+
+export const PySearchResultAsPandasFlatten1 = "tbl.search(np.random.randn(1536)).to_pandas(flatten=1)\n";
+
+export const PySearchResultAsPandasFlattenTrue = "tbl.search(np.random.randn(1536)).to_pandas(flatten=True)\n";
+
+export const PySearchResultAsPyarrow = "tbl.search(np.random.randn(1536)).to_arrow()\n";
+
+export const PySearchResultAsPydantic = "tbl.search(np.random.randn(1536)).to_pydantic(LanceSchema)\n";
+
+export const PySearchResultAsyncAsList = "await (await async_tbl.search(np.random.randn(1536))).to_list()\n";
+
+export const PySearchResultAsyncAsPandas = "await (await async_tbl.search(np.random.randn(1536))).to_pandas()\n";
+
+export const PySearchResultAsyncAsPyarrow = "await (await async_tbl.search(np.random.randn(1536))).to_arrow()\n";
+
+export const PySelectVectorColumn = "import pyarrow as pa\n\nschema = pa.schema([\n    pa.field(\"id\", pa.int32()),\n    pa.field(\n        \"image\",\n        pa.struct([pa.field(\"embedding\", pa.list_(pa.float32(), 2))]),\n    ),\n])\ntable = db.create_table(\n    \"nested\",\n    data=[{\"id\": 0, \"image\": {\"embedding\": [0.0, 1.0]}}],\n    schema=schema,\n)\n\n# Inferred: the only vector leaf is `image.embedding`.\ntable.search([0.0, 1.0]).limit(1).to_list()\n\n# Explicit: required when more than one vector column matches.\ntable.search([0.0, 1.0], vector_column_name=\"image.embedding\").limit(1).to_list()\n";
+
+export const PyVectorSearchPostfilter = "results_post_filtered = (\n    table.search(query_embed)\n    .where(\"label > 1\", prefilter=False)\n    .select([\"text\", \"keywords\", \"label\"])\n    .limit(5)\n    .to_pandas()\n)\n\nprint(\"Vector search results with post-filter:\")\nprint(results_post_filtered)\n";
+
+export const PyVectorSearchPrefilter = "from datasets import load_dataset\n\n# Load query vector from dataset\nquery_dataset = load_dataset(\"sunhaozhepy/ag_news_sbert_keywords_embeddings\", split=\"test[5000:5001]\")\nprint(f\"Query keywords: {query_dataset[0]['keywords']}\")\nquery_embed = query_dataset[\"keywords_embeddings\"][0]\n\n# Open table and perform search\ntable_name = \"lancedb-enterprise-quickstart\"\ntable = db.open_table(table_name)\n\n# Vector search with filters (pre-filtering is the default)\nsearch_results = (\n    table.search(query_embed)\n    .where(\"label > 2\")\n    .select([\"text\", \"keywords\", \"label\"])\n    .limit(5)\n    .to_pandas()\n)\n\nprint(\"Search results (with pre-filtering):\")\nprint(search_results)\n";
+
+export const TsBatchSearch = "// Batch query\nconsole.log(\"Performing batch vector search...\");\nconst batchSize = 5;\nconst queryVectors = Array.from({ length: batchSize }, () =>\n  Array.from({ length: dimensions }, () => Math.random() * 2 - 1),\n);\nlet batchQuery = table.search(queryVectors[0]) as lancedb.VectorQuery;\nfor (let i = 1; i < batchSize; i++) {\n  batchQuery = batchQuery.addQueryVector(queryVectors[i]);\n}\nconst batchResults = await batchQuery\n  .select([\"text\", \"keywords\", \"label\"])\n  .limit(5)\n  .toArray();\nconsole.log(\"Batch vector search results:\");\nconsole.log(batchResults);\n";
+
+export const TsBinarySearch = "const tbl = await db.createTable(\"binary_vectors\", data, {\n  mode: \"overwrite\",\n});\nawait tbl.createIndex(\"vector\", {\n  config: lancedb.Index.ivfFlat({\n    numPartitions: 10,\n    distanceType: \"hamming\",\n  }),\n});\n\nconst query = Array(32)\n  .fill(1)\n  .map(() => Math.floor(Math.random() * 255));\nconst results = await tbl.query().nearestTo(query).limit(10).toArray();\n";
+
+export const TsBruteForceSearch = "const tbl = await db.openTable(\"my_vectors\");\n\nconst results1 = await tbl.search(Array(128).fill(1.2)).limit(3).toArray();\n";
+
+export const TsBypassVectorIndex = "await table\n  .query()\n  .nearestTo(embedding)\n  .bypassVectorIndex()\n  .limit(5)\n  .toArray();\n";
+
+export const TsDistanceRange = "const results3 = await (\n  tbl.search(Array(128).fill(1.2)) as lancedb.VectorQuery\n)\n  .distanceType(\"cosine\")\n  .distanceRange(0.1, 0.2)\n  .limit(10)\n  .toArray();\n";
+
+export const TsExactVsApproximate = "// Indexed ANN search without refinement (fast, approximate `_distance`)\nconst fastResults = await (table.search(embedding) as lancedb.VectorQuery)\n  .limit(10)\n  .toArray();\n\n// Recompute distances on full vectors for reranked candidates\nconst exactDistanceResults = await (\n  table.search(embedding) as lancedb.VectorQuery\n)\n  .limit(10)\n  .refineFactor(1)\n  .toArray();\n\n// Rerank a larger candidate set for better recall (higher latency)\nconst higherRecallResults = await (\n  table.search(embedding) as lancedb.VectorQuery\n)\n  .limit(10)\n  .refineFactor(20)\n  .toArray();\n";
+
+export const TsFastSearch = "await table\n  .query()\n  .nearestTo(embedding)\n  .fastSearch()\n  .limit(5)\n  .toArray();\n";
+
+export const TsImport = "import * as lancedb from \"@lancedb/lancedb\";\n";
+
+export const TsImportBinUtil = "import { Field, FixedSizeList, Int32, Schema, Uint8 } from \"apache-arrow\";\n";
+
+export const TsIndexNestedColumn = "await table.createIndex(\"image.embedding\");\n";
+
+export const TsIngestBinaryData = "const schema = new Schema([\n  new Field(\"id\", new Int32(), true),\n  new Field(\"vec\", new FixedSizeList(32, new Field(\"item\", new Uint8()))),\n]);\nconst data = lancedb.makeArrowTable(\n  Array(1_000)\n    .fill(0)\n    .map((_, i) => ({\n      // the 256 bits would be store in 32 bytes,\n      // if your data is already in this format, you can skip the packBits step\n      id: i,\n      vec: lancedb.packBits(Array(256).fill(i % 2)),\n    })),\n  { schema: schema },\n);\n\nconst tbl = await db.createTable(\"binary_table\", data);\nawait tbl.createIndex(\"vec\", {\n  config: lancedb.Index.ivfFlat({\n    numPartitions: 10,\n    distanceType: \"hamming\",\n  }),\n});\n";
+
+export const TsSearch1 = "const db = await lancedb.connect(databaseDir);\nconst tbl = await db.openTable(\"my_vectors\");\n\nconst results1 = await tbl.search(Array(128).fill(1.2)).limit(10).toArray();\n";
+
+export const TsSearch2 = "const results2 = await (\n  tbl.search(Array(128).fill(1.2)) as lancedb.VectorQuery\n)\n  .distanceType(\"cosine\")\n  .limit(10)\n  .toArray();\n";
+
+export const TsSearchBinaryData = "const query = Array(32)\n  .fill(1)\n  .map(() => Math.floor(Math.random() * 255));\nconst results = await tbl.query().nearestTo(query).limit(10).toArrow();\n";
+
+export const TsSelectVectorColumn = "const table = await db.openTable(\"nested\");\n\n// Inferred: LanceDB finds the single nested vector leaf automatically.\nawait table.query().nearestTo([0.0, 1.0]).limit(1).toArray();\n\n// Explicit: required when more than one vector column matches.\nawait table\n  .query()\n  .nearestTo([0.0, 1.0])\n  .column(\"image.embedding\")\n  .limit(1)\n  .toArray();\n";
+
+export const TsVectorSearchPostfilter = "const vectorResultsWithPostFilter = await (\n  table.search(queryEmbed) as lancedb.VectorQuery\n)\n  .where(\"label > 2\")\n  .postfilter()\n  .select([\"text\", \"keywords\", \"label\"])\n  .limit(5)\n  .toArray();\n\nconsole.log(\"Vector search results with post-filter:\");\nconsole.log(vectorResultsWithPostFilter);\n";
+
+export const TsVectorSearchPrefilter = "// Generate a sample 768-dimension embedding vector (typical for BERT-based models)\n// In real applications, you would get this from an embedding model\nconst dimensions = 768;\nconst queryEmbed = Array.from(\n  { length: dimensions },\n  () => Math.random() * 2 - 1,\n);\n\n// Open table and perform search\nconst tableName = \"lancedb-enterprise-quickstart\";\nconst table = await db.openTable(tableName);\n\n// Vector search with filters (pre-filtering is the default)\nconst vectorResults = await table\n  .search(queryEmbed)\n  .where(\"label > 2\")\n  .select([\"text\", \"keywords\", \"label\"])\n  .limit(5)\n  .toArray();\n\nconsole.log(\"Search results (with pre-filtering):\");\nconsole.log(vectorResults);\n";
+
+export const RsBatchSearch = "// Search multiple query vectors in one call. Each result row carries a\n// `query_index` mapping it back to the query it matched.\nlet mut results = table\n    .vector_search(&query_1)?\n    .add_query_vector(&query_2)?\n    .limit(5)\n    .execute()\n    .await?;\nwhile let Some(batch) = results.try_next().await? {\n    println!(\"{:?}\", batch);\n}\n";
+
+export const RsBinarySearch = "// Binary vectors use `hamming` distance over the packed uint8 bytes.\nlet query: Arc<dyn Array> = Arc::new(UInt8Array::from(vec![1u8; NUM_BYTES as usize]));\nlet mut results = tbl\n    .vector_search(query)?\n    .distance_type(DistanceType::Hamming)\n    .limit(10)\n    .execute()\n    .await?;\nwhile let Some(batch) = results.try_next().await? {\n    println!(\"{:?}\", batch);\n}\n";
+
+export const RsBruteForceSearch = "// A plain vector search returns the top-k closest rows.\nlet mut results = table.vector_search(&query_vector)?.limit(3).execute().await?;\nwhile let Some(batch) = results.try_next().await? {\n    println!(\"{:?}\", batch);\n}\n";
+
+export const RsBypassVectorIndex = "// Force an exhaustive (flat) scan for exact, ground-truth results.\nlet mut results = table\n    .vector_search(&query_vector)?\n    .bypass_vector_index()\n    .limit(5)\n    .execute()\n    .await?;\nwhile let Some(batch) = results.try_next().await? {\n    println!(\"{:?}\", batch);\n}\n";
+
+export const RsConfigureDistanceMetric = "// Use the same distance metric the index was trained with.\nlet mut results = table\n    .vector_search(&query_vector)?\n    .distance_type(DistanceType::Cosine)\n    .limit(10)\n    .execute()\n    .await?;\nwhile let Some(batch) = results.try_next().await? {\n    println!(\"{:?}\", batch);\n}\n";
+
+export const RsExactVsApproximate = "// Approximate ANN search (fast, distances may come from the index representation)\nlet mut fast_results = table.vector_search(&query_vector)?.limit(10).execute().await?;\nwhile let Some(batch) = fast_results.try_next().await? {\n    println!(\"{:?}\", batch);\n}\n\n// Rerank a larger candidate set on full vectors for better recall\nlet mut refined_results = table\n    .vector_search(&query_vector)?\n    .limit(10)\n    .refine_factor(20)\n    .execute()\n    .await?;\nwhile let Some(batch) = refined_results.try_next().await? {\n    println!(\"{:?}\", batch);\n}\n";
+
+export const RsFastSearch = "// Skip unindexed data for lower latency.\nlet mut results = table\n    .vector_search(&query_vector)?\n    .fast_search()\n    .limit(5)\n    .execute()\n    .await?;\nwhile let Some(batch) = results.try_next().await? {\n    println!(\"{:?}\", batch);\n}\n";
+
+export const RsSearchDistanceRange = "// Only return rows whose distance falls within [0.1, 0.5).\nlet mut results = table\n    .vector_search(&query_vector)?\n    .distance_range(Some(0.1), Some(0.5))\n    .execute()\n    .await?;\nwhile let Some(batch) = results.try_next().await? {\n    println!(\"{:?}\", batch);\n}\n";
+
+export const RsVectorSearchPostfilter = "// Apply the filter after vector search by calling postfilter().\nlet mut results = table\n    .vector_search(&query_vector)?\n    .only_if(\"id > 100\")\n    .postfilter()\n    .select(Select::columns(&[\"id\"]))\n    .limit(5)\n    .execute()\n    .await?;\nwhile let Some(batch) = results.try_next().await? {\n    println!(\"{:?}\", batch);\n}\n";
+
+export const RsVectorSearchPrefilter = "// Prefiltering is the default: the filter is applied before vector search.\nlet mut results = table\n    .vector_search(&query_vector)?\n    .only_if(\"id > 100\")\n    .select(Select::columns(&[\"id\"]))\n    .limit(5)\n    .execute()\n    .await?;\nwhile let Some(batch) = results.try_next().await? {\n    println!(\"{:?}\", batch);\n}\n";
+
diff --git a/docs/web/snippets/search_examples.mdx b/docs/web/snippets/search_examples.mdx
new file mode 100644
index 000000000..a9aeb5e7f
--- /dev/null
+++ b/docs/web/snippets/search_examples.mdx
@@ -0,0 +1,32 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyFilterSearch = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-search-\") as uri:\n    db = lancedb.connect(uri)\n    table = db.create_table(\n        \"documents\",\n        [\n            {\n                \"id\": 1,\n                \"text\": \"A puppy playing in the park\",\n                \"category\": \"animals\",\n                \"vector\": [1.0, 0.0],\n            },\n            {\n                \"id\": 2,\n                \"text\": \"A dog resting at home\",\n                \"category\": \"animals\",\n                \"vector\": [0.9, 0.1],\n            },\n            {\n                \"id\": 3,\n                \"text\": \"A guide to growing tomatoes\",\n                \"category\": \"gardening\",\n                \"vector\": [0.0, 1.0],\n            },\n        ],\n    )\n    rows = (\n        table.search([0.0, 1.0])\n        .where(\"category = 'animals'\", prefilter=True)\n        .select([\"id\", \"text\", \"category\", \"_distance\"])\n        .limit(2)\n        .to_list()\n    )\n    print(rows)\n";
+
+export const PyHybridSearch = "from tempfile import TemporaryDirectory\n\nimport lancedb\nfrom lancedb.index import FTS\n\nwith TemporaryDirectory(prefix=\"lancedb-search-\") as uri:\n    db = lancedb.connect(uri)\n    table = db.create_table(\n        \"documents\",\n        [\n            {\n                \"id\": 1,\n                \"text\": \"A puppy playing in the park\",\n                \"category\": \"animals\",\n                \"vector\": [1.0, 0.0],\n            },\n            {\n                \"id\": 2,\n                \"text\": \"A dog resting at home\",\n                \"category\": \"animals\",\n                \"vector\": [0.9, 0.1],\n            },\n            {\n                \"id\": 3,\n                \"text\": \"A guide to growing tomatoes\",\n                \"category\": \"gardening\",\n                \"vector\": [0.0, 1.0],\n            },\n        ],\n    )\n    from lancedb.rerankers import RRFReranker\n\n    table.create_index(\"text\", config=FTS())\n    rows = (\n        table.search(\n            query_type=\"hybrid\", vector_column_name=\"vector\", fts_columns=\"text\"\n        )\n        .vector([1.0, 0.0])\n        .text(\"puppy\")\n        .rerank(RRFReranker())\n        .limit(2)\n        .to_list()\n    )\n    print(rows)\n";
+
+export const PyQueryPlan = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-search-\") as uri:\n    db = lancedb.connect(uri)\n    table = db.create_table(\n        \"documents\",\n        [\n            {\n                \"id\": 1,\n                \"text\": \"A puppy playing in the park\",\n                \"category\": \"animals\",\n                \"vector\": [1.0, 0.0],\n            },\n            {\n                \"id\": 2,\n                \"text\": \"A dog resting at home\",\n                \"category\": \"animals\",\n                \"vector\": [0.9, 0.1],\n            },\n            {\n                \"id\": 3,\n                \"text\": \"A guide to growing tomatoes\",\n                \"category\": \"gardening\",\n                \"vector\": [0.0, 1.0],\n            },\n        ],\n    )\n    query = table.search([1.0, 0.0]).select([\"id\", \"text\", \"_distance\"]).limit(2)\n    print(\"Plan:\", query.explain_plan())\n    print(\"Measured execution:\", query.analyze_plan())\n    rows = query.to_list()\n    print(rows)\n";
+
+export const PyTextSearch = "from tempfile import TemporaryDirectory\n\nimport lancedb\nfrom lancedb.index import FTS\n\nwith TemporaryDirectory(prefix=\"lancedb-search-\") as uri:\n    db = lancedb.connect(uri)\n    table = db.create_table(\n        \"documents\",\n        [\n            {\n                \"id\": 1,\n                \"text\": \"A puppy playing in the park\",\n                \"category\": \"animals\",\n            },\n            {\n                \"id\": 2,\n                \"text\": \"A dog resting at home\",\n                \"category\": \"animals\",\n            },\n            {\n                \"id\": 3,\n                \"text\": \"A guide to growing tomatoes\",\n                \"category\": \"gardening\",\n            },\n        ],\n    )\n    table.create_index(\"text\", config=FTS())\n    rows = (\n        table.search(\"puppy\", query_type=\"fts\", fts_columns=\"text\")\n        .select([\"id\", \"text\", \"_score\"])\n        .limit(2)\n        .to_list()\n    )\n    print(rows)\n";
+
+export const PyVectorSearch = "from tempfile import TemporaryDirectory\n\nimport lancedb\n\nwith TemporaryDirectory(prefix=\"lancedb-search-\") as uri:\n    db = lancedb.connect(uri)\n    table = db.create_table(\n        \"documents\",\n        [\n            {\n                \"id\": 1,\n                \"text\": \"A puppy playing in the park\",\n                \"category\": \"animals\",\n                \"vector\": [1.0, 0.0],\n            },\n            {\n                \"id\": 2,\n                \"text\": \"A dog resting at home\",\n                \"category\": \"animals\",\n                \"vector\": [0.9, 0.1],\n            },\n            {\n                \"id\": 3,\n                \"text\": \"A guide to growing tomatoes\",\n                \"category\": \"gardening\",\n                \"vector\": [0.0, 1.0],\n            },\n        ],\n    )\n    rows = (\n        table.search([1.0, 0.0])\n        .distance_type(\"l2\")\n        .select([\"id\", \"text\", \"_distance\"])\n        .limit(2)\n        .to_list()\n    )\n    print(rows)\n";
+
+export const TsFilterSearchBody = "const lancedb = await import(\"@lancedb/lancedb\");\nconst { mkdtemp, rm } = await import(\"node:fs/promises\");\nconst { tmpdir } = await import(\"node:os\");\nconst { join } = await import(\"node:path\");\nconst uri = await mkdtemp(join(tmpdir(), \"lancedb-search-\"));\ntry {\n  const db = await lancedb.connect(uri);\n  const table = await db.createTable(\"documents\", [\n    {\n      id: 1,\n      text: \"A puppy playing in the park\",\n      category: \"animals\",\n      vector: [1.0, 0.0],\n    },\n    {\n      id: 2,\n      text: \"A dog resting at home\",\n      category: \"animals\",\n      vector: [0.9, 0.1],\n    },\n    {\n      id: 3,\n      text: \"A guide to growing tomatoes\",\n      category: \"gardening\",\n      vector: [0.0, 1.0],\n    },\n  ]);\n  const rows = await table\n    .vectorSearch([0.0, 1.0])\n    .where(\"category = 'animals'\")\n    .select([\"id\", \"text\", \"category\", \"_distance\"])\n    .limit(2)\n    .toArray();\n  console.log(rows);\n";
+
+export const TsFilterSearchTail = "} finally {\n  await rm(uri, { recursive: true, force: true });\n}\n";
+
+export const TsHybridSearchBody = "const lancedb = await import(\"@lancedb/lancedb\");\nconst { mkdtemp, rm } = await import(\"node:fs/promises\");\nconst { tmpdir } = await import(\"node:os\");\nconst { join } = await import(\"node:path\");\nconst uri = await mkdtemp(join(tmpdir(), \"lancedb-search-\"));\ntry {\n  const db = await lancedb.connect(uri);\n  const table = await db.createTable(\"documents\", [\n    {\n      id: 1,\n      text: \"A puppy playing in the park\",\n      category: \"animals\",\n      vector: [1.0, 0.0],\n    },\n    {\n      id: 2,\n      text: \"A dog resting at home\",\n      category: \"animals\",\n      vector: [0.9, 0.1],\n    },\n    {\n      id: 3,\n      text: \"A guide to growing tomatoes\",\n      category: \"gardening\",\n      vector: [0.0, 1.0],\n    },\n  ]);\n  await table.createIndex(\"text\", { config: lancedb.Index.fts() });\n  const reranker = await lancedb.rerankers.RRFReranker.create();\n  const rows = await table\n    .vectorSearch([1.0, 0.0])\n    .fullTextSearch(\"puppy\", { columns: [\"text\"] })\n    .rerank(reranker)\n    .limit(2)\n    .toArray();\n  console.log(rows);\n";
+
+export const TsHybridSearchTail = "} finally {\n  await rm(uri, { recursive: true, force: true });\n}\n";
+
+export const TsQueryPlanBody = "const lancedb = await import(\"@lancedb/lancedb\");\nconst { mkdtemp, rm } = await import(\"node:fs/promises\");\nconst { tmpdir } = await import(\"node:os\");\nconst { join } = await import(\"node:path\");\nconst uri = await mkdtemp(join(tmpdir(), \"lancedb-search-\"));\ntry {\n  const db = await lancedb.connect(uri);\n  const table = await db.createTable(\"documents\", [\n    {\n      id: 1,\n      text: \"A puppy playing in the park\",\n      category: \"animals\",\n      vector: [1.0, 0.0],\n    },\n    {\n      id: 2,\n      text: \"A dog resting at home\",\n      category: \"animals\",\n      vector: [0.9, 0.1],\n    },\n    {\n      id: 3,\n      text: \"A guide to growing tomatoes\",\n      category: \"gardening\",\n      vector: [0.0, 1.0],\n    },\n  ]);\n  const query = table\n    .vectorSearch([1.0, 0.0])\n    .select([\"id\", \"text\", \"_distance\"])\n    .limit(2);\n  console.log(\"Plan:\", await query.explainPlan());\n  console.log(\"Measured execution:\", await query.analyzePlan());\n  const rows = await query.toArray();\n  console.log(rows);\n";
+
+export const TsQueryPlanTail = "} finally {\n  await rm(uri, { recursive: true, force: true });\n}\n";
+
+export const TsTextSearchBody = "const lancedb = await import(\"@lancedb/lancedb\");\nconst { mkdtemp, rm } = await import(\"node:fs/promises\");\nconst { tmpdir } = await import(\"node:os\");\nconst { join } = await import(\"node:path\");\nconst uri = await mkdtemp(join(tmpdir(), \"lancedb-search-\"));\ntry {\n  const db = await lancedb.connect(uri);\n  const table = await db.createTable(\"documents\", [\n    { id: 1, text: \"A puppy playing in the park\", category: \"animals\" },\n    { id: 2, text: \"A dog resting at home\", category: \"animals\" },\n    { id: 3, text: \"A guide to growing tomatoes\", category: \"gardening\" },\n  ]);\n  await table.createIndex(\"text\", { config: lancedb.Index.fts() });\n  const rows = await table\n    .query()\n    .fullTextSearch(\"puppy\", { columns: [\"text\"] })\n    .select([\"id\", \"text\", \"_score\"])\n    .limit(2)\n    .toArray();\n  console.log(rows);\n";
+
+export const TsTextSearchTail = "} finally {\n  await rm(uri, { recursive: true, force: true });\n}\n";
+
+export const TsVectorSearchBody = "const lancedb = await import(\"@lancedb/lancedb\");\nconst { mkdtemp, rm } = await import(\"node:fs/promises\");\nconst { tmpdir } = await import(\"node:os\");\nconst { join } = await import(\"node:path\");\nconst uri = await mkdtemp(join(tmpdir(), \"lancedb-search-\"));\ntry {\n  const db = await lancedb.connect(uri);\n  const table = await db.createTable(\"documents\", [\n    {\n      id: 1,\n      text: \"A puppy playing in the park\",\n      category: \"animals\",\n      vector: [1.0, 0.0],\n    },\n    {\n      id: 2,\n      text: \"A dog resting at home\",\n      category: \"animals\",\n      vector: [0.9, 0.1],\n    },\n    {\n      id: 3,\n      text: \"A guide to growing tomatoes\",\n      category: \"gardening\",\n      vector: [0.0, 1.0],\n    },\n  ]);\n  const rows = await table\n    .vectorSearch([1.0, 0.0])\n    .distanceType(\"l2\")\n    .select([\"id\", \"text\", \"_distance\"])\n    .limit(2)\n    .toArray();\n  console.log(rows);\n";
+
+export const TsVectorSearchTail = "} finally {\n  await rm(uri, { recursive: true, force: true });\n}\n";
+
diff --git a/docs/web/snippets/search_filter_search.mdx b/docs/web/snippets/search_filter_search.mdx
new file mode 100644
index 000000000..efe325aae
--- /dev/null
+++ b/docs/web/snippets/search_filter_search.mdx
@@ -0,0 +1,6 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const RsExampleBody = "use arrow_array::types::Float32Type;\nuse arrow_array::{FixedSizeListArray, Int64Array, RecordBatch, RecordBatchIterator, StringArray};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures::TryStreamExt;\nuse lancedb::query::{ExecutableQuery, QueryBase, Select};\nuse std::sync::Arc;\n\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n    let directory = tempfile::tempdir()?;\n    let db = lancedb::connect(directory.path().to_str().unwrap())\n        .execute()\n        .await?;\n    let schema = Arc::new(Schema::new(vec![\n        Field::new(\"id\", DataType::Int64, false),\n        Field::new(\"text\", DataType::Utf8, false),\n        Field::new(\"category\", DataType::Utf8, false),\n        Field::new(\n            \"vector\",\n            DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 2),\n            true,\n        ),\n    ]));\n    let batch = RecordBatch::try_new(\n        schema.clone(),\n        vec![\n            Arc::new(Int64Array::from(vec![1, 2, 3])),\n            Arc::new(StringArray::from(vec![\n                \"A puppy playing in the park\",\n                \"A dog resting at home\",\n                \"A guide to growing tomatoes\",\n            ])),\n            Arc::new(StringArray::from(vec![\"animals\", \"animals\", \"gardening\"])),\n            Arc::new(\n                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n                    [[1.0, 0.0], [0.9, 0.1], [0.0, 1.0]]\n                        .into_iter()\n                        .map(|v| Some(v.into_iter().map(Some).collect::<Vec<_>>())),\n                    2,\n                ),\n            ),\n        ],\n    )?;\n    let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema);\n    let table = db\n        .create_table(\n            \"documents\",\n            Box::new(reader) as Box<dyn arrow_array::RecordBatchReader + Send>,\n        )\n        .execute()\n        .await?;\n    let query = table\n        .vector_search(&[0.0_f32, 1.0])?\n        .only_if(\"category = 'animals'\")\n        .select(Select::columns(&[\"id\", \"text\", \"category\", \"_distance\"]))\n        .limit(2);\n    let rows: Vec<RecordBatch> = query.execute().await?.try_collect().await?;\n    println!(\"{rows:?}\");\n";
+
+export const RsExampleTail = "    Ok(())\n}\n";
+
diff --git a/docs/web/snippets/search_hybrid_search.mdx b/docs/web/snippets/search_hybrid_search.mdx
new file mode 100644
index 000000000..492e14f54
--- /dev/null
+++ b/docs/web/snippets/search_hybrid_search.mdx
@@ -0,0 +1,6 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const RsExampleBody = "use arrow_array::types::Float32Type;\nuse arrow_array::{FixedSizeListArray, Int64Array, RecordBatch, RecordBatchIterator, StringArray};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures::TryStreamExt;\nuse lance_index::scalar::FullTextSearchQuery;\nuse lancedb::index::{Index, scalar::FtsIndexBuilder};\nuse lancedb::query::{ExecutableQuery, QueryBase};\nuse std::sync::Arc;\n\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n    let directory = tempfile::tempdir()?;\n    let db = lancedb::connect(directory.path().to_str().unwrap())\n        .execute()\n        .await?;\n    let schema = Arc::new(Schema::new(vec![\n        Field::new(\"id\", DataType::Int64, false),\n        Field::new(\"text\", DataType::Utf8, false),\n        Field::new(\"category\", DataType::Utf8, false),\n        Field::new(\n            \"vector\",\n            DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 2),\n            true,\n        ),\n    ]));\n    let batch = RecordBatch::try_new(\n        schema.clone(),\n        vec![\n            Arc::new(Int64Array::from(vec![1, 2, 3])),\n            Arc::new(StringArray::from(vec![\n                \"A puppy playing in the park\",\n                \"A dog resting at home\",\n                \"A guide to growing tomatoes\",\n            ])),\n            Arc::new(StringArray::from(vec![\"animals\", \"animals\", \"gardening\"])),\n            Arc::new(\n                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n                    [[1.0, 0.0], [0.9, 0.1], [0.0, 1.0]]\n                        .into_iter()\n                        .map(|v| Some(v.into_iter().map(Some).collect::<Vec<_>>())),\n                    2,\n                ),\n            ),\n        ],\n    )?;\n    let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema);\n    let table = db\n        .create_table(\n            \"documents\",\n            Box::new(reader) as Box<dyn arrow_array::RecordBatchReader + Send>,\n        )\n        .execute()\n        .await?;\n    table\n        .create_index(&[\"text\"], Index::FTS(FtsIndexBuilder::default()))\n        .execute()\n        .await?;\n    let query = table\n        .vector_search(&[1.0_f32, 0.0])?\n        .full_text_search(FullTextSearchQuery::new(\"puppy\".into()).with_column(\"text\".into())?)\n        .rerank(Arc::new(lancedb::rerankers::rrf::RRFReranker::default()))\n        .limit(2);\n    let rows: Vec<RecordBatch> = query.execute().await?.try_collect().await?;\n    println!(\"{rows:?}\");\n";
+
+export const RsExampleTail = "    Ok(())\n}\n";
+
diff --git a/docs/web/snippets/search_query_plan.mdx b/docs/web/snippets/search_query_plan.mdx
new file mode 100644
index 000000000..fbfe156c9
--- /dev/null
+++ b/docs/web/snippets/search_query_plan.mdx
@@ -0,0 +1,6 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const RsExampleBody = "use arrow_array::types::Float32Type;\nuse arrow_array::{FixedSizeListArray, Int64Array, RecordBatch, RecordBatchIterator, StringArray};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures::TryStreamExt;\nuse lancedb::query::{ExecutableQuery, QueryBase, Select};\nuse std::sync::Arc;\n\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n    let directory = tempfile::tempdir()?;\n    let db = lancedb::connect(directory.path().to_str().unwrap())\n        .execute()\n        .await?;\n    let schema = Arc::new(Schema::new(vec![\n        Field::new(\"id\", DataType::Int64, false),\n        Field::new(\"text\", DataType::Utf8, false),\n        Field::new(\"category\", DataType::Utf8, false),\n        Field::new(\n            \"vector\",\n            DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 2),\n            true,\n        ),\n    ]));\n    let batch = RecordBatch::try_new(\n        schema.clone(),\n        vec![\n            Arc::new(Int64Array::from(vec![1, 2, 3])),\n            Arc::new(StringArray::from(vec![\n                \"A puppy playing in the park\",\n                \"A dog resting at home\",\n                \"A guide to growing tomatoes\",\n            ])),\n            Arc::new(StringArray::from(vec![\"animals\", \"animals\", \"gardening\"])),\n            Arc::new(\n                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n                    [[1.0, 0.0], [0.9, 0.1], [0.0, 1.0]]\n                        .into_iter()\n                        .map(|v| Some(v.into_iter().map(Some).collect::<Vec<_>>())),\n                    2,\n                ),\n            ),\n        ],\n    )?;\n    let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema);\n    let table = db\n        .create_table(\n            \"documents\",\n            Box::new(reader) as Box<dyn arrow_array::RecordBatchReader + Send>,\n        )\n        .execute()\n        .await?;\n    let query = table\n        .vector_search(&[1.0_f32, 0.0])?\n        .select(Select::columns(&[\"id\", \"text\", \"_distance\"]))\n        .limit(2);\n    println!(\"Plan: {}\", query.explain_plan(false).await?);\n    println!(\"Measured execution: {}\", query.analyze_plan().await?);\n    let rows: Vec<RecordBatch> = query.execute().await?.try_collect().await?;\n    println!(\"{rows:?}\");\n";
+
+export const RsExampleTail = "    Ok(())\n}\n";
+
diff --git a/docs/web/snippets/search_text_search.mdx b/docs/web/snippets/search_text_search.mdx
new file mode 100644
index 000000000..86745b652
--- /dev/null
+++ b/docs/web/snippets/search_text_search.mdx
@@ -0,0 +1,6 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const RsExampleBody = "use arrow_array::{Int64Array, RecordBatch, RecordBatchIterator, StringArray};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures::TryStreamExt;\nuse lance_index::scalar::FullTextSearchQuery;\nuse lancedb::index::{Index, scalar::FtsIndexBuilder};\nuse lancedb::query::{ExecutableQuery, QueryBase, Select};\nuse std::sync::Arc;\n\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n    let directory = tempfile::tempdir()?;\n    let db = lancedb::connect(directory.path().to_str().unwrap())\n        .execute()\n        .await?;\n    let schema = Arc::new(Schema::new(vec![\n        Field::new(\"id\", DataType::Int64, false),\n        Field::new(\"text\", DataType::Utf8, false),\n        Field::new(\"category\", DataType::Utf8, false),\n    ]));\n    let batch = RecordBatch::try_new(\n        schema.clone(),\n        vec![\n            Arc::new(Int64Array::from(vec![1, 2, 3])),\n            Arc::new(StringArray::from(vec![\n                \"A puppy playing in the park\",\n                \"A dog resting at home\",\n                \"A guide to growing tomatoes\",\n            ])),\n            Arc::new(StringArray::from(vec![\"animals\", \"animals\", \"gardening\"])),\n        ],\n    )?;\n    let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema);\n    let table = db\n        .create_table(\n            \"documents\",\n            Box::new(reader) as Box<dyn arrow_array::RecordBatchReader + Send>,\n        )\n        .execute()\n        .await?;\n    table\n        .create_index(&[\"text\"], Index::FTS(FtsIndexBuilder::default()))\n        .execute()\n        .await?;\n    let query = table\n        .query()\n        .full_text_search(FullTextSearchQuery::new(\"puppy\".into()).with_column(\"text\".into())?)\n        .select(Select::columns(&[\"id\", \"text\", \"_score\"]))\n        .limit(2);\n    let rows: Vec<RecordBatch> = query.execute().await?.try_collect().await?;\n    println!(\"{rows:?}\");\n";
+
+export const RsExampleTail = "    Ok(())\n}\n";
+
diff --git a/docs/web/snippets/search_vector_search.mdx b/docs/web/snippets/search_vector_search.mdx
new file mode 100644
index 000000000..47966f5d4
--- /dev/null
+++ b/docs/web/snippets/search_vector_search.mdx
@@ -0,0 +1,6 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const RsExampleBody = "use arrow_array::types::Float32Type;\nuse arrow_array::{FixedSizeListArray, Int64Array, RecordBatch, RecordBatchIterator, StringArray};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures::TryStreamExt;\nuse lancedb::query::{ExecutableQuery, QueryBase, Select};\nuse std::sync::Arc;\n\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n    let directory = tempfile::tempdir()?;\n    let db = lancedb::connect(directory.path().to_str().unwrap())\n        .execute()\n        .await?;\n    let schema = Arc::new(Schema::new(vec![\n        Field::new(\"id\", DataType::Int64, false),\n        Field::new(\"text\", DataType::Utf8, false),\n        Field::new(\"category\", DataType::Utf8, false),\n        Field::new(\n            \"vector\",\n            DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 2),\n            true,\n        ),\n    ]));\n    let batch = RecordBatch::try_new(\n        schema.clone(),\n        vec![\n            Arc::new(Int64Array::from(vec![1, 2, 3])),\n            Arc::new(StringArray::from(vec![\n                \"A puppy playing in the park\",\n                \"A dog resting at home\",\n                \"A guide to growing tomatoes\",\n            ])),\n            Arc::new(StringArray::from(vec![\"animals\", \"animals\", \"gardening\"])),\n            Arc::new(\n                FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n                    [[1.0, 0.0], [0.9, 0.1], [0.0, 1.0]]\n                        .into_iter()\n                        .map(|v| Some(v.into_iter().map(Some).collect::<Vec<_>>())),\n                    2,\n                ),\n            ),\n        ],\n    )?;\n    let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema);\n    let table = db\n        .create_table(\n            \"documents\",\n            Box::new(reader) as Box<dyn arrow_array::RecordBatchReader + Send>,\n        )\n        .execute()\n        .await?;\n    let query = table\n        .vector_search(&[1.0_f32, 0.0])?\n        .distance_type(lancedb::DistanceType::L2)\n        .select(Select::columns(&[\"id\", \"text\", \"_distance\"]))\n        .limit(2);\n    let rows: Vec<RecordBatch> = query.execute().await?.try_collect().await?;\n    println!(\"{rows:?}\");\n";
+
+export const RsExampleTail = "    Ok(())\n}\n";
+
diff --git a/docs/web/snippets/sentence-transformers.mdx b/docs/web/snippets/sentence-transformers.mdx
new file mode 100644
index 000000000..bf4aec126
--- /dev/null
+++ b/docs/web/snippets/sentence-transformers.mdx
@@ -0,0 +1,14 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const TsQuickstartConnect = "const db = await lancedb.connect(\"data/sample-lancedb\");\n";
+
+export const TsQuickstartCreateTable = "const table = await db.createEmptyTable(\"words\", wordsSchema, {\n  mode: \"overwrite\",\n});\nawait table.add([{ text: \"hello world\" }, { text: \"goodbye world\" }]);\n";
+
+export const TsQuickstartImports = "import * as lancedb from \"@lancedb/lancedb\";\nimport \"@lancedb/lancedb/embedding/transformers\";\nimport { Utf8 } from \"apache-arrow\";\n";
+
+export const TsQuickstartInitModel = "const model = (await lancedb.embedding\n  .getRegistry()\n  .get(\"huggingface\")\n  ?.create()) as lancedb.embedding.EmbeddingFunction;\n";
+
+export const TsQuickstartQuery = "const query = \"greetings\";\nconst actual = (await table.search(query).limit(1).toArray())[0];\nconsole.log(actual.text);\n";
+
+export const TsQuickstartSchema = "const wordsSchema = lancedb.embedding.LanceSchema({\n  text: model.sourceField(new Utf8()),\n  vector: model.vectorField(),\n});\n";
+
diff --git a/docs/web/snippets/simple.mdx b/docs/web/snippets/simple.mdx
new file mode 100644
index 000000000..92710bfc4
--- /dev/null
+++ b/docs/web/snippets/simple.mdx
@@ -0,0 +1,24 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const RsAdd = "let new_data = create_some_records()?;\ntbl.add(new_data).execute().await.unwrap();\n";
+
+export const RsConnect = "#[tokio::main]\nasync fn main() -> Result<()> {\n    if std::path::Path::new(\"data\").exists() {\n        std::fs::remove_dir_all(\"data\").unwrap();\n    }\n    // --8<-- [start:connect_uri]\n    let uri = \"data/sample-lancedb\";\n    let db = connect(uri).execute().await?;\n    // --8<-- [end:connect_uri]\n\n    // --8<-- [start:list_names]\n    println!(\"{:?}\", db.table_names().execute().await?);\n    // --8<-- [end:list_names]\n    let tbl = create_table(&db).await?;\n    create_index(&tbl).await?;\n    let batches = search(&tbl).await?;\n    println!(\"{:?}\", batches);\n\n    create_empty_table(&db).await.unwrap();\n\n    // --8<-- [start:delete]\n    tbl.delete(\"id > 24\").await.unwrap();\n    // --8<-- [end:delete]\n\n    // --8<-- [start:drop_table]\n    db.drop_table(\"my_table\").await.unwrap();\n    // --8<-- [end:drop_table]\n    Ok(())\n}\n";
+
+export const RsConnectUri = "let uri = \"data/sample-lancedb\";\nlet db = connect(uri).execute().await?;\n";
+
+export const RsCreateEmptyTable = "let schema = Arc::new(Schema::new(vec![\n    Field::new(\"id\", DataType::Int32, false),\n    Field::new(\"item\", DataType::Utf8, true),\n]));\ndb.create_empty_table(\"empty_table\", schema).execute().await\n";
+
+export const RsCreateIndex = "table.create_index(&[\"vector\"], Index::Auto).execute().await\n";
+
+export const RsCreateTable = "let initial_data = create_some_records()?;\nlet tbl = db\n    .create_table(\"my_table\", initial_data)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsDelete = "tbl.delete(\"id > 24\").await.unwrap();\n";
+
+export const RsDropTable = "db.drop_table(\"my_table\").await.unwrap();\n";
+
+export const RsListNames = "println!(\"{:?}\", db.table_names().execute().await?);\n";
+
+export const RsOpenExistingTbl = "let table = db.open_table(\"my_table\").execute().await.unwrap();\n";
+
+export const RsSearch = "table\n    .query()\n    .limit(2)\n    .nearest_to(&[1.0; 128])?\n    .execute()\n    .await?\n    .try_collect::<Vec<_>>()\n    .await\n";
+
diff --git a/docs/web/snippets/storage.mdx b/docs/web/snippets/storage.mdx
new file mode 100644
index 000000000..942fd8aec
--- /dev/null
+++ b/docs/web/snippets/storage.mdx
@@ -0,0 +1,64 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyStorageAzureAccount = "db = lancedb.connect(\n    \"az://my-container/my-database\",\n    storage_options={\n        \"account_name\": \"some-account\",\n        \"account_key\": \"some-key\",\n    },\n)\n";
+
+export const PyStorageAzureSas = "db = lancedb.connect(\n    \"az://my-container/my-database\",\n    storage_options={\n        \"azure_storage_account_name\": \"some-account\",\n        \"azure_storage_sas_token\": \"<sas-token>\",\n    },\n)\n";
+
+export const PyStorageConnectAzure = "db = lancedb.connect(\"az://bucket/path\")\n";
+
+export const PyStorageConnectGcs = "db = lancedb.connect(\"gs://bucket/path\")\n";
+
+export const PyStorageConnectS3 = "db = lancedb.connect(\"s3://bucket/path\")\n";
+
+export const PyStorageConnectTimeout = "db = lancedb.connect(\n    \"s3://bucket/path\",\n    storage_options={\"timeout\": \"60s\"},\n)\n";
+
+export const PyStorageGcsServiceAccount = "db = lancedb.connect(\n    \"gs://my-bucket/my-database\",\n    storage_options={\n        \"service_account\": \"path/to/service-account.json\",\n    },\n)\n";
+
+export const PyStorageS3Ddb = "db = lancedb.connect(\n    \"s3+ddb://bucket/path?ddbTableName=my-dynamodb-table\",\n)\n";
+
+export const PyStorageS3DdbLocal = "db = lancedb.connect(\n    \"s3+ddb://bucket/path?ddbTableName=my-dynamodb-table\",\n    storage_options={\n        \"endpoint\": \"http://localhost:4566\",\n        \"dynamodb_endpoint\": \"http://localhost:4566\",\n        \"allow_http\": \"true\",\n    },\n)\n";
+
+export const PyStorageS3Express = "db = lancedb.connect(\n    \"s3://my-bucket--use1-az4--x-s3/path\",\n    storage_options={\n        \"region\": \"us-east-1\",\n        \"s3_express\": \"true\",\n    },\n)\n";
+
+export const PyStorageS3Minio = "db = lancedb.connect(\n    \"s3://bucket/path\",\n    storage_options={\n        \"region\": \"us-east-1\",\n        \"endpoint\": \"http://minio:9000\",\n    },\n)\n";
+
+export const PyStorageS3SseKms = "db = lancedb.connect(\n    \"s3://bucket/path\",\n    storage_options={\n        \"aws_server_side_encryption\": \"aws:kms\",\n        \"aws_sse_kms_key_id\": \"<your-kms-key-id-or-arn>\",\n    },\n)\n";
+
+export const PyStorageTableTimeout = "table = db.create_table(\n    \"table\",\n    [{\"a\": 1, \"b\": 2}],\n    storage_options={\"timeout\": \"60s\"},\n)\n";
+
+export const PyStorageTigrisConnect = "db = lancedb.connect(\n    \"s3://your-bucket/path\",\n    storage_options={\n        \"endpoint\": \"https://t3.storage.dev\",\n        \"region\": \"auto\",\n    },\n)\n";
+
+export const PyStorageCosConnect = "db = lancedb.connect(\n    \"cos://my-bucket/my-database\",\n    storage_options={\n        \"secret_id\": \"<tencent-cloud-secret-id>\",\n        \"secret_key\": \"<tencent-cloud-secret-key>\",\n        \"region\": \"ap-guangzhou\",\n    },\n)\n";
+
+export const PyStorageGoosefsConnect = "db = lancedb.connect(\"goosefs://my-namespace/my-database\")\n";
+
+export const TsStorageAzureAccount = "async function storageAzureAccount() {\n  const db = await lancedb.connect(\n    \"az://my-container/my-database\",\n    {\n      storageOptions: {\n        accountName: \"some-account\",\n        accountKey: \"some-key\",\n      },\n    },\n  );\n  return db;\n}\n";
+
+export const TsStorageAzureSas = "async function storageAzureSas() {\n  const db = await lancedb.connect(\n    \"az://my-container/my-database\",\n    {\n      storageOptions: {\n        azureStorageAccountName: \"some-account\",\n        azureStorageSasToken: \"<sas-token>\",\n      },\n    },\n  );\n  return db;\n}\n";
+
+export const TsStorageConnectAzure = "async function storageConnectAzure() {\n  const db = await lancedb.connect(\"az://bucket/path\");\n  return db;\n}\n";
+
+export const TsStorageConnectGcs = "async function storageConnectGcs() {\n  const db = await lancedb.connect(\"gs://bucket/path\");\n  return db;\n}\n";
+
+export const TsStorageConnectS3 = "async function storageConnectS3() {\n  const db = await lancedb.connect(\"s3://bucket/path\");\n  return db;\n}\n";
+
+export const TsStorageConnectTimeout = "async function storageConnectTimeout() {\n  const db = await lancedb.connect(\"s3://bucket/path\", {\n    storageOptions: { timeout: \"60s\" },\n  });\n  return db;\n}\n";
+
+export const TsStorageGcsServiceAccount = "async function storageGcsServiceAccount() {\n  const db = await lancedb.connect(\n    \"gs://my-bucket/my-database\",\n    {\n      storageOptions: {\n        serviceAccount: \"path/to/service-account.json\",\n      },\n    },\n  );\n  return db;\n}\n";
+
+export const TsStorageS3Ddb = "async function storageS3Ddb() {\n  const db = await lancedb.connect(\n    \"s3+ddb://bucket/path?ddbTableName=my-dynamodb-table\",\n  );\n  return db;\n}\n";
+
+export const TsStorageS3DdbLocal = "async function storageS3DdbLocal() {\n  const db = await lancedb.connect(\n    \"s3+ddb://bucket/path?ddbTableName=my-dynamodb-table\",\n    {\n      storageOptions: {\n        endpoint: \"http://localhost:4566\",\n        dynamodbEndpoint: \"http://localhost:4566\",\n        allowHttp: \"true\",\n      },\n    },\n  );\n  return db;\n}\n";
+
+export const TsStorageS3Express = "async function storageS3Express() {\n  const db = await lancedb.connect(\n    \"s3://my-bucket--use1-az4--x-s3/path\",\n    {\n      storageOptions: {\n        region: \"us-east-1\",\n        s3Express: \"true\",\n      },\n    },\n  );\n  return db;\n}\n";
+
+export const TsStorageS3Minio = "async function storageS3Minio() {\n  const db = await lancedb.connect(\"s3://bucket/path\", {\n    storageOptions: {\n      region: \"us-east-1\",\n      endpoint: \"http://minio:9000\",\n    },\n  });\n  return db;\n}\n";
+
+export const TsStorageS3SseKms = "async function storageS3SseKms() {\n  const db = await lancedb.connect(\"s3://bucket/path\", {\n    storageOptions: {\n      awsServerSideEncryption: \"aws:kms\",\n      awsSseKmsKeyId: \"<your-kms-key-id-or-arn>\",\n    },\n  });\n  return db;\n}\n";
+
+export const TsStorageTableTimeout = "async function storageTableTimeout() {\n  const db = await lancedb.connect(\"s3://bucket/path\");\n  const table = await db.createTable(\n    \"table\",\n    [{ a: 1, b: 2 }],\n    { storageOptions: { timeout: \"60s\" } },\n  );\n  return table;\n}\n";
+
+export const TsStorageTigrisConnect = "async function storageTigrisConnect() {\n  const db = await lancedb.connect(\n    \"s3://your-bucket/path\",\n    {\n      storageOptions: {\n        endpoint: \"https://t3.storage.dev\",\n        region: \"auto\",\n      },\n    },\n  );\n  return db;\n}\n";
+
+export const TsStorageGoosefsConnect = "async function storageGoosefsConnect() {\n  const db = await lancedb.connect(\"goosefs://my-namespace/my-database\");\n  return db;\n}\n";
+
diff --git a/docs/web/snippets/tables.mdx b/docs/web/snippets/tables.mdx
new file mode 100644
index 000000000..28b9ce172
--- /dev/null
+++ b/docs/web/snippets/tables.mdx
@@ -0,0 +1,372 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyAddColumnsCalculated = "# Add a discounted price column (10% discount)\ntable.add_columns({\"discounted_price\": \"cast((price * 0.9) as float)\"})\n";
+
+export const PyAddColumnsDefaultValues = "# Add a stock status column with default value\ntable.add_columns({\"in_stock\": \"cast(true as boolean)\"})\n";
+
+export const PyAddColumnsNullable = "# Add a nullable timestamp column\ntable.add_columns({\"last_ordered\": \"cast(NULL as timestamp)\"})\n";
+
+export const PyAddDataNestedModel = "from lancedb.pydantic import LanceModel, Vector\nfrom pydantic import BaseModel\n\nclass Document(BaseModel):\n    content: str\n    source: str\n\nclass NestedSchema(LanceModel):\n    id: str\n    vector: Vector(128)\n    document: Document\n\n# Create table with nested schema\ntable_name = \"nested_model_example\"\ntable = db.create_table(table_name, schema=NestedSchema, mode=\"overwrite\")\n";
+
+export const PyAddDataPydanticModel = "from lancedb.pydantic import LanceModel, Vector\n\n# Define a Pydantic model\nclass Content(LanceModel):\n    movie_id: int\n    vector: Vector(128)\n    genres: str\n    title: str\n    imdb_id: int\n\n    @property\n    def imdb_url(self) -> str:\n        return f\"https://www.imdb.com/title/tt{self.imdb_id}\"\n\n# Create table with Pydantic model schema\ntable_name = \"pydantic_example\"\ntable = db.create_table(table_name, schema=Content, mode=\"overwrite\")\n";
+
+export const PyAddDataToTable = "import pyarrow as pa\n\n# create an empty table with schema\ndata = [\n    {\"vector\": [3.1, 4.1], \"item\": \"foo\", \"price\": 10.0},\n    {\"vector\": [5.9, 26.5], \"item\": \"bar\", \"price\": 20.0},\n    {\"vector\": [10.2, 100.8], \"item\": \"baz\", \"price\": 30.0},\n    {\"vector\": [1.4, 9.5], \"item\": \"fred\", \"price\": 40.0},\n]\n\nschema = pa.schema(\n    [\n        pa.field(\"vector\", pa.list_(pa.float32(), 2)),\n        pa.field(\"item\", pa.utf8()),\n        pa.field(\"price\", pa.float32()),\n    ]\n)\n\ntable_name = \"basic_ingestion_example\"\ntable = db.create_table(table_name, schema=schema, mode=\"overwrite\")\n# Add data\ntable.add(data)\n";
+
+export const PyAddFeatureColumnsSql = "table.add_columns(\n    {\n        \"price_per_id\": \"cast(price / id as float)\",\n        \"price_log\": \"ln(price)\",\n        \"price_score\": \"cast(price / (price + 100.0) as float)\",\n    }\n)\n";
+
+export const PyAddFromDataset = "import pyarrow.dataset as ds\n\ndataset = ds.dataset(data_path, format=\"parquet\")\ndb = tmp_db\ntable = db.create_table(\"my_table\", schema=dataset.schema, mode=\"overwrite\")\ntable.add(dataset)\n";
+
+export const PyAlterColumnsDataType = "# Change price from int32 to int64 for larger numbers\ntable.alter_columns({\"path\": \"price\", \"data_type\": pa.int64()})\n";
+
+export const PyAlterColumnsMultiple = "# Rename, change type, and make nullable in one operation\ntable.alter_columns(\n    {\n        \"path\": \"sale_price\",\n        \"rename\": \"final_price\",\n        \"data_type\": pa.float64(),\n        \"nullable\": True,\n    }\n)\n";
+
+export const PyAlterColumnsNullable = "# Make the name column nullable\ntable.alter_columns({\"path\": \"name\", \"nullable\": True})\n";
+
+export const PyAlterColumnsRename = "# Rename discount_price to sale_price\ntable.alter_columns({\"path\": \"discount_price\", \"rename\": \"sale_price\"})\n";
+
+export const PyAlterColumnsWithExpression = "# For custom transforms, create a new column from a SQL expression.\nexpression_table = tmp_db.create_table(\n    \"schema_evolution_expression_example\",\n    [{\"id\": 1, \"price_text\": \"$100\"}],\n    mode=\"overwrite\",\n)\n\nexpression_table.add_columns(\n    {\"price_numeric\": \"cast(replace(price_text, '$', '') as int)\"}\n)\nexpression_table.drop_columns([\"price_text\"])\nexpression_table.alter_columns({\"path\": \"price_numeric\", \"rename\": \"price\"})\n";
+
+export const PyAlterVectorColumn = "vector_dim = 768  # Your embedding dimension\ntable_name = \"vector_alter_example\"\ndb = tmp_db\ndata = [\n    {\n        \"id\": 1,\n        \"embedding\": np.random.random(vector_dim).tolist(),\n    },\n]\ntable = db.create_table(table_name, data, mode=\"overwrite\")\n\ntable.alter_columns(\n    dict(path=\"embedding\", data_type=pa.list_(pa.float32(), vector_dim))\n)\n";
+
+export const PyBatchDataInsertion = "import pyarrow as pa\n\ndef make_batches():\n    for i in range(5):  # Create 5 batches\n        yield pa.RecordBatch.from_arrays(\n            [\n                pa.array([[3.1, 4.1], [5.9, 26.5]], pa.list_(pa.float32(), 2)),\n                pa.array([f\"item{i * 2 + 1}\", f\"item{i * 2 + 2}\"]),\n                pa.array([float((i * 2 + 1) * 10), float((i * 2 + 2) * 10)]),\n            ],\n            [\"vector\", \"item\", \"price\"],\n        )\n\nschema = pa.schema(\n    [\n        pa.field(\"vector\", pa.list_(pa.float32(), 2)),\n        pa.field(\"item\", pa.utf8()),\n        pa.field(\"price\", pa.float32()),\n    ]\n)\n# Create table with batches\ntable_name = \"batch_ingestion_example\"\ntable = db.create_table(table_name, make_batches(), schema=schema, mode=\"overwrite\")\n";
+
+export const PyBranchCreate = "# Fork an isolated, writable branch from main's latest version.\n# `create` returns a table handle scoped to the new branch.\nbranch = table.branches.create(\"exp\")\n";
+
+export const PyBranchDelete = "# Delete the branch and its branch-local history. Data on main is safe.\ntable.branches.delete(\"exp\")\n";
+
+export const PyBranchIndex = "# Build and validate indexes on a branch before using the configuration on\n# main.\ndev = products.branches.create(\"index-dev\")\n\n# A vector (ANN) index and a full-text search index, both branch-scoped.\ndev.create_index(\n    \"vector\",\n    config=IvfPq(distance_type=\"cosine\", num_partitions=1, num_sub_vectors=2),\n)\ndev.create_index(\"text\", config=FTS())\n\n# Both indexes live only on the branch; main still has none.\nprint([ix.name for ix in dev.list_indices()])  # branch: two indexes\nprint([ix.name for ix in products.list_indices()])  # main: [] (untouched)\n";
+
+export const PyBranchReopen = "# Reopen an existing branch by name from the table handle...\nchecked_out = table.branches.checkout(\"exp\")\n# ...or open it directly from the database connection.\nbranch_handle = db.open_table(\"quotes_branches_example\", branch=\"exp\")\nprint(checked_out.count_rows(), branch_handle.count_rows())  # both 4\n";
+
+export const PyBranchUpsertToMain = "# This is a row-level upsert, not a merge of branch histories.\n# `merge_insert` updates matching rows and inserts new rows using a stable\n# unique key. Filter the branch read if you only want to apply some results.\nrows_to_apply = candidate.to_arrow()\n(\n    table.merge_insert(\"id\")\n    .when_matched_update_all()  # update rows that already exist on main\n    .when_not_matched_insert_all()  # insert rows that are new on the branch\n    .execute(rows_to_apply)\n)\n";
+
+export const PyBranchWrite = "# Writes land on the branch handle only; main is left untouched.\nbranch.add([{\"id\": 4, \"author\": \"Lancelot\", \"quote\": \"For the realm!\"}])\nprint(branch.count_rows())  # 4 rows on the branch\nprint(table.count_rows())  # 3 rows; main is unaffected\n\n# List every branch, each mapped to its metadata (including its fork point).\nprint(table.branches.list())\n";
+
+export const PyConsistencyCheckoutLatest = "uri = str(tmp_db.uri)\nwriter_db = lancedb.connect(uri)\nreader_db = lancedb.connect(uri)\nwriter_table = writer_db.create_table(\n    \"consistency_checkout_latest_table\", [{\"id\": 1}], mode=\"overwrite\"\n)\nreader_table = reader_db.open_table(\"consistency_checkout_latest_table\")\n\nwriter_table.add([{\"id\": 2}])\nrows_before_refresh = reader_table.count_rows()\nprint(f\"Rows before checkout_latest: {rows_before_refresh}\")\n\nreader_table.checkout_latest()\nrows_after_refresh = reader_table.count_rows()\nprint(f\"Rows after checkout_latest: {rows_after_refresh}\")\n";
+
+export const PyConsistencyEventual = "from datetime import timedelta\n\nuri = str(tmp_db.uri)\nwriter_db = lancedb.connect(uri)\nreader_db = lancedb.connect(uri, read_consistency_interval=timedelta(seconds=3600))\nwriter_table = writer_db.create_table(\n    \"consistency_eventual_table\", [{\"id\": 1}], mode=\"overwrite\"\n)\nreader_table = reader_db.open_table(\"consistency_eventual_table\")\nwriter_table.add([{\"id\": 2}])\nrows_after_write = reader_table.count_rows()\nprint(f\"Rows visible before eventual refresh interval: {rows_after_write}\")\n";
+
+export const PyConsistencyStrong = "from datetime import timedelta\n\nuri = str(tmp_db.uri)\nwriter_db = lancedb.connect(uri)\nreader_db = lancedb.connect(uri, read_consistency_interval=timedelta(0))\nwriter_table = writer_db.create_table(\n    \"consistency_strong_table\", [{\"id\": 1}], mode=\"overwrite\"\n)\nreader_table = reader_db.open_table(\"consistency_strong_table\")\nwriter_table.add([{\"id\": 2}])\nrows_after_write = reader_table.count_rows()\nprint(f\"Rows visible with strong consistency: {rows_after_write}\")\n";
+
+export const PyCreateEmptyTable = "import pyarrow as pa\n\nschema = pa.schema(\n    [\n        pa.field(\"vector\", pa.list_(pa.float32(), 2)),\n        pa.field(\"item\", pa.string()),\n        pa.field(\"price\", pa.float32()),\n    ]\n)\ndb = tmp_db\ntbl = db.create_table(\"test_empty_table\", schema=schema, mode=\"overwrite\")\n";
+
+export const PyCreateEmptyTablePydantic = "from lancedb.pydantic import LanceModel, Vector\n\nclass Item(LanceModel):\n    vector: Vector(2)\n    item: str\n    price: float\n\ndb = tmp_db\ntbl = db.create_table(\n    \"test_empty_table_new\", schema=Item.to_arrow_schema(), mode=\"overwrite\"\n)\n";
+
+export const PyCreateTableConflictHandling = "# Idempotent open: reuse the existing table if it exists.\n# The provided data is ignored; the schema is validated against the\n# existing table and a mismatch raises an error.\ntbl = db.create_table(\"conflict_table\", data, exist_ok=True)\n\n# Overwrite: drop the existing table and create a new one with the\n# provided data. This permanently discards the old table's data.\ntbl = db.create_table(\"conflict_table\", data, mode=\"overwrite\")\n";
+
+export const PyCreateTableCustomSchema = "import pyarrow as pa\n\ncustom_schema = pa.schema(\n    [\n        pa.field(\"vector\", pa.list_(pa.float32(), 4)),\n        pa.field(\"lat\", pa.float32()),\n        pa.field(\"long\", pa.float32()),\n    ]\n)\n\ndata = [\n    {\"vector\": [1.1, 1.2, 1.3, 1.4], \"lat\": 45.5, \"long\": -122.7},\n    {\"vector\": [0.2, 1.8, 0.4, 3.6], \"lat\": 40.1, \"long\": -74.1},\n]\ndb = tmp_db\ntbl = db.create_table(\n    \"my_table_custom_schema\", data, schema=custom_schema, mode=\"overwrite\"\n)\n";
+
+export const PyCreateTableFromArrow = "import numpy as np\nimport pyarrow as pa\n\ndim = 16\ntotal = 2\nschema = pa.schema(\n    [pa.field(\"vector\", pa.list_(pa.float16(), dim)), pa.field(\"text\", pa.string())]\n)\ndata = pa.Table.from_arrays(\n    [\n        pa.array(\n            [np.random.randn(dim).astype(np.float16) for _ in range(total)],\n            pa.list_(pa.float16(), dim),\n        ),\n        pa.array([\"foo\", \"bar\"]),\n    ],\n    [\"vector\", \"text\"],\n)\ndb = tmp_db\ntbl = db.create_table(\"f16_tbl\", data, schema=schema, mode=\"overwrite\")\n";
+
+export const PyCreateTableFromDicts = "data = [\n    {\"vector\": [1.1, 1.2], \"lat\": 45.5, \"long\": -122.7},\n    {\"vector\": [0.2, 1.8], \"lat\": 40.1, \"long\": -74.1},\n]\ndb = tmp_db\ndb.create_table(\"test_table\", data, mode=\"overwrite\")\ntbl = db[\"test_table\"]\ntbl.head()\n";
+
+export const PyCreateTableFromIterator = "import pyarrow as pa\n\nschema = pa.schema(\n    [\n        pa.field(\"vector\", pa.list_(pa.float32(), 4)),\n        pa.field(\"item\", pa.utf8()),\n        pa.field(\"price\", pa.float32()),\n    ]\n)\n\ndef make_batches():\n    for i in range(5):\n        yield pa.RecordBatch.from_arrays(\n            [\n                pa.array(\n                    [[3.1, 4.1, 5.1, 6.1], [5.9, 26.5, 4.7, 32.8]],\n                    pa.list_(pa.float32(), 4),\n                ),\n                pa.array([\"foo\", \"bar\"]),\n                pa.array([10.0, 20.0]),\n            ],\n            [\"vector\", \"item\", \"price\"],\n        )\n\ndb = tmp_db\ndb.create_table(\"batched_table\", make_batches(), schema=schema, mode=\"overwrite\")\n";
+
+export const PyCreateTableFromPandas = "import pandas as pd\n\ndata = pd.DataFrame(\n    {\n        \"vector\": [[1.1, 1.2, 1.3, 1.4], [0.2, 1.8, 0.4, 3.6]],\n        \"lat\": [45.5, 40.1],\n        \"long\": [-122.7, -74.1],\n    }\n)\ndb = tmp_db\ndb.create_table(\"my_table_pandas\", data, mode=\"overwrite\")\ndb[\"my_table_pandas\"].head()\n";
+
+export const PyCreateTableFromPolars = "import polars as pl\n\ndata = pl.DataFrame(\n    {\n        \"vector\": [[3.1, 4.1], [5.9, 26.5]],\n        \"item\": [\"foo\", \"bar\"],\n        \"price\": [10.0, 20.0],\n    }\n)\ndb = tmp_db\ntbl = db.create_table(\"my_table_pl\", data, mode=\"overwrite\")\n";
+
+export const PyCreateTableFromPydantic = "from lancedb.pydantic import LanceModel, Vector\n\nclass Content(LanceModel):\n    movie_id: int\n    vector: Vector(128)\n    genres: str\n    title: str\n    imdb_id: int\n\n    @property\n    def imdb_url(self) -> str:\n        return f\"https://www.imdb.com/title/tt{self.imdb_id}\"\n\ndb = tmp_db\ntbl = db.create_table(\"movielens_small\", schema=Content, mode=\"overwrite\")\n";
+
+export const PyCreateTableNestedSchema = "from lancedb.pydantic import LanceModel, Vector\n\n# --8<-- [start:tables_document_model]\nfrom pydantic import BaseModel\n\nclass Document(BaseModel):\n    content: str\n    source: str\n\n# --8<-- [end:tables_document_model]\n\nclass NestedSchema(LanceModel):\n    id: str\n    vector: Vector(1536)\n    document: Document\n\ndb = tmp_db\ntbl = db.create_table(\"nested_table\", schema=NestedSchema, mode=\"overwrite\")\n";
+
+export const PyDeleteOperation = "# delete data\npredicate = \"id = 3\"\ntable.delete(predicate)\n";
+
+export const PyDropColumnsMultiple = "# Remove the second temporary column\ntable.drop_columns([\"temp_col2\"])\n";
+
+export const PyDropColumnsSingle = "# Remove the first temporary column\ntable.drop_columns([\"temp_col1\"])\n";
+
+export const PyDropTable = "db = tmp_db\n# Create a table first\ndata = [{\"vector\": [1.1, 1.2], \"lat\": 45.5}]\ndb.create_table(\"my_table\", data, mode=\"overwrite\")\n\n# Drop the table\ndb.drop_table(\"my_table\")\n";
+
+export const PyInsertIfNotExists = "import pyarrow as pa\n\ntable = db.create_table(\n    \"users_example\",\n    data=pa.table(\n        {\n            \"id\": [1, 2],\n            \"name\": [\"Alice\", \"Bob\"],\n            \"login_count\": [10, 20],\n        }\n    ),\n    mode=\"overwrite\",\n)\n\nincoming_users = pa.table(\n    {\n        \"id\": [2, 3],\n        \"name\": [\"Bobby\", \"Charlie\"],\n        \"login_count\": [21, 5],\n    }\n)\n\n(table.merge_insert(\"id\").when_not_matched_insert_all().execute(incoming_users))\n";
+
+export const PyMergeDeleteMissingBySource = "import pyarrow as pa\n\ntable = db.create_table(\n    \"users_example\",\n    data=pa.table(\n        {\n            \"id\": [1, 2, 3],\n            \"name\": [\"Alice\", \"Bob\", \"Charlie\"],\n            \"login_count\": [10, 20, 5],\n        }\n    ),\n    mode=\"overwrite\",\n)\n\nincoming_users = pa.table(\n    {\n        \"id\": [2, 3],\n        \"name\": [\"Bobby\", \"Charlie\"],\n        \"login_count\": [21, 5],\n    }\n)\n\n(\n    table.merge_insert(\"id\")\n    .when_matched_update_all()\n    .when_not_matched_insert_all()\n    .when_not_matched_by_source_delete()\n    .execute(incoming_users)\n)\n";
+
+export const PyMergeMatchedUpdateOnly = "import pyarrow as pa\n\ntable = db.create_table(\n    \"users_example\",\n    data=pa.table(\n        {\n            \"id\": [1, 2],\n            \"name\": [\"Alice\", \"Bob\"],\n            \"login_count\": [10, 20],\n        }\n    ),\n    mode=\"overwrite\",\n)\n\nincoming_users = pa.table(\n    {\n        \"id\": [2, 3],\n        \"name\": [\"Bobby\", \"Charlie\"],\n        \"login_count\": [21, 5],\n    }\n)\n\n(table.merge_insert(\"id\").when_matched_update_all().execute(incoming_users))\n";
+
+export const PyMergePartialColumns = "import pyarrow as pa\n\ntable = db.create_table(\n    \"users_example\",\n    data=pa.table(\n        {\n            \"id\": [1, 2],\n            \"name\": [\"Alice\", \"Bob\"],\n            \"login_count\": [10, 20],\n        }\n    ),\n    mode=\"overwrite\",\n)\n\nincoming_users = pa.table(\n    {\n        \"id\": [2, 3],\n        \"name\": [\"Bobby\", \"Charlie\"],\n    }\n)\n\n(\n    table.merge_insert(\"id\")\n    .when_matched_update_all()\n    .when_not_matched_insert_all()\n    .execute(incoming_users)\n)\n";
+
+export const PyMergeUpdateInsert = "import pyarrow as pa\n\ntable = db.create_table(\n    \"users_example\",\n    data=pa.table(\n        {\n            \"id\": [1, 2],\n            \"name\": [\"Alice\", \"Bob\"],\n            \"login_count\": [10, 20],\n        }\n    ),\n    mode=\"overwrite\",\n)\n\nincoming_users = pa.table(\n    {\n        \"id\": [2, 3],\n        \"name\": [\"Bobby\", \"Charlie\"],\n        \"login_count\": [21, 5],\n    }\n)\n\n(\n    table.merge_insert(\"id\")\n    .when_matched_update_all()\n    .when_not_matched_insert_all()\n    .execute(incoming_users)\n)\n";
+
+export const PyOpenExistingTable = "db = tmp_db\n# Create a table first\ndata = [{\"vector\": [1.1, 1.2], \"lat\": 45.5, \"long\": -122.7}]\ndb.create_table(\"test_table\", data, mode=\"overwrite\")\n\n# List table names\nprint(db.list_tables().tables)\n\n# Open existing table\ntbl = db.open_table(\"test_table\")\n";
+
+export const PySchemaAddSetup = "import numpy as np\n\ndata = [\n    {\n        \"id\": 1,\n        \"name\": \"Laptop\",\n        \"price\": 1200.00,\n        \"vector\": np.random.random(128).tolist(),\n    },\n    {\n        \"id\": 2,\n        \"name\": \"Smartphone\",\n        \"price\": 800.00,\n        \"vector\": np.random.random(128).tolist(),\n    },\n    {\n        \"id\": 3,\n        \"name\": \"Headphones\",\n        \"price\": 150.00,\n        \"vector\": np.random.random(128).tolist(),\n    },\n]\ntable = db.create_table(\"schema_evolution_add_example\", data, mode=\"overwrite\")\n";
+
+export const PySchemaAlterSetup = "table_name = \"schema_evolution_alter_example\"\nif data is None:\n    data = [\n        {\n            \"id\": 1,\n            \"name\": \"Laptop\",\n            \"price\": 1200,\n            \"discount_price\": 1080.0,\n            \"vector\": np.random.random(128).tolist(),\n        },\n        {\n            \"id\": 2,\n            \"name\": \"Smartphone\",\n            \"price\": 800,\n            \"discount_price\": 720.0,\n            \"vector\": np.random.random(128).tolist(),\n        },\n    ]\nschema = pa.schema(\n    {\n        \"id\": pa.int64(),\n        \"name\": pa.string(),\n        \"price\": pa.int32(),\n        \"discount_price\": pa.float64(),\n        \"vector\": pa.list_(pa.float32(), 128),\n    }\n)\ntable = tmp_db.create_table(table_name, data, schema=schema, mode=\"overwrite\")\n";
+
+export const PySchemaDropSetup = "if data is None:\n    data = [\n        {\n            \"id\": 1,\n            \"name\": \"Laptop\",\n            \"price\": 1200.00,\n            \"temp_col1\": \"X\",\n            \"temp_col2\": 100,\n            \"vector\": np.random.random(128).tolist(),\n        },\n        {\n            \"id\": 2,\n            \"name\": \"Smartphone\",\n            \"price\": 800.00,\n            \"temp_col1\": \"Y\",\n            \"temp_col2\": 200,\n            \"vector\": np.random.random(128).tolist(),\n        },\n        {\n            \"id\": 3,\n            \"name\": \"Headphones\",\n            \"price\": 150.00,\n            \"temp_col1\": \"Z\",\n            \"temp_col2\": 300,\n            \"vector\": np.random.random(128).tolist(),\n        },\n    ]\ntable = tmp_db.create_table(\"schema_evolution_drop_example\", data, mode=\"overwrite\")\n";
+
+export const PySchemaFieldMetadataMerge = "# Set two metadata keys on the `category` field.\nres = table.update_field_metadata(\n    {\"path\": \"category\", \"metadata\": {\"unit\": \"label\", \"pii\": \"false\"}}\n)\nprint(res.version)\n\n# Merge: add a new key, delete one with None, keep the rest.\ntable.update_field_metadata(\n    {\"path\": \"category\", \"metadata\": {\"source\": \"import\", \"pii\": None}}\n)\n\n# Arrow stores field metadata as bytes.\nassert table.schema.field(\"category\").metadata == {\n    b\"unit\": b\"label\",\n    b\"source\": b\"import\",\n}\n";
+
+export const PySchemaFieldMetadataReplace = "table.update_field_metadata(\n    {\n        \"path\": \"category\",\n        \"metadata\": {\"owner\": \"search-team\"},\n        \"replace\": True,\n    }\n)\n";
+
+export const PyTablesBasicConnect = "import lancedb\n\nuri = \"data/sample-lancedb\"\ndb = lancedb.connect(uri)\n";
+
+export const PyTablesDocumentModel = "from pydantic import BaseModel\n\nclass Document(BaseModel):\n    content: str\n    source: str\n";
+
+export const PyTablesImports = "import lancedb\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\nimport pytest\nfrom numpy.random import randint, random\n";
+
+export const PyTablesTzValidator = "from datetime import datetime\nfrom zoneinfo import ZoneInfo\n\nfrom lancedb.pydantic import LanceModel\nfrom pydantic import Field, ValidationError, ValidationInfo, field_validator\n\ntzname = \"America/New_York\"\ntz = ZoneInfo(tzname)\n\nclass TestModel(LanceModel):\n    dt_with_tz: datetime = Field(json_schema_extra={\"tz\": tzname})\n\n    @field_validator(\"dt_with_tz\")\n    @classmethod\n    def tz_must_match(cls, dt: datetime) -> datetime:\n        assert dt.tzinfo == tz\n        return dt\n\nok = TestModel(dt_with_tz=datetime.now(tz))\n\ntry:\n    TestModel(dt_with_tz=datetime.now(ZoneInfo(\"Asia/Shanghai\")))\n    assert 0 == 1, \"this should raise ValidationError\"\nexcept ValidationError:\n    print(\"A ValidationError was raised.\")\n    pass\n";
+
+export const PyUpdateConnectEnterprise = "import lancedb\n\ndb = lancedb.connect(\n    uri=\"db://your-project-slug\",\n    api_key=\"your-api-key\",\n    region=\"us-east-1\",\n)\n";
+
+export const PyUpdateConnectLocal = "import lancedb\n\ndb = lancedb.connect(\"./data\")\n";
+
+export const PyUpdateExampleTableSetup = "import pyarrow as pa\n\ntable = db.create_table(\n    \"users_example\",\n    data=pa.table(\n        {\n            \"id\": [1, 2],\n            \"name\": [\"Alice\", \"Bob\"],\n            \"login_count\": [10, 20],\n        }\n    ),\n    mode=\"overwrite\",\n)\n";
+
+export const PyUpdateOperation = "import pyarrow as pa\n\ntable = db.create_table(\n    \"users_example\",\n    data=pa.table(\n        {\n            \"id\": [1, 2],\n            \"name\": [\"Alice\", \"Bob\"],\n            \"login_count\": [10, 20],\n        }\n    ),\n    mode=\"overwrite\",\n)\ntable.update(where=\"id = 2\", values={\"name\": \"Bobby\"})\n";
+
+export const PyUpdateOptimizeCleanup = "from datetime import timedelta\n\ntable.optimize(cleanup_older_than=timedelta(days=1))\n";
+
+export const PyUpdateUsingSql = "import pyarrow as pa\n\ntable = db.create_table(\n    \"users_example\",\n    data=pa.table(\n        {\n            \"id\": [1, 2],\n            \"name\": [\"Alice\", \"Bob\"],\n            \"login_count\": [10, 20],\n        }\n    ),\n    mode=\"overwrite\",\n)\ntable.update(where=\"id = 2\", values_sql={\"login_count\": \"login_count + 1\"})\n";
+
+export const PyVersioningAddData = "more_data = [\n    {\n        \"id\": 4,\n        \"author\": \"Richard Daniel Sanchez\",\n        \"quote\": \"That's the way the news goes!\",\n    },\n    {\"id\": 5, \"author\": \"Morty\", \"quote\": \"Aww geez, Rick!\"},\n]\ntable.add(more_data)\n";
+
+export const PyVersioningBasicSetup = "import pyarrow as pa\n\ndb = tmp_db\n\ntable_name = \"quotes_versioning_example\"\ndata = [\n    {\"id\": 1, \"author\": \"Richard\", \"quote\": \"Wubba Lubba Dub Dub!\"},\n    {\"id\": 2, \"author\": \"Morty\", \"quote\": \"Rick, what's going on?\"},\n    {\n        \"id\": 3,\n        \"author\": \"Richard\",\n        \"quote\": \"I turned myself into a pickle, Morty!\",\n    },\n]\n\n# Define schema\nschema = pa.schema(\n    [\n        pa.field(\"id\", pa.int64()),\n        pa.field(\"author\", pa.string()),\n        pa.field(\"quote\", pa.string()),\n    ]\n)\n\ntable = db.create_table(table_name, data, schema=schema, mode=\"overwrite\")\n";
+
+export const PyVersioningCheckInitialVersion = "versions = table.list_versions()\ncurrent_version = table.version\nprint(f\"Number of versions after creation: {len(versions)}\")\nprint(f\"Current version: {current_version}\")\n";
+
+export const PyVersioningCheckVersionsAfterMod = "versions = table.list_versions()\nversion_count_after_mod = len(versions)\nversion_after_mod = table.version\nprint(f\"Number of versions after modifications: {version_count_after_mod}\")\nprint(f\"Current version: {version_after_mod}\")\n";
+
+export const PyVersioningCheckoutLatest = "table.checkout_latest()\n";
+
+export const PyVersioningDeleteData = "table.delete(\"author = 'Morty'\")\nrows_after_deletion = table.count_rows()\nprint(f\"Number of rows after deletion: {rows_after_deletion}\")\n";
+
+export const PyVersioningListAllVersions = "versions = table.list_versions()\nfor v in versions:\n    print(f\"Version {v['version']}, created at {v['timestamp']}\")\n";
+
+export const PyVersioningRollback = "table.restore(version_after_mod)\nversions = table.list_versions()\nversion_count_after_rollback = len(versions)\nprint(f\"Total number of versions after rollback: {version_count_after_rollback}\")\n";
+
+export const PyVersioningTags = "# Create a tag pointing at a specific version\ntable.tags.create(\"baseline\", 1)\ntable.tags.create(\"with-edits\", table.version)\n\n# List all tags on this table\nprint(table.tags.list())\n\n# Look up the version a tag points at\nprint(table.tags.get_version(\"baseline\"))\n\n# Move an existing tag to a different version\ntable.tags.update(\"baseline\", 2)\n\n# Check out a version by tag name\ntable.checkout(\"baseline\")\nprint(table.version)\n\n# Delete a tag (does not delete the underlying version)\ntable.tags.delete(\"with-edits\")\n\n# Return to the latest version\ntable.checkout_latest()\n";
+
+export const PyVersioningUpdateData = "table.update(where=\"author='Richard'\", values={\"author\": \"Richard Daniel Sanchez\"})\nrows_after_update = table.count_rows(\"author = 'Richard Daniel Sanchez'\")\nprint(f\"Rows updated to Richard Daniel Sanchez: {rows_after_update}\")\n";
+
+export const TsAddColumnsCalculated = "// Add a discounted price column (10% discount)\nawait schemaAddTable.addColumns([\n  {\n    name: \"discounted_price\",\n    valueSql: \"cast((price * 0.9) as float)\",\n  },\n]);\n";
+
+export const TsAddColumnsDefaultValues = "// Add a stock status column with default value\nawait schemaAddTable.addColumns([\n  {\n    name: \"in_stock\",\n    valueSql: \"cast(true as boolean)\",\n  },\n]);\n";
+
+export const TsAddColumnsNullable = "// Add a nullable timestamp column\nawait schemaAddTable.addColumns([\n  {\n    name: \"last_ordered\",\n    valueSql: \"cast(NULL as timestamp)\",\n  },\n]);\n";
+
+export const TsAddFeatureColumnsSql = "await schemaAddTable.addColumns([\n  {\n    name: \"price_per_id\",\n    valueSql: \"cast(price / id as float)\",\n  },\n  {\n    name: \"price_log\",\n    valueSql: \"ln(price)\",\n  },\n  {\n    name: \"price_score\",\n    valueSql: \"cast(price / (price + 100.0) as float)\",\n  },\n]);\n";
+
+export const TsAddProgress = "// Track ingestion progress as batches are written.\nawait table.add(moreData, {\n  progress: (p) => {\n    const rows = `${p.outputRows}/${p.totalRows ?? \"?\"} rows`;\n    const size = `${p.outputBytes} bytes, ${p.elapsedSeconds.toFixed(1)}s`;\n    const tasks = `${p.activeTasks}/${p.totalTasks} tasks active`;\n    const done = p.done ? \" \\u2014 done\" : \"\";\n    console.log(`wrote ${rows} (${size}, ${tasks})${done}`);\n  },\n});\n";
+
+export const TsAlterColumnsDataType = "// Change price from int32 to int64 for larger numbers\nawait schemaAlterTable.alterColumns([\n  { path: \"price\", dataType: new arrow.Int64() },\n]);\n";
+
+export const TsAlterColumnsMultiple = "// Rename, change type, and make nullable in one operation\nawait schemaAlterTable.alterColumns([\n  {\n    path: \"sale_price\",\n    rename: \"final_price\",\n    dataType: new arrow.Float64(),\n    nullable: true,\n  },\n]);\n";
+
+export const TsAlterColumnsNullable = "// Make the name column nullable\nawait schemaAlterTable.alterColumns([{ path: \"name\", nullable: true }]);\n";
+
+export const TsAlterColumnsRename = "// Rename discount_price to sale_price\nawait schemaAlterTable.alterColumns([\n  { path: \"discount_price\", rename: \"sale_price\" },\n]);\n";
+
+export const TsAlterColumnsWithExpression = "// For custom transforms, create a new column from a SQL expression.\nconst expressionTable = await db.createTable(\n  \"schema_evolution_expression_example\",\n  [{ id: 1, price_text: \"$100\" }],\n  { mode: \"overwrite\" },\n);\n\nawait expressionTable.addColumns([\n  {\n    name: \"price_numeric\",\n    valueSql: \"cast(replace(price_text, '$', '') as int)\",\n  },\n]);\nawait expressionTable.dropColumns([\"price_text\"]);\nawait expressionTable.alterColumns([\n  { path: \"price_numeric\", rename: \"price\" },\n]);\n";
+
+export const TsAlterVectorColumn = "const oldDim = 384;\nconst newDim = 1024;\nconst vectorSchema = new arrow.Schema([\n  new arrow.Field(\"id\", new arrow.Int64()),\n  new arrow.Field(\n    \"embedding\",\n    new arrow.FixedSizeList(\n      oldDim,\n      new arrow.Field(\"item\", new arrow.Float16(), true),\n    ),\n    true,\n  ),\n]);\nconst vectorData = lancedb.makeArrowTable(\n  [{ id: 1, embedding: Array.from({ length: oldDim }, () => Math.random()) }],\n  { schema: vectorSchema },\n);\nconst vectorTable = await db.createTable(\"vector_alter_example\", vectorData, {\n  mode: \"overwrite\",\n});\n\n// Changing FixedSizeList dimensions (384 -> 1024) is not supported via alterColumns.\n// Use addColumns + dropColumns + alterColumns(rename) to replace the column.\nawait vectorTable.addColumns([\n  {\n    name: \"embedding_v2\",\n    valueSql: `arrow_cast(NULL, 'FixedSizeList(${newDim}, Float16)')`,\n  },\n]);\nawait vectorTable.dropColumns([\"embedding\"]);\nawait vectorTable.alterColumns([{ path: \"embedding_v2\", rename: \"embedding\" }]);\n";
+
+export const TsBranchCreate = "// Fork an isolated, writable branch from main's latest version.\n// `create` returns a table handle scoped to the new branch.\nconst branch = await branches.create(\"exp\");\n";
+
+export const TsBranchDelete = "// Delete the branch and its branch-local history. Data on main is safe.\nawait branches.delete(\"exp\");\n";
+
+export const TsBranchIndex = "// Build and validate indexes on a branch before using the configuration on\n// main.\nconst dev = await productBranches.create(\"index-dev\");\n\n// A vector (ANN) index and a full-text search index, both branch-scoped.\nawait dev.createIndex(\"vector\", {\n  config: lancedb.Index.ivfPq({\n    distanceType: \"cosine\",\n    numPartitions: 1,\n    numSubVectors: 2,\n  }),\n});\nawait dev.createIndex(\"text\", { config: lancedb.Index.fts() });\n\n// Both indexes live only on the branch; main still has none.\nconsole.log((await dev.listIndices()).map((ix) => ix.name)); // branch: two indexes\nconsole.log((await products.listIndices()).map((ix) => ix.name)); // main: [] (untouched)\n";
+
+export const TsBranchReopen = "// Reopen an existing branch by name from the table handle...\nconst checkedOut = await branches.checkout(\"exp\");\n// ...or open it directly from the database connection.\nconst branchHandle = await db.openTable(\n  \"quotes_branches_example\",\n  undefined,\n  { branch: \"exp\" },\n);\nconsole.log(await checkedOut.countRows(), await branchHandle.countRows()); // both 4\n";
+
+export const TsBranchUpsertToMain = "// This is a row-level upsert, not a merge of branch histories.\n// `mergeInsert` updates matching rows and inserts new rows using a stable\n// unique key. Filter the branch read if you only want to apply some results.\nconst rowsToApply = await candidate.toArrow();\nawait table\n  .mergeInsert(\"id\")\n  .whenMatchedUpdateAll() // update rows that already exist on main\n  .whenNotMatchedInsertAll() // insert rows that are new on the branch\n  .execute(rowsToApply);\n";
+
+export const TsBranchWrite = "// Writes land on the branch handle only; main is left untouched.\nawait branch.add([{ id: 4, author: \"Lancelot\", quote: \"For the realm!\" }]);\nconsole.log(await branch.countRows()); // 4 rows on the branch\nconsole.log(await table.countRows()); // 3 rows; main is unaffected\n\n// List every branch, each mapped to its metadata (including its fork point).\nconsole.log(await branches.list());\n";
+
+export const TsConsistencyCheckoutLatest = "const checkoutWriterDb = await lancedb.connect(databaseDir);\nconst checkoutReaderDb = await lancedb.connect(databaseDir);\nconst checkoutWriterTable = await checkoutWriterDb.createTable(\n  \"consistency_checkout_latest_table\",\n  [{ id: 1 }],\n  { mode: \"overwrite\" },\n);\nconst checkoutReaderTable = await checkoutReaderDb.openTable(\n  \"consistency_checkout_latest_table\",\n);\nawait checkoutWriterTable.add([{ id: 2 }]);\nconst rowsBeforeRefresh = await checkoutReaderTable.countRows();\nconsole.log(`Rows before checkoutLatest: ${rowsBeforeRefresh}`);\nawait checkoutReaderTable.checkoutLatest();\nconst rowsAfterRefresh = await checkoutReaderTable.countRows();\nconsole.log(`Rows after checkoutLatest: ${rowsAfterRefresh}`);\n";
+
+export const TsConsistencyEventual = "const eventualWriterDb = await lancedb.connect(databaseDir);\nconst eventualReaderDb = await lancedb.connect(databaseDir, {\n  readConsistencyInterval: 3600,\n});\nconst eventualWriterTable = await eventualWriterDb.createTable(\n  \"consistency_eventual_table\",\n  [{ id: 1 }],\n  { mode: \"overwrite\" },\n);\nconst eventualReaderTable = await eventualReaderDb.openTable(\n  \"consistency_eventual_table\",\n);\nawait eventualWriterTable.add([{ id: 2 }]);\nconst eventualRowsAfterWrite = await eventualReaderTable.countRows();\nconsole.log(\n  `Rows visible before eventual refresh interval: ${eventualRowsAfterWrite}`,\n);\n";
+
+export const TsConsistencyStrong = "const strongWriterDb = await lancedb.connect(databaseDir);\nconst strongReaderDb = await lancedb.connect(databaseDir, {\n  readConsistencyInterval: 0,\n});\nconst strongWriterTable = await strongWriterDb.createTable(\n  \"consistency_strong_table\",\n  [{ id: 1 }],\n  { mode: \"overwrite\" },\n);\nconst strongReaderTable = await strongReaderDb.openTable(\n  \"consistency_strong_table\",\n);\nawait strongWriterTable.add([{ id: 2 }]);\nconst strongRowsAfterWrite = await strongReaderTable.countRows();\nconsole.log(`Rows visible with strong consistency: ${strongRowsAfterWrite}`);\n";
+
+export const TsCreateEmptyTable = "const emptySchema = new arrow.Schema([\n  new arrow.Field(\n    \"vector\",\n    new arrow.FixedSizeList(\n      2,\n      new arrow.Field(\"item\", new arrow.Float32(), true),\n    ),\n  ),\n  new arrow.Field(\"item\", new arrow.Utf8()),\n  new arrow.Field(\"price\", new arrow.Float32()),\n]);\nconst emptyTable = await db.createEmptyTable(\n  \"test_empty_table\",\n  emptySchema,\n  {\n    mode: \"overwrite\",\n  },\n);\n";
+
+export const TsCreateTableConflictHandling = "// Idempotent open: reuse the existing table if it exists.\n// The provided data is ignored; the schema is validated against the\n// existing table and a mismatch raises an error.\nlet conflictTable = await db.createTable(\"conflict_table\", data, {\n  existOk: true,\n});\n\n// Overwrite: drop the existing table and create a new one with the\n// provided data. This permanently discards the old table's data.\nconflictTable = await db.createTable(\"conflict_table\", data, {\n  mode: \"overwrite\",\n});\n";
+
+export const TsCreateTableCustomSchema = "const customSchema = new arrow.Schema([\n  new arrow.Field(\n    \"vector\",\n    new arrow.FixedSizeList(\n      4,\n      new arrow.Field(\"item\", new arrow.Float32(), true),\n    ),\n  ),\n  new arrow.Field(\"lat\", new arrow.Float32()),\n  new arrow.Field(\"long\", new arrow.Float32()),\n]);\n\nconst customSchemaData = lancedb.makeArrowTable(\n  [\n    { vector: [1.1, 1.2, 1.3, 1.4], lat: 45.5, long: -122.7 },\n    { vector: [0.2, 1.8, 0.4, 3.6], lat: 40.1, long: -74.1 },\n  ],\n  { schema: customSchema },\n);\nconst customSchemaTable = await db.createTable(\n  \"my_table_custom_schema\",\n  customSchemaData,\n  { mode: \"overwrite\" },\n);\n";
+
+export const TsCreateTableFromArrow = "const arrowSchema = new arrow.Schema([\n  new arrow.Field(\n    \"vector\",\n    new arrow.FixedSizeList(\n      16,\n      new arrow.Field(\"item\", new arrow.Float32(), true),\n    ),\n  ),\n  new arrow.Field(\"text\", new arrow.Utf8()),\n]);\nconst arrowData = lancedb.makeArrowTable(\n  [\n    { vector: Array(16).fill(0.1), text: \"foo\" },\n    { vector: Array(16).fill(0.2), text: \"bar\" },\n  ],\n  { schema: arrowSchema },\n);\nconst arrowTable = await db.createTable(\"f32_tbl\", arrowData, {\n  mode: \"overwrite\",\n});\n";
+
+export const TsCreateTableFromDicts = "type Location = {\n  vector: number[];\n  lat: number;\n  long: number;\n};\n\nconst data: Location[] = [\n  { vector: [1.1, 1.2], lat: 45.5, long: -122.7 },\n  { vector: [0.2, 1.8], lat: 40.1, long: -74.1 },\n];\nconst table = await db.createTable(\"test_table\", data, {\n  mode: \"overwrite\",\n});\n";
+
+export const TsCreateTableFromIterator = "const batchSchema = new arrow.Schema([\n  new arrow.Field(\n    \"vector\",\n    new arrow.FixedSizeList(\n      4,\n      new arrow.Field(\"item\", new arrow.Float32(), true),\n    ),\n  ),\n  new arrow.Field(\"item\", new arrow.Utf8()),\n  new arrow.Field(\"price\", new arrow.Float32()),\n]);\n\nconst tableForBatches = await db.createEmptyTable(\n  \"batched_table\",\n  batchSchema,\n  {\n    mode: \"overwrite\",\n  },\n);\n\nconst rows = Array.from({ length: 10 }, (_, i) => ({\n  vector: [i + 0.1, i + 0.2, i + 0.3, i + 0.4],\n  item: `item-${i + 1}`,\n  price: (i + 1) * 10,\n}));\n\nconst chunkSize = 2;\nfor (let i = 0; i < rows.length; i += chunkSize) {\n  const batch = lancedb.makeArrowTable(rows.slice(i, i + chunkSize), {\n    schema: batchSchema,\n  });\n  await tableForBatches.add(batch);\n}\n";
+
+export const TsDeleteOperation = "// delete data\nconst predicate = \"id = 3\";\nawait table.delete(predicate);\n";
+
+export const TsDropColumnsMultiple = "// Remove the second temporary column\nawait schemaDropTable.dropColumns([\"temp_col2\"]);\n";
+
+export const TsDropColumnsSingle = "// Remove the first temporary column\nawait schemaDropTable.dropColumns([\"temp_col1\"]);\n";
+
+export const TsDropTable = "await db.createTable(\"my_table\", [{ vector: [1.1, 1.2], lat: 45.5 }], {\n  mode: \"overwrite\",\n});\n\nawait db.dropTable(\"my_table\");\n";
+
+export const TsInsertIfNotExists = "const table = await db.createTable(\n  \"users_example\",\n  [\n    { id: 1, name: \"Alice\", login_count: 10 },\n    { id: 2, name: \"Bob\", login_count: 20 },\n  ],\n  { mode: \"overwrite\" },\n);\n\nconst incomingUsers = [\n  { id: 2, name: \"Bobby\", login_count: 21 },\n  { id: 3, name: \"Charlie\", login_count: 5 },\n];\n\nawait table\n  .mergeInsert(\"id\")\n  .whenNotMatchedInsertAll()\n  .execute(incomingUsers);\n";
+
+export const TsMergeDeleteMissingBySource = "const table = await db.createTable(\n  \"users_example\",\n  [\n    { id: 1, name: \"Alice\", login_count: 10 },\n    { id: 2, name: \"Bob\", login_count: 20 },\n    { id: 3, name: \"Charlie\", login_count: 5 },\n  ],\n  { mode: \"overwrite\" },\n);\n\nconst incomingUsers = [\n  { id: 2, name: \"Bobby\", login_count: 21 },\n  { id: 3, name: \"Charlie\", login_count: 5 },\n];\n\nawait table\n  .mergeInsert(\"id\")\n  .whenMatchedUpdateAll()\n  .whenNotMatchedInsertAll()\n  .whenNotMatchedBySourceDelete()\n  .execute(incomingUsers);\n";
+
+export const TsMergeMatchedUpdateOnly = "const table = await db.createTable(\n  \"users_example\",\n  [\n    { id: 1, name: \"Alice\", login_count: 10 },\n    { id: 2, name: \"Bob\", login_count: 20 },\n  ],\n  { mode: \"overwrite\" },\n);\n\nconst incomingUsers = [\n  { id: 2, name: \"Bobby\", login_count: 21 },\n  { id: 3, name: \"Charlie\", login_count: 5 },\n];\n\nawait table\n  .mergeInsert(\"id\")\n  .whenMatchedUpdateAll()\n  .execute(incomingUsers);\n";
+
+export const TsMergePartialColumns = "const table = await db.createTable(\n  \"users_example\",\n  [\n    { id: 1, name: \"Alice\", login_count: 10 },\n    { id: 2, name: \"Bob\", login_count: 20 },\n  ],\n  { mode: \"overwrite\" },\n);\n\nconst incomingUsers = [\n  { id: 2, name: \"Bobby\" },\n  { id: 3, name: \"Charlie\" },\n];\n\nawait table\n  .mergeInsert(\"id\")\n  .whenMatchedUpdateAll()\n  .whenNotMatchedInsertAll()\n  .execute(incomingUsers);\n";
+
+export const TsMergeUpdateInsert = "const table = await db.createTable(\n  \"users_example\",\n  [\n    { id: 1, name: \"Alice\", login_count: 10 },\n    { id: 2, name: \"Bob\", login_count: 20 },\n  ],\n  { mode: \"overwrite\" },\n);\n\nconst incomingUsers = [\n  { id: 2, name: \"Bobby\", login_count: 21 },\n  { id: 3, name: \"Charlie\", login_count: 5 },\n];\n\nawait table\n  .mergeInsert(\"id\")\n  .whenMatchedUpdateAll()\n  .whenNotMatchedInsertAll()\n  .execute(incomingUsers);\n";
+
+export const TsOpenExistingTable = "const openTableData = [{ vector: [1.1, 1.2], lat: 45.5, long: -122.7 }];\nawait db.createTable(\"test_table_open\", openTableData, {\n  mode: \"overwrite\",\n});\n\nconsole.log(await db.tableNames());\n\nconst openedTable = await db.openTable(\"test_table_open\");\n";
+
+export const TsSchemaAddSetup = "const schemaAddData = [\n  {\n    id: 1,\n    name: \"Laptop\",\n    price: 1200.0,\n    vector: Array.from({ length: 128 }, () => Math.random()),\n  },\n  {\n    id: 2,\n    name: \"Smartphone\",\n    price: 800.0,\n    vector: Array.from({ length: 128 }, () => Math.random()),\n  },\n  {\n    id: 3,\n    name: \"Headphones\",\n    price: 150.0,\n    vector: Array.from({ length: 128 }, () => Math.random()),\n  },\n];\nconst schemaAddTable = await db.createTable(\n  \"schema_evolution_add_example\",\n  schemaAddData,\n  { mode: \"overwrite\" },\n);\n";
+
+export const TsSchemaAlterSetup = "const schemaAlter = new arrow.Schema([\n  new arrow.Field(\"id\", new arrow.Int64()),\n  new arrow.Field(\"name\", new arrow.Utf8()),\n  new arrow.Field(\"price\", new arrow.Int32()),\n  new arrow.Field(\"discount_price\", new arrow.Float64()),\n  new arrow.Field(\n    \"vector\",\n    new arrow.FixedSizeList(\n      128,\n      new arrow.Field(\"item\", new arrow.Float32(), true),\n    ),\n  ),\n]);\nconst schemaAlterData = lancedb.makeArrowTable(\n  [\n    {\n      id: 1,\n      name: \"Laptop\",\n      price: 1200,\n      discount_price: 1080.0,\n      vector: Array.from({ length: 128 }, () => Math.random()),\n    },\n    {\n      id: 2,\n      name: \"Smartphone\",\n      price: 800,\n      discount_price: 720.0,\n      vector: Array.from({ length: 128 }, () => Math.random()),\n    },\n  ],\n  { schema: schemaAlter },\n);\nconst schemaAlterTable = await db.createTable(\n  \"schema_evolution_alter_example\",\n  schemaAlterData,\n  { mode: \"overwrite\" },\n);\n";
+
+export const TsSchemaDropSetup = "const schemaDropData = [\n  {\n    id: 1,\n    name: \"Laptop\",\n    price: 1200.0,\n    temp_col1: \"X\",\n    temp_col2: 100,\n    vector: Array.from({ length: 128 }, () => Math.random()),\n  },\n  {\n    id: 2,\n    name: \"Smartphone\",\n    price: 800.0,\n    temp_col1: \"Y\",\n    temp_col2: 200,\n    vector: Array.from({ length: 128 }, () => Math.random()),\n  },\n  {\n    id: 3,\n    name: \"Headphones\",\n    price: 150.0,\n    temp_col1: \"Z\",\n    temp_col2: 300,\n    vector: Array.from({ length: 128 }, () => Math.random()),\n  },\n];\nconst schemaDropTable = await db.createTable(\n  \"schema_evolution_drop_example\",\n  schemaDropData,\n  { mode: \"overwrite\" },\n);\n";
+
+export const TsSchemaFieldMetadataMerge = "// Set two metadata keys on the `category` field.\nconst res = await fieldMetadataTable.updateFieldMetadata([\n  { path: \"category\", metadata: { unit: \"label\", pii: \"false\" } },\n]);\nconsole.log(res.version);\n\n// Merge: add a new key, delete one via null, keep the rest.\nawait fieldMetadataTable.updateFieldMetadata([\n  { path: \"category\", metadata: { source: \"import\", pii: null } },\n]);\n";
+
+export const TsSchemaFieldMetadataReplace = "await fieldMetadataTable.updateFieldMetadata([\n  {\n    path: \"category\",\n    metadata: { owner: \"search-team\" },\n    replace: true,\n  },\n]);\n";
+
+export const TsUpdateConnectEnterprise = "const db = await lancedb.connect(\"db://your-project-slug\", {\n  apiKey: \"your-api-key\",\n  region: \"us-east-1\",\n});\n";
+
+export const TsUpdateConnectLocal = "const db = await lancedb.connect(\"./data\");\n";
+
+export const TsUpdateExampleTableSetup = "const table = await db.createTable(\n  \"users_example\",\n  [\n    { id: 1, name: \"Alice\", login_count: 10 },\n    { id: 2, name: \"Bob\", login_count: 20 },\n  ],\n  { mode: \"overwrite\" },\n);\n";
+
+export const TsUpdateOperation = "const table = await db.createTable(\n  \"users_example\",\n  [\n    { id: 1, name: \"Alice\", login_count: 10 },\n    { id: 2, name: \"Bob\", login_count: 20 },\n  ],\n  { mode: \"overwrite\" },\n);\nawait table.update({ where: \"id = 2\", values: { name: \"Bobby\" } });\n";
+
+export const TsUpdateOptimizeCleanup = "const olderThan = new Date();\nolderThan.setDate(olderThan.getDate() - 1);\nawait table.optimize({ cleanupOlderThan: olderThan });\n";
+
+export const TsUpdateUsingSql = "const table = await db.createTable(\n  \"users_example\",\n  [\n    { id: 1, name: \"Alice\", login_count: 10 },\n    { id: 2, name: \"Bob\", login_count: 20 },\n  ],\n  { mode: \"overwrite\" },\n);\nawait table.update({\n  where: \"id = 2\",\n  valuesSql: { login_count: \"login_count + 1\" },\n});\n";
+
+export const TsVersioningAddData = "const moreData = [\n  {\n    id: 4,\n    author: \"Richard Daniel Sanchez\",\n    quote: \"That's the way the news goes!\",\n  },\n  { id: 5, author: \"Morty\", quote: \"Aww geez, Rick!\" },\n];\nawait table.add(moreData);\n";
+
+export const TsVersioningBasicSetup = "const tableName = \"quotes_versioning_example\";\nconst data = [\n  { id: 1, author: \"Richard\", quote: \"Wubba Lubba Dub Dub!\" },\n  { id: 2, author: \"Morty\", quote: \"Rick, what's going on?\" },\n  {\n    id: 3,\n    author: \"Richard\",\n    quote: \"I turned myself into a pickle, Morty!\",\n  },\n];\nconst table = await db.createTable(tableName, data, { mode: \"overwrite\" });\n";
+
+export const TsVersioningCheckInitialVersion = "const versions = await table.listVersions();\nconst currentVersion = await table.version();\nconsole.log(`Number of versions after creation: ${versions.length}`);\nconsole.log(`Current version: ${currentVersion}`);\n";
+
+export const TsVersioningCheckVersionsAfterMod = "const versionsAfterMod = await table.listVersions();\nconst versionCountAfterMod = versionsAfterMod.length;\nconst versionAfterMod = await table.version();\nconsole.log(\n  `Number of versions after modifications: ${versionCountAfterMod}`,\n);\nconsole.log(`Current version: ${versionAfterMod}`);\n";
+
+export const TsVersioningCheckoutLatest = "await table.checkoutLatest();\n";
+
+export const TsVersioningDeleteData = "await table.delete(\"author = 'Morty'\");\nconst rowsAfterDeletion = await table.countRows();\nconsole.log(`Number of rows after deletion: ${rowsAfterDeletion}`);\n";
+
+export const TsVersioningListAllVersions = "const allVersions = await table.listVersions();\nfor (const v of allVersions) {\n  console.log(`Version ${v.version}, created at ${v.timestamp}`);\n}\n";
+
+export const TsVersioningRollback = "await table.checkout(versionAfterMod);\nawait table.restore();\nconst versionsAfterRollback = await table.listVersions();\nconst versionCountAfterRollback = versionsAfterRollback.length;\nconsole.log(\n  `Total number of versions after rollback: ${versionCountAfterRollback}`,\n);\n";
+
+export const TsVersioningTags = "const tags = await tagsTable.tags();\n\n// Create a tag pointing at a specific version\nawait tags.create(\"baseline\", 1);\nawait tags.create(\"with-edits\", await tagsTable.version());\n\n// List all tags on this table\nconsole.log(await tags.list());\n\n// Look up the version a tag points at\nconsole.log(await tags.getVersion(\"baseline\"));\n\n// Move an existing tag to a different version\nawait tags.update(\"baseline\", 2);\n\n// Check out a version by tag name\nawait tagsTable.checkout(\"baseline\");\nconsole.log(await tagsTable.version());\n\n// Delete a tag (does not delete the underlying version)\nawait tags.delete(\"with-edits\");\n\n// Return to the latest version\nawait tagsTable.checkoutLatest();\n";
+
+export const TsVersioningUpdateData = "await table.update({\n  where: \"author = 'Richard'\",\n  values: { author: \"Richard Daniel Sanchez\" },\n});\nconst rowsAfterUpdate = await table.countRows(\n  \"author = 'Richard Daniel Sanchez'\",\n);\nconsole.log(`Rows updated to Richard Daniel Sanchez: ${rowsAfterUpdate}`);\n";
+
+export const RsAddColumnsCalculated = "// Add a discounted price column (10% discount)\nschema_add_table\n    .add_columns(\n        NewColumnTransform::SqlExpressions(vec![(\n            \"discounted_price\".to_string(),\n            \"cast((price * 0.9) as float)\".to_string(),\n        )]),\n        None,\n    )\n    .await\n    .unwrap();\n";
+
+export const RsAddColumnsDefaultValues = "// Add a stock status column with default value\nschema_add_table\n    .add_columns(\n        NewColumnTransform::SqlExpressions(vec![(\n            \"in_stock\".to_string(),\n            \"cast(true as boolean)\".to_string(),\n        )]),\n        None,\n    )\n    .await\n    .unwrap();\n";
+
+export const RsAddColumnsNullable = "// Add a nullable timestamp column\nschema_add_table\n    .add_columns(\n        NewColumnTransform::SqlExpressions(vec![(\n            \"last_ordered\".to_string(),\n            \"cast(NULL as timestamp)\".to_string(),\n        )]),\n        None,\n    )\n    .await\n    .unwrap();\n";
+
+export const RsAddFeatureColumnsSql = "schema_add_table\n    .add_columns(\n        NewColumnTransform::SqlExpressions(vec![\n            (\n                \"price_per_id\".to_string(),\n                \"cast(price / id as float)\".to_string(),\n            ),\n            (\"price_log\".to_string(), \"ln(price)\".to_string()),\n            (\n                \"price_score\".to_string(),\n                \"cast(price / (price + 100.0) as float)\".to_string(),\n            ),\n        ]),\n        None,\n    )\n    .await\n    .unwrap();\n";
+
+export const RsAlterColumnsDataType = "// Change price from int32 to int64 for larger numbers\nschema_alter_table\n    .alter_columns(&[ColumnAlteration::new(\"price\".to_string()).cast_to(DataType::Int64)])\n    .await\n    .unwrap();\n";
+
+export const RsAlterColumnsMultiple = "// Rename, change type, and make nullable in one operation\nschema_alter_table\n    .alter_columns(&[ColumnAlteration::new(\"sale_price\".to_string())\n        .rename(\"final_price\".to_string())\n        .cast_to(DataType::Float64)\n        .set_nullable(true)])\n    .await\n    .unwrap();\n";
+
+export const RsAlterColumnsNullable = "// Make the name column nullable\nschema_alter_table\n    .alter_columns(&[ColumnAlteration::new(\"name\".to_string()).set_nullable(true)])\n    .await\n    .unwrap();\n";
+
+export const RsAlterColumnsRename = "// Rename discount_price to sale_price\nschema_alter_table\n    .alter_columns(&[ColumnAlteration::new(\"discount_price\".to_string())\n        .rename(\"sale_price\".to_string())])\n    .await\n    .unwrap();\n";
+
+export const RsAlterColumnsWithExpression = "// For custom transforms, create a new column from a SQL expression.\nlet expression_schema = Arc::new(Schema::new(vec![\n    Field::new(\"id\", DataType::Int64, false),\n    Field::new(\"price_text\", DataType::Utf8, false),\n]));\nlet expression_batch = RecordBatch::try_new(\n    expression_schema.clone(),\n    vec![\n        Arc::new(Int64Array::from(vec![1])),\n        Arc::new(StringArray::from(vec![\"$100\"])),\n    ],\n)\n.unwrap();\nlet expression_reader: Box<dyn RecordBatchReader + Send> = Box::new(RecordBatchIterator::new(\n    vec![Ok(expression_batch)].into_iter(),\n    expression_schema.clone(),\n));\nlet expression_table = db\n    .create_table(\"schema_evolution_expression_example\", expression_reader)\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n\nexpression_table\n    .add_columns(\n        NewColumnTransform::SqlExpressions(vec![(\n            \"price_numeric\".to_string(),\n            \"cast(replace(price_text, '$', '') as int)\".to_string(),\n        )]),\n        None,\n    )\n    .await\n    .unwrap();\nexpression_table.drop_columns(&[\"price_text\"]).await.unwrap();\nexpression_table\n    .alter_columns(&[ColumnAlteration::new(\"price_numeric\".to_string())\n        .rename(\"price\".to_string())])\n    .await\n    .unwrap();\n";
+
+export const RsAlterVectorColumn = "let old_dim = 384;\nlet new_dim = 1024;\nlet vector_schema = Arc::new(Schema::new(vec![\n    Field::new(\"id\", DataType::Int64, false),\n    Field::new(\n        \"embedding\",\n        DataType::FixedSizeList(\n            Arc::new(Field::new(\"item\", DataType::Float32, true)),\n            old_dim,\n        ),\n        true,\n    ),\n]));\nlet vector_batch = RecordBatch::try_new(\n    vector_schema.clone(),\n    vec![\n        Arc::new(Int64Array::from(vec![1])),\n        Arc::new(\n            FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n                vec![Some(vec![Some(0.1_f32); old_dim as usize])],\n                old_dim,\n            ),\n        ),\n    ],\n)\n.unwrap();\nlet vector_reader: Box<dyn RecordBatchReader + Send> =\n    Box::new(RecordBatchIterator::new(vec![Ok(vector_batch)].into_iter(), vector_schema.clone()));\nlet vector_table = db\n    .create_table(\"vector_alter_example\", vector_reader)\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n\n// Changing FixedSizeList dimensions (384 -> 1024) is not supported via alter_columns.\n// Use add_columns + drop_columns + alter_columns(rename) to replace the column.\nvector_table\n    .add_columns(\n        NewColumnTransform::SqlExpressions(vec![(\n            \"embedding_v2\".to_string(),\n            format!(\"arrow_cast(NULL, 'FixedSizeList({}, Float32)')\", new_dim),\n        )]),\n        None,\n    )\n    .await\n    .unwrap();\nvector_table.drop_columns(&[\"embedding\"]).await.unwrap();\nvector_table\n    .alter_columns(&[ColumnAlteration::new(\"embedding_v2\".to_string())\n        .rename(\"embedding\".to_string())])\n    .await\n    .unwrap();\n";
+
+export const RsBranchCreate = "// Fork an isolated, writable branch from main's latest version.\n// `create_branch` returns a table handle scoped to the new branch.\nlet branch = branches_table\n    .create_branch(\"exp\", Ref::Version(None, None))\n    .await\n    .unwrap();\n";
+
+export const RsBranchDelete = "// Delete the branch and its branch-local history. Data on main is safe.\nbranches_table.delete_branch(\"exp\").await.unwrap();\n";
+
+export const RsBranchIndex = "use lancedb::index::scalar::FtsIndexBuilder;\nuse lancedb::index::Index;\n\n// Build and validate indexes on a branch before using the configuration on\n// main.\nlet dev = products\n    .create_branch(\"index-dev\", Ref::Version(None, None))\n    .await\n    .unwrap();\n\n// A vector (ANN) index and a full-text search index, both branch-scoped.\ndev.create_index(&[\"vector\"], Index::Auto)\n    .execute()\n    .await\n    .unwrap();\ndev.create_index(&[\"text\"], Index::FTS(FtsIndexBuilder::default()))\n    .execute()\n    .await\n    .unwrap();\n\n// Both indexes live only on the branch; main still has none.\nprintln!(\"Branch indexes: {}\", dev.list_indices().await.unwrap().len()); // 2\nprintln!(\"Main indexes: {}\", products.list_indices().await.unwrap().len()); // 0\n";
+
+export const RsBranchReopen = "// Reopen an existing branch by name from the table handle...\nlet checked_out = branches_table.checkout_branch(\"exp\", None).await.unwrap();\n// ...or open it directly via the connection's builder.\nlet opened = db\n    .open_table(\"quotes_branches_example\")\n    .branch(\"exp\")\n    .execute()\n    .await\n    .unwrap();\nprintln!(\n    \"Reopened rows: {}, {}\",\n    checked_out.count_rows(None).await.unwrap(),\n    opened.count_rows(None).await.unwrap()\n); // both 4\n";
+
+export const RsBranchUpsertToMain = "// This is a row-level upsert, not a merge of branch histories.\n// `merge_insert` updates matching rows and inserts new rows using a stable\n// unique key. Filter the branch read if you only want to apply some results.\nlet schema = candidate.schema().await.unwrap();\nlet batches = candidate\n    .query()\n    .execute()\n    .await\n    .unwrap()\n    .try_collect::<Vec<_>>()\n    .await\n    .unwrap();\nlet rows_to_apply = RecordBatchIterator::new(batches.into_iter().map(Ok), schema);\n\nlet mut merge = branches_table.merge_insert(&[\"id\"]);\nmerge\n    .when_matched_update_all(None) // update rows that already exist on main\n    .when_not_matched_insert_all(); // insert rows that are new on the branch\nmerge.execute(Box::new(rows_to_apply)).await.unwrap();\n";
+
+export const RsBranchWrite = "// Writes land on the branch handle only; main is left untouched.\nbranch\n    .add(make_quotes_reader(vec![(4, \"Lancelot\", \"For the realm!\")]))\n    .execute()\n    .await\n    .unwrap();\nprintln!(\"Branch rows: {}\", branch.count_rows(None).await.unwrap()); // 4\nprintln!(\"Main rows: {}\", branches_table.count_rows(None).await.unwrap()); // 3\n\n// List every branch, each mapped to its metadata (including its fork point).\nprintln!(\"Branches: {:?}\", branches_table.list_branches().await.unwrap());\n";
+
+export const RsConsistencyCheckoutLatest = "let checkout_writer_db = connect(&db_uri).execute().await.unwrap();\nlet checkout_reader_db = connect(&db_uri).execute().await.unwrap();\nlet checkout_writer_table = checkout_writer_db\n    .create_table(\n        \"consistency_checkout_latest_table\",\n        make_users_reader(vec![1], vec![\"Alice\"], None),\n    )\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\nlet checkout_reader_table = checkout_reader_db\n    .open_table(\"consistency_checkout_latest_table\")\n    .execute()\n    .await\n    .unwrap();\ncheckout_writer_table\n    .add(make_users_reader(vec![2], vec![\"Bob\"], None))\n    .execute()\n    .await\n    .unwrap();\nlet rows_before_refresh = checkout_reader_table.count_rows(None).await.unwrap();\nprintln!(\"Rows before checkout_latest: {}\", rows_before_refresh);\ncheckout_reader_table.checkout_latest().await.unwrap();\nlet rows_after_refresh = checkout_reader_table.count_rows(None).await.unwrap();\nprintln!(\"Rows after checkout_latest: {}\", rows_after_refresh);\n";
+
+export const RsConsistencyEventual = "let eventual_writer_db = connect(&db_uri).execute().await.unwrap();\nlet eventual_reader_db = connect(&db_uri)\n    .read_consistency_interval(StdDuration::from_secs(3600))\n    .execute()\n    .await\n    .unwrap();\nlet eventual_writer_table = eventual_writer_db\n    .create_table(\n        \"consistency_eventual_table\",\n        make_users_reader(vec![1], vec![\"Alice\"], None),\n    )\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\nlet eventual_reader_table = eventual_reader_db\n    .open_table(\"consistency_eventual_table\")\n    .execute()\n    .await\n    .unwrap();\neventual_writer_table\n    .add(make_users_reader(vec![2], vec![\"Bob\"], None))\n    .execute()\n    .await\n    .unwrap();\nlet eventual_rows_after_write = eventual_reader_table.count_rows(None).await.unwrap();\nprintln!(\n    \"Rows visible before eventual refresh interval: {}\",\n    eventual_rows_after_write\n);\n";
+
+export const RsConsistencyStrong = "let strong_writer_db = connect(&db_uri).execute().await.unwrap();\nlet strong_reader_db = connect(&db_uri)\n    .read_consistency_interval(StdDuration::from_secs(0))\n    .execute()\n    .await\n    .unwrap();\nlet strong_writer_table = strong_writer_db\n    .create_table(\n        \"consistency_strong_table\",\n        make_users_reader(vec![1], vec![\"Alice\"], None),\n    )\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\nlet strong_reader_table = strong_reader_db\n    .open_table(\"consistency_strong_table\")\n    .execute()\n    .await\n    .unwrap();\nstrong_writer_table\n    .add(make_users_reader(vec![2], vec![\"Bob\"], None))\n    .execute()\n    .await\n    .unwrap();\nlet strong_rows_after_write = strong_reader_table.count_rows(None).await.unwrap();\nprintln!(\n    \"Rows visible with strong consistency: {}\",\n    strong_rows_after_write\n);\n";
+
+export const RsCreateEmptyTable = "let empty_schema = Arc::new(Schema::new(vec![\n    Field::new(\n        \"vector\",\n        DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 2),\n        false,\n    ),\n    Field::new(\"item\", DataType::Utf8, false),\n    Field::new(\"price\", DataType::Float32, false),\n]));\nlet empty_table = db\n    .create_empty_table(\"test_empty_table\", empty_schema)\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsCreateTableConflictHandling = "// Idempotent open: reuse the existing table if it exists.\n// The provided data is ignored; the schema is validated against the\n// existing table and a mismatch raises an error.\nlet _conflict_table = db\n    .create_table(\"conflict_table\", exist_ok_reader)\n    .mode(CreateTableMode::exist_ok(|req| req))\n    .execute()\n    .await\n    .unwrap();\n\n// Overwrite: drop the existing table and create a new one with the\n// provided data. This permanently discards the old table's data.\nlet conflict_table = db\n    .create_table(\"conflict_table\", overwrite_reader)\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsCreateTableCustomSchema = "let custom_schema = Arc::new(Schema::new(vec![\n    Field::new(\n        \"vector\",\n        DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 4),\n        false,\n    ),\n    Field::new(\"lat\", DataType::Float32, false),\n    Field::new(\"long\", DataType::Float32, false),\n]));\n\nlet custom_batch = RecordBatch::try_new(\n    custom_schema.clone(),\n    vec![\n        Arc::new(\n            FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n                vec![\n                    Some(vec![Some(1.1), Some(1.2), Some(1.3), Some(1.4)]),\n                    Some(vec![Some(0.2), Some(1.8), Some(0.4), Some(3.6)]),\n                ],\n                4,\n            ),\n        ),\n        Arc::new(Float32Array::from(vec![45.5, 40.1])),\n        Arc::new(Float32Array::from(vec![-122.7, -74.1])),\n    ],\n)\n.unwrap();\nlet custom_reader: Box<dyn RecordBatchReader + Send> =\n    Box::new(RecordBatchIterator::new(vec![Ok(custom_batch)].into_iter(), custom_schema.clone()));\nlet custom_table = db\n    .create_table(\"my_table_custom_schema\", custom_reader)\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsCreateTableFromArrow = "let arrow_schema = Arc::new(Schema::new(vec![\n    Field::new(\n        \"vector\",\n        DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 16),\n        false,\n    ),\n    Field::new(\"text\", DataType::Utf8, false),\n]));\n\nlet arrow_batch = RecordBatch::try_new(\n    arrow_schema.clone(),\n    vec![\n        Arc::new(\n            FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n                vec![Some(vec![Some(0.1); 16]), Some(vec![Some(0.2); 16])],\n                16,\n            ),\n        ),\n        Arc::new(StringArray::from(vec![\"foo\", \"bar\"])),\n    ],\n)\n.unwrap();\nlet arrow_reader: Box<dyn RecordBatchReader + Send> =\n    Box::new(RecordBatchIterator::new(vec![Ok(arrow_batch)].into_iter(), arrow_schema.clone()));\nlet arrow_table = db\n    .create_table(\"arrow_table_example\", arrow_reader)\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsCreateTableFromDicts = "struct Location {\n    vector: [f32; 2],\n    lat: f32,\n    long: f32,\n}\n\nlet data = vec![\n    Location {\n        vector: [1.1, 1.2],\n        lat: 45.5,\n        long: -122.7,\n    },\n    Location {\n        vector: [0.2, 1.8],\n        lat: 40.1,\n        long: -74.1,\n    },\n];\n\nlet schema = Arc::new(Schema::new(vec![\n    Field::new(\n        \"vector\",\n        DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 2),\n        false,\n    ),\n    Field::new(\"lat\", DataType::Float32, false),\n    Field::new(\"long\", DataType::Float32, false),\n]));\n\nlet batch = RecordBatch::try_new(\n    schema.clone(),\n    vec![\n        Arc::new(\n            FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n                data.iter()\n                    .map(|row| Some(row.vector.iter().copied().map(Some).collect::<Vec<_>>())),\n                2,\n            ),\n        ),\n        Arc::new(Float32Array::from_iter_values(\n            data.iter().map(|row| row.lat),\n        )),\n        Arc::new(Float32Array::from_iter_values(\n            data.iter().map(|row| row.long),\n        )),\n    ],\n)\n.unwrap();\nlet reader: Box<dyn RecordBatchReader + Send> =\n    Box::new(RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema.clone()));\nlet table = db\n    .create_table(\"test_table\", reader)\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsCreateTableFromIterator = "let batch_schema = Arc::new(Schema::new(vec![\n    Field::new(\n        \"vector\",\n        DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 4),\n        false,\n    ),\n    Field::new(\"item\", DataType::Utf8, false),\n    Field::new(\"price\", DataType::Float32, false),\n]));\n\nlet batches = (0..5)\n    .map(|i| {\n        RecordBatch::try_new(\n            batch_schema.clone(),\n            vec![\n                Arc::new(\n                    FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n                        vec![\n                            Some(vec![Some(3.1 + i as f32), Some(4.1), Some(5.1), Some(6.1)]),\n                            Some(vec![\n                                Some(5.9),\n                                Some(26.5 + i as f32),\n                                Some(4.7),\n                                Some(32.8),\n                            ]),\n                        ],\n                        4,\n                    ),\n                ),\n                Arc::new(StringArray::from(vec![\n                    format!(\"item{}\", i * 2 + 1),\n                    format!(\"item{}\", i * 2 + 2),\n                ])),\n                Arc::new(Float32Array::from(vec![\n                    ((i * 2 + 1) * 10) as f32,\n                    ((i * 2 + 2) * 10) as f32,\n                ])),\n            ],\n        )\n        .unwrap()\n    })\n    .collect::<Vec<_>>();\n\nlet batch_reader: Box<dyn RecordBatchReader + Send> =\n    Box::new(RecordBatchIterator::new(batches.into_iter().map(Ok), batch_schema.clone()));\nlet batch_table = db\n    .create_table(\"batched_table\", batch_reader)\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsDeleteOperation = "// delete data\nlet predicate = \"id = 3\";\ntable.delete(predicate).await.unwrap();\n";
+
+export const RsDropColumnsMultiple = "// Remove the second temporary column\nschema_drop_table.drop_columns(&[\"temp_col2\"]).await.unwrap();\n";
+
+export const RsDropColumnsSingle = "// Remove the first temporary column\nschema_drop_table.drop_columns(&[\"temp_col1\"]).await.unwrap();\n";
+
+export const RsDropTable = "let drop_schema = Arc::new(Schema::new(vec![\n    Field::new(\n        \"vector\",\n        DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 2),\n        false,\n    ),\n    Field::new(\"lat\", DataType::Float32, false),\n]));\nlet drop_batch = RecordBatch::try_new(\n    drop_schema.clone(),\n    vec![\n        Arc::new(\n            FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n                vec![Some(vec![Some(1.1), Some(1.2)])],\n                2,\n            ),\n        ),\n        Arc::new(Float32Array::from(vec![45.5])),\n    ],\n)\n.unwrap();\nlet drop_reader: Box<dyn RecordBatchReader + Send> =\n    Box::new(RecordBatchIterator::new(vec![Ok(drop_batch)].into_iter(), drop_schema.clone()));\ndb.create_table(\"my_table\", drop_reader)\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n\ndb.drop_table(\"my_table\", &[]).await.unwrap();\n";
+
+export const RsInsertIfNotExists = "let table = db\n    .create_table(\n        \"users_example\",\n        make_users_reader(vec![1, 2], vec![\"Alice\", \"Bob\"], Some(vec![10, 20])),\n    )\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n\nlet mut merge_insert = table.merge_insert(&[\"id\"]);\nmerge_insert.when_not_matched_insert_all();\nmerge_insert\n    .execute(make_users_reader(\n        vec![2, 3],\n        vec![\"Bobby\", \"Charlie\"],\n        Some(vec![21, 5]),\n    ))\n    .await\n    .unwrap();\n";
+
+export const RsMergeDeleteMissingBySource = "let table = db\n    .create_table(\n        \"users_example\",\n        make_users_reader(\n            vec![1, 2, 3],\n            vec![\"Alice\", \"Bob\", \"Charlie\"],\n            Some(vec![10, 20, 5]),\n        ),\n    )\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n\nlet mut merge_insert = table.merge_insert(&[\"id\"]);\nmerge_insert\n    .when_matched_update_all(None)\n    .when_not_matched_insert_all()\n    .when_not_matched_by_source_delete(None);\nmerge_insert\n    .execute(make_users_reader(\n        vec![2, 3],\n        vec![\"Bobby\", \"Charlie\"],\n        Some(vec![21, 5]),\n    ))\n    .await\n    .unwrap();\n";
+
+export const RsMergeMatchedUpdateOnly = "let table = db\n    .create_table(\n        \"users_example\",\n        make_users_reader(vec![1, 2], vec![\"Alice\", \"Bob\"], Some(vec![10, 20])),\n    )\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n\nlet mut merge_insert = table.merge_insert(&[\"id\"]);\nmerge_insert.when_matched_update_all(None);\nmerge_insert\n    .execute(make_users_reader(\n        vec![2, 3],\n        vec![\"Bobby\", \"Charlie\"],\n        Some(vec![21, 5]),\n    ))\n    .await\n    .unwrap();\n";
+
+export const RsMergePartialColumns = "let table = db\n    .create_table(\n        \"users_example\",\n        make_users_reader(vec![1, 2], vec![\"Alice\", \"Bob\"], Some(vec![10, 20])),\n    )\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n\nlet mut merge_insert = table.merge_insert(&[\"id\"]);\nmerge_insert\n    .when_matched_update_all(None)\n    .when_not_matched_insert_all();\nmerge_insert\n    .execute(make_users_reader(vec![2, 3], vec![\"Bobby\", \"Charlie\"], None))\n    .await\n    .unwrap();\n";
+
+export const RsMergeUpdateInsert = "let table = db\n    .create_table(\n        \"users_example\",\n        make_users_reader(vec![1, 2], vec![\"Alice\", \"Bob\"], Some(vec![10, 20])),\n    )\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n\nlet mut merge_insert = table.merge_insert(&[\"id\"]);\nmerge_insert\n    .when_matched_update_all(None)\n    .when_not_matched_insert_all();\nmerge_insert\n    .execute(make_users_reader(\n        vec![2, 3],\n        vec![\"Bobby\", \"Charlie\"],\n        Some(vec![21, 5]),\n    ))\n    .await\n    .unwrap();\n";
+
+export const RsOpenExistingTable = "let open_schema = Arc::new(Schema::new(vec![\n    Field::new(\n        \"vector\",\n        DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 2),\n        false,\n    ),\n    Field::new(\"lat\", DataType::Float32, false),\n    Field::new(\"long\", DataType::Float32, false),\n]));\nlet open_batch = RecordBatch::try_new(\n    open_schema.clone(),\n    vec![\n        Arc::new(\n            FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n                vec![Some(vec![Some(1.1), Some(1.2)])],\n                2,\n            ),\n        ),\n        Arc::new(Float32Array::from(vec![45.5])),\n        Arc::new(Float32Array::from(vec![-122.7])),\n    ],\n)\n.unwrap();\nlet open_reader: Box<dyn RecordBatchReader + Send> =\n    Box::new(RecordBatchIterator::new(vec![Ok(open_batch)].into_iter(), open_schema.clone()));\ndb.create_table(\"test_table\", open_reader)\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n\nprintln!(\"{:?}\", db.table_names().execute().await.unwrap());\n\nlet opened_table = db.open_table(\"test_table\").execute().await.unwrap();\n";
+
+export const RsSchemaAddSetup = "let schema_add_schema = Arc::new(Schema::new(vec![\n    Field::new(\"id\", DataType::Int64, false),\n    Field::new(\"name\", DataType::Utf8, false),\n    Field::new(\"price\", DataType::Float64, false),\n    Field::new(\n        \"vector\",\n        DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 128),\n        false,\n    ),\n]));\nlet schema_add_batch = RecordBatch::try_new(\n    schema_add_schema.clone(),\n    vec![\n        Arc::new(Int64Array::from(vec![1, 2, 3])),\n        Arc::new(StringArray::from(vec![\"Laptop\", \"Smartphone\", \"Headphones\"])),\n        Arc::new(Float64Array::from(vec![1200.0, 800.0, 150.0])),\n        Arc::new(\n            FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n                vec![\n                    Some(vec![Some(0.1_f32); 128]),\n                    Some(vec![Some(0.2_f32); 128]),\n                    Some(vec![Some(0.3_f32); 128]),\n                ],\n                128,\n            ),\n        ),\n    ],\n)\n.unwrap();\nlet schema_add_reader: Box<dyn RecordBatchReader + Send> = Box::new(RecordBatchIterator::new(\n    vec![Ok(schema_add_batch)].into_iter(),\n    schema_add_schema.clone(),\n));\nlet schema_add_table = db\n    .create_table(\"schema_evolution_add_example\", schema_add_reader)\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsSchemaAlterSetup = "let schema_alter_schema = Arc::new(Schema::new(vec![\n    Field::new(\"id\", DataType::Int64, false),\n    Field::new(\"name\", DataType::Utf8, false),\n    Field::new(\"price\", DataType::Int32, false),\n    Field::new(\"discount_price\", DataType::Float64, false),\n    Field::new(\n        \"vector\",\n        DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 128),\n        false,\n    ),\n]));\nlet schema_alter_batch = RecordBatch::try_new(\n    schema_alter_schema.clone(),\n    vec![\n        Arc::new(Int64Array::from(vec![1, 2])),\n        Arc::new(StringArray::from(vec![\"Laptop\", \"Smartphone\"])),\n        Arc::new(Int32Array::from(vec![1200, 800])),\n        Arc::new(Float64Array::from(vec![1080.0, 720.0])),\n        Arc::new(\n            FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n                vec![Some(vec![Some(0.1_f32); 128]), Some(vec![Some(0.2_f32); 128])],\n                128,\n            ),\n        ),\n    ],\n)\n.unwrap();\nlet schema_alter_reader: Box<dyn RecordBatchReader + Send> = Box::new(RecordBatchIterator::new(\n    vec![Ok(schema_alter_batch)].into_iter(),\n    schema_alter_schema.clone(),\n));\nlet schema_alter_table = db\n    .create_table(\"schema_evolution_alter_example\", schema_alter_reader)\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsSchemaDropSetup = "let schema_drop_schema = Arc::new(Schema::new(vec![\n    Field::new(\"id\", DataType::Int64, false),\n    Field::new(\"name\", DataType::Utf8, false),\n    Field::new(\"price\", DataType::Float64, false),\n    Field::new(\"temp_col1\", DataType::Utf8, false),\n    Field::new(\"temp_col2\", DataType::Int32, false),\n    Field::new(\n        \"vector\",\n        DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 128),\n        false,\n    ),\n]));\nlet schema_drop_batch = RecordBatch::try_new(\n    schema_drop_schema.clone(),\n    vec![\n        Arc::new(Int64Array::from(vec![1, 2, 3])),\n        Arc::new(StringArray::from(vec![\"Laptop\", \"Smartphone\", \"Headphones\"])),\n        Arc::new(Float64Array::from(vec![1200.0, 800.0, 150.0])),\n        Arc::new(StringArray::from(vec![\"X\", \"Y\", \"Z\"])),\n        Arc::new(Int32Array::from(vec![100, 200, 300])),\n        Arc::new(\n            FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(\n                vec![\n                    Some(vec![Some(0.1_f32); 128]),\n                    Some(vec![Some(0.2_f32); 128]),\n                    Some(vec![Some(0.3_f32); 128]),\n                ],\n                128,\n            ),\n        ),\n    ],\n)\n.unwrap();\nlet schema_drop_reader: Box<dyn RecordBatchReader + Send> = Box::new(RecordBatchIterator::new(\n    vec![Ok(schema_drop_batch)].into_iter(),\n    schema_drop_schema.clone(),\n));\nlet schema_drop_table = db\n    .create_table(\"schema_evolution_drop_example\", schema_drop_reader)\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsSchemaFieldMetadataMerge = "// Set two metadata keys on the `category` field.\nlet res = field_metadata_table\n    .update_field_metadata(&[FieldMetadataUpdate::new(\"category\")\n        .set(\"unit\", \"label\")\n        .set(\"pii\", \"false\")])\n    .await\n    .unwrap();\nprintln!(\"version: {}\", res.version);\n\n// Merge: add a new key, delete one with `.remove`, keep the rest.\nfield_metadata_table\n    .update_field_metadata(&[FieldMetadataUpdate::new(\"category\")\n        .set(\"source\", \"import\")\n        .remove(\"pii\")])\n    .await\n    .unwrap();\n";
+
+export const RsSchemaFieldMetadataReplace = "field_metadata_table\n    .update_field_metadata(&[FieldMetadataUpdate::new(\"category\")\n        .set(\"owner\", \"search-team\")\n        .replace()])\n    .await\n    .unwrap();\n";
+
+export const RsUpdateConnectEnterprise = "let uri = \"db://your-project-slug\";\nlet api_key = \"your-api-key\";\nlet region = \"us-east-1\";\n";
+
+export const RsUpdateConnectLocal = "let db = connect(\"./data\").execute().await.unwrap();\n";
+
+export const RsUpdateExampleTableSetup = "let table = db\n    .create_table(\n        \"users_example\",\n        make_users_reader(vec![1, 2], vec![\"Alice\", \"Bob\"], Some(vec![10, 20])),\n    )\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsUpdateMakeUsersReader = "fn make_users_reader(\n    ids: Vec<i64>,\n    names: Vec<&str>,\n    login_counts: Option<Vec<i64>>,\n) -> Box<dyn RecordBatchReader + Send> {\n    let mut fields = vec![\n        Field::new(\"id\", DataType::Int64, false),\n        Field::new(\"name\", DataType::Utf8, false),\n    ];\n    let mut columns: Vec<Arc<dyn Array>> =\n        vec![Arc::new(Int64Array::from(ids)), Arc::new(StringArray::from(names))];\n\n    if let Some(login_counts) = login_counts {\n        fields.push(Field::new(\"login_count\", DataType::Int64, true));\n        columns.push(Arc::new(Int64Array::from(login_counts)));\n    }\n\n    let schema = Arc::new(Schema::new(fields));\n    let batch = RecordBatch::try_new(schema.clone(), columns).unwrap();\n    let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema);\n    Box::new(reader)\n}\n";
+
+export const RsUpdateOperation = "let table = db\n    .create_table(\n        \"users_example\",\n        make_users_reader(vec![1, 2], vec![\"Alice\", \"Bob\"], Some(vec![10, 20])),\n    )\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\ntable\n    .update()\n    .only_if(\"id = 2\")\n    .column(\"name\", \"'Bobby'\")\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsUpdateOptimizeCleanup = "table\n    .optimize(OptimizeAction::Prune {\n        older_than: Some(Duration::days(1)),\n        delete_unverified: None,\n        error_if_tagged_old_versions: None,\n    })\n    .await\n    .unwrap();\n";
+
+export const RsUpdateUsingSql = "let table = db\n    .create_table(\n        \"users_example\",\n        make_users_reader(vec![1, 2], vec![\"Alice\", \"Bob\"], Some(vec![10, 20])),\n    )\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\ntable\n    .update()\n    .only_if(\"id = 2\")\n    .column(\"login_count\", \"login_count + 1\")\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsVersioningAddData = "let more_data = vec![\n    (4, \"Richard Daniel Sanchez\", \"That's the way the news goes!\"),\n    (5, \"Morty\", \"Aww geez, Rick!\"),\n];\ntable\n    .add(make_quotes_reader(more_data))\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsVersioningBasicSetup = "let table_name = \"quotes_versioning_example\";\nlet data = vec![\n    (1, \"Richard\", \"Wubba Lubba Dub Dub!\"),\n    (2, \"Morty\", \"Rick, what's going on?\"),\n    (3, \"Richard\", \"I turned myself into a pickle, Morty!\"),\n];\n\nlet table = db\n    .create_table(table_name, make_quotes_reader(data))\n    .mode(CreateTableMode::Overwrite)\n    .execute()\n    .await\n    .unwrap();\n";
+
+export const RsVersioningCheckInitialVersion = "let versions = table.list_versions().await.unwrap();\nlet current_version = table.version().await.unwrap();\nprintln!(\"Number of versions after creation: {}\", versions.len());\nprintln!(\"Current version: {}\", current_version);\n";
+
+export const RsVersioningCheckVersionsAfterMod = "let versions_after_mod = table.list_versions().await.unwrap();\nlet version_count_after_mod = versions_after_mod.len();\nlet version_after_mod = table.version().await.unwrap();\nprintln!(\n    \"Number of versions after modifications: {}\",\n    version_count_after_mod\n);\nprintln!(\"Current version: {}\", version_after_mod);\n";
+
+export const RsVersioningCheckoutLatest = "table.checkout_latest().await.unwrap();\n";
+
+export const RsVersioningDeleteData = "table.delete(\"author = 'Morty'\").await.unwrap();\nlet rows_after_deletion = table.count_rows(None).await.unwrap();\nprintln!(\"Number of rows after deletion: {}\", rows_after_deletion);\n";
+
+export const RsVersioningListAllVersions = "let all_versions = table.list_versions().await.unwrap();\nfor v in &all_versions {\n    println!(\"Version {}, created at {}\", v.version, v.timestamp);\n}\n";
+
+export const RsVersioningMakeQuotesReader = "fn make_quotes_reader(rows: Vec<(i64, &str, &str)>) -> Box<dyn RecordBatchReader + Send> {\n    let ids: Vec<i64> = rows.iter().map(|(id, _, _)| *id).collect();\n    let authors: Vec<&str> = rows.iter().map(|(_, author, _)| *author).collect();\n    let quotes: Vec<&str> = rows.iter().map(|(_, _, quote)| *quote).collect();\n\n    let schema = Arc::new(Schema::new(vec![\n        Field::new(\"id\", DataType::Int64, false),\n        Field::new(\"author\", DataType::Utf8, false),\n        Field::new(\"quote\", DataType::Utf8, false),\n    ]));\n\n    let batch = RecordBatch::try_new(\n        schema.clone(),\n        vec![\n            Arc::new(Int64Array::from(ids)),\n            Arc::new(StringArray::from(authors)),\n            Arc::new(StringArray::from(quotes)),\n        ],\n    )\n    .unwrap();\n    let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema);\n    Box::new(reader)\n}\n";
+
+export const RsVersioningRollback = "table.checkout(version_after_mod).await.unwrap();\ntable.restore().await.unwrap();\nlet versions_after_rollback = table.list_versions().await.unwrap();\nlet version_count_after_rollback = versions_after_rollback.len();\nprintln!(\n    \"Total number of versions after rollback: {}\",\n    version_count_after_rollback\n);\n";
+
+export const RsVersioningTags = "let mut tags = tags_table.tags().await.unwrap();\n\n// Create a tag pointing at a specific version\ntags.create(\"baseline\", 1).await.unwrap();\nlet current_version = tags_table.version().await.unwrap();\ntags.create(\"with-edits\", current_version).await.unwrap();\n\n// List all tags on this table\nlet all_tags = tags.list().await.unwrap();\nprintln!(\"Tags: {:?}\", all_tags);\n\n// Look up the version a tag points at\nlet baseline_version = tags.get_version(\"baseline\").await.unwrap();\nprintln!(\"baseline -> v{}\", baseline_version);\n\n// Move an existing tag to a different version\ntags.update(\"baseline\", 2).await.unwrap();\n\n// Check out a version by tag name (separate method in Rust)\ntags_table.checkout_tag(\"baseline\").await.unwrap();\nprintln!(\"Current version: {}\", tags_table.version().await.unwrap());\n\n// Delete a tag (does not delete the underlying version)\ntags.delete(\"with-edits\").await.unwrap();\n\n// Return to the latest version\ntags_table.checkout_latest().await.unwrap();\n";
+
+export const RsVersioningUpdateData = "table\n    .update()\n    .only_if(\"author = 'Richard'\")\n    .column(\"author\", \"'Richard Daniel Sanchez'\")\n    .execute()\n    .await\n    .unwrap();\nlet rows_after_update = table\n    .count_rows(Some(\"author = 'Richard Daniel Sanchez'\".to_string()))\n    .await\n    .unwrap();\nprintln!(\n    \"Rows updated to Richard Daniel Sanchez: {}\",\n    rows_after_update\n);\n";
+
diff --git a/docs/web/sql/data-types.mdx b/docs/web/sql/data-types.mdx
new file mode 100644
index 000000000..8c7fa1781
--- /dev/null
+++ b/docs/web/sql/data-types.mdx
@@ -0,0 +1,154 @@
+---
+title: "SQL data types"
+sidebarTitle: "Data Types"
+description: "How Lance columns appear to SQL, including the types SQL has no name for."
+icon: shapes
+keywords: ["sql types", "arrow", "cast", "pseudo-columns", "vector", "blob", "json"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+There is no SQL type layer over Lance storage. The table provider hands
+DataFusion the dataset's Arrow schema directly, so a column is the same object
+to the query engine as it is to the format.
+
+The consequence is that SQL type names here are **spellings of Arrow types**,
+not a system that maps onto them. [Data types](/sql/data-types) documents the
+storage side; this page covers what changes when you reach a column from SQL.
+
+## The Arrow types a column can be {#supported}
+
+| Category | Types |
+| --- | --- |
+| Null | `null` |
+| Boolean | `bool` |
+| Integer | `int8`, `int16`, `int32`, `int64` and their `uint` counterparts |
+| Floating point | `float16`, `float32`, `float64` |
+| String | `utf8`, `large_utf8` |
+| Binary | `binary`, `large_binary`, `fixed_size_binary(n)` |
+| Date and time | `date32`, `date64`, `time32`, `time64`, `timestamp` (with or without timezone), `duration` |
+| Nested | `struct`, `list`, `large_list`, `fixed_size_list`, `map` |
+| Dictionary | `dictionary(key, value)` |
+
+Two limits are worth stating because they surface as schema errors rather than
+as documentation:
+
+- A `map` with `keys_sorted = true` is rejected. Sorted-key maps do not survive
+  the round trip through the Python bindings, so they are refused rather than
+  silently unsorted.
+- Not every type is legal as a `fixed_size_list` child. The vector case is,
+  which is what matters in practice.
+
+## SQL names for Arrow types {#names}
+
+| SQL | Arrow |
+| --- | --- |
+| `BOOLEAN` | `Boolean` |
+| `TINYINT` `SMALLINT` `INT` `BIGINT` | `Int8` `Int16` `Int32` `Int64` |
+| `UTINYINT` … `UBIGINT` | the unsigned counterparts |
+| `REAL` / `FLOAT` | `Float32` |
+| `DOUBLE` | `Float64` |
+| `VARCHAR` / `TEXT` / `STRING` | `Utf8` |
+| `BYTEA` / `BINARY` | `Binary` |
+| `DATE` | `Date32` |
+| `TIME` | `Time64` |
+| `TIMESTAMP` | `Timestamp`, with or without a timezone |
+| `INTERVAL` | `Interval` |
+| `STRUCT` / `LIST` / `MAP` | the nested equivalents |
+
+DataFusion's [type reference](https://datafusion.apache.org/user-guide/sql/data_types.html)
+describes the full mapping.
+
+## Types SQL has no name for {#no-name}
+
+Three column kinds you will meet that no SQL type name covers.
+
+### Vectors {#vectors}
+
+A dense vector column has a fixed dimension, commonly represented as
+`FixedSizeList<Float32, dim>`. Other supported vector element types are
+possible; inspect the column with [`arrow_typeof`](/sql/scalar-functions#type) rather than assuming Float32.
+It is selectable and testable for nullness:
+
+```sql
+SELECT id FROM documents WHERE vector IS NOT NULL
+```
+
+<Warning>
+A list representation does not imply that ordinary arithmetic operators
+perform vector arithmetic. Use supported distance functions for similarity
+calculations. SQL vector search depends on the Enterprise server version and query
+plan; see [SQL](/basics/sql#no-vector-sql).
+</Warning>
+
+### JSON {#json}
+
+Stored as JSONB in `large_binary` with the `lance.json` extension name. It reads
+through accessor functions rather than a `->` operator:
+
+```sql
+SELECT json_get_string(data, 'name') FROM events
+WHERE json_get_int(data, 'status') = 200
+```
+
+`json_get`, its typed variants, and `json_extract` are **index-accelerated** by a
+JSON scalar index. See [Functions](/sql/scalar-functions#json).
+
+### Blobs {#blobs}
+
+A blob column is read either as its binary contents or as a **description** — a
+pointer plus metadata, without fetching the bytes. That choice changes the
+column's Arrow type, and therefore the schema your query sees.
+
+<Warning>
+The default reads blob columns as descriptions and other binary columns as
+binary. A query selecting a blob column and expecting bytes gets a struct.
+</Warning>
+
+### bfloat16 {#bfloat16}
+
+Sixteen-bit floats in the bfloat16 layout, stored as `fixed_size_binary(2)`.
+Useful as a vector element type when halving the memory matters more than the
+precision does.
+
+## Geospatial {#geospatial}
+
+<Warning>
+Lance defines geospatial types and `st_*` functions on
+[GeoArrow](https://geoarrow.org), but they sit behind an optional build feature
+that **LanceDB does not ship**. Calling `st_intersects` and friends returns an
+error naming the missing feature rather than an unknown-function error, and the
+spatial index is gated the same way.
+</Warning>
+
+Geometry can still be stored -- it is binary, and binary columns work -- but
+nothing interprets it. Filter on a bounding box held in ordinary numeric
+columns if you need spatial predicates today.
+
+## Pseudo-columns {#pseudo-columns}
+
+Availability depends on the SQL table provider. The ordinary table provider
+exposes the stored dataset schema; naming a Lance scan metadata field does
+not make it available as a SQL column. The `fts()` provider adds `_score` to
+its schema, so `_score` is included in `SELECT * FROM fts(...)`.
+
+| Column | Type | |
+| --- | --- | --- |
+| `_rowid` | `UInt64` | Lance scan row identifier; stability requires stable row ids |
+| `_rowaddr` | `UInt64` | physical row address |
+| `_rowoffset` | `UInt64` | offset within the fragment |
+| `_score` | `Float32` | relevance, from [`fts()`](/sql/functions#fts) |
+
+`_rowid`, `_rowaddr`, and `_rowoffset` describe Lance scan metadata, not
+columns automatically registered on an ordinary SQL table. Select `_score`
+from an `fts()` relation explicitly when you want a projection containing it.
+
+## Casting {#casting}
+
+```sql
+SELECT CAST(identifier AS VARCHAR) FROM documents
+SELECT TRY_CAST(maybe_number AS BIGINT) FROM raw
+```
+
+`TRY_CAST` yields null rather than failing the query on a value that will not
+convert. See [Operators](/sql/operators#casting).
diff --git a/docs/web/sql/ddl/function.mdx b/docs/web/sql/ddl/function.mdx
new file mode 100644
index 000000000..f0782008d
--- /dev/null
+++ b/docs/web/sql/ddl/function.mdx
@@ -0,0 +1,91 @@
+---
+title: "Function"
+sidebarTitle: "Function"
+description: "CREATE FUNCTION, SHOW FUNCTIONS, and DROP FUNCTION."
+icon: function
+keywords: ["create function", "drop function", "show functions", "sql", "udf"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+## CREATE FUNCTION {#create}
+
+```sql
+CREATE FUNCTION f(x int64) RETURNS int64
+LANGUAGE python
+AS 'def f(x): return x * 2'
+WITH (pip = 'numpy==1.26.4', python_version = '3.11')
+```
+
+Parameter and result types are Arrow type names, not SQL ones: `int64`,
+`float64`, `utf8`, `bool`, `date32`, and lists such as `list<float32>` or
+`fixed_size_list<float32, 768>`. `BIGINT` is refused. A parameter accepts nulls
+unless it is declared `NOT NULL`.
+
+The body is Python source. Its dependencies are declared with it and are part
+of the **immutable function version** -- changing one produces a new version
+rather than altering the registered one, so a column filled by an earlier
+version is unaffected.
+
+| Option | |
+| --- | --- |
+| `pip` | pip requirements for the worker environment |
+| `conda` | conda packages instead of pip |
+| `conda_channels` | channels in priority order; requires `conda` |
+| `python_version` | worker Python version; `3.12` when omitted, whatever Python you send the statement from |
+| `gpu` | `'true'` when every execution needs a GPU |
+
+**Errors and limits**
+
+| | |
+| --- | --- |
+| `LANGUAGE` other than `python` | refused |
+| `pip` and `conda` together | refused -- pick one |
+| `conda_channels` without `conda` | refused |
+| `gpu` set to anything but `'true'` | refused |
+| An empty body | refused |
+| Any other option | refused, and the message lists what is supported |
+
+<Note>
+**Enterprise job type:** `create_function`. Registration returns a job because
+the function is baked into an image before the version becomes usable.
+</Note>
+
+## SHOW FUNCTIONS {#show}
+
+```sql
+SHOW FUNCTIONS
+```
+
+Lists registered functions and their versions. Since a version is immutable
+and a column pins the version that fills it, this is how you find which
+versions are still in use before dropping one.
+
+## DROP FUNCTION {#drop}
+
+```sql
+DROP FUNCTION embed WITH (version = '1')
+DROP FUNCTION IF EXISTS embed WITH (version = '1')
+DROP FUNCTION embed WITH (version = '1', cascade = 'true')
+```
+
+`version` is required -- a function is a set of immutable versions, and
+dropping is always of one specific version, never "the function" as a whole.
+
+<Warning>
+Without `cascade = 'true'`, dropping a version that a column still pins fails
+rather than breaking that column silently. Passing it drops the version and
+lets columns pinned to it stop refreshing -- they keep their last-filled
+values, per [Guarantees](/basics/guarantees#jobs).
+</Warning>
+
+## What to read next {#next}
+
+<CardGroup cols={2}>
+  <Card title="Function" icon="function" href="/management/function">
+    Registering one from the SDK, and diagnosing environment mismatches.
+  </Card>
+  <Card title="Computed columns" icon="table-columns" href="/feature-engineering/add-computed-column">
+    Filling a column with a function instead of an expression.
+  </Card>
+</CardGroup>
diff --git a/docs/web/sql/ddl/index.mdx b/docs/web/sql/ddl/index.mdx
new file mode 100644
index 000000000..3181210c0
--- /dev/null
+++ b/docs/web/sql/ddl/index.mdx
@@ -0,0 +1,59 @@
+---
+title: "Index"
+sidebarTitle: "Index"
+description: "CREATE INDEX and DROP INDEX — one column, with the algorithm chosen by USING."
+icon: bolt
+keywords: ["create index", "drop index", "sql"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+## CREATE INDEX {#create}
+
+```sql
+CREATE INDEX ON documents USING btree (title)
+CREATE INDEX text_search ON documents USING fts (text)
+```
+
+`USING` is required: it names the algorithm. The scalar algorithms are `btree`,
+`bitmap` and `label_list`; `fts` builds a full-text index; and the vector
+algorithms are `ivf_pq`, `ivf_rq`, `ivf_flat`, `ivf_hnsw_sq` and `ivf_hnsw_pq`,
+where the deployment supports them. Index options go in a `WITH (...)` clause.
+
+<Note>
+In LanceDB Enterprise, SQL `CREATE INDEX` covers **one column**. A name between
+`INDEX` and `ON` names the index, where the deployment's configuration allows
+it; without one, the server names it after the column, as in `title_idx`. For a
+multi-column index, use the SDK's [`create_index`](/indexing).
+</Note>
+
+<Note>
+**Enterprise job type:** `reindex_` followed by the algorithm, such as
+`reindex_btree` or `reindex_ivf_pq`; the name says which algorithm is building,
+not a different feature. An SDK build that must not replace an existing index
+runs as `create_` followed by the algorithm instead. See
+[Build & Repair](/indexing/reindexing).
+</Note>
+
+## DROP INDEX {#drop}
+
+```sql
+DROP INDEX documents.title_idx
+```
+
+Takes the qualified `table.index` name. Find it with the SDK's
+[`list_indices`](/indexing) if you created the index without
+naming it yourself. A deployment builds the index in the background after
+`CREATE INDEX` returns; until that build finishes, the index is not listed and
+`DROP INDEX` reports it as not found.
+
+## What to read next {#next}
+
+<CardGroup cols={2}>
+  <Card title="Indexing" icon="bolt" href="/indexing">
+    Choosing an index type from the SDK.
+  </Card>
+  <Card title="Build & Repair" icon="refresh" href="/indexing/reindexing">
+    Keeping an index current as data arrives.
+  </Card>
+</CardGroup>
diff --git a/docs/web/sql/ddl/job.mdx b/docs/web/sql/ddl/job.mdx
new file mode 100644
index 000000000..bc985d34d
--- /dev/null
+++ b/docs/web/sql/ddl/job.mdx
@@ -0,0 +1,90 @@
+---
+title: "Job"
+sidebarTitle: "Job"
+description: "SHOW JOBS, SHOW ERRORS, and controlling a running job with PAUSE, RESUME and CANCEL."
+icon: hourglass-half
+keywords: ["show jobs", "show errors", "pause job", "resume job", "cancel job", "sql"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+A [job](/basics/jobs) is not a catalog object -- it has no schema and is not
+namespaced -- but it has its own SQL statements, grouped here the same way the
+catalog objects are.
+
+## SHOW JOBS {#show}
+
+```sql
+SHOW JOBS
+```
+
+Every job the server knows about -- id, type, state, creation time. The SQL
+counterpart of `list_jobs`.
+
+## SHOW ERRORS {#errors}
+
+```sql
+SHOW ERRORS ON <table> [FOR JOB '<job-id>'] [LIMIT <n>]
+```
+
+```sql
+SHOW ERRORS ON documents
+SHOW ERRORS ON documents FOR JOB 'job-abc' LIMIT 100
+```
+
+Why rows failed. Reach for this when a [refresh](/sql/dml/refresh) reports
+fewer filled rows than you expected.
+
+**Errors and limits**
+
+| | |
+| --- | --- |
+| `LIMIT` | must be between 1 and 100,000 |
+| Failed jobs scanned without `FOR JOB` | 32 most recent |
+| Tables the server does not open directly | not supported |
+
+Without `FOR JOB` this surveys recent failures and stops at 32 jobs. When the
+listing stops at your `LIMIT`, the message says so -- narrow with `FOR JOB`
+rather than raising the limit blindly.
+
+## PAUSE JOB {#pause}
+
+```sql
+PAUSE JOB 'j-ci-abc-def'
+```
+
+## RESUME JOB {#resume}
+
+```sql
+RESUME JOB 'j-ci-abc-def'
+```
+
+## CANCEL JOB {#cancel}
+
+```sql
+CANCEL JOB 'j-ci-abc-def'
+```
+
+The job id is always a quoted string literal. All three take exactly one and
+nothing after it -- a second token on the line is a syntax error rather than
+being ignored.
+
+<Note>
+Cancellation is a request, matching the SDK's
+[`cancel()`](/basics/jobs#managing): it does not guarantee the job stops before
+doing more work, and asking twice or cancelling a finished job does nothing.
+`CANCEL JOB` itself can be unavailable on an Enterprise cluster with no job registry
+configured, which is a deployment condition rather than something you can work
+around from SQL.
+</Note>
+
+## What to read next {#next}
+
+<CardGroup cols={2}>
+  <Card title="Query and job" icon="hourglass-half" href="/basics/jobs">
+    The handle these statements are the SQL view of.
+  </Card>
+  <Card title="Background jobs" icon="gears" href="/maintenance/background-jobs">
+    Job types you did not start yourself.
+  </Card>
+</CardGroup>
diff --git a/docs/web/sql/ddl/materialized-view.mdx b/docs/web/sql/ddl/materialized-view.mdx
new file mode 100644
index 000000000..19dba5aa4
--- /dev/null
+++ b/docs/web/sql/ddl/materialized-view.mdx
@@ -0,0 +1,72 @@
+---
+title: "Materialized View"
+sidebarTitle: "Materialized View"
+description: "CREATE MATERIALIZED VIEW and SHOW MATERIALIZED VIEWS; refreshing is a REFRESH statement."
+icon: table
+keywords: ["materialized view", "create materialized view", "show materialized views", "sql"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+A materialized view stores a query's result as rows, kept current by a refresh
+-- the counterpart to a [view](/sql/ddl/view), which stores only the query.
+
+<Note>
+Materialized views are off by default in LanceDB Enterprise and have to be enabled
+there. Which statements and query shapes a deployment accepts, whether it needs
+the source table to have stable row ids, and how it refreshes a view depend on
+its LanceDB Enterprise version and configuration.
+</Note>
+
+## CREATE MATERIALIZED VIEW {#create}
+
+```sql
+CREATE MATERIALIZED VIEW <name> AS SELECT ...
+```
+
+Once created, a materialized view can be queried like a table. Use the
+statements below to list, refresh and drop it. For SDK methods, see
+[Materialized views](/management/materialized-view#create).
+
+## SHOW MATERIALIZED VIEWS {#show}
+
+```sql
+SHOW MATERIALIZED VIEWS
+```
+
+<Note>
+This asks the server for its views, as the SDK's `list_materialized_views` does
+on a `db://` connection.
+</Note>
+
+## Refreshing one {#refresh}
+
+```sql
+REFRESH MATERIALIZED VIEW chunks
+```
+
+See [REFRESH](/sql/dml/refresh#refresh-materialized-view) for the full syntax,
+and compatibility notes for older server versions.
+
+## Dropping one {#drop}
+
+A materialized view **is** a table, so it is dropped with
+[`DROP TABLE`](/sql/ddl/table#drop) -- which is also why dropping one removes
+stored rows, unlike dropping a [view](/sql/ddl/view#drop).
+
+<Note>
+**Enterprise job type:** `mat_view_refresh` for a refresh, or `sql` on a
+deployment that rebuilds views through SQL. Dropping one runs the same job as
+dropping any table, `drop_table` or `drop_object`.
+</Note>
+
+## What to read next {#next}
+
+<CardGroup cols={2}>
+  <Card title="Materialized View" icon="table" href="/management/materialized-view">
+    Creating one from the SDK.
+  </Card>
+  <Card title="Refresh a materialized view" icon="arrows-rotate" href="/feature-engineering/materialized-views">
+    How a refresh works, locally and in LanceDB Enterprise.
+  </Card>
+</CardGroup>
diff --git a/docs/web/sql/ddl/namespace.mdx b/docs/web/sql/ddl/namespace.mdx
new file mode 100644
index 000000000..efd209b02
--- /dev/null
+++ b/docs/web/sql/ddl/namespace.mdx
@@ -0,0 +1,55 @@
+---
+title: "Namespace"
+sidebarTitle: "Namespace"
+description: "CREATE SCHEMA and DROP SCHEMA — the SQL spelling of namespace DDL."
+icon: folder-tree
+keywords: ["namespace", "schema", "create schema", "drop schema", "sql"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+A [namespace](/management/namespace) is addressed in SQL as a `SCHEMA` -- the
+standard's name for the same concept.
+
+## CREATE SCHEMA {#create}
+
+```sql
+CREATE SCHEMA analytics
+CREATE SCHEMA db1.`ns1$ns2`
+CREATE SCHEMA IF NOT EXISTS `ns1$ns2`
+```
+
+A nested namespace is one backtick-quoted name with `$` between its
+components: `` db1.`ns1$ns2` `` creates `ns2` inside `ns1` in database `db1`,
+provided `ns1` already exists. The part before the dot is the database, so a
+three-part path such as `db1.ns1.ns2` is rejected. `public` is the database's
+root namespace, which is never created or dropped.
+
+`IF NOT EXISTS` makes the statement a no-op rather than an error when the
+namespace is already there.
+
+## DROP SCHEMA {#drop}
+
+```sql
+DROP SCHEMA db1.analytics
+DROP SCHEMA IF EXISTS `a$b`
+DROP SCHEMA `a$b` RESTRICT
+```
+
+<Warning>
+`CASCADE` is rejected: **"DROP SCHEMA CASCADE is not supported; drop the
+objects inside first."** A namespace's tables, views and other objects must be
+dropped explicitly before the namespace itself can go. `RESTRICT` -- the
+default -- states that requirement rather than changing it.
+</Warning>
+
+## What to read next {#next}
+
+<CardGroup cols={2}>
+  <Card title="Namespace" icon="folder-tree" href="/management/namespace">
+    The SDK equivalent, and how a namespace fits the catalog hierarchy.
+  </Card>
+  <Card title="Table" icon="table" href="/sql/ddl/table">
+    Creating tables inside a namespace.
+  </Card>
+</CardGroup>
diff --git a/docs/web/sql/ddl/table.mdx b/docs/web/sql/ddl/table.mdx
new file mode 100644
index 000000000..05406e677
--- /dev/null
+++ b/docs/web/sql/ddl/table.mdx
@@ -0,0 +1,186 @@
+---
+title: "Table"
+sidebarTitle: "Table"
+description: "CREATE, DESCRIBE, ALTER, CLONE and DROP TABLE."
+icon: table
+keywords: ["create table", "alter table", "drop table", "describe", "clone", "ddl"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+## CREATE TABLE {#create}
+
+```sql
+CREATE TABLE documents (id BIGINT, title VARCHAR, created_at TIMESTAMP)
+CREATE TABLE recent AS SELECT * FROM documents WHERE created_at > now() - INTERVAL '7 days'
+```
+
+Column types are Arrow types under SQL names -- see [Data types](/sql/data-types).
+A table inside a [namespace](/sql/ddl/namespace) is written as a dotted path:
+`prod.events.clicks`.
+
+## DESCRIBE {#describe}
+
+```sql
+DESCRIBE documents
+```
+
+The table's columns and types, without a plan -- the SQL counterpart of
+`table.schema`.
+
+## ALTER TABLE {#alter}
+
+What `ALTER TABLE ... ADD COLUMN` does depends on what follows the column
+name.
+
+### A plain column {#plain-column}
+
+SQL `ADD COLUMN` requires an `AS` binding, so a plain column such as
+`ALTER TABLE t ADD COLUMN c INT` is rejected. Add an empty, nullable column
+with the SDK instead -- see
+[Add nullable columns](/management/table#schema-add-nullable-columns).
+
+### A computed column {#computed-column}
+
+```sql
+ALTER TABLE t ADD COLUMN doubled AS value * 2
+```
+
+`AS <expression>` declares a [computed column](/feature-engineering/add-computed-column).
+The column is committed empty and the expression is stored, not evaluated --
+declaring one on a billion-row table costs what declaring one on an empty table
+costs. [`REFRESH COLUMN`](/sql/dml/refresh) fills it.
+
+{/*
+Draft for the Functions release.
+
+### A function column {#function-column}
+
+```sql
+ALTER TABLE t ADD COLUMN embedded AS embed(name) WITH (version = 'fv_1')
+```
+
+When the expression calls a [registered function](/sql/ddl/function), the
+column is filled by running that function on workers. `version` pins which
+immutable function version fills it, so a later registration does not silently
+change what the column means.
+
+Several modifiers narrow what gets computed:
+
+| | |
+| --- | --- |
+| `FILTER (WHERE pred)` | only rows matching `pred` are computed |
+| `IGNORE NULLS` | rows with null inputs are skipped |
+| `OVER (PARTITION BY col)` | the function sees rows grouped by `col` |
+
+A function returning several values fills several columns at once:
+
+```sql
+ALTER TABLE t ADD COLUMN (label, score) AS classify(text) WITH (version = 'fv_1')
+```
+
+```sql
+ALTER TABLE t ADD COLUMN c AS f(x) FILTER (WHERE keep) WITH (version = 'fv_1')
+ALTER TABLE t ADD COLUMN c AS f(x) WITH (num_workers = '2')
+```
+
+<Note>
+`WITH` on a plain computed column accepts only `version`. Resource options such
+as `num_workers` apply to function columns, where there are workers to size.
+</Note>
+*/}
+
+### Table properties {#properties}
+
+```sql
+ALTER TABLE t SET UNENFORCED PRIMARY KEY (id)
+```
+
+Records which column identifies a row. **Unenforced** is not a hedge in the
+naming: uniqueness is not checked on write, and declaring it does not make
+duplicates impossible. It tells the planner what to assume. The column must be
+declared `NOT NULL`.
+
+```sql
+ALTER TABLE t SET MEMWAL
+ALTER TABLE t SET MEMWAL WITH (sharding = 'bucket', column = 'id', buckets = 4)
+ALTER TABLE t SET MEMWAL WITH (maintained_indexes = '')
+ALTER TABLE t UNSET MEMWAL
+```
+
+A write-ahead log makes small frequent writes cheap by batching them out of the
+commit path. `sharding` and `buckets` spread the log so concurrent writers do
+not contend on one place; `bucket` sharding names its `column`, which must be
+the table's unenforced primary key, and inserts into the table then need a
+scalar index on that key. `maintained_indexes` chooses which indexes are kept
+current against WAL rows. A table with a computed column cannot take a
+write-ahead log. See
+[Background jobs](/maintenance/background-jobs#wal) for how the log gets
+folded back into the table.
+
+<Warning>
+[`TABLESAMPLE`](/sql/dql/sampling) is refused on a WAL-backed table, because
+rows a sample would draw from are not all in the fragments yet.
+</Warning>
+
+**Errors and limits**
+
+| | |
+| --- | --- |
+| `ALTER TABLE ... DROP COLUMN` | not supported |
+
+Dropping, renaming, or retyping a column is an SDK operation --
+[`drop_columns`](/management/table#schema) and
+[`alter_columns`](/management/table#schema) -- not a SQL statement.
+
+## CLONE {#clone}
+
+```sql
+CREATE TABLE docs_snapshot CLONE documents
+CREATE TABLE docs_at_v42 CLONE documents AT 42
+CREATE TABLE docs_at_release CLONE documents AT TAG 'release-1'
+CREATE TABLE docs_yesterday CLONE documents AT TIMESTAMP '2026-09-01T00:00:00Z'
+```
+
+A **shallow clone**: the new table shares the source's data files rather than
+copying them, so it costs one metadata commit regardless of the source's size.
+
+`AT` pins the point in the source's history to clone. A bare integer is a
+version and a bare quoted string is a timestamp; `VERSION`, `TAG` and
+`TIMESTAMP` say so explicitly, and a tag always needs its keyword -- a bare
+quoted string cannot be both a tag name and a timestamp.
+
+<Warning>
+The clone depends on the source's files continuing to exist. If the source is
+[compacted](/maintenance/cleanup) past the retention window, the version the
+clone points at can be pruned. The SDK's
+[`clone_table`](/management/table#rename-clone) also offers a deep clone for
+when the copy has to outlive the source's history.
+</Warning>
+
+## DROP TABLE {#drop}
+
+```sql
+DROP TABLE documents
+```
+
+<Warning>
+Removes the table and every version of it. There is no undrop.
+</Warning>
+
+<Note>
+**Enterprise job type:** `drop_table` or `drop_object`, depending on how the
+deployment stores its catalog. See
+[Background jobs](/maintenance/background-jobs#teardown).
+</Note>
+
+## What to read next {#next}
+
+<CardGroup cols={2}>
+  <Card title="Table" icon="table" href="/management/table">
+    The SDK equivalent, and what a table carries.
+  </Card>
+  <Card title="Schema evolution" icon="boxes-stacked" href="/management/table#schema">
+    Renaming, retyping and dropping columns from the SDK.
+  </Card>
+</CardGroup>
diff --git a/docs/web/sql/ddl/view.mdx b/docs/web/sql/ddl/view.mdx
new file mode 100644
index 000000000..8b4337fac
--- /dev/null
+++ b/docs/web/sql/ddl/view.mdx
@@ -0,0 +1,98 @@
+---
+title: "View"
+sidebarTitle: "View"
+description: "CREATE VIEW and DROP VIEW — a saved query, evaluated when read."
+icon: eye
+keywords: ["create view", "drop view", "sql", "catalog"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+A view stores a **query**, not rows. Reading it runs the query against current
+data, so a view is never stale -- and never faster than the query it wraps. It
+is the counterpart to a [materialized view](/sql/ddl/materialized-view), which
+stores rows and is brought up to date by a refresh; both are
+[catalog objects](/basics/catalog) defined by a query, differing only in when
+that query runs.
+
+There is no SDK method for a view, because the thing it stores *is* a SQL
+statement.
+
+## CREATE VIEW {#create}
+
+```sql
+CREATE VIEW "recent_matches" AS
+SELECT t1.floats
+FROM "documents" AS t1
+INNER JOIN "categories" AS t2 ON t1.ints = t2.ints
+```
+
+The statement returns the view's schema -- column name, data type, nullability
+-- resolved from the query at creation time. Anything the
+[dialect](/sql/functions) supports can define one: joins, predicates,
+aggregates, window functions.
+
+## Reading one {#reading}
+
+A view is queried like a table:
+
+```sql
+SELECT * FROM "recent_matches" WHERE floats > 0.5
+```
+
+Predicates applied to the view compose with the query inside it rather than
+filtering its output afterwards, so a selective filter does not pay for rows
+the view would have produced and then discarded.
+
+## DROP VIEW {#drop}
+
+```sql
+DROP VIEW "recent_matches"
+```
+
+Removes the saved query. Unlike dropping a table, no data is removed -- there
+was none to begin with.
+
+## Listing {#listing}
+
+Views and tables share a namespace, and both are things you can query. The
+catalog distinguishes them:
+
+| | |
+| --- | --- |
+| view names | the views in the database |
+| table names | the tables |
+| relation names | everything queryable -- both |
+
+Use the relation listing when the question is "what can I select from," and the
+separate listings when the distinction matters -- before dropping, for
+instance, since dropping a view removes only the saved query while dropping a
+table removes data.
+
+## Choosing between a view and a materialized view {#choosing}
+
+| | View | Materialized view |
+| --- | --- | --- |
+| Stores | the query | the rows |
+| Freshness | always current | as of the last refresh |
+| Read cost | the full query, every read | a table scan |
+| Write cost | none | a refresh |
+
+Reach for a **view** when the query is cheap, or when reading stale rows would
+be wrong. Reach for a **materialized view** when the query is expensive and the
+data changes less often than it is read.
+
+The privileges are separate too: `CREATE_VIEW` and `CREATE_MATERIALIZED_VIEW`
+are distinct grants on a namespace, so the ability to create one does not imply
+the other. See [Authorization](/governance/authorization).
+
+## What to read next {#next}
+
+<CardGroup cols={2}>
+  <Card title="Materialized View" icon="table" href="/sql/ddl/materialized-view">
+    The stored-rows counterpart.
+  </Card>
+  <Card title="Management" icon="folder-tree" href="/management">
+    Where views fit among the other catalog objects.
+  </Card>
+</CardGroup>
diff --git a/docs/web/sql/dml/copy.mdx b/docs/web/sql/dml/copy.mdx
new file mode 100644
index 000000000..658b20b59
--- /dev/null
+++ b/docs/web/sql/dml/copy.mdx
@@ -0,0 +1,27 @@
+---
+title: "Copy"
+sidebarTitle: "Copy"
+description: "COPY ... TO — writing a query result to object storage as files."
+icon: file-export
+keywords: ["copy", "export", "parquet", "sql"]
+---
+
+<Warning>
+Recent deployment versions refuse `COPY` over the SQL endpoint with "COPY is not
+supported". On those, export a result by reading it through a Flight SQL client
+or an SDK and writing the files from your application.
+</Warning>
+
+```sql
+COPY (SELECT id, title FROM documents) TO 's3://bucket/export/' STORED AS PARQUET
+```
+
+Writes a query result to object storage. This is the export path when the
+consumer is another engine rather than an application -- results land as files
+rather than streaming to a client.
+
+## What to read next {#next}
+
+<Card title="Insert" icon="plus" href="/sql/dml/insert">
+  Bringing rows in, rather than exporting them.
+</Card>
diff --git a/docs/web/sql/dml/delete.mdx b/docs/web/sql/dml/delete.mdx
new file mode 100644
index 000000000..90965062b
--- /dev/null
+++ b/docs/web/sql/dml/delete.mdx
@@ -0,0 +1,31 @@
+---
+title: "Delete"
+sidebarTitle: "Delete"
+description: "DELETE FROM ... WHERE — rows are marked, not erased."
+icon: trash
+keywords: ["delete", "sql"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+```sql
+DELETE FROM documents WHERE created_at < TIMESTAMP '2025-01-01 00:00:00'
+```
+
+Compare a timestamp column with a typed literal, as above, or with
+`CAST('2025-01-01' AS TIMESTAMP)`. A bare string such as `'2025-01-01'` is
+rejected in a `DELETE` predicate, even though a `SELECT` accepts it.
+
+Deletion marks rows rather than erasing them, so the data remains recoverable
+for as long as the version holding it survives
+[retention](/maintenance/cleanup).
+
+Commits a new table version, so [versioning](/management/table#versioning) and
+the [write guarantees](/basics/guarantees#writes) apply -- including that the
+version advances even when the statement affects zero rows.
+
+## What to read next {#next}
+
+<Card title="Delete" icon="trash" href="/ingestion/delete">
+  The SDK equivalent.
+</Card>
diff --git a/docs/web/sql/dml/insert.mdx b/docs/web/sql/dml/insert.mdx
new file mode 100644
index 000000000..61fcb9994
--- /dev/null
+++ b/docs/web/sql/dml/insert.mdx
@@ -0,0 +1,33 @@
+---
+title: "Insert"
+sidebarTitle: "Insert"
+description: "INSERT INTO, including INSERT ... SELECT to move rows server-side."
+icon: plus
+keywords: ["insert", "insert into", "sql"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+```sql
+INSERT INTO documents (id, title) VALUES (1, 'Merlin'), (2, 'Arthur')
+
+INSERT INTO archive SELECT * FROM documents WHERE created_at < '2026-01-01'
+```
+
+`INSERT ... SELECT` is the form worth knowing: it moves rows between tables
+server-side, without a round trip through your process.
+
+Commits a new table version, so [versioning](/management/table#versioning) and
+the [write guarantees](/basics/guarantees#writes) apply -- including that the
+version advances even when the statement affects zero rows.
+
+## What to read next {#next}
+
+<CardGroup cols={2}>
+  <Card title="Insert" icon="plus" href="/ingestion/insert">
+    The SDK equivalent.
+  </Card>
+  <Card title="Merge Insert" icon="code-merge" href="/ingestion/merge-insert">
+    Upsert -- matching incoming rows against existing keys -- has no SQL form.
+  </Card>
+</CardGroup>
diff --git a/docs/web/sql/dml/refresh.mdx b/docs/web/sql/dml/refresh.mdx
new file mode 100644
index 000000000..1a14adef8
--- /dev/null
+++ b/docs/web/sql/dml/refresh.mdx
@@ -0,0 +1,100 @@
+---
+title: "REFRESH"
+sidebarTitle: "REFRESH"
+description: "Filling computed columns and bringing materialized views up to date."
+icon: arrows-rotate
+keywords: ["refresh column", "refresh materialized view", "backfill", "cascade"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+LanceDB extensions with no DataFusion equivalent. They are what makes
+[feature engineering](/feature-engineering) reachable from SQL: a column
+declared by [DDL](/sql/ddl/table#computed-column) starts empty, and a refresh fills it.
+
+## REFRESH COLUMN {#refresh-column}
+
+```sql
+REFRESH COLUMN <column> ON [TABLE] <table>
+  [WHERE <predicate>]
+  [WITH (<option> = <value> [, ...])]
+```
+
+```sql
+REFRESH COLUMN doubled ON my_table
+REFRESH COLUMN embedding ON documents WHERE created_at > '2026-01-01'
+```
+
+A refresh gives values to rows that lack them, recomputes rows whose inputs
+changed, and **leaves other filled rows alone**. That makes it idempotent, cheap
+to repeat as data arrives, and safe to re-run after a failure -- though a re-run
+can recompute values the failed run had already written.
+
+`WHERE` scopes the refresh, which is how you fill a slice and check it before
+committing to the whole table.
+
+### Options {#refresh-column-options}
+
+| Option | Meaning |
+| --- | --- |
+| `num_workers` | how many workers to use |
+| `cpu_millis` | CPU budget per worker |
+| `memory_bytes` | memory budget per worker |
+| `gpu_count` | GPUs per worker |
+| `batch_size` | rows a worker takes at a time |
+| `error_policy` | `'skip'` continues past failing rows instead of failing the job |
+
+```sql
+REFRESH COLUMN embedding ON documents
+  WITH (cpu_millis = 8000, memory_bytes = 17179869184, gpu_count = 2)
+```
+
+`num_workers` sets a count; the budget options size each worker. They are
+alternatives rather than a pair.
+
+{/*
+Draft for the Functions release.
+
+<Note>
+`error_policy` and `num_workers` are refused for a column filled by a registered
+[function](/management/function) with its own resource budget -- use the CPU,
+memory and GPU options there.
+</Note>
+*/}
+
+## REFRESH MATERIALIZED VIEW {#refresh-materialized-view}
+
+```sql
+REFRESH MATERIALIZED VIEW <name>
+```
+
+```sql
+REFRESH MATERIALIZED VIEW chunks
+```
+
+A refresh updates the view's stored result. Refresh views in dependency order,
+starting with their source views.
+
+<Note>
+Some older server versions accepted `WITH (full = true)` and `CASCADE`.
+Those options are not accepted by the SQL implementation described here.
+Use the basic statement above; do not assume that the options accepted by a
+local SDK's `refresh()` method are also SQL options.
+</Note>
+
+## Watching a refresh {#watching}
+
+Both statements submit work that outlives the call. Track it with
+[`SHOW JOBS`](/sql/ddl/job#show), and diagnose failures with
+[`SHOW ERRORS`](/sql/ddl/job#errors).
+
+## See also {#see-also}
+
+<CardGroup cols={2}>
+  <Card title="Refresh a column" icon="arrows-rotate" href="/feature-engineering/refresh-column">
+    The same operation from the SDKs, and what a refresh guarantees.
+  </Card>
+  <Card title="Table" icon="table" href="/sql/ddl/table#alter">
+    Declaring the columns this fills.
+  </Card>
+</CardGroup>
diff --git a/docs/web/sql/dml/update.mdx b/docs/web/sql/dml/update.mdx
new file mode 100644
index 000000000..c4332d185
--- /dev/null
+++ b/docs/web/sql/dml/update.mdx
@@ -0,0 +1,34 @@
+---
+title: "Update"
+sidebarTitle: "Update"
+description: "UPDATE ... SET ... WHERE — and why it does not recompute derived columns."
+icon: pen
+keywords: ["update", "sql"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+```sql
+UPDATE documents SET title = upper(title) WHERE category = 'film'
+```
+
+Assignments take any expression the [dialect](/sql/functions) supports,
+including references to other columns of the same row. Without a `WHERE`
+clause every row is updated.
+
+<Warning>
+An update does not recompute [computed columns](/feature-engineering/add-computed-column)
+that derive from the column you changed. Those rows keep the value computed from
+the old input until the next refresh recomputes them -- see
+[Guarantees](/basics/guarantees#jobs).
+</Warning>
+
+Commits a new table version, so [versioning](/management/table#versioning) and
+the [write guarantees](/basics/guarantees#writes) apply -- including that the
+version advances even when the statement affects zero rows.
+
+## What to read next {#next}
+
+<Card title="Update" icon="pen" href="/ingestion/update">
+  The SDK equivalent.
+</Card>
diff --git a/docs/web/sql/dql/aggregation.mdx b/docs/web/sql/dql/aggregation.mdx
new file mode 100644
index 000000000..48ae8453f
--- /dev/null
+++ b/docs/web/sql/dql/aggregation.mdx
@@ -0,0 +1,120 @@
+---
+title: "Aggregation"
+sidebarTitle: "Aggregation"
+description: "GROUP BY, HAVING, and the aggregate function set."
+icon: layer-group
+keywords: ["group by", "having", "count", "sum", "avg", "aggregate", "approximate"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+Use SQL aggregates to summarize records, either across a whole table or
+within groups. Common examples include counts by category, totals and averages.
+
+## Grouping {#grouping}
+
+```sql
+SELECT category, count(*) AS n, avg(score) AS mean_score
+FROM documents
+GROUP BY category
+HAVING count(*) > 10
+ORDER BY n DESC
+```
+
+`WHERE` filters rows before grouping; `HAVING` filters groups after. Use `WHERE` for row-level conditions and `HAVING` for conditions on aggregate
+results. The planner may push eligible predicates earlier in the query.
+
+Grouping by several expressions produces one row per distinct combination:
+
+```sql
+SELECT category, source, count(*) FROM documents GROUP BY category, source
+```
+
+## Aggregate without grouping {#whole-table}
+
+Omitting `GROUP BY` aggregates the whole table into one row -- the usual way to
+ask how big something is:
+
+```sql
+SELECT count(*), min(created_at), max(created_at) FROM documents
+```
+
+`count(*)` counts rows; `count(col)` counts non-null values, which is how you
+measure how populated a [computed column](/feature-engineering/add-computed-column)
+is before deciding to refresh it again.
+
+## The function set {#functions}
+
+Registered in DataFusion 54. Names below are what is actually available.
+
+**General**
+
+```text
+count  sum  min  max  avg  median
+bit_and  bit_or  bit_xor  bool_and  bool_or
+first_value  last_value  nth_value  grouping
+```
+
+**Statistical**
+
+```text
+stddev  stddev_pop  stddev_samp
+var  var_pop  var_samp
+corr  covar_pop  covar_samp
+regr_slope  regr_intercept  regr_count  regr_r2
+regr_avgx  regr_avgy  regr_sxx  regr_syy  regr_sxy
+```
+
+**Approximate**
+
+```text
+approx_distinct  approx_median
+approx_percentile_cont  approx_percentile_cont_with_weight
+percentile_cont
+```
+
+**Collecting**
+
+```text
+array_agg  string_agg
+```
+
+The approximate functions are the ones worth reaching for on a large table.
+`approx_distinct` answers "roughly how many distinct values" for a fraction of
+what `count(DISTINCT ...)` costs, which can help with high-cardinality columns. Measure the accuracy and runtime
+for your workload.
+
+`DISTINCT` works inside an aggregate: `count(DISTINCT category)`. `FILTER`
+narrows one aggregate without narrowing the others:
+
+```sql
+SELECT
+    count(*) AS total,
+    count(*) FILTER (WHERE score > 0.9) AS confident
+FROM documents
+```
+
+## Aggregating a sample {#sampling}
+
+For an estimate over a very large table, aggregate a
+[sample](/sql/dql/sampling) rather than the whole thing:
+
+```sql
+SELECT category, count(*) * 100 AS estimated
+FROM documents TABLESAMPLE (1)
+GROUP BY category
+```
+
+For a uniform percentage sample, counts and sums can be scaled by the inverse
+of the sample fraction to estimate whole-table values. Do not apply that scaling
+to averages, minima or maxima. Small samples can miss rare categories; consider
+the sampling method and uncertainty when interpreting the result.
+
+## What aggregation does not do {#not}
+
+Aggregates run over rows the query already produced. Whether a statement can
+include [vector search](/search/vector-search)
+is described under [SQL vector search](/basics/sql#no-vector-sql). If your server
+does not support that query form, run the
+search in an SDK and aggregate its result, or materialize the search output
+first.
diff --git a/docs/web/sql/dql/explain.mdx b/docs/web/sql/dql/explain.mdx
new file mode 100644
index 000000000..8c296c2d5
--- /dev/null
+++ b/docs/web/sql/dql/explain.mdx
@@ -0,0 +1,53 @@
+---
+title: "Explain"
+sidebarTitle: "Explain"
+description: "EXPLAIN, EXPLAIN ANALYZE and EXPLAIN VERBOSE — reading and measuring a plan from SQL."
+icon: map
+keywords: ["explain", "explain analyze", "explain verbose", "query plan"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+```sql
+EXPLAIN SELECT * FROM documents WHERE identifier < 1000
+EXPLAIN ANALYZE SELECT * FROM documents WHERE identifier < 1000
+```
+
+| Statement | What it returns |
+| --- | --- |
+| `EXPLAIN <query>` | Logical and physical plan, without executing the query. |
+| `EXPLAIN ANALYZE <query>` | Executes the query and annotates each operator with runtime metrics (rows, timing). |
+| `EXPLAIN VERBOSE <query>` | Adds intermediate optimizer plans on top of `EXPLAIN`. |
+
+The result is a small relation with `plan_type` and `plan` columns, read
+through the same client you use for regular queries -- see
+[Connecting](/sql#connecting).
+
+SQL plans and the SDK's
+[`explain_plan` and `analyze_plan`](/search/explain) output share scan and index
+operators. Use the same approach to inspect index coverage and filter pushdown;
+SQL can also add joins, aggregates and other operators:
+
+<CardGroup cols={2}>
+  <Card title="What it shows" icon="map" href="/search/explain#inspect-a-query-plan">
+    Reading a plan: operators, pushdown, index usage.
+  </Card>
+  <Card title="What it measures" icon="gauge" href="/search/explain#analyze-a-query-plan">
+    Reading the metrics, and a worked before/after.
+  </Card>
+</CardGroup>
+
+## What to look for {#what-to-look-for}
+
+| Sign | Means |
+| --- | --- |
+| `ScalarIndexQuery` | the predicate used a scalar index |
+| `LanceScan` with high `bytes_read` | inspect the selected columns, filter pushdown and index use |
+| `FilterExec` above a search | the filter ran after search rather than pushing into it |
+| `ANNSubIndex` / `ANNIvfPartition` | the vector index was used |
+
+A `LanceScan` feeding a join is the common one worth acting on -- see
+[Joins](/sql/dql/join#performance).
+
+For a table's columns and types without a plan, see
+[`DESCRIBE`](/sql/ddl/table#describe).
diff --git a/docs/web/sql/dql/fts.mdx b/docs/web/sql/dql/fts.mdx
new file mode 100644
index 000000000..71673645f
--- /dev/null
+++ b/docs/web/sql/dql/fts.mdx
@@ -0,0 +1,148 @@
+---
+title: "Full-Text Search"
+sidebarTitle: "Full-Text Search"
+description: "The fts() table function — match, fuzzy, phrase, boolean, boost and multi-match queries from SQL."
+icon: magnifying-glass
+keywords: ["fts", "sql", "full text search", "fuzzy", "phrase", "boolean", "boost", "multi-match"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+Full-text search is a **table function**, `fts(table, query)`, rather than a
+predicate -- it produces a relevance score alongside the matching rows, and a
+`WHERE` clause has nowhere to put one. The two string-literal arguments are
+the table name and a JSON-encoded query, built with the same query-builder
+classes used from the SDK and serialized with `.to_json()`.
+
+## Setup {#setup}
+
+Run these statements in order, sending one statement per SQL request.
+Semicolons separate the statements for clients that execute a script.
+
+```sql
+CREATE TABLE my_docs (id INT, text STRING, category STRING, author STRING);
+
+INSERT INTO my_docs VALUES
+    (1, 'The happy puppy runs merrily in the park', 'animals', 'Alice'),
+    (2, 'A curious kitten jumps quickly over the fence', 'animals', 'Bob'),
+    (3, 'The puppy catches a ball with great enthusiasm', 'sports', 'Alice'),
+    (5, 'Puppy training requires patience and dedication', 'training', 'Alice');
+
+CREATE INDEX ON my_docs USING fts (text);
+```
+
+<Warning>
+Phrase queries need position information, which costs extra index size --
+add `WITH (with_position = true)` to `CREATE INDEX`. Without it,
+[phrase queries](#phrase) find nothing. See
+[FTS parameters](/indexing/fts-index#fts-parameters).
+</Warning>
+
+## Basic search {#basic}
+
+The query builder produces the JSON string used as the second SQL argument.
+The SQL examples below spell out the equivalent match object so they can be
+run directly after the setup above.
+
+```python
+from lancedb.query import MatchQuery
+
+query = MatchQuery("puppy", "text")
+json_query = query.to_json()
+```
+
+```sql
+SELECT id, text, category
+FROM fts('my_docs', '{"match":{"column":"text","terms":"puppy"}}')
+LIMIT 5
+```
+
+By default the top 5 matches come back in **arbitrary order** -- select
+`_score` and order by it for relevance ranking:
+
+```sql
+SELECT id, text, _score
+FROM fts('my_docs', '{"match":{"column":"text","terms":"puppy"}}')
+ORDER BY _score DESC
+LIMIT 5
+```
+
+`_score` is a pseudo-column carrying the query's BM25 relevance for the row;
+it behaves like any other column, but exists only in a relation that came
+from `fts()`. See [Data types](/sql/data-types#pseudo-columns).
+
+## Fuzzy {#fuzzy}
+
+Tolerates typos, up to a character-edit budget:
+
+```python
+MatchQuery("pupy", "text", fuzziness=2)
+```
+
+## Phrase {#phrase}
+
+Exact phrase, or with `slop` to allow words between terms:
+
+```python
+from lancedb.query import PhraseQuery
+
+PhraseQuery("happy puppy", "text")
+PhraseQuery("puppy park", "text", slop=2)   # up to 2 words between them
+```
+
+## Boolean {#boolean}
+
+Combine queries with `&` (AND) or `|` (OR):
+
+```python
+MatchQuery("puppy", "text") & MatchQuery("happy", "text")
+MatchQuery("puppy", "text") | MatchQuery("kitten", "text")
+```
+
+## Boost {#boost}
+
+Weight a query up or down relative to another:
+
+```python
+from lancedb.query import BoostQuery
+
+BoostQuery(
+    positive=MatchQuery("puppy", "text"),
+    negative=MatchQuery("kitten", "text"),
+    negative_boost=0.2,
+)
+```
+
+## Multi-match {#multi-match}
+
+Search several columns at once, optionally weighting them differently:
+
+```python
+from lancedb.query import MultiMatchQuery
+
+MultiMatchQuery("puppy", ["text", "category"])
+MultiMatchQuery("puppy", ["text", "category"], boosts=[2.0, 1.0])  # text weighted 2x
+```
+
+## Combining with SQL {#combining}
+
+`fts()` is a relation, so it composes with the rest of the SQL surface --
+filter, join, or aggregate over it same as any table:
+
+```sql
+SELECT id, text, category
+FROM fts('my_docs', '{"match":{"column":"text","terms":"puppy"}}')
+WHERE category = 'animals'
+LIMIT 5
+```
+
+## What to read next {#next}
+
+<CardGroup cols={2}>
+  <Card title="Full-Text Search" icon="magnifying-glass" href="/search/full-text-search">
+    The query-builder equivalent, and every query form in full.
+  </Card>
+  <Card title="FTS Index" icon="bolt" href="/indexing/fts-index">
+    Tokenizers, and the position information phrase queries need.
+  </Card>
+</CardGroup>
diff --git a/docs/web/sql/dql/join.mdx b/docs/web/sql/dql/join.mdx
new file mode 100644
index 000000000..7d4f84a8b
--- /dev/null
+++ b/docs/web/sql/dql/join.mdx
@@ -0,0 +1,72 @@
+---
+title: "Joins"
+sidebarTitle: "Joins"
+description: "Combining rows from more than one table, including across namespaces."
+icon: link
+keywords: ["join", "inner", "left", "outer", "cross", "namespace"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+Use SQL joins to combine rows from two or more tables.
+
+## The join types {#types}
+
+```sql
+SELECT d.title, c.name
+FROM documents AS d
+JOIN categories AS c ON c.id = d.category_id
+```
+
+| | What it keeps |
+| --- | --- |
+| `[INNER] JOIN` | rows matching on both sides |
+| `LEFT [OUTER] JOIN` | every left row, nulls where the right has no match |
+| `RIGHT [OUTER] JOIN` | every right row |
+| `FULL [OUTER] JOIN` | every row from both |
+| `CROSS JOIN` | every combination -- the product of the two |
+
+`USING (col)` is shorthand for an equality join on a shared column name, and
+avoids repeating it in the select list.
+
+## Across namespaces {#namespaces}
+
+Tables in different [namespaces](/management/namespace) join by their dotted paths -- the
+catalog is one catalog, so there is nothing to federate:
+
+```sql
+SELECT c.id, u.email
+FROM prod.events.clicks AS c
+JOIN prod.reference.users AS u ON u.id = c.user_id
+```
+
+## Joining a search result {#search}
+
+[`fts()`](/sql/functions#fts) is a table function, so its result is a relation
+and joins like any other. This example assumes `my_docs` has `text` and
+`category_id` columns, a full-text index on `text`, and a `categories` table
+with `id` and `name` columns:
+
+```sql
+SELECT c.name, count(*) AS hits
+FROM fts('my_docs', '{"match":{"column":"text","terms":"puppy"}}') AS m
+JOIN categories AS c ON c.id = m.category_id
+GROUP BY c.name
+ORDER BY hits DESC
+```
+
+That composition is the reason full-text search is a relation rather than a
+predicate -- a `WHERE` clause has nowhere to put the relevance score.
+
+## Performance {#performance}
+
+A [scalar index](/indexing/scalar-index) can accelerate supported filters on
+the input tables. An index on a join key does not by itself guarantee an
+index lookup join. Use SQL [`EXPLAIN`](/sql/dql/explain) to inspect the scans,
+filters, and join chosen for the complete statement.
+
+<Warning>
+Avoid selecting blob or vector columns you do not need on either side of a join.
+The join multiplies rows before projection is applied in some plans, so a wide
+column can be materialized many more times than the result has rows.
+</Warning>
diff --git a/docs/web/sql/dql/sampling.mdx b/docs/web/sql/dql/sampling.mdx
new file mode 100644
index 000000000..8b36fa275
--- /dev/null
+++ b/docs/web/sql/dql/sampling.mdx
@@ -0,0 +1,102 @@
+---
+title: "Random sampling"
+sidebarTitle: "Sampling"
+description: "TABLESAMPLE: draw a random subset of a table, by rows or by fragments."
+icon: dice
+keywords: ["tablesample", "sampling", "bernoulli", "system", "repeatable", "random"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+Use `TABLESAMPLE` to explore a subset of a table or estimate aggregate
+statistics. The sampling method determines how much data is read and whether
+the table's physical layout influences the sample.
+
+```sql
+SELECT * FROM documents TABLESAMPLE (10)        -- 10 percent
+SELECT * FROM documents TABLESAMPLE (500 ROWS)  -- 500 rows
+```
+
+A bare number is a **percentage**. Add `ROWS` for a count instead.
+
+## Rows or fragments {#methods}
+
+The standard calls these two methods of one clause. They are different
+operations, and the difference matters on Lance:
+
+```sql
+SELECT * FROM documents TABLESAMPLE BERNOULLI (10)
+SELECT * FROM documents TABLESAMPLE SYSTEM (10)
+```
+
+| Method | Aliases | What it picks |
+| --- | --- | --- |
+| `BERNOULLI` | `ROW` | rows, each considered independently |
+| `SYSTEM` | `BLOCK` | **Lance fragments**, and takes whatever rows they hold |
+
+`BERNOULLI` gives a genuinely uniform sample and reads across the whole table to
+get it. `SYSTEM` reads far less -- it skips entire fragments -- but the sample is
+only as uniform as the data's layout. If rows were written in an order that
+correlates with what you are measuring, a fragment sample inherits that
+correlation.
+
+Use `SYSTEM` for a quick look at the data and `BERNOULLI` when you need
+independent row sampling. Omitting the method gives
+`BERNOULLI`.
+
+## Repeatability {#repeatable}
+
+Without a seed, every execution resamples -- running the same query twice gives
+different rows. `REPEATABLE` fixes the draw:
+
+```sql
+SELECT * FROM documents TABLESAMPLE (10) REPEATABLE (42)
+```
+
+Use the same seed and table snapshot to reproduce a sampling run. For a fixed
+evaluation set, save the selected row IDs or materialize the sample and retain
+its version. A seed alone does not preserve membership after source changes
+or changes to the physical layout.
+
+## Where the clause can go {#placement}
+
+It attaches to a table in `FROM`, including inside a subquery, a CTE, or either
+side of a join:
+
+```sql
+SELECT * FROM (SELECT * FROM documents TABLESAMPLE (10)) d
+
+SELECT * FROM a TABLESAMPLE (5 ROWS) JOIN b ON a.id = b.id
+
+SELECT * FROM t a TABLESAMPLE (10) JOIN t b TABLESAMPLE (10) ON a.id = b.id
+```
+
+Each side of a join samples independently. Sampling both sides of a join
+shrinks the *result* far more than either fraction suggests, because a row only
+survives if both its sides were drawn.
+
+## Unsupported sampling forms {#refused}
+
+Sampling is semantics, not a hint. A clause that were read and then not applied
+would be a wrong answer rather than a missing feature, so anything that cannot
+be honoured exactly is an error:
+
+| | |
+| --- | --- |
+| `TABLESAMPLE (101)` | a percentage outside `0..=100` is refused, not clamped |
+| `TABLESAMPLE (1.5 ROWS)` | a row count must be whole |
+| `TABLESAMPLE (BUCKET 1 OUT OF 4)` | bucket sampling is not supported |
+| `(SELECT ...) d TABLESAMPLE (10)` | a subquery's result cannot be sampled; sample the table inside it |
+| the `SAMPLE` spelling | use `TABLESAMPLE` |
+| the `OFFSET` modifier | not supported |
+
+<Note>
+`TABLESAMPLE` is also refused on a WAL-backed table, where the rows a sample
+would draw from are not all in the fragments yet.
+</Note>
+
+## Sampling outside SQL {#sdk}
+
+For a training loop rather than a query, the SDKs read a table in a shuffled
+order without going through SQL at all -- see [Training](/training/data-loading), where a
+permutation drives the data loader.
diff --git a/docs/web/sql/dql/select.mdx b/docs/web/sql/dql/select.mdx
new file mode 100644
index 000000000..202a3d60a
--- /dev/null
+++ b/docs/web/sql/dql/select.mdx
@@ -0,0 +1,136 @@
+---
+title: "SELECT"
+sidebarTitle: "SELECT"
+description: "Projection, filtering, ordering, and composing queries out of other queries."
+icon: table-columns
+keywords: ["select", "where", "order by", "limit", "cte", "union", "distinct"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+```sql
+[WITH cte AS (...)]
+SELECT [ALL | DISTINCT] select_expr [, ...]
+FROM from_item [[JOIN ...] ON ...]
+[WHERE predicate]
+[GROUP BY expr [, ...]] [HAVING predicate]
+[WINDOW name AS (...)]
+[ORDER BY expr [ASC | DESC] [NULLS FIRST | LAST] [, ...]]
+[LIMIT n] [OFFSET n]
+```
+
+## Projection {#projection}
+
+<CodeGroup>
+```sql SQL
+SELECT id, title, identifier FROM documents
+```
+
+```python Python icon="python"
+tbl.search().select(["id", "title", "identifier"]).to_arrow()
+```
+
+```typescript TypeScript icon="square-js"
+await tbl.query().select(["id", "title", "identifier"]).toArrow();
+```
+</CodeGroup>
+
+Name the columns you need instead of using `SELECT *`, particularly for
+tables containing images, video or large vectors. Projection lets the engine
+avoid loading unnecessary columns, although columns used by filters, joins or
+other expressions may still need to be read.
+
+Row metadata is available as [pseudo-columns](/sql/data-types#pseudo-columns)
+(`_rowid`, `_rowaddr`, `_rowoffset`) but only when named, so they stay out of
+`SELECT *`.
+
+`DISTINCT` deduplicates the projected rows.
+
+## Filtering {#filtering}
+
+<CodeGroup>
+```sql SQL
+SELECT * FROM documents
+WHERE identifier > 0 AND identifier < 1000000
+```
+
+```python Python icon="python"
+tbl.search().where("identifier > 0 AND identifier < 1000000").to_arrow()
+```
+
+```typescript TypeScript icon="square-js"
+await tbl.query().where("identifier > 0 AND identifier < 1000000").toArrow();
+```
+</CodeGroup>
+
+The SDK `where` takes the same expression language, so a predicate moves between
+them unchanged. A supported predicate can use a scalar index; confirm the chosen plan with
+[`explain_plan`](/search/explain). See
+[Filtering](/search/filtering) for the operators and their pushdown behaviour.
+
+## Ordering and limiting {#order-limit}
+
+```sql
+SELECT id, title FROM documents
+ORDER BY created_at DESC NULLS LAST
+LIMIT 100 OFFSET 200
+```
+
+`ORDER BY` on a large table is a sort over the whole result, so pair it with a
+`LIMIT` unless you genuinely need every row ordered.
+
+## Set operations {#set-operations}
+
+`UNION`, `UNION ALL`, `INTERSECT` and `EXCEPT` combine whole queries:
+
+```sql
+SELECT id FROM documents WHERE category = 'film'
+UNION ALL
+SELECT id FROM archived_documents WHERE category = 'film'
+```
+
+`UNION` deduplicates and `UNION ALL` does not, which makes `UNION ALL` the
+cheaper one when you already know the inputs are disjoint.
+
+## Common table expressions {#ctes}
+
+```sql
+WITH recent AS (
+    SELECT * FROM documents WHERE created_at > now() - INTERVAL '7 days'
+)
+SELECT category, count(*) FROM recent GROUP BY category
+```
+
+A CTE is scoped to the statement. One you want to keep is a
+[view](/management/view); one whose rows you want stored is a
+[materialized view](/management/materialized-view).
+
+## Subqueries {#subqueries}
+
+Subqueries are allowed anywhere an expression or a relation is -- in `FROM`, in
+`WHERE` with `IN` or `EXISTS`, and as a scalar in the select list:
+
+```sql
+SELECT title
+FROM documents
+WHERE category_id IN (SELECT id FROM categories WHERE active)
+```
+
+## Namespaced tables {#namespaces}
+
+A table inside a [namespace](/management/namespace) is written as a dotted path:
+
+```sql
+SELECT * FROM prod.events.clicks
+```
+
+## Reading the plan {#plans}
+
+`EXPLAIN` and `EXPLAIN ANALYZE` work as DataFusion defines them, and produce the
+same operators the SDK methods do:
+
+```sql
+EXPLAIN ANALYZE SELECT * FROM documents WHERE identifier < 1000
+```
+
+See [Explain](/search/explain) and [Explain Analyze](/search/explain).
diff --git a/docs/web/sql/dql/subqueries.mdx b/docs/web/sql/dql/subqueries.mdx
new file mode 100644
index 000000000..48887da72
--- /dev/null
+++ b/docs/web/sql/dql/subqueries.mdx
@@ -0,0 +1,77 @@
+---
+title: "Subqueries"
+sidebarTitle: "Subqueries"
+description: "Queries inside queries: scalar, IN, EXISTS, correlated, and derived tables."
+icon: code-branch
+keywords: ["subquery", "exists", "in", "correlated", "derived table", "lateral"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+A subquery is allowed anywhere an expression or a relation is.
+
+## As a relation {#derived}
+
+```sql
+SELECT * FROM (
+    SELECT category, count(*) AS n FROM documents GROUP BY category
+) AS counts
+WHERE n > 100
+```
+
+A derived table needs an alias. Its result cannot be
+[sampled](/sql/dql/sampling); sample the table inside it instead:
+
+```sql
+SELECT * FROM (SELECT * FROM documents TABLESAMPLE (10)) d
+```
+
+## As a scalar {#scalar}
+
+```sql
+SELECT title, (SELECT count(*) FROM revisions r WHERE r.doc_id = d.id) AS revisions
+FROM documents AS d
+```
+
+A scalar subquery returns one column and at most one row. No rows produces
+`NULL`; more than one row is an error.
+
+## IN and EXISTS {#in-exists}
+
+```sql
+SELECT title FROM documents
+WHERE category_id IN (SELECT id FROM categories WHERE active)
+
+SELECT title FROM documents AS d
+WHERE EXISTS (SELECT 1 FROM revisions r WHERE r.doc_id = d.id)
+```
+
+Use `EXISTS` to test whether a matching row exists and `IN` to test membership
+in a set of values. The optimizer may rewrite either into a join; inspect
+`EXPLAIN` before choosing one for performance reasons.
+
+`NOT IN` and `NOT EXISTS` differ on nulls: `NOT IN` against a set containing a
+null yields no rows, because the comparison is unknown rather than false. Prefer
+`NOT EXISTS` unless you have ruled nulls out.
+
+## Correlated subqueries {#correlated}
+
+A subquery referencing a column from the enclosing query, such as `d.id` above,
+is correlated, and is conceptually evaluated per outer row. The planner usually
+rewrites it into a join.
+
+<Note>
+Check with [`EXPLAIN`](/sql/dql/explain) when a correlated subquery is slow. A plan
+showing a join has been optimized; one showing a repeated scan has not, and
+rewriting it as an explicit [join](/sql/dql/join) is the fix.
+</Note>
+
+## CTEs as an alternative {#ctes}
+
+A [CTE](/sql/dql/select#ctes) is a named subquery, and is usually more readable when
+the same subquery appears twice or when nesting gets deep.
+
+```sql
+WITH active AS (SELECT id FROM categories WHERE active)
+SELECT title FROM documents WHERE category_id IN (SELECT id FROM active)
+```
diff --git a/docs/web/sql/dql/window.mdx b/docs/web/sql/dql/window.mdx
new file mode 100644
index 000000000..ed02994c3
--- /dev/null
+++ b/docs/web/sql/dql/window.mdx
@@ -0,0 +1,100 @@
+---
+title: "Window functions"
+sidebarTitle: "Window Functions"
+description: "Compute over a set of rows related to the current one, without collapsing them."
+icon: chart-simple
+keywords: ["window", "over", "partition by", "row_number", "rank", "lag", "lead"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+A window function computes across a set of rows related to the current one and
+returns a value **per row**. That is the difference from
+[aggregation](/sql/dql/aggregation), which collapses the rows it covers.
+
+```sql
+SELECT
+    title,
+    score,
+    row_number() OVER (PARTITION BY category ORDER BY score DESC) AS rank
+FROM documents
+```
+
+Every row survives; each one gains its rank within its category.
+
+## The window {#window}
+
+```sql
+OVER (
+    [PARTITION BY expr [, ...]]
+    [ORDER BY expr [, ...]]
+    [ROWS | RANGE BETWEEN frame_start AND frame_end]
+)
+```
+
+`PARTITION BY` splits the rows into independent groups. `ORDER BY` orders them
+inside each partition. Supply an ordering for useful ranks, and a stable
+tie-breaker when selecting one row from several equal values. The frame clause narrows the window further, which is
+how running totals and moving averages are expressed:
+
+```sql
+SELECT
+    created_at,
+    sum(amount) OVER (ORDER BY created_at ROWS UNBOUNDED PRECEDING) AS running_total,
+    avg(amount) OVER (ORDER BY created_at ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS trailing_7
+FROM transactions
+```
+
+A `WINDOW` clause names a definition when several functions share one:
+
+```sql
+SELECT
+    row_number() OVER w,
+    lag(score) OVER w
+FROM documents
+WINDOW w AS (PARTITION BY category ORDER BY score DESC)
+```
+
+## The functions {#functions}
+
+Registered in DataFusion 54.
+
+**Ranking**
+
+```text
+row_number  rank  dense_rank  percent_rank  cume_dist  ntile
+```
+
+**Offset and positional**
+
+```text
+lag  lead  first_value  last_value  nth_value
+```
+
+**Aggregates**
+
+Any [aggregate function](/sql/dql/aggregation#functions) used with `OVER` becomes a
+window function. This is how running totals and moving averages are written.
+
+## Deduplicating with a window {#dedup}
+
+The common practical use: keep one row per key, chosen by some ordering. A
+window ranks, and an outer query filters:
+
+```sql
+WITH ranked AS (
+    SELECT *, row_number() OVER (PARTITION BY doc_id ORDER BY version DESC) AS rn
+    FROM revisions
+)
+SELECT * FROM ranked WHERE rn = 1
+```
+
+The outer query is needed because a window function cannot appear in `WHERE` --
+windows are computed after filtering, so the predicate would have nothing to
+read.
+
+<Note>
+Window functions sort within each partition, so on a large table they are among
+the more expensive things you can ask for. Filter first, and check the cost with
+[Explain Analyze](/search/explain).
+</Note>
diff --git a/docs/web/sql/functions.mdx b/docs/web/sql/functions.mdx
new file mode 100644
index 000000000..79c4258b7
--- /dev/null
+++ b/docs/web/sql/functions.mdx
@@ -0,0 +1,100 @@
+---
+title: "Functions"
+sidebarTitle: "Functions"
+description: "Where to find each kind of function, and the dialect version they come from."
+icon: function
+keywords: ["functions", "datafusion", "scalar", "aggregate", "window", "fts", "table function"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+LanceDB executes SQL with [Apache DataFusion](https://datafusion.apache.org),
+currently **version 54**. The function set is DataFusion's default registration
+plus what Lance adds.
+
+| | |
+| --- | --- |
+| [Scalar functions](/sql/scalar-functions) | per row — math, string, regex, date/time, array, struct, JSON |
+| [Aggregate functions](/sql/dql/aggregation#functions) | per group — `count`, `sum`, statistical, approximate |
+| [Window functions](/sql/dql/window#functions) | per window — ranking, offset, running aggregates |
+| [Table functions](#table-functions) | produce a relation rather than a value |
+
+<Note>
+Function availability is version-specific. DataFusion adds functions in most
+releases, so one documented on `main` may not exist in 54. When a name is not
+recognised, check it against the reference for that version before assuming a
+bug.
+</Note>
+
+## Table functions {#table-functions}
+
+A table function appears in `FROM` and produces a relation, which is what lets
+it return columns a scalar function has nowhere to put.
+
+### `fts(table, query)` {#fts}
+
+Full-text search is a table function rather than a predicate, because it
+produces relevance alongside the matching rows. The following query assumes
+`my_docs` has `id` and `text` columns and a full-text index on `text`; see the
+[setup example](/sql/dql/fts#setup):
+
+```sql
+SELECT id, text, _score
+FROM fts('my_docs', '{"match":{"column":"text","terms":"puppy"}}')
+ORDER BY _score DESC
+LIMIT 5
+```
+
+The second argument is a string literal containing a JSON query object. A bare
+query string such as `'puppy'` is rejected. Match, fuzzy, phrase, boolean, boost
+and multi-match queries use this JSON encoding.
+
+Its schema contains the dataset's columns plus `_score`, so every stored
+column is available in the result. Projections push into the scan.
+Being a relation is also what makes it compose — join it, filter it, aggregate
+over it. This join additionally requires `my_docs.category_id` and a
+`categories` table with `id` and `name` columns:
+
+```sql
+SELECT c.name, count(*) AS hits
+FROM fts('my_docs', '{"match":{"column":"text","terms":"puppy"}}') AS m
+JOIN categories AS c ON c.id = m.category_id
+GROUP BY c.name
+ORDER BY hits DESC
+```
+
+For a keyword test that needs no score, the
+[`contains_tokens`](/sql/scalar-functions#contains-tokens) predicate is
+index-accelerated and usable directly in `WHERE`.
+
+See [Full-Text Search](/sql/dql/fts) for the fuzzy, phrase, boolean, boost and
+multi-match query forms.
+
+### `_score` {#score}
+
+A pseudo-column produced by `fts()`, carrying relevance for the row. It behaves
+like any other column — order by it, filter on it, project it — and exists only
+in a relation that came from `fts()`. See
+[Data types](/sql/data-types#pseudo-columns).
+
+### `generate_series` {#generate-series}
+
+```sql
+SELECT * FROM generate_series(1, 10)
+```
+
+Rows from a range, useful for building a dense axis to left-join sparse data
+onto.
+
+## What Lance accelerates {#accelerated}
+
+Two function families use an index rather than scanning, which is the difference
+between a fast predicate and a slow one:
+
+| | Index used |
+| --- | --- |
+| [`contains_tokens`](/sql/scalar-functions#contains-tokens) | full-text index |
+| [`json_get`, typed variants, `json_extract`](/sql/scalar-functions#json) | JSON scalar index |
+
+Confirm with [`EXPLAIN`](/sql/dql/explain): an accelerated predicate shows an
+index node rather than a `LanceScan` followed by a filter.
diff --git a/docs/web/sql/index.mdx b/docs/web/sql/index.mdx
new file mode 100644
index 000000000..a3f411c81
--- /dev/null
+++ b/docs/web/sql/index.mdx
@@ -0,0 +1,106 @@
+---
+title: "SQL reference"
+sidebarTitle: "Overview"
+description: "SQL statements, expressions and data types supported by LanceDB Enterprise."
+icon: table-list
+keywords: ["sql", "datafusion", "flight sql", "reference", "dialect"]
+---
+
+LanceDB Enterprise supports SQL through an Arrow Flight SQL endpoint. This
+reference covers statement syntax, data types and expressions. To set up a
+client and run your first query, see [SQL](/basics/sql).
+
+## The dialect {#dialect}
+
+LanceDB uses [Apache DataFusion](https://datafusion.apache.org) for SQL planning
+and execution. These pages describe LanceDB's supported statements and
+extensions. DataFusion's reference is useful for expression syntax, but does
+not establish that every DataFusion statement or feature is exposed by
+LanceDB.
+
+## Connecting {#connecting}
+
+Use Python's `execute_query` or a Flight SQL client after configuring the
+[Enterprise connection](/basics/sql#sql-is-remote). TypeScript applications
+use a Flight SQL client for SQL queries.
+
+```python
+reader = db.execute_query("SELECT id, text FROM my_docs LIMIT 10")
+for batch in reader:
+    print(batch)
+```
+
+SQL results arrive as Arrow record batches. Iterating over the reader avoids
+collecting the entire result in application memory. See
+[Queries](/basics/query) for result handling.
+
+## Usage {#usage}
+
+The endpoint executes one statement per request and is meant for the queries
+and DDL/DML pages below. Reach for the SDKs instead for table-management
+operations that have no SQL form, such as a merge insert -- see
+[What SQL cannot do](#cannot).
+
+## Reference {#reference}
+
+### DDL, by object {#ddl}
+
+| | |
+| --- | --- |
+| [Namespace](/sql/ddl/namespace) | `CREATE SCHEMA`, `DROP SCHEMA` |
+| [Table](/sql/ddl/table) | `CREATE`, `DESCRIBE`, `ALTER`, `CLONE`, `DROP TABLE` |
+| [View](/sql/ddl/view) | `CREATE VIEW`, `DROP VIEW` |
+| [Materialized view](/sql/ddl/materialized-view) | `CREATE MATERIALIZED VIEW`, `SHOW MATERIALIZED VIEWS` |
+| [Index](/sql/ddl/index) | `CREATE INDEX`, `DROP INDEX` |
+| [Job](/sql/ddl/job) | `SHOW JOBS`, `SHOW ERRORS`, `PAUSE`/`RESUME`/`CANCEL JOB` |
+
+{/*
+Draft for the Functions release. Add this row to the table above, after Index:
+| [Function](/sql/ddl/function) | `CREATE FUNCTION`, `SHOW FUNCTIONS`, `DROP FUNCTION` |
+*/}
+
+### DML {#dml}
+
+| | |
+| --- | --- |
+| [Insert](/sql/dml/insert) | `INSERT INTO`, including `INSERT ... SELECT` |
+| [Update](/sql/dml/update) | `UPDATE ... SET` |
+| [Delete](/sql/dml/delete) | `DELETE FROM` |
+| [Copy](/sql/dml/copy) | `COPY` -- refused by recent deployment versions |
+| [Refresh](/sql/dml/refresh) | filling computed columns and materialized views |
+
+### DQL {#dql}
+
+| | |
+| --- | --- |
+| [Data types](/sql/data-types) | how Lance columns appear to SQL, and pseudo-columns |
+| [Select](/sql/dql/select) | projection, filtering, ordering, set operations, CTEs |
+| [Subqueries](/sql/dql/subqueries) | scalar, `IN`, `EXISTS`, correlated, derived tables |
+| [Joins](/sql/dql/join) | inner, outer, cross, and namespace-qualified paths |
+| [Aggregation](/sql/dql/aggregation) | `GROUP BY`, `HAVING`, the aggregate set |
+| [Window functions](/sql/dql/window) | `OVER`, framing, ranking and offset functions |
+| [Sampling](/sql/dql/sampling) | `TABLESAMPLE`, by rows or by fragments |
+| [Full-Text Search](/sql/dql/fts) | the `fts()` table function, and its query forms |
+| [Explain](/sql/dql/explain) | `EXPLAIN`, `EXPLAIN ANALYZE`, `EXPLAIN VERBOSE` |
+
+### Expressions {#expressions}
+
+| | |
+| --- | --- |
+| [Operators](/sql/operators) | comparison, logical, arithmetic, literals, precedence |
+| [Functions](/sql/functions) | where to find each kind, and table functions |
+| [Scalar functions](/sql/scalar-functions) | the per-row catalogue, and what Lance accelerates |
+| [Prepared statements](/sql/prepared-statements) | preparing through a Flight SQL client |
+
+## SQL limitations and alternatives {#cannot}
+
+| | |
+| --- | --- |
+| Vector search | See [SQL vector search](/basics/sql#no-vector-sql) for supported query forms and server-version requirements. The [SDK query builder](/search/vector-search) also supports vector search. |
+| Merge insert | no SQL form -- needs a source relation and a per-match policy |
+| `information_schema` | not enabled, so `SHOW TABLES` and `SHOW COLUMNS` fail |
+| `COPY` | refused by recent deployment versions -- see [Copy](/sql/dml/copy) |
+| `PREPARE` / `EXECUTE` | preparation is a [protocol feature](/sql/prepared-statements), not a statement |
+| Run on a local database | no engine to plan the statement |
+
+See [Limits](/basics/limits#sql) for the bounds these pages enforce.
diff --git a/docs/web/sql/operators.mdx b/docs/web/sql/operators.mdx
new file mode 100644
index 000000000..05ccbc3b4
--- /dev/null
+++ b/docs/web/sql/operators.mdx
@@ -0,0 +1,85 @@
+---
+title: "Operators and literals"
+sidebarTitle: "Operators"
+description: "Comparison, logical and arithmetic operators, literals, and precedence."
+icon: plus-minus
+keywords: ["operators", "literals", "precedence", "null", "cast", "interval"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+LanceDB uses DataFusion's SQL operators. This page describes comparison,
+logical and arithmetic operators, literal values, type conversions and
+operator precedence.
+
+## Comparison {#comparison}
+
+| | |
+| --- | --- |
+| `=` `!=` `<>` `<` `<=` `>` `>=` | ordinary comparison |
+| `IS NULL` / `IS NOT NULL` | null test — `= NULL` is never true |
+| `IS DISTINCT FROM` | comparison that treats null as a value |
+| `BETWEEN a AND b` | inclusive range |
+| `IN (...)` | set membership |
+| `LIKE` / `ILIKE` | pattern match, `%` and `_` |
+| `~` `~*` `!~` | regular expression match |
+
+<Note>
+For keyword matching on a text column, [`contains_tokens`](/sql/scalar-functions#contains-tokens)
+is index-accelerated where `LIKE` is not. Reach for `LIKE` only when you need
+its wildcard semantics.
+</Note>
+
+## Logical {#logical}
+
+`AND`, `OR`, `NOT`. Three-valued: a comparison against null is **unknown**, and
+`NOT unknown` is still unknown. A `WHERE` clause keeps only rows where the
+predicate is true, so unknown behaves like false there — but not inside `NOT
+IN`, which is the usual place this bites. See
+[Subqueries](/sql/dql/subqueries#in-exists).
+
+## Arithmetic {#arithmetic}
+
+`+` `-` `*` `/` `%`, with the usual precedence. String concatenation is `||`.
+
+<Warning>
+Arithmetic over a vector column is **list** arithmetic, not scalar arithmetic —
+a vector is a `FixedSizeList<Float32>`. When the operation you want is a
+similarity comparison, use [vector search](/search/vector-search); whether SQL
+can express it [depends on the Enterprise server version](/basics/sql#no-vector-sql).
+</Warning>
+
+## Literals {#literals}
+
+| | |
+| --- | --- |
+| String | `'text'` — doubled quote to escape: `'it''s'` |
+| Number | `42`, `3.14` |
+| Boolean | `TRUE`, `FALSE` |
+| Null | `NULL` |
+| Date / timestamp | `DATE '2026-01-01'`, `TIMESTAMP '2026-01-01 12:00:00'` |
+| Interval | `INTERVAL '7 days'` |
+| Array | `[1, 2, 3]` |
+
+Identifiers are quoted with double quotes when they need to be — a reserved word
+or a name with mixed case: `SELECT "order" FROM "MyTable"`.
+
+## Casting {#casting}
+
+```sql
+SELECT CAST(identifier AS VARCHAR) AS with_cast, identifier::VARCHAR AS with_colons
+FROM documents
+```
+
+Both forms work. Give each its own alias when one projection uses both: they
+produce the same expression name, and a projection refuses two columns with
+one name. `TRY_CAST` returns null instead of erroring on a value that
+will not convert, which is the one to use over a column you do not control.
+
+## Precedence {#precedence}
+
+Highest to lowest: `::` → unary `-` → `* / %` → `+ -` → `||` → comparison →
+`IS` → `NOT` → `AND` → `OR`.
+
+Parenthesize anything mixing `AND` and `OR`. `a OR b AND c` is `a OR (b AND c)`,
+which is rarely what was meant.
diff --git a/docs/web/sql/prepared-statements.mdx b/docs/web/sql/prepared-statements.mdx
new file mode 100644
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@@ -0,0 +1,61 @@
+---
+title: "Prepared statements"
+sidebarTitle: "Prepared Statements"
+description: "What a deployment does when a Flight SQL client prepares a statement or binds parameter values."
+icon: bolt
+keywords: ["prepared statement", "flight sql", "parameters", "bind"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+A Flight SQL client can prepare a statement before it runs it. A deployment
+accepts that, but it keeps no plan between runs and does not accept parameter
+values.
+
+## Prepare through a Flight SQL client {#protocol}
+
+<Warning>
+There is no SQL `PREPARE` / `EXECUTE` here. Preparation is part of the
+**Arrow Flight SQL protocol**, so you reach it through your client rather than
+by sending a statement.
+</Warning>
+
+That distinction matters when porting SQL from another engine: a script
+containing `PREPARE name AS SELECT ...` will not run.
+
+## Prepare and execute {#flow}
+
+A Flight SQL client takes these steps, whatever it calls them:
+
+1. **Prepare:** send the SQL and get back a handle and the result schema. The
+   statement is parsed and planned at this point, so a syntax or name error
+   surfaces here rather than at execution.
+2. **Execute:** run the handle and read the result. The deployment plans the
+   statement again each time it runs, so reusing a handle does not save
+   planning time.
+3. **Close:** release the handle.
+
+## Parameters {#parameters}
+
+A deployment does not accept parameter values for a prepared statement. A
+client that binds values to placeholders such as `$1` and `$2` gets a "not
+implemented" error when it executes the statement. A statement with fixed,
+trusted values written into it runs as usual:
+
+```sql
+SELECT id, title FROM documents WHERE category = 'news' AND score > 0.5
+```
+
+Do not put untrusted input, such as a value from a user or a request, directly
+into the statement text. Quoting it does not protect the statement the way
+binding a parameter would.
+
+## When to use prepared statements {#when}
+
+Prepare a statement when your client requires it, or to get a statement's
+result schema and catch syntax and name errors without running it. Preparing
+does not make a repeated statement faster.
+
+To give a reusable query a name, create a [view](/management/view). To store
+its result for later queries, create a
+[materialized view](/management/materialized-view).
diff --git a/docs/web/sql/scalar-functions.mdx b/docs/web/sql/scalar-functions.mdx
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+---
+title: "Scalar functions"
+sidebarTitle: "Scalar Functions"
+description: "The per-row function set: math, string, regex, date/time, array, struct, and what Lance adds."
+icon: calculator
+keywords: ["scalar functions", "string functions", "math functions", "regex", "array functions", "json"]
+---
+
+<Badge color="red">Enterprise</Badge> [SQL runs in LanceDB Enterprise.](/sql)
+
+Functions that take a value and return a value, one row at a time. The set is
+based on DataFusion's registration plus Lance and deployment-specific
+functions. The names below are selected examples, not an exhaustive catalog
+for every deployment. Availability depends on the deployed version and build.
+
+For per-group functions see [Aggregation](/sql/dql/aggregation); for per-window,
+[Window functions](/sql/dql/window).
+
+## Math {#math}
+
+```text
+abs  ceil  floor  round  trunc  signum  power  sqrt  cbrt  exp
+ln  log  log2  log10  factorial  gcd  lcm  pi  random  nanvl
+isnan  iszero  cot  degrees  radians  to_degrees  to_radians
+sin  cos  tan  asin  acos  atan  atan2
+sinh  cosh  tanh  asinh  acosh  atanh
+```
+
+## String {#string}
+
+```text
+ascii  bit_length  btrim  chr  concat  concat_ws  contains  ends_with
+starts_with  levenshtein  lower  upper  ltrim  rtrim  octet_length
+repeat  replace  split_part  to_hex  uuid
+```
+
+Unicode-aware variants:
+
+```text
+character_length  find_in_set  initcap  left  right  lpad  rpad
+reverse  strpos  substr  substr_index  substring  translate
+```
+
+<Note>
+For keyword matching on a text column, [`contains_tokens`](#contains-tokens) is
+index-accelerated where `contains` and `LIKE` are not.
+</Note>
+
+## Regular expressions {#regex}
+
+```text
+regexp_count  regexp_instr  regexp_like  regexp_match  regexp_replace
+```
+
+## Date and time {#datetime}
+
+```text
+current_date  current_time  date_bin  date_part  date_trunc
+from_unixtime  to_unixtime  make_date  make_time  to_char
+to_date  to_time  to_local_time
+```
+
+`date_bin` is the one worth knowing for analytics: it buckets timestamps into
+fixed-width intervals, which is how you build a time series without a
+self-join.
+
+```sql
+SELECT date_bin(INTERVAL '1 hour', created_at) AS hour, count(*)
+FROM events GROUP BY hour ORDER BY hour
+```
+
+## Arrays and maps {#arrays}
+
+```text
+make_array  array_has  array_filter  array_transform  array_any_match
+array_compact  array_normalize  array_slice  arrays_zip  cardinality
+flatten  range  generate_series
+```
+
+```text
+map  map_keys  map_values  map_entries  map_extract
+```
+
+Distance functions over arrays:
+
+```text
+cosine_distance  distance  exact_distance  inner_product
+```
+
+<Warning>
+Distance functions can evaluate arrays per row. Some deployment versions also
+recognize `ORDER BY distance(vector, query_vector) LIMIT k` over a table scan
+and plan it as vector search, with indexed or flat search as appropriate.
+Where available, `exact_distance` evaluates distance without that rewrite.
+Confirm the
+functions supported by your deployment and inspect SQL
+[`EXPLAIN`](/sql/dql/explain); the function name alone does not guarantee an
+index-backed plan. See [SQL vector search](/basics/sql#no-vector-sql).
+</Warning>
+
+## Structs and fields {#structs}
+
+```text
+named_struct  get_field  coalesce  nullif  nvl  nvl2  greatest  least
+union_extract  union_tag
+```
+
+## Crypto and encoding {#crypto}
+
+```text
+digest  md5  sha224  sha256  sha384  sha512  encode  decode
+```
+
+## Type functions {#type}
+
+```text
+arrow_cast  arrow_try_cast  arrow_typeof  arrow_field  arrow_metadata
+with_metadata  version
+```
+
+Use `arrow_typeof` to inspect the Arrow data type of a column or expression:
+
+```sql
+SELECT arrow_typeof(vector) FROM documents LIMIT 1
+-- FixedSizeList(Field { name: "item", data_type: Float32, .. }, 1536)
+```
+
+`arrow_cast` casts to an Arrow type by name, reaching types SQL has no spelling
+for. See [Data types](/sql/data-types).
+
+## What Lance adds {#lance}
+
+These are registered by Lance on top of DataFusion's set.
+
+### `contains_tokens(column, tokens)` {#contains-tokens}
+
+```sql
+SELECT * FROM my_docs WHERE contains_tokens(text, 'fox jumps dog')
+```
+
+True when the column contains **all** the tokens, split on whitespace and
+punctuation. Equivalent to an `fts()` match query with fuzziness off, `AND`
+semantics and the simple tokenizer.
+
+**Index-accelerated.** When the column has a full-text index that suits the
+query, the planner uses it instead of scanning — which is the reason to prefer
+it over `LIKE` for keyword tests.
+
+### JSON accessors {#json}
+
+| Function | Returns |
+| --- | --- |
+| `json_get(col, key)` | the value at `key`, still JSON |
+| `json_get_string(col, key)` | `Utf8` |
+| `json_get_int(col, key)` | `Int64` |
+| `json_get_float(col, key)` | `Float64` |
+| `json_get_bool(col, key)` | `Boolean` |
+| `json_extract(col, path)` | `Utf8`: the value as JSON text, by path |
+| `json_extract_with_type(col, path)` | a struct of the value as JSON and a type tag, by path |
+| `json_exists(col, path)` | `Boolean` |
+| `json_array_contains(col, value)` | `Boolean` |
+| `json_array_length(col)` | length |
+
+`json_get` returns JSON, so a typed accessor can read a field inside it:
+
+```sql
+SELECT * FROM events
+WHERE json_get_string(json_get(data, 'user'), 'name') = 'Alice'
+```
+
+That works one level down. A `json_get` nested inside another `json_get` is
+refused, so reach a deeper value with `json_extract` and a JSONPath instead. It
+returns JSON text, so a string compares with its quotes:
+
+```sql
+SELECT * FROM events
+WHERE json_extract(data, '$.user.profile.name') = '"Alice"'
+```
+
+**`json_get`, its typed variants, and `json_extract` are index-accelerated** by a
+JSON scalar index on the column. The rest evaluate per row.
+
+### Geospatial {#geo}
+
+<Warning>
+Lance defines `st_*` functions on GeoArrow, but they sit behind a build feature
+**LanceDB does not ship**. Calling one returns an error naming the missing
+feature rather than an unknown-function error, and the spatial index is gated
+the same way.
+</Warning>
+
+## Additional scalar functions {#escape-hatch}
+
+SQL queries can call functions registered with the server, including functions
+not listed on this page. Consult
+[DataFusion's scalar function reference](https://datafusion.apache.org/user-guide/sql/scalar_functions.html)
+for version 54. Functions documented for a newer DataFusion release may not be
+available in your LanceDB Enterprise version.
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+<svg width="512" height="512" viewBox="0 0 512 512" xmlns="http://www.w3.org/2000/svg" role="img" aria-labelledby="title desc">
+  <title id="title">Large scale vector search (1.5B)</title>
+  <desc id="desc">Abstract visualization of billions of vectors clustered and searched</desc>
+  <defs>
+    <linearGradient id="bg" x1="0%" y1="0%" x2="100%" y2="100%">
+      <stop offset="0%" stop-color="#030712" />
+      <stop offset="50%" stop-color="#111827" />
+      <stop offset="100%" stop-color="#1f2937" />
+    </linearGradient>
+    <radialGradient id="pulse" cx="50%" cy="50%" r="50%">
+      <stop offset="0%" stop-color="#a78bfa" stop-opacity="1" />
+      <stop offset="100%" stop-color="#38bdf8" stop-opacity="0" />
+    </radialGradient>
+    <linearGradient id="node" x1="0%" y1="0%" x2="100%" y2="100%">
+      <stop offset="0%" stop-color="#c084fc" />
+      <stop offset="100%" stop-color="#22d3ee" />
+    </linearGradient>
+    <filter id="glow" x="-50%" y="-50%" width="200%" height="200%">
+      <feGaussianBlur stdDeviation="6" result="blur" />
+      <feMerge>
+        <feMergeNode in="blur" />
+        <feMergeNode in="SourceGraphic" />
+      </feMerge>
+    </filter>
+  </defs>
+
+  <rect width="512" height="512" rx="36" fill="url(#bg)" />
+
+  <g opacity="0.4" stroke="#1f2937">
+    <path d="M64 128h384M64 224h384M64 320h384M64 416h384" stroke-width="1.5" />
+    <path d="M128 64v384M224 64v384M320 64v384M416 64v384" stroke-width="1.5" />
+  </g>
+
+  <circle cx="256" cy="256" r="170" fill="url(#pulse)" opacity="0.45" />
+
+  <g stroke="rgba(255,255,255,0.15)" stroke-width="2" fill="none">
+    <path d="M140 180 Q256 120 372 180" />
+    <path d="M120 260 Q256 360 392 260" />
+    <path d="M160 340 Q256 220 352 340" />
+  </g>
+
+  <g fill="url(#node)" filter="url(#glow)">
+    <circle cx="140" cy="180" r="8" />
+    <circle cx="256" cy="120" r="10" />
+    <circle cx="372" cy="180" r="8" />
+    <circle cx="120" cy="260" r="7" />
+    <circle cx="392" cy="260" r="7" />
+    <circle cx="160" cy="340" r="9" />
+    <circle cx="352" cy="340" r="9" />
+    <circle cx="256" cy="256" r="14" />
+    <circle cx="210" cy="210" r="6" />
+    <circle cx="300" cy="210" r="6" />
+    <circle cx="210" cy="302" r="6" />
+    <circle cx="300" cy="302" r="6" />
+  </g>
+
+  <g fill="#e2e8f0" font-family="'Inter', 'Segoe UI', sans-serif" text-anchor="middle">
+    <text x="256" y="420" font-size="26" font-weight="600">1.5B vectors</text>
+    <text x="256" y="452" font-size="16" opacity="0.8">Large scale LanceDB search</text>
+  </g>
+</svg>
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+<svg xmlns="http://www.w3.org/2000/svg" width="800" height="676" viewBox="0 0 800 676" role="img" aria-labelledby="title desc">
+  <title id="title">LanceDB OSS and Enterprise architecture</title>
+  <desc id="desc">OSS runs the application and embedded library in one process with local disk or object storage. Enterprise separates query serving, read execution and caching, and indexing and background workers. The control plane governs the service. Enterprise persists tables in object storage. Both offerings use the Lance format, without automatically sharing storage.</desc>
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+    <marker id="arrow" markerWidth="8" markerHeight="8" refX="7" refY="4" orient="auto" markerUnits="userSpaceOnUse"><path d="M1 1 7 4 1 7" fill="none" stroke="#596776" stroke-width="1.7" stroke-linecap="round" stroke-linejoin="round"/></marker>
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new file mode 100644
index 000000000..e74f01300
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"></image>
+  </defs>
+</svg>
\ No newline at end of file
diff --git a/docs/web/static/assets/tutorials/build-with-ai-agents/camelot/data/camelot.json b/docs/web/static/assets/tutorials/build-with-ai-agents/camelot/data/camelot.json
new file mode 100644
index 000000000..5851d2b21
--- /dev/null
+++ b/docs/web/static/assets/tutorials/build-with-ai-agents/camelot/data/camelot.json
@@ -0,0 +1,106 @@
+[
+  {
+    "id": 1,
+    "name": "King Arthur",
+    "role": "King of Camelot",
+    "description": "The legendary ruler of Camelot, wielder of Excalibur, and leader of the Knights of the Round Table.",
+    "stats": {
+      "strength": 2,
+      "courage": 5,
+      "magic": 1,
+      "wisdom": 4
+    },
+    "img": "data/img/arthur.jpg"
+  },
+  {
+    "id": 2,
+    "name": "Merlin",
+    "role": "Wizard and Advisor",
+    "description": "A powerful wizard and prophet who mentors Arthur and shapes the destiny of Camelot through magic and foresight.",
+    "stats": {
+      "strength": 2,
+      "courage": 4,
+      "magic": 5,
+      "wisdom": 5
+    },
+    "img": "data/img/merlin.jpg"
+  },
+  {
+    "id": 3,
+    "name": "Queen Guinevere",
+    "role": "Queen of Camelot",
+    "description": "Arthur's queen, admired for her grace and diplomacy, whose romances and loyalties influence Camelot's fate.",
+    "stats": {
+      "strength": 1,
+      "courage": 3,
+      "magic": 1,
+      "wisdom": 4
+    },
+    "img": "data/img/guinevere.jpg"
+  },
+  {
+    "id": 4,
+    "name": "Sir Lancelot",
+    "role": "Knight of the Round Table",
+    "description": "Arthur's most skilled knight, famed for unmatched combat prowess and his tragic love for Queen Guinevere.",
+    "stats": {
+      "strength": 5,
+      "courage": 5,
+      "magic": 1,
+      "wisdom": 3
+    },
+    "img": "data/img/sir_lancelot.jpg"
+  },
+  {
+    "id": 5,
+    "name": "Sir Gawain",
+    "role": "Knight of the Round Table",
+    "description": "A noble and honorable knight known for his courtesy and his encounter with the Green Knight.",
+    "stats": {
+      "strength": 4,
+      "courage": 5,
+      "magic": 1,
+      "wisdom": 4
+    },
+    "img": "data/img/sir_gawain.jpg"
+  },
+  {
+    "id": 6,
+    "name": "Sir Galahad",
+    "role": "Knight of the Round Table",
+    "description": "The purest and most virtuous knight, chosen to achieve the Holy Grail due to his unwavering spiritual purity.",
+    "stats": {
+      "strength": 4,
+      "courage": 5,
+      "magic": 2,
+      "wisdom": 5
+    },
+    "img": "data/img/sir_galahad.jpg"
+  },
+  {
+    "id": 7,
+    "name": "Sir Percival",
+    "role": "Knight of the Round Table",
+    "description": "A loyal and innocent knight whose bravery and sincerity make him one of the key seekers of the Holy Grail.",
+    "stats": {
+      "strength": 4,
+      "courage": 4,
+      "magic": 1,
+      "wisdom": 3
+    },
+    "img": "data/img/sir_percival.jpg"
+  },
+  {
+    "id": 8,
+    "name": "Mordred",
+    "role": "Traitor Knight",
+    "description": "Arthur's treacherous son or nephew who ultimately rebels against him, leading to Camelot's downfall.",
+    "stats": {
+      "strength": 4,
+      "courage": 2,
+      "magic": 1,
+      "wisdom": 2
+    },
+    "img": "data/img/mordred.jpg"
+  }
+]
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@@ -0,0 +1,213 @@
+/**
+ * Cookie consent banner + consent-gated RB2B loader for docs.lancedb.com.
+ *
+ * Mintlify loads every .js file in the content directory on every page, so this
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+  var STORAGE_KEY = "lancedb-cookie-consent"; // "accepted" | "rejected"
+  var POLICY_URL =
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+  var STYLE_ID = "lancedb-cookie-banner-style";
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+  var SETTINGS_HASH = "#cookie-settings";
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+  function readConsent() {
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+  // ---- RB2B -----------------------------------------------------------------
+  // Mirrors RB2B's official SPA guidance: remove and re-append the script on
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+  function loadRb2b() {
+    if (!hasKey() || readConsent() !== "accepted") return;
+    var existing = document.getElementById(SCRIPT_ID);
+    if (existing) existing.remove();
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+  var CSS = [
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+    "#" + BANNER_ID + " a{color:var(--lcb-link);text-decoration:underline;}",
+    "#" + BANNER_ID + " .lcb-actions{display:flex;gap:8px;}",
+    "#" + BANNER_ID + " button{flex:1;cursor:pointer;border:0;border-radius:8px;padding:8px 12px;",
+    "  font:inherit;font-weight:600;}",
+    "#" + BANNER_ID + " .lcb-reject{background:var(--lcb-btn2);color:var(--lcb-btn2-fg);}",
+    "#" + BANNER_ID + " .lcb-accept{background:var(--lcb-accent);color:var(--lcb-accent-fg);}",
+    "#" + BANNER_ID + " button:focus-visible{outline:2px solid var(--lcb-accent);outline-offset:2px;}",
+    "@media (max-width:480px){#" + BANNER_ID + "{left:16px;right:16px;max-width:none;}}",
+    "@media print{#" + BANNER_ID + "{display:none;}}",
+  ].join("\n");
+
+  function injectStyles() {
+    if (document.getElementById(STYLE_ID)) return;
+    var style = document.createElement("style");
+    style.id = STYLE_ID;
+    style.textContent = CSS;
+    document.head.appendChild(style);
+  }
+
+  function closeBanner() {
+    var el = document.getElementById(BANNER_ID);
+    if (el) el.remove();
+  }
+
+  function choose(value) {
+    var previous = readConsent();
+    writeConsent(value);
+    closeBanner();
+    if (value === "accepted") {
+      lastPath = window.location.pathname + window.location.search;
+      loadRb2b();
+    } else if (previous === "accepted") {
+      // RB2B may already be running in this tab; reload so it stops.
+      window.location.reload();
+    }
+  }
+
+  function openBanner() {
+    if (document.getElementById(BANNER_ID)) return;
+    injectStyles();
+
+    var el = document.createElement("div");
+    el.id = BANNER_ID;
+    el.setAttribute("role", "dialog");
+    el.setAttribute("aria-live", "polite");
+    el.setAttribute("aria-labelledby", BANNER_ID + "-title");
+    el.innerHTML =
+      '<p><strong id="' + BANNER_ID + '-title">Cookies on LanceDB Docs</strong>' +
+      "With your permission, we use a third-party service (RB2B) that sets cookies " +
+      "to help us understand which companies and people visit our docs. " +
+      "Nothing loads unless you accept. " +
+      '<a href="' + POLICY_URL + '" target="_blank" rel="noopener">Privacy policy</a></p>' +
+      '<div class="lcb-actions">' +
+      '<button type="button" class="lcb-reject">Decline</button>' +
+      '<button type="button" class="lcb-accept">Accept</button>' +
+      "</div>";
+
+    el.querySelector(".lcb-reject").addEventListener("click", function () {
+      choose("rejected");
+    });
+    el.querySelector(".lcb-accept").addEventListener("click", function () {
+      choose("accepted");
+    });
+
+    document.body.appendChild(el);
+  }
+
+  // Any link to #cookie-settings (e.g. the footer link) reopens the banner.
+  function watchSettingsLinks() {
+    document.addEventListener(
+      "click",
+      function (event) {
+        var link = event.target && event.target.closest && event.target.closest("a[href]");
+        if (!link) return;
+        var href = link.getAttribute("href") || "";
+        if (href.slice(-SETTINGS_HASH.length) !== SETTINGS_HASH) return;
+        event.preventDefault();
+        event.stopPropagation();
+        openBanner();
+      },
+      true
+    );
+  }
+
+  // ---- Init -----------------------------------------------------------------
+  window.lancedbCookieConsent = {
+    open: openBanner,
+    get: readConsent,
+    reset: function () {
+      try {
+        window.localStorage.removeItem(STORAGE_KEY);
+      } catch (e) {}
+      openBanner();
+    },
+  };
+
+  function init() {
+    watchRoutes();
+    watchSettingsLinks();
+    var consent = readConsent();
+    if (consent === "accepted") {
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+      openBanner();
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+    if (window.location.hash === SETTINGS_HASH) openBanner();
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+
+  if (document.readyState === "loading") {
+    document.addEventListener("DOMContentLoaded", init);
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+    init();
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+})();
diff --git a/docs/web/static/styles/style.css b/docs/web/static/styles/style.css
new file mode 100644
index 000000000..5a54326ac
--- /dev/null
+++ b/docs/web/static/styles/style.css
@@ -0,0 +1,39 @@
+:root {
+  --banner-left: #e4d8f8;
+  --banner-right: #e55a2b;
+}
+
+/* Mintlify's KaTeX stylesheet currently serves the AMS font from a broken URL. */
+@font-face {
+  font-family: "LanceDB KaTeX AMS";
+  src: url("https://cdn.jsdelivr.net/npm/katex@0.16.0/dist/fonts/KaTeX_AMS-Regular.woff2") format("woff2");
+  font-display: swap;
+  font-style: normal;
+  font-weight: 400;
+}
+
+.katex .mathbb,
+.katex .textbb {
+  font-family: "LanceDB KaTeX AMS", "KaTeX_AMS", serif !important;
+}
+
+/* Mintlify banner gradient */
+#banner,
+:where(.banner, [data-banner], [class*="Banner_banner"]) {
+  background: linear-gradient(90deg, var(--banner-left) 0%, var(--banner-right) 100%) !important;
+  color: #1b0f09 !important;
+  border: none !important;
+}
+
+#banner a,
+:where(.banner, [data-banner], [class*="Banner_banner"]) a {
+  color: #1b0f09 !important;
+  font-size: 0.9rem;
+  font-weight: 500;
+  text-decoration-color: rgba(27, 15, 9, 0.35);
+}
+
+#banner button,
+:where(.banner, [data-banner], [class*="Banner_banner"]) button {
+  color: #1b0f09 !important;
+}
diff --git a/docs/web/tables/branching.mdx b/docs/web/tables/branching.mdx
new file mode 100644
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--- /dev/null
+++ b/docs/web/tables/branching.mdx
@@ -0,0 +1,441 @@
+---
+title: "Branches"
+sidebarTitle: "Branch"
+description: "Fork isolated, writable lines of table history in LanceDB. Run experiments, backfills, and index rebuilds without disturbing production reads on main."
+icon: "code-branch"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Branches work in the embedded library and in LanceDB Enterprise. Comparing a
+    branch with `main` and promoting its new columns onto `main` is available
+    only in LanceDB Enterprise.
+---
+import {
+    PyConnect,
+    TsConnect,
+    RsConnect,
+    PyConnectEnterprisePlaceholders,
+    TsConnectEnterprisePlaceholders,
+    RsConnectEnterprisePlaceholders,
+} from '/snippets/connection.mdx';
+import {
+    PyBranchCreate as BranchCreate,
+    TsBranchCreate,
+    RsBranchCreate,
+    PyBranchWrite as BranchWrite,
+    TsBranchWrite,
+    RsBranchWrite,
+    PyBranchReopen as BranchReopen,
+    TsBranchReopen,
+    RsBranchReopen,
+    PyBranchDelete as BranchDelete,
+    TsBranchDelete,
+    RsBranchDelete,
+    PyBranchUpsertToMain as BranchUpsertToMain,
+    TsBranchUpsertToMain,
+    RsBranchUpsertToMain,
+    PyBranchIndex as BranchIndex,
+    TsBranchIndex,
+    RsBranchIndex,
+} from '/snippets/tables.mdx';
+
+A branch is an isolated, writable line of history forked from `main` (or from any
+other branch). Anything you do on a branch (e.g., adding rows, changing the schema,
+building an index) stays on that branch, so `main` keeps serving production
+reads exactly as before. Branches are a natural fit when you want to:
+
+- Experiment with a new index, schema change, or reprocessing step without
+  affecting live queries on `main`.
+- Run a backfill or migration you'd like to validate before applying it to
+  `main`.
+- Hand a collaborator a frozen point-in-time fork while you keep writing to `main`.
+
+## How branches relate to versions and tags {#how-branches-relate-to-versions-and-tags}
+
+Every LanceDB table already tracks a linear history of [versions](/management/table#versioning),
+and you can [tag](/management/table#versioning-tag-based-versioning) a version or `checkout`
+one to read it. Branches add the missing piece: a *separate, writable* line of
+history. Where a tag is a read-only label and `checkout` is a read-only view, a
+branch forks from a point in history and then evolves on its own. Creating or
+checking out a branch hands you a new table handle whose reads and writes are
+scoped to that branch. The [comparison table](#branches-vs-tags-vs-versions) at
+the end of this page lays out when to reach for each.
+
+<Note>
+Branches are supported on local and namespace-backed tables in LanceDB OSS, as
+well as on LanceDB Enterprise (remote) tables.
+</Note>
+
+## Connect to a table {#connect-to-a-table}
+
+The branch API is identical no matter how you connect — only the connection
+itself differs between OSS and Enterprise. Establish a connection (`db`) and open
+a `table` (see [Ingesting data](/ingestion)), then use the same branch calls
+in every example that follows.
+
+### LanceDB OSS {#lancedb-oss}
+
+Point LanceDB at a local directory (or an object-storage URI) to use it as an
+embedded library.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyConnect}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    { "import * as lancedb from \"@lancedb/lancedb\";\n\n" }
+    {TsConnect}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    { "use lancedb::connect;\n\n" }
+    {RsConnect}
+    </CodeBlock>
+</CodeGroup>
+
+### LanceDB Enterprise {#lancedb-enterprise}
+
+<Badge color="red">Enterprise</Badge>
+
+Branching on LanceDB Enterprise works the same way as on OSS, apart from the differences
+noted below, but you connect to your
+Enterprise deployment with a `db://` URI, an API key, and your
+region and endpoint. Replace the placeholders below, including `us-east-1` if your
+deployment uses another region. In application code, load credentials from environment
+variables or a secrets manager. Once connected, open a table and use the same branch
+calls in every example that follows.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {PyConnectEnterprisePlaceholders}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsConnectEnterprisePlaceholders}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    { "use lancedb::connect;\n\n" }
+    {RsConnectEnterprisePlaceholders}
+    </CodeBlock>
+</CodeGroup>
+
+## Work with branches {#work-with-branches}
+
+The lifecycle of a branch is short and predictable: fork it, write to it, reopen
+it whenever you need it, and delete it once you're done. The examples below use a
+small `quotes` table with three rows on `main`.
+
+### Create a branch {#create-a-branch}
+
+Forking from `main` returns a table handle scoped to the new branch. `main` is
+the reserved default source, so `create` needs only a name; to fork from
+somewhere else, pass a branch name, a specific version, or both.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {BranchCreate}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsBranchCreate}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsBranchCreate}
+    </CodeBlock>
+</CodeGroup>
+
+### Write to a branch {#write-to-a-branch}
+
+Writes go through the branch handle and stay there — the `main` handle keeps
+reporting its original row count. Listing branches returns a mapping of each
+branch name to its metadata, including the version it was forked from.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {BranchWrite}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsBranchWrite}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsBranchWrite}
+    </CodeBlock>
+</CodeGroup>
+
+### Reopen a branch {#reopen-a-branch}
+
+A branch outlives the handle that created it. Reopen it later by name — either
+from an existing table handle or straight from the connection when you open the
+table. Both routes give you a writable handle tracking the branch's latest state.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {BranchReopen}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsBranchReopen}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsBranchReopen}
+    </CodeBlock>
+</CodeGroup>
+
+### Delete a branch {#delete-a-branch}
+
+Deleting a branch removes it and its branch-local history; `main` is untouched.
+A deployment refuses to delete a branch whose columns have been promoted onto
+`main`, because `main` still depends on it.
+Before deleting a branch, make sure you've retained any results you need — see
+[Apply branch-tested changes to `main`](#apply-branch-tested-changes-to-main)
+below.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {BranchDelete}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsBranchDelete}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsBranchDelete}
+    </CodeBlock>
+</CodeGroup>
+
+## Apply branch-tested changes to `main` {#apply-branch-tested-changes-to-main}
+
+<Warning>
+A branch has its own writable history. Outside of the [diff and cherry-pick
+APIs](#compare-and-merge-a-branch-into-main) — which are available on
+LanceDB Enterprise and promote added columns only — LanceDB does not reconcile
+one branch's history with another or detect conflicts between them. To carry
+other accepted work forward, rerun the validated operation against `main` or
+explicitly write selected results to it.
+</Warning>
+
+How you apply a validated change depends on the type of work:
+
+- **Added columns (Enterprise):** review the branch with `diff`, then promote
+  the new columns onto `main` with `cherry_pick` (`cherryPick` in TypeScript). See
+  [Compare and promote a branch into `main`](#compare-and-merge-a-branch-into-main).
+- **Backfill or transformation:** rerun the validated job against `main`.
+- **Schema change:** apply the same reviewed schema operation to `main`.
+- **Index change:** build the index on `main` using the configuration validated
+  on the branch.
+- **Selected row results:** upsert those rows into `main` using a stable unique
+  key.
+
+### Upsert selected branch rows into `main` {#upsert-selected-branch-rows-into-main}
+
+If the result you want to retain is a set of inserted or updated rows, use
+[`merge_insert`](/ingestion/merge-insert#merge-incoming-rows-by-key) to write them to
+`main`. Despite its name, `merge_insert` is a row-ingestion operation: it
+matches incoming rows by key and does not merge branch histories.
+
+Read the rows you want from the reviewed branch, then upsert them into `main` on
+your key column — `id` in this example. Here, `candidate` is the branch handle:
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {BranchUpsertToMain}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsBranchUpsertToMain}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsBranchUpsertToMain}
+    </CodeBlock>
+</CodeGroup>
+
+With LanceDB Enterprise, the synchronous Python table raises for `to_arrow()`; read
+the branch's rows with a query instead -- see
+[Avoid materializing the whole table](/search#avoid-materializing-the-whole-table).
+
+This operation transfers only the inserted or updated rows in its input. It does
+not transfer branch history, schema changes, indexes, or branch-local deletions,
+and it does not determine which rows changed after the branch was created.
+
+Reapplying an identical payload is idempotent on the key, but the operation is
+not conflict-aware. If `main` has diverged, incoming branch rows can overwrite
+newer values with the same key. Read and upsert the whole branch only when that
+overwrite is intentional; otherwise, filter the branch read to the rows you
+intend to apply.
+
+## Compare and promote a branch into `main` {#compare-and-merge-a-branch-into-main}
+
+<Badge color="red">Enterprise</Badge>
+
+On LanceDB Enterprise, branches expose two review-and-land calls: `diff`
+inspects what a branch has changed relative to `main`, and `cherry_pick`
+(`cherryPick` in TypeScript) promotes the branch's new columns onto `main` in
+place, without reissuing the branch's writes.
+
+<Info>
+`diff` and `cherry_pick` work on Enterprise (remote) tables only. On a local
+table they fail as not supported: Python raises `NotImplementedError`, and
+TypeScript and Rust return an error. A cherry-pick promotes added columns; use
+the [upsert](#upsert-selected-branch-rows-into-main) or rerun patterns above for
+row and index changes.
+</Info>
+
+### Diff a branch {#diff-a-branch}
+
+`diff` reads the branch and `main` without modifying either, and returns what
+has changed: columns added, removed or changed, indexes added or removed, row
+counts, and a list of `errors` -- the reasons a cherry-pick of the branch could
+not land now. An empty list means it could.
+
+<CodeGroup>
+```python Python icon="python"
+diff = table.branches.diff("exp")
+
+print([c["name"] for c in diff["addedColumns"]])  # columns the branch introduced
+print(diff["baseMoved"])        # True if main has moved on since the branch's fork
+for error in diff["errors"]:    # empty when a cherry-pick could land
+    print(error["code"], error["message"])
+```
+
+```typescript TypeScript icon="square-js"
+const branches = await table.branches();
+const diff = await branches.diff("exp");
+
+console.log(diff.addedColumns.map((c) => c.name)); // columns the branch introduced
+console.log(diff.baseMoved);   // true if main has moved on since the branch's fork
+for (const error of diff.errors) {  // empty when a cherry-pick could land
+  console.log(error.code, error.message);
+}
+```
+</CodeGroup>
+
+The result also carries the versions it compared (`parentVersion`,
+`mainVersion`, `branchVersion`), `rowCountMain` and `rowCountBranch`, a
+`rowSummary`, and `removedColumns`, `changedColumns`, `addedIndexes` and
+`removedIndexes`. In Python the result is a dictionary with the same camelCase
+keys.
+
+The error codes are `baseMoved` (`main` has moved on since the version the
+branch was forked from), `rowCountMismatch`, `rowsChanged`, `columnRemoved`,
+`columnChanged`, `nothingToApply`, `noColumnChanges`, `inputColumnDependency`
+and `parentNotMain`. A code the client does not know surfaces as `unknown`, so
+older clients keep working.
+
+### Promote a branch's columns {#merge-a-branch}
+
+`cherry_pick` promotes the columns a branch added onto `main`. The server
+re-evaluates the diff when the request arrives and either lands the change or
+refuses it for the reasons `diff` would report. A refusal is not an exception:
+the call returns a result whose `status` is `"failed"`, with the reasons in
+`diff.errors`, so you can decide what to do next. Cherry-picks are not retried.
+
+Pass `dry_run=True` in Python, or `true` as the second argument in TypeScript,
+to check without landing anything. `preview.promotedColumns` lists the columns
+the cherry-pick would promote, or did.
+
+<CodeGroup>
+```python Python icon="python"
+# Check first; this does not modify main.
+preview = table.branches.cherry_pick("exp", dry_run=True)
+print(preview["status"])                      # "ready" when it would land
+print(preview["preview"]["promotedColumns"])
+
+# Land it.
+result = table.branches.cherry_pick("exp")
+if result["status"] == "cherryPicked":
+    print("landed at main version", result["mainVersionAfter"])
+elif result["status"] == "failed":
+    for error in result["diff"]["errors"]:
+        print(error["code"], error["message"])
+```
+
+```typescript TypeScript icon="square-js"
+const branches = await table.branches();
+
+// Check first; this does not modify main.
+const preview = await branches.cherryPick("exp", true);
+console.log(preview.status);                  // "ready" when it would land
+console.log(preview.preview.promotedColumns);
+
+// Land it.
+const result = await branches.cherryPick("exp");
+if (result.status === "cherryPicked") {
+  console.log("landed at main version", result.mainVersionAfter);
+} else if (result.status === "failed") {
+  for (const error of result.diff.errors) {
+    console.log(error.code, error.message);
+  }
+}
+```
+</CodeGroup>
+
+`status` is `ready` (a dry run that would land), `cherryPicked` (it landed, and
+`mainVersionAfter` is the new version of `main`), `failed` (refused, or for a
+dry run, would be refused; see `diff.errors`), `notImplemented`, or `unknown`
+for a status the client does not know. A branch that does not exist raises a
+not-found error instead of returning a status.
+
+In async Python, await the same calls on an `AsyncTable`:
+`await table.branches.diff("exp")` and
+`await table.branches.cherry_pick("exp", dry_run=True)`. In Rust they are
+`Table::diff_branch` and `Table::cherry_pick`.
+
+## Build indexes on a branch {#build-indexes-on-a-branch}
+
+One of the most useful things a branch buys you is a safe place to build and
+validate an index without affecting what's in production on the `main` branch.
+Fork a branch, create your vector (ANN) and full-text search (FTS)
+indexes on it, and check recall and latency. Once you have selected a
+configuration, build the corresponding index on `main` through your normal
+deployment workflow. Because the indexes live on the branch, queries against
+`main` never see a half-built index and are never slowed down by the branch's
+build.
+
+This pattern is especially valuable on Enterprise deployments, where `main` is
+typically serving production traffic while you tune an index configuration on
+the side. A deployment builds the branch's indexes in the background, so wait
+for them before measuring.
+
+<CodeGroup>
+    <CodeBlock filename="Python" language="Python" icon="python">
+    {BranchIndex}
+    </CodeBlock>
+
+    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
+    {TsBranchIndex}
+    </CodeBlock>
+
+    <CodeBlock filename="Rust" language="Rust" icon="rust">
+    {RsBranchIndex}
+    </CodeBlock>
+</CodeGroup>
+
+Schema changes such as adding, altering, or dropping columns are branch-scoped in
+the same way, so you can stage and review a larger reshaping of a table before
+applying the same schema operations to `main`.
+
+## Branches vs. tags vs. versions {#branches-vs-tags-vs-versions}
+
+Now that you're familiar with branches, you can see how they complement the other ways LanceDB
+give you to work with table history. Choose these approaches based on whether you need to
+*label*, *read*, or *write* data at a point in history:
+
+| Feature | Writable? | Purpose |
+| --- | --- | --- |
+| **[Branch](#work-with-branches)** | ✅ Yes | Write on top of a point in history without touching `main`. |
+| **[Tag](/management/table#versioning-tag-based-versioning)** | ❌ No | Attach a human-readable label to an existing version; protects it from cleanup. |
+| **[`checkout(version)`](/management/table#versioning-rollback-to-previous-versions)** | ❌ No | Read a historical version of `main` without forking. Read-only until you `restore`. |
+
+<Note>
+For linear version history — creating versions, listing them, rolling back, and
+tagging — see the [Versioning and Reproducibility](/management/table#versioning) guide.
+</Note>
diff --git a/docs/web/tables/consistency.mdx b/docs/web/tables/consistency.mdx
new file mode 100644
index 000000000..388655ff8
--- /dev/null
+++ b/docs/web/tables/consistency.mdx
@@ -0,0 +1,90 @@
+---
+title: "Consistency"
+sidebarTitle: "Consistency"
+description: "Understand when readers see changes and choose the freshness your application needs."
+icon: "water"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Configure read freshness in OSS and Enterprise. OSS checks for table updates;
+    Enterprise uses the connection setting to tighten its configured freshness bound.
+---
+
+import { PyReadConsistency } from '/snippets/read_consistency.mdx';
+
+## Table snapshots and freshness {#consistency}
+
+Each table read uses one committed version of the data. **Freshness** describes
+whether that version includes recent writes. For example, after another
+application appends records, an already-open table handle can still read the
+version from before the append.
+
+For example, a dashboard may need the latest updates, while an experiment may
+need to keep reading a specific table version. Use read freshness settings for the dashboard and an explicit table version
+for the experiment.
+
+## Visibility of writes {#visibility}
+
+In **OSS**, a table handle sees its own committed writes. By default, it does
+not automatically check for changes made through other handles or processes.
+Use a read consistency interval or `checkout_latest()` to pick up those changes.
+
+In **Enterprise**, readers use the service's configured freshness policy.
+A connection can request fresher data. When a read must include a completed
+write, use a zero interval or call `checkout_latest()` after the write, with a
+client and server that support these freshness controls.
+
+## Configure read freshness {#configure-consistency-parameters}
+
+Set `read_consistency_interval` when connecting. This controls when reads
+pick up newer table versions; it does not change how writes commit.
+
+| Setting | OSS | Enterprise |
+| --- | --- | --- |
+| **Unset** | Keep the open table version until explicitly refreshed or changed by that handle. | Use the service's configured freshness policy. |
+| **Zero** | Check for the latest committed version on every read. | Request the latest committed version on every read. |
+| **Non-zero interval** | Check for updates on a read once the interval since the last check has elapsed. | Request data no older than the interval allows; the service may return fresher data. |
+
+`checkout_latest()` explicitly refreshes an OSS table handle. On Enterprise it
+asks subsequent reads on that handle to reflect data at least as recent as the
+refresh request. To read a fixed historical version instead, use
+[table versioning](/management/table#versioning).
+
+<Accordion title="Complete Python example: compare two readers">
+
+Install the Python SDK with `pip install lancedb`, then save this as
+`consistency.py` and run `python consistency.py`. It creates a temporary local
+database and removes it when finished. No Enterprise connection is needed.
+
+<CodeBlock filename="consistency.py" language="python" icon="python">
+{PyReadConsistency}
+</CodeBlock>
+
+The default reader sees one row until refreshed. The reader with a zero
+interval sees the new row on its next read.
+
+</Accordion>
+
+## Choose a freshness interval {#choosing}
+
+- **Reads that must include recent writes:** use a zero interval. Checking more
+  frequently can increase read latency and storage requests.
+- **Dashboards with an acceptable delay:** choose an interval within that delay
+  budget, then measure the effect on latency and cost.
+- **Reproducible experiments:** read an explicit table version and retain it for
+  as long as the experiment needs it.
+
+Choose the interval based on the delay your application can tolerate between
+a write completing and a reader seeing it.
+
+## Learn more {#next}
+
+- [Connect](/basics/connect): configure your client.
+- [Table versioning](/management/table#versioning): read and restore retained versions.
+- [Guarantees](/basics/guarantees): understand commit and failure boundaries.
+
+<span id="handle-bad-vectors" />
+
+For invalid vector handling during ingestion, see
+[Create a table](/ingestion/create-table#from-a-custom-schema).
diff --git a/docs/web/training/data-loading.mdx b/docs/web/training/data-loading.mdx
new file mode 100644
index 000000000..fd9df51b2
--- /dev/null
+++ b/docs/web/training/data-loading.mdx
@@ -0,0 +1,480 @@
+---
+title: "Loading Data for Model Training"
+sidebarTitle: "Data Loading & Shuffles"
+description: "Stream, shuffle, transform, and resume model training data with LanceDB."
+icon: boxes-stacked
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Streaming, shuffling and resuming training data work with local,
+    object-storage and deployment tables in the Python SDK.
+---
+
+LanceDB provides an excellent data backend for training machine learning models. For basic use cases, a `Table` can be fed directly into a data loader. For a more complete solution, a `StreamingDataset` adds prefetching, elastic determinism, resumability, multithreaded transformations, and more.
+
+## Basic Data Loading
+
+Most model training frameworks feed data into a model by sequencing through successive batches, a process 
+often referred to as **data loading**. The simplest way to perform data loading is to read a LanceDB table in batches,
+in the order its rows are stored, through an identity `Permutation`. A table itself is not iterable.
+
+<CodeGroup>
+```py Python icon=Python
+import lancedb
+from lancedb.permutation import Permutation
+
+db = lancedb.connect("file://some/db/path")
+table = db.open_table("some_table")
+
+for batch in Permutation.identity(table).with_format("arrow").iter(batch_size=1024):
+    print(batch.to_pydict())
+```
+</CodeGroup>
+
+## Advanced Data Loading
+Advanced data loading scenarios can be handled by the `StreamingDataset`. This object allows you to apply preprocessing before training, pipelined reads, and more. Use this when the training data does not fit in memory, or when you need deterministic global batches across cluster sizes.
+
+<CodeGroup>
+```py Python icon=Python
+import lancedb
+import torch
+from lancedb.streaming import StreamingDataset
+
+db = lancedb.connect("file://some/db/path")
+table = db.open_table("some_table")
+
+dataset = StreamingDataset(table, shuffle_seed=42)
+dataloader = torch.utils.data.DataLoader(
+    dataset,
+    batch_size=128,
+    num_workers=0,
+)
+
+for batch in dataloader:
+    train_step(batch)
+```
+</CodeGroup>
+
+The `StreamingDataset` wraps a LanceDB `Table` and yields plain Python dictionaries by default. Whether iterating directly or through a PyTorch `DataLoader`, only one iterator can be simultaneously active on a `StreamingDataset` instance. 
+
+<Note>
+`StreamingDataset` is built on the `permutation` API and works with both LanceDB OSS and LanceDB Enterprise. The underlying table data can live on local disk or object storage.
+</Note>
+
+To understand the wide range of scenarios handled by `StreamingDataset`, consider a model trained with stochastic gradient descent (SGD) and distributed data parallelism (DDP). In this example, we need to load batches onto multiple GPUs
+across multiple servers. After each batch is processed, the GPUs exchange weights and the next batch is loaded. We
+fix the following terminology:
+
+- **World size** - The number of GPUs being loaded simultaneously. For example, if we have $2$ servers with $4$
+  GPUs each, the world size is $8$.
+- **Rank** - The identifier of an individual GPU. It is an integer in the range `[0, world_size)`.
+  Each rank is allocated a disjoint portion of the source data.
+- **Batch size** - The number of rows processed by one GPU in a single training step. For example, $128$.
+- **Global batch size** - The total number of rows processed across all GPUs in a training step (sometimes called a "minibatch" in machine learning literature). For
+  example, if we have $8$ GPUs with batch size $128$, the global batch size is $1024$.
+
+
+Other concepts, such as read batch size and `num_workers`, are introduced in the relevant sections below.
+
+### Filtering
+
+The `StreamingDataset` object allows you to train models on only the subset of rows that satisfy a user-defined filter, specified using the `filter` parameter. This can be useful for removing large rows, badly-sourced data, and a wide range of other applications. 
+You can also use the `columns` parameter to load only a subset of columns.
+
+<CodeGroup>
+```py Python icon=Python
+ds = StreamingDataset(
+    table,
+    shuffle_seed=42,
+    columns=["image", "label"],       # skip all other columns
+    filter="category = 'train'",      # only training rows
+)
+```
+</CodeGroup>
+
+### Shuffling Rows
+
+In model training, model quality can be hurt by unintentionally learning patterns in the ordering of the source data. Shuffling the dataset before training alleviates this problem. However, fetching rows with scattered sparse reads across object storage can be dramatically slower than fetching contiguous batches. `StreamingDataset` shuffles at one of two granularities, selected by the `shuffle_clump_size` parameter.
+
+<Note>
+`StreamingDataset` shuffle seeds combine `shuffle_seed` with `epoch`; each epoch produces a different permutation while runs
+with the same inputs remain reproducible. Set `shuffle_seed = None` to generate a random seed upon pipeline initialization.
+</Note>
+
+#### Row-Level Shuffle {#one-phase-shuffle}
+
+By default, `shuffle_clump_size` is `None` and rows are shuffled individually: the rows of the source dataset are shuffled according to a pseudorandom permutation, then fed in batches to the accelerator. This gives the best possible randomness, but can exhibit low throughput.
+
+
+<CodeGroup> 
+```py Python icon=Python 
+# 1-phase shuffled read: rows shuffled individually.
+for epoch in range(num_epochs): 
+    ds = StreamingDataset( 
+        table, 
+        shuffle_seed=42, 
+        epoch=epoch, # changes the permutation each epoch 
+    ) 
+    for sample in ds: 
+        train_step(sample)
+
+#Evaluation: deterministic sequential order, no shuffle.
+eval_ds = StreamingDataset(table, shuffle=False)
+for sample in eval_ds:
+    eval_step(sample)
+```
+
+</CodeGroup>
+
+
+#### Clumped Shuffle {#two-phase-shuffle}
+
+Setting `shuffle_clump_size` to an integer shuffles contiguous groups ("clumps") of that many rows instead of single rows. Each clump keeps its rows in storage order and moves as a unit, so storage I/O consists mostly of contiguous reads rather than single-row fetches. Larger clumps improve I/O locality, which matters most on object storage, at the cost of randomness.
+
+<CodeGroup>
+```py Python icon=Python
+# Clumped shuffled read: rows are shuffled in groups of contiguous rows
+# instead of individually, trading a bit of randomness for much cheaper I/O.
+for epoch in range(num_epochs):
+    ds = StreamingDataset(
+        table,
+        shuffle_seed=42,
+        epoch=epoch,
+        shuffle_clump_size=1024,   # contiguous rows that move together
+    )
+    for sample in ds:
+        train_step(sample)
+
+# Evaluation: deterministic sequential order, no shuffle.
+eval_ds = StreamingDataset(table, shuffle=False)
+for sample in eval_ds:
+    eval_step(sample)
+```
+</CodeGroup>
+
+### Parallelism & Elasticity
+
+When loading data, `StreamingDataset` partitions the the source dataset among disjoint parallel threads called **splits**. Each rank is allocated a contiguous group of splits, and each DataLoader worker associated to a rank gets a contiguous subgroup of its rank's splits.
+
+If using `shuffle=False`, data is allocated to splits naively in ordered contiguous blocks of equal size. With `shuffle=True`
+(the default), the rows of the source dataset are shuffled and distributed among splits such that each split receives the same
+number of rows; with `shuffle_clump_size` set, they are shuffled in clumps of that many contiguous rows.
+
+The number of splits defaults to `world_size` if `num_splits` is omitted during initialization. This is not recommended, as a later run using a different world size will then construct a different layout and structure of global batches.
+
+To get **elastic determinism**, choose a fixed `num_splits` that is compatible with every topology you plan to use.
+For example, a run with `num_splits=8` and $4$ GPUs assigns $2$ splits to each rank. The ranks pull from their splits in
+round-robin order, producing the same global batches as a run with $8$ GPUs and the same `num_splits`, `shuffle_seed`,
+and `epoch`.
+
+The following constraints apply:
+
+- `num_splits` must be divisible by `world_size`.
+- With DataLoader worker processes, `num_splits` must be divisible by `world_size * num_workers`.
+- For the same samples to form each global training step across topologies, `global_batch_size` must be a multiple of
+  `num_splits`.
+- The filtered row count must be at least `num_splits`. If it is not evenly divisible by `num_splits`, up to
+  `num_splits - 1` surplus rows are dropped so that every split has the same length.
+
+For example, to switch between $8$ and $6$ GPUs, use a common multiple such as $24$ for `num_splits`. Highly composite values can support many layouts; for example, `num_splits = 48` is compatible with $10$ distinct possible GPU counts. Remember to include `num_workers` when checking divisibility.
+
+<CodeGroup>
+```py Python icon=Python
+import torch.distributed as dist
+
+dist.init_process_group("nccl")
+rank = dist.get_rank()
+world_size = dist.get_world_size()
+
+# num_splits=48 is divisible by many integers,
+# so this dataset works unchanged as you add or remove GPUs.
+ds = StreamingDataset(
+    table,
+    num_splits=48,
+    shuffle_seed=42,
+    epoch=current_epoch,
+    rank=rank,
+    world_size=world_size,
+)
+
+for sample in ds:
+    train_step(sample)
+```
+</CodeGroup>
+
+### Fetching
+
+Traditional datasets have often been designed to yield a single sample at a time upon iteration. However, in modern use cases,  accessing data on object storage one row at a time introduces too much per-call overhead. Thus, `StreamingDataset` fetches data in read batches, whose size is controlled by the `read_batch_size` parameter (defaults = `64`).
+
+In addition to batching requests, the prefetching mechanism reads ahead in the background. While one batch is being processed, `StreamingDataset` reads several subsequent batches. The `io_queue_depth` parameter specifies the number of batches simultaneously kept in flight per split (default = `4`); it replaces `prefetch_batches`, which still works but is deprecated. A larger value provides more buffering against jittery workloads, but also increases memory use and I/O concurrency.
+
+<CodeGroup>
+```py Python icon=Python
+ds = StreamingDataset(
+    table,
+    shuffle_seed=42,
+    read_batch_size=256,   # rows fetched per LanceDB call per split
+    io_queue_depth=8,      # batches to keep in flight per split
+)
+```
+</CodeGroup>
+
+### Transforming
+
+Many model training workloads require performing a transformation to the data after it is loaded from object storage and before it is passed to the GPU. For example, we may need to decode images, tokenize text, or normalize vectors. A transformation function can be provided using the `transform` parameter. 
+
+A transformation function must receive a PyArrow `RecordBatch` and return an iterable. For example, the default transform function returns a list of Python dictionaries. Furthermore, the input expected by PyTorch must match the collation function used in the PyTorch data loader. PyTorch's default collation function accepts a Python dictionary and several other types, and is thus compatible with the default `StreamingDataset` transform function. If your custom transform function returns something different (for example, Arrow record batches), you may need to provide a compatible PyTorch collation function.
+
+
+<CodeGroup>
+```py Python icon=Python
+import pyarrow as pa
+
+def normalize(batch: pa.RecordBatch) -> list[dict]:
+    # This pure-Python loop holds the GIL and is shown for illustration only.
+    # In practice, prefer a library like torchvision or numpy that releases the
+    # GIL so the ThreadPoolExecutor can run transforms in parallel.
+    rows = batch.to_pylist()
+    for row in rows:
+        row["image"] = [v / 255.0 for v in row["image"]]
+    return rows
+
+ds = StreamingDataset(table, shuffle_seed=42, transform=normalize)
+```
+</CodeGroup>
+
+`StreamingDataset` manages expensive transforms through a
+`ThreadPoolExecutor` whose worker count equals the number of available CPUs. Furthermore, transformations are applied to batches (instead of individual samples) to amortize per-batch overhead. 
+
+##### DataLoader Workers
+
+The thread-based transformation model that `StreamingDataset` uses by default is only effective when the transform
+function releases the GIL. This is true for many Python scientific libraries, including NumPy, PyArrow, and
+TorchVision, but not for pure-Python transforms. PyTorch can launch multiple DataLoader worker processes per rank,
+and `StreamingDataset` uses `get_worker_info()` to give each worker a non-overlapping group of splits.
+
+Multiprocessing adds pickling and transfer overhead and increases memory use. Start with `num_workers=0`, which keeps
+loading in the rank's main process, and add workers only after confirming an unavoidable GIL bottleneck. If you use
+workers, `num_splits` must be divisible by `world_size * num_workers`. Use the `forkserver` or `spawn` multiprocessing
+context because LanceDB uses internal threads.
+
+```py Python icon=Python
+dataloader = torch.utils.data.DataLoader(
+    dataset,
+    batch_size=batch_size,
+    num_workers=2,
+    multiprocessing_context="forkserver",
+    persistent_workers=True,
+)
+```
+
+By default, `StreamingDataset` serializes enough table state to reopen the table in each worker. For custom connection
+setup, pass a picklable `connection_factory` callable that accepts a table name and returns an open table. This avoids
+serializing connection credentials into worker state.
+
+### Observability
+
+Optimizing data loader performance can be tricky, as runtime bottlenecks are often difficult to locate across various stages of the I/O and CPU pipelines. To help distinguish bottlenecks, `StreamingDataset` exposes several pipeline counters: `raw_queue_depth` shows the number of loaded rows waiting to be transformed,
+and `prefetch_queue_depth` is the number of transformed rows ready to be consumed. `unscanned_rows` and
+`consumed_rows` show how far the current iterator has progressed.
+
+If the `prefetch_queue_depth` is consistently zero but the `raw_queue_depth` is not, then you likely have a CPU
+transformation bottleneck. You should investigate GIL bottlenecks or look for ways to optimize your transformation.
+This can often be done by batching the compute work. If both `prefetch_queue_depth` and `raw_queue_depth` are
+consistently zero while the consumer is waiting, I/O is the likely bottleneck. A larger read batch size or clumped
+shuffling could help.
+
+<CodeGroup>
+```py Python icon=Python
+import threading, time
+
+ds = StreamingDataset(table, shuffle_seed=42)
+
+def log_pipeline_health():
+    while True:
+        print(
+            f"unscanned={ds.unscanned_rows} "
+            f"raw={ds.raw_queue_depth} "
+            f"cooked={ds.prefetch_queue_depth} "
+            f"consumed={ds.consumed_rows}"
+        )
+        time.sleep(1.0)
+
+monitor = threading.Thread(target=log_pipeline_health, daemon=True)
+monitor.start()
+
+for sample in ds:
+    train_step(sample)
+
+print(
+    f"bytes loaded: {ds.bytes_loaded} "
+    f"fetch time: {ds.fetch_time:.2f}s "
+    f"transform time: {ds.transform_time:.2f}s"
+)
+```
+</CodeGroup>
+
+`bytes_loaded` measures raw Arrow buffer bytes before transformation. `bytes_loaded`, `fetch_time`, and `transform_time`
+are cumulative across iterations of the same dataset instance; because work runs concurrently, the summed stage times
+can exceed wall-clock time. The queue depths report rows currently waiting in the pipeline, and the progress counters
+reflect the latest iterator snapshot.
+
+### Checkpointing 
+`StreamingDataset` provides checkpointing in case your model training fails partway through a run. Use `StreamingDataset.state_dict()` to capture the current number of samples consumed from each split in a Python dictionary, and `load_state_dict()` to restore the last saved position. 
+
+A stored `StreamingDataset` state reflects only round-robin cycles that have been completed across all splits; samples completed partway through a cycle at the time of saving can be replayed. To ensure efficient saving, make sure `global_batch_size` is a multiple of `num_splits`, as in the example below. 
+
+<CodeGroup>
+```py Python icon=Python
+import torch
+
+global_batch_size = 48  # one sample per split in each global step
+batch_size = global_batch_size // world_size
+
+dataset = StreamingDataset(
+    table,
+    num_splits=48,
+    shuffle_seed=42,
+    epoch=current_epoch,
+    rank=rank,
+    world_size=world_size,
+)
+dataloader = torch.utils.data.DataLoader(
+    dataset,
+    batch_size=batch_size,
+    num_workers=0,
+)
+
+for step, batch in enumerate(dataloader):
+    train_step(batch)
+
+    if (step + 1) % checkpoint_interval == 0:
+        torch.save(
+            {"model": model.state_dict(), "dataset": dataset.state_dict()},
+            f"checkpoint_{step + 1}.pt",
+        )
+
+# --- resuming after a crash ---
+checkpoint = torch.load("checkpoint_100.pt")
+model.load_state_dict(checkpoint["model"])
+
+dataset_state = checkpoint["dataset"]
+dataset = StreamingDataset(
+    table,
+    num_splits=dataset_state["num_splits"],
+    shuffle_seed=dataset_state["shuffle_seed"],
+    epoch=dataset_state["epoch"],
+    rank=rank,
+    world_size=world_size,  # may differ from the run that saved the checkpoint
+)
+dataset.load_state_dict(dataset_state)
+dataloader = torch.utils.data.DataLoader(
+    dataset,
+    batch_size=global_batch_size // world_size,
+    num_workers=0,
+)
+
+for batch in dataloader:
+    train_step(batch)
+```
+</CodeGroup>
+
+`load_state_dict()` rejects a checkpoint whose `num_splits` or `shuffle_seed` differs from the new dataset. For an
+exact resume, also use the same table snapshot, `epoch`, `shuffle`, `filter`, and other data-selection settings. The
+`world_size` and number of DataLoader workers may change as long as the split and batch-size divisibility constraints
+still hold.
+
+##### Checkpointing with Multiple DataLoader Workers
+
+Python's built-in `torch.utils.data.DataLoader` produces a safe checkpoint when `num_workers = 0`. With `num_workers > 0`, calling `state_dict()` often raises `RuntimeError`. In this case, use `StreamingDataLoader` for multi-worker prefetch and exact resumability. It carries a state snapshot
+alongside every internal batch and commits it to the parent `StreamingDataset` only when that batch is returned to the
+trainer. The trainer receives the same collated batch it would receive from a standard `DataLoader`.
+
+<CodeGroup>
+```py Python icon=Python
+from lancedb.streaming import StreamingDataset, StreamingDataLoader
+
+dataset = StreamingDataset(
+    table,
+    num_splits=48,
+    shuffle_seed=42,
+    epoch=current_epoch,
+    rank=rank,
+    world_size=world_size,
+)
+dataloader = StreamingDataLoader(
+    dataset,
+    batch_size=batch_size,
+    num_workers=4,
+)
+
+for step, batch in enumerate(dataloader):
+    train_step(batch)
+
+    if (step + 1) % checkpoint_interval == 0:
+        torch.save(
+            {"model": model.state_dict(), "dataset": dataset.state_dict()},
+            f"checkpoint_{step + 1}.pt",
+        )
+```
+</CodeGroup>
+
+`StreamingDataLoader` accepts the same arguments as `torch.utils.data.DataLoader`, with a few restrictions:
+
+- `dataset` must be a `StreamingDataset` (subclasses that override `__iter__` are not supported).
+- `in_order = True` is required so that consumer-committed checkpoints stay deterministic.
+- `persistent_workers = True` is not supported, because prefetched worker copies cannot be restored from parent-committed state.
+- `drop_last = True` is not supported, because incomplete tails discarded by worker replicas cannot be checkpointed topology-independently.
+
+With more than one worker, call `state_dict()` at a complete logical step boundary where every split assigned to the
+rank has the same consumed-sample count. Calling it mid-step raises `RuntimeError` asking you to consume more batches
+first.
+
+##### Resuming Across Different Topologies
+
+When training across ranks, each rank owns its own subset of splits and only its own splits have exact progress. To
+resume on a different `world_size`, collect the `state_dict()` from every rank of the previous run and merge them with
+`StreamingDataset.merge_state_dicts` before calling `load_state_dict()` on the new run. The merge is topology-agnostic:
+pass the full list of per-rank states in, and hand the identical merged dict to every rank of the resumed job,
+regardless of whether the topology grew, shrank, or stayed the same.
+
+<CodeGroup>
+```py Python icon=Python
+# On each rank of the previous run:
+state = dataset.state_dict()
+# ...gather `state` from every rank into a single list `states` (for example
+# via torch.distributed all-gather or by saving one file per rank).
+
+merged = StreamingDataset.merge_state_dicts(states)
+
+# On each rank of the resumed run (world_size may differ):
+dataset = StreamingDataset(
+    table,
+    num_splits=merged["num_splits"],
+    shuffle_seed=merged["shuffle_seed"],
+    epoch=merged["epoch"],
+    rank=rank,
+    world_size=new_world_size,
+)
+dataset.load_state_dict(merged)
+```
+</CodeGroup>
+
+## Permutations
+
+In specific use cases, you may need the flexibility to shuffle, split, and select data without using the full `StreamingDataset` object. In these cases, use `Permutation`, the lower-level functionality on which `StreamingDataset`
+is built. A `Permutation` defines a customizable row ordering, and supports map-style access through `__getitem__()`
+and batched access through `__getitems__()`.
+
+##### Version Pinning
+
+A `Permutation` is pinned to the version of its base table at the time it was constructed, and every read (including future reads
+from a `StreamingDataset` that wraps it, and every DataLoader worker after it starts) resolves against that pinned
+version. This makes iteration deterministic across compactions and worker restarts: the permutation only addresses rows
+that existed when it was built, and rows appended to the base table afterwards are not visible through the existing
+permutation.
+
+To include newly appended rows, build a new `Permutation` (and any `StreamingDataset` that depends on it)
+against the updated table. Permutations written before this behavior shipped continue to
+read as they did before.
diff --git a/docs/web/training/object-detection.mdx b/docs/web/training/object-detection.mdx
new file mode 100644
index 000000000..735b0af45
--- /dev/null
+++ b/docs/web/training/object-detection.mdx
@@ -0,0 +1,368 @@
+---
+title: "Object Detection for AV Perception"
+sidebarTitle: "Object Detection"
+description: End-to-end fine-tuning of a Faster R-CNN object detector on curated dashcam slices, using LanceDB as the data backbone from raw frames to checkpoints.
+icon: car
+---
+
+This example walks through fine-tuning an autonomous vehicle (AV) perception model on targeted failure-mode slices of [BDD100K](https://www.bdd100k.com/) — riders, nighttime pedestrians, and distant pedestrians — using LanceDB as a single multimodal table from raw JPEG bytes through to the PyTorch training loop.
+
+The full pipeline lives in the [lancedb/training](https://github.com/lancedb/training/tree/main/object-detection) repository. This page focuses on the parts most relevant to training: defining curated splits as materialized views, loading them through the [`Permutation`](/training/data-loading) API, and pinning checkpoints to an exact data version.
+
+## What you get {#what-you-get}
+
+Fine-tuning Faster R-CNN ResNet50 FPN v2 for 10 epochs on each curated slice (batch size 64, AMP, A100), starting from the same COCO-pretrained checkpoint and evaluating on the matching validation view:
+
+| Failure mode | Metric | Baseline (COCO) | Fine-tuned | Δ% |
+|---|---|---|---|---|
+| **Nighttime pedestrian** | mAP@0.5 | 0.4025 | **0.5192** | **+29.0%** |
+|                          | Recall  | 0.5923 | **0.7570** | **+27.8%** |
+| **Rider**                | mAP@0.5 | 0.5563 | **0.6676** | **+20.0%** |
+|                          | Recall  | 0.6788 | **0.7847** | **+15.6%** |
+| **Distant pedestrian**   | mAP@0.5 | 0.4746 | **0.5788** | **+22.0%** |
+|                          | Recall  | 0.6794 | **0.8024** | **+18.1%** |
+
+No external data added — only training-distribution correction via SQL filters over a single Lance table. Each panel below shows the same frame with three overlaid predictions: **green** = ground truth · **red** = pretrained COCO baseline · **blue** = fine-tuned model.
+
+![Rider detection — ground truth vs baseline vs fine-tuned](/static/assets/images/training/rider_04.jpg)
+
+![Nighttime pedestrian detection — ground truth vs baseline vs fine-tuned](/static/assets/images/training/nighttime_person_01.jpg)
+
+![Distant pedestrian detection — ground truth vs baseline vs fine-tuned](/static/assets/images/training/distant_person_00.jpg)
+
+The rest of the page walks through the pipeline that produced these checkpoints.
+
+## The failure modes {#the-failure-modes}
+
+A perception model fine-tuned on a generic dataset typically misses the long-tail scenarios that matter most in deployment. Three common failure modes drive this example:
+
+| Failure mode | Curation signal |
+|---|---|
+| **Riders** (person on bike/motorcycle) | `has_rider = true` |
+| **Nighttime pedestrians** | `timeofday = 'night' AND has_person = true` |
+| **Distant pedestrians** | `has_person = true AND person_bbox_area_pct < 30.0` |
+
+Each curated slice becomes a [Geneva materialized view](/geneva/jobs/materialized-views) — a named, refreshable SQL filter over the source table — and the training script loads it by name. New footage flows in through `add()` → `backfill()` → `refresh()`; no manifests, no exports, no reshuffling on disk.
+
+## 1\. Schema {#1-schema}
+
+The source table holds raw image bytes alongside structured annotations. Bounding boxes are stored as a parallel list (one element per box) rather than a nested struct so they remain directly queryable with SQL.
+
+```py Python icon=Python
+import pyarrow as pa
+
+BDD_SCHEMA = pa.schema([
+    pa.field("image_id",   pa.string()),
+    pa.field("split",      pa.string()),         # "train" | "val"
+    pa.field("image_bytes", pa.large_binary()),  # raw JPEG
+    pa.field("width",      pa.int32()),
+    pa.field("height",     pa.int32()),
+
+    # scene metadata
+    pa.field("weather",    pa.string()),
+    pa.field("scene",      pa.string()),
+    pa.field("timeofday",  pa.string()),
+
+    # annotations — parallel lists, one element per box
+    pa.field("ann_categories", pa.list_(pa.string())),
+    pa.field("ann_bboxes",     pa.list_(pa.list_(pa.float32()))),
+    pa.field("ann_occluded",   pa.list_(pa.bool_())),
+])
+```
+
+Ingestion streams `pa.RecordBatch`es of raw frames + annotations directly into a Lance table — no intermediate preprocessing job. The table can live on local disk, S3, GCS, or Azure; everything downstream (backfills, views, the training loader) opens it in place via `lancedb.connect("s3://...")` with no local copy step.
+
+## 2\. Backfill curation features with Geneva {#2-backfill-curation-features-with-geneva}
+
+Curation signals are added as columns on the same table using [Geneva backfilling](/geneva/jobs/backfilling). Backfills are incremental and checkpointed: re-running the command after new footage arrives only computes the new rows.
+
+```py Python icon=Python
+import pyarrow as pa
+from geneva.transformer import udf
+
+# Tier 1 — CPU, derived from annotations alone
+@udf(data_type=pa.bool_(), input_columns=["ann_categories"])
+def has_rider(ann_categories: list[str]) -> bool:
+    return "rider" in (ann_categories or [])
+
+# Tier 2 — GPU, runs a Faster R-CNN to find the largest detected person
+# as a percentage of frame area. <30% = a distant or small pedestrian,
+# the hard case we want to upweight in training.
+@udf(data_type=pa.float32(),
+     input_columns=["image_bytes", "width", "height"],
+     cuda=True, num_gpus=1)
+class PersonBboxAreaPct:
+    def __init__(self):
+        self._model = None
+
+    def __call__(self, image_bytes, width, height):
+        # lazy model load — runs once per Ray worker, then reused
+        ...
+```
+
+Run the backfill against the live table:
+
+```py Python icon=Python
+import geneva
+
+gconn = geneva.connect("data/bdd100k/lancedb")
+tbl   = gconn.open_table("bdd100k")
+
+tbl.add_columns({"has_rider": has_rider})
+tbl.add_columns({"person_bbox_area_pct": PersonBboxAreaPct()})
+
+with gconn.local_ray_context():
+    tbl.backfill("has_rider")
+    tbl.backfill("person_bbox_area_pct")
+```
+
+<Note>
+Because the curation features are flat scalar columns on the same table, all four retrieval modes — SQL, full-text search, vector search, and SQL-filtered vector search — work directly without joins or exports. See [Geneva backfilling](/geneva/jobs/backfilling) for how a backfill fills only the rows that lack values.
+</Note>
+
+## 3\. Define training splits as materialized views {#3-define-training-splits-as-materialized-views}
+
+A training split is a named SQL filter, not a CSV manifest. Each view stays in sync with the source table and bumps its `version` on every refresh — the link between a checkpoint and the exact data that produced it.
+
+```py Python icon=Python
+import geneva
+
+gconn = geneva.connect("data/bdd100k/lancedb")
+gtbl  = gconn.open_table("bdd100k")
+
+VIEWS = {
+    "bdd100k_rider_train":
+        "has_rider = true AND split = 'train'",
+    "bdd100k_rider_val":
+        "has_rider = true AND split = 'val'",
+    "bdd100k_nighttime_person_train":
+        "timeofday = 'night' AND has_person = true AND split = 'train'",
+    "bdd100k_nighttime_person_val":
+        "timeofday = 'night' AND has_person = true AND split = 'val'",
+    "bdd100k_distant_person_train":
+        "has_person = true AND person_bbox_area_pct < 30.0 AND split = 'train'",
+    "bdd100k_distant_person_val":
+        "has_person = true AND person_bbox_area_pct < 30.0 AND split = 'val'",
+}
+
+with gconn.local_ray_context():
+    for name, sql_filter in VIEWS.items():
+        query = gtbl.search().where(sql_filter)
+        mv = gconn.create_materialized_view(name, query)
+        mv.refresh()
+        print(f"[{name}]  {mv.count_rows()} rows  (version {mv.version})")
+```
+
+## 4\. PyTorch DataLoader via the Permutation API {#4-pytorch-dataloader-via-the-permutation-api}
+
+The training script doesn't know about the filter — it opens a view by name and reads through the [`Permutation`](/training/data-loading) API. Each DataLoader worker reopens its own connection lazily, reads Arrow batches directly from Lance (zero-copy, no intermediate file format), and the collate function decodes the whole batch in one pass. `Permutation` provides random-access indexing over the table, so shuffling is a cheap pointer rewrite rather than a full-dataset shuffle on disk.
+
+```py Python icon=Python
+import lancedb
+import torch
+import torchvision.io as tio
+from lancedb.permutation import Permutation
+
+DETECTION_COLS = ["image_bytes", "ann_categories", "ann_bboxes"]
+
+class LanceDetectionDataset(torch.utils.data.Dataset):
+    def __init__(self, uri: str, table_name: str):
+        self.uri, self.table_name = uri, table_name
+        self._perm = None
+        self.length = len(lancedb.connect(uri).open_table(table_name))
+
+    def __len__(self):
+        return self.length
+
+    def __getstate__(self):
+        # Permutation holds Rust async state — zero it so each worker reopens
+        state = self.__dict__.copy()
+        state["_perm"] = None
+        return state
+
+    def _ensure_open(self):
+        if self._perm is None:
+            tbl = lancedb.connect(self.uri).open_table(self.table_name)
+            self._perm = (
+                Permutation.identity(tbl)
+                .select_columns(DETECTION_COLS)
+                .with_format("arrow")  # zero-copy
+            )
+
+    def __getitems__(self, indices: list[int]):
+        self._ensure_open()
+        return self._perm.__getitems__(indices)
+```
+
+The collate function decodes JPEG bytes and converts BDD category strings into COCO class IDs (so the comparison against the pretrained checkpoint is valid):
+
+```py Python icon=Python
+BDD_LABEL_MAP = {
+    "person": 1, "rider": 1, "bicycle": 2, "car": 3, "motorcycle": 4,
+    "bus": 6, "train": 7, "truck": 8, "traffic light": 10,
+}
+
+
+def detection_collate(batch):
+    images, targets = [], []
+    for raw, cats, bboxes in zip(
+        batch.column("image_bytes").to_pylist(),
+        batch.column("ann_categories").to_pylist(),
+        batch.column("ann_bboxes").to_pylist(),
+    ):
+        buf = torch.frombuffer(bytearray(raw), dtype=torch.uint8)
+        images.append(tio.decode_image(buf, tio.ImageReadMode.RGB).float() / 255.0)
+
+        valid_boxes, valid_labels = [], []
+        for cat, box in zip(cats or [], bboxes or []):
+            lid = BDD_LABEL_MAP.get(cat)
+            if lid is None or box[2] <= box[0] or box[3] <= box[1]:
+                continue
+            valid_boxes.append(box)
+            valid_labels.append(lid)
+        targets.append({
+            "boxes":  torch.tensor(valid_boxes  or [], dtype=torch.float32).reshape(-1, 4),
+            "labels": torch.tensor(valid_labels or [], dtype=torch.int64),
+        })
+    return images, targets
+```
+
+Wire it into a standard `torch.utils.data.DataLoader`:
+
+```py Python icon=Python
+def make_loader(uri, table_name, batch_size=64, num_workers=8, shuffle=False):
+    dataset = LanceDetectionDataset(uri, table_name)
+    sampler = torch.utils.data.RandomSampler(dataset) if shuffle else None
+    return torch.utils.data.DataLoader(
+        dataset,
+        batch_size=batch_size,
+        sampler=sampler,
+        num_workers=num_workers,
+        collate_fn=detection_collate,
+        pin_memory=torch.cuda.is_available(),
+        persistent_workers=(num_workers > 0),
+        multiprocessing_context="spawn" if num_workers > 0 else None,
+    )
+```
+
+<Note>
+`with_format("arrow")` keeps batches as zero-copy `pa.RecordBatch`es — no per-row Python boxing, no pickling between worker and main. Each DataLoader worker reopens its own `Permutation` after fork (the Rust async handle is cleared in `__getstate__`), so reads scale with `num_workers` and stream straight from the underlying object store. JPEG decode overlaps with GPU compute via `pin_memory` + `prefetch_factor`, which is what keeps the loader from becoming the bottleneck on a fast GPU.
+</Note>
+
+## 5\. Fine-tune Faster R-CNN {#5-fine-tune-faster-r-cnn}
+
+The training loop is plain PyTorch — the Lance integration ends at the loader. Mixed precision is enabled on CUDA for ~2× speedup on Ampere GPUs.
+
+```py Python icon=Python
+import time
+import torch
+from torchvision.models.detection import (
+    fasterrcnn_resnet50_fpn_v2, FasterRCNN_ResNet50_FPN_V2_Weights,
+)
+
+device  = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+use_amp = device.type == "cuda"
+
+# COCO pretrained weights — head left intact since BDD uses a subset of COCO IDs
+model = fasterrcnn_resnet50_fpn_v2(
+    weights=FasterRCNN_ResNet50_FPN_V2_Weights.COCO_V1
+).to(device)
+
+train_loader = make_loader("data/bdd100k/lancedb",
+                           "bdd100k_rider_train",
+                           batch_size=64, num_workers=14, shuffle=True)
+val_loader   = make_loader("data/bdd100k/lancedb",
+                           "bdd100k_rider_val",
+                           batch_size=64, num_workers=14)
+
+optimizer = torch.optim.SGD(
+    [p for p in model.parameters() if p.requires_grad],
+    lr=0.04, momentum=0.9, weight_decay=1e-4,
+)
+scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1)
+scaler    = torch.cuda.amp.GradScaler() if use_amp else None
+
+for epoch in range(1, 11):
+    model.train()
+    t0 = time.time()
+    for images, targets in train_loader:
+        images  = [img.to(device) for img in images]
+        targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
+        if all(t["labels"].numel() == 0 for t in targets):
+            continue
+
+        with torch.cuda.amp.autocast(enabled=use_amp):
+            losses = sum(model(images, targets).values())
+
+        optimizer.zero_grad()
+        if use_amp:
+            scaler.scale(losses).backward()
+            scaler.unscale_(optimizer)
+            torch.nn.utils.clip_grad_norm_(model.parameters(), 10.0)
+            scaler.step(optimizer)
+            scaler.update()
+        else:
+            losses.backward()
+            torch.nn.utils.clip_grad_norm_(model.parameters(), 10.0)
+            optimizer.step()
+
+    scheduler.step()
+    print(f"epoch {epoch}  ({time.time() - t0:.1f}s)")
+```
+
+## 6\. Pin the checkpoint to a data version {#6-pin-the-checkpoint-to-a-data-version}
+
+Every Lance table — including a materialized view — exposes a monotonically increasing `version`. Logging it next to the weights gives a permanent, deterministic link between a checkpoint and the exact data snapshot that produced it.
+
+```py Python icon=Python
+import json
+from pathlib import Path
+
+train_tbl = lancedb.connect("data/bdd100k/lancedb").open_table("bdd100k_rider_train")
+
+out = Path("checkpoints/rider")
+out.mkdir(parents=True, exist_ok=True)
+torch.save(model.state_dict(), out / "fasterrcnn_bdd_finetuned.pt")
+with open(out / "metadata.json", "w") as f:
+    json.dump({
+        "train_table":   train_tbl.name,
+        "table_version": train_tbl.version,
+        "row_count":     len(train_tbl),
+    }, f, indent=2)
+```
+
+To reproduce a run, [time-travel](/management/table#versioning) the view to the recorded version before opening the loader:
+
+```py Python icon=Python
+tbl = lancedb.connect("data/bdd100k/lancedb").open_table("bdd100k_rider_train")
+tbl.checkout(version=7)   # exact snapshot the checkpoint was trained on
+```
+
+## 7\. Continuous updates {#7-continuous-updates}
+
+When new footage arrives, the same three calls update every downstream view — no view definitions change, no training-script edits required:
+
+```py Python icon=Python
+# 1. ingest the new footage into the source table
+table.add(new_record_batches)
+
+# 2. backfill computes only the new rows (incremental, checkpointed)
+with gconn.local_ray_context():
+    tbl.backfill("has_rider")
+    tbl.backfill("person_bbox_area_pct")
+
+# 3. refresh appends qualifying new rows to every materialized view
+for view_name in gconn.table_names():
+    if view_name == "bdd100k":
+        continue
+    mv = gconn.open_table(view_name)
+    before = mv.count_rows()
+    mv.refresh()
+    print(f"[{view_name}]  {before} → {mv.count_rows()} rows  (version {mv.version})")
+```
+
+The next training run picks up the new data automatically — and pins itself to the new `version`.
+
+## Full source {#full-source}
+
+The complete code, including a synthetic-data mode for pipeline verification (`--synthetic 500`), GPU UDFs for CLIP embeddings and dHash deduplication, and the EDA notebook, is in this [GitHub repository](https://github.com/lancedb/training/tree/main/object-detection).
diff --git a/docs/web/training/torch.mdx b/docs/web/training/torch.mdx
new file mode 100644
index 000000000..53d33e39c
--- /dev/null
+++ b/docs/web/training/torch.mdx
@@ -0,0 +1,227 @@
+---
+title: "PyTorch Integration"
+sidebarTitle: "PyTorch"
+description: Learn how to use LanceDB with PyTorch for training and inference.
+icon: fire
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    The PyTorch integration works with local, object-storage and deployment
+    tables in the Python SDK.
+---
+
+Use LanceDB tables as input to a PyTorch `DataLoader`. Select the columns you
+need, choose a batch format, and read data in parallel for training or inference.
+
+## Quickstart {#quickstart}
+
+The `Table` class in LanceDB implements a contract for a PyTorch
+[Dataset](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.Dataset).
+ This means you can simply use a LanceDB table in a PyTorch dataloader directly.
+
+```py Python icon=Python 
+import lancedb
+import torch
+import pyarrow as pa
+from lancedb.util import tbl_to_tensor
+
+mem_db = lancedb.connect("memory://")
+table = mem_db.create_table("test_table", pa.table({"a": range(1000)}))
+
+# Any LanceDB table can be used as a PyTorch Dataset
+dataloader = torch.utils.data.DataLoader(
+    table, batch_size=1024, shuffle=True, collate_fn=tbl_to_tensor
+)
+
+for batch in dataloader:
+    print(batch)
+```
+
+Although the `Table` class in LanceDB implements the `torch.utils.data.Dataset` interface, you may find that using
+a table [Permutation](/training/data-loading) is more flexible.
+
+```py Python icon=Python 
+from lancedb.permutation import Permutation
+
+permutation = Permutation.identity(table)
+dataloader = torch.utils.data.DataLoader(permutation)
+```
+
+## Output Formats {#output-formats}
+
+By default, a `Table` data loader will emit Arrow data. `collate_fn` is PyTorch's batching hook: PyTorch calls it to
+turn the fetched items into one batch. PyTorch's default collate function only knows how to combine tensors, NumPy
+arrays, numbers, dicts, and lists, so it does not accept Arrow data directly. When using a `Table` directly, pass
+LanceDB's `lancedb.util.tbl_to_tensor` helper as PyTorch's `collate_fn`; it converts numeric Arrow columns into a
+column-major `torch.Tensor` with shape `(columns, rows)`.
+
+`Permutation` works differently: its default output is a list of Python dicts, which PyTorch's default collate function
+can batch into a dict of tensors. This is usually more convenient when you are getting started. However, there is a
+significant performance penalty converting from Arrow, Lance's internal representation, to this default format. If you
+want the default PyTorch dict-of-tensors behavior, use a `Permutation` as-is; if you want direct Arrow-to-tensor
+conversion, either pass `lancedb.util.tbl_to_tensor` as `collate_fn` with a direct `Table` or configure a `Permutation`
+with one of the transform formats described below.
+
+To address this, the `Permutation` class provides a set of builtin transform functions that can be applied to map
+the Arrow data in different ways.  The `arrow` and `polars` formats will always avoid data copies.  However, `numpy`,
+`pandas`, and `torch_col` formats will also avoid data copies in most cases.  The `python`, `python_col`, and
+`torch` formats will all require at least one full copy of the data and are the slowest options.
+
+### Using the torch column format with a torch data loader {#using-the-torch-column-format-with-a-torch-data-loader}
+
+The `torch_col` format is the most efficient way to convert from Arrow to a `torch.Tensor`.  It will convert the
+entire Arrow batch to a _column-major_ `torch.Tensor`.  In other words, given C columns and R rows, the resulting
+Tensor will have shape `(C, R)`.  However, this format generates an error if you are using a
+`torch.utils.data.DataLoader` with the default collation function:
+
+```text
+TypeError: stack(): argument 'tensors' (position 1) must be tuple of Tensors, not Tensor
+```
+
+This error occurs because the default collation function does not currently expect a single two-dimensional tensor.
+It expects a list of tensors which it will then stack.  This is what is output by the `torch` format but that format
+requires a data copy.  To avoid this error, and avoid data copies, you will need to provide a custom collation function
+in addition to specifying the `torch_col` format.
+
+```py Python icon=Python 
+from lancedb.permutation import Permutation
+
+permutation = Permutation.identity(table).with_format("torch_col")
+dataloader = torch.utils.data.DataLoader(permutation, collate_fn=lambda x: x)
+```
+
+This will now output a single two-dimensional tensor for each batch.
+
+## Selecting columns {#selecting-columns}
+
+By default, the `Table` class will return all columns in the table when used as input to PyTorch. If you only need
+a subset of columns, you can significantly reduce your I/O requirements by selecting only the columns you need.  The
+`Permutation` class provides a `select_columns` method that provides this functionality.
+
+```py Python icon=Python 
+import lancedb
+import pyarrow as pa
+import torch
+from lancedb.permutation import Permutation
+
+db = lancedb.connect("memory://")
+prompts = db.create_table(
+    "prompts",
+    pa.table(
+        {
+            "id": range(4),
+            "prompt": ["north", "south", "east", "west"],
+            "embedding": [[0.1, 0.2], [0.3, 0.4], [0.5, 0.6], [0.7, 0.8]],
+        }
+    ),
+)
+
+permutation = Permutation.identity(prompts).select_columns(["id", "prompt"])
+dataloader = torch.utils.data.DataLoader(
+    permutation, batch_size=1024, shuffle=True
+)
+
+for batch in dataloader:
+    print(sorted(batch))  # ['id', 'prompt']: the embedding is never read
+```
+
+Each batch is a dict of the selected columns, the default `Permutation` format
+described above.
+
+## Using multiple DataLoader workers {#using-multiple-dataloader-workers}
+
+Set `num_workers > 0` to read from LanceDB in multiple PyTorch worker processes. `Permutation` objects and tables in LanceDB Enterprise are picklable, so each worker reopens the table after it starts. A local table is not, so wrap it in a `Permutation`, as below, to read it from several workers.
+
+Prefer the `forkserver` start method when using multiple workers. LanceDB uses internal threads, so the default `fork` method is unsafe; `forkserver` avoids that while being cheaper to start than `spawn`, and became the default on Linux and other supported POSIX platforms in Python
+3.14. macOS and Windows default to `spawn`. See
+[Python's start-method documentation](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods). See [the performance guide](/search#performance) for more multiprocessing guidance.
+
+<Note>
+`forkserver` is only available on POSIX systems (Linux and macOS). On Windows, use `spawn`, the only available start method.
+</Note>
+
+```py Python icon=Python 
+import torch
+from lancedb.permutation import Permutation
+
+permutation = Permutation.identity(table)
+dataloader = torch.utils.data.DataLoader(
+    permutation,
+    batch_size=1024,
+    shuffle=True,
+    num_workers=4,
+    multiprocessing_context="forkserver",
+    persistent_workers=True,
+)
+```
+
+### Remote tables in DataLoader workers {#remote-tables-in-dataloader-workers}
+
+Remote LanceDB Enterprise tables (`db://...`) work the same way: workers reopen the table from the pickled connection state. Replace the URI, API key, region and endpoint below with your deployment details. In application code, load credentials from environment variables or a secrets manager.
+
+```py Python icon=Python 
+import lancedb
+import torch
+from lancedb.util import tbl_to_tensor
+
+db = lancedb.connect(
+    "db://my-database",
+    api_key="your-api-key",
+    region="us-east-1",
+    host_override="https://your-endpoint",
+)
+table = db.open_table("my_table")
+
+dataloader = torch.utils.data.DataLoader(
+    table,
+    batch_size=512,
+    num_workers=4,
+    multiprocessing_context="forkserver",
+    collate_fn=tbl_to_tensor,
+)
+```
+
+<Note>
+This sends the connection state, including the API key, to each worker. Use a connection factory if credentials should be loaded inside the worker or your `client_config` contains a non-serializable `header_provider`.
+</Note>
+
+### Providing a custom connection factory {#providing-a-custom-connection-factory}
+
+`Permutation.with_connection_factory` lets each worker reopen the base table with custom logic. The factory takes the table name, returns a LanceDB table, and must be picklable.
+
+This example loads the API key inside each worker. Set it in the shell before
+starting the Python program, then replace the database URI, region and endpoint
+in the factory with your deployment details:
+
+```shell
+export LANCEDB_API_KEY="your-api-key"
+```
+
+```py Python icon=Python 
+import os
+import lancedb
+import torch
+from lancedb.permutation import Permutation
+
+def open_table(name: str):
+    db = lancedb.connect(
+        "db://my-database",
+        api_key=os.environ["LANCEDB_API_KEY"],
+        region="us-east-1",
+        host_override="https://your-endpoint",
+    )
+    return db.open_table(name)
+
+table = open_table("my_table")
+permutation = (
+    Permutation.identity(table)
+    .with_connection_factory(open_table)
+)
+dataloader = torch.utils.data.DataLoader(
+    permutation,
+    batch_size=512,
+    num_workers=4,
+    multiprocessing_context="forkserver",
+)
+```
diff --git a/docs/web/training/vlm-finetuning.mdx b/docs/web/training/vlm-finetuning.mdx
new file mode 100644
index 000000000..89c385328
--- /dev/null
+++ b/docs/web/training/vlm-finetuning.mdx
@@ -0,0 +1,522 @@
+---
+title: "Fine-tuning a VLM on TextVQA"
+sidebarTitle: "VLM Fine-tuning"
+description: End-to-end fine-tuning of Qwen2.5-VL on a curated TextVQA slice, using LanceDB and Geneva to materialize expensive vision-language features once and train from cached columns.
+icon: image
+---
+
+This example walks through a vision-language model (VLM) fine-tuning pipeline for [TextVQA](https://textvqa.org/), where the task is to answer questions that require reasoning over text _inside an image_. The base model is `Qwen2.5-VL-3B-Instruct`, fine-tuned with the [QLoRA](https://arxiv.org/abs/2305.14314) method. The data backbone is one Lance table that evolves from raw multimodal rows into training-ready features.
+
+The key idea is simple: in this QLoRA fine-tuning setup, we freeze the VLM's image encoder and train only a small adapter on the language-model side. We call that encoder the **vision tower** in this example: it is the part of the model that turns image pixels into visual hidden states before the language model reads them alongside the text prompt.
+
+Because the vision tower's weights do not change during fine-tuning, its output for a given image also does not change. That means the pipeline can compute those visual hidden states once, store them as a fixed-size Lance column, and reuse them in every epoch instead of recomputing them in every training step. This also helps the run fit comfortably on a small GPU, because the training job does not need to keep the vision encoder active or pay for its forward pass on every batch.
+
+<CardGroup cols={2}>
+  <Card
+    title="Open the Colab demo"
+    icon="play"
+    href="https://colab.research.google.com/github/lancedb/training/blob/vlm-textvqa/examples/vlm-textvqa/notebooks/colab_textvqa_lance.ipynb"
+  >
+    Run the Colab-sized workflow on a free T4: download the pre-baked Lance subset, explore it, benchmark Lance vs Parquet, fine-tune with QLoRA, and evaluate base vs tuned answers.
+  </Card>
+  <Card
+    title="View the full demo source"
+    icon="github"
+    href="https://github.com/lancedb/tmls-2026-demo"
+  >
+    Full demo repository with the notebook, Geneva UDFs, direct backfill fallback, dataloader, training loop, and evaluation scripts.
+  </Card>
+</CardGroup>
+
+The Colab notebook uses a pre-baked subset of the TextVQA dataset: it downloads a curated Lance subset whose expensive feature columns have already been computed. This page explains the complete end-to-end pipeline that produced that subset, then shows how the notebook applies it to produce a fine-tuned model that improves performance on the TextVQA task.
+
+## What you get {#what-you-get}
+
+On the curated `text_dense` TextVQA slice, the demo fine-tunes `Qwen2.5-VL-3B-Instruct` with QLoRA and evaluates on held-out images:
+
+| Setup | TextVQA accuracy |
+|---|---|
+| Base model | 0.799 |
+| LoRA-tuned model | **0.820** |
+| Lift | **+2.1 percentage points** |
+
+The larger point is not the absolute score, because you could just as well fine-tune a better base model on more data. The main takeaways are the workflow and quality-of-life improvements that you get when you combine LanceDB and Geneva:
+
+1. **Add expensive features** as new columns without rewriting the raw dataset.
+2. **Read fixed-size model features efficiently** for shuffled PyTorch batches.
+3. **Iterate quickly** from feature idea to scalable CPU/GPU backfill, using Geneva UDFs.
+
+## Why LanceDB fits this workflow {#why-lancedb-fits-this-workflow}
+
+VLM fine-tuning pipelines spend a lot of time between "I have an experiment idea" and "I trained the model." LanceDB shortens that loop in three places.
+
+<CardGroup cols={3}>
+  <Card title="Cheap feature evolution" icon="plus">
+    Lance can append derived columns such as `ocr_token_count`, `dhash`, `vision_tower_hiddens`, and tokenized SFT prompts without rewriting the existing image/question/answer columns or managing sidecar files.
+  </Card>
+  <Card title="Efficient training reads" icon="gauge">
+    Lance is optimized for scans and random access over fixed-size lists, which are common in model training: embeddings, hidden states, token IDs, masks, and labels.
+  </Card>
+  <Card title="Fast experiment turnaround" icon="flask">
+    Geneva lets AI engineers express feature work as UDFs, run those UDFs across CPU or GPU workers, and materialize the results directly into the same Lance table.
+  </Card>
+</CardGroup>
+
+In this pipeline, those three properties combine into the core optimization: compute the VLM vision features once, store them cheaply, then train by reading only the cached columns the model needs.
+
+## Pipeline overview {#pipeline-overview}
+
+The runnable demo uses the exact Colab subset hosted at [`lance-format/textvqa-lance-colab`](https://huggingface.co/datasets/lance-format/textvqa-lance-colab). It is derived from the Lance-formatted TextVQA corpus and stores inline JPEG bytes, questions, answers, OCR tokens, object classes, CLIP image/question embeddings, and the cached training features used by this example. The full demo pipeline adds three tiers of derived features on top.
+
+<Card title="Tier 1: text features" icon="font">
+  Cheap CPU columns such as `question_length`, `answer_length`, `question_type`, and `ocr_token_count`.
+</Card>
+
+<Card title="Tier 2: light image features" icon="image">
+  Image-derived columns such as `dhash`, computed by decoding the JPEG once and storing a perceptual hash.
+</Card>
+
+<Card title="Tier 3: VLM training features" icon="microchip">
+  GPU-heavy columns: `vision_tower_hiddens` plus SFT token fields (`input_ids`, `attention_mask`, `labels`).
+</Card>
+
+The Colab notebook's workflow starts after all three tiers have been computed. It downloads a small curated subset and runs the training/evaluation path without needing to run Geneva or the vision-tower backfill on the notebook GPU.
+
+## 1\. Start with a multimodal LanceDB table {#1-start-with-a-multimodal-lancedb-table}
+
+The base schema comes from the TextVQA Lance dataset. One row contains the image bytes, natural-language question, reference answers, OCR tokens, scene tags, and retrieval embeddings.
+
+```py Python icon=Python
+import pyarrow as pa
+
+BASE_SCHEMA = pa.schema([
+    pa.field("id",            pa.int64()),
+    pa.field("image",         pa.large_binary()),
+    pa.field("image_id",      pa.string()),
+    pa.field("question_id",   pa.string()),
+    pa.field("question",      pa.string()),
+    pa.field("answers",       pa.list_(pa.string())),
+    pa.field("answer",        pa.string()),
+    pa.field("image_emb",     pa.list_(pa.float32(), 512)),
+    pa.field("question_emb",  pa.list_(pa.float32(), 512)),
+    pa.field("ocr_tokens",    pa.list_(pa.string())),
+    pa.field("image_classes", pa.list_(pa.string())),
+    pa.field("set_name",      pa.string()),
+])
+```
+
+Because the raw image, text, OCR, and embedding features live together, the same table supports curation, retrieval, feature engineering, and training. For example, the notebook can run a text-to-image retrieval demo by searching `image_emb` with a question embedding that already exists in the row.
+
+## 2\. Add feature columns with Geneva {#2-add-feature-columns-with-geneva}
+
+Geneva turns feature engineering into UDF definitions plus backfills. The UDFs can be simple text functions, image-processing functions, or stateful GPU model calls.
+
+The Tier 1 features are ordinary CPU UDFs:
+
+```py Python icon=Python
+import re
+import pyarrow as pa
+from geneva.transformer import udf
+
+_QUESTION_TYPE_PATTERNS = [
+    ("how_many", re.compile(r"^\s*how\s+many\b", re.IGNORECASE)),
+    ("what_brand", re.compile(r"^\s*what\s+(is\s+the\s+)?(brand|company|make)\b", re.IGNORECASE)),
+    ("what", re.compile(r"^\s*what\b", re.IGNORECASE)),
+]
+
+@udf(data_type=pa.string(), input_columns=["question"])
+def question_type(question: str) -> str:
+    for label, pattern in _QUESTION_TYPE_PATTERNS:
+        if pattern.search(question or ""):
+            return label
+    return "other"
+
+@udf(data_type=pa.int32(), input_columns=["ocr_tokens"])
+def ocr_token_count(ocr_tokens: list[str] | None) -> int:
+    return len(ocr_tokens) if ocr_tokens else 0
+```
+
+The Tier 3 feature is heavier: run Qwen2.5-VL's frozen vision tower once, then store the merged visual hidden states as a fixed-size fp16 list.
+
+```py Python icon=Python
+IMAGE_PX = 560
+LLM_TOKENS_PER_IMAGE = 400
+VISION_HIDDEN = 2048
+
+@udf(
+    data_type=pa.list_(pa.float16(), LLM_TOKENS_PER_IMAGE * VISION_HIDDEN),
+    input_columns=["image"],
+)
+class VisionTowerEmbedder:
+    def __init__(self):
+        self._model = None
+        self._processor = None
+
+    def _lazy_load(self):
+        if self._model is not None:
+            return
+        import torch
+        from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
+
+        self._torch = torch
+        self._model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen2.5-VL-3B-Instruct",
+            torch_dtype=torch.bfloat16,
+            device_map="cuda:0",
+        ).model.visual.eval()
+        self._processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct")
+
+    def __call__(self, image: bytes) -> list[float]:
+        self._lazy_load()
+        # Decode image, resize to IMAGE_PX, run the frozen vision tower,
+        # and return fp16[400, 2048] flattened as one fixed-size list.
+        ...
+```
+
+The fixed shape matters. With `IMAGE_PX = 560`, Qwen2.5-VL produces 400 merged visual tokens, each with hidden size 2048. That becomes one `fp16[400 * 2048]` column per row. Training can scan and randomly access that column without decoding images or running the vision tower in the hot loop, saving GPU compute at training time.
+
+Run the tiered backfill with Geneva:
+
+```bash bash icon="terminal"
+python -m vlm.backfill_geneva --tier 1     # CPU text columns
+python -m vlm.backfill_geneva --tier 2     # image decode + dhash
+python -m vlm.backfill_geneva --tier 3     # vision tower + SFT tokens
+```
+
+<Tip>
+The same Tier 3 work can be done manually by creating PyArrow batches and calling Lance's column-evolution APIs directly. The demo repo includes [`backfill_direct.py`](https://github.com/lancedb/tmls-2026-demo/blob/main/vlm/backfill_direct.py) for that path. Geneva is the preferred abstraction when you want to scale the same feature code across CPU or GPU workers and keep backfills incremental.
+</Tip>
+
+See the full UDF registry in [`vlm/geneva_udfs.py`](https://github.com/lancedb/tmls-2026-demo/blob/main/vlm/geneva_udfs.py) and the backfill driver in [`vlm/backfill_geneva.py`](https://github.com/lancedb/tmls-2026-demo/blob/main/vlm/backfill_geneva.py).
+
+## 3\. Curate a training slice {#3-curate-a-training-slice}
+
+The demo uses a `text_dense` slice: TextVQA examples whose images contain many OCR tokens. The slice was chosen empirically because it gave the clearest LoRA lift over the already-strong base model.
+
+```py Python icon=Python
+TEXT_DENSE_OCR_THRESHOLD = 16
+
+def matches_text_dense(row: dict) -> bool:
+    return len(row.get("ocr_tokens") or []) >= TEXT_DENSE_OCR_THRESHOLD
+```
+
+The Colab-ready bake ingests a small train split, backfills Tier 3 on that train table, ingests a held-out validation split, and optionally pushes the result to Hugging Face:
+
+```bash bash icon="terminal"
+python -m vlm.colab_prepare \
+  --out data/colab \
+  --slice text_dense \
+  --train-rows 600 \
+  --val-rows 400 \
+  --hf-repo lance-format/textvqa-lance-colab \
+  --push
+```
+
+The train table contains cached Tier 3 columns because training reads them directly. The validation table keeps raw images because evaluation should run the full VLM on unseen images.
+
+## 4\. Explore the prepared table {#4-explore-the-prepared-table}
+
+Before training, it helps to look at the actual task. Each row pairs an image with a question whose answer is often visible as text in the image: a product label, phone screen, sign, book spine, or package.
+
+<CardGroup cols={2}>
+  <Card
+    title="Q: what is the name of the airline on the sugar packet?"
+    img="/static/assets/images/training/vlm-finetuning/textvqa-domino-sugar.jpg"
+  >
+    **A:** TWA
+
+    **OCR:** 7h the Finest... 74 1E TWA 8 SALT REESE PEPPER
+  </Card>
+  <Card
+    title="Q: what time is displayed?"
+    img="/static/assets/images/training/vlm-finetuning/textvqa-phone-time.jpg"
+  >
+    **A:** 12:39 am
+
+    **OCR:** AT&T 12:39 AM TV CS WATCH P PANDORA YouTube Ustream
+  </Card>
+  <Card
+    title="Q: what brand of building block is this?"
+    img="/static/assets/images/training/vlm-finetuning/textvqa-lego-box.jpg"
+  >
+    **A:** lego
+
+    **OCR:** LEGO CITY Ages/edades 5-12 POLICE B-403 4473 112 112 pcs
+  </Card>
+  <Card
+    title="Q: what is printed in red?"
+    img="/static/assets/images/training/vlm-finetuning/textvqa-warning-sign.jpg"
+  >
+    **A:** warning
+
+    **OCR:** WARNING Controlled Area Itis unlawf enter thisre without permission nstallation
+  </Card>
+</CardGroup>
+
+The notebook downloads the public pre-baked subset:
+
+```py Python icon=Python
+from huggingface_hub import snapshot_download
+import lancedb
+import os
+
+local = snapshot_download(
+    repo_id="lance-format/textvqa-lance-colab",
+    repo_type="dataset",
+    local_dir="data/colab",
+)
+
+def open_tbl(path: str):
+    name = os.path.basename(path).removesuffix(".lance")
+    return lancedb.connect(os.path.dirname(path)).open_table(name)
+
+train_tbl = open_tbl(f"{local}/textvqa_colab_train.lance")
+val_tbl = open_tbl(f"{local}/textvqa_colab_val.lance")
+```
+
+Because the table also ships CLIP embeddings, you can run cross-modal retrieval without loading a model:
+
+```py Python icon=Python
+import numpy as np
+
+seed = (
+    train_tbl.search()
+    .select(["question", "question_emb"])
+    .limit(40)
+    .to_arrow()
+    .to_pylist()[11]
+)
+
+hits = (
+    train_tbl.search(
+        np.asarray(seed["question_emb"], dtype=np.float32),
+        vector_column_name="image_emb",
+    )
+    .select(["image", "question", "answer", "_distance"])
+    .limit(5)
+    .to_arrow()
+    .to_pylist()
+)
+```
+
+This is the same table that later feeds training. There is no separate feature store, image directory, Parquet export, or manifest to keep synchronized.
+
+## 5\. Benchmark Lance vs Parquet-style reads {#5-benchmark-lance-vs-parquet-style-reads}
+
+Many training pipelines start with Parquet. Parquet is excellent for columnar analytics, but training commonly needs shuffled batches and fixed-size tensor columns. The notebook compares Lance and Parquet on two access patterns:
+
+| Column group | Why it matters |
+|---|---|
+| `image`, `question`, `answer` | Raw multimodal rows: the baseline "decode and tokenize during training" path. |
+| `vision_tower_hiddens` | Cached fixed-size fp16 VLM features: the optimized training path. |
+
+The notebook mirrors those column groups to uncompressed Parquet, then measures sequential scans and shuffled random batches:
+
+```py Python icon=Python
+RAW = ["image", "question", "answer"]
+VEC = ["vision_tower_hiddens"]
+BATCH = 8
+
+lance_ds = train_tbl.to_lance()
+n = train_tbl.count_rows()
+
+def seq(ds, cols):
+    t0 = time.time()
+    for _ in ds.to_batches(columns=cols, batch_size=BATCH):
+        pass
+    return n / (time.time() - t0)
+
+def shuf(ds, cols, num_batches=20):
+    batches = [
+        sorted(rng.choice(n, BATCH, replace=False).tolist())
+        for _ in range(num_batches)
+    ]
+    t0 = time.time()
+    for idx in batches:
+        ds.take(idx, columns=cols)
+    return (num_batches * BATCH) / (time.time() - t0)
+```
+
+One Colab run produced the following throughput:
+
+| Throughput, rows/s | LanceDB | Parquet |
+|---|---:|---:|
+| `image` + `question` + `answer`, sequential | 2,603 | 8,311 |
+| `image` + `question` + `answer`, shuffled | 2,613 | 352 |
+| `vision_tower_hiddens` fp16, sequential | 1,452 | 90 |
+| `vision_tower_hiddens` fp16, shuffled | 2,149 | -- |
+
+The takeaways are workload-specific:
+
+- For a traditional sequential scan over raw image/question/answer columns, Parquet is faster in this run: 8,311 rows/s vs 2,603 rows/s.
+- For shuffled raw multimodal batches, Lance is faster because training reads scattered rows repeatedly instead of streaming the file once.
+- For cached fp16 fixed-size arrays, Lance is about 16x faster than Parquet on the sequential scan. This is the training-relevant path in this example: the model reads `vision_tower_hiddens`, token IDs, masks, and labels as fixed-size columns.
+- The benchmark intentionally skips the Parquet fp16 shuffled case. Parquet would re-decode whole row groups for each random batch, which is slow enough to distract from the real use case. The sequential fp16 row already shows the layout gap, while Lance shuffled reads remain fast.
+
+The numbers shown above are central to the example. The Tier 3 feature is only useful if the storage format can read it efficiently in the way a trainer actually needs: projected columns, repeated scans, and shuffled batches. **Lance specializes in exactly that access pattern**, including fixed-size list columns stored on disk.
+
+## 6\. Load cached columns with the Permutation API {#6-load-cached-columns-with-the-permutation-api}
+
+The training DataLoader projects only the columns needed by the cached training loop:
+
+```py Python icon=Python
+from lancedb.permutation import Permutation
+
+CACHED_COLS = [
+    "vision_tower_hiddens",
+    "input_ids",
+    "attention_mask",
+    "labels",
+]
+
+class LancePermutationDataset(torch.utils.data.Dataset):
+    def __init__(self, uri: str, table_name: str):
+        self.uri = uri
+        self.table_name = table_name
+        self._perm = None
+        self.length = len(lancedb.connect(uri).open_table(table_name))
+
+    def __len__(self):
+        return self.length
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["_perm"] = None
+        return state
+
+    def _ensure_open(self):
+        if self._perm is None:
+            tbl = lancedb.connect(self.uri).open_table(self.table_name)
+            self._perm = (
+                Permutation.identity(tbl)
+                .select_columns(CACHED_COLS)
+                .with_format("arrow")
+            )
+
+    def __getitems__(self, indices: list[int]):
+        self._ensure_open()
+        return self._perm.__getitems__(indices)
+```
+
+Each worker opens its own `Permutation`, reads Arrow batches directly from Lance, and avoids per-row Python object conversion until the collate function converts arrays into tensors.
+
+The training batch contains:
+
+| Field | Shape |
+|---|---|
+| `vision_hiddens` | `fp16[B, 400, 2048]` |
+| `input_ids` | `int64[B, 512]` |
+| `attention_mask` | `int64[B, 512]` |
+| `labels` | `int64[B, 512]` |
+
+## 7\. Fine-tune without loading the vision tower {#7-fine-tune-without-loading-the-vision-tower}
+
+The training process loads the language-model side of Qwen2.5-VL in 4-bit, deletes the vision tower, and wraps the LLM projections with LoRA adapters.
+
+During the forward pass, the model embeds the token IDs, finds the `<|image_pad|>` positions, and inserts the cached visual hidden states into those positions:
+
+```py Python icon=Python
+def forward_cached(model, batch, image_pad_id: int):
+    base = model.get_base_model() if hasattr(model, "get_base_model") else model
+    inner = base.model
+
+    inputs_embeds = inner.get_input_embeddings()(batch.input_ids)
+    _, _, hidden_dim = inputs_embeds.shape
+
+    mask = (
+        (batch.input_ids == image_pad_id)
+        .unsqueeze(-1)
+        .expand_as(inputs_embeds)
+    )
+
+    vision_flat = batch.vision_hiddens.to(inputs_embeds.dtype).reshape(-1, hidden_dim)
+    inputs_embeds = inputs_embeds.masked_scatter(mask, vision_flat)
+
+    return model(
+        inputs_embeds=inputs_embeds,
+        attention_mask=batch.attention_mask,
+        labels=batch.labels,
+    ).loss
+```
+
+At this point, the LanceDB integration is done. The rest is plain PyTorch: optimizer, gradient accumulation, checkpointing, and saving the LoRA adapter.
+
+```py Python icon=Python
+loader = make_cached_loader(
+    "data/colab/textvqa_colab_train.lance",
+    batch_size=2,
+    shuffle=True,
+)
+
+for batch in loader:
+    batch = batch.to(device)
+    loss = forward_cached(model, batch, image_pad_id)
+    (loss / grad_accum).backward()
+    ...
+```
+
+This produces a training log like the following. The loss falls as the adapter learns from the cached features, and peak VRAM stays at 5.3 GB because QLoRA trains without keeping the vision tower active:
+
+```text
+step  10/300  loss=2.6694  5.9 samples/s
+step  20/300  loss=2.3133  6.1 samples/s
+                 .
+                 .
+                 .
+step 290/300  loss=0.0359  6.3 samples/s
+step 300/300  loss=0.4750  6.3 samples/s
+saved adapter to runs/colab_lora/lora | peak VRAM 5.3 GB
+```
+
+The training loop pays zero per-step cost for image decode, vision-tower forward, or prompt tokenization. Those costs were moved into feature engineering, where LanceDB and Geneva make them durable, incremental, and reusable.
+
+## 8\. Evaluate on held-out images {#8-evaluate-on-held-out-images}
+
+Evaluation uses the held-out validation table and loads the full VLM, including the vision tower. That is intentional: inference should see raw unseen images, not the cached train features.
+
+```py Python icon=Python
+rows = (
+    val_tbl.search()
+    .select(["image", "question", "answer", "answers"])
+    .limit(256)
+    .to_arrow()
+    .to_pylist()
+)
+
+base_model, processor = load_model(adapter_dir=None, load_4bit=True)
+tuned_model, processor = load_model(adapter_dir="runs/colab_lora/lora", load_4bit=True)
+
+base_score = score_textvqa(base_model, processor, rows)
+tuned_score = score_textvqa(tuned_model, processor, rows)
+```
+
+In this end-to-end example, the held-out curated validation split produced:
+
+| Model | TextVQA accuracy |
+|---|---:|
+| Base `Qwen2.5-VL-3B-Instruct` | 0.799 |
+| QLoRA-tuned adapter | **0.820** |
+| Lift | **+2.1 percentage points** |
+
+The tuned adapter is not meant to be a state-of-the-art TextVQA checkpoint. It is the proof point for the pipeline: the same Lance table supports curation, feature engineering, efficient training reads, and evaluation on held-out raw images.
+
+The notebook renders side-by-side examples: image, question, base answer, tuned answer, and ground truth. This closes the loop from feature idea to trained model while keeping the source data, derived features, training batches, and evaluation split in Lance.
+
+## Full source {#full-source}
+
+The complete demo implementation with helper scripts and usage instructions is in [this repo](https://github.com/lancedb/tmls-2026-demo).
+
+<CardGroup cols={2}>
+  <Card title="Notebook" icon="book-open" href="https://github.com/lancedb/training/blob/vlm-textvqa/examples/vlm-textvqa/notebooks/colab_textvqa_lance.ipynb">
+    The runnable Colab workflow: download, explore, benchmark, train, and evaluate.
+  </Card>
+  <Card title="UDF registry" icon="code" href="https://github.com/lancedb/tmls-2026-demo/blob/main/vlm/geneva_udfs.py">
+    Tier 1, Tier 2, and Tier 3 feature definitions.
+  </Card>
+  <Card title="Backfill driver" icon="server" href="https://github.com/lancedb/tmls-2026-demo/blob/main/vlm/backfill_geneva.py">
+    Feature materialization for the tiers above.
+  </Card>
+  <Card title="Training loop" icon="microchip" href="https://github.com/lancedb/tmls-2026-demo/blob/main/vlm/train_qwen25vl_lora.py">
+    QLoRA training from cached Lance columns.
+  </Card>
+</CardGroup>
diff --git a/docs/web/training/why-lancedb.mdx b/docs/web/training/why-lancedb.mdx
new file mode 100644
index 000000000..af10d4edc
--- /dev/null
+++ b/docs/web/training/why-lancedb.mdx
@@ -0,0 +1,119 @@
+---
+title: "Why LanceDB for Training"
+sidebarTitle: "Why LanceDB"
+description: "Use LanceDB as the multimodal data layer for model training, fine-tuning, curation, and feature engineering workflows."
+icon: fire
+---
+
+LanceDB is built for AI teams that need a practical data layer between raw multimodal datasets and model training.
+Instead of moving data through separate systems for curation, feature engineering, search, manifests, and training,
+you can keep the whole workflow attached to one versioned LanceDB table.
+
+That table can hold images, video, audio, text, annotations, metadata, embeddings, tokenized fields, model outputs,
+quality signals, and training-ready tensors. As the dataset evolves, LanceDB lets you add new columns, filter rows,
+pin versions, and read batches without rewriting the original data.
+
+![Training data lifecycle: Curation, Feature Engineering, Search and Retrieval, Training](/static/assets/images/overview/training-data-lifecycle.svg)
+
+The LanceDB multimodal lakehouse connects curation, feature engineering, retrieval, and training data access
+through Lance tables. Data loaders read these tables into training frameworks, which run the model training.
+
+## A connected data lifecycle {#a-connected-data-lifecycle}
+
+Training pipelines usually need more than a pile of files. They need curation, derived features, reproducible splits,
+fast random access, and a clean path into frameworks such as PyTorch. LanceDB keeps these pieces connected through
+the same table model, whether you organize a workflow as one table or several related tables.
+
+<Steps>
+  <Step title="Curate and slice the dataset">
+    Use filters, vector search, full-text search, and retrieval workflows to find the examples that matter: hard negatives,
+    long-tail failure modes, duplicate clusters, low-quality samples, or targeted fine-tuning slices.
+  </Step>
+  <Step title="Engineer features in place">
+    Add embeddings, detections, OCR output, labels, token IDs, hidden states, deduplication flags, or quality scores as
+    new columns. Lance's columnar layout and schema evolution avoid rewriting large raw media columns when you add features.
+  </Step>
+  <Step title="Create reproducible splits">
+    Build filtered splits and materialized views from the table instead of exporting CSV manifests. Data versions and tags
+    make it possible to tie a checkpoint back to the exact rows and features used for training.
+  </Step>
+  <Step title="Load batches for training">
+    Use fast random access and column projection to read only the columns a training step needs. LanceDB tables can be read
+    from local storage or object storage, and integrate with data loading patterns such as PyTorch datasets.
+  </Step>
+</Steps>
+
+## Lance as the foundation {#lance-as-the-foundation}
+
+LanceDB is built on [Lance](https://lance.org/), an open-source lakehouse format designed for multimodal AI data.
+The table below highlights the Lance features that enable the multimodal lakehouse on top.
+
+| Capability | Why it matters for training |
+|---|---|
+| **Multimodal columns** | Store raw bytes, annotations, metadata, embeddings, and features together. |
+| **Fast random access** | Support shuffled and sampled reads without reshuffling the dataset on disk. |
+| **Column projection** | Read only images, tokens, labels, embeddings, or hidden states needed by a given run. |
+| **Schema evolution** | Add new feature columns without rewriting existing media columns. |
+| **Versioning** | Reproduce experiments against the same table snapshot, even as the dataset evolves. |
+| **Search and filtering** | Find and materialize useful training slices directly from the table. |
+
+## Search inside training workflows {#search-inside-training-workflows}
+
+Search is not limited to QA systems, agents, or production retrieval apps. It is also a practical way to inspect,
+curate, and improve training data:
+
+- Find visually similar examples when debugging model failures.
+- Retrieve hard negatives or near-duplicates for contrastive training.
+- Combine vector search, full-text search, and metadata filters to build targeted fine-tuning slices.
+- Reuse the same table for both offline curation and production retrieval.
+
+In LanceDB, retrieval and training workflows can operate over the same multimodal tables instead of forcing teams to
+manage separate data systems for each stage.
+
+## Projects using LanceDB for training workflows {#projects-using-lancedb-for-training-workflows}
+
+<CardGroup cols={1}>
+  <Card
+    title="stable-worldmodel"
+    icon="github"
+    href="https://github.com/galilai-group/stable-worldmodel"
+  >
+    A platform for reproducible world-model research built on a LanceDB data layer, reporting faster data loading on Push-T workloads.
+  </Card>
+  <Card
+    title="le-wm"
+    icon="github"
+    href="https://github.com/lucas-maes/le-wm"
+  >
+    A joint-embedding predictive world model from pixels, trained on the stable-worldmodel platform and its LanceDB data layer.
+  </Card>
+  <Card
+    title="lerobot-lancedb"
+    icon="github"
+    href="https://github.com/lancedb/lerobot-lancedb"
+  >
+    A drop-in LanceDB backend for Hugging Face LeRobot datasets with faster loading across robotics datasets.
+  </Card>
+</CardGroup>
+
+In the world-model ecosystem, [stable-worldmodel](https://github.com/galilai-group/stable-worldmodel) reports
+3-4x faster data loading on Push-T versus HDF5 / MP4 at a fraction of the disk footprint. Across these projects,
+LanceDB and Lance provide the multimodal data layer that keeps raw observations, annotations, features, and training
+access patterns in one format instead of scattering them across task-specific stores.
+
+## Next steps {#next-steps}
+
+<CardGroup cols={2}>
+  <Card title="Data loading and shuffles" icon="boxes-stacked" href="/training/data-loading">
+    Learn how to use LanceDB permutations to select rows, project columns, split datasets, and shuffle training reads.
+  </Card>
+  <Card title="PyTorch integration" icon="fire" href="/training/torch">
+    Use LanceDB tables and permutations with `torch.utils.data.DataLoader`.
+  </Card>
+  <Card title="Object detection example" icon="car" href="/training/object-detection">
+    Fine-tune an AV perception model on curated failure-mode slices backed by one LanceDB table.
+  </Card>
+  <Card title="VLM fine-tuning example" icon="image" href="/training/vlm-finetuning">
+    Fine-tune a VLM on TextVQA using LanceDB and Geneva to cache expensive training features.
+  </Card>
+</CardGroup>
diff --git a/docs/web/tutorials/agents/agentic-rag.mdx b/docs/web/tutorials/agents/agentic-rag.mdx
new file mode 100644
index 000000000..505ae0791
--- /dev/null
+++ b/docs/web/tutorials/agents/agentic-rag.mdx
@@ -0,0 +1,23 @@
+---
+title: "Agentic RAG"
+sidebarTitle: "Agentic RAG"
+description: "Several agents collaborating to retrieve and answer."
+---
+
+Builds a RAG system where multiple agents collaborate -- retrieving
+information, checking it, and generating answers -- rather than a single
+retrieve-then-generate pass.
+
+<Note>
+This example is a runnable notebook in the
+[vectordb-recipes](https://github.com/lancedb/vectordb-recipes) repository rather than a page here.
+</Note>
+
+<CardGroup cols={2}>
+  <Card title="Open in Colab" icon="google" href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Agentic_RAG/main.ipynb">
+    Run it end to end in the browser.
+  </Card>
+  <Card title="View on GitHub" icon="github" href="https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/Agentic_RAG">
+    Read the source.
+  </Card>
+</CardGroup>
diff --git a/docs/web/tutorials/agents/contextual-rag.mdx b/docs/web/tutorials/agents/contextual-rag.mdx
new file mode 100644
index 000000000..5f332d819
--- /dev/null
+++ b/docs/web/tutorials/agents/contextual-rag.mdx
@@ -0,0 +1,24 @@
+---
+title: "Contextual RAG"
+sidebarTitle: "Contextual RAG"
+description: "Prepend LLM-generated context to each chunk before embedding it."
+---
+
+Addresses the "lost in the middle" problem in retrieval. An LLM generates a
+succinct piece of context for each document chunk, which is prepended to the
+chunk before embedding -- so a chunk carries enough surrounding meaning to be
+retrieved for the right queries.
+
+<Note>
+This example is a runnable notebook in the
+[vectordb-recipes](https://github.com/lancedb/vectordb-recipes) repository rather than a page here.
+</Note>
+
+<CardGroup cols={2}>
+  <Card title="Open in Colab" icon="google" href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-RAG/Anthropic_Contextual_RAG.ipynb">
+    Run it end to end in the browser.
+  </Card>
+  <Card title="View on GitHub" icon="github" href="https://github.com/lancedb/vectordb-recipes/tree/main/examples/Contextual-RAG">
+    Read the source.
+  </Card>
+</CardGroup>
diff --git a/docs/web/tutorials/agents/hyde.mdx b/docs/web/tutorials/agents/hyde.mdx
new file mode 100644
index 000000000..76d674fae
--- /dev/null
+++ b/docs/web/tutorials/agents/hyde.mdx
@@ -0,0 +1,24 @@
+---
+title: "HyDE: hypothetical document embeddings"
+sidebarTitle: "HyDE"
+description: "Generate a hypothetical answer, then retrieve documents similar to it."
+---
+
+An LLM writes a hypothetical document answering the query, and that document --
+not the query -- is embedded and used for retrieval. It works because a
+plausible answer sits nearer the real answers in embedding space than a question
+does.
+
+<Note>
+This example is a runnable notebook in the
+[vectordb-recipes](https://github.com/lancedb/vectordb-recipes) repository rather than a page here.
+</Note>
+
+<CardGroup cols={2}>
+  <Card title="Open in Colab" icon="google" href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Advance-RAG-with-HyDE/main.ipynb">
+    Run it end to end in the browser.
+  </Card>
+  <Card title="View on GitHub" icon="github" href="https://github.com/lancedb/vectordb-recipes/tree/main/examples/Advance-RAG-with-HyDE">
+    Read the source.
+  </Card>
+</CardGroup>
diff --git a/docs/web/tutorials/agents/late-chunking.mdx b/docs/web/tutorials/agents/late-chunking.mdx
new file mode 100644
index 000000000..4b6a12b29
--- /dev/null
+++ b/docs/web/tutorials/agents/late-chunking.mdx
@@ -0,0 +1,23 @@
+---
+title: "Late chunking"
+sidebarTitle: "Late Chunking"
+description: "Retrieve whole documents first, chunk them afterwards."
+---
+
+Inverts the usual order: documents are retrieved first and chunked just before
+synthesis. Chunking late preserves context that pre-chunking discards, since a
+chunk is cut with knowledge of the query it is answering.
+
+<Note>
+This example is a runnable notebook in the
+[vectordb-recipes](https://github.com/lancedb/vectordb-recipes) repository rather than a page here.
+</Note>
+
+<CardGroup cols={2}>
+  <Card title="Open in Colab" icon="google" href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Advanced_RAG_Late_Chunking/Late_Chunking_(Chunked_Pooling).ipynb">
+    Run it end to end in the browser.
+  </Card>
+  <Card title="View on GitHub" icon="github" href="https://github.com/lancedb/vectordb-recipes/tree/main/examples/Advanced_RAG_Late_Chunking">
+    Read the source.
+  </Card>
+</CardGroup>
diff --git a/docs/web/tutorials/agents/matryoshka-embeddings.mdx b/docs/web/tutorials/agents/matryoshka-embeddings.mdx
new file mode 100644
index 000000000..6a6bc7f44
--- /dev/null
+++ b/docs/web/tutorials/agents/matryoshka-embeddings.mdx
@@ -0,0 +1,23 @@
+---
+title: "Matryoshka embeddings"
+sidebarTitle: "Matryoshka Embeddings"
+description: "Nested, variable-size embeddings with LanceDB and LlamaIndex."
+---
+
+A RAG pipeline built on Matryoshka embeddings with LlamaIndex. The embeddings
+nest, so one model serves several dimensionalities -- store the full vector and
+retrieve with a truncated one when the cheaper comparison is good enough.
+
+<Note>
+This example is a runnable notebook in the
+[vectordb-recipes](https://github.com/lancedb/vectordb-recipes) repository rather than a page here.
+</Note>
+
+<CardGroup cols={2}>
+  <Card title="Open in Colab" icon="google" href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/RAG-with_MatryoshkaEmbed-Llamaindex/main.ipynb">
+    Run it end to end in the browser.
+  </Card>
+  <Card title="View on GitHub" icon="github" href="https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/RAG-with_MatryoshkaEmbed-Llamaindex">
+    Read the source.
+  </Card>
+</CardGroup>
diff --git a/docs/web/tutorials/agents/multimodal-agent/index.mdx b/docs/web/tutorials/agents/multimodal-agent/index.mdx
new file mode 100644
index 000000000..7c0e4ecaf
--- /dev/null
+++ b/docs/web/tutorials/agents/multimodal-agent/index.mdx
@@ -0,0 +1,71 @@
+---
+title: "Multimodal Agent"
+sidebarTitle: "Multimodal Agent"
+description: "Build an AI agent that understands both text and images to help users find recipes using LanceDB and PydanticAI"
+---
+
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pxavAGoXa-KSh_4HxNpvP2AjHPcIRpbq?usp=sharing)
+
+Ever wanted to combine the power of text and images in a single AI agent? In this tutorial,
+you'll build an agent that can understand both text and images to help users discover recipes that are relevant to them. The approach shown combines LanceDB's multimodal capabilities with [Pydantic AI](https://ai.pydantic.dev/) for the agentic workflow.
+
+## Key Technologies {#key-technologies}
+
+- **LanceDB**: Embedded retrieval library and multimodal lakehouse for efficient storage and retrieval
+- **PydanticAI**: Modern AI agent framework with type safety
+- **Sentence Transformers**: Text embeddings for semantic search
+- **CLIP**: Vision-language model for image understanding
+- **Streamlit**: Interactive web application framework
+
+## Tutorial Overview {#tutorial-overview}
+
+### Option 1: Notebook {#option-1-notebook}
+The notebook shows how to work through the steps and prepare a small sample recipe dataset, generate both text and image
+embeddings, store everything efficiently in LanceDB, and then build a PydanticAI agent with custom tools to
+query it. You'll finish by testing the agent against a few example questions to see the full multimodal flow
+end to end.
+
+<Card title="Notebook" href="https://colab.research.google.com/drive/1pxavAGoXa-KSh_4HxNpvP2AjHPcIRpbq?usp=sharing">
+This simple tutorial provides a step-by-step workflow with a small demo dataset of 4 examples.
+No local setup required - just click and start learning about multimodal agents.
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pxavAGoXa-KSh_4HxNpvP2AjHPcIRpbq?usp=sharing)
+</Card>
+
+### Option 2: Demo Application (Local Setup) {#option-2-demo-application-local-setup}
+The demo application is the full codebase: you'll download and process a real recipe dataset with thousands
+of items, run a Streamlit chat interface that supports image upload, and follow a structure that includes
+production-minded touches like error handling, logging, and monitoring. Everything you need to deploy is
+included.
+
+<Card icon="download" title="Download Tutorial Files" href="https://github.com/lancedb/vectordb-recipes/tree/main/applications/multimodal-recipe-agent">
+Download the files for the full demo application here.
+</Card>
+
+### Dataset Information {#dataset-information}
+- **Source**: [Kaggle Recipe Dataset](https://www.kaggle.com/datasets/pes12017000148/food-ingredients-and-recipe-dataset-with-images)
+- **Size**: Thousands of recipes with images
+- **Format**: CSV file with recipe data and image references
+
+### Setup {#setup}
+
+<CodeGroup>
+```bash bash icon="code"
+# 1. Extract the downloaded files to a folder
+# 2. Navigate to the folder in terminal
+cd multimodal-recipe-agent
+
+# 3. Install dependencies with uv
+uv sync
+
+# 4. Download the Kaggle dataset
+# Visit: https://www.kaggle.com/datasets/pes12017000148/food-ingredients-and-recipe-dataset-with-images
+# Extract recipes.csv to the data/ folder
+
+# 5. Import the dataset
+uv run python import.py
+
+# 6. Run the complete Streamlit chat application
+uv run streamlit run app.py
+```
+</CodeGroup>
diff --git a/docs/web/tutorials/agents/nvidia-rag-blueprint/index.mdx b/docs/web/tutorials/agents/nvidia-rag-blueprint/index.mdx
new file mode 100644
index 000000000..667c876c1
--- /dev/null
+++ b/docs/web/tutorials/agents/nvidia-rag-blueprint/index.mdx
@@ -0,0 +1,175 @@
+---
+title: "NVIDIA RAG Blueprint with LanceDB"
+sidebarTitle: "NVIDIA RAG Blueprint"
+description: "Use LanceDB as the retrieval layer for NVIDIA RAG Blueprint with a Docker-first, retrieval-only reference integration."
+---
+
+## What this tutorial shows {#what-this-tutorial-shows}
+
+If you are using [NVIDIA RAG Blueprints](https://build.nvidia.com/blueprints) and want to evaluate LanceDB in that stack, this tutorial gives you a concrete starting point. It shows how to use LanceDB as the retrieval layer for a Docker-based NVIDIA RAG deployment with a small, script-driven reference integration where LanceDB OSS is embedded directly in the NVIDIA containers, the collection is prepared ahead of time, and the RAG server retrieves from it for search and generation. The example is intentionally retrieval-only, but it also includes hybrid search and reranker selection so you can see how LanceDB fits into a realistic NVIDIA retrieval workflow.
+
+<Tip>
+The runnable example for this tutorial lives in the
+[VectorDB recipes repository](https://github.com/lancedb/vectordb-recipes/tree/main/examples/nvidia-rag-blueprint-lancedb).
+</Tip>
+
+
+## How NVIDIA organizes vector databases {#how-nvidia-organizes-vector-databases}
+
+NVIDIA's [RAG Blueprint documentation](https://docs.nvidia.com/rag/latest/readme.html) effectively describes three different patterns for vector database support.
+1. There are built-in backends such as Milvus, where NVIDIA already owns both ingestion and
+retrieval.
+2. There are built-in alternatives such as Elasticsearch, where NVIDIA still owns
+the end-to-end flow but switches the backend through configuration.
+3. Then, there is the custom vector database path, where you implement a `VDBRag` backend yourself and register it in NVIDIA's
+factory.
+
+The LanceDB example shown below fits into the third category. More specifically, it follows NVIDIA's
+**retrieval-only** custom backend path: the data is prepared in LanceDB ahead of time, and NVIDIA
+RAG Blueprint is then pointed at that existing collection for search and generation. It does not
+yet teach NVIDIA's ingestor how to write new documents into LanceDB automatically.
+
+## Deployment model {#deployment-model}
+
+This reference integration uses **LanceDB OSS as an embedded retrieval library**, not as a separate
+database service. In practice, `APP_VECTORSTORE_NAME` is set to `lancedb`, `APP_VECTORSTORE_URL`
+points to a local filesystem path inside the NVIDIA containers, the LanceDB collection is prepared
+ahead of time, and the NVIDIA RAG server loads the LanceDB adapter to retrieve directly from that
+local dataset.
+
+## What the recipe contains {#what-the-recipe-contains}
+
+The recipe at
+[`examples/nvidia-rag-blueprint-lancedb`](https://github.com/lancedb/vectordb-recipes/tree/main/examples/nvidia-rag-blueprint-lancedb)
+is organized around a small number of practical pieces. The data-prep script builds a demo
+LanceDB collection from scratch, generates embeddings through the LanceDB embedding registry, and
+creates a full-text index so hybrid retrieval works immediately. The adapter file shows the
+retrieval-only integration point for NVIDIA RAG Blueprint, while the Docker override and NVIDIA
+change guide show the minimal configuration and source changes needed to run the example against
+NVIDIA's containers.
+
+## End-to-end flow {#end-to-end-flow}
+
+### 1\. Prepare the LanceDB collection {#1-prepare-the-lancedb-collection}
+
+From the [recipe directory](https://github.com/lancedb/vectordb-recipes/tree/main/examples/nvidia-rag-blueprint-lancedb):
+
+```bash
+uv sync
+uv run prepare_lancedb.py --embedder demo-keyword --reranker mrr
+```
+
+That script creates:
+
+- a local LanceDB dataset under `data/`
+- a collection named `nvidia_blueprint_demo`
+- automatic embeddings generated at ingest time
+- an FTS index for hybrid search
+
+The default embedder is an offline demo embedder so the example stays easy to run. If you want a
+more realistic setup, the same script can switch to a sentence-transformers embedder.
+
+### 2\. Patch the NVIDIA blueprint {#2-patch-the-nvidia-blueprint}
+
+Follow the instructions in the recipe's
+[`nvidia_blueprint_changes.md`](https://github.com/lancedb/vectordb-recipes/tree/main/examples/nvidia-rag-blueprint-lancedb/nvidia_blueprint_changes.md).
+The essential changes are:
+
+- add LanceDB dependencies to the NVIDIA environment
+- copy `lancedb_vdb.py` into the NVIDIA source tree
+- register the `lancedb` branch in NVIDIA's VDB factory
+
+NVIDIA's [RAG blueprint documentation](https://docs.nvidia.com/rag/latest/readme.html) and custom-VDB guide provide useful background if you want more context before applying the LanceDB-specific changes.
+
+### 3\. Start the Docker deployment {#3-start-the-docker-deployment}
+
+Set the absolute path to the recipe directory:
+
+```bash
+export LANCEDB_RECIPE_DIR=/absolute/path/to/vectordb-recipes/examples/nvidia-rag-blueprint-lancedb
+```
+
+Then from the NVIDIA repo root:
+
+```bash
+docker compose \
+  -f deploy/compose/docker-compose-rag-server.yaml \
+  -f "$LANCEDB_RECIPE_DIR"/docker-compose.override.yml \
+  up -d --build
+
+docker compose \
+  -f deploy/compose/docker-compose-ingestor-server.yaml \
+  -f "$LANCEDB_RECIPE_DIR"/docker-compose.override.yml \
+  up -d --build
+```
+
+The key environment values are:
+
+- `APP_VECTORSTORE_NAME=lancedb`
+- `APP_VECTORSTORE_URL=/opt/lancedb-recipe/data`
+- `COLLECTION_NAME=nvidia_blueprint_demo`
+- `APP_VECTORSTORE_SEARCHTYPE=hybrid`
+- `LANCEDB_RERANKER=mrr`
+
+## Verifying the integration {#verifying-the-integration}
+
+### Search {#search}
+
+```bash
+curl -X POST http://localhost:8081/v1/search \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "query": "How do I replace Milvus in the NVIDIA RAG blueprint with LanceDB?",
+    "use_knowledge_base": true,
+    "collection_names": ["nvidia_blueprint_demo"],
+    "vdb_top_k": 3,
+    "reranker_top_k": 0
+  }'
+```
+
+### Generate {#generate}
+
+```bash
+curl -N -X POST http://localhost:8081/v1/generate \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "messages": [{"role":"user","content":"Summarize the LanceDB integration approach."}],
+    "use_knowledge_base": true,
+    "collection_names": ["nvidia_blueprint_demo"],
+    "vdb_top_k": 3,
+    "reranker_top_k": 0
+  }'
+```
+
+## Hybrid retrieval and rerankers {#hybrid-retrieval-and-rerankers}
+
+This example is meant to prove more than a trivial vector lookup.
+
+- LanceDB hybrid retrieval combines vector search with full-text search
+- the recipe creates the FTS index as part of dataset prep
+- the adapter supports `RRFReranker`, `MRRReranker`, and `CrossEncoderReranker`
+- the default example uses `MRRReranker`, not a plain weighted linear combination
+
+That matters for NVIDIA partner workloads because product names, storage platforms, and technical
+jargon often need exact lexical matching as well as semantic retrieval.
+
+## How this can be extended {#how-this-can-be-extended}
+
+The current example follows NVIDIA's **custom retrieval-only backend** path. In practice, that
+means the LanceDB collection is created ahead of time and NVIDIA RAG Blueprint is then pointed at
+that existing collection for search and generation. The sample data in `prepare_lancedb.py` exists
+only to make that flow runnable end to end: it creates a small local collection, inserts a few
+documents, generates embeddings, and builds an FTS index so the NVIDIA side has something real to
+query.
+
+A fuller integration is possible. NVIDIA's custom `VDBRag` interface also supports the pattern used
+by built-in backends such as Milvus and Elasticsearch, where NVIDIA owns both ingestion and
+retrieval. To make LanceDB work that way, a complete LanceDB backend would need to implement the
+ingestion methods NVIDIA documents, especially `create_collection` and `write_to_index`, along with
+the retrieval and collection-management methods expected by the rest of the stack.
+
+The open work is in defining how NVIDIA's ingestor should write
+records into LanceDB, how that storage is shared between the ingestor and the RAG server, and how
+document and collection metadata should be exposed so the broader NVIDIA APIs behave correctly.
+Until those pieces exist, this example should be read as: prepare LanceDB first, then let NVIDIA
+retrieve from it.
diff --git a/docs/web/tutorials/agents/time-travel-rag/index.mdx b/docs/web/tutorials/agents/time-travel-rag/index.mdx
new file mode 100644
index 000000000..fd653bf50
--- /dev/null
+++ b/docs/web/tutorials/agents/time-travel-rag/index.mdx
@@ -0,0 +1,181 @@
+---
+title: "Time-Travel RAG with versioned data"
+sidebarTitle: "Time-travel RAG"
+description: "Build a RAG example that retrieves documents from a selected table version."
+---
+
+<Tip>
+All the scripts and code for this tutorial are available in the
+[vectorDB recipes](https://github.com/lancedb/vectordb-recipes/tree/main/examples/time-travel-rag) repository. 
+</Tip>
+
+## Use case: Financial services regulatory knowledge base {#use-case-financial-services-regulatory-knowledge-base}
+
+Imagine you're a major investment bank. Your team is tasked with building a critical Retrieval-Augmented Generation (RAG) system. This system must provide instant, accurate answers to compliance officers about ever-changing financial regulations. A wrong or out-of-date answer isn't just an inconvenience—it could lead to multi-million dollar fines, reputational damage, and regulatory audits.
+
+Your knowledge base is a living entity, constantly evolving with:
+
+* Daily regulatory updates from government bodies.
+* New internal policy documents and interpretations.
+* A/B testing of different embedding models and text chunking strategies to improve accuracy.
+
+This dynamic environment creates a series of high-stakes challenges that traditional
+vector databases are ill-equipped to handle.
+
+## Pain points solved by LanceDB {#pain-points-solved-by-lancedb}
+
+1. "Our RAG gave different answers yesterday versus today. Which version was used in the official compliance report?" Without versioning, you can't prove what the AI knew at a specific point in time, making audits impossible.
+
+2. "The new embedding model we deployed corrupted half the vectors. Can we instantly roll back our 10TB dataset?" With traditional systems, a rollback means a painful, hours-long (or days-long) process of re-indexing from a backup, leading to significant downtime.
+
+3. "Regulators want to audit an AI-assisted decision from three months ago. How can we prove what data the model had access to at that exact moment?" Reproducibility is key for compliance. You must be able to reconstruct the exact state of the knowledge base for any historical query.
+
+4. "We need to A/B test a new chunking strategy, but we can't disrupt the production system or duplicate the entire dataset." Experimentation is vital for improvement, but it can't come at the cost of production stability or a massive infrastructure bill.
+
+LanceDB's [column-based data evolution](/management/table#schema) and [time-travel capabilities](https://docs.lancedb.com/management/table#versioning) help preserve the dataset used for a particular answer. This tutorial demonstrates
+versioned retrieval; production auditing also needs application logs, retention
+policies and access controls.
+
+## Dataset: The U.S. Federal Register {#dataset-the-us-federal-register}
+
+To make this use case realistic, we'll use a perfect real-world dataset: The U.S. Federal Register, the official daily journal of the United States Government.
+
+It contains all new rules, proposed rules, and notices from federal agencies. It is the canonical source for regulatory changes, and it's updated every business day. It even has a public API, allowing us to simulate the real-time ingestion of new documents.
+
+An example output of the workflow defined in main.py
+is shown below.
+
+```bash main.py expandable
+--- Initializing Database Environment ---
+Removed old database at ./lancedb
+Loading embedding model: all-MiniLM-L6-v2...
+
+--- STEP 1: Initial Data Ingestion ---
+
+Fetching 500 documents for publication date: 2024-08-19...
+Successfully fetched 86 documents.
+Embedding 86 documents...
+Batches: 100%|███████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00,  7.12it/s]
+Successfully embedded 86 documents.
+
+Creating table 'federal_register'...
+✅ Table 'federal_register' created. Version: 1, Rows: 86
+
+--- STEP 2: Simulating Sequential Daily Updates ---
+
+Fetching 500 documents for publication date: 2024-08-20...
+Successfully fetched 102 documents.
+Embedding 102 documents...
+Batches: 100%|███████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 10.66it/s]
+Successfully embedded 102 documents.
+✅ Data added to 'federal_register'. New Version: 2, Total Rows: 188
+
+Fetching 500 documents for publication date: 2024-08-21...
+Successfully fetched 114 documents.
+Embedding 114 documents...
+Batches: 100%|███████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 10.19it/s]
+Successfully embedded 114 documents.
+✅ Data added to 'federal_register'. New Version: 3, Total Rows: 302
+
+
+========================================================
+= PART 1: AUDITING KNOWLEDGE BASE ACROSS TIME  =
+========================================================
+
+Running audit for query: 'cybersecurity reporting requirements for public companies'
+
+Attempting to open 'federal_register' and checkout version 1...
+✅ Successfully checked out Version 1 of 'federal_register'. Total rows: 86
+Querying table 'federal_register' (Version 1)...
+
+--- Top Result for Version: V1 (all-MiniLM-L6-v2) ---
+📄 Title: Public Company Accounting Oversight Board; Extension of Approval Periods for Proposed Rules on a Firm's System of Quality Control and Related Amendments to PCAOB Standards, Proposed Rules on Amendments Related to Aspects of Designing and Performing Audit Procedures That Involve Technology-Assisted Analysis of Information in Electronic Form, and Proposed Rules on Amendment to PCAOB Rule 3502 Governing Contributory Liability
+🗓️  Date: 2024-08-19
+📏 Distance: 1.1667
+📝 Abstract:
+[No abstract available for this document]
+--------------------------------------
+
+Attempting to open 'federal_register' and checkout version 2...
+✅ Successfully checked out Version 2 of 'federal_register'. Total rows: 188
+Querying table 'federal_register' (Version 2)...
+
+--- Top Result for Version: V2 (all-MiniLM-L6-v2) ---
+📄 Title: Information Collection Being Reviewed by the Federal Communications Commission Under Delegated Authority
+🗓️  Date: 2024-08-20
+📏 Distance: 1.1436
+📝 Abstract:
+As part of its continuing effort to reduce paperwork burdens, and as required by the Paperwork
+Reduction Act (PRA) of 1995, the Federal Communications Commission (FCC or the Commission) invites
+the general public and other Federal agencies to take this opportunity to comment on the following
+information collection. Comments are requested concerning: whether the proposed collection of
+information is necessary for the proper performance of the functions of the Commission, including
+whether the information shall have practical utility; the accuracy of the Commission's burden
+estimate; ways to enhance the quality, utility, and clarity of the information collected; ways to
+minimize the burden of the collection of information on the respondents, including the use of
+automated collection techniques or other forms of information technology; and ways to further reduce
+the information collection burden on small business concerns with fewer than 25 employees. The FCC
+may not conduct or sponsor a collection of information unless it displays a currently valid control
+number. No person shall be subject to any penalty for failing to comply with a collection of
+information subject to the PRA that does not display a valid Office of Management and Budget (OMB)
+control number.
+--------------------------------------
+
+Attempting to open 'federal_register' and checkout version 3...
+✅ Successfully checked out Version 3 of 'federal_register'. Total rows: 302
+Querying table 'federal_register' (Version 3)...
+
+--- Top Result for Version: V3 (all-MiniLM-L6-v2) ---
+📄 Title: Equipment, Systems, and Network Information Security Protection
+🗓️  Date: 2024-08-21
+📏 Distance: 1.0942
+📝 Abstract:
+This proposed rulemaking would impose new design standards to address cybersecurity threats for
+transport category airplanes, engines, and propellers. The intended effect of this proposed action
+is to standardize the FAA's criteria for addressing cybersecurity threats, reducing certification
+costs and time while maintaining the same level of safety provided by current special conditions.
+--------------------------------------
+
+✅ Date-based audit complete. Results show how knowledge evolves over time. This demonstrates LanceDB's powerful [versioning capabilities](/tutorials/tables/consistency#versioning) for maintaining audit trails.
+
+
+=============================================================
+= PART 2: A/B TESTING DIFFERENT EMBEDDING MODELS  =
+=============================================================
+Loading embedding model: all-mpnet-base-v2...
+Embedding 302 documents...
+Batches: 100%|█████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:03<00:00,  2.55it/s]
+Successfully embedded 302 documents.
+
+Creating table 'federal_register_experimental'...
+✅ Table 'federal_register_experimental' created. Version: 1, Rows: 302
+
+Comparing search results for the same data with different models:
+Querying table 'federal_register' (Version 3)...
+
+--- Top Result for Version: Latest Prod V3 (all-MiniLM-L6-v2) ---
+📄 Title: Equipment, Systems, and Network Information Security Protection
+🗓️  Date: 2024-08-21
+📏 Distance: 1.0942
+📝 Abstract:
+This proposed rulemaking would impose new design standards to address cybersecurity threats for
+transport category airplanes, engines, and propellers. The intended effect of this proposed action
+is to standardize the FAA's criteria for addressing cybersecurity threats, reducing certification
+costs and time while maintaining the same level of safety provided by current special conditions.
+--------------------------------------
+Querying table 'federal_register_experimental' (Version 1)...
+
+--- Top Result for Version: Experimental (all-mpnet-base-v2) ---
+📄 Title: Commission Information Collection Activities (FERC-725B); Comment Request; Extension
+🗓️  Date: 2024-08-20
+📏 Distance: 1.0827
+📝 Abstract:
+In compliance with the requirements of the Paperwork Reduction Act of 1995, the Federal Energy
+Regulatory Commission (Commission or FERC) is soliciting public comment on the currently approved
+information collection, FERC-725B, Mandatory Reliability Standards, Critical Infrastructure
+Protection (CIP) (Update for CIP-012-1 to version CIP-012-02) Cyber Security--Communications between
+Control Centers. The 60-day notice comment period ended on July 23, 2024, with no comments received.
+--------------------------------------
+
+✅ A/B test complete. Notice the difference in relevance (distance score) between models. This showcases how LanceDB enables [experimentation with different embedding models](/docs/embeddings/) without disrupting production systems.
+```
diff --git a/docs/web/tutorials/feature-engineering/feature-engineering-101.mdx b/docs/web/tutorials/feature-engineering/feature-engineering-101.mdx
new file mode 100644
index 000000000..7f6110162
--- /dev/null
+++ b/docs/web/tutorials/feature-engineering/feature-engineering-101.mdx
@@ -0,0 +1,23 @@
+---
+title: "Feature engineering 101"
+sidebarTitle: "Feature Engineering 101"
+description: "Add derived feature columns to an existing table."
+---
+
+Adds new derived features to a table, the pattern documented under
+[data evolution](/feature-engineering/data-evolution) and
+[computed columns](/feature-engineering/add-computed-column).
+
+<Note>
+This example is a runnable notebook in the
+[vectordb-recipes](https://github.com/lancedb/vectordb-recipes) repository rather than a page here.
+</Note>
+
+<CardGroup cols={2}>
+  <Card title="Open in Colab" icon="google" href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/feature-engineering/feature-engineering-101.ipynb">
+    Run it end to end in the browser.
+  </Card>
+  <Card title="View on GitHub" icon="github" href="https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/feature-engineering/feature-engineering-101.ipynb">
+    Read the source.
+  </Card>
+</CardGroup>
diff --git a/docs/web/tutorials/feature-engineering/materialized-views.mdx b/docs/web/tutorials/feature-engineering/materialized-views.mdx
new file mode 100644
index 000000000..5e1a77bd4
--- /dev/null
+++ b/docs/web/tutorials/feature-engineering/materialized-views.mdx
@@ -0,0 +1,23 @@
+---
+title: "Materialized views"
+sidebarTitle: "Materialized Views"
+description: "Persist query results as physical tables and keep them current."
+---
+
+Creates [materialized views](/management/materialized-view): query results
+persisted as physical tables, brought up to date by a
+[refresh](/feature-engineering/materialized-views).
+
+<Note>
+This example is a runnable notebook in the
+[vectordb-recipes](https://github.com/lancedb/vectordb-recipes) repository rather than a page here.
+</Note>
+
+<CardGroup cols={2}>
+  <Card title="Open in Colab" icon="google" href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/feature-engineering/materialized-views.ipynb">
+    Run it end to end in the browser.
+  </Card>
+  <Card title="View on GitHub" icon="github" href="https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/feature-engineering/materialized-views.ipynb">
+    Read the source.
+  </Card>
+</CardGroup>
diff --git a/docs/web/tutorials/index.mdx b/docs/web/tutorials/index.mdx
new file mode 100644
index 000000000..6ada57c9a
--- /dev/null
+++ b/docs/web/tutorials/index.mdx
@@ -0,0 +1,42 @@
+---
+title: "Tutorials"
+sidebarTitle: "Overview"
+description: "Worked examples of building with LanceDB, grouped by what they build."
+---
+
+Every tutorial is one page in the sidebar, grouped by what it builds. The
+documentation explains how a feature works; these show it doing something.
+
+Examples marked as notebooks live in the
+[vectordb-recipes](https://github.com/lancedb/vectordb-recipes) repository and
+are runnable in Colab; the rest are written up here.
+
+## Where to start {#start}
+
+<CardGroup cols={2}>
+  <Card title="Search" icon="magnifying-glass" href="/tutorials/search/hybrid-search-beir">
+    Vector, multi-vector, hybrid and full-text retrieval.
+  </Card>
+  <Card title="RAG" icon="robot" href="/tutorials/agents/contextual-rag">
+    Retrieval-augmented generation and agent applications.
+  </Card>
+  <Card title="Feature engineering" icon="wand-magic-sparkles" href="/tutorials/feature-engineering/feature-engineering-101">
+    Deriving columns, materialized views, experimenting on branches.
+  </Card>
+  <Card title="Training" icon="graduation-cap" href="/training/object-detection">
+    Curating data and training models directly on a table.
+  </Card>
+</CardGroup>
+
+## The features these use {#read-the-docs}
+
+| Feature | What it covers |
+|:--------|:------------|
+| [Vector search](/search/vector-search) | Similarity search, distance metrics, and tuning it. |
+| [Full-text search](/search/full-text-search) | Keyword search over text columns. |
+| [Hybrid search](/search/hybrid-search) | Keyword and vector search combined. |
+| [Multi-vector search](/search/vector-search) | Several embeddings per document. |
+| [Reranking](/reranking) | Reordering results to improve the top-k. |
+| [Feature engineering](/feature-engineering) | Deriving columns and tables from data already stored. |
+| [Materialized views](/management/materialized-view) | A table defined by a query, brought up to date by each refresh. |
+| [Training](/training/data-loading) | Reading a table into a data loader. |
diff --git a/docs/web/tutorials/search/hybrid-search-beir.mdx b/docs/web/tutorials/search/hybrid-search-beir.mdx
new file mode 100644
index 000000000..3db8e95ce
--- /dev/null
+++ b/docs/web/tutorials/search/hybrid-search-beir.mdx
@@ -0,0 +1,25 @@
+---
+title: "Hybrid search and reranking on BEIR"
+sidebarTitle: "Hybrid Search on BEIR"
+description: "Combine semantic and keyword search on the BEIR benchmark, then rerank the results."
+---
+
+Uses LanceDB's built-in [hybrid search](/search/hybrid-search), which combines the
+strengths of semantic and full-text search. Working against the BEIR dataset, it
+shows how searching for both the meaning of a query and the specific keywords in
+it produces more relevant results than either alone, and how a
+[reranker](/reranking) refines what comes back.
+
+<Note>
+This example is a runnable notebook in the
+[vectordb-recipes](https://github.com/lancedb/vectordb-recipes) repository rather than a page here.
+</Note>
+
+<CardGroup cols={2}>
+  <Card title="Open in Colab" icon="google" href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Inbuilt-Hybrid-Search/Inbuilt_Hybrid_Search_with_LanceDB.ipynb">
+    Run it end to end in the browser.
+  </Card>
+  <Card title="View on GitHub" icon="github" href="https://github.com/lancedb/vectordb-recipes/tree/main/examples/Inbuilt-Hybrid-Search">
+    Read the source.
+  </Card>
+</CardGroup>
diff --git a/docs/web/tutorials/search/multivector-needle-in-a-haystack.mdx b/docs/web/tutorials/search/multivector-needle-in-a-haystack.mdx
new file mode 100644
index 000000000..ba5cefe35
--- /dev/null
+++ b/docs/web/tutorials/search/multivector-needle-in-a-haystack.mdx
@@ -0,0 +1,330 @@
+---
+title: "Finding the Needle in a Haystack: Comparing Multi-vector Search Strategies"
+sidebarTitle: "Needle in a Haystack"
+description: "Tutorial on token-level retrieval with LanceDB multivector search."
+---
+
+![](/static/assets/images/search/multivector/multivector-1.png)
+
+In the development of advanced search and retrieval systems, moving from keyword matching to semantic understanding is a critical step. However, a key distinction exists between finding a relevant document and locating a specific piece of information within that document with precision. While there are techniques that perform well for retrieving documents, most of them work by extracting summarized semantic meaning of the document.
+
+This can be seen as these models trying to understand the "gist" of the documents. Both single-vector search and late-interaction approaches work well for these conditions with various tradeoffs involved. But there's another type of problem where the goal is not just to understand the overall topic of a document in general, but to also specifically account for the requested detail within the document. This "needle in a haystack" problem is a significant challenge, and addressing it is essential for building high-precision retrieval systems.
+
+This guide provides a technical analysis of multivector search for high-precision information retrieval. We will examine various optimization strategies and analyze their performance. This guide should be seen as complementary to resources like [this blog post by Answer.AI](https://www.answer.ai/posts/colbert-pooling.html) on ColBERT pooling, which explains how pooling strategies can be effective for document-level retrieval. Here, we will demonstrate why those same techniques can be counterproductive when precision at an intra-document, token level is the primary objective.
+
+To reproduce the work below, see the code [here](https://github.com/lancedb/research/tree/main/multivector-needle-haystack-bench).
+
+## The Dataset {#the-dataset}
+
+This task is different from benchmarks like BEIR, which focus on text-based doc retrieval, finding the most relevant documents from a large collection. Here, we want *intra-document localization*, where the goal is to find a precise piece of information within a single, dense document, in a multimodal setting.
+
+### The Task: The Document Haystack Dataset {#the-task-the-document-haystack-dataset}
+
+Our benchmark is built on the **[AmazonScience/document-haystack](https://huggingface.co/datasets/AmazonScience/document-haystack)** dataset, which contains 25 visually complex source documents (e.g., financial reports, academic papers). To create a rigorous test, our evaluation follows a per-document methodology:
+
+*   We process each of the 25 source documents independently.
+*   **Table Creation:** For a single source document (e.g., "AIG"), we ingest all pages from all of its page-length variants (from 5 to 200 pages long). This creates a temporary LanceDB table containing approximately 1,230 pages.
+*   **The Task:** We then query this table using a set of "needle" questions, where the goal is to retrieve the **exact page number** containing the answer. A successful retrieval means the correct page number is within the top K results.
+*   **Target metric:** We measure both retrieval accuracy (Hit@K) and the average search latency for each query against this table.
+
+**Dataset example**
+The documents contain "text needles" like these
+
+![](/static/assets/images/search/multivector/multivector-2.png)
+![](/static/assets/images/search/multivector/multivector-3.png)
+
+During evaluation, the queries processed are somewhat like this:
+```
+What is the secret currency in the document?
+What is the secret object #3 in the document?
+```
+The intention of this task is to find the page which has the text needle that answers this questions
+
+
+## Models and Architectures {#models-and-architectures}
+
+Our testbed includes a baseline single-vector model and a family of advanced multivector models. 
+
+### Single-Vector (Bi-Encoder) Baseline: CLIP ViT-Base-Patch32 {#single-vector-bi-encoder-baseline-clip-vit-base-patch32}
+A bi-encoder maps an entire piece of content (a query, a document page) to a *single* vector. The search process is simple: pre-compute one vector for every page, and at query time, find the page vector closest to the query vector.
+
+*   **Strength:** Speed and simplicity.
+*   **Weakness:** This creates an **information bottleneck**. All the nuanced details, keywords, and semantic relationships on a page must be compressed into a single, fixed-size vector. For finding a needle, this is like trying to describe a specific person's face using only one word.
+
+### Multi-vector (Late-Interaction) Models {#multi-vector-late-interaction-models}
+
+Multi-vector models, pioneered by ColBERT, take a different approach. Instead of one vector per page, they generate a *set of vectors* for each page—one for every token (or image patch).
+
+*   **Mechanism (MaxSim):** The search process is more sophisticated. For each token in the query, the system finds the most similar token on the page. These maximum similarity scores are then summed up to get the final relevance score. This "late-interaction" preserves fine-grained, token-level details.
+*   **The Models:** We used several vision-language models adapted for this architecture, including `ColPali`, `ColQwen2`, and `ColSmol`. While their underlying transformer backbones differ, they all share the ColBERT philosophy of representing documents as a bag of contextualized token embeddings.
+
+![](/static/assets/images/search/multivector/multivector-4.png)
+
+## Different Retrieval Strategies Used {#different-retrieval-strategies-used}
+
+A full multivector search is powerful but computationally intensive. Here are five strategies for managing it, complete with LanceDB implementation details.
+
+
+### 1\. `base`: The Gold Standard (Full Multi-vector Search) {#1-base-the-gold-standard-full-multi-vector-search}
+
+This is the pure, baseline late-interaction search. It offers the highest potential for accuracy by considering every token.
+
+**LanceDB also integrates with ConteXtualized Token Retriever (XTR)** , an advanced retrieval model that prioritizes the most semantically important document tokens during search. This integration enhances the quality of search results by focusing on the most relevant token matches.
+**LanceDB Implementation:**
+
+```python Python icon="python"
+import lancedb
+import pyarrow as pa
+
+# Schema for multivector data
+# Assumes embeddings are 128-dimensional
+schema = pa.schema([
+    pa.field("page_num", pa.int32()),
+    pa.field("vector", pa.list_(pa.list_(pa.float32(), 128)))
+])
+
+db = lancedb.connect("./lancedb")
+tbl = db.create_table("document_pages_base", schema=schema)
+
+# Ingesting multi-token embeddings for a page
+# multi_token_embeddings is a NumPy array of shape (num_tokens, 128)
+tbl.add([{"page_num": 1, "vector": multi_token_embeddings.tolist()}])
+
+# Searching with a multi-token query
+# query_multi_vector is also shape (num_query_tokens, 128)
+results = tbl.search(query_multi_vector).limit(5).to_list()
+```
+
+### 2\. `flatten`: Mean Pooling {#2-flatten-mean-pooling}
+
+This strategy "flattens" the set of token vectors into a single vector by averaging them. This transforms the search into a standard, fast approximate nearest neighbor (ANN) search.
+
+**LanceDB Implementation:**
+
+```python Python icon="python"
+# Schema for single-vector data
+schema_flat = pa.schema([
+    pa.field("page_num", pa.int32()),
+    pa.field("vector", pa.list_(pa.float32(), 128))
+])
+tbl_flat = db.create_table("document_pages_flat", schema=schema_flat)
+
+# Ingesting the mean-pooled vector
+mean_vector = multi_token_embeddings.mean(axis=0)
+tbl_flat.add([{"page_num": 1, "vector": mean_vector.tolist()}])
+
+# Searching with a single averaged query vector
+query_mean_vector = query_multi_vector.mean(axis=0)
+results = tbl_flat.search(query_mean_vector).limit(5).to_list()
+```
+
+### 3\. Max pooling {#3-max-pooling}
+
+This is a variation of `flatten`. `max_pooling` takes the element-wise max across all token vectors instead of the mean. The implementation is identical to `flatten`, just with a different aggregation method (`.max(axis=0)`).
+
+### 4\. `flatten and multivector rerank`: The Hybrid Optimization {#4-flatten-and-multivector-rerank-the-hybrid-optimization}
+This two-stage strategy aims for the best of both worlds. First, use a fast, pooled-vector search to find a set of promising candidates. Then, run the full, accurate multivector search on *only* those candidates.
+
+**LanceDB Implementation:**
+This requires a table with two vector columns.
+
+```python Python icon="python"
+# Schema with both flat and multivector columns
+schema_rerank = pa.schema([
+    pa.field("page_num", pa.int32()),
+    pa.field("vector_flat", pa.list_(pa.float32(), 128)),
+    pa.field("vector_multi", pa.list_(pa.list_(pa.float32(), 128)))
+])
+tbl_rerank = db.create_table("document_pages_rerank", schema=schema_rerank)
+
+# Ingest both vectors
+tbl_rerank.add([
+    {
+        "page_num": 1,
+        "vector_flat": multi_token_embeddings.mean(axis=0).tolist(),
+        "vector_multi": multi_token_embeddings.tolist()
+    }
+])
+
+# --- Two-Stage Search ---
+# Stage 1: Fast search on the flat vector
+query_flat = query_multi_vector.mean(axis=0)
+candidates = tbl_rerank.search(query_flat, vector_column_name="vector_flat") \
+                       .limit(100) \
+                       .with_row_id(True) \
+                       .to_pandas()
+
+# Stage 2: Precise multivector search on candidates
+candidate_ids = tuple(candidates["_rowid"].to_list())
+final_results = tbl_rerank.search(query_multi_vector, vector_column_name="vector_multi") \
+                          .where(f"_rowid IN {candidate_ids}") \
+                          .limit(5) \
+                          .to_list()
+```
+
+### 5\. `hierarchical token pooling`: Compressing the Haystack {#5-hierarchical-token-pooling-compressing-the-haystack}
+
+This is an indexing-time strategy that aims to reduce the storage footprint and computational cost of multivector search by reducing the number of vectors per document. Instead of using every token vector, it clusters semantically similar tokens together and replaces them with a single, averaged vector.
+
+*   **Mechanism:** For each document, it computes the similarity between all token vectors, performs hierarchical clustering to group them, and then mean-pools the vectors within each cluster. This results in a smaller, more compact set of token vectors representing the document.
+*   **Goal:** To reduce memory and disk usage while attempting to preserve the most important semantic information, potentially offering a middle ground between the high accuracy of `base` search and the speed of pooled methods.
+
+**LanceDB Implementation:**
+The schema is identical to the `base` multivector search, but the data is pre-processed before ingestion.
+
+```python Python icon="python"
+from utils import pool_embeddings_hierarchical
+import numpy as np
+
+# Schema is the same as the base multivector schema
+schema = pa.schema([
+    pa.field("page_num", pa.int32()),
+    pa.field("vector", pa.list_(pa.list_(pa.float32(), 128)))
+])
+tbl_hierarchical = db.create_table("document_pages_hierarchical", schema=schema)
+
+# Pool the embeddings before ingestion
+# multi_token_embeddings is a NumPy array of shape (num_tokens, 128)
+pooled_embeddings = pool_embeddings_hierarchical(
+    multi_token_embeddings,
+    pool_factor=4  # Reduce vector count by a factor of 4
+)
+
+# Ingest the smaller set of multi-token embeddings
+# pooled_embeddings is now shape (approx. num_tokens / 4, 128)
+tbl_hierarchical.add([{"page_num": 1, "vector": pooled_embeddings.tolist()}])
+
+# Search is identical to the base multivector search
+results = tbl_hierarchical.search(query_multi_vector).limit(5).to_list()
+```
+
+## The Results {#the-results}
+
+For a "needle in a haystack" task, retrieval accuracy is the primary metric of success. The benchmark results reveal a significant performance gap between the full multivector search strategy and common optimization techniques.
+
+### Baseline Performance: Single-Vector Bi-Encoder {#baseline-performance-single-vector-bi-encoder}
+
+First, we establish a baseline using a standard single-vector bi-encoder model, `openai/clip-vit-base-patch32`. This represents a common approach to semantic search but, as the data shows, is ill-suited for this task's precision requirements.
+
+| Model | Strategy | Hit@1 | Hit@5 | Hit@20 | Avg. Latency (s) |
+| :--- | :--- | :--- | :--- | :--- | :--- |
+| `openai/clip-vit-base-patch32` | `base` | 1.6% | 4.7% | 11.8% | **0.008 s** |
+
+With a Hit@20 rate of just under 12%, the baseline model struggles to reliably locate the correct page. This performance level is insufficient for applications requiring high precision.
+
+### Multi-vector Model Performance {#multi-vector-model-performance}
+
+We now examine the performance of multivector models using different strategies. The following table compares the `base` (full multivector), `flatten` (mean pooling), and `rerank` (hybrid) strategies across several late-interaction models.
+
+| Model | Strategy | Hit@1 | Hit@5 | Hit@20 | Avg. Latency (s) |
+| :--- | :--- | :--- | :--- | :--- | :--- |
+| `vidore/colqwen2-v1.0` | `flatten` | 1.9% | 5.5% | 11.9% | 0.010 s |
+| `vidore/colqwen2-v1.0` | `flatten and multivector rerank` | 0.3% | 1.5% | 7.3% | 0.692 s |
+| **`vidore/colqwen2-v1.0`** | **`hierarchical token pooling`** | **13.7%** | **60.5%** | **91.6%** | **0.144 s** |
+| **`vidore/colqwen2-v1.0`** | **`base`** | **14.0%** | **65.4%** | **95.5%** | 0.668 s |
+| `vidore/colpali-v1.3` | `flatten` | 1.7% | 4.5% | 9.3% | 0.008 s |
+| `vidore/colpali-v1.3` | `flatten and multivector rerank` | 0.6% | 2.3% | 6.9% | 0.949 s |
+| **`vidore/colpali-v1.3`** | **`hierarchical token pooling`** | **10.8%** | **41.7%** | **64.8%** | **0.189 s** |
+| **`vidore/colpali-v1.3`** | **`base`** | **11.3%** | **42.3%** | **65.6%** | 0.936 s |
+| `vidore/colSmol-256M` | `flatten` | 1.6% | 4.7% | 10.5% | 0.008 s |
+| `vidore/colSmol-256M` | `flatten and multivector rerank` | 0.3% | 1.6% | 7.0% | 0.853 s |
+| **`vidore/colSmol-256M`** | **`base`** | **14.4%** | **64.0%** | **91.7%** | 0.848 s |
+
+The data shows a consistent pattern: the `base` strategy outperforms all other techniques. The flattned pooling and reranking strategies perform no better than the single-vector baseline. However, hierarchical token pooling seems like a decent alternative to base considering speed vs accuracy tradeoff. Let's look at the numbers in detail.
+
+### In-Depth Analysis of Pooling Strategies {#in-depth-analysis-of-pooling-strategies}
+
+To further understand the failure of optimization techniques, we compared different methods for pooling token vectors into a single vector: `mean` (`flatten`), `max`.
+
+| Model & Pooling Strategy | Hit@1 | Hit@5 | Hit@20 | Avg. Latency (s) |
+| :--- | :--- | :--- | :--- | :--- |
+| `vidore/colqwen2-v1.0` (`mean_pooling`) | 1.9% | 5.5% | 11.9% | 0.010 s |
+| `vidore/colqwen2-v1.0` (`max_pooling`) | 1.4% | 4.2% | 11.2% | 0.011 s |
+| **`vidore/colqwen2-v1.0` (`base`)** | **14.0%** | **65.4%** | **95.5%** | 0.668 s |
+
+All flattened pooling methods perform poorly, confirming that the aggregation of token vectors into a single representation loses the fine-grained detail required for this task.
+
+**Finding the Right Trade-Off**
+
+![](/static/assets/images/search/multivector/multivector-5.png)
+
+1.  **The Failure of Simple Pooling:** The `flatten` (mean pooling) and `max_pooling` strategies fail to improve upon the baseline. This is because their aggressive compression **destroys the essential localization signal**. The resulting single vector represents the *topic* of the page, not the *specific needle* on it.
+2.  **The Failure of `flatten and multivector rerank`:** This hybrid strategy is the *worst-performing* of all. The reason is a fundamental flaw in its design for this task: the first stage uses a simple pooled vector to retrieve candidates. Since this pooling eliminates the localization signal, the initial candidate set is effectively random.
+3.  **`hierarchical token pooling`:** By clustering and pooling tokens at indexing time, it reduces the number of vectors per page (in our case, by a factor of 4). This intelligently compresses the data, while preserving enough token-level detail in multivector setting. It achieves a **Hit@20 of 91.6%**, only slightly behind the `base` strategy's 95.5%, but is significantly faster.
+4.  **`base` multivector Search:** The vanilla, un-optimized `base` multivector search remains the most accurate strategy. Preserving every token vector provides the highest guarantee of finding the needle, but this comes at the highest computational cost.
+
+### Latency: {#latency}
+
+![](/static/assets/images/search/multivector/multivector-6.png)
+
+
+The "optimizations" are not all created equal. While simple pooling is fast, its inaccuracy makes it unusable. Hierarchical pooling, however, offers a compelling balance of speed and accuracy.
+
+| Strategy (on `vidore/colqwen2-v1.0`) | Avg. Search Latency (s) | Hit@20 Accuracy |
+| :--- | :--- | :--- |
+| `flatten` (Fast but Ineffective) | **0.010 s** | 11.9% |
+| `flatten and multivector rerank` (Slower and Ineffective) | 0.692 s | 7.3% |
+| **`hierarchical token pooling` (Accurate & Fast)** | **0.144 s** | **91.6%** |
+| `base` (Most Accurate) | 0.668 s | **95.5%** |
+_Latency reported is as seen on NVIDIA H100 GPUs_
+
+## Practical Considerations {#practical-considerations}
+
+The accuracy of `base` multivector search is impressive, but its computational intensity has historically limited its use.  `hierarchical token pooling` as a viable strategy creates a new, practical sweet spot on the accuracy-latency curve, making high-precision search accessible for a wider range of applications.
+
+### Search Latency and Computational Complexity {#search-latency-and-computational-complexity}
+
+
+As the benchmark data shows, the search latency for `base` multivector search is orders of magnitude higher than for single-vector (or pooled-vector) search. It's important to note that the reported ~670ms latency is an average from per-document evaluations. In this benchmark, each of the 25 documents is processed independently. All pages from a single document's variants (ranging from 5 to 200 pages) are ingested into a temporary table, resulting in a table size of approximately **1,230 rows (pages)** per evaluation. The search is performed on this table, and then the table is discarded. This highlights a significant performance cost even on a relatively small, per-document scale. This stems from a fundamental difference in computational complexity:
+
+*   **Modern ANN Search (for single vectors):** Algorithms like HNSW (Hierarchical Navigable Small World) provide sub-linear search times, often close to `O(log N)`, where `N` is the number of items in the index. This allows them to scale to billions of vectors with millisecond-level latency.
+*   **Late-Interaction Search (Multi-vector):** The search process is far more intensive. For each query, it must compute similarity scores between query tokens and the tokens of many candidate documents. The complexity is closer to `O(M * Q * D)`, where `M` is the number of candidate documents to score, `Q` is the number of query tokens, and `D` is the average number of tokens per document. `Hierarchical token pooling` directly attacks this problem by reducing `D`, leading to a significant reduction in search latency.
+
+### When to Use Multi-Vector Search {#when-to-use-multi-vector-search}
+
+Given these constraints, the choice of strategy depends on the specific requirements of the application.
+
+*   **For Maximum Precision (`base`):** In domains where the cost of missing the needle is extremely high, the full `base` search is the most reliable option.
+*   **For a Balance of Precision and Performance (`hierarchical token pooling`):** This is the ideal choice for many applications. It makes high-precision search practical for larger datasets and more interactive use cases where the sub-second latency of the `base` search may be too high. It significantly lowers the barrier to entry for adopting multivector search. It should still not be seen as a drop-in replacement for ANN, as it still requires more computational resources than single-vector search.
+*   **For General-Purpose Document Retrieval (`flatten` / single-vector):** For large-scale retrieval where understanding the "gist" is sufficient or where in cases where large-context text-based models suffice, single-vector search remains the most practical and scalable solution.
+
+## Appendix: Full Benchmark Results {#appendix-full-benchmark-results}
+
+The full benchmark results are shown below.
+
+```text
+| name                                        |   _runtime |   _step |    _timestamp | _wandb             |   avg_inference_latency |   avg_search_latency | hit_rates                                                                                                                                 | model_name                   | strategy             |
+|:--------------------------------------------|-----------:|--------:|--------------:|:-------------------|------------------------:|---------------------:|:------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------|:---------------------|
+| vidore/colqwen2-v0.1_base                   |      13410 |       0 |   1.75873e+09 | {'runtime': 13410} |              0.0418646  |           0.751151   | {'1': 0.1355151515151515, '10': 0.888, '20': 0.9597575757575758, '3': 0.3936969696969697, '5': 0.6349090909090909}                        | vidore/colqwen2-v0.1         | base                 |
+| vidore/colqwen2-v1.0_rerank                 |      13008 |       0 |   1.75873e+09 | {'runtime': 13008} |              0.0426252  |           0.692482   | {'1': 0.003393939393939394, '10': 0.03296969696969697, '20': 0.07296969696969698, '3': 0.010666666666666666, '5': 0.015272727272727271}   | vidore/colqwen2-v1.0         | rerank               |
+| vidore/colpali-v1.3_flatten                 |       2998 |       0 |   1.75872e+09 | {'runtime': 2998}  |              0.0296511  |           0.00833224 | {'1': 0.017454545454545455, '10': 0.06448484848484848, '20': 0.09333333333333334, '3': 0.034666666666666665, '5': 0.04509090909090909}    | vidore/colpali-v1.3          | flatten              |
+| vidore/colqwen2-v0.1_rerank                 |      13512 |       0 |   1.75873e+09 | {'runtime': 13512} |              0.0418855  |           0.662372   | {'1': 0.002909090909090909, '10': 0.026424242424242423, '20': 0.05987878787878788, '3': 0.0075151515151515155, '5': 0.014545454545454544} | vidore/colqwen2-v0.1         | rerank               |
+| vidore/colqwen2-v1.0_base                   |      12933 |       0 |   1.75873e+09 | {'runtime': 12933} |              0.0416839  |           0.667898   | {'1': 0.14012121212121212, '10': 0.8846060606060606, '20': 0.9553939393939394, '3': 0.4111515151515152, '5': 0.6538181818181819}          | vidore/colqwen2-v1.0         | base                 |
+| vidore/colpali-v1.3_rerank                  |      12090 |       0 |   1.75873e+09 | {'runtime': 12090} |              0.0310783  |           0.949047   | {'1': 0.006060606060606061, '10': 0.03878787878787879, '20': 0.06933333333333333, '3': 0.014545454545454544, '5': 0.022787878787878788}   | vidore/colpali-v1.3          | rerank               |
+| vidore/colqwen2-v1.0_flatten                |       4846 |       0 |   1.75874e+09 | {'runtime': 4846}  |              0.0504031  |           0.010443   | {'1': 0.018666666666666668, '10': 0.07854545454545454, '20': 0.11903030303030304, '3': 0.041212121212121214, '5': 0.055030303030303034}   | vidore/colqwen2-v1.0         | flatten              |
+| vidore/colqwen2-v0.1_base                   |      10838 |       0 |   1.75874e+09 | {'runtime': 10838} |              0.04627    |           0.692836   | {'1': 0.13575757575757577, '10': 0.8870303030303031, '20': 0.96, '3': 0.3941818181818182, '5': 0.6351515151515151}                        | vidore/colqwen2-v0.1         | base                 |
+| vidore/colqwen2-v0.1_flatten                |       4828 |       0 |   1.75874e+09 | {'runtime': 4828}  |              0.0486335  |           0.0103028  | {'1': 0.018424242424242423, '10': 0.07224242424242425, '20': 0.10521212121212122, '3': 0.03903030303030303, '5': 0.05212121212121213}     | vidore/colqwen2-v0.1         | flatten              |
+| vidore/colpali-v1.3_base                    |      10253 |       0 |   1.75874e+09 | {'runtime': 10253} |              0.0312334  |           0.93611    | {'1': 0.11272727272727272, '10': 0.551030303030303, '20': 0.6555151515151515, '3': 0.29987878787878786, '5': 0.4232727272727273}          | vidore/colpali-v1.3          | base                 |
+| vidore/colqwen2-v0.1_flatten                |       4745 |       0 |   1.75874e+09 | {'runtime': 4745}  |              0.0472825  |           0.00990508 | {'1': 0.018424242424242423, '10': 0.07296969696969698, '20': 0.10496969696969696, '3': 0.03951515151515152, '5': 0.05236363636363636}     | vidore/colqwen2-v0.1         | flatten              |
+| vidore/colqwen2.5-v0.2_base                 |      17218 |       0 |   1.75875e+09 | {'runtime': 17218} |              0.0540356  |           0.694855   | {'1': 0.11903030303030304, '10': 0.7127272727272728, '20': 0.8366060606060606, '3': 0.336, '5': 0.5258181818181819}                       | vidore/colqwen2.5-v0.2       | base                 |
+| vidore/colqwen2.5-v0.2_rerank               |      16859 |       0 |   1.75875e+09 | {'runtime': 16859} |              0.0518383  |           0.71693    | {'1': 0.0026666666666666666, '10': 0.025212121212121213, '20': 0.060848484848484846, '3': 0.006787878787878788, '5': 0.01187878787878788} | vidore/colqwen2.5-v0.2       | rerank               |
+| vidore/colqwen2-v0.1_rerank                 |       9484 |       0 |   1.75875e+09 | {'runtime': 9484}  |              0.0445599  |           0.691609   | {'1': 0.005333333333333333, '10': 0.030545454545454542, '20': 0.064, '3': 0.010666666666666666, '5': 0.017696969696969697}                | vidore/colqwen2-v0.1         | rerank               |
+| vidore/colSmol-256M_flatten                 |       6822 |       0 |   1.75875e+09 | {'runtime': 6822}  |              0.0404544  |           0.00822329 | {'1': 0.01575757575757576, '10': 0.06836363636363636, '20': 0.10496969696969696, '3': 0.03442424242424243, '5': 0.04678787878787879}      | vidore/colSmol-256M          | flatten              |
+| vidore/colSmol-500M_base                    |      12681 |       0 |   1.75875e+09 | {'runtime': 12681} |              0.0408026  |           0.850902   | {'1': 0.136, '10': 0.8029090909090909, '20': 0.8993939393939394, '3': 0.3806060606060606, '5': 0.5975757575757575}                        | vidore/colSmol-500M          | base                 |
+| vidore/colSmol-500M_rerank                  |      13066 |       0 |   1.75876e+09 | {'runtime': 13066} |              0.0440974  |           0.927632   | {'1': 0.003636363636363637, '10': 0.028606060606060607, '20': 0.07054545454545455, '3': 0.00896969696969697, '5': 0.015030303030303033}   | vidore/colSmol-500M          | rerank               |
+| vidore/colSmol-256M_rerank                  |      12646 |       0 |   1.75876e+09 | {'runtime': 12646} |              0.0391553  |           0.853279   | {'1': 0.003393939393939394, '10': 0.02909090909090909, '20': 0.07006060606060606, '3': 0.008727272727272728, '5': 0.015515151515151517}   | vidore/colSmol-256M          | rerank               |
+| vidore/colqwen2.5-v0.2_flatten              |       6348 |       0 |   1.75876e+09 | {'runtime': 6348}  |              0.0509971  |           0.00772653 | {'1': 0.017696969696969697, '10': 0.06545454545454546, '20': 0.09284848484848485, '3': 0.03515151515151515, '5': 0.045575757575757575}    | vidore/colqwen2.5-v0.2       | flatten              |
+| vidore/colSmol-256M_base                    |      11554 |       0 |   1.75876e+09 | {'runtime': 11554} |              0.0366467  |           0.848463   | {'1': 0.1435151515151515, '10': 0.8426666666666667, '20': 0.9173333333333332, '3': 0.40824242424242424, '5': 0.6404848484848484}          | vidore/colSmol-256M          | base                 |
+| vidore/colSmol-500M_flatten                 |       6395 |       0 |   1.75876e+09 | {'runtime': 6395}  |              0.0384664  |           0.00716238 | {'1': 0.018424242424242423, '10': 0.07345454545454545, '20': 0.11393939393939394, '3': 0.03903030303030303, '5': 0.05090909090909091}     | vidore/colSmol-500M          | flatten              |
+| openai/clip-vit-base-patch32_base           |        815 |       0 |   1.75876e+09 | {'runtime': 815}   |              0.00533487 |           0.00794629 | {'1': 0.016, '10': 0.07636363636363637, '20': 0.11757575757575756, '3': 0.03296969696969697, '5': 0.04703030303030303}                    | openai/clip-vit-base-patch32 | base                 |
+| vidore/colqwen2-v0.1_max_pooling            |       8758 |       0 |   1.7595e+09  | {'runtime': 8758}  |              0.0985753  |           0.0106716  | {'1': 0.015515151515151517, '10': 0.07296969696969698, '20': 0.11248484848484848, '3': 0.032484848484848484, '5': 0.04703030303030303}    | vidore/colqwen2-v0.1         | max_pooling          |
+| vidore/colpali-v1.3_max_pooling             |       4728 |       0 |   1.75949e+09 | {'runtime': 4728}  |              0.0718573  |           0.0103053  | {'1': 0.011393939393939394, '10': 0.05672727272727273, '20': 0.08872727272727272, '3': 0.02666666666666667, '5': 0.03709090909090909}     | vidore/colpali-v1.3          | max_pooling          |
+| vidore/colqwen2-v1.0_max_pooling            |       8760 |       0 |   1.7595e+09  | {'runtime': 8760}  |              0.0981102  |           0.0106316  | {'1': 0.013575757575757576, '10': 0.06933333333333333, '20': 0.112, '3': 0.02812121212121212, '5': 0.041939393939393936}                  | vidore/colqwen2-v1.0         | max_pooling          |
+| vidore/colqwen2-v0.1_max_pooling            |       7696 |       0 |   1.7595e+09  | {'runtime': 7696}  |              0.105203   |           0.011907   | {'1': 0.016242424242424242, '10': 0.07321212121212121, '20': 0.1132121212121212, '3': 0.03442424242424243, '5': 0.04945454545454545}      | vidore/colqwen2-v0.1         | max_pooling          |
+| vidore/colSmol-256M_max_pooling             |      13982 |       0 |   1.75951e+09 | {'runtime': 13982} |              0.0999708  |           0.0121211  | {'1': 0.00993939393939394, '10': 0.05818181818181818, '20': 0.09187878787878788, '3': 0.025212121212121213, '5': 0.037575757575757575}    | vidore/colSmol-256M          | max_pooling          |
+| vidore/colSmol-500M_max_pooling             |      14191 |       0 |   1.75951e+09 | {'runtime': 14191} |              0.111858   |           0.0121703  | {'1': 0.012363636363636365, '10': 0.07539393939393939, '20': 0.13187878787878787, '3': 0.02787878787878788, '5': 0.0416969696969697}      | vidore/colSmol-500M          | max_pooling          |
+| vidore/colqwen2-v0.1_hierarchical_pooling   |       4507 |       0 |   1.7599e+09  | {'runtime': 4507}  |              0.0320023  |           0.133653   | {'1': 0.1296969696969697, '10': 0.8504242424242424, '20': 0.9343030303030304, '3': 0.37527272727272726, '5': 0.6041212121212122}          | vidore/colqwen2-v0.1         | hierarchical_pooling |
+| vidore/colpali-v1.3_hierarchical_pooling    |       5816 |       0 |   1.7599e+09  | {'runtime': 5816}  |              0.0217625  |           0.188727   | {'1': 0.10763636363636364, '10': 0.5372121212121213, '20': 0.6482424242424243, '3': 0.29333333333333333, '5': 0.4167272727272727}         | vidore/colpali-v1.3          | hierarchical_pooling |
+| vidore/colqwen2.5-v0.2_hierarchical_pooling |       8430 |       0 |   1.75991e+09 | {'runtime': 8430}  |              0.043073   |           0.141276   | {'1': 0.11103030303030303, '10': 0.6892121212121212, '20': 0.8203636363636364, '3': 0.3185454545454545, '5': 0.4989090909090909}          | vidore/colqwen2.5-v0.2       | hierarchical_pooling |
+| vidore/colqwen2-v1.0_hierarchical_pooling   |       5685 |       0 |   1.75991e+09 | {'runtime': 5685}  |              0.0343824  |           0.144062   | {'1': 0.13745454545454547, '10': 0.822060606060606, '20': 0.9156363636363636, '3': 0.3856969696969697, '5': 0.6050909090909091}           | vidore/colqwen2-v1.0         | hierarchical_pooling |
+```
\ No newline at end of file
diff --git a/docs/web/tutorials/search/multivector-xtr.mdx b/docs/web/tutorials/search/multivector-xtr.mdx
new file mode 100644
index 000000000..cb6ed2c3e
--- /dev/null
+++ b/docs/web/tutorials/search/multivector-xtr.mdx
@@ -0,0 +1,27 @@
+---
+title: "Multi-vector search with XTR"
+sidebarTitle: "Multi-vector XTR"
+description: "Represent a document with many vectors, and speed retrieval with ConteXtualized Token Retrieval."
+---
+
+Covers [multi-vector search](/search/vector-search) enhanced by XTR
+(ConteXtualized Token Retriever): representing complex data with several vectors
+for more nuanced meaning, and using XTR to speed retrieval by prioritizing the
+most important tokens.
+
+For a comparison of retrieval strategies on a token-level benchmark, see
+[Needle in a haystack](/tutorials/search/multivector-needle-in-a-haystack).
+
+<Note>
+This example is a runnable notebook in the
+[vectordb-recipes](https://github.com/lancedb/vectordb-recipes) repository rather than a page here.
+</Note>
+
+<CardGroup cols={2}>
+  <Card title="Open in Colab" icon="google" href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multivector_xtr/main.ipynb">
+    Run it end to end in the browser.
+  </Card>
+  <Card title="View on GitHub" icon="github" href="https://github.com/lancedb/vectordb-recipes/tree/main/examples/multivector_xtr">
+    Read the source.
+  </Card>
+</CardGroup>
diff --git a/docs/web/tutorials/search/ner-search.mdx b/docs/web/tutorials/search/ner-search.mdx
new file mode 100644
index 000000000..462475246
--- /dev/null
+++ b/docs/web/tutorials/search/ner-search.mdx
@@ -0,0 +1,23 @@
+---
+title: "NER-powered vector search"
+sidebarTitle: "NER Search"
+description: "Extract named entities and embed them to sharpen retrieval."
+---
+
+Uses Named Entity Recognition to power vector search. Extracting entities --
+people, places, organizations -- and embedding them alongside the text improves
+accuracy when queries are about specific things rather than general topics.
+
+<Note>
+This example is a runnable notebook in the
+[vectordb-recipes](https://github.com/lancedb/vectordb-recipes) repository rather than a page here.
+</Note>
+
+<CardGroup cols={2}>
+  <Card title="Open in Colab" icon="google" href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search/NER_powered_Semantic_Search_with_LanceDB.ipynb">
+    Run it end to end in the browser.
+  </Card>
+  <Card title="View on GitHub" icon="github" href="https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/NER-powered-Semantic-Search">
+    Read the source.
+  </Card>
+</CardGroup>
diff --git a/docs/web/tutorials/search/reddit-summarizer.mdx b/docs/web/tutorials/search/reddit-summarizer.mdx
new file mode 100644
index 000000000..7d562ba9e
--- /dev/null
+++ b/docs/web/tutorials/search/reddit-summarizer.mdx
@@ -0,0 +1,23 @@
+---
+title: "Reddit concept summarizer"
+sidebarTitle: "Reddit Summarizer"
+description: "An end-to-end pipeline from Reddit text to embeddings to summarization and search."
+---
+
+A complete pipeline: acquire text from Reddit, turn it into embeddings, store
+and manage those vectors in LanceDB, then build summarization and semantic
+search on top. Useful as a shape for any ingest-embed-serve workflow.
+
+<Note>
+This example is a runnable notebook in the
+[vectordb-recipes](https://github.com/lancedb/vectordb-recipes) repository rather than a page here.
+</Note>
+
+<CardGroup cols={2}>
+  <Card title="Open in Colab" icon="google" href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Reddit-summarization-and-search/subreddit_summarization_querying.ipynb">
+    Run it end to end in the browser.
+  </Card>
+  <Card title="View on GitHub" icon="github" href="https://github.com/lancedb/vectordb-recipes/tree/main/examples/Reddit-summarization-and-search">
+    Read the source.
+  </Card>
+</CardGroup>
diff --git a/docs/web/tutorials/search/vector-arithmetic.mdx b/docs/web/tutorials/search/vector-arithmetic.mdx
new file mode 100644
index 000000000..d5adc2b79
--- /dev/null
+++ b/docs/web/tutorials/search/vector-arithmetic.mdx
@@ -0,0 +1,24 @@
+---
+title: "Semantic result merging"
+sidebarTitle: "Vector Arithmetic"
+description: "Add and subtract embeddings to steer a query toward or away from a concept."
+---
+
+Explores vector arithmetic: manipulating embeddings to capture relationships a
+single query vector cannot. Adding or subtracting the vector representation of a
+concept shifts the query, which makes searches more nuanced than a literal
+similarity lookup.
+
+<Note>
+This example is a runnable notebook in the
+[vectordb-recipes](https://github.com/lancedb/vectordb-recipes) repository rather than a page here.
+</Note>
+
+<CardGroup cols={2}>
+  <Card title="Open in Colab" icon="google" href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Vector-Arithmetic-with-LanceDB/main.ipynb">
+    Run it end to end in the browser.
+  </Card>
+  <Card title="View on GitHub" icon="github" href="https://github.com/lancedb/vectordb-recipes/tree/main/examples/Vector-Arithmetic-with-LanceDB">
+    Read the source.
+  </Card>
+</CardGroup>
diff --git a/docs/web/tutorials/search/video-search.mdx b/docs/web/tutorials/search/video-search.mdx
new file mode 100644
index 000000000..0a8323597
--- /dev/null
+++ b/docs/web/tutorials/search/video-search.mdx
@@ -0,0 +1,24 @@
+---
+title: "Semantic search across videos"
+sidebarTitle: "Video Search"
+description: "Embed video with V-JEPA and search it by similarity."
+---
+
+Builds a video search application on V-JEPA (Video Joint Embedding Predictive
+Architecture). Generates vector embeddings for video content and uses
+[vector search](/search/vector-search) to find visually similar clips -- a
+multimodal case where the searchable column is not text.
+
+<Note>
+This example is a runnable notebook in the
+[vectordb-recipes](https://github.com/lancedb/vectordb-recipes) repository rather than a page here.
+</Note>
+
+<CardGroup cols={2}>
+  <Card title="Open in Colab" icon="google" href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/v-jepa-video-search/intra-video.ipynb">
+    Run it end to end in the browser.
+  </Card>
+  <Card title="View on GitHub" icon="github" href="https://github.com/lancedb/vectordb-recipes/tree/main/examples/v-jepa-video-search">
+    Read the source.
+  </Card>
+</CardGroup>
diff --git a/java/README.md b/java/README.md
index c46c8174b..0e54d20e4 100644
--- a/java/README.md
+++ b/java/README.md
@@ -29,7 +29,7 @@ LanceNamespace namespaceClient = LanceDbNamespaceClientBuilder.newBuilder()
     .build();
 ```
 
-## MemWAL LSM write path
+## MemWAL write path
 
 Most table operations reach LanceDB through the `LanceNamespace` above, which is
 generated from the Lance Namespace specification. The MemWAL LSM routes are not part
@@ -37,27 +37,27 @@ of that specification, so they are issued through a separate client:
 
 ```java
 import com.lancedb.LanceDbRestClient;
-import com.lancedb.LanceDbTableLsm;
-import com.lancedb.LsmWriteSpec;
+import com.lancedb.LanceDbTableMemWal;
+import com.lancedb.MemWalWriteSpec;
 
 LanceDbRestClient client = LanceDbNamespaceClientBuilder.newBuilder()
     .apiKey("your_lancedb_cloud_api_key")
     .database("your_database_name")
     .buildRestClient();
 
-LanceDbTableLsm lsm = new LanceDbTableLsm(client, "my_table");
+LanceDbTableMemWal memWal = new LanceDbTableMemWal(client, "my_table");
 
 // Route future merge_insert upserts through the MemWAL, hash-bucketed by `id`.
-lsm.setLsmWriteSpec(LsmWriteSpec.bucket("id", 16));
+memWal.enableMemWal(MemWalWriteSpec.bucket("id", 16));
 
 // ... merge_insert traffic ...
 
 // Converge the fresh tier into the base table.
-lsm.checkpointLsm();
+memWal.checkpointMemWal();
 
-// Inspect live per-bucket state.
-lsm.getLsmStats().ifPresent(stats -> stats.buckets().forEach(bucket ->
-    System.out.println(bucket.shardId() + ": " + bucket.generations().size() + " L0 generations")));
+// Inspect live per-table-shard state.
+memWal.getMemWalStats().ifPresent(stats -> stats.tableShards().forEach(tableShard ->
+    System.out.println(tableShard.shardId() + ": " + tableShard.sstables().size() + " SSTables")));
 
 client.close();
 ```
@@ -67,12 +67,17 @@ reader usually expects:
 
 | Value | Meaning |
 | --- | --- |
-| unset (null) | Maintain **every** index the MemWAL can, resolved on install |
+| unset (null) | Maintain **every** index the table has, including ones created later |
 | `Collections.emptyList()` | Maintain **none** |
 | `Arrays.asList("id_idx")` | Maintain exactly those |
 
 ## Development
 
+The parent POM includes `https://maven.lance.org` to resolve Lance beta and RC
+dependencies. Use an explicit `lance-core.version`; numbered beta and RC versions
+are Maven releases, not snapshots. Maven Central remains available for stable
+and historical versions, as well as other dependencies.
+
 Build:
 
 ```shell
diff --git a/java/lancedb-core/pom.xml b/java/lancedb-core/pom.xml
index b7cdb9e41..4bc763f6a 100644
--- a/java/lancedb-core/pom.xml
+++ b/java/lancedb-core/pom.xml
@@ -8,7 +8,7 @@
     <parent>
       <groupId>com.lancedb</groupId>
       <artifactId>lancedb-parent</artifactId>
-      <version>0.40.0-beta.11</version>
+      <version>0.41.0-beta.7</version>
       <relativePath>../pom.xml</relativePath>
     </parent>
 
@@ -44,7 +44,30 @@
         <dependency>
             <groupId>com.fasterxml.jackson.core</groupId>
             <artifactId>jackson-databind</artifactId>
-            <version>2.17.1</version>
+        </dependency>
+
+        <!-- Declared directly so downstream consumers resolve the patched versions:
+             the parent's jackson-bom only manages our own build, and otherwise Maven's
+             nearest-wins mediation picks older versions via arrow-vector and
+             lance-namespace-apache-client. -->
+        <dependency>
+            <groupId>com.fasterxml.jackson.core</groupId>
+            <artifactId>jackson-core</artifactId>
+        </dependency>
+
+        <dependency>
+            <groupId>com.fasterxml.jackson.core</groupId>
+            <artifactId>jackson-annotations</artifactId>
+        </dependency>
+
+        <dependency>
+            <groupId>com.fasterxml.jackson.datatype</groupId>
+            <artifactId>jackson-datatype-jsr310</artifactId>
+        </dependency>
+
+        <dependency>
+            <groupId>com.fasterxml.jackson.jaxrs</groupId>
+            <artifactId>jackson-jaxrs-json-provider</artifactId>
         </dependency>
 
         <dependency>
diff --git a/java/lancedb-core/src/main/java/com/lancedb/JsonFields.java b/java/lancedb-core/src/main/java/com/lancedb/JsonFields.java
index b78e2411a..e0a288eaf 100644
--- a/java/lancedb-core/src/main/java/com/lancedb/JsonFields.java
+++ b/java/lancedb-core/src/main/java/com/lancedb/JsonFields.java
@@ -20,12 +20,12 @@ import com.fasterxml.jackson.databind.JsonNode;
  *
  * <p>Every reader fails closed: a missing, null, or wrong-typed field throws rather than
  * defaulting. That mirrors the serde decoding the Rust client applies to the same payloads in
- * {@code rust/lancedb/src/table/lsm_stats.rs}, where a required field has no default and a
+ * {@code rust/lancedb/src/table/memwal_stats.rs}, where a required field has no default and a
  * malformed response is an error rather than a zero.
  *
  * <p>The alternative — Jackson's {@code path()}, which yields a missing node that reads as an empty
- * array or a zero — is unsafe here because {@link LanceDbTableLsm#checkpointLsm()} decides
- * convergence from these numbers. A defaulted {@code generations} array is indistinguishable from a
+ * array or a zero — is unsafe here because {@link LanceDbTableMemWal#checkpointMemWal()} decides
+ * convergence from these numbers. A defaulted {@code sstables} array is indistinguishable from a
  * drained one, so a malformed response would report a checkpoint that never happened.
  */
 final class JsonFields {
diff --git a/java/lancedb-core/src/main/java/com/lancedb/LanceDbNamespaceClientBuilder.java b/java/lancedb-core/src/main/java/com/lancedb/LanceDbNamespaceClientBuilder.java
index da241dfd5..05aed8c74 100644
--- a/java/lancedb-core/src/main/java/com/lancedb/LanceDbNamespaceClientBuilder.java
+++ b/java/lancedb-core/src/main/java/com/lancedb/LanceDbNamespaceClientBuilder.java
@@ -151,8 +151,8 @@ public class LanceDbNamespaceClientBuilder {
    * Build a {@link LanceDbRestClient} for the same endpoint.
    *
    * <p>Needed only for LanceDB routes that the Lance Namespace specification does not cover — the
-   * MemWAL LSM write path, reached through {@link LanceDbTableLsm}. Every other table operation
-   * belongs on the {@link LanceNamespace} from {@link #build()}.
+   * MemWAL write path, reached through {@link LanceDbTableMemWal}. Every other table
+   * operation belongs on the {@link LanceNamespace} from {@link #build()}.
    *
    * <p>The returned client owns an HTTP connection pool; close it when you are done with it.
    *
diff --git a/java/lancedb-core/src/main/java/com/lancedb/LanceDbRestClient.java b/java/lancedb-core/src/main/java/com/lancedb/LanceDbRestClient.java
index baafbb9df..5275c8346 100644
--- a/java/lancedb-core/src/main/java/com/lancedb/LanceDbRestClient.java
+++ b/java/lancedb-core/src/main/java/com/lancedb/LanceDbRestClient.java
@@ -31,9 +31,9 @@ import java.io.UncheckedIOException;
  * specification does not cover.
  *
  * <p>Most table operations reach LanceDB through {@link org.lance.namespace.LanceNamespace}, which
- * is generated from the namespace spec. A handful of routes — the MemWAL LSM write path in
+ * is generated from the namespace spec. A handful of routes — the MemWAL write path in
  * particular — are served by the same endpoint but are not part of that spec, so they are issued
- * directly here. See {@link LanceDbTableLsm}.
+ * directly here. See {@link LanceDbTableMemWal}.
  *
  * <p>Obtain one from {@link LanceDbNamespaceClientBuilder#buildRestClient()}.
  */
@@ -50,8 +50,8 @@ public class LanceDbRestClient implements Closeable {
     this.apiKey = apiKey;
     this.database = database;
     // Automatic retries off, deliberately. The default strategy retries 429 and 503 —
-    // exactly the two statuses LanceDbTableLsm.checkpointLsm() acts on — which would
-    // silently double its explicit retry budget and would also retry compact_lsm in
+    // exactly the two statuses LanceDbTableMemWal.checkpointMemWal() acts on — which would
+    // silently double its explicit retry budget and would also retry compact_memwal in
     // place, where the loop is designed to fall through to a fresh stats poll instead.
     // The checkpoint loop owns the 421/429/503 transitions; the transport must not.
     this.http = HttpClients.custom().disableAutomaticRetries().build();
@@ -98,8 +98,9 @@ public class LanceDbRestClient implements Closeable {
   /**
    * A non-2xx response.
    *
-   * <p>The status is exposed because callers act on it: {@link LanceDbTableLsm#checkpointLsm()}
-   * treats 429 and 503 as retryable and 421 as a lost node claim.
+   * <p>The status is exposed because callers act on it: {@link
+   * LanceDbTableMemWal#checkpointMemWal()} treats 429 and 503 as retryable and 421 as a lost node
+   * claim.
    */
   public static class HttpException extends RuntimeException {
     private static final long serialVersionUID = 1L;
diff --git a/java/lancedb-core/src/main/java/com/lancedb/LanceDbTableLsm.java b/java/lancedb-core/src/main/java/com/lancedb/LanceDbTableMemWal.java
similarity index 56%
rename from java/lancedb-core/src/main/java/com/lancedb/LanceDbTableLsm.java
rename to java/lancedb-core/src/main/java/com/lancedb/LanceDbTableMemWal.java
index 23b18199e..75c6c33b1 100644
--- a/java/lancedb-core/src/main/java/com/lancedb/LanceDbTableLsm.java
+++ b/java/lancedb-core/src/main/java/com/lancedb/LanceDbTableMemWal.java
@@ -22,11 +22,11 @@ import java.util.Optional;
 import java.util.OptionalLong;
 
 /**
- * The MemWAL LSM write path for one LanceDB Cloud or Enterprise table.
+ * The MemWAL write path for one LanceDB Cloud or Enterprise table.
  *
- * <p>Installing an {@link LsmWriteSpec} routes {@code mergeInsert} upserts through Lance's MemWAL —
- * an LSM-style append — instead of the standard merge path. Rows land in an in-memory memtable,
- * seal into L0 generations, and are merged into the base table by compaction.
+ * <p>Installing an {@link MemWalWriteSpec} routes {@code mergeInsert} upserts through Lance's
+ * MemWAL — an LSM-style append — instead of the standard merge path. Rows land in an in-memory
+ * memtable, freeze into SSTables, and are merged into the base table by compaction.
  *
  * <p>These routes are not part of the Lance Namespace specification, so they are issued directly
  * rather than through {@link org.lance.namespace.LanceNamespace}.
@@ -37,23 +37,23 @@ import java.util.OptionalLong;
  *     .database("your_database_name")
  *     .buildRestClient();
  *
- * LanceDbTableLsm lsm = new LanceDbTableLsm(client, "my_table");
- * lsm.setLsmWriteSpec(LsmWriteSpec.bucket("id", 16));
+ * LanceDbTableMemWal memWal = new LanceDbTableMemWal(client, "my_table");
+ * memWal.enableMemWal(MemWalWriteSpec.bucket("id", 16));
  * // ... merge_insert traffic ...
- * lsm.checkpointLsm();
+ * memWal.checkpointMemWal();
  * }</pre>
  */
-public class LanceDbTableLsm {
+public class LanceDbTableMemWal {
 
   /**
-   * Interval between {@code get_lsm_stats} polls during a checkpoint. One interval is roughly one
-   * compaction pass, the granularity at which the answer can change.
+   * Interval between {@code get_memwal_stats} polls during a checkpoint. One interval is roughly
+   * one compaction pass, the granularity at which the answer can change.
    */
   private static final long POLL_INTERVAL_MS = 5_000L;
 
   /**
-   * Cap on re-issues from {@code flushLsm} after a 421, so a crash-looping node cannot turn flush →
-   * compact → 421 → flush into a spin.
+   * Cap on re-issues from {@code flushMemWal} after a 421, so a crash-looping node cannot turn
+   * flush → compact → 421 → flush into a spin.
    *
    * <p>Deliberately not shared with {@link #MAX_RETRIES}: a claim that keeps evaporating is a
    * broken node, while contention is routine and wants a real budget.
@@ -79,7 +79,7 @@ public class LanceDbTableLsm {
    * @param tableIdentifier The table's full identifier, {@code $}-delimited when it sits inside a
    *     namespace, such as {@code analytics$events}.
    */
-  public LanceDbTableLsm(LanceDbRestClient client, String tableIdentifier) {
+  public LanceDbTableMemWal(LanceDbRestClient client, String tableIdentifier) {
     if (client == null) {
       throw new IllegalArgumentException("Client cannot be null");
     }
@@ -91,136 +91,140 @@ public class LanceDbTableLsm {
   }
 
   /**
-   * Install an {@link LsmWriteSpec} on this table, selecting the MemWAL LSM write path for future
-   * {@code mergeInsert} calls.
+   * Install an {@link MemWalWriteSpec} on this table, selecting the MemWAL write path for
+   * future {@code mergeInsert} calls.
    *
    * <p>All variants require the table to have an unenforced primary key; bucket sharding
    * additionally requires it to be the single column being bucketed.
    */
-  public void setLsmWriteSpec(LsmWriteSpec spec) {
+  public void enableMemWal(MemWalWriteSpec spec) {
     if (spec == null) {
       throw new IllegalArgumentException("Spec cannot be null");
     }
-    client.post(route("set_lsm_write_spec"), spec.toRequestBody());
+    client.post(route("enable_memwal"), spec.toRequestBody());
   }
 
   /**
-   * Remove the {@link LsmWriteSpec} from this table, reverting to the standard {@code mergeInsert}
-   * write path.
+   * Remove the {@link MemWalWriteSpec} from this table, reverting to the standard {@code
+   * mergeInsert} write path.
    *
    * <p>Errors if no spec is currently set.
    */
-  public void unsetLsmWriteSpec() {
-    client.post(route("unset_lsm_write_spec"), null);
+  public void disableMemWal() {
+    client.post(route("disable_memwal"), null);
   }
 
   /**
-   * Read the {@link LsmWriteSpec} currently installed on this table.
+   * Read the {@link MemWalWriteSpec} currently installed on this table.
    *
-   * <p>Empty when the LSM write path is not enabled. The returned spec mirrors what was installed,
-   * except that {@link LsmWriteSpec#maintainedIndexes()} always reports the concrete list resolved
-   * when the spec was set — a null selection never round-trips.
+   * <p>Empty when the MemWAL write path is not enabled at all — no spec has been set, or one was
+   * removed with {@link #disableMemWal()}. That is a different answer from a present spec whose
+   * {@link MemWalWriteSpec#maintainedIndexes()} is null, which selects indexes automatically.
+   *
+   * <p>The spec read back is the one that was installed, selection included: null maintains every
+   * supported index the table has now or gains later, an empty list maintains none, and a non-empty
+   * list maintains exactly those. All three round-trip.
    */
-  public Optional<LsmWriteSpec> getLsmWriteSpec() {
-    JsonNode response = client.post(route("get_lsm_write_spec"), null);
-    if (response == null || !response.hasNonNull("lsm_write_spec")) {
+  public Optional<MemWalWriteSpec> getMemWalSpec() {
+    JsonNode response = client.post(route("get_memwal_spec"), null);
+    if (response == null || !response.hasNonNull("memwal_spec")) {
       return Optional.empty();
     }
-    return Optional.of(LsmWriteSpec.fromJson(response.get("lsm_write_spec")));
+    return Optional.of(MemWalWriteSpec.fromJson(response.get("memwal_spec")));
   }
 
   /**
-   * Seal every bucket's active memtable into a new L0 generation.
+   * Freeze every table shard's active memtable into a new SSTable.
    *
-   * <p>Returns once the seal is committed. Sealing an empty memtable is a no-op, so this is safe to
-   * call repeatedly.
+   * <p>Returns once the freeze is committed. Freezing an empty memtable is a no-op, so this is safe
+   * to call repeatedly.
    */
-  public void flushLsm() {
-    client.post(route("flush_lsm"), null);
+  public void flushMemWal() {
+    client.post(route("flush_memwal"), null);
   }
 
   /**
-   * Trigger a background L0 → base compaction pass per bucket.
+   * Trigger a background SSTable compaction pass per table shard.
    *
    * <p>Returns once the passes are <em>dispatched</em>, not once they finish — watch {@link
-   * #getLsmStats}, or use {@link #checkpointLsm} to wait for convergence.
+   * #getMemWalStats}, or use {@link #checkpointMemWal} to wait for convergence.
    */
-  public void compactLsm() {
-    client.post(route("compact_lsm"), null);
+  public void compactMemWal() {
+    client.post(route("compact_memwal"), null);
   }
 
   /**
-   * Read live per-bucket LSM state.
+   * Read live per-table-shard MemWAL state.
    *
-   * <p>Answers "how far behind is my fresh tier", "which bucket is hot", and "why is my fresh-tier
-   * vector search brute-force". Mutates no table state.
+   * <p>Answers "how far behind is my fresh tier", "which table shard is hot", and "why is my
+   * fresh-tier vector search brute-force". Mutates no table state.
    *
-   * <p>Empty only when the LSM write path is not enabled — that is, when the server sends an absent
-   * or null {@code lsm_stats}. A stats object that is present is decoded strictly, and a malformed
-   * one throws rather than decoding to something empty, because {@link #checkpointLsm} reads
-   * convergence out of these numbers and cannot tell a defaulted array from a drained one.
+   * <p>Empty only when the MemWAL write path is not enabled — that is, when the server sends an
+   * absent or null {@code memwal_stats}. A stats object that is present is decoded strictly, and a
+   * malformed one throws rather than decoding to something empty, because {@link #checkpointMemWal}
+   * reads convergence out of these numbers and cannot tell a defaulted array from a drained one.
    *
-   * @param includeGenerationRows Also count rows per L0 generation. Off by default because each
-   *     count opens an uncached Lance dataset.
+   * @param includeSstableRows Also count rows per SSTable. Off by default because each count opens
+   *     an uncached Lance dataset.
    * @throws IllegalStateException if the response is absent or does not decode.
    */
-  public Optional<LsmStats> getLsmStats(boolean includeGenerationRows) {
+  public Optional<MemWalStats> getMemWalStats(boolean includeSstableRows) {
     Map<String, Object> body = new LinkedHashMap<String, Object>();
-    body.put("include_generation_rows", includeGenerationRows);
-    JsonNode response = client.post(route("get_lsm_stats"), body);
+    body.put("include_sstable_rows", includeSstableRows);
+    JsonNode response = client.post(route("get_memwal_stats"), body);
     if (response == null) {
-      throw new IllegalStateException("get_lsm_stats returned an empty response body");
+      throw new IllegalStateException("get_memwal_stats returned an empty response body");
     }
-    JsonNode stats = response.get("lsm_stats");
+    JsonNode stats = response.get("memwal_stats");
     if (stats == null || stats.isNull()) {
       return Optional.empty();
     }
-    return Optional.of(LsmStats.fromJson(stats));
+    return Optional.of(MemWalStats.fromJson(stats));
   }
 
-  /** Equivalent to {@code getLsmStats(false)}. */
-  public Optional<LsmStats> getLsmStats() {
-    return getLsmStats(false);
+  /** Equivalent to {@code getMemWalStats(false)}. */
+  public Optional<MemWalStats> getMemWalStats() {
+    return getMemWalStats(false);
   }
 
   /**
-   * Converge this table's LSM write path into its base table.
+   * Converge this table's MemWAL write path into its base table.
    *
-   * <p>Seals once, fixes a target watermark from the resulting L0, then triggers compaction and
-   * polls until that L0 is gone. The target set is fixed at the start, so generations created
-   * <em>during</em> the checkpoint are ignored — that is what lets it terminate under write load,
-   * and what makes it best-effort: it converges the fresh tier as of some instant. Idempotent,
-   * abandonable at any point, safe on a cadence.
+   * <p>Freezes once, fixes a target watermark from the resulting SSTables, then triggers compaction
+   * and polls until those SSTables are gone. The target set is fixed at the start, so sstables
+   * created <em>during</em> the checkpoint are ignored — that is what lets it terminate under write
+   * load, and what makes it best-effort: it converges the fresh tier as of some instant.
+   * Idempotent, abandonable at any point, safe on a cadence.
    *
-   * <p>The loop runs here, not on the server: {@link #compactLsm} dispatches a pass and returns, so
-   * nothing holds a socket and a client can vanish mid-operation with nothing to reconcile.
+   * <p>The loop runs here, not on the server: {@link #compactMemWal} dispatches a pass and returns,
+   * so nothing holds a socket and a client can vanish mid-operation with nothing to reconcile.
    * Completion is read from generation numbers in the shard manifest — durable state, unlike a
    * count in a compact response, which a concurrent write invalidates.
    *
    * <p>No liveness bound — the caller owns the deadline. The compactor pool is shared across
    * tables, so a checkpoint queued behind unrelated work looks exactly like one that is merging.
    */
-  public void checkpointLsm() {
+  public void checkpointMemWal() {
     for (int reissue = 0; reissue <= MAX_REISSUES; reissue++) {
-      // The seal turns everything written before this call into a generation, so the
-      // watermark has to be read after it. Idempotent: sealing an empty memtable is a
-      // no-op, so a re-issue does not churn empty generations.
-      if (issueVoid(this::flushLsm)) {
+      // The freeze turns everything written before this call into an SSTable, so the
+      // watermark has to be read after it. Idempotent: freezing an empty memtable is a
+      // no-op, so a re-issue does not churn empty sstables.
+      if (issueVoid(this::flushMemWal)) {
         backoff(reissue);
         continue;
       }
 
-      Attempt<Optional<LsmStats>> stats = issue(() -> getLsmStats(false));
+      Attempt<Optional<MemWalStats>> stats = issue(() -> getMemWalStats(false));
       if (stats.lostClaim) {
         backoff(reissue);
         continue;
       }
       if (!stats.value.isPresent()) {
-        // Not WAL-backed; flushLsm would have errored first but for a race.
+        // Not WAL-backed; flushMemWal would have errored first but for a race.
         return;
       }
 
-      Map<String, Long> targets = newestGenerations(stats.value.get());
+      Map<String, Long> targets = newestSstableGenerations(stats.value.get());
       if (targets.isEmpty()) {
         return;
       }
@@ -231,18 +235,18 @@ public class LanceDbTableLsm {
       backoff(reissue);
     }
     throw new IllegalStateException(
-        "checkpointLsm: the owning node kept losing its claim; re-issued from flush the maximum "
+        "checkpointMemWal: the owning node kept losing its claim; re-issued from flush the maximum "
             + "number of times");
   }
 
   /**
-   * Trigger and poll until no bucket holds a generation at or below its target.
+   * Trigger and poll until no table shard holds an SSTable generation at or below its target.
    *
    * @return true when the drain finished, false when the table needs re-claiming from flush.
    */
   private boolean drainToTargets(Map<String, Long> targets) {
     while (true) {
-      Attempt<Optional<LsmStats>> stats = issue(() -> getLsmStats(false));
+      Attempt<Optional<MemWalStats>> stats = issue(() -> getMemWalStats(false));
       if (stats.lostClaim) {
         return false;
       }
@@ -250,21 +254,21 @@ public class LanceDbTableLsm {
         return true;
       }
 
-      // `compacting` is the bucket's compaction latch, held from dispatch until the pass
+      // `compacting` is the table shard's compaction latch, held from dispatch until the pass
       // ends — including while it waits on a pod-wide permit. So it answers one question
-      // only: do not pile on. Buckets with nothing outstanding are skipped, not counted
+      // only: do not pile on. Table shards with nothing outstanding are skipped, not counted
       // as idle.
       long outstanding = 0;
       boolean allCompacting = true;
-      for (BucketStats bucket : stats.value.get().buckets()) {
-        Long target = targets.get(bucket.shardId());
+      for (TableShardStats tableShard : stats.value.get().tableShards()) {
+        Long target = targets.get(tableShard.shardId());
         if (target == null) {
           continue;
         }
-        long remaining = bucket.outstandingGenerations(target);
+        long remaining = tableShard.outstandingSstables(target);
         if (remaining > 0) {
           outstanding += remaining;
-          allCompacting &= bucket.compacting();
+          allCompacting &= tableShard.compacting();
         }
       }
       if (outstanding == 0) {
@@ -273,7 +277,7 @@ public class LanceDbTableLsm {
 
       if (!allCompacting) {
         try {
-          compactLsm();
+          compactMemWal();
         } catch (LanceDbRestClient.HttpException e) {
           if (isLostClaim(e)) {
             return false;
@@ -281,7 +285,7 @@ public class LanceDbTableLsm {
           if (!isRetryable(e)) {
             throw e;
           }
-          // A 429 here means the server could latch no bucket at all, which the poll
+          // A 429 here means the server could latch no table shard at all, which the poll
           // above already handles. Not retried in place: the latch it would contend for
           // is the one doing the work, so fall through and re-read — POLL_INTERVAL_MS is
           // the backoff.
@@ -291,13 +295,13 @@ public class LanceDbTableLsm {
     }
   }
 
-  /** The newest generation held by each bucket, skipping buckets holding none. */
-  private static Map<String, Long> newestGenerations(LsmStats stats) {
+  /** The newest SSTable generation held by each table shard, skipping table shards holding none. */
+  private static Map<String, Long> newestSstableGenerations(MemWalStats stats) {
     Map<String, Long> targets = new HashMap<String, Long>();
-    for (BucketStats bucket : stats.buckets()) {
-      OptionalLong newest = bucket.newestGeneration();
+    for (TableShardStats tableShard : stats.tableShards()) {
+      OptionalLong newest = tableShard.newestSstableGeneration();
       if (newest.isPresent()) {
-        targets.put(bucket.shardId(), newest.getAsLong());
+        targets.put(tableShard.shardId(), newest.getAsLong());
       }
     }
     return targets;
diff --git a/java/lancedb-core/src/main/java/com/lancedb/LsmStats.java b/java/lancedb-core/src/main/java/com/lancedb/LsmStats.java
deleted file mode 100644
index 3496ebc96..000000000
--- a/java/lancedb-core/src/main/java/com/lancedb/LsmStats.java
+++ /dev/null
@@ -1,56 +0,0 @@
-/*
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package com.lancedb;
-
-import com.fasterxml.jackson.databind.JsonNode;
-
-import java.util.ArrayList;
-import java.util.Collections;
-import java.util.List;
-
-/**
- * Live per-bucket LSM state, as returned by {@link LanceDbTableLsm#getLsmStats()}.
- *
- * <p>Nothing here is derived: sums and differences (total L0 bytes, WAL lag) are the caller's to
- * compute. There is no "LSM is off" shape — that case is an empty {@link java.util.Optional},
- * because a stats object of zeros would read as measurements.
- */
-public class LsmStats {
-  private static final String CONTEXT = "lsm stats";
-
-  private final List<BucketStats> buckets;
-
-  LsmStats(List<BucketStats> buckets) {
-    this.buckets = Collections.unmodifiableList(buckets);
-  }
-
-  /** One entry per bucket. */
-  public List<BucketStats> buckets() {
-    return buckets;
-  }
-
-  static LsmStats fromJson(JsonNode node) {
-    JsonFields.requiredObject(node, CONTEXT);
-    List<BucketStats> buckets = new ArrayList<BucketStats>();
-    for (JsonNode bucket : JsonFields.requiredArray(node, "buckets", CONTEXT)) {
-      buckets.add(BucketStats.fromJson(bucket));
-    }
-    return new LsmStats(buckets);
-  }
-
-  @Override
-  public String toString() {
-    return "LsmStats{buckets=" + buckets + "}";
-  }
-}
diff --git a/java/lancedb-core/src/main/java/com/lancedb/MemWalStats.java b/java/lancedb-core/src/main/java/com/lancedb/MemWalStats.java
new file mode 100644
index 000000000..c84220508
--- /dev/null
+++ b/java/lancedb-core/src/main/java/com/lancedb/MemWalStats.java
@@ -0,0 +1,56 @@
+/*
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.lancedb;
+
+import com.fasterxml.jackson.databind.JsonNode;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+
+/**
+ * Live per-table-shard MemWAL state, as returned by {@link LanceDbTableMemWal#getMemWalStats()}.
+ *
+ * <p>Nothing here is derived: sums and differences (total SSTable bytes, WAL lag) are the caller's
+ * to compute. There is no "LSM is off" shape — that case is an empty {@link java.util.Optional},
+ * because a stats object of zeros would read as measurements.
+ */
+public class MemWalStats {
+  private static final String CONTEXT = "memwal stats";
+
+  private final List<TableShardStats> tableShards;
+
+  MemWalStats(List<TableShardStats> tableShards) {
+    this.tableShards = Collections.unmodifiableList(tableShards);
+  }
+
+  /** One entry per table shard. */
+  public List<TableShardStats> tableShards() {
+    return tableShards;
+  }
+
+  static MemWalStats fromJson(JsonNode node) {
+    JsonFields.requiredObject(node, CONTEXT);
+    List<TableShardStats> tableShards = new ArrayList<TableShardStats>();
+    for (JsonNode tableShard : JsonFields.requiredArray(node, "table_shards", CONTEXT)) {
+      tableShards.add(TableShardStats.fromJson(tableShard));
+    }
+    return new MemWalStats(tableShards);
+  }
+
+  @Override
+  public String toString() {
+    return "MemWalStats{tableShards=" + tableShards + "}";
+  }
+}
diff --git a/java/lancedb-core/src/main/java/com/lancedb/LsmWriteSpec.java b/java/lancedb-core/src/main/java/com/lancedb/MemWalWriteSpec.java
similarity index 76%
rename from java/lancedb-core/src/main/java/com/lancedb/LsmWriteSpec.java
rename to java/lancedb-core/src/main/java/com/lancedb/MemWalWriteSpec.java
index da0966910..95f541214 100644
--- a/java/lancedb-core/src/main/java/com/lancedb/LsmWriteSpec.java
+++ b/java/lancedb-core/src/main/java/com/lancedb/MemWalWriteSpec.java
@@ -27,14 +27,16 @@ import java.util.Map;
  *
  * <p>Construct via {@link #bucket}, {@link #identity}, or {@link #unsharded}, then optionally chain
  * {@link #withMaintainedIndexes} and {@link #withWriterConfigDefaults}. Install it with {@link
- * LanceDbTableLsm#setLsmWriteSpec} and remove it with {@link LanceDbTableLsm#unsetLsmWriteSpec}.
+ * LanceDbTableMemWal#enableMemWal} and remove it with {@link LanceDbTableMemWal#disableMemWal}.
  *
  * <p>This is deliberately not {@code org.lance.memwal.InitializeMemWalParams}. That type is Lance's
  * own, and its maintained-index default is the opposite of this one: it defaults to maintaining
  * <em>nothing</em>, while a fresh spec here maintains <em>every</em> index. It also cannot express
  * the null that asks the server to resolve the set.
  */
-public class LsmWriteSpec {
+public class MemWalWriteSpec {
+
+  private static final String CONTEXT = "get_memwal_spec response";
 
   /** How writes are routed to MemWAL shards. */
   public enum Sharding {
@@ -71,7 +73,7 @@ public class LsmWriteSpec {
   private final List<String> maintainedIndexes;
   private final Map<String, String> writerConfigDefaults;
 
-  private LsmWriteSpec(
+  private MemWalWriteSpec(
       Sharding sharding,
       String column,
       Integer numBuckets,
@@ -93,11 +95,11 @@ public class LsmWriteSpec {
    * @param column A non-nested column with a supported scalar type.
    * @param numBuckets The number of buckets, in {@code [1, 1024]}.
    */
-  public static LsmWriteSpec bucket(String column, int numBuckets) {
+  public static MemWalWriteSpec bucket(String column, int numBuckets) {
     if (column == null || column.trim().isEmpty()) {
       throw new IllegalArgumentException("Column cannot be null or empty");
     }
-    return new LsmWriteSpec(
+    return new MemWalWriteSpec(
         Sharding.BUCKET, column, numBuckets, null, new HashMap<String, String>());
   }
 
@@ -109,29 +111,29 @@ public class LsmWriteSpec {
    * with a given primary key must always produce the same {@code column} value, or upserts of that
    * key can land in different shards and a stale version can win.
    */
-  public static LsmWriteSpec identity(String column) {
+  public static MemWalWriteSpec identity(String column) {
     if (column == null || column.trim().isEmpty()) {
       throw new IllegalArgumentException("Column cannot be null or empty");
     }
-    return new LsmWriteSpec(Sharding.IDENTITY, column, null, null, new HashMap<String, String>());
+    return new MemWalWriteSpec(
+        Sharding.IDENTITY, column, null, null, new HashMap<String, String>());
   }
 
   /** No sharding — every write goes to a single MemWAL shard — maintaining every index. */
-  public static LsmWriteSpec unsharded() {
-    return new LsmWriteSpec(Sharding.UNSHARDED, null, null, null, new HashMap<String, String>());
+  public static MemWalWriteSpec unsharded() {
+    return new MemWalWriteSpec(Sharding.UNSHARDED, null, null, null, new HashMap<String, String>());
   }
 
   /**
    * Set the indexes the MemWAL keeps up to date as rows are appended.
    *
-   * <p>Pass {@code null} — the default for a fresh spec — to maintain every index the MemWAL can,
-   * resolved when the spec is installed. That is a snapshot: indexes created later are not
-   * maintained until the spec is unset and set again. Pass an empty list to maintain none.
+   * <p>Pass {@code null} — the default for a fresh spec — to maintain every index the table has,
+   * including ones created later. Pass an empty list to maintain none.
    *
    * <p>Note that {@code null} and the empty list mean opposite things here.
    */
-  public LsmWriteSpec withMaintainedIndexes(List<String> maintainedIndexes) {
-    return new LsmWriteSpec(
+  public MemWalWriteSpec withMaintainedIndexes(List<String> maintainedIndexes) {
+    return new MemWalWriteSpec(
         sharding,
         column,
         numBuckets,
@@ -148,11 +150,11 @@ public class LsmWriteSpec {
    * max_unflushed_memtable_bytes}, and {@code enable_memtable}. Duration knobs carry an {@code _ms}
    * suffix, such as {@code max_wal_flush_interval_ms}.
    */
-  public LsmWriteSpec withWriterConfigDefaults(Map<String, String> writerConfigDefaults) {
+  public MemWalWriteSpec withWriterConfigDefaults(Map<String, String> writerConfigDefaults) {
     if (writerConfigDefaults == null) {
       throw new IllegalArgumentException("writerConfigDefaults cannot be null");
     }
-    return new LsmWriteSpec(
+    return new MemWalWriteSpec(
         sharding,
         column,
         numBuckets,
@@ -176,8 +178,10 @@ public class LsmWriteSpec {
   }
 
   /**
-   * The indexes the MemWAL maintains, or null to have the server resolve every maintainable index
-   * on install. An empty list means none.
+   * Which indexes the MemWAL maintains.
+   *
+   * <p>Null selects automatically: every supported index the table has now or gains later. An empty
+   * list maintains none. A non-empty list maintains exactly those.
    */
   public List<String> maintainedIndexes() {
     return maintainedIndexes == null ? null : Collections.unmodifiableList(maintainedIndexes);
@@ -188,7 +192,7 @@ public class LsmWriteSpec {
     return Collections.unmodifiableMap(writerConfigDefaults);
   }
 
-  /** Render this spec as the {@code set_lsm_write_spec} request body. */
+  /** Render this spec as the {@code enable_memwal} request body. */
   Map<String, Object> toRequestBody() {
     Map<String, Object> shardingBody = new LinkedHashMap<String, Object>();
     shardingBody.put("mode", sharding.wireName());
@@ -208,15 +212,15 @@ public class LsmWriteSpec {
   }
 
   /**
-   * Rebuild a spec from a {@code get_lsm_write_spec} response body.
+   * Rebuild a spec from a {@code get_memwal_spec} response body.
    *
-   * <p>The server always reports a concrete maintained-index list, so a null selection never
-   * round-trips.
+   * <p>A null {@code maintained_indexes} selects every index the table has; an empty list selects
+   * none.
    */
-  static LsmWriteSpec fromJson(JsonNode node) {
+  static MemWalWriteSpec fromJson(JsonNode node) {
     JsonNode shardingNode = node.get("sharding");
     if (shardingNode == null || shardingNode.get("mode") == null) {
-      throw new IllegalStateException("get_lsm_write_spec response has no sharding mode");
+      throw new IllegalStateException("get_memwal_spec response has no sharding mode");
     }
     Sharding sharding = Sharding.fromWireName(shardingNode.get("mode").asText());
 
@@ -224,10 +228,19 @@ public class LsmWriteSpec {
     Integer numBuckets =
         shardingNode.hasNonNull("num_buckets") ? shardingNode.get("num_buckets").asInt() : null;
 
-    List<String> maintainedIndexes = new ArrayList<String>();
-    JsonNode indexesNode = node.get("maintained_indexes");
-    if (indexesNode != null && indexesNode.isArray()) {
+    // Absent or null is the automatic selection; anything else has to be an
+    // array of names. Reading a string or an object as automatic would turn a
+    // malformed response into "maintain every index", which is a policy, not a
+    // parse failure.
+    JsonNode indexesNode = JsonFields.optionalArray(node, "maintained_indexes", CONTEXT);
+    List<String> maintainedIndexes = null;
+    if (indexesNode != null) {
+      maintainedIndexes = new ArrayList<String>();
       for (JsonNode index : indexesNode) {
+        if (!index.isTextual()) {
+          throw new IllegalStateException(
+              CONTEXT + ": maintained_indexes must hold index names, got " + index);
+        }
         maintainedIndexes.add(index.asText());
       }
     }
@@ -240,12 +253,12 @@ public class LsmWriteSpec {
           .forEachRemaining(name -> defaults.put(name, defaultsNode.get(name).asText()));
     }
 
-    return new LsmWriteSpec(sharding, column, numBuckets, maintainedIndexes, defaults);
+    return new MemWalWriteSpec(sharding, column, numBuckets, maintainedIndexes, defaults);
   }
 
   @Override
   public String toString() {
-    return "LsmWriteSpec{sharding="
+    return "MemWalWriteSpec{sharding="
         + sharding
         + ", column="
         + column
diff --git a/java/lancedb-core/src/main/java/com/lancedb/MemtableStats.java b/java/lancedb-core/src/main/java/com/lancedb/MemtableStats.java
index 777e915aa..b9ef5a89d 100644
--- a/java/lancedb-core/src/main/java/com/lancedb/MemtableStats.java
+++ b/java/lancedb-core/src/main/java/com/lancedb/MemtableStats.java
@@ -37,7 +37,7 @@ public class MemtableStats {
     this.indexes = Collections.unmodifiableList(indexes);
   }
 
-  /** The generation this memtable will become once sealed. */
+  /** The generation this memtable will become once frozen. */
   public long generation() {
     return generation;
   }
diff --git a/java/lancedb-core/src/main/java/com/lancedb/GenerationStats.java b/java/lancedb-core/src/main/java/com/lancedb/SsTableStats.java
similarity index 75%
rename from java/lancedb-core/src/main/java/com/lancedb/GenerationStats.java
rename to java/lancedb-core/src/main/java/com/lancedb/SsTableStats.java
index 12222407c..6e57c1ddf 100644
--- a/java/lancedb-core/src/main/java/com/lancedb/GenerationStats.java
+++ b/java/lancedb-core/src/main/java/com/lancedb/SsTableStats.java
@@ -17,21 +17,21 @@ import com.fasterxml.jackson.databind.JsonNode;
 
 import java.util.OptionalLong;
 
-/** One flushed L0 generation. */
-public class GenerationStats {
+/** One SSTable. */
+public class SsTableStats {
   private static final String CONTEXT = "generation stats";
 
   private final long generation;
   private final long bytes;
   private final Long rows;
 
-  GenerationStats(long generation, long bytes, Long rows) {
+  SsTableStats(long generation, long bytes, Long rows) {
     this.generation = generation;
     this.bytes = bytes;
     this.rows = rows;
   }
 
-  /** The generation number. Increases as memtables are sealed into L0. */
+  /** The generation number. Increases as memtables are frozen into SSTables. */
   public long generation() {
     return generation;
   }
@@ -42,16 +42,16 @@ public class GenerationStats {
   }
 
   /**
-   * Rows in this generation, present only when {@code includeGenerationRows} was requested. Off by
+   * Rows in this generation, present only when {@code includeSstableRows} was requested. Off by
    * default because each count opens an uncached Lance dataset.
    */
   public OptionalLong rows() {
     return rows == null ? OptionalLong.empty() : OptionalLong.of(rows);
   }
 
-  static GenerationStats fromJson(JsonNode node) {
+  static SsTableStats fromJson(JsonNode node) {
     JsonFields.requiredObject(node, CONTEXT);
-    return new GenerationStats(
+    return new SsTableStats(
         JsonFields.requiredLong(node, "generation", CONTEXT),
         JsonFields.requiredLong(node, "bytes", CONTEXT),
         JsonFields.optionalLong(node, "rows", CONTEXT));
@@ -59,6 +59,6 @@ public class GenerationStats {
 
   @Override
   public String toString() {
-    return "GenerationStats{generation=" + generation + ", bytes=" + bytes + ", rows=" + rows + "}";
+    return "SsTableStats{generation=" + generation + ", bytes=" + bytes + ", rows=" + rows + "}";
   }
 }
diff --git a/java/lancedb-core/src/main/java/com/lancedb/BucketStats.java b/java/lancedb-core/src/main/java/com/lancedb/TableShardStats.java
similarity index 76%
rename from java/lancedb-core/src/main/java/com/lancedb/BucketStats.java
rename to java/lancedb-core/src/main/java/com/lancedb/TableShardStats.java
index 2a8060c5d..dcbc4fa93 100644
--- a/java/lancedb-core/src/main/java/com/lancedb/BucketStats.java
+++ b/java/lancedb-core/src/main/java/com/lancedb/TableShardStats.java
@@ -22,11 +22,11 @@ import java.util.Optional;
 import java.util.OptionalLong;
 
 /**
- * Live state of one bucket. A table is N buckets on one node; flattening to a single number hides
- * the one hot bucket that is usually why someone opened this endpoint.
+ * Live state of one table shard. A table is N table shards on one node; flattening to a single
+ * number hides the one hot table shard that is usually why someone opened this endpoint.
  */
-public class BucketStats {
-  private static final String CONTEXT = "bucket stats";
+public class TableShardStats {
+  private static final String CONTEXT = "tableShard stats";
 
   private final String shardId;
   private final String status;
@@ -35,11 +35,11 @@ public class BucketStats {
   private final long currentGeneration;
   private final long replayAfterWalEntryPosition;
   private final long walEntryPositionLastSeen;
-  private final List<GenerationStats> generations;
+  private final List<SsTableStats> sstables;
   private final boolean compacting;
   private final List<MemtableStats> memtables;
 
-  BucketStats(
+  TableShardStats(
       String shardId,
       String status,
       long writerEpoch,
@@ -47,7 +47,7 @@ public class BucketStats {
       long currentGeneration,
       long replayAfterWalEntryPosition,
       long walEntryPositionLastSeen,
-      List<GenerationStats> generations,
+      List<SsTableStats> sstables,
       boolean compacting,
       List<MemtableStats> memtables) {
     this.shardId = shardId;
@@ -57,12 +57,12 @@ public class BucketStats {
     this.currentGeneration = currentGeneration;
     this.replayAfterWalEntryPosition = replayAfterWalEntryPosition;
     this.walEntryPositionLastSeen = walEntryPositionLastSeen;
-    this.generations = Collections.unmodifiableList(generations);
+    this.sstables = Collections.unmodifiableList(sstables);
     this.compacting = compacting;
     this.memtables = memtables == null ? null : Collections.unmodifiableList(memtables);
   }
 
-  /** The shard this bucket writes. */
+  /** The id of this table shard. */
   public String shardId() {
     return shardId;
   }
@@ -100,13 +100,13 @@ public class BucketStats {
     return walEntryPositionLastSeen;
   }
 
-  /** Flushed L0 generations not yet merged into the base table. */
-  public List<GenerationStats> generations() {
-    return generations;
+  /** SSTables not yet merged into the base table. */
+  public List<SsTableStats> sstables() {
+    return sstables;
   }
 
   /**
-   * Whether a pass owns this bucket's compaction latch right now. Says <em>a</em> driver is
+   * Whether a pass owns this table shard's compaction latch right now. Says <em>a</em> driver is
    * running, not <em>whose</em>, and the latch is held from dispatch — including while the pass
    * queues for a pod-wide compactor permit. Read it as "do not pile on", never as "mine is
    * progressing".
@@ -115,15 +115,17 @@ public class BucketStats {
     return compacting;
   }
 
-  /** Oldest first, active last. Empty for a {@code "Sealed"} bucket, whose state is torn down. */
+  /**
+   * Oldest first, active last. Empty for a {@code "Sealed"} table shard, whose state is torn down.
+   */
   public Optional<List<MemtableStats>> memtables() {
     return Optional.ofNullable(memtables);
   }
 
-  /** The newest flushed generation, or empty when L0 is empty. */
-  OptionalLong newestGeneration() {
+  /** The newest SSTable generation, or empty when the tier is empty. */
+  OptionalLong newestSstableGeneration() {
     OptionalLong newest = OptionalLong.empty();
-    for (GenerationStats generation : generations) {
+    for (SsTableStats generation : sstables) {
       if (!newest.isPresent() || generation.generation() > newest.getAsLong()) {
         newest = OptionalLong.of(generation.generation());
       }
@@ -132,15 +134,15 @@ public class BucketStats {
   }
 
   /**
-   * How many generations at or below {@code target} are still in L0.
+   * How many SSTables at or below {@code target} are still uncompacted.
    *
    * <p>A count, not a boolean: one pass drains a bounded prefix rather than the whole target set,
    * so a boolean would read as "no progress" for every pass but the last. Compaction drains
    * oldest-first, so this decreases monotonically.
    */
-  long outstandingGenerations(long target) {
+  long outstandingSstables(long target) {
     long count = 0;
-    for (GenerationStats generation : generations) {
+    for (SsTableStats generation : sstables) {
       if (generation.generation() <= target) {
         count++;
       }
@@ -148,11 +150,11 @@ public class BucketStats {
     return count;
   }
 
-  static BucketStats fromJson(JsonNode node) {
+  static TableShardStats fromJson(JsonNode node) {
     JsonFields.requiredObject(node, CONTEXT);
-    List<GenerationStats> generations = new ArrayList<GenerationStats>();
-    for (JsonNode generation : JsonFields.requiredArray(node, "generations", CONTEXT)) {
-      generations.add(GenerationStats.fromJson(generation));
+    List<SsTableStats> sstables = new ArrayList<SsTableStats>();
+    for (JsonNode generation : JsonFields.requiredArray(node, "sstables", CONTEXT)) {
+      sstables.add(SsTableStats.fromJson(generation));
     }
 
     JsonNode memtablesNode = JsonFields.optionalArray(node, "memtables", CONTEXT);
@@ -164,7 +166,7 @@ public class BucketStats {
       }
     }
 
-    return new BucketStats(
+    return new TableShardStats(
         JsonFields.requiredText(node, "shard_id", CONTEXT),
         JsonFields.requiredText(node, "status", CONTEXT),
         JsonFields.requiredLong(node, "writer_epoch", CONTEXT),
@@ -172,21 +174,21 @@ public class BucketStats {
         JsonFields.requiredLong(node, "current_generation", CONTEXT),
         JsonFields.requiredLong(node, "replay_after_wal_entry_position", CONTEXT),
         JsonFields.requiredLong(node, "wal_entry_position_last_seen", CONTEXT),
-        generations,
+        sstables,
         JsonFields.requiredBoolean(node, "compacting", CONTEXT),
         memtables);
   }
 
   @Override
   public String toString() {
-    return "BucketStats{shardId="
+    return "TableShardStats{shardId="
         + shardId
         + ", status="
         + status
         + ", currentGeneration="
         + currentGeneration
-        + ", generations="
-        + generations
+        + ", sstables="
+        + sstables
         + ", compacting="
         + compacting
         + "}";
diff --git a/java/lancedb-core/src/test/java/com/lancedb/LanceDbTableLsmTest.java b/java/lancedb-core/src/test/java/com/lancedb/LanceDbTableMemWalTest.java
similarity index 58%
rename from java/lancedb-core/src/test/java/com/lancedb/LanceDbTableLsmTest.java
rename to java/lancedb-core/src/test/java/com/lancedb/LanceDbTableMemWalTest.java
index e84fa5421..349479988 100644
--- a/java/lancedb-core/src/test/java/com/lancedb/LanceDbTableLsmTest.java
+++ b/java/lancedb-core/src/test/java/com/lancedb/LanceDbTableMemWalTest.java
@@ -46,12 +46,12 @@ import static org.junit.jupiter.api.Assertions.*;
  * <p>The wire assertions mirror the Rust mocked-endpoint tests in {@code
  * rust/lancedb/src/remote/table.rs}, which are the contract these routes have to match.
  */
-public class LanceDbTableLsmTest {
+public class LanceDbTableMemWalTest {
   private static final ObjectMapper MAPPER = new ObjectMapper();
 
   private HttpServer server;
   private LanceDbRestClient client;
-  private LanceDbTableLsm lsm;
+  private LanceDbTableMemWal memWal;
 
   private final List<String> requestPaths = Collections.synchronizedList(new ArrayList<String>());
   private final List<String> requestBodies = Collections.synchronizedList(new ArrayList<String>());
@@ -101,7 +101,7 @@ public class LanceDbTableLsmTest {
             .database("test-db")
             .endpoint("http://127.0.0.1:" + server.getAddress().getPort())
             .buildRestClient();
-    lsm = new LanceDbTableLsm(client, "my_table");
+    memWal = new LanceDbTableMemWal(client, "my_table");
   }
 
   @AfterEach
@@ -115,12 +115,12 @@ public class LanceDbTableLsmTest {
   // ===========================================================================
 
   @Test
-  public void testSetLsmWriteSpecUnsharded() throws Exception {
-    enqueue("set_lsm_write_spec", 200, "");
+  public void testEnableMemWalUnsharded() throws Exception {
+    enqueue("enable_memwal", 200, "");
 
-    lsm.setLsmWriteSpec(LsmWriteSpec.unsharded());
+    memWal.enableMemWal(MemWalWriteSpec.unsharded());
 
-    assertEquals("/v1/table/my_table/set_lsm_write_spec/", requestPaths.get(0));
+    assertEquals("/v1/table/my_table/enable_memwal/", requestPaths.get(0));
     JsonNode body = MAPPER.readTree(requestBodies.get(0));
     assertEquals("unsharded", body.get("sharding").get("mode").asText());
     assertFalse(body.get("sharding").has("column"));
@@ -128,11 +128,11 @@ public class LanceDbTableLsmTest {
   }
 
   @Test
-  public void testSetLsmWriteSpecBucket() throws Exception {
-    enqueue("set_lsm_write_spec", 200, "");
+  public void testEnableMemWalBucket() throws Exception {
+    enqueue("enable_memwal", 200, "");
 
-    lsm.setLsmWriteSpec(
-        LsmWriteSpec.bucket("id", 16).withMaintainedIndexes(Arrays.asList("id_idx")));
+    memWal.enableMemWal(
+        MemWalWriteSpec.bucket("id", 16).withMaintainedIndexes(Arrays.asList("id_idx")));
 
     JsonNode body = MAPPER.readTree(requestBodies.get(0));
     assertEquals("bucket", body.get("sharding").get("mode").asText());
@@ -143,10 +143,10 @@ public class LanceDbTableLsmTest {
   }
 
   @Test
-  public void testSetLsmWriteSpecIdentity() throws Exception {
-    enqueue("set_lsm_write_spec", 200, "");
+  public void testEnableMemWalIdentity() throws Exception {
+    enqueue("enable_memwal", 200, "");
 
-    lsm.setLsmWriteSpec(LsmWriteSpec.identity("tenant"));
+    memWal.enableMemWal(MemWalWriteSpec.identity("tenant"));
 
     JsonNode body = MAPPER.readTree(requestBodies.get(0));
     assertEquals("identity", body.get("sharding").get("mode").asText());
@@ -160,66 +160,121 @@ public class LanceDbTableLsmTest {
    */
   @Test
   public void testMaintainedIndexesNullAndEmptyAreDistinctOnTheWire() throws Exception {
-    enqueue("set_lsm_write_spec", 200, "");
+    enqueue("enable_memwal", 200, "");
 
-    lsm.setLsmWriteSpec(LsmWriteSpec.unsharded());
+    memWal.enableMemWal(MemWalWriteSpec.unsharded());
     JsonNode fresh = MAPPER.readTree(requestBodies.get(0));
     assertTrue(fresh.has("maintained_indexes"), "the key must be present");
     assertTrue(fresh.get("maintained_indexes").isNull(), "a fresh spec sends null, not []");
 
-    lsm.setLsmWriteSpec(
-        LsmWriteSpec.unsharded().withMaintainedIndexes(Collections.<String>emptyList()));
+    memWal.enableMemWal(
+        MemWalWriteSpec.unsharded().withMaintainedIndexes(Collections.<String>emptyList()));
     JsonNode none = MAPPER.readTree(requestBodies.get(1));
     assertTrue(none.get("maintained_indexes").isArray());
     assertEquals(0, none.get("maintained_indexes").size());
   }
 
   @Test
-  public void testSetLsmWriteSpecWriterConfigDefaults() throws Exception {
-    enqueue("set_lsm_write_spec", 200, "");
+  public void testEnableMemWalWriterConfigDefaults() throws Exception {
+    enqueue("enable_memwal", 200, "");
 
     Map<String, String> defaults = new HashMap<String, String>();
     defaults.put("max_memtable_rows", "50000");
-    lsm.setLsmWriteSpec(LsmWriteSpec.unsharded().withWriterConfigDefaults(defaults));
+    memWal.enableMemWal(MemWalWriteSpec.unsharded().withWriterConfigDefaults(defaults));
 
     JsonNode body = MAPPER.readTree(requestBodies.get(0));
     assertEquals("50000", body.get("writer_config_defaults").get("max_memtable_rows").asText());
   }
 
   @Test
-  public void testUnsetLsmWriteSpec() {
-    enqueue("unset_lsm_write_spec", 200, "");
+  public void testDisableMemWal() {
+    enqueue("disable_memwal", 200, "");
 
-    lsm.unsetLsmWriteSpec();
+    memWal.disableMemWal();
 
-    assertEquals("/v1/table/my_table/unset_lsm_write_spec/", requestPaths.get(0));
+    assertEquals("/v1/table/my_table/disable_memwal/", requestPaths.get(0));
     assertEquals("", requestBodies.get(0));
   }
 
   @Test
-  public void testGetLsmWriteSpec() {
+  public void testGetMemWalSpec() {
     enqueue(
-        "get_lsm_write_spec",
+        "get_memwal_spec",
         200,
-        "{\"lsm_write_spec\":{\"sharding\":{\"mode\":\"bucket\",\"column\":\"id\","
+        "{\"memwal_spec\":{\"sharding\":{\"mode\":\"bucket\",\"column\":\"id\","
             + "\"num_buckets\":16},\"maintained_indexes\":[\"id_idx\"],"
             + "\"writer_config_defaults\":{\"durable_write\":\"true\"}}}");
 
-    Optional<LsmWriteSpec> spec = lsm.getLsmWriteSpec();
+    Optional<MemWalWriteSpec> spec = memWal.getMemWalSpec();
 
     assertTrue(spec.isPresent());
-    assertEquals(LsmWriteSpec.Sharding.BUCKET, spec.get().sharding());
+    assertEquals(MemWalWriteSpec.Sharding.BUCKET, spec.get().sharding());
     assertEquals("id", spec.get().column());
     assertEquals(Integer.valueOf(16), spec.get().numBuckets());
     assertEquals(Arrays.asList("id_idx"), spec.get().maintainedIndexes());
     assertEquals("true", spec.get().writerConfigDefaults().get("durable_write"));
   }
 
+  /** A null selection, every index the table has, reads back as null rather than an empty list. */
   @Test
-  public void testGetLsmWriteSpecAbsent() {
-    enqueue("get_lsm_write_spec", 200, "{\"lsm_write_spec\":null}");
+  public void testGetMemWalSpecRoundTripsEveryIndexSelection() {
+    enqueue(
+        "get_memwal_spec",
+        200,
+        "{\"memwal_spec\":{\"sharding\":{\"mode\":\"unsharded\"},"
+            + "\"maintained_indexes\":null,\"writer_config_defaults\":{}}}");
 
-    assertFalse(lsm.getLsmWriteSpec().isPresent());
+    Optional<MemWalWriteSpec> spec = memWal.getMemWalSpec();
+
+    assertTrue(spec.isPresent());
+    assertNull(spec.get().maintainedIndexes());
+  }
+
+  @Test
+  public void testGetMemWalSpecRoundTripsNoIndexSelection() {
+    enqueue(
+        "get_memwal_spec",
+        200,
+        "{\"memwal_spec\":{\"sharding\":{\"mode\":\"unsharded\"},"
+            + "\"maintained_indexes\":[],\"writer_config_defaults\":{}}}");
+
+    Optional<MemWalWriteSpec> spec = memWal.getMemWalSpec();
+
+    assertTrue(spec.isPresent());
+    assertEquals(Collections.emptyList(), spec.get().maintainedIndexes());
+  }
+
+  /**
+   * Null is the automatic selection, so anything that is neither null nor an array has to be
+   * rejected: reading a string or an object as null would turn a malformed response into a policy.
+   */
+  @Test
+  public void testGetMemWalSpecRejectsANonArrayMaintainedIndexes() {
+    enqueue(
+        "get_memwal_spec",
+        200,
+        "{\"memwal_spec\":{\"sharding\":{\"mode\":\"unsharded\"},"
+            + "\"maintained_indexes\":\"id_idx\",\"writer_config_defaults\":{}}}");
+
+    assertThrows(IllegalStateException.class, () -> memWal.getMemWalSpec());
+  }
+
+  @Test
+  public void testGetMemWalSpecRejectsNonStringIndexNames() {
+    enqueue(
+        "get_memwal_spec",
+        200,
+        "{\"memwal_spec\":{\"sharding\":{\"mode\":\"unsharded\"},"
+            + "\"maintained_indexes\":[7],\"writer_config_defaults\":{}}}");
+
+    assertThrows(IllegalStateException.class, () -> memWal.getMemWalSpec());
+  }
+
+  @Test
+  public void testGetMemWalSpecAbsent() {
+    enqueue("get_memwal_spec", 200, "{\"memwal_spec\":null}");
+
+    assertFalse(memWal.getMemWalSpec().isPresent());
   }
 
   // ===========================================================================
@@ -227,15 +282,15 @@ public class LanceDbTableLsmTest {
   // ===========================================================================
 
   @Test
-  public void testGetLsmStats() throws Exception {
-    enqueue("get_lsm_stats", 200, stats(bucket("shard-0", false, 7L, 8L)));
+  public void testGetMemWalStats() throws Exception {
+    enqueue("get_memwal_stats", 200, stats(tableShard("shard-0", false, 7L, 8L)));
 
-    Optional<LsmStats> got = lsm.getLsmStats(true);
+    Optional<MemWalStats> got = memWal.getMemWalStats(true);
 
-    assertEquals("/v1/table/my_table/get_lsm_stats/", requestPaths.get(0));
-    assertTrue(MAPPER.readTree(requestBodies.get(0)).get("include_generation_rows").asBoolean());
+    assertEquals("/v1/table/my_table/get_memwal_stats/", requestPaths.get(0));
+    assertTrue(MAPPER.readTree(requestBodies.get(0)).get("include_sstable_rows").asBoolean());
     assertTrue(got.isPresent());
-    BucketStats decoded = got.get().buckets().get(0);
+    TableShardStats decoded = got.get().tableShards().get(0);
     assertEquals("shard-0", decoded.shardId());
     assertEquals("Active", decoded.status());
     assertEquals(1, decoded.writerEpoch());
@@ -243,30 +298,30 @@ public class LanceDbTableLsmTest {
     assertEquals(9, decoded.currentGeneration());
     assertFalse(decoded.compacting());
     assertEquals(Arrays.asList(7L, 8L), generationNumbers(decoded));
-    assertEquals(1024, decoded.generations().get(0).bytes());
-    assertFalse(decoded.generations().get(0).rows().isPresent(), "rows absent unless requested");
+    assertEquals(1024, decoded.sstables().get(0).bytes());
+    assertFalse(decoded.sstables().get(0).rows().isPresent(), "rows absent unless requested");
     assertFalse(decoded.memtables().isPresent(), "absent memtables stay absent");
   }
 
   /** The optional fields decode when the server does send them. */
   @Test
-  public void testGetLsmStatsDecodesOptionalFields() {
+  public void testGetMemWalStatsDecodesOptionalFields() {
     enqueue(
-        "get_lsm_stats",
+        "get_memwal_stats",
         200,
-        "{\"lsm_stats\":{\"buckets\":[{\"shard_id\":\"shard-0\",\"status\":\"Active\","
+        "{\"memwal_stats\":{\"table_shards\":[{\"shard_id\":\"shard-0\",\"status\":\"Active\","
             + "\"writer_epoch\":1,\"manifest_version\":2,\"current_generation\":9,"
             + "\"replay_after_wal_entry_position\":3,\"wal_entry_position_last_seen\":11,"
-            + "\"generations\":[{\"generation\":7,\"bytes\":1024,\"rows\":42}],"
+            + "\"sstables\":[{\"generation\":7,\"bytes\":1024,\"rows\":42}],"
             + "\"compacting\":true,\"memtables\":[{\"generation\":8,\"rows\":5,"
             + "\"bytes\":64,\"batches\":2,\"indexes\":[\"id_idx\"]}]}]}}");
 
-    BucketStats decoded = lsm.getLsmStats(true).get().buckets().get(0);
+    TableShardStats decoded = memWal.getMemWalStats(true).get().tableShards().get(0);
 
     assertEquals(3, decoded.replayAfterWalEntryPosition());
     assertEquals(11, decoded.walEntryPositionLastSeen());
     assertTrue(decoded.compacting());
-    assertEquals(42, decoded.generations().get(0).rows().getAsLong());
+    assertEquals(42, decoded.sstables().get(0).rows().getAsLong());
     assertTrue(decoded.memtables().isPresent());
     MemtableStats memtable = decoded.memtables().get().get(0);
     assertEquals(8, memtable.generation());
@@ -277,19 +332,19 @@ public class LanceDbTableLsmTest {
   }
 
   @Test
-  public void testGetLsmStatsAbsentWhenLsmDisabled() {
-    enqueue("get_lsm_stats", 200, "{\"lsm_stats\":null}");
+  public void testGetMemWalStatsAbsentWhenMemWalDisabled() {
+    enqueue("get_memwal_stats", 200, "{\"memwal_stats\":null}");
 
-    assertFalse(lsm.getLsmStats().isPresent());
+    assertFalse(memWal.getMemWalStats().isPresent());
   }
 
   @Test
-  public void testGetLsmStatsDefaultsToExcludingGenerationRows() throws Exception {
-    enqueue("get_lsm_stats", 200, stats());
+  public void testGetMemWalStatsDefaultsToExcludingGenerationRows() throws Exception {
+    enqueue("get_memwal_stats", 200, stats());
 
-    lsm.getLsmStats();
+    memWal.getMemWalStats();
 
-    assertFalse(MAPPER.readTree(requestBodies.get(0)).get("include_generation_rows").asBoolean());
+    assertFalse(MAPPER.readTree(requestBodies.get(0)).get("include_sstable_rows").asBoolean());
   }
 
   // ===========================================================================
@@ -298,22 +353,22 @@ public class LanceDbTableLsmTest {
 
   @Test
   public void testFlushAndCompactRoutes() {
-    enqueue("flush_lsm", 200, "");
-    enqueue("compact_lsm", 200, "");
+    enqueue("flush_memwal", 200, "");
+    enqueue("compact_memwal", 200, "");
 
-    lsm.flushLsm();
-    lsm.compactLsm();
+    memWal.flushMemWal();
+    memWal.compactMemWal();
 
-    assertEquals("/v1/table/my_table/flush_lsm/", requestPaths.get(0));
-    assertEquals("/v1/table/my_table/compact_lsm/", requestPaths.get(1));
+    assertEquals("/v1/table/my_table/flush_memwal/", requestPaths.get(0));
+    assertEquals("/v1/table/my_table/compact_memwal/", requestPaths.get(1));
   }
 
   @Test
   public void testHttpErrorCarriesStatus() {
-    enqueue("flush_lsm", 404, "no such table");
+    enqueue("flush_memwal", 404, "no such table");
 
     LanceDbRestClient.HttpException e =
-        assertThrows(LanceDbRestClient.HttpException.class, () -> lsm.flushLsm());
+        assertThrows(LanceDbRestClient.HttpException.class, () -> memWal.flushMemWal());
     assertEquals(404, e.statusCode());
   }
 
@@ -322,97 +377,98 @@ public class LanceDbTableLsmTest {
   // ===========================================================================
 
   @Test
-  public void testCheckpointReturnsWhenLsmDisabled() {
-    enqueue("flush_lsm", 200, "");
-    enqueue("get_lsm_stats", 200, "{\"lsm_stats\":null}");
+  public void testCheckpointReturnsWhenMemWalDisabled() {
+    enqueue("flush_memwal", 200, "");
+    enqueue("get_memwal_stats", 200, "{\"memwal_stats\":null}");
 
-    lsm.checkpointLsm();
+    memWal.checkpointMemWal();
 
-    assertEquals(0, countCalls("compact_lsm"), "nothing to compact when the LSM path is off");
+    assertEquals(0, countCalls("compact_memwal"), "nothing to compact when the LSM path is off");
   }
 
   @Test
   public void testCheckpointReturnsWhenNoGenerationsOutstanding() {
-    enqueue("flush_lsm", 200, "");
-    // A bucket with no L0 generations yields no target, so the drain never starts.
-    enqueue("get_lsm_stats", 200, stats(bucket("shard-0", false)));
+    enqueue("flush_memwal", 200, "");
+    // A table shard with no SSTables yields no target, so the drain never starts.
+    enqueue("get_memwal_stats", 200, stats(tableShard("shard-0", false)));
 
-    lsm.checkpointLsm();
+    memWal.checkpointMemWal();
 
-    assertEquals(0, countCalls("compact_lsm"));
+    assertEquals(0, countCalls("compact_memwal"));
   }
 
   @Test
   public void testCheckpointConvergesOnceTargetGenerationsAreGone() {
-    enqueue("flush_lsm", 200, "");
-    // Watermark read: shard-0 holds generations 7 and 8, so target = 8.
-    enqueue("get_lsm_stats", 200, stats(bucket("shard-0", false, 7L, 8L)));
+    enqueue("flush_memwal", 200, "");
+    // Watermark read: shard-0 holds sstables 7 and 8, so target = 8.
+    enqueue("get_memwal_stats", 200, stats(tableShard("shard-0", false, 7L, 8L)));
     // First drain poll: both still outstanding, nothing compacting -> dispatch a pass.
-    enqueue("get_lsm_stats", 200, stats(bucket("shard-0", false, 7L, 8L)));
+    enqueue("get_memwal_stats", 200, stats(tableShard("shard-0", false, 7L, 8L)));
     // Second drain poll: drained past the target -> done.
-    enqueue("get_lsm_stats", 200, stats(bucket("shard-0", false, 9L)));
-    enqueue("compact_lsm", 200, "");
+    enqueue("get_memwal_stats", 200, stats(tableShard("shard-0", false, 9L)));
+    enqueue("compact_memwal", 200, "");
 
-    lsm.checkpointLsm();
+    memWal.checkpointMemWal();
 
-    assertEquals(1, countCalls("compact_lsm"), "one pass dispatched");
-    assertEquals(3, countCalls("get_lsm_stats"), "watermark read plus two drain polls");
+    assertEquals(1, countCalls("compact_memwal"), "one pass dispatched");
+    assertEquals(3, countCalls("get_memwal_stats"), "watermark read plus two drain polls");
   }
 
   @Test
   public void testCheckpointDoesNotPileOnWhileEveryTargetBucketIsCompacting() {
-    enqueue("flush_lsm", 200, "");
-    enqueue("get_lsm_stats", 200, stats(bucket("shard-0", true, 4L)));
+    enqueue("flush_memwal", 200, "");
+    enqueue("get_memwal_stats", 200, stats(tableShard("shard-0", true, 4L)));
     // Still compacting on the first poll, so no pass is dispatched; then it drains.
-    enqueue("get_lsm_stats", 200, stats(bucket("shard-0", true, 4L)));
-    enqueue("get_lsm_stats", 200, stats(bucket("shard-0", false, 5L)));
+    enqueue("get_memwal_stats", 200, stats(tableShard("shard-0", true, 4L)));
+    enqueue("get_memwal_stats", 200, stats(tableShard("shard-0", false, 5L)));
 
-    lsm.checkpointLsm();
+    memWal.checkpointMemWal();
 
-    assertEquals(0, countCalls("compact_lsm"), "a latched bucket is left alone");
+    assertEquals(0, countCalls("compact_memwal"), "a latched table shard is left alone");
   }
 
   @Test
   public void testCheckpointRetriesFromFlushAfterLostClaim() {
     // 421 on the watermark read: the node lost its claim, so the whole thing restarts
     // from flush rather than retrying the read in place.
-    enqueue("flush_lsm", 200, "");
-    enqueue("get_lsm_stats", 421, "no claim");
-    enqueue("get_lsm_stats", 200, stats(bucket("shard-0", false)));
+    enqueue("flush_memwal", 200, "");
+    enqueue("get_memwal_stats", 421, "no claim");
+    enqueue("get_memwal_stats", 200, stats(tableShard("shard-0", false)));
 
-    lsm.checkpointLsm();
+    memWal.checkpointMemWal();
 
-    assertEquals(2, countCalls("flush_lsm"), "re-issued from flush");
+    assertEquals(2, countCalls("flush_memwal"), "re-issued from flush");
   }
 
   @Test
   public void testCheckpointRetriesRetryableStatusInPlace() {
-    enqueue("flush_lsm", 429, "latch held");
-    enqueue("flush_lsm", 200, "");
-    enqueue("get_lsm_stats", 200, stats(bucket("shard-0", false)));
+    enqueue("flush_memwal", 429, "latch held");
+    enqueue("flush_memwal", 200, "");
+    enqueue("get_memwal_stats", 200, stats(tableShard("shard-0", false)));
 
-    lsm.checkpointLsm();
+    memWal.checkpointMemWal();
 
-    assertEquals(2, countCalls("flush_lsm"), "429 retried in place, not re-issued");
+    assertEquals(2, countCalls("flush_memwal"), "429 retried in place, not re-issued");
   }
 
   @Test
   public void testCheckpointPropagatesTerminalStatus() {
-    enqueue("flush_lsm", 400, "bad request");
+    enqueue("flush_memwal", 400, "bad request");
 
     LanceDbRestClient.HttpException e =
-        assertThrows(LanceDbRestClient.HttpException.class, () -> lsm.checkpointLsm());
+        assertThrows(LanceDbRestClient.HttpException.class, () -> memWal.checkpointMemWal());
     assertEquals(400, e.statusCode());
-    assertEquals(1, countCalls("flush_lsm"), "a terminal status is not retried");
+    assertEquals(1, countCalls("flush_memwal"), "a terminal status is not retried");
   }
 
   @Test
   public void testCheckpointGivesUpAfterRepeatedLostClaims() {
-    enqueue("flush_lsm", 421, "no claim");
+    enqueue("flush_memwal", 421, "no claim");
 
-    IllegalStateException e = assertThrows(IllegalStateException.class, () -> lsm.checkpointLsm());
+    IllegalStateException e =
+        assertThrows(IllegalStateException.class, () -> memWal.checkpointMemWal());
     assertTrue(e.getMessage().contains("kept losing its claim"), e.getMessage());
-    assertEquals(4, countCalls("flush_lsm"), "the initial attempt plus MAX_REISSUES");
+    assertEquals(4, countCalls("flush_memwal"), "the initial attempt plus MAX_REISSUES");
   }
 
   // ===========================================================================
@@ -421,50 +477,51 @@ public class LanceDbTableLsmTest {
 
   /**
    * A stats payload that does not decode must fail closed. Every one of these bodies used to be
-   * read as "no buckets", which is indistinguishable from a drained table, so {@code checkpointLsm}
-   * reported convergence for a checkpoint that never ran.
+   * read as "no table shards", which is indistinguishable from a drained table, so {@code
+   * checkpointMemWal} reported convergence for a checkpoint that never ran.
    */
   @Test
   public void testCheckpointRejectsMalformedStats() {
     Map<String, String> malformed = new LinkedHashMap<String, String>();
     malformed.put("no response body at all", "");
-    malformed.put("stats object with no buckets", "{\"lsm_stats\":{}}");
-    malformed.put("bucket missing its required fields", "{\"lsm_stats\":{\"buckets\":[{}]}}");
+    malformed.put("stats object with no table shards", "{\"memwal_stats\":{}}");
     malformed.put(
-        "bucket missing generations",
-        "{\"lsm_stats\":{\"buckets\":[{\"shard_id\":\"shard-0\",\"status\":\"Active\","
+        "table shard missing its required fields", "{\"memwal_stats\":{\"table_shards\":[{}]}}");
+    malformed.put(
+        "table shard missing sstables",
+        "{\"memwal_stats\":{\"table_shards\":[{\"shard_id\":\"shard-0\",\"status\":\"Active\","
             + "\"writer_epoch\":1,\"manifest_version\":2,\"current_generation\":9,"
             + "\"replay_after_wal_entry_position\":0,\"wal_entry_position_last_seen\":0,"
             + "\"compacting\":false}]}}");
     malformed.put(
         "generation with a non-numeric generation number",
-        "{\"lsm_stats\":{\"buckets\":[{\"shard_id\":\"shard-0\",\"status\":\"Active\","
+        "{\"memwal_stats\":{\"table_shards\":[{\"shard_id\":\"shard-0\",\"status\":\"Active\","
             + "\"writer_epoch\":1,\"manifest_version\":2,\"current_generation\":9,"
             + "\"replay_after_wal_entry_position\":0,\"wal_entry_position_last_seen\":0,"
-            + "\"generations\":[{\"generation\":\"7\",\"bytes\":1024}],"
+            + "\"sstables\":[{\"generation\":\"7\",\"bytes\":1024}],"
             + "\"compacting\":false}]}}");
 
     for (Map.Entry<String, String> each : malformed.entrySet()) {
       setUpFresh();
-      enqueue("flush_lsm", 200, "");
-      enqueue("get_lsm_stats", 200, each.getValue());
+      enqueue("flush_memwal", 200, "");
+      enqueue("get_memwal_stats", 200, each.getValue());
 
       assertThrows(
           IllegalStateException.class,
-          () -> lsm.checkpointLsm(),
+          () -> memWal.checkpointMemWal(),
           each.getKey() + " must not report convergence");
     }
   }
 
-  /** The one shape that legitimately means "this table has no LSM write path". */
+  /** The one shape that legitimately means "this table has no MemWAL write path". */
   @Test
   public void testCheckpointTreatsNullStatsAsNotWalBacked() {
-    enqueue("flush_lsm", 200, "");
-    enqueue("get_lsm_stats", 200, "{\"lsm_stats\":null}");
+    enqueue("flush_memwal", 200, "");
+    enqueue("get_memwal_stats", 200, "{\"memwal_stats\":null}");
 
-    lsm.checkpointLsm();
+    memWal.checkpointMemWal();
 
-    assertEquals(1, countCalls("get_lsm_stats"));
+    assertEquals(1, countCalls("get_memwal_stats"));
   }
 
   // ===========================================================================
@@ -474,40 +531,41 @@ public class LanceDbTableLsmTest {
   /**
    * The transport must not retry on the checkpoint loop's behalf. Apache HttpClient's default
    * strategy retries exactly 429 and 503 — the two statuses {@code isRetryable} owns — which
-   * doubled every budget here and also retried {@code compact_lsm} in place, where the loop is
+   * doubled every budget here and also retried {@code compact_memwal} in place, where the loop is
    * built to fall through to a fresh stats poll instead.
    */
   @Test
   public void testCheckpointRetryBudgetIsNotDoubledByTheTransport() {
-    enqueue("flush_lsm", 429, "latch held");
+    enqueue("flush_memwal", 429, "latch held");
 
     LanceDbRestClient.HttpException e =
-        assertThrows(LanceDbRestClient.HttpException.class, () -> lsm.checkpointLsm());
+        assertThrows(LanceDbRestClient.HttpException.class, () -> memWal.checkpointMemWal());
 
     assertEquals(429, e.statusCode(), "the exhausted budget propagates the last error as itself");
-    assertEquals(9, countCalls("flush_lsm"), "the initial request plus MAX_RETRIES, and no more");
+    assertEquals(
+        9, countCalls("flush_memwal"), "the initial request plus MAX_RETRIES, and no more");
   }
 
   // ===========================================================================
   // harness
   // ===========================================================================
 
-  private static List<Long> generationNumbers(BucketStats bucket) {
+  private static List<Long> generationNumbers(TableShardStats tableShard) {
     List<Long> numbers = new ArrayList<Long>();
-    for (GenerationStats generation : bucket.generations()) {
+    for (SsTableStats generation : tableShard.sstables()) {
       numbers.add(generation.generation());
     }
     return numbers;
   }
 
-  /** Build an {@code lsm_stats} response body from bucket fragments. */
-  private static String stats(String... buckets) {
-    return "{\"lsm_stats\":{\"buckets\":[" + String.join(",", buckets) + "]}}";
+  /** Build a {@code memwal_stats} response body from table shard fragments. */
+  private static String stats(String... tableShards) {
+    return "{\"memwal_stats\":{\"table_shards\":[" + String.join(",", tableShards) + "]}}";
   }
 
-  private static String bucket(String shardId, boolean compacting, Long... generations) {
+  private static String tableShard(String shardId, boolean compacting, Long... sstables) {
     StringBuilder gens = new StringBuilder();
-    for (Long generation : generations) {
+    for (Long generation : sstables) {
       if (gens.length() > 0) {
         gens.append(",");
       }
@@ -517,7 +575,7 @@ public class LanceDbTableLsmTest {
         + shardId
         + "\",\"status\":\"Active\",\"writer_epoch\":1,\"manifest_version\":2,"
         + "\"current_generation\":9,\"replay_after_wal_entry_position\":0,"
-        + "\"wal_entry_position_last_seen\":0,\"generations\":["
+        + "\"wal_entry_position_last_seen\":0,\"sstables\":["
         + gens
         + "],\"compacting\":"
         + compacting
@@ -542,7 +600,7 @@ public class LanceDbTableLsmTest {
     return requestPaths.stream().filter(path -> operationOf(path).equals(operation)).count();
   }
 
-  /** {@code /v1/table/my_table/flush_lsm/} -> {@code flush_lsm}. */
+  /** {@code /v1/table/my_table/flush_memwal/} -> {@code flush_memwal}. */
   private static String operationOf(String path) {
     String[] segments = path.split("/");
     return segments.length == 0 ? "" : segments[segments.length - 1];
diff --git a/java/pom.xml b/java/pom.xml
index 752dacefd..d6d1fdf98 100644
--- a/java/pom.xml
+++ b/java/pom.xml
@@ -6,7 +6,7 @@
 
     <groupId>com.lancedb</groupId>
     <artifactId>lancedb-parent</artifactId>
-    <version>0.40.0-beta.11</version>
+    <version>0.41.0-beta.7</version>
     <packaging>pom</packaging>
     <name>${project.artifactId}</name>
     <description>LanceDB Java SDK Parent POM</description>
@@ -28,7 +28,8 @@
     <properties>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
         <arrow.version>15.0.0</arrow.version>
-        <lance-core.version>13.0.0-beta.14</lance-core.version>
+        <lance-core.version>14.0.0-beta.18</lance-core.version>
+        <jackson.version>2.18.11</jackson.version>
         <spotless.skip>false</spotless.skip>
         <spotless.version>2.30.0</spotless.version>
         <spotless.java.googlejavaformat.version>1.7</spotless.java.googlejavaformat.version>
@@ -62,6 +63,15 @@
 
     <dependencyManagement>
         <dependencies>
+            <!-- Pin every Jackson artifact, including ones pulled in transitively
+                 (e.g. jackson-core), to a single patched version. -->
+            <dependency>
+                <groupId>com.fasterxml.jackson</groupId>
+                <artifactId>jackson-bom</artifactId>
+                <version>${jackson.version}</version>
+                <type>pom</type>
+                <scope>import</scope>
+            </dependency>
             <dependency>
                 <groupId>org.lance</groupId>
                 <artifactId>lance-core</artifactId>
@@ -95,6 +105,16 @@
         </dependencies>
     </dependencyManagement>
 
+    <repositories>
+        <repository>
+            <id>lance</id>
+            <url>https://maven.lance.org</url>
+            <snapshots>
+                <enabled>false</enabled>
+            </snapshots>
+        </repository>
+    </repositories>
+
     <distributionManagement>
         <snapshotRepository>
             <id>ossrh</id>
diff --git a/nodejs/Cargo.toml b/nodejs/Cargo.toml
index 87d4973d2..0a76c20b7 100644
--- a/nodejs/Cargo.toml
+++ b/nodejs/Cargo.toml
@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.40.0-beta.11"
+version = "0.41.0-beta.7"
 publish = false
 license.workspace = true
 description.workspace = true
diff --git a/nodejs/NODEJS_THIRD_PARTY_LICENSES.md b/nodejs/NODEJS_THIRD_PARTY_LICENSES.md
index c0881f61e..550d2888c 100644
--- a/nodejs/NODEJS_THIRD_PARTY_LICENSES.md
+++ b/nodejs/NODEJS_THIRD_PARTY_LICENSES.md
@@ -275,6 +275,7 @@
 [@types/node@20.16.10](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
 [@types/node@20.17.9](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
 [@types/node@22.7.4](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
+[@types/node@25.9.8](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
 [@types/semver@7.5.6](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
 [@types/stack-utils@2.0.3](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
 [@types/tmp@0.2.6](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
@@ -306,6 +307,9 @@
 [apache-arrow@16.0.0](https://github.com/apache/arrow) - Apache-2.0
 [apache-arrow@17.0.0](https://github.com/apache/arrow) - Apache-2.0
 [apache-arrow@18.0.0](https://github.com/apache/arrow) - Apache-2.0
+[apache-arrow@19.0.1](https://github.com/apache/arrow) - Apache-2.0
+[apache-arrow@20.0.0](https://github.com/apache/arrow) - Apache-2.0
+[apache-arrow@21.2.0](https://github.com/apache/arrow-js) - Apache-2.0
 [argparse@1.0.10](https://github.com/nodeca/argparse) - MIT
 [argparse@2.0.1](https://github.com/nodeca/argparse) - Python-2.0
 [array-back@3.1.0](https://github.com/75lb/array-back) - MIT
@@ -348,6 +352,7 @@
 [color@4.2.3](https://github.com/Qix-/color) - MIT
 [combined-stream@1.0.8](https://github.com/felixge/node-combined-stream) - MIT
 [command-line-args@5.2.1](https://github.com/75lb/command-line-args) - MIT
+[command-line-args@6.0.2](https://github.com/75lb/command-line-args) - MIT
 [command-line-usage@7.0.1](https://github.com/75lb/command-line-usage) - MIT
 [concat-map@0.0.1](https://github.com/substack/node-concat-map) - MIT
 [convert-source-map@2.0.0](https://github.com/thlorenz/convert-source-map) - MIT
@@ -398,12 +403,14 @@
 [file-entry-cache@6.0.1](https://github.com/royriojas/file-entry-cache) - MIT
 [fill-range@7.1.1](https://github.com/jonschlinkert/fill-range) - MIT
 [find-replace@3.0.0](https://github.com/75lb/find-replace) - MIT
+[find-replace@5.0.2](https://github.com/75lb/find-replace) - MIT
 [find-up@4.1.0](https://github.com/sindresorhus/find-up) - MIT
 [find-up@5.0.0](https://github.com/sindresorhus/find-up) - MIT
 [flat-cache@3.2.0](https://github.com/jaredwray/flat-cache) - MIT
 [flatbuffers@1.12.0](https://github.com/google/flatbuffers) - Apache*
 [flatbuffers@23.5.26](https://github.com/google/flatbuffers) - Apache*
 [flatbuffers@24.3.25](https://github.com/google/flatbuffers) - Apache-2.0
+[flatbuffers@25.9.23](https://github.com/google/flatbuffers) - Apache-2.0
 [flatted@3.2.9](https://github.com/WebReflection/flatted) - ISC
 [follow-redirects@1.15.6](https://github.com/follow-redirects/follow-redirects) - MIT
 [foreground-child@3.3.0](https://github.com/tapjs/foreground-child) - ISC
@@ -644,6 +651,7 @@
 [uc.micro@2.1.0](https://github.com/markdown-it/uc.micro) - MIT
 [undici-types@5.26.5](https://github.com/nodejs/undici) - MIT
 [undici-types@6.19.8](https://github.com/nodejs/undici) - MIT
+[undici-types@7.24.6](https://github.com/nodejs/undici) - MIT
 [update-browserslist-db@1.0.13](https://github.com/browserslist/update-db) - MIT
 [uri-js@4.4.1](https://github.com/garycourt/uri-js) - BSD-2-Clause
 [uuid@9.0.1](https://github.com/uuidjs/uuid) - MIT
diff --git a/nodejs/__test__/arrow.test.ts b/nodejs/__test__/arrow.test.ts
index df02b4936..376533961 100644
--- a/nodejs/__test__/arrow.test.ts
+++ b/nodejs/__test__/arrow.test.ts
@@ -2,10 +2,8 @@
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 import * as fs from "node:fs";
 import * as vm from "node:vm";
-import * as arrow15 from "apache-arrow-15";
-import * as arrow16 from "apache-arrow-16";
-import * as arrow17 from "apache-arrow-17";
-import * as arrow18 from "apache-arrow-18";
+
+import { type ApacheArrow, arrow15, arrowVersions } from "./arrow_versions";
 
 import {
   Field as CurrentField,
@@ -110,1541 +108,1539 @@ it("preserves field metadata from a provided schema", async function () {
   expect(roundTripped.schema.fields[0].metadata).toEqual(jsonMetadata);
 });
 
-describe.each([arrow15, arrow16, arrow17, arrow18])(
-  "Arrow",
-  (
-    arrow: typeof arrow15 | typeof arrow16 | typeof arrow17 | typeof arrow18,
-  ) => {
-    type ApacheArrow =
-      | typeof arrow15
-      | typeof arrow16
-      | typeof arrow17
-      | typeof arrow18;
-    const {
-      Schema,
-      Field,
-      Binary,
-      Bool,
-      Utf8,
-      Float64,
-      Struct,
-      List,
-      Map_,
-      Int16,
-      Int32,
-      Int64,
-      Float,
-      Float16,
-      Float32,
-      FixedSizeList,
-      Precision,
-      tableFromIPC,
-      DataType,
-      Dictionary,
-      RecordBatch: ArrowRecordBatch,
-      Table: ArrowTable,
-      Uint8: ArrowUint8,
-      makeData: arrowMakeData,
-      vectorFromArray,
+describe.each(arrowVersions)("Arrow %#", (arrow: ApacheArrow) => {
+  const {
+    Schema,
+    Field,
+    Binary,
+    Bool,
+    Utf8,
+    Float64,
+    Struct,
+    List,
+    Map_,
+    Int16,
+    Int32,
+    Int64,
+    Float,
+    Float16,
+    Float32,
+    FixedSizeList,
+    Precision,
+    tableFromIPC,
+    DataType,
+    Dictionary,
+    RecordBatch: ArrowRecordBatch,
+    Table: ArrowTable,
+    Uint8: ArrowUint8,
+    makeData: arrowMakeData,
+    vectorFromArray,
+    // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+  } = <any>arrow;
+  type Schema = ApacheArrow["Schema"];
+  type Table = ApacheArrow["Table"];
+
+  function expectValidMapField(
+    // biome-ignore lint/suspicious/noExplicitAny: Arrow Field types vary across supported versions
+    field: any,
+  ): void {
+    expect(DataType.isMap(field.type)).toBe(true);
+    expect(field.type.keysSorted).toBe(true);
+    expect(field.type.children).toHaveLength(1);
+
+    const entries = field.type.children[0];
+    expect(entries.name).toBe("entries");
+    expect(entries.nullable).toBe(false);
+    expect(DataType.isStruct(entries.type)).toBe(true);
+    expect(entries.type.children).toHaveLength(2);
+
+    const [key, value] = entries.type.children;
+    expect([key.name, value.name]).toEqual(["key", "value"]);
+    expect(key.nullable).toBe(false);
+    expect(DataType.isUtf8(key.type)).toBe(true);
+    expect(value.nullable).toBe(true);
+    expect(DataType.isInt(value.type)).toBe(true);
+    expect(value.type.bitWidth).toBe(32);
+    expect(value.type.isSigned).toBe(true);
+  }
+
+  // Helper method to verify various ways to create a table
+  async function checkTableCreation(
+    tableCreationMethod: (
+      records: Record<string, unknown>[],
+      recordsReversed: Record<string, unknown>[],
+      schema: Schema,
+    ) => Promise<Table>,
+    infersTypes: boolean,
+  ): Promise<void> {
+    const records = sampleRecords();
+    const recordsReversed = [
+      {
+        list: ["anime", "action", "comedy"],
+        struct: { x: 0, y: 0 },
+        string: "hello",
+        number: 7,
+        boolean: false,
+        binary: Buffer.alloc(5),
+      },
+    ];
+    const schema = new Schema([
+      new Field("binary", new Binary(), false),
+      new Field("boolean", new Bool(), false),
+      new Field("number", new Float64(), false),
+      new Field("string", new Utf8(), false),
+      new Field(
+        "struct",
+        new Struct([
+          new Field("x", new Float64(), false),
+          new Field("y", new Float64(), false),
+        ]),
+      ),
+      new Field("list", new List(new Field("item", new Utf8(), false)), false),
+    ]);
+    const table = (await tableCreationMethod(
+      records,
+      recordsReversed,
+      schema,
       // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-    } = <any>arrow;
-    type Schema = ApacheArrow["Schema"];
-    type Table = ApacheArrow["Table"];
+    )) as any;
 
-    function expectValidMapField(
-      // biome-ignore lint/suspicious/noExplicitAny: Arrow Field types vary across supported versions
-      field: any,
-    ): void {
-      expect(DataType.isMap(field.type)).toBe(true);
-      expect(field.type.keysSorted).toBe(true);
-      expect(field.type.children).toHaveLength(1);
+    // We expect deterministic ordering of the fields
+    expect(table.schema.names).toEqual(schema.names);
 
-      const entries = field.type.children[0];
-      expect(entries.name).toBe("entries");
-      expect(entries.nullable).toBe(false);
-      expect(DataType.isStruct(entries.type)).toBe(true);
-      expect(entries.type.children).toHaveLength(2);
+    schema.fields.forEach(
+      (
+        // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+        field: { name: any; type: { toString: () => any } },
+        idx: string | number,
+      ) => {
+        const actualField = table.schema.fields[idx];
+        // Type inference always assumes nullable=true
+        if (infersTypes) {
+          expect(actualField.nullable).toBe(true);
+        } else {
+          expect(actualField.nullable).toBe(false);
+        }
+        expect(table.getChild(field.name)?.type.toString()).toEqual(
+          field.type.toString(),
+        );
+        expect(table.getChildAt(idx)?.type.toString()).toEqual(
+          field.type.toString(),
+        );
+      },
+    );
+  }
 
-      const [key, value] = entries.type.children;
-      expect([key.name, value.name]).toEqual(["key", "value"]);
-      expect(key.nullable).toBe(false);
-      expect(DataType.isUtf8(key.type)).toBe(true);
-      expect(value.nullable).toBe(true);
-      expect(DataType.isInt(value.type)).toBe(true);
-      expect(value.type.bitWidth).toBe(32);
-      expect(value.type.isSigned).toBe(true);
-    }
-
-    // Helper method to verify various ways to create a table
-    async function checkTableCreation(
-      tableCreationMethod: (
-        records: Record<string, unknown>[],
-        recordsReversed: Record<string, unknown>[],
-        schema: Schema,
-      ) => Promise<Table>,
-      infersTypes: boolean,
-    ): Promise<void> {
-      const records = sampleRecords();
-      const recordsReversed = [
-        {
-          list: ["anime", "action", "comedy"],
-          struct: { x: 0, y: 0 },
-          string: "hello",
-          number: 7,
-          boolean: false,
-          binary: Buffer.alloc(5),
-        },
+  describe("The function makeArrowTable", function () {
+    it("accepts snake_case embedding metadata like camelCase", function () {
+      const spellings = [
+        // biome-ignore lint/style/useNamingConvention: the Python wire spelling
+        { source_column: "text", vector_column: "vector" },
+        { sourceColumn: "text", vectorColumn: "vector" },
       ];
-      const schema = new Schema([
-        new Field("binary", new Binary(), false),
-        new Field("boolean", new Bool(), false),
-        new Field("number", new Float64(), false),
-        new Field("string", new Utf8(), false),
-        new Field(
-          "struct",
-          new Struct([
-            new Field("x", new Float64(), false),
-            new Field("y", new Float64(), false),
+      for (const columns of spellings) {
+        const schema = new Schema(
+          [
+            new Field("text", new Utf8(), false),
+            new Field(
+              "vector",
+              new FixedSizeList(3, new Field("item", new Float32(), true)),
+              false,
+            ),
+          ],
+          new Map([
+            [
+              "embedding_functions",
+              JSON.stringify([{ name: "mock", model: {}, ...columns }]),
+            ],
           ]),
-        ),
+        );
+        // The vector field is non-nullable and absent from the data; only a
+        // recognized embedding config makes that acceptable.
+        const table = makeArrowTable([{ text: "hello" }], { schema });
+        expect(table.numRows).toBe(1);
+      }
+    });
+
+    it("will use data types from a provided schema instead of inference", async function () {
+      const schema = new Schema([
+        new Field("a", new Int32(), false),
+        new Field("b", new Float32(), true),
+        new Field("c", new FixedSizeList(3, new Field("item", new Float16()))),
+        new Field("d", new Int64(), true),
+      ]);
+      const table = makeArrowTable(
+        [
+          { a: 1, b: 2, c: [1, 2, 3], d: 9 },
+          { a: 4, b: 5, c: [4, 5, 6], d: 10 },
+          { a: 7, b: 8, c: [7, 8, 9], d: null },
+        ],
+        { schema },
+      );
+
+      const buf = await fromTableToBuffer(table);
+      expect(buf.byteLength).toBeGreaterThan(0);
+
+      const actual = tableFromIPC(buf);
+      expect(actual.numRows).toBe(3);
+      const actualSchema = actual.schema;
+      expect(actualSchema).toEqual(schema);
+      expect(table.getChild("a")?.toJSON()).toEqual([1, 4, 7]);
+      expect(table.getChild("b")?.toJSON()).toEqual([2, 5, 8]);
+      expect(table.getChild("d")?.toJSON()).toEqual([9n, 10n, null]);
+    });
+
+    it("will use a provided FixedSizeList schema with typed array values", function () {
+      const schema = new Schema([
+        new Field("text", new Utf8(), false),
         new Field(
-          "list",
-          new List(new Field("item", new Utf8(), false)),
+          "vector",
+          new FixedSizeList(3, new Field("item", new Float32(), false)),
           false,
         ),
       ]);
-      const table = (await tableCreationMethod(
-        records,
-        recordsReversed,
-        schema,
-        // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-      )) as any;
 
-      // We expect deterministic ordering of the fields
-      expect(table.schema.names).toEqual(schema.names);
+      const table = makeArrowTable(
+        [
+          {
+            text: "foo",
+            vector: new Float32Array([1, 2, 3]),
+          },
+        ],
+        { schema },
+      );
 
-      schema.fields.forEach(
-        (
-          // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-          field: { name: any; type: { toString: () => any } },
-          idx: string | number,
-        ) => {
-          const actualField = table.schema.fields[idx];
-          // Type inference always assumes nullable=true
-          if (infersTypes) {
-            expect(actualField.nullable).toBe(true);
-          } else {
-            expect(actualField.nullable).toBe(false);
-          }
-          expect(table.getChild(field.name)?.type.toString()).toEqual(
-            field.type.toString(),
-          );
-          expect(table.getChildAt(idx)?.type.toString()).toEqual(
-            field.type.toString(),
-          );
+      expect(table.getChild("text")?.toJSON()).toEqual(["foo"]);
+      expect(
+        table
+          .getChild("vector")
+          ?.toJSON()
+          .map((value) => value.toJSON()),
+      ).toEqual([[1, 2, 3]]);
+    });
+
+    it("will assume the column `vector` is FixedSizeList<Float32> by default", async function () {
+      const schema = new Schema([
+        new Field("a", new Float(Precision.DOUBLE), true),
+        new Field("b", new Int64(), true),
+        new Field(
+          "vector",
+          new FixedSizeList(
+            3,
+            new Field("item", new Float(Precision.SINGLE), true),
+          ),
+          true,
+        ),
+      ]);
+      const table = makeArrowTable([
+        { a: 1, b: 2n, vector: [1, 2, 3] },
+        { a: 4, b: 5n, vector: [4, 5, 6] },
+        { a: 7, b: 8n, vector: [7, 8, 9] },
+      ]);
+
+      const buf = await fromTableToBuffer(table);
+      expect(buf.byteLength).toBeGreaterThan(0);
+
+      const actual = tableFromIPC(buf);
+      expect(actual.numRows).toBe(3);
+      const actualSchema = actual.schema;
+      expect(actualSchema).toEqual(schema);
+
+      expect(table.getChild("a")?.toJSON()).toEqual([1, 4, 7]);
+      expect(table.getChild("b")?.toJSON()).toEqual([2n, 5n, 8n]);
+      expect(
+        table
+          .getChild("vector")
+          ?.toJSON()
+          .map((v) => v.toJSON()),
+      ).toEqual([
+        [1, 2, 3],
+        [4, 5, 6],
+        [7, 8, 9],
+      ]);
+    });
+
+    it("can support multiple vector columns", async function () {
+      const schema = new Schema([
+        new Field("a", new Float(Precision.DOUBLE), true),
+        new Field("b", new Float(Precision.DOUBLE), true),
+        new Field(
+          "vec1",
+          new FixedSizeList(3, new Field("item", new Float16(), true)),
+          true,
+        ),
+        new Field(
+          "vec2",
+          new FixedSizeList(3, new Field("item", new Float64(), true)),
+          true,
+        ),
+      ]);
+      const table = makeArrowTable(
+        [
+          { a: 1, b: 2, vec1: [1, 2, 3], vec2: [2, 4, 6] },
+          { a: 4, b: 5, vec1: [4, 5, 6], vec2: [8, 10, 12] },
+          { a: 7, b: 8, vec1: [7, 8, 9], vec2: [14, 16, 18] },
+        ],
+        {
+          vectorColumns: {
+            vec1: { type: new Float16() },
+            vec2: { type: new Float64() },
+          },
         },
       );
-    }
 
-    describe("The function makeArrowTable", function () {
-      it("accepts snake_case embedding metadata like camelCase", function () {
-        const spellings = [
-          // biome-ignore lint/style/useNamingConvention: the Python wire spelling
-          { source_column: "text", vector_column: "vector" },
-          { sourceColumn: "text", vectorColumn: "vector" },
-        ];
-        for (const columns of spellings) {
-          const schema = new Schema(
-            [
-              new Field("text", new Utf8(), false),
-              new Field(
-                "vector",
-                new FixedSizeList(3, new Field("item", new Float32(), true)),
-                false,
-              ),
-            ],
-            new Map([
-              [
-                "embedding_functions",
-                JSON.stringify([{ name: "mock", model: {}, ...columns }]),
-              ],
-            ]),
-          );
-          // The vector field is non-nullable and absent from the data; only a
-          // recognized embedding config makes that acceptable.
-          const table = makeArrowTable([{ text: "hello" }], { schema });
-          expect(table.numRows).toBe(1);
-        }
-      });
+      const buf = await fromTableToBuffer(table);
+      expect(buf.byteLength).toBeGreaterThan(0);
 
-      it("will use data types from a provided schema instead of inference", async function () {
-        const schema = new Schema([
-          new Field("a", new Int32(), false),
-          new Field("b", new Float32(), true),
-          new Field(
-            "c",
-            new FixedSizeList(3, new Field("item", new Float16())),
-          ),
-          new Field("d", new Int64(), true),
-        ]);
-        const table = makeArrowTable(
-          [
-            { a: 1, b: 2, c: [1, 2, 3], d: 9 },
-            { a: 4, b: 5, c: [4, 5, 6], d: 10 },
-            { a: 7, b: 8, c: [7, 8, 9], d: null },
-          ],
-          { schema },
-        );
+      const actual = tableFromIPC(buf);
+      expect(actual.numRows).toBe(3);
+      const actualSchema = actual.schema;
+      expect(actualSchema).toEqual(schema);
+    });
 
-        const buf = await fromTableToBuffer(table);
-        expect(buf.byteLength).toBeGreaterThan(0);
+    it("will detect vector columns when name contains 'vector' or 'embedding'", async function () {
+      // Test various naming patterns that should be detected as vector columns
+      const floatVectorTable = makeArrowTable([
+        {
+          // Float vectors (use decimal values to ensure they're treated as floats)
+          // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
+          user_vector: [1.1, 2.2],
+          // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
+          text_embedding: [3.3, 4.4],
+          // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
+          doc_embeddings: [5.5, 6.6],
+          // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
+          my_vector_field: [7.7, 8.8],
+          // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
+          embedding_model: [9.9, 10.1],
+          // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
+          VECTOR_COL: [11.1, 12.2], // uppercase
+          // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
+          Vector_Mixed: [13.3, 14.4], // mixed case
+        },
+      ]);
 
-        const actual = tableFromIPC(buf);
-        expect(actual.numRows).toBe(3);
-        const actualSchema = actual.schema;
-        expect(actualSchema).toEqual(schema);
-        expect(table.getChild("a")?.toJSON()).toEqual([1, 4, 7]);
-        expect(table.getChild("b")?.toJSON()).toEqual([2, 5, 8]);
-        expect(table.getChild("d")?.toJSON()).toEqual([9n, 10n, null]);
-      });
+      // Check that columns with 'vector' or 'embedding' in name are converted to FixedSizeList
+      const floatVectorColumns = [
+        "user_vector",
+        "text_embedding",
+        "doc_embeddings",
+        "my_vector_field",
+        "embedding_model",
+        "VECTOR_COL",
+        "Vector_Mixed",
+      ];
 
-      it("will use a provided FixedSizeList schema with typed array values", function () {
-        const schema = new Schema([
-          new Field("text", new Utf8(), false),
-          new Field(
-            "vector",
-            new FixedSizeList(3, new Field("item", new Float32(), false)),
-            false,
-          ),
-        ]);
-
-        const table = makeArrowTable(
-          [
-            {
-              text: "foo",
-              vector: new Float32Array([1, 2, 3]),
-            },
-          ],
-          { schema },
-        );
-
-        expect(table.getChild("text")?.toJSON()).toEqual(["foo"]);
+      for (const columnName of floatVectorColumns) {
         expect(
-          table
-            .getChild("vector")
-            ?.toJSON()
-            .map((value) => value.toJSON()),
-        ).toEqual([[1, 2, 3]]);
-      });
-
-      it("will assume the column `vector` is FixedSizeList<Float32> by default", async function () {
-        const schema = new Schema([
-          new Field("a", new Float(Precision.DOUBLE), true),
-          new Field("b", new Int64(), true),
-          new Field(
-            "vector",
-            new FixedSizeList(
-              3,
-              new Field("item", new Float(Precision.SINGLE), true),
-            ),
-            true,
-          ),
-        ]);
-        const table = makeArrowTable([
-          { a: 1, b: 2n, vector: [1, 2, 3] },
-          { a: 4, b: 5n, vector: [4, 5, 6] },
-          { a: 7, b: 8n, vector: [7, 8, 9] },
-        ]);
-
-        const buf = await fromTableToBuffer(table);
-        expect(buf.byteLength).toBeGreaterThan(0);
-
-        const actual = tableFromIPC(buf);
-        expect(actual.numRows).toBe(3);
-        const actualSchema = actual.schema;
-        expect(actualSchema).toEqual(schema);
-
-        expect(table.getChild("a")?.toJSON()).toEqual([1, 4, 7]);
-        expect(table.getChild("b")?.toJSON()).toEqual([2n, 5n, 8n]);
+          DataType.isFixedSizeList(floatVectorTable.getChild(columnName)?.type),
+        ).toBe(true);
+        // Check that float vectors use Float32 by default
         expect(
-          table
-            .getChild("vector")
-            ?.toJSON()
-            .map((v) => v.toJSON()),
-        ).toEqual([
-          [1, 2, 3],
-          [4, 5, 6],
-          [7, 8, 9],
-        ]);
-      });
+          floatVectorTable
+            .getChild(columnName)
+            ?.type.children[0].type.toString(),
+        ).toEqual(new Float32().toString());
+      }
 
-      it("can support multiple vector columns", async function () {
-        const schema = new Schema([
-          new Field("a", new Float(Precision.DOUBLE), true),
-          new Field("b", new Float(Precision.DOUBLE), true),
-          new Field(
-            "vec1",
-            new FixedSizeList(3, new Field("item", new Float16(), true)),
-            true,
-          ),
-          new Field(
-            "vec2",
-            new FixedSizeList(3, new Field("item", new Float64(), true)),
-            true,
-          ),
-        ]);
-        const table = makeArrowTable(
-          [
-            { a: 1, b: 2, vec1: [1, 2, 3], vec2: [2, 4, 6] },
-            { a: 4, b: 5, vec1: [4, 5, 6], vec2: [8, 10, 12] },
-            { a: 7, b: 8, vec1: [7, 8, 9], vec2: [14, 16, 18] },
-          ],
-          {
-            vectorColumns: {
-              vec1: { type: new Float16() },
-              vec2: { type: new Float64() },
-            },
-          },
-        );
+      // Test that regular integer arrays still get treated as float vectors
+      // (since JavaScript doesn't distinguish integers from floats at runtime)
+      const integerArrayTable = makeArrowTable([
+        {
+          // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
+          vector_int: [1, 2], // Regular array with integers - should be Float32
+          // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
+          embedding_int: [3, 4], // Regular array with integers - should be Float32
+        },
+      ]);
 
-        const buf = await fromTableToBuffer(table);
-        expect(buf.byteLength).toBeGreaterThan(0);
-
-        const actual = tableFromIPC(buf);
-        expect(actual.numRows).toBe(3);
-        const actualSchema = actual.schema;
-        expect(actualSchema).toEqual(schema);
-      });
-
-      it("will detect vector columns when name contains 'vector' or 'embedding'", async function () {
-        // Test various naming patterns that should be detected as vector columns
-        const floatVectorTable = makeArrowTable([
-          {
-            // Float vectors (use decimal values to ensure they're treated as floats)
-            // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
-            user_vector: [1.1, 2.2],
-            // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
-            text_embedding: [3.3, 4.4],
-            // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
-            doc_embeddings: [5.5, 6.6],
-            // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
-            my_vector_field: [7.7, 8.8],
-            // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
-            embedding_model: [9.9, 10.1],
-            // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
-            VECTOR_COL: [11.1, 12.2], // uppercase
-            // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
-            Vector_Mixed: [13.3, 14.4], // mixed case
-          },
-        ]);
-
-        // Check that columns with 'vector' or 'embedding' in name are converted to FixedSizeList
-        const floatVectorColumns = [
-          "user_vector",
-          "text_embedding",
-          "doc_embeddings",
-          "my_vector_field",
-          "embedding_model",
-          "VECTOR_COL",
-          "Vector_Mixed",
-        ];
-
-        for (const columnName of floatVectorColumns) {
-          expect(
-            DataType.isFixedSizeList(
-              floatVectorTable.getChild(columnName)?.type,
-            ),
-          ).toBe(true);
-          // Check that float vectors use Float32 by default
-          expect(
-            floatVectorTable
-              .getChild(columnName)
-              ?.type.children[0].type.toString(),
-          ).toEqual(new Float32().toString());
-        }
-
-        // Test that regular integer arrays still get treated as float vectors
-        // (since JavaScript doesn't distinguish integers from floats at runtime)
-        const integerArrayTable = makeArrowTable([
-          {
-            // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
-            vector_int: [1, 2], // Regular array with integers - should be Float32
-            // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
-            embedding_int: [3, 4], // Regular array with integers - should be Float32
-          },
-        ]);
-
-        const integerArrayColumns = ["vector_int", "embedding_int"];
-
-        for (const columnName of integerArrayColumns) {
-          expect(
-            DataType.isFixedSizeList(
-              integerArrayTable.getChild(columnName)?.type,
-            ),
-          ).toBe(true);
-          // Regular integer arrays should use Float32 (avoiding false positives)
-          expect(
-            integerArrayTable
-              .getChild(columnName)
-              ?.type.children[0].type.toString(),
-          ).toEqual(new Float32().toString());
-        }
-
-        // Test normal list should NOT be converted to FixedSizeList
-        const normalListTable = makeArrowTable([
-          {
-            // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
-            normal_list: [15.5, 16.6], // should NOT be detected as vector
-          },
-        ]);
+      const integerArrayColumns = ["vector_int", "embedding_int"];
 
+      for (const columnName of integerArrayColumns) {
         expect(
           DataType.isFixedSizeList(
-            normalListTable.getChild("normal_list")?.type,
+            integerArrayTable.getChild(columnName)?.type,
           ),
-        ).toBe(false);
-        expect(
-          DataType.isList(normalListTable.getChild("normal_list")?.type),
         ).toBe(true);
-      });
-
-      it("will allow different vector column types", async function () {
-        const table = makeArrowTable([{ fp16: [1], fp32: [1], fp64: [1] }], {
-          vectorColumns: {
-            fp16: { type: new Float16() },
-            fp32: { type: new Float32() },
-            fp64: { type: new Float64() },
-          },
-        });
-
+        // Regular integer arrays should use Float32 (avoiding false positives)
         expect(
-          table.getChild("fp16")?.type.children[0].type.toString(),
-        ).toEqual(new Float16().toString());
-        expect(
-          table.getChild("fp32")?.type.children[0].type.toString(),
+          integerArrayTable
+            .getChild(columnName)
+            ?.type.children[0].type.toString(),
         ).toEqual(new Float32().toString());
-        expect(
-          table.getChild("fp64")?.type.children[0].type.toString(),
-        ).toEqual(new Float64().toString());
+      }
+
+      // Test normal list should NOT be converted to FixedSizeList
+      const normalListTable = makeArrowTable([
+        {
+          // biome-ignore lint/style/useNamingConvention: Testing vector column detection patterns
+          normal_list: [15.5, 16.6], // should NOT be detected as vector
+        },
+      ]);
+
+      expect(
+        DataType.isFixedSizeList(normalListTable.getChild("normal_list")?.type),
+      ).toBe(false);
+      expect(
+        DataType.isList(normalListTable.getChild("normal_list")?.type),
+      ).toBe(true);
+    });
+
+    it("will allow different vector column types", async function () {
+      const table = makeArrowTable([{ fp16: [1], fp32: [1], fp64: [1] }], {
+        vectorColumns: {
+          fp16: { type: new Float16() },
+          fp32: { type: new Float32() },
+          fp64: { type: new Float64() },
+        },
       });
 
-      it("will infer FixedSizeList<Float32> from Float32Array values", async function () {
-        const table = makeArrowTable([
-          { id: "a", vector: new Float32Array([0.1, 0.2, 0.3]) },
-          { id: "b", vector: new Float32Array([0.4, 0.5, 0.6]) },
-        ]);
+      expect(table.getChild("fp16")?.type.children[0].type.toString()).toEqual(
+        new Float16().toString(),
+      );
+      expect(table.getChild("fp32")?.type.children[0].type.toString()).toEqual(
+        new Float32().toString(),
+      );
+      expect(table.getChild("fp64")?.type.children[0].type.toString()).toEqual(
+        new Float64().toString(),
+      );
+    });
 
-        expect(DataType.isFixedSizeList(table.getChild("vector")?.type)).toBe(
+    it("will infer FixedSizeList<Float32> from Float32Array values", async function () {
+      const table = makeArrowTable([
+        { id: "a", vector: new Float32Array([0.1, 0.2, 0.3]) },
+        { id: "b", vector: new Float32Array([0.4, 0.5, 0.6]) },
+      ]);
+
+      expect(DataType.isFixedSizeList(table.getChild("vector")?.type)).toBe(
+        true,
+      );
+      const vectorType = table.getChild("vector")?.type;
+      expect(vectorType.listSize).toBe(3);
+      expect(vectorType.children[0].type.toString()).toEqual(
+        new Float32().toString(),
+      );
+    });
+
+    it("will infer FixedSizeList<Uint8> from Uint8Array values", async function () {
+      const table = makeArrowTable([
+        { id: "a", vector: new Uint8Array([1, 2, 3]) },
+        { id: "b", vector: new Uint8Array([4, 5, 6]) },
+      ]);
+
+      expect(DataType.isFixedSizeList(table.getChild("vector")?.type)).toBe(
+        true,
+      );
+      const vectorType = table.getChild("vector")?.type;
+      expect(vectorType.listSize).toBe(3);
+      expect(vectorType.children[0].type.toString()).toEqual(
+        new ArrowUint8().toString(),
+      );
+    });
+
+    it("will use dictionary encoded strings if asked", async function () {
+      const table = makeArrowTable([{ str: "hello" }]);
+      expect(DataType.isUtf8(table.getChild("str")?.type)).toBe(true);
+
+      const tableWithDict = makeArrowTable([{ str: "hello" }], {
+        dictionaryEncodeStrings: true,
+      });
+      expect(DataType.isDictionary(tableWithDict.getChild("str")?.type)).toBe(
+        true,
+      );
+
+      const schema = new Schema([
+        new Field("str", new Dictionary(new Utf8(), new Int32())),
+      ]);
+
+      const tableWithDict2 = makeArrowTable([{ str: "hello" }], { schema });
+      expect(DataType.isDictionary(tableWithDict2.getChild("str")?.type)).toBe(
+        true,
+      );
+    });
+
+    it("will infer data types correctly", async function () {
+      await checkTableCreation(
+        // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+        async (records) => (<any>makeArrowTable)(records),
+        true,
+      );
+    });
+
+    it("will allow matching inferred types across records", function () {
+      expect(() => makeArrowTable([{ value: 1 }, { value: 2 }])).not.toThrow();
+    });
+
+    it("will reject mismatched inferred types across records", function () {
+      expect(() => makeArrowTable([{ value: 1 }, { value: "two" }])).toThrow(
+        "Failed to infer schema for data. Previously inferred type Float64 but found Utf8 for field value at row 1. Consider providing an explicit schema.",
+      );
+    });
+
+    it.each([
+      ["ArrayBuffer", new Uint8Array([104]).buffer],
+      ["URL", new URL("https://example.com/")],
+      ["empty object", {}],
+      [
+        "class instance with enumerable fields",
+        Object.assign(new Blob(["x"]), { position: 0 }),
+      ],
+    ])("rejects %s values without a schema", (_name, value) => {
+      expect(() => makeArrowTable([{ id: 1, value }])).toThrow(
+        /field value at row 0/,
+      );
+      expect(() => makeArrowTable([{ value }])).toThrow(/field value at row 0/);
+    });
+
+    it.each([
+      ["Binary", new Binary(), new Uint8Array([104]).buffer],
+      ["Utf8", new Utf8(), new URL("https://example.com/")],
+    ])("rejects %s values with a schema", (_name, type, value) => {
+      const schema = new Schema([new Field("value", type, true)]);
+      expect(() => makeArrowTable([{ value }], { schema })).toThrow(
+        /field value at row 0/,
+      );
+    });
+
+    it("rejects unsupported values in nested struct fields", () => {
+      const schema = new Schema([
+        new Field(
+          "metadata",
+          new Struct([new Field("bytes", new Binary(), true)]),
           true,
-        );
-        const vectorType = table.getChild("vector")?.type;
-        expect(vectorType.listSize).toBe(3);
-        expect(vectorType.children[0].type.toString()).toEqual(
-          new Float32().toString(),
-        );
-      });
+        ),
+      ]);
+      expect(() =>
+        makeArrowTable(
+          [{ metadata: { bytes: new Uint8Array([104]).buffer } }],
+          { schema },
+        ),
+      ).toThrow(/field metadata\.bytes at row 0/);
+    });
 
-      it("will infer FixedSizeList<Uint8> from Uint8Array values", async function () {
-        const table = makeArrowTable([
-          { id: "a", vector: new Uint8Array([1, 2, 3]) },
-          { id: "b", vector: new Uint8Array([4, 5, 6]) },
-        ]);
-
-        expect(DataType.isFixedSizeList(table.getChild("vector")?.type)).toBe(
+    it("rejects unsupported objects in list elements", () => {
+      const schema = new Schema([
+        new Field(
+          "images",
+          new List(new Field("item", new Binary(), true)),
           true,
-        );
-        const vectorType = table.getChild("vector")?.type;
-        expect(vectorType.listSize).toBe(3);
-        expect(vectorType.children[0].type.toString()).toEqual(
-          new ArrowUint8().toString(),
-        );
-      });
+        ),
+      ]);
+      expect(() =>
+        makeArrowTable([{ images: [new Blob(["abc"])] }], { schema }),
+      ).toThrow(/field images\[0\] at row 0/);
+    });
 
-      it("will use dictionary encoded strings if asked", async function () {
-        const table = makeArrowTable([{ str: "hello" }]);
-        expect(DataType.isUtf8(table.getChild("str")?.type)).toBe(true);
-
-        const tableWithDict = makeArrowTable([{ str: "hello" }], {
-          dictionaryEncodeStrings: true,
-        });
-        expect(DataType.isDictionary(tableWithDict.getChild("str")?.type)).toBe(
-          true,
-        );
-
-        const schema = new Schema([
-          new Field("str", new Dictionary(new Utf8(), new Int32())),
-        ]);
-
-        const tableWithDict2 = makeArrowTable([{ str: "hello" }], { schema });
-        expect(
-          DataType.isDictionary(tableWithDict2.getChild("str")?.type),
-        ).toBe(true);
-      });
-
-      it("will infer data types correctly", async function () {
-        await checkTableCreation(
-          // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-          async (records) => (<any>makeArrowTable)(records),
-          true,
-        );
-      });
-
-      it("will allow matching inferred types across records", function () {
-        expect(() =>
-          makeArrowTable([{ value: 1 }, { value: 2 }]),
-        ).not.toThrow();
-      });
-
-      it("will reject mismatched inferred types across records", function () {
-        expect(() => makeArrowTable([{ value: 1 }, { value: "two" }])).toThrow(
-          "Failed to infer schema for data. Previously inferred type Float64 but found Utf8 for field value at row 1. Consider providing an explicit schema.",
-        );
-      });
-
-      it.each([
-        ["ArrayBuffer", new Uint8Array([104]).buffer],
-        ["URL", new URL("https://example.com/")],
-        ["empty object", {}],
-        [
-          "class instance with enumerable fields",
-          Object.assign(new Blob(["x"]), { position: 0 }),
-        ],
-      ])("rejects %s values without a schema", (_name, value) => {
-        expect(() => makeArrowTable([{ id: 1, value }])).toThrow(
-          /field value at row 0/,
-        );
-        expect(() => makeArrowTable([{ value }])).toThrow(
-          /field value at row 0/,
-        );
-      });
-
-      it.each([
-        ["Binary", new Binary(), new Uint8Array([104]).buffer],
-        ["Utf8", new Utf8(), new URL("https://example.com/")],
-      ])("rejects %s values with a schema", (_name, type, value) => {
-        const schema = new Schema([new Field("value", type, true)]);
-        expect(() => makeArrowTable([{ value }], { schema })).toThrow(
-          /field value at row 0/,
-        );
-      });
-
-      it("rejects unsupported values in nested struct fields", () => {
-        const schema = new Schema([
-          new Field(
-            "metadata",
-            new Struct([new Field("bytes", new Binary(), true)]),
-            true,
-          ),
-        ]);
-        expect(() =>
-          makeArrowTable(
-            [{ metadata: { bytes: new Uint8Array([104]).buffer } }],
-            { schema },
-          ),
-        ).toThrow(/field metadata\.bytes at row 0/);
-      });
-
-      it("rejects unsupported objects in list elements", () => {
-        const schema = new Schema([
-          new Field(
-            "images",
-            new List(new Field("item", new Binary(), true)),
-            true,
-          ),
-        ]);
-        expect(() =>
-          makeArrowTable([{ images: [new Blob(["abc"])] }], { schema }),
-        ).toThrow(/field images\[0\] at row 0/);
-      });
-
-      it("rejects unsupported objects in nested lists", () => {
-        const schema = new Schema([
-          new Field(
-            "images",
-            new List(
-              new Field(
-                "item",
-                new List(new Field("item", new Binary(), true)),
-                true,
-              ),
+    it("rejects unsupported objects in nested lists", () => {
+      const schema = new Schema([
+        new Field(
+          "images",
+          new List(
+            new Field(
+              "item",
+              new List(new Field("item", new Binary(), true)),
+              true,
             ),
-            true,
           ),
-        ]);
-        expect(() =>
-          makeArrowTable([{ images: [[new Blob(["abc"])]] }], { schema }),
-        ).toThrow(/field images\[0\]\[0\] at row 0/);
-      });
+          true,
+        ),
+      ]);
+      expect(() =>
+        makeArrowTable([{ images: [[new Blob(["abc"])]] }], { schema }),
+      ).toThrow(/field images\[0\]\[0\] at row 0/);
+    });
 
-      it("rejects unsupported nested struct fields in lists", () => {
-        const schema = new Schema([
-          new Field(
-            "items",
-            new List(
-              new Field(
-                "item",
-                new Struct([new Field("bytes", new Binary(), true)]),
-                true,
-              ),
+    it("rejects unsupported nested struct fields in lists", () => {
+      const schema = new Schema([
+        new Field(
+          "items",
+          new List(
+            new Field(
+              "item",
+              new Struct([new Field("bytes", new Binary(), true)]),
+              true,
             ),
-            true,
           ),
-        ]);
-        expect(() =>
-          makeArrowTable(
-            [{ items: [{ bytes: new Uint8Array([104]).buffer }] }],
-            { schema },
-          ),
-        ).toThrow(/field items\[0\]\.bytes at row 0/);
+          true,
+        ),
+      ]);
+      expect(() =>
+        makeArrowTable([{ items: [{ bytes: new Uint8Array([104]).buffer }] }], {
+          schema,
+        }),
+      ).toThrow(/field items\[0\]\.bytes at row 0/);
+    });
+
+    it("will ignore generated dictionary IDs when comparing inferred types", function () {
+      const table = makeArrowTable([{ str: "a" }, { str: "b" }], {
+        dictionaryEncodeStrings: true,
       });
 
-      it("will ignore generated dictionary IDs when comparing inferred types", function () {
-        const table = makeArrowTable([{ str: "a" }, { str: "b" }], {
-          dictionaryEncodeStrings: true,
-        });
+      expect(table.getChild("str")?.toJSON()).toEqual(["a", "b"]);
+    });
 
-        expect(table.getChild("str")?.toJSON()).toEqual(["a", "b"]);
-      });
+    it("will preserve null values without treating them as type mismatches", function () {
+      for (const records of [
+        [{ vector: [1, 2, 3] }, { vector: null }],
+        [{ vector: null }, { vector: [1, 2, 3] }],
+      ]) {
+        const table = makeArrowTable(records);
 
-      it("will preserve null values without treating them as type mismatches", function () {
-        for (const records of [
-          [{ vector: [1, 2, 3] }, { vector: null }],
-          [{ vector: null }, { vector: [1, 2, 3] }],
-        ]) {
-          const table = makeArrowTable(records);
+        expect(table.numRows).toBe(2);
+        expect(table.getChild("vector")?.nullCount).toBe(1);
+      }
+    });
 
-          expect(table.numRows).toBe(2);
-          expect(table.getChild("vector")?.nullCount).toBe(1);
-        }
-      });
-
-      it("will preserve empty variable-size lists", function () {
-        for (const records of [
-          [{ items: [1] }, { items: [] }],
-          [{ items: [] }, { items: [1] }],
-        ]) {
-          const table = makeArrowTable(records);
-          expect(
-            table
-              .getChild("items")
-              ?.toJSON()
-              .map((value) => value.toJSON()),
-          ).toEqual(records.map((record) => record.items));
-        }
-      });
-
-      it("will propagate deferred evidence through nested lists", function () {
-        for (const records of [
-          [{ items: [1] }, { items: [null] }],
-          [{ items: [null] }, { items: [1] }],
-          [{ items: [null, 1] }, { items: [2, null] }],
-        ]) {
-          const table = makeArrowTable(records);
-          expect(
-            table
-              .getChild("items")
-              ?.toJSON()
-              .map((value) => value.toJSON()),
-          ).toEqual(records.map((record) => record.items));
-        }
-
-        const nestedRecords = [{ items: [[1]] }, { items: [[null]] }];
-        const nestedTable = makeArrowTable(nestedRecords);
+    it("will preserve empty variable-size lists", function () {
+      for (const records of [
+        [{ items: [1] }, { items: [] }],
+        [{ items: [] }, { items: [1] }],
+      ]) {
+        const table = makeArrowTable(records);
         expect(
-          nestedTable
+          table
             .getChild("items")
             ?.toJSON()
-            .map((value) =>
-              value
-                .toJSON()
-                .map((nestedValue: { toJSON: () => unknown[] }) =>
-                  nestedValue.toJSON(),
-                ),
-            ),
-        ).toEqual(nestedRecords.map((record) => record.items));
-      });
+            .map((value) => value.toJSON()),
+        ).toEqual(records.map((record) => record.items));
+      }
+    });
 
-      it("will reject incompatible deferred evidence within a list", function () {
-        for (const items of [
-          [[], 1],
-          [1, []],
-          [[null], 1],
-          [1, [null]],
-        ]) {
-          expect(() => makeArrowTable([{ items }])).toThrow(
-            "Failed to infer data type for field items at row 0.",
-          );
-        }
-      });
+    it("will propagate deferred evidence through nested lists", function () {
+      for (const records of [
+        [{ items: [1] }, { items: [null] }],
+        [{ items: [null] }, { items: [1] }],
+        [{ items: [null, 1] }, { items: [2, null] }],
+      ]) {
+        const table = makeArrowTable(records);
+        expect(
+          table
+            .getChild("items")
+            ?.toJSON()
+            .map((value) => value.toJSON()),
+        ).toEqual(records.map((record) => record.items));
+      }
 
-      it("will reject empty fixed-size lists", function () {
-        expect(() =>
-          makeArrowTable([{ vector: [1, 2, 3] }, { vector: [] }]),
-        ).toThrow(
-          "Failed to infer schema for data. Previously inferred type FixedSizeList[3]<Float32> but found List[0] for field vector at row 1.",
+      const nestedRecords = [{ items: [[1]] }, { items: [[null]] }];
+      const nestedTable = makeArrowTable(nestedRecords);
+      expect(
+        nestedTable
+          .getChild("items")
+          ?.toJSON()
+          .map((value) =>
+            value
+              .toJSON()
+              .map((nestedValue: { toJSON: () => unknown[] }) =>
+                nestedValue.toJSON(),
+              ),
+          ),
+      ).toEqual(nestedRecords.map((record) => record.items));
+    });
+
+    it("will reject incompatible deferred evidence within a list", function () {
+      for (const items of [
+        [[], 1],
+        [1, []],
+        [[null], 1],
+        [1, [null]],
+      ]) {
+        expect(() => makeArrowTable([{ items }])).toThrow(
+          "Failed to infer data type for field items at row 0.",
         );
-      });
+      }
+    });
 
-      it("will reject inferred leaf and branch shape changes", function () {
-        expect(() =>
-          makeArrowTable([{ value: 1 }, { value: { nested: 2 } }]),
-        ).toThrow(
-          "Failed to infer schema for data. Previously inferred type Float64 but found Struct for field value at row 1.",
+    it("will reject empty fixed-size lists", function () {
+      expect(() =>
+        makeArrowTable([{ vector: [1, 2, 3] }, { vector: [] }]),
+      ).toThrow(
+        "Failed to infer schema for data. Previously inferred type FixedSizeList[3]<Float32> but found List[0] for field vector at row 1.",
+      );
+    });
+
+    it("will reject inferred leaf and branch shape changes", function () {
+      expect(() =>
+        makeArrowTable([{ value: 1 }, { value: { nested: 2 } }]),
+      ).toThrow(
+        "Failed to infer schema for data. Previously inferred type Float64 but found Struct for field value at row 1.",
+      );
+      expect(() =>
+        makeArrowTable([{ value: { nested: 1 } }, { value: 2 }]),
+      ).toThrow(
+        "Failed to infer schema for data. Previously inferred type Struct but found Float64 for field value at row 1.",
+      );
+    });
+
+    it("will allow null values around inferred struct values", function () {
+      for (const { records, nullIndex } of [
+        {
+          records: [{ value: null }, { value: { nested: 2 } }],
+          nullIndex: 0,
+        },
+        {
+          records: [{ value: { nested: 1 } }, { value: null }],
+          nullIndex: 1,
+        },
+      ]) {
+        const table = makeArrowTable(records);
+        const values = table.getChild("value");
+
+        expect(values?.nullCount).toBe(1);
+        expect(values?.get(nullIndex)).toBeNull();
+      }
+    });
+
+    it("will allow null values around struct values with a schema", function () {
+      const schema = new Schema([
+        new Field("value", new Struct([new Field("nested", new Int32())])),
+      ]);
+      for (const { records, nullIndex } of [
+        {
+          records: [{ value: null }, { value: { nested: 2 } }],
+          nullIndex: 0,
+        },
+        {
+          records: [{ value: { nested: 1 } }, { value: null }],
+          nullIndex: 1,
+        },
+      ]) {
+        const table = makeArrowTable(records, { schema });
+        const values = table.getChild("value");
+
+        expect(values?.nullCount).toBe(1);
+        expect(values?.get(nullIndex)).toBeNull();
+      }
+    });
+
+    it("will reject non-object values for struct fields in a schema", function () {
+      const schema = new Schema([
+        new Field("value", new Struct([new Field("nested", new Int32())])),
+      ]);
+      for (const records of [
+        [{ value: 5 }],
+        [{ value: 5 }, { value: { nested: 1 } }],
+        [{ value: { nested: 1 } }, { value: "oops" }],
+      ]) {
+        expect(() => makeArrowTable(records, { schema })).toThrow(
+          /Expected a struct value for field value at row \d, got/,
         );
-        expect(() =>
-          makeArrowTable([{ value: { nested: 1 } }, { value: 2 }]),
-        ).toThrow(
-          "Failed to infer schema for data. Previously inferred type Struct but found Float64 for field value at row 1.",
-        );
-      });
+      }
+    });
 
-      it("will allow null values around inferred struct values", function () {
-        for (const { records, nullIndex } of [
-          {
-            records: [{ value: null }, { value: { nested: 2 } }],
-            nullIndex: 0,
-          },
-          {
-            records: [{ value: { nested: 1 } }, { value: null }],
-            nullIndex: 1,
-          },
-        ]) {
-          const table = makeArrowTable(records);
-          const values = table.getChild("value");
-
-          expect(values?.nullCount).toBe(1);
-          expect(values?.get(nullIndex)).toBeNull();
-        }
-      });
-
-      it("will allow a schema to be provided", async function () {
-        await checkTableCreation(
-          async (records, _, schema) =>
-            // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-            (<any>makeArrowTable)(records, { schema }),
-          false,
-        );
-      });
-
-      it("will use the field order of any provided schema", async function () {
-        await checkTableCreation(
-          async (_, recordsReversed, schema) =>
-            // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-            (<any>makeArrowTable)(recordsReversed, { schema }),
-          false,
-        );
-      });
-
-      it("will make an empty table", async function () {
-        await checkTableCreation(
+    it("will allow a schema to be provided", async function () {
+      await checkTableCreation(
+        async (records, _, schema) =>
           // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-          async (_, __, schema) => (<any>makeArrowTable)([], { schema }),
-          false,
-        );
-      });
+          (<any>makeArrowTable)(records, { schema }),
+        false,
+      );
+    });
 
-      it("will allow subsets of columns if nullable", async function () {
-        const schema = new Schema([
-          new Field("a", new Int64(), true),
+    it("will use the field order of any provided schema", async function () {
+      await checkTableCreation(
+        async (_, recordsReversed, schema) =>
+          // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+          (<any>makeArrowTable)(recordsReversed, { schema }),
+        false,
+      );
+    });
+
+    it("will make an empty table", async function () {
+      await checkTableCreation(
+        // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+        async (_, __, schema) => (<any>makeArrowTable)([], { schema }),
+        false,
+      );
+    });
+
+    it("will allow subsets of columns if nullable", async function () {
+      const schema = new Schema([
+        new Field("a", new Int64(), true),
+        new Field(
+          "s",
+          new Struct([
+            new Field("x", new Int32(), true),
+            new Field("y", new Int32(), true),
+          ]),
+          true,
+        ),
+        new Field("d", new Int16(), true),
+      ]);
+
+      const table = makeArrowTable([{ a: 1n }], { schema });
+      expect(table.numCols).toBe(1);
+      expect(table.numRows).toBe(1);
+
+      const table2 = makeArrowTable([{ a: 1n, d: 2 }], { schema });
+      expect(table2.numCols).toBe(2);
+
+      const table3 = makeArrowTable([{ s: { y: 3 } }], { schema });
+      expect(table3.numCols).toBe(1);
+      const expectedSchema = new Schema([
+        new Field("s", new Struct([new Field("y", new Int32(), true)]), true),
+      ]);
+      expect(table3.schema).toEqual(expectedSchema);
+    });
+
+    it("will work even if columns are sparsely provided", async function () {
+      const sparseRecords = [{ a: 1n }, { b: 2n }, { c: 3n }, { d: 4n }];
+      const table = makeArrowTable(sparseRecords);
+      expect(table.numCols).toBe(4);
+      expect(table.numRows).toBe(4);
+
+      const schema = new Schema([
+        new Field("a", new Int64(), true),
+        new Field("b", new Int32(), true),
+        new Field("c", new Int64(), true),
+        new Field("d", new Int16(), true),
+      ]);
+      const table2 = makeArrowTable(sparseRecords, { schema });
+      expect(table2.numCols).toBe(4);
+      expect(table2.numRows).toBe(4);
+      expect(table2.schema).toEqual(schema);
+    });
+
+    it("will handle missing columns in schema alignment when using embeddings", async function () {
+      const schema = new Schema(
+        [
+          new Field("domain", new Utf8(), true),
+          new Field("name", new Utf8(), true),
+          new Field("description", new Utf8(), true),
+        ],
+        new Map([["embedding_functions", JSON.stringify([])]]),
+      );
+
+      const data = [
+        { domain: "google.com", name: "Google" },
+        { domain: "facebook.com", name: "Facebook" },
+      ];
+
+      const table = await convertToTable(data, undefined, { schema });
+
+      expect(table.numCols).toBe(3);
+      expect(table.numRows).toBe(2);
+
+      const descriptionColumn = table.getChild("description");
+      expect(descriptionColumn).toBeDefined();
+      expect(descriptionColumn?.nullCount).toBe(2);
+      expect(descriptionColumn?.toArray()).toEqual([null, null]);
+
+      expect(table.getChild("domain")?.toArray()).toEqual([
+        "google.com",
+        "facebook.com",
+      ]);
+      expect(table.getChild("name")?.toArray()).toEqual(["Google", "Facebook"]);
+    });
+
+    it("will handle completely missing nested struct columns", async function () {
+      const schema = new Schema(
+        [
+          new Field("id", new Utf8(), true),
+          new Field("name", new Utf8(), true),
           new Field(
-            "s",
+            "metadata",
             new Struct([
-              new Field("x", new Int32(), true),
-              new Field("y", new Int32(), true),
+              new Field("version", new Int32(), true),
+              new Field("author", new Utf8(), true),
+              new Field(
+                "tags",
+                new List(new Field("item", new Utf8(), true)),
+                true,
+              ),
             ]),
             true,
           ),
-          new Field("d", new Int16(), true),
-        ]);
-
-        const table = makeArrowTable([{ a: 1n }], { schema });
-        expect(table.numCols).toBe(1);
-        expect(table.numRows).toBe(1);
-
-        const table2 = makeArrowTable([{ a: 1n, d: 2 }], { schema });
-        expect(table2.numCols).toBe(2);
-
-        const table3 = makeArrowTable([{ s: { y: 3 } }], { schema });
-        expect(table3.numCols).toBe(1);
-        const expectedSchema = new Schema([
-          new Field("s", new Struct([new Field("y", new Int32(), true)]), true),
-        ]);
-        expect(table3.schema).toEqual(expectedSchema);
-      });
-
-      it("will work even if columns are sparsely provided", async function () {
-        const sparseRecords = [{ a: 1n }, { b: 2n }, { c: 3n }, { d: 4n }];
-        const table = makeArrowTable(sparseRecords);
-        expect(table.numCols).toBe(4);
-        expect(table.numRows).toBe(4);
-
-        const schema = new Schema([
-          new Field("a", new Int64(), true),
-          new Field("b", new Int32(), true),
-          new Field("c", new Int64(), true),
-          new Field("d", new Int16(), true),
-        ]);
-        const table2 = makeArrowTable(sparseRecords, { schema });
-        expect(table2.numCols).toBe(4);
-        expect(table2.numRows).toBe(4);
-        expect(table2.schema).toEqual(schema);
-      });
-
-      it("will handle missing columns in schema alignment when using embeddings", async function () {
-        const schema = new Schema(
-          [
-            new Field("domain", new Utf8(), true),
-            new Field("name", new Utf8(), true),
-            new Field("description", new Utf8(), true),
-          ],
-          new Map([["embedding_functions", JSON.stringify([])]]),
-        );
-
-        const data = [
-          { domain: "google.com", name: "Google" },
-          { domain: "facebook.com", name: "Facebook" },
-        ];
-
-        const table = await convertToTable(data, undefined, { schema });
-
-        expect(table.numCols).toBe(3);
-        expect(table.numRows).toBe(2);
-
-        const descriptionColumn = table.getChild("description");
-        expect(descriptionColumn).toBeDefined();
-        expect(descriptionColumn?.nullCount).toBe(2);
-        expect(descriptionColumn?.toArray()).toEqual([null, null]);
-
-        expect(table.getChild("domain")?.toArray()).toEqual([
-          "google.com",
-          "facebook.com",
-        ]);
-        expect(table.getChild("name")?.toArray()).toEqual([
-          "Google",
-          "Facebook",
-        ]);
-      });
-
-      it("will handle completely missing nested struct columns", async function () {
-        const schema = new Schema(
-          [
-            new Field("id", new Utf8(), true),
-            new Field("name", new Utf8(), true),
-            new Field(
-              "metadata",
-              new Struct([
-                new Field("version", new Int32(), true),
-                new Field("author", new Utf8(), true),
-                new Field(
-                  "tags",
-                  new List(new Field("item", new Utf8(), true)),
-                  true,
-                ),
-              ]),
-              true,
-            ),
-          ],
-          new Map([["embedding_functions", JSON.stringify([])]]),
-        );
-
-        const data = [
-          { id: "doc1", name: "Document 1" },
-          { id: "doc2", name: "Document 2" },
-        ];
-
-        const table = await convertToTable(data, undefined, { schema });
-
-        expect(table.numCols).toBe(3);
-        expect(table.numRows).toBe(2);
-
-        const buf = await fromTableToBuffer(table);
-        const retrievedTable = tableFromIPC(buf);
-
-        const rows = [];
-        for (let i = 0; i < retrievedTable.numRows; i++) {
-          rows.push(retrievedTable.get(i));
-        }
-
-        expect(rows[0].metadata.version).toBe(null);
-        expect(rows[0].metadata.author).toBe(null);
-        expect(rows[0].metadata.tags).toBe(null);
-        expect(rows[0].id).toBe("doc1");
-        expect(rows[0].name).toBe("Document 1");
-      });
-
-      it("will handle partially missing nested struct fields", async function () {
-        const schema = new Schema(
-          [
-            new Field("id", new Utf8(), true),
-            new Field(
-              "metadata",
-              new Struct([
-                new Field("version", new Int32(), true),
-                new Field("author", new Utf8(), true),
-                new Field("created_at", new Utf8(), true),
-              ]),
-              true,
-            ),
-          ],
-          new Map([["embedding_functions", JSON.stringify([])]]),
-        );
-
-        const data = [
-          { id: "doc1", metadata: { version: 1, author: "Alice" } },
-          { id: "doc2", metadata: { version: 2 } },
-        ];
-
-        const table = await convertToTable(data, undefined, { schema });
-
-        expect(table.numCols).toBe(2);
-        expect(table.numRows).toBe(2);
-
-        const metadataColumn = table.getChild("metadata");
-        expect(metadataColumn).toBeDefined();
-        expect(metadataColumn?.type.toString()).toBe(
-          "Struct<{version:Int32, author:Utf8, created_at:Utf8}>",
-        );
-      });
-
-      it("will handle multiple levels of nested structures", async function () {
-        const schema = new Schema(
-          [
-            new Field("id", new Utf8(), true),
-            new Field(
-              "config",
-              new Struct([
-                new Field("database", new Utf8(), true),
-                new Field(
-                  "connection",
-                  new Struct([
-                    new Field("host", new Utf8(), true),
-                    new Field("port", new Int32(), true),
-                    new Field(
-                      "ssl",
-                      new Struct([
-                        new Field("enabled", new Bool(), true),
-                        new Field("cert_path", new Utf8(), true),
-                      ]),
-                      true,
-                    ),
-                  ]),
-                  true,
-                ),
-              ]),
-              true,
-            ),
-          ],
-          new Map([["embedding_functions", JSON.stringify([])]]),
-        );
-
-        const data = [
-          {
-            id: "config1",
-            config: {
-              database: "postgres",
-              connection: { host: "localhost" },
-            },
-          },
-          {
-            id: "config2",
-            config: { database: "mysql" },
-          },
-          {
-            id: "config3",
-          },
-        ];
-
-        const table = await convertToTable(data, undefined, { schema });
-
-        expect(table.numCols).toBe(2);
-        expect(table.numRows).toBe(3);
-
-        const configColumn = table.getChild("config");
-        expect(configColumn).toBeDefined();
-        expect(configColumn?.type.toString()).toBe(
-          "Struct<{database:Utf8, connection:Struct<{host:Utf8, port:Int32, ssl:Struct<{enabled:Bool, cert_path:Utf8}>}>}>",
-        );
-      });
-
-      it("will handle missing columns in Arrow table input when using embeddings", async function () {
-        const incompleteTable = makeArrowTable([
-          { domain: "google.com", name: "Google" },
-          { domain: "facebook.com", name: "Facebook" },
-        ]);
-
-        const schema = new Schema(
-          [
-            new Field("domain", new Utf8(), true),
-            new Field("name", new Utf8(), true),
-            new Field("description", new Utf8(), true),
-          ],
-          new Map([["embedding_functions", JSON.stringify([])]]),
-        );
-
-        const buf = await fromDataToBuffer(incompleteTable, undefined, schema);
-
-        expect(buf.byteLength).toBeGreaterThan(0);
-
-        const retrievedTable = tableFromIPC(buf);
-        expect(retrievedTable.numCols).toBe(3);
-        expect(retrievedTable.numRows).toBe(2);
-
-        const descriptionColumn = retrievedTable.getChild("description");
-        expect(descriptionColumn).toBeDefined();
-        expect(descriptionColumn?.nullCount).toBe(2);
-        expect(descriptionColumn?.toArray()).toEqual([null, null]);
-
-        expect(retrievedTable.getChild("domain")?.toArray()).toEqual([
-          "google.com",
-          "facebook.com",
-        ]);
-        expect(retrievedTable.getChild("name")?.toArray()).toEqual([
-          "Google",
-          "Facebook",
-        ]);
-      });
-
-      it("should correctly retain values in nested struct fields", async function () {
-        const testData = [
-          {
-            id: "doc1",
-            vector: [1, 2, 3],
-            metadata: {
-              filePath: "/path/to/file1.ts",
-              startLine: 10,
-              endLine: 20,
-              text: "function test() { return true; }",
-            },
-          },
-          {
-            id: "doc2",
-            vector: [4, 5, 6],
-            metadata: {
-              filePath: "/path/to/file2.ts",
-              startLine: 30,
-              endLine: 40,
-              text: "function test2() { return false; }",
-            },
-          },
-        ];
-
-        const table = makeArrowTable(testData);
-
-        const metadataField = table.schema.fields.find(
-          (f) => f.name === "metadata",
-        );
-        expect(metadataField).toBeDefined();
-        // biome-ignore lint/suspicious/noExplicitAny: accessing fields in different Arrow versions
-        const childNames = metadataField?.type.children.map((c: any) => c.name);
-        expect(childNames).toEqual([
-          "filePath",
-          "startLine",
-          "endLine",
-          "text",
-        ]);
-
-        const buf = await fromTableToBuffer(table);
-        const retrievedTable = tableFromIPC(buf);
-
-        const rows = [];
-        for (let i = 0; i < retrievedTable.numRows; i++) {
-          rows.push(retrievedTable.get(i));
-        }
-
-        const firstRow = rows[0];
-        expect(firstRow.id).toBe("doc1");
-        expect(firstRow.vector.toJSON()).toEqual([1, 2, 3]);
-        expect(firstRow.metadata.filePath).toBe("/path/to/file1.ts");
-        expect(firstRow.metadata.startLine).toBe(10);
-        expect(firstRow.metadata.endLine).toBe(20);
-        expect(firstRow.metadata.text).toBe("function test() { return true; }");
-      });
-    });
-
-    class DummyEmbedding extends EmbeddingFunction<string> {
-      toJSON(): Partial<FunctionOptions> {
-        return {};
-      }
-
-      async computeSourceEmbeddings(data: string[]): Promise<number[][]> {
-        return data.map(() => [0.0, 0.0]);
-      }
-
-      ndims(): number {
-        return 2;
-      }
-
-      embeddingDataType() {
-        return new Float16();
-      }
-    }
-
-    class DummyEmbeddingWithNoDimension extends EmbeddingFunction<string> {
-      toJSON(): Partial<FunctionOptions> {
-        return {};
-      }
-
-      embeddingDataType() {
-        return new Float16();
-      }
-
-      async computeSourceEmbeddings(data: string[]): Promise<number[][]> {
-        return data.map(() => [0.0, 0.0]);
-      }
-    }
-    const dummyEmbeddingConfig: EmbeddingFunctionConfig = {
-      sourceColumn: "string",
-      function: new DummyEmbedding(),
-    };
-
-    const dummyEmbeddingConfigWithNoDimension: EmbeddingFunctionConfig = {
-      sourceColumn: "string",
-      function: new DummyEmbeddingWithNoDimension(),
-    };
-
-    describe("convertToTable", function () {
-      it("will infer data types correctly", async function () {
-        await checkTableCreation(
-          // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-          async (records) => await (<any>convertToTable)(records),
-          true,
-        );
-      });
-
-      it("will allow a schema to be provided", async function () {
-        await checkTableCreation(
-          async (records, _, schema) =>
-            // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-            await (<any>convertToTable)(records, undefined, { schema }),
-          false,
-        );
-      });
-
-      it("will use the field order of any provided schema", async function () {
-        await checkTableCreation(
-          async (_, recordsReversed, schema) =>
-            // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-            await (<any>convertToTable)(recordsReversed, undefined, { schema }),
-          false,
-        );
-      });
-
-      it("will make an empty table", async function () {
-        await checkTableCreation(
-          async (_, __, schema) =>
-            // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-            await (<any>convertToTable)([], undefined, { schema }),
-          false,
-        );
-      });
-
-      it("will apply embeddings", async function () {
-        const records = sampleRecords();
-        const table = await convertToTable(records, dummyEmbeddingConfig);
-        expect(DataType.isFixedSizeList(table.getChild("vector")?.type)).toBe(
-          true,
-        );
-        expect(
-          table.getChild("vector")?.type.children[0].type.toString(),
-        ).toEqual(new Float16().toString());
-      });
-
-      it("will fail if missing the embedding source column", async function () {
-        await expect(
-          convertToTable([{ id: 1 }], dummyEmbeddingConfig),
-        ).rejects.toThrow("'string' was not present");
-      });
-
-      it("use embeddingDimension if embedding missing from table", async function () {
-        const schema = new Schema([new Field("string", new Utf8(), false)]);
-        // Simulate getting an empty Arrow table (minus embedding) from some other source
-        // In other words, we aren't starting with records
-        const table = makeEmptyTable(schema);
-
-        // If the embedding specifies the dimension we are fine
-        await fromTableToBuffer(table, dummyEmbeddingConfig);
-
-        // We can also supply a schema and should be ok
-        const schemaWithEmbedding = new Schema([
-          new Field("string", new Utf8(), false),
-          new Field(
-            "vector",
-            new FixedSizeList(2, new Field("item", new Float16(), false)),
-            false,
-          ),
-        ]);
-        await fromTableToBuffer(
-          table,
-          dummyEmbeddingConfigWithNoDimension,
-          schemaWithEmbedding,
-        );
-
-        // Otherwise we will get an error
-        await expect(
-          fromTableToBuffer(table, dummyEmbeddingConfigWithNoDimension),
-        ).rejects.toThrow("does not specify `embeddingDimension`");
-      });
-
-      it("will apply embeddings to an empty table", async function () {
-        const schema = new Schema([
-          new Field("string", new Utf8(), false),
-          new Field(
-            "vector",
-            new FixedSizeList(2, new Field("item", new Float16(), false)),
-            false,
-          ),
-        ]);
-        const table = await convertToTable([], dummyEmbeddingConfig, {
-          schema,
-        });
-        expect(DataType.isFixedSizeList(table.getChild("vector")?.type)).toBe(
-          true,
-        );
-        expect(
-          table.getChild("vector")?.type.children[0].type.toString(),
-        ).toEqual(new Float16().toString());
-      });
-
-      it("will complain if embeddings present but schema missing embedding column", async function () {
-        const schema = new Schema([new Field("string", new Utf8(), false)]);
-        await expect(
-          convertToTable([], dummyEmbeddingConfig, { schema }),
-        ).rejects.toThrow("column vector was missing");
-      });
-
-      it("will skip embedding application if already applied", async function () {
-        const records = sampleRecords();
-        const table = await convertToTable(records, dummyEmbeddingConfig);
-
-        // fromTableToBuffer will try and apply the embeddings again
-        // but should skip since the column already has non-null values
-        const result = await fromTableToBuffer(table, dummyEmbeddingConfig);
-        expect(result.byteLength).toBeGreaterThan(0);
-      });
-    });
-
-    describe("makeEmptyTable", function () {
-      it("will make an empty table", async function () {
-        await checkTableCreation(
-          // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-          async (_, __, schema) => (<any>makeEmptyTable)(schema),
-          false,
-        );
-      });
-
-      it("will make an empty table with a Map field", async function () {
-        const schema = new Schema([
-          new Field(
-            "attributes",
-            new Map_(
-              new Field(
-                "entries",
-                new Struct([
-                  new Field("key", new Utf8(), false),
-                  new Field("value", new Int32(), true),
-                ]),
-                false,
-              ),
-              true,
-            ),
-          ),
-        ]);
-
-        const table = makeEmptyTable(schema);
-
-        expectValidMapField(table.schema.fields[0]);
-
-        const buffer = await fromTableToBuffer(table);
-        const roundTripped = tableFromIPC(buffer);
-
-        expectValidMapField(roundTripped.schema.fields[0]);
-      });
-
-      it("preserves string schema metadata", function () {
-        const metadata = new Map([["source", "fixture"]]);
-        const schema = new Schema(
-          [new Field("value", new Int32(), true)],
-          metadata,
-        );
-
-        expect(makeEmptyTable(schema).schema.metadata.get("source")).toBe(
-          "fixture",
-        );
-      });
-
-      it.each([
-        ["non-string keys", new Map<unknown, unknown>([[42, "fixture"]])],
-        ["non-string values", new Map<unknown, unknown>([["source", 42]])],
-        [
-          "non-string keys and values",
-          new Map<unknown, unknown>([[42, false]]),
         ],
-      ])("rejects schema metadata with %s", function (_, metadataLike) {
-        const metadata = metadataLike as unknown as Map<string, string>;
-        const schema = new Schema(
-          [new Field("value", new Int32(), true)],
-          metadata,
-        );
+        new Map([["embedding_functions", JSON.stringify([])]]),
+      );
 
-        expect(() => makeEmptyTable(schema)).toThrow(
-          "Expected metadata, if present, to be a Map<string, string> but it had non-string keys or values",
-        );
-      });
+      const data = [
+        { id: "doc1", name: "Document 1" },
+        { id: "doc2", name: "Document 2" },
+      ];
+
+      const table = await convertToTable(data, undefined, { schema });
+
+      expect(table.numCols).toBe(3);
+      expect(table.numRows).toBe(2);
+
+      const buf = await fromTableToBuffer(table);
+      const retrievedTable = tableFromIPC(buf);
+
+      const rows = [];
+      for (let i = 0; i < retrievedTable.numRows; i++) {
+        rows.push(retrievedTable.get(i));
+      }
+
+      expect(rows[0].metadata.version).toBe(null);
+      expect(rows[0].metadata.author).toBe(null);
+      expect(rows[0].metadata.tags).toBe(null);
+      expect(rows[0].id).toBe("doc1");
+      expect(rows[0].name).toBe("Document 1");
     });
 
-    describe("when using two versions of arrow", function () {
-      it("preserves a dictionary shared by multiple fields", async function () {
-        const values = ["alpha", "beta", "alpha"];
-        const dictionaryVector = vectorFromArray(values);
-        const batch = new ArrowRecordBatch({
-          first: dictionaryVector.data[0],
-          second: dictionaryVector.data[0],
-        });
-        const table = new ArrowTable([batch]);
-
-        const sanitized = sanitizeTable(table);
-        expect([...sanitized.getChild("first")!]).toEqual(values);
-        expect([...sanitized.getChild("second")!]).toEqual(values);
-        const firstType = sanitized.schema.fields[0].type as {
-          dictionary: unknown;
-        };
-        const secondType = sanitized.schema.fields[1].type as {
-          dictionary: unknown;
-        };
-        expect(secondType.dictionary).toBe(firstType.dictionary);
-        expect(sanitized.batches[0].data.children[1].dictionary).toBe(
-          sanitized.batches[0].data.children[0].dictionary,
-        );
-
-        const buf = await fromDataToBuffer(table);
-        const actual = currentTableFromIPC(buf);
-        expect([...actual.getChild("first")!]).toEqual(values);
-        expect([...actual.getChild("second")!]).toEqual(values);
-      });
-
-      it("preserves shared dictionary data from another Arrow version", async function () {
-        const values = ["alpha", "beta", "alpha"];
-        const dictionaryVector = vectorFromArray(values);
-        const firstBatch = new ArrowRecordBatch({
-          label: dictionaryVector.slice(0, 2).data[0],
-        });
-        const secondBatch = new ArrowRecordBatch({
-          label: dictionaryVector.slice(2).data[0],
-        });
-        const table = new ArrowTable([firstBatch, secondBatch]);
-
-        const sanitized = sanitizeTable(table);
-        expect([...sanitized.getChild("label")!]).toEqual(values);
-
-        const dictionaries = sanitized.batches.map(
-          (batch) => batch.data.children[0].dictionary,
-        );
-        expect(dictionaries[0]).toBeInstanceOf(CurrentVector);
-        expect(dictionaries[1]).toBe(dictionaries[0]);
-
-        const buf = await fromDataToBuffer(table);
-        const actual = currentTableFromIPC(buf);
-        expect([...actual.getChild("label")!]).toEqual(values);
-      });
-
-      it("preserves shared chunks in growing dictionaries", async function () {
-        const type = new Dictionary(new Utf8(), new Int32(), 42, false);
-        const firstDictionary = vectorFromArray(["alpha", "beta"], new Utf8());
-        const secondDictionary = firstDictionary.concat(
-          vectorFromArray(["gamma"], new Utf8()),
-        );
-        const firstData = arrowMakeData({
-          type,
-          data: Int32Array.from([0, 1]),
-          dictionary: firstDictionary,
-        });
-        const secondData = arrowMakeData({
-          type,
-          data: Int32Array.from([2]),
-          dictionary: secondDictionary,
-        });
-        const table = new ArrowTable([
-          new ArrowRecordBatch({ label: firstData }),
-          new ArrowRecordBatch({ label: secondData }),
-        ]);
-
-        const sanitized = sanitizeTable(table);
-        const expected = ["alpha", "beta", "gamma"];
-        expect([...sanitized.getChild("label")!]).toEqual(expected);
-        const firstLocalDictionary =
-          sanitized.batches[0].data.children[0].dictionary!;
-        const secondLocalDictionary =
-          sanitized.batches[1].data.children[0].dictionary!;
-        expect(secondLocalDictionary.data[0]).toBe(
-          firstLocalDictionary.data[0],
-        );
-
-        const buf = await fromTableToBuffer(sanitized);
-        const actual = currentTableFromIPC(buf);
-        expect([...actual.getChild("label")!]).toEqual(expected);
-      });
-
-      it("can serialize list data from another Arrow version", async function () {
-        const values = [["anime", "action"], [], null];
-        const vector = vectorFromArray(
-          values,
-          new List(new Field("item", new Utf8(), true)),
-        );
-        const table = new ArrowTable({ tags: vector });
-
-        const buf = await fromDataToBuffer(table);
-        const actual = currentTableFromIPC(buf);
-        const actualTags = actual.getChild("tags");
-
-        expect(actualTags?.get(0)?.toJSON()).toEqual(values[0]);
-        expect(actualTags?.get(1)?.toJSON()).toEqual(values[1]);
-        expect(actualTags?.get(2)).toBeNull();
-      });
-
-      it("can still import data", async function () {
-        const schema = new arrow15.Schema([
-          new arrow15.Field("id", new arrow15.Int32()),
-          new arrow15.Field(
-            "vector",
-            new arrow15.FixedSizeList(
-              1024,
-              new arrow15.Field("item", new arrow15.Float32(), true),
-            ),
+    it("will handle partially missing nested struct fields", async function () {
+      const schema = new Schema(
+        [
+          new Field("id", new Utf8(), true),
+          new Field(
+            "metadata",
+            new Struct([
+              new Field("version", new Int32(), true),
+              new Field("author", new Utf8(), true),
+              new Field("created_at", new Utf8(), true),
+            ]),
+            true,
           ),
-          new arrow15.Field(
-            "struct",
-            new arrow15.Struct([
-              new arrow15.Field(
-                "nested",
-                new arrow15.Dictionary(
-                  new arrow15.Utf8(),
-                  new arrow15.Int32(),
-                  1,
-                  true,
-                ),
-              ),
-              new arrow15.Field(
-                "ts_with_tz",
-                new arrow15.TimestampNanosecond("some_tz"),
-              ),
-              new arrow15.Field(
-                "ts_no_tz",
-                new arrow15.TimestampNanosecond(null),
+        ],
+        new Map([["embedding_functions", JSON.stringify([])]]),
+      );
+
+      const data = [
+        { id: "doc1", metadata: { version: 1, author: "Alice" } },
+        { id: "doc2", metadata: { version: 2 } },
+      ];
+
+      const table = await convertToTable(data, undefined, { schema });
+
+      expect(table.numCols).toBe(2);
+      expect(table.numRows).toBe(2);
+
+      const metadataColumn = table.getChild("metadata");
+      expect(metadataColumn).toBeDefined();
+      expect(metadataColumn?.type.toString()).toBe(
+        "Struct<{version:Int32, author:Utf8, created_at:Utf8}>",
+      );
+    });
+
+    it("will handle multiple levels of nested structures", async function () {
+      const schema = new Schema(
+        [
+          new Field("id", new Utf8(), true),
+          new Field(
+            "config",
+            new Struct([
+              new Field("database", new Utf8(), true),
+              new Field(
+                "connection",
+                new Struct([
+                  new Field("host", new Utf8(), true),
+                  new Field("port", new Int32(), true),
+                  new Field(
+                    "ssl",
+                    new Struct([
+                      new Field("enabled", new Bool(), true),
+                      new Field("cert_path", new Utf8(), true),
+                    ]),
+                    true,
+                  ),
+                ]),
+                true,
               ),
             ]),
+            true,
           ),
-          // biome-ignore lint/suspicious/noExplicitAny: skip
-        ]) as any;
-        schema.metadataVersion = arrow15.MetadataVersion.V5;
-        const table = makeArrowTable([], { schema });
+        ],
+        new Map([["embedding_functions", JSON.stringify([])]]),
+      );
 
-        const buf = await fromTableToBuffer(table);
-        expect(buf.byteLength).toBeGreaterThan(0);
-        const actual = tableFromIPC(buf);
-        const actualSchema = actual.schema;
-        expect(actualSchema.fields.length).toBe(3);
+      const data = [
+        {
+          id: "config1",
+          config: {
+            database: "postgres",
+            connection: { host: "localhost" },
+          },
+        },
+        {
+          id: "config2",
+          config: { database: "mysql" },
+        },
+        {
+          id: "config3",
+        },
+      ];
 
-        // Deep equality gets hung up on some very minor unimportant differences
-        // between arrow version 13 and 15 which isn't really what we're testing for
-        // and so we do our own comparison that just checks name/type/nullability
-        function compareFields(lhs: arrow15.Field, rhs: arrow15.Field) {
-          expect(lhs.name).toEqual(rhs.name);
-          expect(lhs.nullable).toEqual(rhs.nullable);
-          expect(lhs.typeId).toEqual(rhs.typeId);
-          if ("children" in lhs.type && lhs.type.children !== null) {
-            const lhsChildren = lhs.type.children as arrow15.Field[];
-            lhsChildren.forEach((child: arrow15.Field, idx) => {
-              compareFields(child, rhs.type.children[idx]);
-            });
-          }
-        }
+      const table = await convertToTable(data, undefined, { schema });
+
+      expect(table.numCols).toBe(2);
+      expect(table.numRows).toBe(3);
+
+      const configColumn = table.getChild("config");
+      expect(configColumn).toBeDefined();
+      expect(configColumn?.type.toString()).toBe(
+        "Struct<{database:Utf8, connection:Struct<{host:Utf8, port:Int32, ssl:Struct<{enabled:Bool, cert_path:Utf8}>}>}>",
+      );
+    });
+
+    it("will handle missing columns in Arrow table input when using embeddings", async function () {
+      const incompleteTable = makeArrowTable([
+        { domain: "google.com", name: "Google" },
+        { domain: "facebook.com", name: "Facebook" },
+      ]);
+
+      const schema = new Schema(
+        [
+          new Field("domain", new Utf8(), true),
+          new Field("name", new Utf8(), true),
+          new Field("description", new Utf8(), true),
+        ],
+        new Map([["embedding_functions", JSON.stringify([])]]),
+      );
+
+      const buf = await fromDataToBuffer(incompleteTable, undefined, schema);
+
+      expect(buf.byteLength).toBeGreaterThan(0);
+
+      const retrievedTable = tableFromIPC(buf);
+      expect(retrievedTable.numCols).toBe(3);
+      expect(retrievedTable.numRows).toBe(2);
+
+      const descriptionColumn = retrievedTable.getChild("description");
+      expect(descriptionColumn).toBeDefined();
+      expect(descriptionColumn?.nullCount).toBe(2);
+      expect(descriptionColumn?.toArray()).toEqual([null, null]);
+
+      expect(retrievedTable.getChild("domain")?.toArray()).toEqual([
+        "google.com",
+        "facebook.com",
+      ]);
+      expect(retrievedTable.getChild("name")?.toArray()).toEqual([
+        "Google",
+        "Facebook",
+      ]);
+    });
+
+    it("should correctly retain values in nested struct fields", async function () {
+      const testData = [
+        {
+          id: "doc1",
+          vector: [1, 2, 3],
+          metadata: {
+            filePath: "/path/to/file1.ts",
+            startLine: 10,
+            endLine: 20,
+            text: "function test() { return true; }",
+          },
+        },
+        {
+          id: "doc2",
+          vector: [4, 5, 6],
+          metadata: {
+            filePath: "/path/to/file2.ts",
+            startLine: 30,
+            endLine: 40,
+            text: "function test2() { return false; }",
+          },
+        },
+      ];
+
+      const table = makeArrowTable(testData);
+
+      const metadataField = table.schema.fields.find(
+        (f) => f.name === "metadata",
+      );
+      expect(metadataField).toBeDefined();
+      // biome-ignore lint/suspicious/noExplicitAny: accessing fields in different Arrow versions
+      const childNames = metadataField?.type.children.map((c: any) => c.name);
+      expect(childNames).toEqual(["filePath", "startLine", "endLine", "text"]);
+
+      const buf = await fromTableToBuffer(table);
+      const retrievedTable = tableFromIPC(buf);
+
+      const rows = [];
+      for (let i = 0; i < retrievedTable.numRows; i++) {
+        rows.push(retrievedTable.get(i));
+      }
+
+      const firstRow = rows[0];
+      expect(firstRow.id).toBe("doc1");
+      expect(firstRow.vector.toJSON()).toEqual([1, 2, 3]);
+      expect(firstRow.metadata.filePath).toBe("/path/to/file1.ts");
+      expect(firstRow.metadata.startLine).toBe(10);
+      expect(firstRow.metadata.endLine).toBe(20);
+      expect(firstRow.metadata.text).toBe("function test() { return true; }");
+    });
+  });
+
+  class DummyEmbedding extends EmbeddingFunction<string> {
+    toJSON(): Partial<FunctionOptions> {
+      return {};
+    }
+
+    async computeSourceEmbeddings(data: string[]): Promise<number[][]> {
+      return data.map(() => [0.0, 0.0]);
+    }
+
+    ndims(): number {
+      return 2;
+    }
+
+    embeddingDataType() {
+      return new Float16();
+    }
+  }
+
+  class DummyEmbeddingWithNoDimension extends EmbeddingFunction<string> {
+    toJSON(): Partial<FunctionOptions> {
+      return {};
+    }
+
+    embeddingDataType() {
+      return new Float16();
+    }
+
+    async computeSourceEmbeddings(data: string[]): Promise<number[][]> {
+      return data.map(() => [0.0, 0.0]);
+    }
+  }
+  const dummyEmbeddingConfig: EmbeddingFunctionConfig = {
+    sourceColumn: "string",
+    function: new DummyEmbedding(),
+  };
+
+  const dummyEmbeddingConfigWithNoDimension: EmbeddingFunctionConfig = {
+    sourceColumn: "string",
+    function: new DummyEmbeddingWithNoDimension(),
+  };
+
+  describe("convertToTable", function () {
+    it("will infer data types correctly", async function () {
+      await checkTableCreation(
         // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-        actualSchema.fields.forEach((field: any, idx: string | number) => {
-          compareFields(field, actualSchema.fields[idx]);
-        });
-      });
+        async (records) => await (<any>convertToTable)(records),
+        true,
+      );
     });
 
-    describe("converting record batches to buffers", function () {
-      it("can convert to buffered record batch and back again", async function () {
-        const records = [
-          { text: "dog", vector: [0.1, 0.2] },
-          { text: "cat", vector: [0.3, 0.4] },
-        ];
-        const table = await convertToTable(records);
-        const batch = table.batches[0];
-
-        const buffer = await fromRecordBatchToBuffer(batch);
-        const result = await fromBufferToRecordBatch(buffer);
-
-        expect(JSON.stringify(batch.toArray())).toEqual(
-          JSON.stringify(result?.toArray()),
-        );
-      });
-
-      it("converting from buffer returns null if buffer has no record batches", async function () {
-        const result = await fromBufferToRecordBatch(Buffer.from([0x01, 0x02])); // bad data
-        expect(result).toEqual(null);
-      });
+    it("will allow a schema to be provided", async function () {
+      await checkTableCreation(
+        async (records, _, schema) =>
+          // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+          await (<any>convertToTable)(records, undefined, { schema }),
+        false,
+      );
     });
 
-    describe("boolean null handling", function () {
-      it("should handle null values in nullable boolean fields", () => {
-        const { makeArrowTable } = require("../lancedb/arrow");
-        const schema = new Schema([new Field("test", new arrow.Bool(), true)]);
-
-        // Test with all null values
-        const data = [{ test: null }];
-        const table = makeArrowTable(data, { schema });
-
-        expect(table.numRows).toBe(1);
-        expect(table.schema.names).toEqual(["test"]);
-        expect(table.getChild("test")!.get(0)).toBeNull();
-      });
-
-      it("should handle mixed null and non-null boolean values", () => {
-        const { makeArrowTable } = require("../lancedb/arrow");
-        const schema = new Schema([new Field("test", new Bool(), true)]);
-
-        // Test with mixed values
-        const data = [{ test: true }, { test: null }, { test: false }];
-        const table = makeArrowTable(data, { schema });
-
-        expect(table.numRows).toBe(3);
-        expect(table.getChild("test")!.get(0)).toBe(true);
-        expect(table.getChild("test")!.get(1)).toBeNull();
-        expect(table.getChild("test")!.get(2)).toBe(false);
-      });
+    it("will use the field order of any provided schema", async function () {
+      await checkTableCreation(
+        async (_, recordsReversed, schema) =>
+          // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+          await (<any>convertToTable)(recordsReversed, undefined, { schema }),
+        false,
+      );
     });
 
-    // Test for the undefined values bug fix
-    describe("undefined values handling", () => {
-      it("should handle mixed undefined and actual values", () => {
-        const schema = new Schema([
-          new Field("text", new Utf8(), true), // nullable
-          new Field("number", new Int32(), true), // nullable
-          new Field("bool", new Bool(), true), // nullable
-        ]);
+    it("will make an empty table", async function () {
+      await checkTableCreation(
+        async (_, __, schema) =>
+          // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+          await (<any>convertToTable)([], undefined, { schema }),
+        false,
+      );
+    });
 
-        const data = [
-          { text: undefined, number: 42, bool: true },
-          { text: "hello", number: undefined, bool: false },
-          { text: "world", number: 123, bool: undefined },
-        ];
-        const table = makeArrowTable(data, { schema });
+    it("will apply embeddings", async function () {
+      const records = sampleRecords();
+      const table = await convertToTable(records, dummyEmbeddingConfig);
+      expect(DataType.isFixedSizeList(table.getChild("vector")?.type)).toBe(
+        true,
+      );
+      expect(
+        table.getChild("vector")?.type.children[0].type.toString(),
+      ).toEqual(new Float16().toString());
+    });
 
-        const result = table.toArray();
-        expect(result).toHaveLength(3);
-        expect(result[0].text).toBe(null);
-        expect(result[0].number).toBe(42);
-        expect(result[0].bool).toBe(true);
-        expect(result[1].text).toBe("hello");
-        expect(result[1].number).toBe(null);
-        expect(result[1].bool).toBe(false);
-        expect(result[2].text).toBe("world");
-        expect(result[2].number).toBe(123);
-        expect(result[2].bool).toBe(null);
+    it("will fail if missing the embedding source column", async function () {
+      await expect(
+        convertToTable([{ id: 1 }], dummyEmbeddingConfig),
+      ).rejects.toThrow("'string' was not present");
+    });
+
+    it("use embeddingDimension if embedding missing from table", async function () {
+      const schema = new Schema([new Field("string", new Utf8(), false)]);
+      // Simulate getting an empty Arrow table (minus embedding) from some other source
+      // In other words, we aren't starting with records
+      const table = makeEmptyTable(schema);
+
+      // If the embedding specifies the dimension we are fine
+      await fromTableToBuffer(table, dummyEmbeddingConfig);
+
+      // We can also supply a schema and should be ok
+      const schemaWithEmbedding = new Schema([
+        new Field("string", new Utf8(), false),
+        new Field(
+          "vector",
+          new FixedSizeList(2, new Field("item", new Float16(), false)),
+          false,
+        ),
+      ]);
+      await fromTableToBuffer(
+        table,
+        dummyEmbeddingConfigWithNoDimension,
+        schemaWithEmbedding,
+      );
+
+      // Otherwise we will get an error
+      await expect(
+        fromTableToBuffer(table, dummyEmbeddingConfigWithNoDimension),
+      ).rejects.toThrow("does not specify `embeddingDimension`");
+    });
+
+    it("will apply embeddings to an empty table", async function () {
+      const schema = new Schema([
+        new Field("string", new Utf8(), false),
+        new Field(
+          "vector",
+          new FixedSizeList(2, new Field("item", new Float16(), false)),
+          false,
+        ),
+      ]);
+      const table = await convertToTable([], dummyEmbeddingConfig, {
+        schema,
+      });
+      expect(DataType.isFixedSizeList(table.getChild("vector")?.type)).toBe(
+        true,
+      );
+      expect(
+        table.getChild("vector")?.type.children[0].type.toString(),
+      ).toEqual(new Float16().toString());
+    });
+
+    it("will complain if embeddings present but schema missing embedding column", async function () {
+      const schema = new Schema([new Field("string", new Utf8(), false)]);
+      await expect(
+        convertToTable([], dummyEmbeddingConfig, { schema }),
+      ).rejects.toThrow("column vector was missing");
+    });
+
+    it("will skip embedding application if already applied", async function () {
+      const records = sampleRecords();
+      const table = await convertToTable(records, dummyEmbeddingConfig);
+
+      // fromTableToBuffer will try and apply the embeddings again
+      // but should skip since the column already has non-null values
+      const result = await fromTableToBuffer(table, dummyEmbeddingConfig);
+      expect(result.byteLength).toBeGreaterThan(0);
+    });
+  });
+
+  describe("makeEmptyTable", function () {
+    it("will make an empty table", async function () {
+      await checkTableCreation(
+        // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+        async (_, __, schema) => (<any>makeEmptyTable)(schema),
+        false,
+      );
+    });
+
+    it("will make an empty table with a Map field", async function () {
+      const schema = new Schema([
+        new Field(
+          "attributes",
+          new Map_(
+            new Field(
+              "entries",
+              new Struct([
+                new Field("key", new Utf8(), false),
+                new Field("value", new Int32(), true),
+              ]),
+              false,
+            ),
+            true,
+          ),
+        ),
+      ]);
+
+      const table = makeEmptyTable(schema);
+
+      expectValidMapField(table.schema.fields[0]);
+
+      const buffer = await fromTableToBuffer(table);
+      const roundTripped = tableFromIPC(buffer);
+
+      expectValidMapField(roundTripped.schema.fields[0]);
+    });
+
+    it("preserves string schema metadata", function () {
+      const metadata = new Map([["source", "fixture"]]);
+      const schema = new Schema(
+        [new Field("value", new Int32(), true)],
+        metadata,
+      );
+
+      expect(makeEmptyTable(schema).schema.metadata.get("source")).toBe(
+        "fixture",
+      );
+    });
+
+    it.each([
+      ["non-string keys", new Map<unknown, unknown>([[42, "fixture"]])],
+      ["non-string values", new Map<unknown, unknown>([["source", 42]])],
+      ["non-string keys and values", new Map<unknown, unknown>([[42, false]])],
+    ])("rejects schema metadata with %s", function (_, metadataLike) {
+      const metadata = metadataLike as unknown as Map<string, string>;
+      const schema = new Schema(
+        [new Field("value", new Int32(), true)],
+        metadata,
+      );
+
+      expect(() => makeEmptyTable(schema)).toThrow(
+        "Expected metadata, if present, to be a Map<string, string> but it had non-string keys or values",
+      );
+    });
+  });
+
+  describe("when using two versions of arrow", function () {
+    it("preserves a dictionary shared by multiple fields", async function () {
+      const values = ["alpha", "beta", "alpha"];
+      const dictionaryVector = vectorFromArray(values);
+      const batch = new ArrowRecordBatch({
+        first: dictionaryVector.data[0],
+        second: dictionaryVector.data[0],
+      });
+      const table = new ArrowTable([batch]);
+
+      const sanitized = sanitizeTable(table);
+      expect([...sanitized.getChild("first")!]).toEqual(values);
+      expect([...sanitized.getChild("second")!]).toEqual(values);
+      const firstType = sanitized.schema.fields[0].type as {
+        dictionary: unknown;
+      };
+      const secondType = sanitized.schema.fields[1].type as {
+        dictionary: unknown;
+      };
+      expect(secondType.dictionary).toBe(firstType.dictionary);
+      expect(sanitized.batches[0].data.children[1].dictionary).toBe(
+        sanitized.batches[0].data.children[0].dictionary,
+      );
+
+      const buf = await fromDataToBuffer(table);
+      const actual = currentTableFromIPC(buf);
+      expect([...actual.getChild("first")!]).toEqual(values);
+      expect([...actual.getChild("second")!]).toEqual(values);
+    });
+
+    it("preserves shared dictionary data from another Arrow version", async function () {
+      const values = ["alpha", "beta", "alpha"];
+      const dictionaryVector = vectorFromArray(values);
+      const firstBatch = new ArrowRecordBatch({
+        label: dictionaryVector.slice(0, 2).data[0],
+      });
+      const secondBatch = new ArrowRecordBatch({
+        label: dictionaryVector.slice(2).data[0],
+      });
+      const table = new ArrowTable([firstBatch, secondBatch]);
+
+      const sanitized = sanitizeTable(table);
+      expect([...sanitized.getChild("label")!]).toEqual(values);
+
+      const dictionaries = sanitized.batches.map(
+        (batch) => batch.data.children[0].dictionary,
+      );
+      expect(dictionaries[0]).toBeInstanceOf(CurrentVector);
+      expect(dictionaries[1]).toBe(dictionaries[0]);
+
+      const buf = await fromDataToBuffer(table);
+      const actual = currentTableFromIPC(buf);
+      expect([...actual.getChild("label")!]).toEqual(values);
+    });
+
+    it("preserves shared chunks in growing dictionaries", async function () {
+      const type = new Dictionary(new Utf8(), new Int32(), 42, false);
+      const firstDictionary = vectorFromArray(["alpha", "beta"], new Utf8());
+      const secondDictionary = firstDictionary.concat(
+        vectorFromArray(["gamma"], new Utf8()),
+      );
+      const firstData = arrowMakeData({
+        type,
+        data: Int32Array.from([0, 1]),
+        dictionary: firstDictionary,
+      });
+      const secondData = arrowMakeData({
+        type,
+        data: Int32Array.from([2]),
+        dictionary: secondDictionary,
+      });
+      const table = new ArrowTable([
+        new ArrowRecordBatch({ label: firstData }),
+        new ArrowRecordBatch({ label: secondData }),
+      ]);
+
+      const sanitized = sanitizeTable(table);
+      const expected = ["alpha", "beta", "gamma"];
+      expect([...sanitized.getChild("label")!]).toEqual(expected);
+      const firstLocalDictionary =
+        sanitized.batches[0].data.children[0].dictionary!;
+      const secondLocalDictionary =
+        sanitized.batches[1].data.children[0].dictionary!;
+      expect(secondLocalDictionary.data[0]).toBe(firstLocalDictionary.data[0]);
+
+      const buf = await fromTableToBuffer(sanitized);
+      const actual = currentTableFromIPC(buf);
+      expect([...actual.getChild("label")!]).toEqual(expected);
+    });
+
+    it("can serialize list data from another Arrow version", async function () {
+      const values = [["anime", "action"], [], null];
+      const vector = vectorFromArray(
+        values,
+        new List(new Field("item", new Utf8(), true)),
+      );
+      const table = new ArrowTable({ tags: vector });
+
+      const buf = await fromDataToBuffer(table);
+      const actual = currentTableFromIPC(buf);
+      const actualTags = actual.getChild("tags");
+
+      expect(actualTags?.get(0)?.toJSON()).toEqual(values[0]);
+      expect(actualTags?.get(1)?.toJSON()).toEqual(values[1]);
+      expect(actualTags?.get(2)).toBeNull();
+    });
+
+    it("can still import data", async function () {
+      const schema = new arrow15.Schema([
+        new arrow15.Field("id", new arrow15.Int32()),
+        new arrow15.Field(
+          "vector",
+          new arrow15.FixedSizeList(
+            1024,
+            new arrow15.Field("item", new arrow15.Float32(), true),
+          ),
+        ),
+        new arrow15.Field(
+          "struct",
+          new arrow15.Struct([
+            new arrow15.Field(
+              "nested",
+              new arrow15.Dictionary(
+                new arrow15.Utf8(),
+                new arrow15.Int32(),
+                1,
+                true,
+              ),
+            ),
+            new arrow15.Field(
+              "ts_with_tz",
+              new arrow15.TimestampNanosecond("some_tz"),
+            ),
+            new arrow15.Field(
+              "ts_no_tz",
+              new arrow15.TimestampNanosecond(null),
+            ),
+          ]),
+        ),
+        // biome-ignore lint/suspicious/noExplicitAny: skip
+      ]) as any;
+      schema.metadataVersion = arrow15.MetadataVersion.V5;
+      const table = makeArrowTable([], { schema });
+
+      const buf = await fromTableToBuffer(table);
+      expect(buf.byteLength).toBeGreaterThan(0);
+      const actual = tableFromIPC(buf);
+      const actualSchema = actual.schema;
+      expect(actualSchema.fields.length).toBe(3);
+
+      // Deep equality gets hung up on some very minor unimportant differences
+      // between arrow version 13 and 15 which isn't really what we're testing for
+      // and so we do our own comparison that just checks name/type/nullability
+      function compareFields(lhs: arrow15.Field, rhs: arrow15.Field) {
+        expect(lhs.name).toEqual(rhs.name);
+        expect(lhs.nullable).toEqual(rhs.nullable);
+        expect(lhs.typeId).toEqual(rhs.typeId);
+        if ("children" in lhs.type && lhs.type.children !== null) {
+          const lhsChildren = lhs.type.children as arrow15.Field[];
+          lhsChildren.forEach((child: arrow15.Field, idx) => {
+            compareFields(child, rhs.type.children[idx]);
+          });
+        }
+      }
+      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+      actualSchema.fields.forEach((field: any, idx: string | number) => {
+        compareFields(field, actualSchema.fields[idx]);
       });
     });
-  },
-);
+  });
+
+  describe("converting record batches to buffers", function () {
+    it("can convert to buffered record batch and back again", async function () {
+      const records = [
+        { text: "dog", vector: [0.1, 0.2] },
+        { text: "cat", vector: [0.3, 0.4] },
+      ];
+      const table = await convertToTable(records);
+      const batch = table.batches[0];
+
+      const buffer = await fromRecordBatchToBuffer(batch);
+      const result = await fromBufferToRecordBatch(buffer);
+
+      expect(JSON.stringify(batch.toArray())).toEqual(
+        JSON.stringify(result?.toArray()),
+      );
+    });
+
+    it("converting from buffer returns null if buffer has no record batches", async function () {
+      const result = await fromBufferToRecordBatch(Buffer.from([0x01, 0x02])); // bad data
+      expect(result).toEqual(null);
+    });
+  });
+
+  describe("boolean null handling", function () {
+    it("should handle null values in nullable boolean fields", () => {
+      const { makeArrowTable } = require("../lancedb/arrow");
+      const schema = new Schema([new Field("test", new arrow.Bool(), true)]);
+
+      // Test with all null values
+      const data = [{ test: null }];
+      const table = makeArrowTable(data, { schema });
+
+      expect(table.numRows).toBe(1);
+      expect(table.schema.names).toEqual(["test"]);
+      expect(table.getChild("test")!.get(0)).toBeNull();
+    });
+
+    it("should handle mixed null and non-null boolean values", () => {
+      const { makeArrowTable } = require("../lancedb/arrow");
+      const schema = new Schema([new Field("test", new Bool(), true)]);
+
+      // Test with mixed values
+      const data = [{ test: true }, { test: null }, { test: false }];
+      const table = makeArrowTable(data, { schema });
+
+      expect(table.numRows).toBe(3);
+      expect(table.getChild("test")!.get(0)).toBe(true);
+      expect(table.getChild("test")!.get(1)).toBeNull();
+      expect(table.getChild("test")!.get(2)).toBe(false);
+    });
+  });
+
+  // Test for the undefined values bug fix
+  describe("undefined values handling", () => {
+    it("should handle mixed undefined and actual values", () => {
+      const schema = new Schema([
+        new Field("text", new Utf8(), true), // nullable
+        new Field("number", new Int32(), true), // nullable
+        new Field("bool", new Bool(), true), // nullable
+      ]);
+
+      const data = [
+        { text: undefined, number: 42, bool: true },
+        { text: "hello", number: undefined, bool: false },
+        { text: "world", number: 123, bool: undefined },
+      ];
+      const table = makeArrowTable(data, { schema });
+
+      const result = table.toArray();
+      expect(result).toHaveLength(3);
+      expect(result[0].text).toBe(null);
+      expect(result[0].number).toBe(42);
+      expect(result[0].bool).toBe(true);
+      expect(result[1].text).toBe("hello");
+      expect(result[1].number).toBe(null);
+      expect(result[1].bool).toBe(false);
+      expect(result[2].text).toBe("world");
+      expect(result[2].number).toBe(123);
+      expect(result[2].bool).toBe(null);
+    });
+  });
+});
diff --git a/nodejs/__test__/arrow_versions.ts b/nodejs/__test__/arrow_versions.ts
new file mode 100644
index 000000000..bfbbfa5ef
--- /dev/null
+++ b/nodejs/__test__/arrow_versions.ts
@@ -0,0 +1,39 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+// Every Apache Arrow release the package accepts as a peer dependency, as
+// separately installed copies so the tests can feed objects built by one
+// Arrow version into a LanceDB running on another.  When a new Arrow major
+// is released, add the alias to `package.json` and extend this list; the
+// test files pick the change up from here.
+import * as arrow15 from "apache-arrow-15";
+import * as arrow16 from "apache-arrow-16";
+import * as arrow17 from "apache-arrow-17";
+import * as arrow18 from "apache-arrow-18";
+import * as arrow19 from "apache-arrow-19";
+import * as arrow20 from "apache-arrow-20";
+import * as arrow21 from "apache-arrow-21";
+
+export { arrow15, arrow16, arrow17, arrow18, arrow19, arrow20, arrow21 };
+
+export type ApacheArrow =
+  | typeof arrow15
+  | typeof arrow16
+  | typeof arrow17
+  | typeof arrow18
+  | typeof arrow19
+  | typeof arrow20
+  | typeof arrow21;
+
+export const arrowVersions: ApacheArrow[] = [
+  arrow15,
+  arrow16,
+  arrow17,
+  arrow18,
+  arrow19,
+  arrow20,
+  arrow21,
+];
+
+/** The newest supported Arrow release, for tests that only need one. */
+export const latestArrow = arrow21;
diff --git a/nodejs/__test__/blob.test.ts b/nodejs/__test__/blob.test.ts
index 5c01897b2..b41d5716f 100644
--- a/nodejs/__test__/blob.test.ts
+++ b/nodejs/__test__/blob.test.ts
@@ -1,9 +1,25 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-import { Field, Int64, List, Schema, Struct, Utf8 } from "apache-arrow";
-import { makeArrowTable } from "../lancedb/arrow";
+import {
+  Field,
+  Float,
+  Float32,
+  Int32,
+  Int64,
+  List,
+  Schema,
+  Struct,
+  Utf8,
+} from "apache-arrow";
+import {
+  convertToTable,
+  makeArrowTable,
+  resolveBlobInputs,
+} from "../lancedb/arrow";
 import { BlobFile, blob, coerceBlobValue, isBlobField } from "../lancedb/blob";
+import { EmbeddingFunction, LanceSchema } from "../lancedb/embedding";
+import { getRegistry } from "../lancedb/embedding/registry";
 
 describe("blob()", () => {
   it("marks the field as lance.blob.v2", () => {
@@ -46,6 +62,13 @@ describe("blob()", () => {
   });
 });
 
+class BlobStruct {
+  constructor(
+    public data?: unknown,
+    public uri?: unknown,
+  ) {}
+}
+
 describe("coerceBlobValue", () => {
   it.each([
     ["Buffer", Buffer.from("x"), { data: Buffer.from("x"), uri: null }],
@@ -65,6 +88,41 @@ describe("coerceBlobValue", () => {
       { uri: "s3://bucket/key" },
       { data: null, uri: "s3://bucket/key" },
     ],
+    [
+      "ArrayBuffer",
+      new Uint8Array([120]).buffer,
+      { data: new Uint8Array([120]), uri: null },
+    ],
+    [
+      "URL",
+      new URL("https://example.com/blob"),
+      { data: null, uri: "https://example.com/blob" },
+    ],
+    [
+      "data struct with ArrayBuffer",
+      { data: new Uint8Array([121]).buffer },
+      { data: new Uint8Array([121]), uri: null },
+    ],
+    [
+      "uri struct with URL",
+      { uri: new URL("file:///tmp/a.png") },
+      { data: null, uri: "file:///tmp/a.png" },
+    ],
+    [
+      "class instance with Uint8Array data",
+      new BlobStruct(new Uint8Array([122])),
+      { data: new Uint8Array([122]), uri: null },
+    ],
+    [
+      "class instance with ArrayBuffer data",
+      new BlobStruct(new Uint8Array([122]).buffer),
+      { data: new Uint8Array([122]), uri: null },
+    ],
+    [
+      "class instance with URL uri",
+      new BlobStruct(undefined, new URL("s3://bucket/key")),
+      { data: null, uri: "s3://bucket/key" },
+    ],
     ["null", null, null],
   ])("accepts %s", (_name, input, expected) => {
     expect(coerceBlobValue(input)).toEqual(expected);
@@ -76,7 +134,7 @@ describe("coerceBlobValue", () => {
     [
       "Int16Array",
       new Int16Array([1]),
-      /Blob data must be Buffer or Uint8Array/,
+      /Blob data must be Buffer, Uint8Array, ArrayBuffer, or Blob/,
     ],
     [
       "both data and uri",
@@ -88,11 +146,160 @@ describe("coerceBlobValue", () => {
       { data: null, uri: null },
       /exactly one of 'data' or 'uri'/,
     ],
+    ["Blob", new Blob(["x"]), /must be read asynchronously/],
+    ["data struct with Blob", { data: new Blob(["x"]) }, /asynchronously/],
+    [
+      "class instance with Blob data",
+      new BlobStruct(new Blob(["x"])),
+      /asynchronously/,
+    ],
+    [
+      "unknown struct field",
+      { data: Buffer.from("y"), mime: "image/png" },
+      /Blob struct values only support 'data' and 'uri', got 'mime'/,
+    ],
+    [
+      "misspelled struct field",
+      { uri: "s3://bucket/key", dat: Buffer.from("y") },
+      /got 'dat'/,
+    ],
+    [
+      "SharedArrayBuffer",
+      new SharedArrayBuffer(2),
+      /Blob column values must be Buffer, Uint8Array, ArrayBuffer, Blob/,
+    ],
+    ["Date", new Date(0), /Blob column values must be/],
   ])("rejects %s", (_name, input, message) => {
     expect(() => coerceBlobValue(input)).toThrow(message);
   });
 });
 
+describe("resolveBlobInputs", () => {
+  it("reads Blob and File values in blob columns only", async () => {
+    const schema = new Schema([
+      new Field("name", new Utf8(), true),
+      blob("image"),
+      new Field("images", new List(blob("image")), true),
+    ]);
+    const shared = new Blob(["same"]);
+    const notABlobColumn = new Blob(["other"]);
+    const data = [
+      {
+        name: notABlobColumn,
+        image: new File(["file"], "a.txt"),
+        images: [shared, { data: shared }, null],
+      },
+      { name: "x", image: Buffer.from("buf"), images: null },
+    ];
+    const resolved = await resolveBlobInputs(data, schema);
+
+    expect(resolved[0].name).toBe(notABlobColumn);
+    expect(Buffer.from(resolved[0].image as Uint8Array).toString()).toBe(
+      "file",
+    );
+    const images = resolved[0].images as [Uint8Array, { data: Uint8Array }];
+    expect(Buffer.from(images[0]).toString()).toBe("same");
+    expect(Buffer.from(images[1].data).toString()).toBe("same");
+    // Rows without Blob values and the caller's records are left alone.
+    expect(resolved[1]).toBe(data[1]);
+    expect(data[0].images?.[0]).toBe(shared);
+  });
+
+  it("reads each distinct Blob once, bare or inside data structs", async () => {
+    class CountingBlob extends Blob {
+      calls = 0;
+      async arrayBuffer(): Promise<ArrayBuffer> {
+        this.calls++;
+        return super.arrayBuffer();
+      }
+    }
+    const shared = new CountingBlob(["same"]);
+    const schema = new Schema([blob("image")]);
+    const resolved = await resolveBlobInputs(
+      [
+        { image: { data: shared } },
+        { image: { data: shared } },
+        { image: shared },
+      ],
+      schema,
+    );
+    expect(shared.calls).toBe(1);
+    const first = (resolved[0].image as { data: Uint8Array }).data;
+    expect(Buffer.from(first).toString()).toBe("same");
+    expect((resolved[1].image as { data: Uint8Array }).data).toBe(first);
+    expect(resolved[2].image).toBe(first);
+  });
+
+  it("reads a Blob held by a class instance", async () => {
+    const schema = new Schema([blob("image")]);
+    const [row] = await resolveBlobInputs(
+      [{ image: new BlobStruct(new Blob(["hi"])) }],
+      schema,
+    );
+    expect(
+      Buffer.from((row.image as { data: Uint8Array }).data).toString(),
+    ).toBe("hi");
+  });
+
+  it("returns the input when there is nothing to read", async () => {
+    const data = [{ image: Buffer.from("x") }];
+    const schema = new Schema([blob("image")]);
+    await expect(resolveBlobInputs(data, schema)).resolves.toBe(data);
+    await expect(resolveBlobInputs(data)).resolves.toBe(data);
+  });
+});
+
+describe("convertToTable blob columns with embedding functions", () => {
+  // Embedding metadata routes rows through ensureNestedFieldsExist, which must
+  // leave blob values for the blob coercion instead of treating them as structs.
+  class LengthEmbedding extends EmbeddingFunction<string> {
+    ndims() {
+      return 2;
+    }
+    embeddingDataType(): Float {
+      return new Float32();
+    }
+    async computeSourceEmbeddings(data: string[]) {
+      return data.map((text) => [text.length, 1]);
+    }
+  }
+  getRegistry().register("blob-test-length")(LengthEmbedding);
+  const func = getRegistry().get<LengthEmbedding>("blob-test-length")!.create();
+  const embeddingSchema = LanceSchema({
+    text: func.sourceField(new Utf8()),
+    vector: func.vectorField(),
+  });
+  const schema = new Schema(
+    [...embeddingSchema.fields, blob("image")],
+    embeddingSchema.metadata,
+  );
+
+  it.each([
+    ["Uint8Array", new Uint8Array([104, 105]), "hi", null],
+    ["ArrayBuffer", new Uint8Array([104, 105]).buffer, "hi", null],
+    ["Blob", new Blob(["hi"]), "hi", null],
+    ["URI string", "s3://b/k", null, "s3://b/k"],
+    ["URL", new URL("s3://b/k"), null, "s3://b/k"],
+    ["data struct", { data: new Uint8Array([104, 105]) }, "hi", null],
+  ])("accepts %s input", async (_name, image, data, uri) => {
+    const table = await convertToTable([{ text: "a", image }], undefined, {
+      schema,
+    });
+    const column = table.getChild("image")!;
+    expect(column.nullCount).toBe(0);
+    const bytes = column.getChild("data")!.get(0);
+    expect(bytes == null ? null : Buffer.from(bytes).toString()).toBe(data);
+    expect(column.getChild("uri")!.get(0)).toBe(uri);
+  });
+
+  it("writes a missing blob as null", async () => {
+    const table = await convertToTable([{ text: "a" }], undefined, { schema });
+    const column = table.getChild("image")!;
+    expect(column.nullCount).toBe(1);
+    expect(column.get(0)).toBeNull();
+  });
+});
+
 describe("BlobFile", () => {
   it("rejects constructing BlobFile without a native handle", () => {
     expect(() => new (BlobFile as unknown as { new (): BlobFile })()).toThrow(
@@ -103,26 +310,122 @@ describe("BlobFile", () => {
 
 describe("makeArrowTable blob columns", () => {
   it.each([
-    ["ArrayBuffer", new Uint8Array([104]).buffer],
-    ["Blob", new Blob(["hello"])],
-    ["File", new File(["hello"], "hello.txt")],
-    ["URL", new URL("https://example.com/blob")],
     ["ReadableStream", new ReadableStream()],
-    ["data struct with ArrayBuffer", { data: new Uint8Array([104]).buffer }],
-    ["uri struct with URL", { uri: new URL("https://example.com/blob") }],
+    ["Int16Array", new Int16Array([1])],
   ])("rejects %s input rather than writing a null blob", (_name, image) => {
     const schema = new Schema([blob("image")]);
     expect(() => makeArrowTable([{ image }], { schema })).toThrow(/image/);
   });
 
-  it("rejects Blob instances with enumerable fields as one invalid value", () => {
+  it.each([
+    ["Blob", new Blob(["hello"])],
+    ["File", new File(["hello"], "hello.txt")],
+    ["data struct with Blob", { data: new Blob(["hello"]) }],
+    [
+      "Blob with enumerable fields",
+      Object.assign(new Blob(["hello"]), { position: 0 }),
+    ],
+  ])("points %s input at the async APIs", (_name, image) => {
     const schema = new Schema([blob("image")]);
-    const image = Object.assign(new Blob(["hello"]), { position: 0 });
-    expect(() => makeArrowTable([{ image }], { schema })).toThrow(
-      "Unsupported object value for field image at row 0.",
+    expect(() => makeArrowTable([{ id: 1, image }], { schema })).toThrow(
+      /blob field image at row 0: Blob and File values must be read asynchronously/,
     );
   });
 
+  it.each([
+    ["ArrayBuffer", new Uint8Array([104, 105]).buffer, "hi", null],
+    [
+      "data struct with ArrayBuffer",
+      { data: new Uint8Array([104, 105]).buffer },
+      "hi",
+      null,
+    ],
+    ["URL", new URL("https://example.com/a"), null, "https://example.com/a"],
+    [
+      "uri struct with URL",
+      { uri: new URL("file:///tmp/a.png") },
+      null,
+      "file:///tmp/a.png",
+    ],
+  ])("accepts %s input", (_name, image, data, uri) => {
+    const schema = new Schema([blob("image")]);
+    const input = { image };
+    const table = makeArrowTable([input], { schema });
+    const column = table.getChild("image")!;
+    expect(column.nullCount).toBe(0);
+    const bytes = column.getChild("data")!.get(0);
+    expect(bytes == null ? null : Buffer.from(bytes).toString()).toBe(data);
+    expect(column.getChild("uri")!.get(0)).toBe(uri);
+    // The caller's record is left untouched.
+    expect(input.image).toBe(image);
+  });
+
+  it.each([
+    ["Uint8Array", new Uint8Array([104, 105]), "hi", null],
+    ["URI string", "s3://b/k", null, "s3://b/k"],
+    ["data struct", { data: new Uint8Array([104, 105]) }, "hi", null],
+  ])("accepts %s input after a null row", (_name, image, data, uri) => {
+    const schema = new Schema([blob("image")]);
+    const table = makeArrowTable([{ image: null }, { image }], { schema });
+    const column = table.getChild("image")!;
+    expect(column.nullCount).toBe(1);
+    expect(column.get(0)).toBeNull();
+    const bytes = column.getChild("data")!.get(1);
+    expect(bytes == null ? null : Buffer.from(bytes).toString()).toBe(data);
+    expect(column.getChild("uri")!.get(1)).toBe(uri);
+  });
+
+  it("accepts a null struct before one holding a blob", () => {
+    const schema = new Schema([
+      new Field("info", new Struct([blob("image")]), true),
+    ]);
+    const table = makeArrowTable(
+      [{ info: null }, { info: { image: new Uint8Array([97]) } }],
+      { schema },
+    );
+    const info = table.getChild("info")!;
+    expect(info.get(0)).toBeNull();
+    const image = (info.get(1) as { image: { data: Uint8Array } }).image;
+    expect(Buffer.from(image.data).toString()).toBe("a");
+  });
+
+  it("skips a missing blob column named like an Object.prototype member", () => {
+    const schema = new Schema([
+      new Field("id", new Int32(), true),
+      blob("constructor"),
+    ]);
+    const table = makeArrowTable([{ id: 1 }], { schema });
+    expect(table.schema.fields.map((f) => f.name)).toEqual(["id"]);
+  });
+
+  it("accepts ArrayBuffer and URL inside lists and structs", () => {
+    const schema = new Schema([
+      new Field("images", new List(blob("image")), true),
+      new Field("info", new Struct([blob("image")]), true),
+    ]);
+    const table = makeArrowTable(
+      [
+        {
+          images: [new Uint8Array([97]).buffer, new URL("s3://b/k")],
+          info: { image: new Uint8Array([98]).buffer },
+        },
+      ],
+      { schema },
+    );
+    const images = Array.from(
+      table.getChild("images")!.get(0) as Iterable<{
+        data: Uint8Array | null;
+        uri: string | null;
+      }>,
+    );
+    expect(Buffer.from(images[0].data!).toString()).toBe("a");
+    expect(images[1].uri).toBe("s3://b/k");
+    const info = table.getChild("info")!.get(0) as {
+      image: { data: Uint8Array };
+    };
+    expect(Buffer.from(info.image.data).toString()).toBe("b");
+  });
+
   it("coerces Buffer input onto a blob field", () => {
     const schema = new Schema([
       new Field("id", new Int64(), true),
diff --git a/nodejs/__test__/catalog.test.ts b/nodejs/__test__/catalog.test.ts
index f9158c94f..f8b14ca6f 100644
--- a/nodejs/__test__/catalog.test.ts
+++ b/nodejs/__test__/catalog.test.ts
@@ -80,9 +80,12 @@ describe("remote catalog", () => {
         const second = await catalog.connectDatabase("other");
         expect(await second.tableNames()).toEqual([]);
         expect(await first.tableNames()).toEqual([]);
-        expect(
-          await catalog.listDatabases({ limit: 1, pageToken: "a/b" }),
-        ).toEqual({ databases: ["team/search"], pageToken: "next" });
+        const names = catalog.listDatabases({ pageLimit: 1, pageToken: "a/b" });
+        expect(await names.next()).toEqual({
+          done: false,
+          value: "team/search",
+        });
+        expect(names.pageToken()).toBe("next");
         await catalog.dropDatabase("team/search", { ignoreMissing: true });
         expect(requests[0].url).toBe("/v1/namespace/team%2Fsearch/create");
         expect(requests[0].body).toEqual({ mode: "ExistOk" });
@@ -129,14 +132,175 @@ describe("remote catalog", () => {
     );
   });
 
+  it("lazily traverses pages, including empty pages, and exposes cached state", async () => {
+    await withCatalog(
+      [
+        // biome-ignore lint/style/useNamingConvention: server wire format
+        [200, { namespaces: ["a", "b"], page_token: "empty" }],
+        // biome-ignore lint/style/useNamingConvention: server wire format
+        [200, { namespaces: [], page_token: "last" }],
+        // biome-ignore lint/style/useNamingConvention: server wire format
+        [200, { namespaces: ["c", "d"], page_token: "" }],
+      ],
+      async (catalog, requests) => {
+        const names = catalog.listDatabases({ pageLimit: 2 });
+        expect(names[Symbol.asyncIterator]()).toBe(names);
+        expect(requests).toHaveLength(0);
+        expect(names.numPageResults()).toBe(0);
+        expect(names.pageToken()).toBeUndefined();
+        expect(await names.next()).toEqual({ done: false, value: "a" });
+        expect(names.numPageResults()).toBe(1);
+        expect(names.pageToken()).toBe("empty");
+        expect(await names.next()).toEqual({ done: false, value: "b" });
+        expect(requests).toHaveLength(1);
+        expect(await names.next()).toEqual({ done: false, value: "c" });
+        expect(requests).toHaveLength(3);
+        expect(names.pageToken()).toBeUndefined();
+        expect(names.numPageResults()).toBe(1);
+        const remaining = [];
+        for await (const name of names) remaining.push(name);
+        expect(remaining).toEqual(["d"]);
+        expect(names.numPageResults()).toBe(0);
+        expect(await names.next()).toEqual({ done: true, value: undefined });
+        expect(requests.map((request) => request.url)).toEqual([
+          "/v1/namespace/$/list?limit=2",
+          "/v1/namespace/$/list?limit=2&page_token=empty",
+          "/v1/namespace/$/list?limit=2&page_token=last",
+        ]);
+      },
+    );
+  });
+
+  it("resumes from a saved token after draining the cache", async () => {
+    await withCatalog(
+      [
+        // biome-ignore lint/style/useNamingConvention: server wire format
+        [200, { namespaces: ["a", "b"], page_token: "next/token" }],
+        [200, { namespaces: ["c"] }],
+      ],
+      async (catalog, requests) => {
+        const names = catalog.listDatabases({
+          pageToken: "start/token",
+          pageLimit: 2,
+        });
+        expect(names.pageToken()).toBe("start/token");
+        expect(await names.next()).toEqual({ done: false, value: "a" });
+        while (names.numPageResults() > 0) await names.next();
+        const resumed = catalog.listDatabases({
+          pageToken: names.pageToken(),
+          pageLimit: 2,
+        });
+        const results = [];
+        for await (const name of resumed) results.push(name);
+        expect(results).toEqual(["c"]);
+        expect(requests.map((request) => request.url)).toEqual([
+          "/v1/namespace/$/list?limit=2&page_token=start%2Ftoken",
+          "/v1/namespace/$/list?limit=2&page_token=next%2Ftoken",
+        ]);
+      },
+    );
+  });
+
+  it("terminates on errors and retains the failed request token", async () => {
+    await withCatalog(
+      [
+        // biome-ignore lint/style/useNamingConvention: server wire format
+        [200, { namespaces: ["a"], page_token: "retry" }],
+        [400, {}],
+        [200, { namespaces: ["b"] }],
+      ],
+      async (catalog, requests) => {
+        const names = catalog.listDatabases();
+        await names.next();
+        await expect(names.next()).rejects.toThrow();
+        expect(names.pageToken()).toBe("retry");
+        expect(names.numPageResults()).toBe(0);
+        expect(await names.next()).toEqual({ done: true, value: undefined });
+        expect(requests).toHaveLength(2);
+        const resumed = catalog.listDatabases({ pageToken: names.pageToken() });
+        expect(await resumed.next()).toEqual({ done: false, value: "b" });
+        expect(await resumed.next()).toEqual({ done: true, value: undefined });
+      },
+    );
+  });
+
+  it("handles an empty listing and concurrent advances", async () => {
+    await withCatalog(
+      [
+        [200, { namespaces: [] }],
+        [200, { namespaces: ["a", "b"] }],
+      ],
+      async (catalog, requests) => {
+        const empty = catalog.listDatabases();
+        expect(await empty.next()).toEqual({ done: true, value: undefined });
+        expect(await empty.next()).toEqual({ done: true, value: undefined });
+        const names = catalog.listDatabases();
+        const pending = [names.next(), names.next(), names.next()];
+        expect(names.numPageResults()).toBe(0);
+        expect(names.pageToken()).toBeUndefined();
+        const results = await Promise.all(pending);
+        expect(
+          results
+            .filter((result) => !result.done)
+            .map((result) => result.value)
+            .sort(),
+        ).toEqual(["a", "b"]);
+        expect(results.filter((result) => result.done)).toHaveLength(1);
+        expect(requests).toHaveLength(2);
+      },
+    );
+  });
+
   it("validates endpoints and pagination", async () => {
     await expect(connectCatalog("/tmp/catalog")).rejects.toThrow();
     const catalog = await connectCatalog("http://127.0.0.1:1");
-    for (const limit of [0, -1, 1.5, 2147483648]) {
-      await expect(catalog.listDatabases({ limit })).rejects.toThrow("limit");
+    for (const limit of [
+      0,
+      -1,
+      1.5,
+      2147483648,
+      Number.NaN,
+      Number.POSITIVE_INFINITY,
+    ]) {
+      expect(() => catalog.listDatabases({ pageLimit: limit })).toThrow(
+        "limit",
+      );
     }
-    await expect(catalog.connectDatabase("a$b")).rejects.toThrow(
-      "Invalid database name",
-    );
   });
+
+  it.each([
+    "",
+    ".",
+    "..",
+    "/db",
+    "db/",
+    "a//b",
+    "a/./b",
+    "a/../b",
+    "a b",
+    "a:b",
+    "a$b",
+    "a%b",
+    "a?b",
+    "a#b",
+    "a\\b",
+    "café",
+    "a\nb",
+  ])(
+    "rejects invalid database name %j before sending requests",
+    async (name) => {
+      await withCatalog([], async (catalog, requests) => {
+        for (const method of [
+          "createDatabase",
+          "connectDatabase",
+          "dropDatabase",
+        ] as const) {
+          await expect(catalog[method](name)).rejects.toThrow(
+            "Invalid database name",
+          );
+        }
+        expect(requests).toEqual([]);
+      });
+    },
+  );
 });
diff --git a/nodejs/__test__/connection.test.ts b/nodejs/__test__/connection.test.ts
index 816f8c3de..895c2a7c5 100644
--- a/nodejs/__test__/connection.test.ts
+++ b/nodejs/__test__/connection.test.ts
@@ -1,7 +1,8 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-import { readdirSync } from "fs";
+import { mkdirSync, readdirSync } from "fs";
+import { join } from "path";
 import { Field, Float64, Schema } from "apache-arrow";
 import * as tmp from "tmp";
 import {
@@ -180,6 +181,32 @@ describe("given a connection", () => {
     expect(seen).toEqual(created);
   });
 
+  it("should list all names and use a default page size of 100", async () => {
+    const names = Array.from(
+      { length: 130 },
+      (_, i) => `t${i.toString().padStart(3, "0")}`,
+    );
+    for (const name of names) {
+      mkdirSync(join(tmpDir.name, `${name}.lance`));
+    }
+    await expect(db.tableNames()).resolves.toEqual(names);
+    await expect(db.tableNames({ limit: 0 })).resolves.toEqual([]);
+    const first = await db.listTables();
+    expect(first.tables).toEqual(names.slice(0, 100));
+    expect(first.pageToken).toBeDefined();
+    const second = await db.listTables({ pageToken: first.pageToken });
+    expect(second.tables).toEqual(names.slice(100));
+    expect(second.pageToken).toBeUndefined();
+    await expect(db.listTables({ limit: 200 })).resolves.toEqual({
+      tables: names,
+      pageToken: undefined,
+    });
+    await expect(db.listTables({ limit: 0 })).resolves.toEqual({
+      tables: [],
+      pageToken: undefined,
+    });
+  });
+
   it("should list tables in a namespace", async () => {
     const db = await connect(tmpDir.name, {
       // biome-ignore lint/style/useNamingConvention: opaque backend property key, must match Rust
diff --git a/nodejs/__test__/materialized_view.test.ts b/nodejs/__test__/materialized_view.test.ts
index 6739320aa..3e47a00e9 100644
--- a/nodejs/__test__/materialized_view.test.ts
+++ b/nodejs/__test__/materialized_view.test.ts
@@ -16,15 +16,11 @@ describe("materialized views", () => {
   beforeEach(async () => {
     tmpDir = tmp.dirSync({ unsafeCleanup: true });
     db = await connect(tmpDir.name);
-    await db.createTable(
-      "people",
-      [
-        { name: "ada", age: 36 },
-        { name: "kid", age: 7 },
-        { name: "grace", age: 85 },
-      ],
-      { storageOptions: { newTableEnableStableRowIds: "true" } },
-    );
+    await db.createTable("people", [
+      { name: "ada", age: 36 },
+      { name: "kid", age: 7 },
+      { name: "grace", age: 85 },
+    ]);
   });
   afterEach(() => tmpDir.removeCallback());
 
@@ -52,19 +48,9 @@ describe("materialized views", () => {
       "SELECT * FROM people",
     );
 
-    for (const [format, fn] of [
-      [3, "vector_duplicate_pairs"],
-      [4, "vector_dedup"],
-    ]) {
-      const native = `SELECT * FROM ${fn}('images', 3, 'phash', 4)`;
-      expect(read(JSON.stringify({ format, query: native })).query).toBe(
-        native,
-      );
-    }
-
     // A newer writer's layout is reported, never guessed at.
     for (const newer of [
-      `{"format":5,"query":${JSON.stringify(query)}}`,
+      `{"format":3,"query":${JSON.stringify(query)}}`,
       '{"kind":"select_v3","source_table":"people"}',
     ]) {
       expect(() => read(newer)).toThrow(/cannot refresh/);
@@ -106,7 +92,7 @@ describe("materialized views", () => {
     );
   });
 
-  it("refreshes incrementally after an append", async () => {
+  it("fully refreshes after an append", async () => {
     const view = await db.createMaterializedView("copy", "people", {
       withNoData: true,
     });
@@ -115,18 +101,18 @@ describe("materialized views", () => {
     const people = await db.openTable("people");
     await people.add([{ name: "alan", age: 41 }]);
     const result = await view.refresh();
-    expect(result.mode).toBe("incremental");
-    expect(Number(result.rowsWritten)).toBe(1);
+    expect(result.mode).toBe("rebuild");
+    expect(Number(result.rowsWritten)).toBe(4);
     expect(await view.table().countRows()).toBe(4);
 
-    expect((await view.refresh()).mode).toBe("no_op");
+    expect((await view.refresh()).mode).toBe("rebuild");
   });
 
   it("lists views and rejects non-views", async () => {
     await db.createMaterializedView("adults", "people", {
       where: "age >= 18",
     });
-    expect(await db.listMaterializedViews()).toEqual(["adults"]);
+    expect(await collect(db.listMaterializedViews())).toEqual(["adults"]);
     await expect(db.openMaterializedView("people")).rejects.toThrow(
       "not a materialized view",
     );
@@ -135,7 +121,7 @@ describe("materialized views", () => {
     );
 
     await db.dropMaterializedView("adults");
-    expect(await db.listMaterializedViews()).toEqual([]);
+    expect(await collect(db.listMaterializedViews())).toEqual([]);
   });
 
   it("returns a job when dropping a view asynchronously", async () => {
@@ -144,7 +130,7 @@ describe("materialized views", () => {
     const job = await db.dropMaterializedViewAsync("adults");
     expect(job.id).toBeNull();
     await job.wait();
-    expect(await db.listMaterializedViews()).toEqual([]);
+    expect(await collect(db.listMaterializedViews())).toEqual([]);
   });
 
   it("rejects an invalid expression at create time", async () => {
@@ -161,7 +147,7 @@ describe("materialized views", () => {
         db.createMaterializedView("bad", "people", { limit }),
       ).rejects.toThrow("non-negative integer");
     }
-    expect(await db.listMaterializedViews()).toEqual([]);
+    expect(await collect(db.listMaterializedViews())).toEqual([]);
 
     const view = await db.createMaterializedView("copy", "people");
     for (const sourceVersion of [-1, 1.5, Infinity, NaN]) {
@@ -172,9 +158,7 @@ describe("materialized views", () => {
   });
 
   it("quotes bare select names", async () => {
-    await db.createTable("odd_names", [{ "order item": "widget" }], {
-      storageOptions: { newTableEnableStableRowIds: "true" },
-    });
+    await db.createTable("odd_names", [{ "order item": "widget" }]);
     const view = await db.createMaterializedView("quoted", "odd_names", {
       select: ["order item"],
       withNoData: true,
@@ -183,10 +167,15 @@ describe("materialized views", () => {
     expect(Number(result.rowsWritten)).toBe(1);
   });
 
-  it("requires stable row ids on the source", async () => {
+  it("does not require stable row ids on the source", async () => {
     await db.createTable("plain", [{ x: 1 }]);
-    await expect(db.createMaterializedView("v", "plain")).rejects.toThrow(
-      "stable row ids",
-    );
+    const view = await db.createMaterializedView("v", "plain");
+    expect(await view.table().countRows()).toBe(1);
   });
 });
+
+async function collect<T>(items: AsyncIterable<T>): Promise<T[]> {
+  const result: T[] = [];
+  for await (const item of items) result.push(item);
+  return result;
+}
diff --git a/nodejs/__test__/query.test.ts b/nodejs/__test__/query.test.ts
index 5f3e68b16..786f04015 100644
--- a/nodejs/__test__/query.test.ts
+++ b/nodejs/__test__/query.test.ts
@@ -110,6 +110,129 @@ describe("Query outputSchema", () => {
   });
 });
 
+describe("Query empty results", () => {
+  let tmpDir: tmp.DirResult;
+  let table: Table;
+
+  beforeEach(async () => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+    const db = await connect(tmpDir.name);
+    const schema = new Schema(
+      [
+        new Field("id", new Int64(), false),
+        new Field(
+          "text",
+          new Utf8(),
+          true,
+          new Map([["description", "searchable text"]]),
+        ),
+        new Field(
+          "vector",
+          new FixedSizeList(2, new Field("item", new Float32())),
+          true,
+        ),
+      ],
+      new Map([["source", "empty-query-regression"]]),
+    );
+    table = await db.createTable(
+      "test",
+      makeArrowTable([{ id: 1n, text: "hello", vector: [1, 2] }], { schema }),
+    );
+  });
+
+  afterEach(() => {
+    tmpDir.removeCallback();
+  });
+
+  it.each([
+    {
+      name: "filtered scan",
+      query: (table: Table) => table.query().where("id < 0"),
+      fields: ["id", "text", "vector"],
+    },
+    {
+      name: "zero limit",
+      query: (table: Table) => table.query().limit(0),
+      fields: ["id", "text", "vector"],
+    },
+    {
+      name: "column projection",
+      query: (table: Table) =>
+        table.query().where("id < 0").select(["text", "id"]),
+      fields: ["text", "id"],
+    },
+    {
+      name: "dynamic projection",
+      query: (table: Table) =>
+        table.query().where("id < 0").select({ doubled: "id * 2" }),
+      fields: ["doubled"],
+    },
+    {
+      name: "filtered vector search",
+      query: (table: Table) =>
+        table.search([0.5, 0.5]).where("id < 0").limit(3),
+      fields: ["id", "text", "vector", "_distance"],
+    },
+    {
+      name: "vector projection with row id",
+      query: (table: Table) =>
+        table
+          .vectorSearch([0.5, 0.5])
+          .where("id < 0")
+          .select(["id"])
+          .withRowId(),
+      fields: ["id", "_distance", "_rowid"],
+    },
+    {
+      name: "fast search without an index",
+      query: (table: Table) => table.search([0.5, 0.5]).fastSearch(),
+      fields: ["id", "text", "vector", "_distance"],
+    },
+    {
+      name: "take with no offsets",
+      query: (table: Table) => table.takeOffsets([]).select(["text"]),
+      fields: ["text"],
+    },
+  ])("preserves the output schema for $name", async ({ query, fields }) => {
+    const result = await query(table).toArrow({ maxBatchLength: 1 });
+
+    expect(result.numRows).toBe(0);
+    expect(result.schema.fields.map((field) => field.name)).toEqual(fields);
+    for (const name of fields) {
+      expect(result.getChild(name)).not.toBeNull();
+      expect(result.getChild(name)?.length).toBe(0);
+    }
+  });
+
+  it("preserves types, nullability, and metadata", async () => {
+    const result = await table.query().where("id < 0").toArrow();
+    const schema = await table.schema();
+
+    expect(result.schema).toEqual(schema);
+    expect(result.getChild("id")?.type.toString()).toBe("Int64");
+    expect(result.schema.fields[0].nullable).toBe(false);
+    expect(result.schema.fields[1].metadata.get("description")).toBe(
+      "searchable text",
+    );
+    expect(result.schema.metadata.get("source")).toBe("empty-query-regression");
+  });
+
+  it("preserves the score column for full-text search with no matches", async () => {
+    await table.createIndex("text", { config: Index.fts() });
+    const result = await table
+      .search("missing", "fts")
+      .select(["id"])
+      .toArrow();
+
+    expect(result.numRows).toBe(0);
+    expect(result.schema.fields.map((field) => field.name)).toEqual([
+      "id",
+      "_score",
+    ]);
+    expect(result.getChild("_score")?.type.toString()).toBe("Float32");
+  });
+});
+
 describe("Search pagination", () => {
   let tmpDir: tmp.DirResult;
   let table: Table;
@@ -404,3 +527,106 @@ describe("Query orderBy", () => {
     ]);
   });
 });
+
+describe("IVF search effort", () => {
+  let directory: tmp.DirResult;
+  let table: Table;
+  const vector = Array.from({ length: 16 }, (_, i) => (i === 0 ? 1 : 0));
+
+  beforeEach(async () => {
+    directory = tmp.dirSync({ unsafeCleanup: true });
+    const db = await connect(directory.name);
+    const rows = Array.from({ length: 512 }, (_, id) => ({
+      id,
+      vector: Array.from(
+        { length: 16 },
+        (_, j) =>
+          (Math.floor(id / 32) === j ? 1 : 0) + Math.sin(id * 17 + j) * 0.001,
+      ),
+    }));
+    table = await db.createTable("effort", rows.slice(0, 256));
+    await table.add(rows.slice(256));
+    await table.createIndex("vector", {
+      config: Index.ivfFlat({ numPartitions: 16 }),
+    });
+  });
+
+  afterEach(() => {
+    directory.removeCallback();
+  });
+
+  it.each([
+    [null, 6],
+    [0, 1],
+    [0.25, 3],
+    [0.5, 6],
+    [0.75, 10],
+    [1, 16],
+  ])(
+    "preserves effort %s through execution and probe precedence",
+    async (effort, expected) => {
+      const partitions = async (query: ReturnType<Table["vectorSearch"]>) => {
+        const plan = await query.limit(8).analyzePlan();
+        const matches = [...plan.matchAll(/partitions_searched=(\d+)/g)];
+        expect(matches.length).toBeGreaterThan(0);
+        return matches.reduce((total, match) => total + Number(match[1]), 0);
+      };
+      expect(
+        await partitions(table.vectorSearch(vector).searchEffort(effort)),
+      ).toBe(expected);
+      expect(
+        await partitions(
+          table.vectorSearch(vector).nprobes(1).searchEffort(effort),
+        ),
+      ).toBe(effort === null ? 1 : expected);
+      expect(
+        await partitions(
+          table.vectorSearch(vector).searchEffort(effort).nprobes(1),
+        ),
+      ).toBe(effort === null ? 1 : expected);
+      expect(
+        await partitions(
+          table
+            .vectorSearch(vector)
+            .searchEffort(effort)
+            .nprobes(1)
+            .searchEffort(null),
+        ),
+      ).toBe(1);
+      expect(
+        await partitions(
+          table.vectorSearch(vector).searchEffort(effort).maximumNprobes(2),
+        ),
+      ).toBeLessThanOrEqual(2);
+      const actual = await table
+        .vectorSearch(vector)
+        .searchEffort(effort)
+        .limit(8)
+        .toArray();
+      const exact = await table
+        .vectorSearch(vector)
+        .bypassVectorIndex()
+        .limit(8)
+        .toArray();
+      const ids = new Set(exact.map((row) => row.id));
+      expect(
+        actual.filter((row) => ids.has(row.id)).length / 8,
+      ).toBeGreaterThanOrEqual(0.5);
+    },
+  );
+
+  it.each([
+    -0.1,
+    1.1,
+    Number.NaN,
+    Number.POSITIVE_INFINITY,
+    Number.NEGATIVE_INFINITY,
+  ])("rejects invalid effort %s before execution", (effort) => {
+    expect(() => table.vectorSearch(vector).searchEffort(effort)).toThrow(
+      "search_effort must be finite and in [0, 1]",
+    );
+    expect(() =>
+      table.vectorSearch(vector).fullTextSearch("dog").searchEffort(effort),
+    ).toThrow("search_effort must be finite and in [0, 1]");
+  });
+});
diff --git a/nodejs/__test__/registry.test.ts b/nodejs/__test__/registry.test.ts
index 973ad8a25..5c92941d6 100644
--- a/nodejs/__test__/registry.test.ts
+++ b/nodejs/__test__/registry.test.ts
@@ -3,13 +3,10 @@
 
 import * as apiArrow from "apache-arrow";
 
-import * as arrow15 from "apache-arrow-15";
-import * as arrow16 from "apache-arrow-16";
-import * as arrow17 from "apache-arrow-17";
-import * as arrow18 from "apache-arrow-18";
-
 import * as tmp from "tmp";
 
+import { arrowVersions } from "./arrow_versions";
+
 import { connect } from "../lancedb";
 import {
   EmbeddingFunction,
@@ -18,7 +15,7 @@ import {
 } from "../lancedb/embedding";
 import { getRegistry, register } from "../lancedb/embedding/registry";
 
-describe.each([arrow15, arrow16, arrow17, arrow18])("LanceSchema", (arrow) => {
+describe.each(arrowVersions)("LanceSchema", (arrow) => {
   test("should preserve input order", async () => {
     const schema = LanceSchema({
       id: new arrow.Int32(),
@@ -29,7 +26,7 @@ describe.each([arrow15, arrow16, arrow17, arrow18])("LanceSchema", (arrow) => {
   });
 });
 
-describe.each([arrow15, arrow16, arrow17, arrow18])("Registry", (arrow) => {
+describe.each(arrowVersions)("Registry", (arrow) => {
   let tmpDir: tmp.DirResult;
   beforeEach(() => {
     tmpDir = tmp.dirSync({ unsafeCleanup: true });
@@ -240,7 +237,7 @@ describe("Registry.setVar", () => {
         return data.map(() => [1, 2, 3]);
       }
       embeddingDataType() {
-        return new arrow18.Float32() as apiArrow.Float;
+        return new apiArrow.Float32();
       }
       protected getSensitiveKeys() {
         return ["secretKey"];
diff --git a/nodejs/__test__/remote.test.ts b/nodejs/__test__/remote.test.ts
index 898ba533e..c06cb737e 100644
--- a/nodejs/__test__/remote.test.ts
+++ b/nodejs/__test__/remote.test.ts
@@ -14,12 +14,21 @@ import {
   TlsConfig,
   connect,
 } from "../lancedb";
+import {
+  Table as ArrowTable,
+  Field,
+  Int64,
+  RecordBatch,
+  Schema,
+  tableToIPC,
+} from "../lancedb/arrow";
 import {
   HeaderProvider,
   OAuthHeaderProvider,
   StaticHeaderProvider,
 } from "../lancedb/header";
 import { Index } from "../lancedb/indices";
+import { LocalTable } from "../lancedb/table";
 
 // Test-only header providers
 class CustomProvider extends HeaderProvider {
@@ -85,6 +94,146 @@ async function withMockDatabase(
 }
 
 describe("remote connection", () => {
+  it("reports unsupported operations with backend-neutral errors", async () => {
+    await withMockDatabase(
+      (req, res) => {
+        expect(req.url).toBe("/v1/table/test/describe/");
+        res.writeHead(200, { "Content-Type": "application/json" }).end(
+          JSON.stringify({
+            version: 1,
+            schema: {
+              fields: [
+                { name: "id", type: { type: "int64" }, nullable: false },
+                {
+                  name: "vector",
+                  type: {
+                    type: "fixed_size_list",
+                    fields: [
+                      { name: "item", type: { type: "float" }, nullable: true },
+                    ],
+                    length: 2,
+                  },
+                  nullable: false,
+                },
+              ],
+            },
+          }),
+        );
+      },
+      async (db) => {
+        const table = (await db.openTable("test")) as LocalTable;
+        const cases: [() => Promise<unknown>, string][] = [
+          [() => table.usesV2ManifestPaths(), "uses_v2_manifest_paths"],
+          [() => table.migrateManifestPathsV2(), "migrate_manifest_paths_v2"],
+          [() => table.optimize(), "optimize"],
+          [
+            () => table.createIndex("vector", { config: Index.hnswPq() }),
+            "IVF_HNSW_PQ",
+          ],
+        ];
+        for (const [run, operation] of cases) {
+          let message = `LanceDBError: not supported: ${operation} is not supported for remote tables.`;
+          if (operation === "IVF_HNSW_PQ") {
+            message += " Please use IVF_HNSW_SQ instead.";
+          }
+          await expect(run()).rejects.toThrow(new Error(message));
+        }
+      },
+    );
+  });
+
+  it("rejects nprobes(0) before sending a query", async () => {
+    const requests: string[] = [];
+    await withMockDatabase(
+      (req, res) => {
+        requests.push(req.url ?? "");
+        if (req.url === "/v1/table/test/describe/") {
+          res
+            .writeHead(200, { "Content-Type": "application/json" })
+            .end(JSON.stringify({ version: 1, schema: { fields: [] } }));
+        } else {
+          res.writeHead(404).end();
+        }
+      },
+      async (db) => {
+        const table = await db.openTable("test");
+        expect(() => table.vectorSearch([0, 0]).nprobes(0)).toThrow(
+          "Invalid input, nprobes must be greater than 0",
+        );
+        expect(() =>
+          table.query().nearestTo([0, 0]).fullTextSearch("dog").nprobes(0),
+        ).toThrow("Invalid input, nprobes must be greater than 0");
+        await expect(
+          table
+            .vectorSearch(Promise.resolve([0, 0]))
+            .nprobes(0)
+            .toArrow(),
+        ).rejects.toThrow("Invalid input, nprobes must be greater than 0");
+      },
+    );
+    expect(requests).toEqual(["/v1/table/test/describe/"]);
+  });
+
+  it.each([false, true])(
+    "preserves an empty query's schema with an empty batch: %s",
+    async (withEmptyBatch) => {
+      const schema = new Schema(
+        [new Field("doubled", new Int64(), false)],
+        new Map([["source", "query-output"]]),
+      );
+      const result = new ArrowTable(
+        schema,
+        withEmptyBatch ? [new RecordBatch(schema, undefined)] : [],
+      );
+      const response = Buffer.from(tableToIPC(result, "stream"));
+      let queryRequests = 0;
+
+      await withMockDatabase(
+        (req, res) => {
+          if (req.url?.endsWith("/describe/")) {
+            res.writeHead(200, { "Content-Type": "application/json" }).end(
+              JSON.stringify({
+                name: "items",
+                version: 1,
+                schema: {
+                  fields: [
+                    { name: "id", type: { type: "int64" }, nullable: false },
+                  ],
+                },
+              }),
+            );
+          } else if (req.url?.endsWith("/query/")) {
+            queryRequests++;
+            req.resume();
+            req.on("end", () => {
+              res
+                .writeHead(200, {
+                  "Content-Type": "application/vnd.apache.arrow.stream",
+                })
+                .end(response);
+            });
+          } else {
+            res.writeHead(404).end();
+          }
+        },
+        async (db) => {
+          const table = await db.openTable("items");
+          const result = await table
+            .query()
+            .where("id < 0")
+            .select({ doubled: "id * 2" })
+            .toArrow();
+
+          expect(result.numRows).toBe(0);
+          expect(result.schema).toEqual(schema);
+          expect(result.getChild("doubled")?.length).toBe(0);
+        },
+      );
+
+      expect(queryRequests).toBe(1);
+    },
+  );
+
   it("rejects the external blob opt-in without blocking regular adds", async () => {
     let insertRequests = 0;
     const describeRequests: string[] = [];
@@ -158,7 +307,9 @@ describe("remote connection", () => {
           .end(JSON.stringify({ views: ["daily_sales"] }));
       },
       async (db) => {
-        expect(await db.listMaterializedViews()).toEqual(["daily_sales"]);
+        expect(await collect(db.listMaterializedViews())).toEqual([
+          "daily_sales",
+        ]);
       },
     );
   });
@@ -208,7 +359,7 @@ describe("remote connection", () => {
           .end(JSON.stringify({ views: ["adults"] }));
       },
       async (db) => {
-        expect(await db.listViews()).toEqual(["adults"]);
+        expect(await collect(db.listViews())).toEqual(["adults"]);
       },
     );
 
@@ -325,6 +476,15 @@ describe("remote connection", () => {
     );
   });
 
+  it("rejects a zero blob request concurrency limit", async () => {
+    await expect(
+      connect("db://dev", {
+        apiKey: "fake",
+        clientConfig: { blobRequestConcurrency: 0 },
+      }),
+    ).rejects.toThrow("blob_request_concurrency");
+  });
+
   it("shows the full error messages on retry errors", async () => {
     await withMockDatabase(
       (_req, res) => {
@@ -1277,7 +1437,7 @@ describe("remote connection jobs surface", () => {
         });
       },
       async (db) => {
-        const jobs = await db.listJobs();
+        const jobs = await collect(db.listJobs());
         expect(jobs.map((job) => job.jobId)).toEqual(["job-1", "job-2"]);
         expect(jobs[0].state).toEqual("running");
         expect(jobs[1].state).toEqual("finished");
@@ -1349,4 +1509,171 @@ describe("remote connection jobs surface", () => {
       },
     );
   });
+
+  it("leaves numDeletedRows absent when the server omits it", async () => {
+    // A server that predates num_deleted_rows says nothing about deleted rows
+    const statsReply =
+      '{"total_bytes":1,"num_rows":3,"num_indices":0,"fragment_stats":' +
+      '{"num_fragments":1,"num_small_fragments":0,' +
+      '"lengths":{"min":3,"max":3,"mean":3,"p25":3,"p50":3,"p75":3,"p99":3}}}';
+
+    await withMockDatabase(
+      (req, res) => {
+        const path = req.url ?? "";
+        const body = path.endsWith("/describe/")
+          ? JSON.stringify({ name: "t", version: 1, schema: { fields: [] } })
+          : statsReply;
+        res.writeHead(200, { "Content-Type": "application/json" }).end(body);
+      },
+      async (db) => {
+        const stats = await (await db.openTable("t")).stats();
+        expect("numDeletedRows" in stats).toBe(false);
+        expect(stats.numDeletedRows).toBeUndefined();
+      },
+    );
+  });
+});
+
+async function collect<T>(items: AsyncIterable<T>): Promise<T[]> {
+  const result: T[] = [];
+  for await (const item of items) result.push(item);
+  return result;
+}
+
+describe("lazy resource listings", () => {
+  for (const kind of ["views", "materializedViews", "jobs"] as const) {
+    const listing = (
+      db: Connection,
+      options: { pageToken?: string; pageLimit?: number } = {},
+    ) => {
+      if (kind === "jobs") return db.listJobs(options);
+      if (kind === "views") return db.listViews(["team"], options);
+      return db.listMaterializedViews(options);
+    };
+    const page = (names: string[], token?: string) => {
+      const items =
+        kind === "jobs"
+          ? names.map((name) => ({
+              // biome-ignore lint/style/useNamingConvention: server wire format
+              job_id: name,
+              table: "t",
+              // biome-ignore lint/style/useNamingConvention: server wire format
+              job_type: "create_index",
+              state: "in_progress",
+              // biome-ignore lint/style/useNamingConvention: server wire format
+              created_at_millis: 1,
+            }))
+          : names;
+      return {
+        [kind === "jobs" ? "jobs" : "views"]: items,
+        // biome-ignore lint/style/useNamingConvention: server wire format
+        page_token: token,
+      };
+    };
+
+    it(`${kind} fetches lazily, skips empty pages, and resumes`, async () => {
+      const requests: { url: string; body: Record<string, unknown> }[] = [];
+      const responses = [
+        page(["a", "b"], "empty"),
+        page([], "last"),
+        page(["c", "d"], ""),
+      ];
+      await withMockDatabase(
+        async (req, res) => {
+          const chunks: Buffer[] = [];
+          for await (const chunk of req) chunks.push(Buffer.from(chunk));
+          const body = Buffer.concat(chunks).toString();
+          requests.push({
+            url: req.url ?? "",
+            body: body ? JSON.parse(body) : {},
+          });
+          res
+            .writeHead(200, { "content-type": "application/json" })
+            .end(JSON.stringify(responses.shift()));
+        },
+        async (db) => {
+          const items = listing(db, { pageToken: "start/token", pageLimit: 2 });
+          expect(requests).toHaveLength(0);
+          expect(items.pageToken()).toBe("start/token");
+          expect(items.numPageResults()).toBe(0);
+          expect((await items.next()).done).toBe(false);
+          expect(items.numPageResults()).toBe(1);
+          expect(items.pageToken()).toBe("empty");
+          await items.next();
+          expect(requests).toHaveLength(1);
+          const resumed = listing(db, {
+            pageToken: items.pageToken(),
+            pageLimit: 2,
+          });
+          await resumed.next();
+          expect(resumed.numPageResults()).toBe(1);
+          expect(resumed.pageToken()).toBeUndefined();
+          expect(requests).toHaveLength(3);
+          await resumed.next();
+          expect((await resumed.next()).done).toBe(true);
+          expect((await resumed.next()).done).toBe(true);
+          for (const [i, request] of requests.entries()) {
+            const token = ["start/token", "empty", "last"][i];
+            const url = new URL(request.url, "http://localhost");
+            if (kind === "jobs") {
+              expect(url.pathname).toBe("/v1/jobs/list");
+              expect(request.body).toEqual({
+                // biome-ignore lint/style/useNamingConvention: server wire format
+                page_token: token,
+                limit: 2,
+              });
+            } else {
+              expect(url.pathname).toBe(
+                kind === "views"
+                  ? "/v1/namespace/team/view/list"
+                  : "/v1/namespace/$/materialized_view/list",
+              );
+              expect(url.searchParams.get("page_token")).toBe(token);
+              expect(url.searchParams.get("limit")).toBe("2");
+            }
+          }
+        },
+      );
+    });
+
+    it(`${kind} retains failed tokens and serializes concurrent advances`, async () => {
+      let requests = 0;
+      await withMockDatabase(
+        (_req, res) => {
+          requests += 1;
+          res
+            .writeHead(requests === 1 ? 401 : 200, {
+              "content-type": "application/json",
+            })
+            .end(JSON.stringify(requests === 1 ? {} : page(["a", "b"])));
+        },
+        async (db) => {
+          const failed = listing(db, { pageToken: "retry" });
+          await expect(failed.next()).rejects.toThrow();
+          expect(failed.pageToken()).toBe("retry");
+          expect((await failed.next()).done).toBe(true);
+          expect(requests).toBe(1);
+          const resumed = listing(db, { pageToken: failed.pageToken() });
+          const results = await Promise.all([
+            resumed.next(),
+            resumed.next(),
+            resumed.next(),
+          ]);
+          expect(results.filter((result) => result.done)).toHaveLength(1);
+          expect(requests).toBe(2);
+          for (const limit of [
+            0,
+            -1,
+            1.5,
+            Number.NaN,
+            Number.POSITIVE_INFINITY,
+            2147483648,
+          ]) {
+            expect(() => listing(db, { pageLimit: limit })).toThrow("limit");
+          }
+          expect(requests).toBe(2);
+        },
+      );
+    });
+  }
 });
diff --git a/nodejs/__test__/sanitize.test.ts b/nodejs/__test__/sanitize.test.ts
index 587805d53..97856a55b 100644
--- a/nodejs/__test__/sanitize.test.ts
+++ b/nodejs/__test__/sanitize.test.ts
@@ -2,7 +2,13 @@
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 import * as arrow from "../lancedb/arrow";
-import { sanitizeField, sanitizeMap, sanitizeType } from "../lancedb/sanitize";
+import {
+  sanitizeField,
+  sanitizeMap,
+  sanitizeSchema,
+  sanitizeType,
+} from "../lancedb/sanitize";
+import { arrow21 } from "./arrow_versions";
 
 describe("sanitize", function () {
   describe("sanitizeType function", function () {
@@ -59,6 +65,8 @@ describe("sanitize", function () {
       ["durationmicrosecond", new arrow.DurationMicrosecond()],
       ["durationmillisecond", new arrow.DurationMillisecond()],
       ["durationsecond", new arrow.DurationSecond()],
+      ["largebinary", new arrow.LargeBinary()],
+      ["largeutf8", new arrow.LargeUtf8()],
     ] as const;
 
     it.each(allTypeNameTestCases)(
@@ -89,6 +97,110 @@ describe("sanitize", function () {
         "Unrecognized type name in schema: invalid_type",
       );
     });
+
+    // These types only exist in Arrow 20 (IntervalMonthDayNano) and 21 (the
+    // rest), so the objects come from a second copy of the newest release.  The
+    // library itself may be running on an older Arrow (see the CI matrix), in
+    // which case sanitizing them has to fail with a clear message instead.
+    const hostArrow = arrow as unknown as Record<string, unknown>;
+    const hostSupportsNewerTypes = typeof hostArrow.Utf8View === "function";
+    const whenSupported = hostSupportsNewerTypes ? describe : describe.skip;
+    const whenUnsupported = hostSupportsNewerTypes ? describe.skip : describe;
+
+    const newerTypeTestCases = [
+      ["Utf8View", () => new arrow21.Utf8View()],
+      ["BinaryView", () => new arrow21.BinaryView()],
+      ["IntervalMonthDayNano", () => new arrow21.IntervalMonthDayNano()],
+      [
+        "LargeList",
+        () =>
+          new arrow21.LargeList(new arrow21.Field("item", new arrow21.Utf8())),
+      ],
+    ] as const;
+
+    whenSupported("types added in Arrow 20 and 21", function () {
+      it.each(newerTypeTestCases)(
+        "should sanitize a %s type from another copy of Arrow",
+        function (name, makeForeign) {
+          const foreign = makeForeign();
+          const result = sanitizeType(foreign);
+          // Interval subclasses share `Type.Interval`, so the sanitized type
+          // is the generic `Interval` carrying the same unit, as for the
+          // other units.
+          const expected = name === "IntervalMonthDayNano" ? "Interval" : name;
+          expect(result).toBeInstanceOf(hostArrow[expected] as () => unknown);
+          expect(result.typeId).toBe(foreign.typeId);
+          expect(result.toString()).toBe(foreign.toString());
+        },
+      );
+
+      it.each([
+        ["binaryview", "BinaryView"],
+        ["utf8view", "Utf8View"],
+        ["intervalmonthdaynano", "Interval"],
+      ])('should map type name "%s" to %s', function (name, expected) {
+        expect(sanitizeType(name)).toBeInstanceOf(
+          hostArrow[expected] as () => unknown,
+        );
+      });
+
+      it("should sanitize the child field of a LargeList", function () {
+        const foreign = new arrow21.LargeList(
+          new arrow21.Field("item", new arrow21.Int32(), false),
+        );
+        const result = sanitizeType(foreign) as arrow.List;
+        expect(result.children[0]).toBeInstanceOf(arrow.Field);
+        expect(result.children[0].name).toBe("item");
+        expect(result.children[0].nullable).toBe(false);
+        const child = result.children[0].type;
+        expect(arrow.DataType.isInt(child)).toBe(true);
+        expect((child as arrow.Int).bitWidth).toBe(32);
+        expect((child as arrow.Int).isSigned).toBe(true);
+      });
+    });
+
+    whenUnsupported("types newer than the installed Arrow", function () {
+      it.each(
+        newerTypeTestCases.filter(([name]) => name !== "IntervalMonthDayNano"),
+      )(
+        "should explain that %s needs a newer apache-arrow",
+        function (name, makeForeign) {
+          expect(() => sanitizeType(makeForeign())).toThrow(
+            `The Arrow type ${name} requires apache-arrow`,
+          );
+        },
+      );
+
+      it("should sanitize IntervalMonthDayNano to a generic Interval", () => {
+        // The subclass shares `Type.Interval`, which every supported Arrow
+        // release has; only the unit is new.
+        const result = sanitizeType(new arrow21.IntervalMonthDayNano());
+        expect(result).toBeInstanceOf(arrow.Interval);
+        expect((result as arrow.Interval).unit).toBe(
+          arrow21.IntervalUnit.MONTH_DAY_NANO,
+        );
+      });
+    });
+
+    it("should rebuild a schema from a second copy of the same Arrow version", function () {
+      // Arrow 21 makes `instanceof Schema` succeed across copies of the library
+      // but not `instanceof <DataType>`; the schema must still be rebuilt.
+      const foreign = new arrow21.Schema([
+        new arrow21.Field("x", new arrow21.Float32(), true),
+      ]);
+      // Only Arrow 21+ overrides `Symbol.hasInstance` this way.
+      const crossCopyInstanceof = Object.getOwnPropertySymbols(
+        arrow.Schema,
+      ).includes(Symbol.hasInstance);
+      expect(foreign instanceof arrow.Schema).toBe(crossCopyInstanceof);
+      const result = sanitizeSchema(foreign);
+      expect(result).not.toBe(foreign);
+      const type = result.fields[0].type;
+      expect(type).toBeInstanceOf(arrow.DataType);
+      expect(arrow.DataType.isFloat(type)).toBe(true);
+      expect((type as arrow.Float).precision).toBe(arrow.Precision.SINGLE);
+      expect(() => arrow.makeBuilder({ type })).not.toThrow();
+    });
   });
 
   describe("sanitizeField function", function () {
diff --git a/nodejs/__test__/table.test.ts b/nodejs/__test__/table.test.ts
index ae257455d..f53f859b8 100644
--- a/nodejs/__test__/table.test.ts
+++ b/nodejs/__test__/table.test.ts
@@ -2,17 +2,14 @@
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 import * as fs from "fs";
+import { getEventListeners } from "node:events";
 import { pathToFileURL } from "node:url";
 import * as path from "path";
 import * as tmp from "tmp";
 
-import * as arrow15 from "apache-arrow-15";
-import * as arrow16 from "apache-arrow-16";
-import * as arrow17 from "apache-arrow-17";
-import * as arrow18 from "apache-arrow-18";
-
 import {
   AutoQuery,
+  type BlobInput,
   Connection,
   MatchQuery,
   PhraseQuery,
@@ -55,19 +52,21 @@ import {
   instanceOfFullTextQuery,
 } from "../lancedb/query";
 import { LocalTable } from "../lancedb/table";
+import {
+  type ApacheArrow,
+  arrow18,
+  arrowVersions,
+  latestArrow,
+} from "./arrow_versions";
 
-describe.each([arrow15, arrow16, arrow17, arrow18])(
+describe.each(arrowVersions)(
   "Given a table",
   // biome-ignore lint/suspicious/noExplicitAny: <explanation>
   (arrow: any) => {
     let tmpDir: tmp.DirResult;
     let table: Table;
 
-    const schema:
-      | import("apache-arrow-15").Schema
-      | import("apache-arrow-16").Schema
-      | import("apache-arrow-17").Schema
-      | import("apache-arrow-18").Schema = new arrow.Schema([
+    const schema: InstanceType<ApacheArrow["Schema"]> = new arrow.Schema([
       new arrow.Field("id", new arrow.Float64(), true),
     ]);
 
@@ -92,6 +91,38 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
       await expect(table.countRows()).resolves.toBe(3);
     });
 
+    it("creates and adds Arrow tables with large string and binary columns", async () => {
+      const data = new arrow.Table({
+        text: arrow.vectorFromArray(["alpha", "beta"], new arrow.LargeUtf8()),
+        bytes: arrow.vectorFromArray(
+          [Buffer.from("one"), Buffer.from("two")],
+          new arrow.LargeBinary(),
+        ),
+      });
+      const conn = await connect(tmpDir.name);
+      const largeTable = await conn.createTable("large_columns", data);
+      await largeTable.add(data);
+
+      const rows = await largeTable.query().toArray();
+      expect(rows.map((row) => row.text)).toEqual([
+        "alpha",
+        "beta",
+        "alpha",
+        "beta",
+      ]);
+      expect(rows.map((row) => Buffer.from(row.bytes).toString())).toEqual([
+        "one",
+        "two",
+        "one",
+        "two",
+      ]);
+      const fields = (await largeTable.schema()).fields;
+      expect(fields.map((field) => field.type.typeId)).toEqual([
+        arrow.Type.LargeUtf8,
+        arrow.Type.LargeBinary,
+      ]);
+    });
+
     it("should support a foreign Float64 vector schema end to end", async () => {
       const conn = await connect(tmpDir.name);
       const schema = new arrow.Schema([
@@ -283,6 +314,7 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
         },
         numIndices: 0,
         numRows: 3,
+        numDeletedRows: 0,
         // Full on-disk size of the two data files, footers and metadata included.
         totalBytes: 550,
       });
@@ -293,6 +325,15 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
       const statsWithIndex = await table.stats();
       expect(statsWithIndex.numIndices).toBe(1);
       expect(statsWithIndex.totalBytes).toBeGreaterThan(550);
+
+      // Both rows with id = 1 are deleted, but that empties the second
+      // fragment, which is dropped outright rather than kept with a deletion
+      // file, so only the row marked in the surviving fragment is counted.
+      await table.delete("id = 1");
+      const stats = await table.stats();
+      expect(stats.numRows).toBe(1);
+      expect(stats.numDeletedRows).toBe(1);
+      expect(stats.fragmentStats.numFragments).toBe(1);
     });
 
     it("should overwrite data if asked", async () => {
@@ -599,11 +640,11 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
       );
     });
 
-    it("should expose useLsm on takeRowIds as the base-only escape hatch", async () => {
+    it("should expose useMemWal on takeRowIds as the base-only escape hatch", async () => {
       await table.add([{ id: 1 }, { id: 2 }, { id: 3 }]);
-      // useLsm(false) is reachable on TakeQuery (the escape hatch for MemWAL tables,
+      // useMemWal(false) is reachable on TakeQuery (the escape hatch for MemWAL tables,
       // where take-by-row-id auto-routes to the LSM scanner and is rejected).
-      const res = await table.takeRowIds([0, 2]).useLsm(false).toArray();
+      const res = await table.takeRowIds([0, 2]).useMemWal(false).toArray();
       expect(res.map((r) => r.id)).toEqual([1, 3]);
     });
 
@@ -709,6 +750,114 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
 );
 
 // https://github.com/lancedb/lancedb/issues/1963
+// These types were added in Arrow 21, so they are built with a second copy of
+// that release rather than the per-version matrix above, and reading them back
+// needs the library's own Arrow to know them too.
+const hostArrowSupportsViewTypes =
+  typeof (arrow as unknown as Record<string, unknown>).Utf8View === "function";
+(hostArrowSupportsViewTypes ? it : it.skip)(
+  "creates and adds tables with LargeList, Utf8View and BinaryView columns",
+  async () => {
+    const tmpDir = tmp.dirSync({ unsafeCleanup: true });
+    const db = await connect(tmpDir.name);
+    const makeData = () =>
+      new latestArrow.Table({
+        id: latestArrow.vectorFromArray([1, 2], new latestArrow.Int32()),
+        tags: latestArrow.vectorFromArray(
+          [["a", "b"], ["c"]],
+          new latestArrow.LargeList(
+            new latestArrow.Field("item", new latestArrow.Utf8(), true),
+          ),
+        ),
+        text: latestArrow.vectorFromArray(
+          ["short", "a string that is longer than the twelve inline bytes"],
+          new latestArrow.Utf8View(),
+        ),
+        bytes: latestArrow.vectorFromArray(
+          [new Uint8Array([1, 2, 3]), new Uint8Array(20).fill(7)],
+          new latestArrow.BinaryView(),
+        ),
+      });
+    const table = await db.createTable("views", makeData());
+    await table.add(makeData());
+
+    const schema = await table.schema();
+    // Take the enum from the Arrow 21 copy: the host Arrow may predate
+    // `Type.LargeList`, and this file still has to type-check there.
+    expect(schema.fields.map((f) => f.type.typeId)).toEqual([
+      latestArrow.Type.Int,
+      latestArrow.Type.LargeList,
+      // Lance stores the view types as their offset-based equivalents.
+      latestArrow.Type.Utf8,
+      latestArrow.Type.Binary,
+    ]);
+    const rows = await table.query().toArrow();
+    expect(rows.numRows).toBe(4);
+    const row = rows.get(1)!.toJSON();
+    expect(row.id).toBe(2);
+    expect(row.tags.toJSON()).toEqual(["c"]);
+    expect(row.text).toBe(
+      "a string that is longer than the twelve inline bytes",
+    );
+    expect(Array.from(row.bytes)).toEqual(Array(20).fill(7));
+
+    // Arrow JS 21 serializes a *sliced* view column with a truncated views
+    // buffer; the library rebuilds view columns as Utf8/Binary so this works.
+    const sliced = makeData().slice(1, 2);
+    expect(sliced.getChild("text")!.data[0].offset).toBe(1);
+    const slicedTable = await db.createTable("views_sliced", sliced);
+    await slicedTable.add(sliced);
+    const slicedRows = await slicedTable.query().toArrow();
+    expect(slicedRows.numRows).toBe(2);
+    for (const i of [0, 1]) {
+      const r = slicedRows.get(i)!.toJSON();
+      expect(r.id).toBe(2);
+      expect(r.text).toBe(
+        "a string that is longer than the twelve inline bytes",
+      );
+      expect(Array.from(r.bytes)).toEqual(Array(20).fill(7));
+    }
+
+    // Regression: a field with an integer-like name. JavaScript enumerates
+    // such keys first, so a key-ordered rebuild would pair "1" with the text
+    // column's values. The batches are built explicitly to pin field order.
+    const numericSchema = new latestArrow.Schema([
+      new latestArrow.Field("text", new latestArrow.Utf8View(), true),
+      new latestArrow.Field("1", new latestArrow.Utf8View(), true),
+      new latestArrow.Field("n", new latestArrow.Int32(), true),
+    ]);
+    const column = (
+      values: unknown[],
+      type: InstanceType<typeof latestArrow.DataType>,
+    ) => latestArrow.vectorFromArray(values, type).data[0];
+    const numericBatch = new latestArrow.RecordBatch(
+      numericSchema,
+      latestArrow.makeData({
+        type: new latestArrow.Struct(numericSchema.fields),
+        length: 3,
+        nullCount: 0,
+        children: [
+          column(["t0", "t1", "t2"], new latestArrow.Utf8View()),
+          column(["one0", "one1", "one2"], new latestArrow.Utf8View()),
+          column([0, 1, 2], new latestArrow.Int32()),
+        ],
+      }),
+    );
+    const numericData = new latestArrow.Table(numericSchema, [numericBatch]);
+    const numericTable = await db.createTable("views_numeric", numericData);
+    await numericTable.add(numericData.slice(1));
+    const numericRows = (await numericTable.query().toArrow()).toArray();
+    expect(numericRows.map((r) => r.toJSON())).toEqual([
+      { text: "t0", "1": "one0", n: 0 },
+      { text: "t1", "1": "one1", n: 1 },
+      { text: "t2", "1": "one2", n: 2 },
+      { text: "t1", "1": "one1", n: 1 },
+      { text: "t2", "1": "one2", n: 2 },
+    ]);
+    tmpDir.removeCallback();
+  },
+);
+
 it("should query documents with LangChain PDF metadata", async () => {
   const tmpDir = tmp.dirSync({ unsafeCleanup: true });
   try {
@@ -1073,10 +1222,18 @@ describe("When creating an index", () => {
       .toArrow();
     expect(rst.numRows).toBe(2);
 
+    expect(() => tbl.vectorSearch(queryVec).nprobes(0)).toThrow(
+      "Invalid input, nprobes must be greater than 0",
+    );
+    expect(() =>
+      tbl.query().nearestTo(queryVec).fullTextSearch("dog").nprobes(0),
+    ).toThrow("Invalid input, nprobes must be greater than 0");
     expect(() => tbl.query().nearestTo(queryVec).minimumNprobes(0)).toThrow(
       "Invalid input, minimum_nprobes must be greater than 0",
     );
-    expect(() => tbl.query().nearestTo(queryVec).maximumNprobes(5)).toThrow(
+    expect(() =>
+      tbl.query().nearestTo(queryVec).minimumNprobes(10).maximumNprobes(5),
+    ).toThrow(
       "Invalid input, maximum_nprobes must be greater than or equal to minimum_nprobes",
     );
 
@@ -2439,7 +2596,7 @@ describe("when dealing with blob columns", () => {
       new Field("id", new Int64(), true),
       blob("image"),
     ]);
-    const row = { id: 1n, image: new Uint8Array([104]).buffer };
+    const row = { id: 1n, image: new ReadableStream() };
 
     await expect(db.createTable("invalid", [row], { schema })).rejects.toThrow(
       /field image at row 0/,
@@ -2469,6 +2626,8 @@ describe("when dealing with blob columns", () => {
   it.each([
     ["URI string", (uri: string) => uri],
     ["URI struct", (uri: string) => ({ uri })],
+    ["URL", (uri: string) => new URL(uri)],
+    ["URL struct", (uri: string) => ({ uri: new URL(uri) })],
   ])(
     "round-trips an external blob from a %s after reopening",
     async (_label, blobValue) => {
@@ -2562,6 +2721,63 @@ describe("when dealing with blob columns", () => {
     expect(bytes[0]).toEqual(payload);
   });
 
+  it("accepts ArrayBuffer, Blob, and File in createTable and add", async () => {
+    const db = await connect(tmpDir.name);
+    const schema = new Schema([
+      new Field("id", new Int64(), true),
+      blob("image"),
+    ]);
+    const rows: { id: bigint; image: BlobInput }[] = [
+      { id: 1n, image: new TextEncoder().encode("array-buffer").buffer },
+      { id: 2n, image: new Blob(["blob"]) },
+      { id: 3n, image: { data: new File(["file"], "f.txt") } },
+    ];
+    const table = await db.createTable("widened", rows.slice(0, 2), {
+      schema,
+    });
+    await table.add(rows.slice(2));
+
+    const results = await table.query().select(["id"]).withRowId().toArray();
+    results.sort((a, b) => Number(a.id - b.id));
+    const bytes = await table.fetchBlobs(
+      "image",
+      results.map((row) => row._rowid as bigint),
+    );
+    expect(bytes.map((b) => b?.toString())).toEqual([
+      "array-buffer",
+      "blob",
+      "file",
+    ]);
+  });
+
+  it("accepts Blob values in mergeInsert", async () => {
+    const db = await connect(tmpDir.name);
+    const schema = new Schema([
+      new Field("id", new Int64(), true),
+      blob("image"),
+    ]);
+    const table = await db.createTable(
+      "merge_blobs",
+      [{ id: 1n, image: Buffer.from("old") }],
+      { schema },
+    );
+    await table
+      .mergeInsert("id")
+      .whenMatchedUpdateAll()
+      .whenNotMatchedInsertAll()
+      .execute([
+        { id: 1n, image: new Blob(["new"]) },
+        { id: 2n, image: new Uint8Array([104, 105]).buffer },
+      ]);
+    const results = await table.query().select(["id"]).withRowId().toArray();
+    results.sort((a, b) => Number(a.id - b.id));
+    const bytes = await table.fetchBlobs(
+      "image",
+      results.map((row) => row._rowid as bigint),
+    );
+    expect(bytes.map((b) => b?.toString())).toEqual(["new", "hi"]);
+  });
+
   it("discovers blob columns", async () => {
     const { table } = await openBlobTable();
     expect(await table.blobColumns()).toEqual(["image"]);
@@ -2601,6 +2817,39 @@ describe("when dealing with blob columns", () => {
     expect(Buffer.from(await files[1]!.read()).toString()).toBe("beta");
   });
 
+  it("rejects a whole blob read batch when one row ID was deleted", async () => {
+    const { table, rowIds } = await openBlobTable();
+    await table.delete("id = 2");
+
+    await expect(table.fetchBlobs("image", rowIds)).rejects.toThrow(
+      `first missing row ids: [${rowIds[1]}]`,
+    );
+    await expect(table.fetchBlobFiles("image", rowIds)).rejects.toThrow(
+      `first missing row ids: [${rowIds[1]}]`,
+    );
+  });
+
+  it("adds a LargeBinary Arrow column to a blob table", async () => {
+    const db = await connect(tmpDir.name);
+    const schema = new Schema([new Field("id", new Int64()), blob("image")]);
+    const table = await db.createTable(
+      "blobs",
+      [{ id: 1n, image: Buffer.from("alpha") }],
+      { schema },
+    );
+    const payload = Buffer.from("beta");
+    const data = new arrow18.Table({
+      id: arrow18.vectorFromArray([2n], new arrow18.Int64()),
+      image: arrow18.vectorFromArray([payload], new arrow18.LargeBinary()),
+    });
+    await table.add(data);
+
+    const rows = await table.query().withRowId().toArray();
+    const rowId = rows.find((row) => row.id === 2n)!._rowid as bigint;
+    const [actual] = await table.fetchBlobs("image", [rowId]);
+    expect(actual).toEqual(payload);
+  });
+
   it("reads a half-open range", async () => {
     const { table, rowIds } = await openBlobTable();
     const files = await table.fetchBlobFiles("image", rowIds);
@@ -2625,6 +2874,148 @@ describe("when dealing with blob columns", () => {
     ).rejects.toThrow(/exceeds blob size/);
   });
 
+  it("reads at most maxBytes and advances the cursor", async () => {
+    const { table, rowIds } = await openBlobTable();
+    const [handle] = await table.fetchBlobFiles("image", rowIds);
+    expect((await handle!.read(2n)).toString()).toBe("al");
+    expect(await handle!.tell()).toBe(2n);
+    expect((await handle!.read(2n)).toString()).toBe("ph");
+    expect((await handle!.read(10n)).toString()).toBe("a");
+    expect(await handle!.tell()).toBe(5n);
+    expect(await handle!.read(1n)).toEqual(Buffer.alloc(0));
+  });
+
+  it("seeks the cursor", async () => {
+    const { table, rowIds, alpha } = await openBlobTable();
+    const [handle] = await table.fetchBlobFiles("image", rowIds);
+    await handle!.seek(3n);
+    expect(await handle!.tell()).toBe(3n);
+    expect((await handle!.read()).toString()).toBe("ha");
+    await handle!.seek(0n);
+    expect(await handle!.read()).toEqual(alpha);
+  });
+
+  it("rejects a negative seek position", async () => {
+    const { table, rowIds } = await openBlobTable();
+    const [handle] = await table.fetchBlobFiles("image", rowIds);
+    await expect(handle!.seek(-1n)).rejects.toThrow(/cannot be negative/);
+  });
+
+  it("readRanges returns one buffer per range in order", async () => {
+    const { table, rowIds } = await openBlobTable();
+    const [handle] = await table.fetchBlobFiles("image", rowIds);
+    const buffers = await handle!.readRanges([
+      { start: 3n, end: 5n },
+      { start: 0n, end: 2n },
+      { start: 3n, end: 5n },
+    ]);
+    expect(buffers.map((b) => b.toString())).toEqual(["ha", "al", "ha"]);
+    expect(await handle!.tell()).toBe(0n);
+  });
+
+  it("fails when a readRanges end is past the blob size", async () => {
+    const { table, rowIds, alpha } = await openBlobTable();
+    const [handle] = await table.fetchBlobFiles("image", rowIds);
+    await expect(
+      handle!.readRanges([
+        { start: 0n, end: 1n },
+        { start: 0n, end: BigInt(alpha.length + 1) },
+      ]),
+    ).rejects.toThrow(/exceeds blob size/);
+  });
+
+  it("fails reads after close and closes once", async () => {
+    const { table, rowIds } = await openBlobTable();
+    const [handle] = await table.fetchBlobFiles("image", rowIds);
+    expect(handle!.isClosed()).toBe(false);
+    await handle!.close();
+    expect(handle!.isClosed()).toBe(true);
+    await expect(handle!.close()).resolves.toBeUndefined();
+    await expect(handle!.read()).rejects.toThrow(/already closed/);
+    await expect(handle!.read(1n)).rejects.toThrow(/already closed/);
+    await expect(handle!.readRange(0n, 1n)).rejects.toThrow(/already closed/);
+    await expect(handle!.readRanges([{ start: 0n, end: 1n }])).rejects.toThrow(
+      /already closed/,
+    );
+    await expect(handle!.seek(0n)).rejects.toThrow(/already closed/);
+    await expect(handle!.tell()).rejects.toThrow(/already closed/);
+    expect(handle!.size()).toBe(5n);
+  });
+
+  it("rejects reads with an already aborted signal", async () => {
+    const { table, rowIds, alpha } = await openBlobTable();
+    const [handle] = await table.fetchBlobFiles("image", rowIds);
+    const signal = AbortSignal.abort();
+    const abortError = { name: "AbortError" };
+    await expect(handle!.read(undefined, { signal })).rejects.toMatchObject(
+      abortError,
+    );
+    await expect(handle!.read(2n, { signal })).rejects.toMatchObject(
+      abortError,
+    );
+    await expect(handle!.readRange(0n, 1n, { signal })).rejects.toMatchObject(
+      abortError,
+    );
+    await expect(
+      handle!.readRanges([{ start: 0n, end: 1n }], { signal }),
+    ).rejects.toMatchObject(abortError);
+    await expect(
+      table.fetchBlobs("image", rowIds, { signal }),
+    ).rejects.toMatchObject(abortError);
+    await expect(
+      table.fetchBlobFiles("image", rowIds, { signal }),
+    ).rejects.toMatchObject(abortError);
+    expect(await handle!.read()).toEqual(alpha);
+  });
+
+  it("rejects with the abort reason", async () => {
+    const { table, rowIds } = await openBlobTable();
+    const [handle] = await table.fetchBlobFiles("image", rowIds);
+    const reason = new Error("caller gave up");
+    await expect(
+      handle!.readRange(0n, 1n, { signal: AbortSignal.abort(reason) }),
+    ).rejects.toBe(reason);
+  });
+
+  it("reads with a signal and removes its abort listener", async () => {
+    const { table, rowIds, alpha, beta } = await openBlobTable();
+    const controller = new AbortController();
+    const { signal } = controller;
+    const [handle] = await table.fetchBlobFiles("image", rowIds, { signal });
+    expect((await handle!.readRange(0n, 2n, { signal })).toString()).toBe("al");
+    expect(await table.fetchBlobs("image", rowIds, { signal })).toEqual([
+      alpha,
+      beta,
+      null,
+    ]);
+    expect(getEventListeners(signal, "abort")).toHaveLength(0);
+    controller.abort();
+    expect(await handle!.read()).toEqual(alpha);
+  });
+
+  it("cancels an in-flight read", async () => {
+    const db = await connect(tmpDir.name);
+    const schema = new Schema([
+      new Field("id", new Int64(), true),
+      blob("payload", { inlineSizeThreshold: 0, dedicatedSizeThreshold: 1 }),
+    ]);
+    const payload = Buffer.alloc(32 * 1024 * 1024, 7);
+    const table = await db.createTable("large_blobs", [{ id: 1n, payload }], {
+      schema,
+    });
+    const [row] = await table.query().withRowId().toArray();
+    const [handle] = await table.fetchBlobFiles("payload", [
+      row._rowid as bigint,
+    ]);
+    const controller = new AbortController();
+    const pending = handle!.readRange(0n, handle!.size(), {
+      signal: controller.signal,
+    });
+    controller.abort();
+    await expect(pending).rejects.toMatchObject({ name: "AbortError" });
+    expect(await handle!.readRange(0n, 4n)).toEqual(payload.subarray(0, 4));
+  });
+
   it("rejects fetchBlobs on a non-blob column", async () => {
     const { table, rowIds } = await openBlobTable();
     await expect(table.fetchBlobs("id", rowIds)).rejects.toThrow(/blob/i);
@@ -2650,7 +3041,7 @@ describe("when dealing with blob columns", () => {
     expect(bytes[0]!.equals(payload)).toBe(true);
   });
 
-  it("creates and adds list blob columns", async () => {
+  it("creates list blobs without advertising unfetchable elements", async () => {
     const db = await connect(tmpDir.name);
     const schema = new Schema([
       new Field("id", new Int64(), true),
@@ -2669,7 +3060,16 @@ describe("when dealing with blob columns", () => {
       { id: 3n, images: [gamma, null] },
       { id: 4n, images: [] },
     ]);
-    expect(await table.blobColumns()).toEqual(["images.image"]);
+    expect(await table.blobColumns()).toEqual([]);
+    const [first] = await table.query().withRowId().toArray();
+    for (const column of ["images", "images.image"]) {
+      await expect(
+        table.fetchBlobs(column, [first._rowid as bigint]),
+      ).rejects.toThrow(/blobs inside lists cannot be fetched/);
+      await expect(
+        table.fetchBlobFiles(column, [first._rowid as bigint]),
+      ).rejects.toThrow(/blobs inside lists cannot be fetched/);
+    }
     const rows = await table.query().toArray();
     const byId = new Map(rows.map((row) => [Number(row.id), row]));
     expect(descriptorSizes(byId.get(1)!.images)).toEqual([
@@ -2715,6 +3115,7 @@ describe("when dealing with blob columns", () => {
         ],
       },
     ]);
+    expect(await table.blobColumns()).toEqual([]);
     const rows = await table.query().toArray();
     const byId = new Map(rows.map((row) => [Number(row.id), row]));
     expect(
@@ -3007,7 +3408,7 @@ it("passes cleanupOlderThan to the native binding as an absolute timestamp", asy
   expect(optimize).toHaveBeenCalledWith(cutoff.getTime(), true);
 });
 
-describe.each([arrow15, arrow16, arrow17, arrow18])(
+describe.each(arrowVersions)(
   "when optimizing a dataset",
   // biome-ignore lint/suspicious/noExplicitAny: <explanation>
   (arrow: any) => {
@@ -3673,6 +4074,28 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
       );
     });
 
+    test.each(["Klingon", "english"])(
+      "rejects unsupported full text language %s with a catchable error",
+      (language) => {
+        expect(() => Index.fts({ language })).toThrow(
+          new Error(
+            `LanceDB does not support the requested language: '${language}'`,
+          ),
+        );
+      },
+    );
+
+    test("full text search with a supported language", async () => {
+      const db = await connect(tmpDir.name);
+      const table = await db.createTable("test", [{ text: "running" }]);
+      await table.createIndex("text", {
+        config: Index.fts({ language: "English", stem: true }),
+      });
+
+      const results = await table.search("run").toArray();
+      expect(results.map((row) => row.text)).toEqual(["running"]);
+    });
+
     test("full text search without lowercase", async () => {
       const db = await connect(tmpDir.name);
       const data = [
@@ -3803,6 +4226,20 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
       expect(mustNotResults.length).toBe(1);
     });
 
+    test("full text search code tokenizer", async () => {
+      const db = await connect(tmpDir.name);
+      const table = await db.createTable("test", [
+        { id: 1, text: "def getUserName(user_id): return user_id" },
+        { id: 2, text: "def parseFile(file_path): return file_path" },
+      ]);
+      await table.createIndex("text", {
+        config: Index.fts({ baseTokenizer: "code" }),
+      });
+
+      const results = await table.search("getUserName", "fts").toArray();
+      expect(results.map((row) => row.id)).toEqual([1]);
+    });
+
     test("full text search ngram", async () => {
       const db = await connect(tmpDir.name);
       const data = [
@@ -4191,7 +4628,7 @@ describe("setUnenforcedPrimaryKey", () => {
   });
 });
 
-describe("setLsmWriteSpec / unsetLsmWriteSpec", () => {
+describe("enableMemWal / disableMemWal", () => {
   let tmpDir: tmp.DirResult;
 
   beforeEach(() => {
@@ -4211,16 +4648,16 @@ describe("setLsmWriteSpec / unsetLsmWriteSpec", () => {
     const table = await makeTable(conn);
 
     await table.setUnenforcedPrimaryKey("id");
-    await table.setLsmWriteSpec({
+    await table.enableMemWal({
       specType: "bucket",
       column: "id",
       numBuckets: 4,
     });
-    await table.unsetLsmWriteSpec();
+    await table.disableMemWal();
     // A second unset errors — there is no spec left to remove.
-    await expect(table.unsetLsmWriteSpec()).rejects.toThrow();
+    await expect(table.disableMemWal()).rejects.toThrow();
     // A fresh spec can be installed after unset.
-    await table.setLsmWriteSpec({
+    await table.enableMemWal({
       specType: "bucket",
       column: "id",
       numBuckets: 8,
@@ -4232,8 +4669,8 @@ describe("setLsmWriteSpec / unsetLsmWriteSpec", () => {
     const table = await makeTable(conn);
 
     await table.setUnenforcedPrimaryKey("id");
-    await table.setLsmWriteSpec({ specType: "unsharded" });
-    await table.unsetLsmWriteSpec();
+    await table.enableMemWal({ specType: "unsharded" });
+    await table.disableMemWal();
   });
 
   it("installs an identity spec", async () => {
@@ -4241,8 +4678,8 @@ describe("setLsmWriteSpec / unsetLsmWriteSpec", () => {
     const table = await makeTable(conn);
 
     await table.setUnenforcedPrimaryKey("id");
-    await table.setLsmWriteSpec({ specType: "identity", column: "id" });
-    await table.unsetLsmWriteSpec();
+    await table.enableMemWal({ specType: "identity", column: "id" });
+    await table.disableMemWal();
   });
 
   it("rejects an invalid spec", async () => {
@@ -4252,7 +4689,7 @@ describe("setLsmWriteSpec / unsetLsmWriteSpec", () => {
     await table.setUnenforcedPrimaryKey("id");
     // num_buckets out of range.
     await expect(
-      table.setLsmWriteSpec({
+      table.enableMemWal({
         specType: "bucket",
         column: "id",
         numBuckets: 0,
@@ -4260,7 +4697,7 @@ describe("setLsmWriteSpec / unsetLsmWriteSpec", () => {
     ).rejects.toThrow();
     // Column mismatch.
     await expect(
-      table.setLsmWriteSpec({
+      table.enableMemWal({
         specType: "bucket",
         column: "missing",
         numBuckets: 4,
@@ -4268,13 +4705,13 @@ describe("setLsmWriteSpec / unsetLsmWriteSpec", () => {
     ).rejects.toThrow();
   });
 
-  it("reads back the installed spec via getLsmWriteSpec", async () => {
+  it("reads back the installed spec via getMemWalSpec", async () => {
     const conn = await connect(tmpDir.name);
     const table = await makeTable(conn);
     await table.setUnenforcedPrimaryKey("id");
 
     // Nothing installed yet.
-    expect(await table.getLsmWriteSpec()).toBeUndefined();
+    expect(await table.getMemWalSpec()).toBeUndefined();
 
     // A real scalar index is needed to name it as a maintained index.
     await table.add([{ id: 1 }, { id: 2 }, { id: 3 }]);
@@ -4285,14 +4722,14 @@ describe("setLsmWriteSpec / unsetLsmWriteSpec", () => {
     // defaults. Lance writer-config keys are canonically snake_case.
     // biome-ignore lint/style/useNamingConvention: Lance writer-config keys are snake_case
     const writerConfigDefaults = { durable_write: "false" };
-    await table.setLsmWriteSpec({
+    await table.enableMemWal({
       specType: "bucket",
       column: "id",
       numBuckets: 4,
       maintainedIndexes: [indexName],
       writerConfigDefaults,
     });
-    const spec = await table.getLsmWriteSpec();
+    const spec = await table.getMemWalSpec();
     expect(spec).toBeDefined();
     expect(spec?.specType).toBe("bucket");
     expect(spec?.column).toBe("id");
@@ -4301,19 +4738,19 @@ describe("setLsmWriteSpec / unsetLsmWriteSpec", () => {
     expect(spec?.writerConfigDefaults).toEqual(writerConfigDefaults);
 
     // After unset, undefined again.
-    await table.unsetLsmWriteSpec();
-    expect(await table.getLsmWriteSpec()).toBeUndefined();
+    await table.disableMemWal();
+    expect(await table.getMemWalSpec()).toBeUndefined();
 
     // Identity round-trips (column recovered from the schema).
-    await table.setLsmWriteSpec({ specType: "identity", column: "id" });
-    const identity = await table.getLsmWriteSpec();
+    await table.enableMemWal({ specType: "identity", column: "id" });
+    const identity = await table.getMemWalSpec();
     expect(identity?.specType).toBe("identity");
     expect(identity?.column).toBe("id");
-    await table.unsetLsmWriteSpec();
+    await table.disableMemWal();
 
     // Unsharded round-trips (no routing column).
-    await table.setLsmWriteSpec({ specType: "unsharded" });
-    const unsharded = await table.getLsmWriteSpec();
+    await table.enableMemWal({ specType: "unsharded" });
+    const unsharded = await table.getMemWalSpec();
     expect(unsharded?.specType).toBe("unsharded");
     expect(unsharded?.column).toBeFalsy();
   });
@@ -4342,7 +4779,7 @@ describe("LSM merge insert", () => {
     ]);
     await table.setUnenforcedPrimaryKey("id");
     // numBuckets = 1: every row routes to the single bucket.
-    await table.setLsmWriteSpec({
+    await table.enableMemWal({
       specType: "bucket",
       column: "id",
       numBuckets: 1,
@@ -4367,17 +4804,17 @@ describe("LSM merge insert", () => {
     expect(res.version).toBe(0);
     expect(res.numInsertedRows).toBe(0);
 
-    await table.closeLsmWriters();
+    await table.closeMemWalWriters();
   });
 
-  it("falls back to the standard path with useLsm(false)", async () => {
+  it("falls back to the standard path with useMemWal(false)", async () => {
     const conn = await connect(tmpDir.name);
     const table = await bucketTable(conn);
 
     const res = await table
       .mergeInsert("id")
       .whenNotMatchedInsertAll()
-      .useLsm(false)
+      .useMemWal(false)
       .execute([
         { id: "b", value: 9 },
         { id: "e", value: 5 },
@@ -4400,7 +4837,7 @@ describe("LSM merge insert", () => {
     expect(res.numRows).toBe(1);
   });
 
-  it("rejects a non-upsert merge under an LSM spec", async () => {
+  it("rejects a non-upsert merge under a MemWAL spec", async () => {
     const conn = await connect(tmpDir.name);
     const table = await bucketTable(conn);
 
@@ -4426,22 +4863,24 @@ describe("LSM merge insert", () => {
     const lsm = await table.query().toArray();
     expect(lsm.map((r) => r.id).sort()).toEqual(["a", "b", "c"]);
 
-    // useLsm(false) bypasses the MemWAL and reads the base table only.
-    const baseOnly = await table.query().useLsm(false).toArray();
+    // useMemWal(false) bypasses the MemWAL and reads the base table only.
+    const baseOnly = await table.query().useMemWal(false).toArray();
     expect(baseOnly.map((r) => r.id).sort()).toEqual(["a", "b"]);
   });
 
-  it("reads the base table when no LSM spec is installed", async () => {
+  it("reads the base table when no MemWAL spec is installed", async () => {
     const conn = await connect(tmpDir.name);
     const table = await conn.createEmptyTable(
       "plain",
       new arrow.Schema([new arrow.Field("id", new arrow.Utf8(), false)]),
     );
-    // No spec: default read and useLsm(false) both succeed against the base table.
+    // No spec: default read and useMemWal(false) both succeed against the base table.
     await expect(table.query().toArray()).resolves.toBeDefined();
-    await expect(table.query().useLsm(false).toArray()).resolves.toBeDefined();
-    // useLsm(true) demands MemWAL routing; without a spec it errors.
-    await expect(table.query().useLsm(true).toArray()).rejects.toThrow();
+    await expect(
+      table.query().useMemWal(false).toArray(),
+    ).resolves.toBeDefined();
+    // useMemWal(true) demands MemWAL routing; without a spec it errors.
+    await expect(table.query().useMemWal(true).toArray()).rejects.toThrow();
   });
 });
 
@@ -4459,7 +4898,7 @@ describe("LSM convergence and stats", () => {
       new arrow.Schema([new arrow.Field("id", new arrow.Utf8(), false)]),
     );
     await table.setUnenforcedPrimaryKey("id");
-    await table.setLsmWriteSpec({ specType: "unsharded" });
+    await table.enableMemWal({ specType: "unsharded" });
     return table;
   }
 
@@ -4467,34 +4906,34 @@ describe("LSM convergence and stats", () => {
   // rejects them rather than answering. What is asserted here is that the
   // bindings reach the core at all; the behavior against a real endpoint is
   // covered by the mocked endpoint tests in rust/lancedb/src/remote/table.rs.
-  it("rejects flushLsm on a local table", async () => {
+  it("rejects flushMemWal on a local table", async () => {
     const conn = await connect(tmpDir.name);
     const table = await lsmTable(conn);
 
-    await expect(table.flushLsm()).rejects.toThrow(/not supported/i);
+    await expect(table.flushMemWal()).rejects.toThrow(/not supported/i);
   });
 
-  it("rejects compactLsm on a local table", async () => {
+  it("rejects compactMemWal on a local table", async () => {
     const conn = await connect(tmpDir.name);
     const table = await lsmTable(conn);
 
-    await expect(table.compactLsm()).rejects.toThrow(/not supported/i);
+    await expect(table.compactMemWal()).rejects.toThrow(/not supported/i);
   });
 
-  it("rejects getLsmStats on a local table", async () => {
+  it("rejects getMemWalStats on a local table", async () => {
     const conn = await connect(tmpDir.name);
     const table = await lsmTable(conn);
 
-    await expect(table.getLsmStats()).rejects.toThrow(/not supported/i);
-    await expect(table.getLsmStats(true)).rejects.toThrow(/not supported/i);
+    await expect(table.getMemWalStats()).rejects.toThrow(/not supported/i);
+    await expect(table.getMemWalStats(true)).rejects.toThrow(/not supported/i);
   });
 
-  it("rejects checkpointLsm on a local table", async () => {
+  it("rejects checkpointMemWal on a local table", async () => {
     const conn = await connect(tmpDir.name);
     const table = await lsmTable(conn);
 
-    // checkpointLsm seals first, so it surfaces flushLsm's rejection.
-    await expect(table.checkpointLsm()).rejects.toThrow(/not supported/i);
+    // checkpointMemWal freezes first, so it surfaces flushMemWal's rejection.
+    await expect(table.checkpointMemWal()).rejects.toThrow(/not supported/i);
   });
 });
 
diff --git a/nodejs/lancedb/arrow.ts b/nodejs/lancedb/arrow.ts
index df875a808..3130da8eb 100644
--- a/nodejs/lancedb/arrow.ts
+++ b/nodejs/lancedb/arrow.ts
@@ -40,7 +40,12 @@ import {
 } from "apache-arrow";
 import { Buffers } from "apache-arrow/data";
 import { typedArrayToArrowType } from "./arrow_type";
-import { coerceBlobValue, isBlobField } from "./blob";
+import {
+  blobToRead,
+  coerceBlobValue,
+  isBlobField,
+  withBlobBytes,
+} from "./blob";
 import { type EmbeddingFunction } from "./embedding/embedding_function";
 import {
   EmbeddingFunctionConfig,
@@ -66,11 +71,20 @@ export type SchemaLike =
 export type FieldLike =
   | Field
   | {
-      type: string;
+      type: string | DataTypeLike;
       name: string;
       nullable: boolean;
       metadata?: Map<string, string>;
     };
+/**
+ * A `DataType` from any copy or version of apache-arrow.
+ *
+ * Arrow 21 brands its classes with `unique symbol` properties, so a type
+ * object from a second copy of the library no longer satisfies the `DataType`
+ * type of this one even though it is structurally identical. Inputs that only
+ * need to be sanitized accept this looser shape instead.
+ */
+export type DataTypeLike = DataType | { readonly typeId: number };
 
 /**
  * Create an Arrow field backed by LanceDB's JSON extension type.
@@ -440,6 +454,24 @@ export function makeArrowTable(
     );
   }
 
+  if (schema !== undefined) {
+    // Validate blob values up front and give every one the full
+    // `{ data, uri }` shape, so inference sees the same struct whether a row
+    // passed bytes, a URI, or a partial struct.
+    data = mapBlobInputs(data, schema, (value, field, row) => {
+      if (value === undefined) {
+        return value;
+      }
+      try {
+        return coerceBlobValue(value);
+      } catch (e) {
+        throw new Error(
+          `Invalid value for blob field ${field} at row ${row}: ${(e as Error).message}`,
+        );
+      }
+    });
+  }
+
   let schemaMetadata = schema?.metadata || new Map<string, string>();
   if (metadata !== undefined) {
     schemaMetadata = new Map([...schemaMetadata, ...metadata]);
@@ -501,6 +533,133 @@ function containsBlobField(field: Field): boolean {
   );
 }
 
+type BlobInputVisitor = (value: unknown, field: string, row: number) => unknown;
+
+/**
+ * Calls `visit` on every value that lands in a blob column of `schema`,
+ * including blobs nested in structs and lists, and replaces it with the
+ * result. Records and arrays are copied only where a value changed, so the
+ * caller's data is never mutated.
+ */
+function mapBlobInputs(
+  data: Array<Record<string, unknown>>,
+  schema: Schema,
+  visit: BlobInputVisitor,
+): Array<Record<string, unknown>> {
+  if (!schema.fields.some(containsBlobField)) {
+    return data;
+  }
+  return data.map((record, row) =>
+    isObject(record)
+      ? mapBlobFields(record, schema.fields, "", row, visit)
+      : record,
+  );
+}
+
+function mapBlobFields(
+  record: Record<string, unknown>,
+  fields: Field[],
+  prefix: string,
+  row: number,
+  visit: BlobInputVisitor,
+): Record<string, unknown> {
+  let out: Record<string, unknown> | undefined;
+  for (const field of fields) {
+    if (!containsBlobField(field) || !Object.hasOwn(record, field.name)) {
+      continue;
+    }
+    const value = record[field.name];
+    const mapped = mapBlobField(
+      value,
+      field,
+      `${prefix}${field.name}`,
+      row,
+      visit,
+    );
+    if (mapped !== value) {
+      out ??= { ...record };
+      out[field.name] = mapped;
+    }
+  }
+  return out ?? record;
+}
+
+function mapBlobField(
+  value: unknown,
+  field: Field,
+  label: string,
+  row: number,
+  visit: BlobInputVisitor,
+): unknown {
+  if (isBlobField(field)) {
+    return visit(value, label, row);
+  }
+  if (field.type instanceof Struct && isObject(value)) {
+    return mapBlobFields(value, field.type.children, `${label}.`, row, visit);
+  }
+  if (isList(field.type) && Array.isArray(value)) {
+    const child = field.type.children[0];
+    let out: unknown[] | undefined;
+    for (const [index, element] of value.entries()) {
+      const mapped = mapBlobField(
+        element,
+        child,
+        `${label}[${index}]`,
+        row,
+        visit,
+      );
+      if (mapped !== element) {
+        out ??= [...value];
+        out[index] = mapped;
+      }
+    }
+    return out ?? value;
+  }
+  return value;
+}
+
+/**
+ * Reads `Blob` / `File` values in the blob columns of `schema` into bytes so
+ * the synchronous conversion in {@link makeArrowTable} can accept them.
+ * Returns `data` itself when there is nothing to read.
+ */
+export async function resolveBlobInputs(
+  data: Array<Record<string, unknown>>,
+  schema?: SchemaLike,
+): Promise<Array<Record<string, unknown>>> {
+  if (schema === undefined || schema === null) {
+    return data;
+  }
+  const sanitized = sanitizeSchema(schema);
+  // Keyed by the Blob itself, so rows sharing one Blob (bare or as
+  // `{ data }`) read it once and share the bytes.
+  const reads = new Map<Blob, Promise<Uint8Array>>();
+  mapBlobInputs(data, sanitized, (value) => {
+    const source = blobToRead(value);
+    if (source !== undefined && !reads.has(source)) {
+      reads.set(
+        source,
+        source.arrayBuffer().then((buffer) => new Uint8Array(buffer)),
+      );
+    }
+    return value;
+  });
+  if (reads.size === 0) {
+    return data;
+  }
+  const bytes = new Map(
+    await Promise.all(
+      [...reads].map(async ([source, read]) => [source, await read] as const),
+    ),
+  );
+  return mapBlobInputs(data, sanitized, (value) => {
+    const source = blobToRead(value);
+    return source === undefined
+      ? value
+      : withBlobBytes(value, bytes.get(source) as Uint8Array);
+  });
+}
+
 function isObject(value: unknown): value is Record<string, unknown> {
   return (
     typeof value === "object" &&
@@ -1004,6 +1163,11 @@ export async function convertToTable(
     );
   }
 
+  processedData = await resolveBlobInputs(
+    processedData,
+    makeTableOptions?.schema,
+  );
+
   const table = makeArrowTable(processedData, makeTableOptions);
   return await applyEmbeddings(table, embeddings, makeTableOptions?.schema);
 }
@@ -1059,6 +1223,71 @@ export async function fromRecordsToStreamBuffer(
   return Buffer.from(await writer.toUint8Array());
 }
 
+// `Type.Utf8View` / `Type.BinaryView` as numbers: the enum members only exist
+// in Arrow 21+, and this module compiles against every supported release.
+const UTF8_VIEW_TYPE_ID = 24;
+const BINARY_VIEW_TYPE_ID = 23;
+
+/**
+ * Copy a Utf8View / BinaryView `Data` into a single `Data` of `type`.
+ */
+function materializeViewData(child: ArrowData, type: DataType): ArrowData {
+  const builder = makeBuilder({ type, nullValues: [null] });
+  for (const value of new Vector([child])) {
+    builder.append(value);
+  }
+  return builder.finish().flush();
+}
+
+/**
+ * Rebuild any top-level Utf8View / BinaryView column as Utf8 / Binary.
+ *
+ * Lance stores the view types as their offset-based equivalents anyway, so
+ * nothing is lost. Doing it here also sidesteps an Arrow JS 21 bug: its IPC
+ * writer emits a truncated views buffer for a *sliced* view array, which the
+ * Rust reader rejects with "Need at least N bytes in buffers[0]".
+ *
+ * The record batches are rebuilt positionally rather than through a
+ * `Record<string, Vector>`: JavaScript enumerates integer-like keys first, so
+ * a field named e.g. `"1"` would otherwise be paired with the wrong column.
+ *
+ * Tables without view columns are returned as-is.
+ */
+function materializeViewColumns(table: ArrowTable): ArrowTable {
+  const replacements = new Map<number, DataType>();
+  table.schema.fields.forEach((field, i) => {
+    if (field.type.typeId === UTF8_VIEW_TYPE_ID) {
+      replacements.set(i, new Utf8());
+    } else if (field.type.typeId === BINARY_VIEW_TYPE_ID) {
+      replacements.set(i, new Binary());
+    }
+  });
+  if (replacements.size === 0) {
+    return table;
+  }
+  const fields = table.schema.fields.map((field, i) => {
+    const type = replacements.get(i);
+    return type === undefined
+      ? field
+      : new Field(field.name, type, field.nullable, field.metadata);
+  });
+  const schema = new Schema(fields, table.schema.metadata);
+  const batches = table.batches.map((batch) => {
+    const children = batch.data.children.map((child, i) => {
+      const type = replacements.get(i);
+      return type === undefined ? child : materializeViewData(child, type);
+    });
+    const data = makeData({
+      type: new Struct(fields),
+      length: batch.numRows,
+      nullCount: 0,
+      children,
+    });
+    return new RecordBatch(schema, data);
+  });
+  return new ArrowTable(schema, batches);
+}
+
 /**
  * Serialize an Arrow Table into a buffer using the Arrow IPC File serialization
  *
@@ -1075,7 +1304,9 @@ export async function fromTableToBuffer(
   if (schema !== undefined && schema !== null) {
     schema = sanitizeSchema(schema);
   }
-  const tableWithEmbeddings = await applyEmbeddings(table, embeddings, schema);
+  const tableWithEmbeddings = materializeViewColumns(
+    await applyEmbeddings(table, embeddings, schema),
+  );
   validateBlobSchema(tableWithEmbeddings.schema);
   const writer = RecordBatchFileWriter.writeAll(tableWithEmbeddings);
   return Buffer.from(await writer.toUint8Array());
@@ -1146,7 +1377,8 @@ export async function fromRecordBatchToBuffer(
 export async function fromRecordBatchToStreamBuffer(
   batch: RecordBatch,
 ): Promise<Buffer> {
-  const writer = RecordBatchStreamWriter.writeAll([batch]);
+  const table = materializeViewColumns(new ArrowTable([batch]));
+  const writer = RecordBatchStreamWriter.writeAll(table);
   return Buffer.from(await writer.toUint8Array());
 }
 
@@ -1163,7 +1395,9 @@ export async function fromTableToStreamBuffer(
   embeddings?: EmbeddingFunctionConfig,
   schema?: SchemaLike,
 ): Promise<Buffer> {
-  const tableWithEmbeddings = await applyEmbeddings(table, embeddings, schema);
+  const tableWithEmbeddings = materializeViewColumns(
+    await applyEmbeddings(table, embeddings, schema),
+  );
   const writer = RecordBatchStreamWriter.writeAll(tableWithEmbeddings);
   return Buffer.from(await writer.toUint8Array());
 }
@@ -1290,7 +1524,7 @@ export function ensureNestedFieldsExist(
     for (const field of schema.fields) {
       if (field.name in row) {
         if (
-          field.type.constructor.name === "Struct" &&
+          isPlainStructField(field) &&
           row[field.name] !== null &&
           row[field.name] !== undefined
         ) {
@@ -1307,10 +1541,9 @@ export function ensureNestedFieldsExist(
       } else {
         // Keep a missing struct valid while filling each of its children with
         // null. This is distinct from an explicitly null struct value.
-        completeRow[field.name] =
-          field.type.constructor.name === "Struct"
-            ? ensureStructFieldsExist({}, field.type as Struct)
-            : null;
+        completeRow[field.name] = isPlainStructField(field)
+          ? ensureStructFieldsExist({}, field.type as Struct)
+          : null;
       }
     }
 
@@ -1318,6 +1551,15 @@ export function ensureNestedFieldsExist(
   });
 }
 
+/**
+ * Blob fields are Arrow structs, but their values are bytes, URIs, or
+ * `{ data } | { uri }` inputs that the blob coercion in `makeArrowTable`
+ * handles, so they must not be filled in like ordinary structs.
+ */
+function isPlainStructField(field: Field): boolean {
+  return field.type.constructor.name === "Struct" && !isBlobField(field);
+}
+
 /**
  * Recursively ensures that all fields in a struct type exist in the data,
  * filling missing fields with null values.
@@ -1331,7 +1573,7 @@ function ensureStructFieldsExist(
   for (const childField of structType.children) {
     if (childField.name in data) {
       if (
-        childField.type.constructor.name === "Struct" &&
+        isPlainStructField(childField) &&
         data[childField.name] !== null &&
         data[childField.name] !== undefined
       ) {
@@ -1347,10 +1589,9 @@ function ensureStructFieldsExist(
     } else {
       // Keep a missing struct valid while filling each of its children with
       // null. This is distinct from an explicitly null struct value.
-      completeStruct[childField.name] =
-        childField.type.constructor.name === "Struct"
-          ? ensureStructFieldsExist({}, childField.type as Struct)
-          : null;
+      completeStruct[childField.name] = isPlainStructField(childField)
+        ? ensureStructFieldsExist({}, childField.type as Struct)
+        : null;
     }
   }
 
diff --git a/nodejs/lancedb/blob.ts b/nodejs/lancedb/blob.ts
index 244faac09..7b0276f14 100644
--- a/nodejs/lancedb/blob.ts
+++ b/nodejs/lancedb/blob.ts
@@ -12,7 +12,46 @@ const DEDICATED_SIZE_THRESHOLD_KEY =
 const PACK_FILE_SIZE_THRESHOLD_KEY =
   "lance-encoding:blob-pack-file-size-threshold";
 
-export type BlobInput = {
+/**
+ * Bytes accepted for a blob value. `ArrayBuffer` is wrapped without copying.
+ * `Blob` and `File` are read with `arrayBuffer()`, which only the async write
+ * paths ({@link Connection.createTable}, {@link Table.add},
+ * {@link Table.mergeInsert}) can do.
+ */
+export type BlobData = Buffer | Uint8Array | ArrayBuffer | Blob;
+
+/** A URI accepted for a blob value. A `URL` is stored as its `href`. */
+export type BlobUri = string | URL;
+
+/**
+ * A value accepted for a `lance.blob.v2` column (see {@link blob}).
+ *
+ * Either inline bytes, a URI pointing at external bytes, or a struct that sets
+ * exactly one of `data` or `uri`. `null` and `undefined` write a null blob.
+ *
+ * @example
+ * ```ts
+ * import { pathToFileURL } from "node:url";
+ * import type { BlobInput } from "@lancedb/lancedb";
+ *
+ * const rows: { id: bigint; image: BlobInput }[] = [
+ *   { id: 1n, image: await (await fetch(url)).arrayBuffer() },
+ *   { id: 2n, image: pathToFileURL("/data/cat.png") },
+ *   { id: 3n, image: { data: new Blob(["hello"]) } },
+ * ];
+ * await table.add(rows);
+ * ```
+ */
+export type BlobInput =
+  | BlobData
+  | BlobUri
+  | { data: BlobData; uri?: null }
+  | { data?: null; uri: BlobUri }
+  | null
+  | undefined;
+
+/** A blob value normalized to the columns of the blob struct. */
+export type BlobValue = {
   data: Buffer | Uint8Array | null;
   uri: string | null;
 };
@@ -42,6 +81,8 @@ export type BlobOptions = {
  *
  * Query results are descriptors, not payload bytes. Use {@link Table.fetchBlobs}
  * or {@link Table.fetchBlobFiles} to read bytes.
+ * Blobs inside lists can be written and queried as descriptors, but cannot
+ * currently be fetched because a row ID does not identify a list element.
  *
  * @example
  * ```ts
@@ -141,13 +182,16 @@ export class BlobFile {
   }
 
   /**
-   * Reads from the cursor to the end and advances the cursor.
+   * Reads from the cursor and advances the cursor.
    *
-   * A second call returns an empty buffer. {@link BlobFile.readRange} does
-   * not move the cursor.
+   * Reads to the end when `maxBytes` is omitted, or at most `maxBytes` bytes
+   * otherwise. Returns an empty buffer at the end of the blob.
+   * {@link BlobFile.readRange} does not move the cursor.
    */
-  read(): Promise<Buffer> {
-    return this.inner.read();
+  read(maxBytes?: bigint, options?: BlobReadOptions): Promise<Buffer> {
+    return runWithSignal(options?.signal, (signal) =>
+      this.inner.read(maxBytes, signal),
+    );
   }
 
   /**
@@ -155,12 +199,167 @@ export class BlobFile {
    *
    * Fails when `end` is past the blob size. Does not move the cursor.
    */
-  readRange(start: bigint, end: bigint): Promise<Buffer> {
-    return this.inner.readRange(start, end);
+  readRange(
+    start: bigint,
+    end: bigint,
+    options?: BlobReadOptions,
+  ): Promise<Buffer> {
+    return runWithSignal(options?.signal, (signal) =>
+      this.inner.readRange(start, end, signal),
+    );
+  }
+
+  /**
+   * Reads several half-open byte ranges. Returns one buffer per range, in
+   * the order given.
+   *
+   * Fails when any `end` is past the blob size. Does not move the cursor.
+   */
+  readRanges(
+    ranges: BlobRange[],
+    options?: BlobReadOptions,
+  ): Promise<Buffer[]> {
+    return runWithSignal(options?.signal, (signal) =>
+      this.inner.readRanges(ranges, signal),
+    );
+  }
+
+  /** Moves the cursor to `position`, in bytes from the start of the blob. */
+  seek(position: bigint): Promise<void> {
+    return this.inner.seek(position);
+  }
+
+  /** Returns the cursor position in bytes. */
+  tell(): Promise<bigint> {
+    return this.inner.tell();
+  }
+
+  /**
+   * Releases the handle. Reads after `close()` fail. Calling it again does
+   * nothing.
+   */
+  close(): Promise<void> {
+    return this.inner.close();
+  }
+
+  /** Returns true after {@link BlobFile.close}. */
+  isClosed(): boolean {
+    return this.inner.isClosed();
   }
 }
 
-export function coerceBlobValue(value: unknown): BlobInput | null {
+/** Options for blob reads. */
+export type BlobReadOptions = {
+  /**
+   * Cancels the read. The call rejects with `signal.reason`, and the native
+   * read stops, including in-flight requests to a remote table.
+   */
+  signal?: AbortSignal;
+};
+
+/** @ignore */
+export async function runWithSignal<T>(
+  signal: AbortSignal | undefined,
+  run: (signal: AbortSignal | undefined) => Promise<T>,
+): Promise<T> {
+  if (signal === undefined) {
+    return run(undefined);
+  }
+  signal.throwIfAborted();
+  // napi-rs replaces `onabort` on the signal it receives, so pass a private
+  // signal and forward the caller's abort to it.
+  const controller = new AbortController();
+  const onAbort = () => controller.abort();
+  signal.addEventListener("abort", onAbort, { once: true });
+  try {
+    return await run(controller.signal);
+  } catch (err) {
+    if (signal.aborted) {
+      throw signal.reason;
+    }
+    throw err;
+  } finally {
+    signal.removeEventListener("abort", onAbort);
+  }
+}
+
+/** A half-open byte range `[start, end)` for {@link BlobFile.readRanges}. */
+export type BlobRange = {
+  start: bigint;
+  end: bigint;
+};
+
+/**
+ * Rewrites the synchronous-only widenings of {@link BlobInput} (`ArrayBuffer`,
+ * `URL`) into `Uint8Array` and URI strings, keeping the value's shape. Other
+ * values are returned unchanged and validated later by {@link coerceBlobValue}.
+ *
+ * Throws on `Blob` / `File`, which must be read into bytes first.
+ */
+export function normalizeBlobInput(value: unknown): unknown {
+  if (blobToRead(value) !== undefined) {
+    throw new Error(
+      "Blob and File values must be read asynchronously. Pass them to " +
+        "createTable, add, or mergeInsert, or convert them with " +
+        "`new Uint8Array(await value.arrayBuffer())`",
+    );
+  }
+  if (value instanceof ArrayBuffer) {
+    return new Uint8Array(value);
+  }
+  if (value instanceof URL) {
+    return value.href;
+  }
+  if (isBlobStruct(value)) {
+    // Read `data` and `uri` explicitly: spreading a class instance would drop
+    // fields defined as getters on its prototype.
+    const out: Record<string, unknown> = {
+      ...value,
+      data: value.data,
+      uri: value.uri,
+    };
+    let changed = false;
+    if (value.data instanceof ArrayBuffer) {
+      out.data = new Uint8Array(value.data);
+      changed = true;
+    }
+    if (value.uri instanceof URL) {
+      out.uri = value.uri.href;
+      changed = true;
+    }
+    return changed ? out : value;
+  }
+  return value;
+}
+
+/**
+ * Returns the `Blob` / `File` a blob value needs read, either the value itself
+ * or its `data` field, or `undefined` when there is nothing to read.
+ */
+export function blobToRead(value: unknown): Blob | undefined {
+  if (isBlobLike(value)) {
+    return value;
+  }
+  if (isBlobStruct(value) && isBlobLike(value.data)) {
+    return value.data;
+  }
+  return undefined;
+}
+
+/**
+ * Replaces the `Blob` that {@link blobToRead} found in `value` with `bytes`,
+ * keeping the value's shape.
+ */
+export function withBlobBytes(value: unknown, bytes: Uint8Array): unknown {
+  if (isBlobLike(value)) {
+    return bytes;
+  }
+  const record = value as Record<string, unknown>;
+  return { ...record, uri: record.uri, data: bytes };
+}
+
+export function coerceBlobValue(input: unknown): BlobValue | null {
+  const value = normalizeBlobInput(input);
   if (value == null) {
     return null;
   }
@@ -168,7 +367,9 @@ export function coerceBlobValue(value: unknown): BlobInput | null {
     return { data: value, uri: null };
   }
   if (ArrayBuffer.isView(value)) {
-    throw new Error("Blob data must be Buffer or Uint8Array");
+    throw new Error(
+      "Blob data must be Buffer, Uint8Array, ArrayBuffer, or Blob",
+    );
   }
   if (typeof value === "string") {
     if (value === "") {
@@ -176,23 +377,37 @@ export function coerceBlobValue(value: unknown): BlobInput | null {
     }
     return { data: null, uri: value };
   }
-  if (typeof value === "object") {
-    const record = value as Record<string, unknown>;
+  if (isBlobStruct(value)) {
+    const record = value;
     if (!("data" in record) && !("uri" in record)) {
       throw new Error(
         "Blob struct values must include a 'data' or 'uri' field",
       );
     }
+    const unknownKeys = Object.keys(record).filter(
+      (key) => key !== "data" && key !== "uri",
+    );
+    if (unknownKeys.length > 0) {
+      throw new Error(
+        `Blob struct values only support 'data' and 'uri', got ${unknownKeys
+          .map((key) => `'${key}'`)
+          .join(", ")}`,
+      );
+    }
     const uri = record.uri;
     if (uri === "") {
       throw new Error("Blob uri cannot be empty");
     }
     if (uri != null && typeof uri !== "string") {
-      throw new Error(`Blob uri must be a string or null, got ${typeof uri}`);
+      throw new Error(
+        `Blob uri must be a string, URL, or null, got ${typeof uri}`,
+      );
     }
     const data = record.data;
     if (data != null && !isBlobBytes(data)) {
-      throw new Error("Blob data must be Buffer, Uint8Array, or null");
+      throw new Error(
+        "Blob data must be Buffer, Uint8Array, ArrayBuffer, Blob, or null",
+      );
     }
     const bytes = (data as Buffer | Uint8Array | null | undefined) ?? null;
     const uriValue = uri ?? null;
@@ -204,7 +419,39 @@ export function coerceBlobValue(value: unknown): BlobInput | null {
     return { data: bytes, uri: uriValue };
   }
   throw new Error(
-    "Blob column values must be Buffer, Uint8Array, a URI string, null, or { data?, uri? }",
+    "Blob column values must be Buffer, Uint8Array, ArrayBuffer, Blob, a URI string or URL, null, or { data?, uri? }",
+  );
+}
+
+function isBlobLike(value: unknown): value is Blob {
+  return typeof Blob !== "undefined" && value instanceof Blob;
+}
+
+/**
+ * Whether `value` is a `{ data?, uri? }` struct: any plain object, or any
+ * other object with a `data` or `uri` field (such as a class instance), apart
+ * from the binary, `Blob`, and `URL` values that are blob inputs themselves.
+ */
+function isBlobStruct(value: unknown): value is Record<string, unknown> {
+  if (
+    typeof value !== "object" ||
+    value === null ||
+    Array.isArray(value) ||
+    ArrayBuffer.isView(value) ||
+    value instanceof ArrayBuffer ||
+    (typeof SharedArrayBuffer !== "undefined" &&
+      value instanceof SharedArrayBuffer) ||
+    isBlobLike(value) ||
+    value instanceof URL
+  ) {
+    return false;
+  }
+  const prototype = Object.getPrototypeOf(value);
+  return (
+    prototype === Object.prototype ||
+    prototype === null ||
+    "data" in value ||
+    "uri" in value
   );
 }
 
diff --git a/nodejs/lancedb/catalog.ts b/nodejs/lancedb/catalog.ts
index 5cfe2c5ea..e344d8187 100644
--- a/nodejs/lancedb/catalog.ts
+++ b/nodejs/lancedb/catalog.ts
@@ -5,9 +5,9 @@ import { Connection, LocalConnection } from "./connection";
 import { HeaderProvider } from "./header";
 import {
   JsHeaderProvider,
-  ListDatabasesResponse,
   Catalog as NativeCatalog,
   CatalogOptions as NativeCatalogOptions,
+  DatabaseNames as NativeDatabaseNames,
 } from "./native.js";
 import { OAuthConfig } from "./oauth";
 
@@ -21,7 +21,41 @@ export interface CatalogOptions
     | (() => Record<string, string> | Promise<Record<string, string>>);
 }
 
-export type { ListDatabasesResponse } from "./native.js";
+/**
+ * A lazy async iterator of database names with pagination state.
+ * Returned by {@link Catalog.listDatabases}.
+ */
+export class DatabaseNames implements AsyncIterableIterator<string> {
+  /** @hidden */
+  constructor(private readonly inner: NativeDatabaseNames) {}
+
+  [Symbol.asyncIterator](): AsyncIterableIterator<string> {
+    return this;
+  }
+
+  /** Fetch the next name, requesting another page only when needed. */
+  async next(): Promise<IteratorResult<string>> {
+    const value = await this.inner.next();
+    return value == null
+      ? { done: true, value: undefined }
+      : { done: false, value };
+  }
+
+  /** Number of names available without another REST request. */
+  numPageResults(): number {
+    return this.inner.numPageResults();
+  }
+
+  /**
+   * Token for the next REST request. Initially this is the supplied starting token.
+   * Returns undefined after the final page is fetched, even if names remain cached.
+   * Drain the cache before saving a token to avoid skipping names when resuming.
+   * A failed request terminates iteration and retains its token for a new iterator.
+   */
+  pageToken(): string | undefined {
+    return this.inner.pageToken() ?? undefined;
+  }
+}
 
 /** A remote catalog manages databases through the server's root namespace. */
 export class Catalog {
@@ -56,21 +90,35 @@ export class Catalog {
     await this.inner.dropDatabase(name, options.ignoreMissing);
   }
 
-  /** List one page of databases; pass pageToken from a response for the next page. */
-  async listDatabases(
-    options: { limit?: number; pageToken?: string } = {},
-  ): Promise<ListDatabasesResponse> {
+  /**
+   * Iterate lazily over all database names using `for await...of`.
+   * pageToken resumes from a saved token; omitted starts at the beginning.
+   * pageLimit limits each REST response, not the total; omitted uses the server default.
+   * Request errors are raised during iteration and terminate the iterator.
+   *
+   * @example
+   * ```ts
+   * for await (const name of catalog.listDatabases({ pageLimit: 20 })) {
+   *   console.log(name);
+   * }
+   * ```
+   */
+  listDatabases(
+    options: { pageLimit?: number; pageToken?: string } = {},
+  ): DatabaseNames {
     if (
-      options.limit !== undefined &&
-      (!Number.isInteger(options.limit) ||
-        options.limit <= 0 ||
-        options.limit > 2147483647)
+      options.pageLimit !== undefined &&
+      (!Number.isInteger(options.pageLimit) ||
+        options.pageLimit <= 0 ||
+        options.pageLimit > 2147483647)
     ) {
       throw new Error(
         "Database list limit must be an integer between 1 and 2147483647",
       );
     }
-    return this.inner.listDatabases(options.limit, options.pageToken);
+    return new DatabaseNames(
+      this.inner.listDatabases(options.pageToken, options.pageLimit),
+    );
   }
 }
 
@@ -82,7 +130,9 @@ export class Catalog {
  * ```ts
  * const catalog = await connectCatalog("https://my-server.example", { apiKey: "secret" });
  * const db = await catalog.createDatabase("analytics", { existOk: true });
- * const page = await catalog.listDatabases({ limit: 20 });
+ * for await (const name of catalog.listDatabases({ pageLimit: 20 })) {
+ *   console.log(name);
+ * }
  * ```
  */
 export async function connectCatalog(
diff --git a/nodejs/lancedb/connection.ts b/nodejs/lancedb/connection.ts
index 5e9641202..11341a8c6 100644
--- a/nodejs/lancedb/connection.ts
+++ b/nodejs/lancedb/connection.ts
@@ -8,6 +8,7 @@ import {
   fromTableToStreamBuffer,
   isArrowTable,
   makeArrowTable,
+  resolveBlobInputs,
 } from "./arrow";
 import {
   Table as ArrowTable,
@@ -16,6 +17,7 @@ import {
 } from "./arrow";
 import { EmbeddingFunctionConfig, getRegistry } from "./embedding/registry";
 import { Job } from "./job";
+import { Listing, ListingOptions, validateListingOptions } from "./listing";
 import {
   MaterializedView,
   MaterializedViewSelect,
@@ -148,7 +150,7 @@ export interface TableNamesOptions {
    * the last table name from the previous page.
    */
   startAfter?: string;
-  /** An optional limit to the number of results to return. */
+  /** The maximum number of names to return. Omitted returns all names; zero returns none. */
   limit?: number;
 }
 
@@ -162,6 +164,7 @@ export interface ListTablesOptions {
   pageToken?: string;
   /**
    * An upper bound on how many tables to return.
+   * Defaults to 100. Zero returns an empty page without a continuation token.
    *
    * A page may hold fewer than this and still not be the last one, so keep
    * going while the response carries a page token rather than while pages are
@@ -324,10 +327,6 @@ export abstract class Connection {
    * The view is populated before creation returns. Set `withNoData` to create
    * only its definition and empty backing table. The view is a normal table:
    * it can be queried, indexed and searched, and it appears in `tableNames`.
-   * The source table must have stable row ids (create it with
-   * the `newTableEnableStableRowIds` storage option); they keep the view's
-   * provenance valid across source compactions and cannot be enabled after
-   * a table exists.
    */
   abstract createMaterializedView(
     name: string,
@@ -350,9 +349,11 @@ export abstract class Connection {
   /**
    * The names of the materialized views in this database.
    *
-   * Found by reading every table's schema, so this costs an open per table.
+   * Iterate lazily with `for await...of`. Local listings inspect table schemas
+   * one page at a time. Options control page size and the starting token.
+   * See {@link Listing} for pagination state and error behavior.
    */
-  abstract listMaterializedViews(): Promise<string[]>;
+  abstract listMaterializedViews(options?: ListingOptions): Listing<string>;
 
   /**
    * Drop the materialized view named `name`.
@@ -424,8 +425,13 @@ export abstract class Connection {
    * The names of the views in one namespace.
    *
    * Names only; a definition comes from {@link describeView}.
+   * Iterate lazily with `for await...of`. See {@link Listing} for pagination
+   * state and error behavior; options control page size and the starting token.
    */
-  abstract listViews(namespacePath?: string[]): Promise<string[]>;
+  abstract listViews(
+    namespacePath?: string[],
+    options?: ListingOptions,
+  ): Listing<string>;
 
   abstract openTable(
     name: string,
@@ -640,8 +646,12 @@ export abstract class Connection {
    */
   abstract openJob(jobId: string): Promise<Job>;
 
-  /** List server-side jobs across the database's tables. */
-  abstract listJobs(): Promise<JobInfo[]>;
+  /**
+   * Iterate lazily over server-side jobs across the database's tables.
+   * Use `for await...of`. Options control page size and the starting token.
+   * See {@link Listing} for pagination state and error behavior.
+   */
+  abstract listJobs(options?: ListingOptions): Listing<JobInfo>;
 
   /**
    * Request cancellation of a server-side job by id.
@@ -744,8 +754,11 @@ export class LocalConnection extends Connection {
     return new MaterializedView(new LocalTable(innerTable));
   }
 
-  async listMaterializedViews(): Promise<string[]> {
-    return await this.inner.listMaterializedViews();
+  listMaterializedViews(options: ListingOptions = {}): Listing<string> {
+    validateListingOptions(options);
+    return new Listing(
+      this.inner.listMaterializedViews(options.pageToken, options.pageLimit),
+    );
   }
 
   async dropMaterializedView(
@@ -791,8 +804,18 @@ export class LocalConnection extends Connection {
     return new Job(await this.inner.dropViewAsync(name, namespacePath ?? []));
   }
 
-  async listViews(namespacePath?: string[]): Promise<string[]> {
-    return this.inner.listViews(namespacePath ?? []);
+  listViews(
+    namespacePath?: string[],
+    options: ListingOptions = {},
+  ): Listing<string> {
+    validateListingOptions(options);
+    return new Listing(
+      this.inner.listViews(
+        namespacePath ?? [],
+        options.pageToken,
+        options.pageLimit,
+      ),
+    );
   }
 
   async listTables(
@@ -1060,8 +1083,11 @@ export class LocalConnection extends Connection {
     return new Job(await this.inner.openJob(jobId));
   }
 
-  async listJobs(): Promise<JobInfo[]> {
-    return this.inner.listJobs();
+  listJobs(options: ListingOptions = {}): Listing<JobInfo> {
+    validateListingOptions(options);
+    return new Listing(
+      this.inner.listJobs(options.pageToken, options.pageLimit),
+    );
   }
 
   async cancelJob(jobId: string): Promise<boolean> {
@@ -1133,7 +1159,11 @@ async function parseTableData(
   if (isArrowTable(data)) {
     table = sanitizeTable(data);
   } else {
-    table = makeArrowTable(data as Record<string, unknown>[], options);
+    const records = await resolveBlobInputs(
+      data as Record<string, unknown>[],
+      options?.schema,
+    );
+    table = makeArrowTable(records, options);
   }
   if (streaming) {
     const buf = await fromTableToStreamBuffer(
diff --git a/nodejs/lancedb/index.ts b/nodejs/lancedb/index.ts
index ed6889232..3254a0742 100644
--- a/nodejs/lancedb/index.ts
+++ b/nodejs/lancedb/index.ts
@@ -84,7 +84,14 @@ export {
 } from "./arrow";
 
 export { blob, isBlobField, BlobFile } from "./blob";
-export type { BlobOptions } from "./blob";
+export type {
+  BlobData,
+  BlobInput,
+  BlobOptions,
+  BlobRange,
+  BlobReadOptions,
+  BlobUri,
+} from "./blob";
 
 export {
   Connection,
@@ -160,10 +167,10 @@ export {
   WriteProgress,
   FtsToken,
   TokenizeTableOptions,
-  LsmWriteSpec,
-  LsmStats,
-  BucketStats,
-  GenerationStats,
+  MemWalWriteSpec,
+  MemWalStats,
+  TableShardStats,
+  SsTableStats,
   MemtableStats,
   ColumnAlteration,
   FieldMetadataUpdate,
@@ -195,6 +202,7 @@ export * as rerankers from "./rerankers";
 export {
   SchemaLike,
   TableLike,
+  DataTypeLike,
   FieldLike,
   RecordBatchLike,
   DataLike,
@@ -638,6 +646,8 @@ export async function connectNamespace(
 export {
   Catalog,
   CatalogOptions,
-  ListDatabasesResponse,
+  DatabaseNames,
   connectCatalog,
 } from "./catalog";
+
+export { Listing, ListingOptions } from "./listing";
diff --git a/nodejs/lancedb/indices.ts b/nodejs/lancedb/indices.ts
index bc9e19300..43d1cf1b5 100644
--- a/nodejs/lancedb/indices.ts
+++ b/nodejs/lancedb/indices.ts
@@ -491,6 +491,7 @@ export type BaseTokenizer =
   | "whitespace"
   | "raw"
   | "ngram"
+  | "code"
   | "icu"
   | "icu/split"
   | `jieba/${string}`
@@ -520,6 +521,8 @@ export interface FtsOptions {
    *
    * "raw" - Raw tokenizer. This tokenizer does not split the text into tokens and indexes the entire text as a single token.
    *
+   * "code" - Code tokenizer. This tokenizer keeps snake_case and camelCase identifiers as single tokens.
+   *
    * "icu" - ICU dictionary-based word segmentation.
    *
    * "icu/split" - ICU segmentation with simple-style delimiter splitting.
diff --git a/nodejs/lancedb/listing.ts b/nodejs/lancedb/listing.ts
new file mode 100644
index 000000000..87333a28f
--- /dev/null
+++ b/nodejs/lancedb/listing.ts
@@ -0,0 +1,73 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+/** Pagination controls for lazy resource listings. */
+export interface ListingOptions {
+  /** Starting continuation token; omitted starts at the beginning. */
+  pageToken?: string;
+  /** Maximum results per request, not a limit on the whole listing. */
+  pageLimit?: number;
+}
+
+/** @internal */
+export function validateListingOptions(options: ListingOptions): void {
+  if (
+    options.pageLimit !== undefined &&
+    (!Number.isInteger(options.pageLimit) ||
+      options.pageLimit <= 0 ||
+      options.pageLimit > 2147483647)
+  ) {
+    throw new Error(
+      "Listing page limit must be an integer between 1 and 2147483647",
+    );
+  }
+}
+
+/**
+ * A lazy async iterator over database resources with pagination state.
+ * Requests occur during iteration; request errors terminate the iterator.
+ *
+ * @example
+ * ```ts
+ * for await (const name of connection.listViews([], { pageLimit: 20 })) {
+ *   console.log(name);
+ * }
+ * ```
+ */
+export class Listing<T> implements AsyncIterableIterator<T> {
+  /** @hidden */
+  constructor(
+    private readonly inner: {
+      next(): Promise<T | null | undefined>;
+      numPageResults(): number;
+      pageToken(): string | null | undefined;
+    },
+  ) {}
+
+  [Symbol.asyncIterator](): AsyncIterableIterator<T> {
+    return this;
+  }
+
+  /** Fetch the next item, requesting another page only when needed. */
+  async next(): Promise<IteratorResult<T>> {
+    const value = await this.inner.next();
+    return value == null
+      ? { done: true, value: undefined }
+      : { done: false, value };
+  }
+
+  /** Number of cached items available without another request. */
+  numPageResults(): number {
+    return this.inner.numPageResults();
+  }
+
+  /**
+   * Token for the next request. Initially this is the supplied starting token.
+   * Returns undefined after fetching the final page, even with cached items.
+   * Drain the cache before saving a token to avoid skipping items on resumption.
+   * Failed requests terminate iteration and retain their token for a new iterator.
+   */
+  pageToken(): string | undefined {
+    return this.inner.pageToken() ?? undefined;
+  }
+}
diff --git a/nodejs/lancedb/materialized_view.ts b/nodejs/lancedb/materialized_view.ts
index a59a3a5f6..7cf1690e0 100644
--- a/nodejs/lancedb/materialized_view.ts
+++ b/nodejs/lancedb/materialized_view.ts
@@ -9,10 +9,9 @@ export const DEFINITION_META_KEY = "mv.definition";
 
 /**
  * The newest stored layout this version reads: `{"format": N, "query": "<SQL>"}`,
- * format 2 being a query with `GROUP BY`, format 3 native pairs and format 4
- * native dedup results.
+ * format 2 being a query with `GROUP BY`.
  */
-export const DEFINITION_FORMAT = 4;
+export const DEFINITION_FORMAT = 2;
 
 /**
  * The query that defines a materialized view, as stored:
@@ -188,23 +187,14 @@ export class MaterializedView {
   /**
    * Recompute the view from its source.
    *
-   * The refresh is incremental when the source's changes can be reconciled
-   * into the view -- rows added, changed or removed since the last one --
-   * and otherwise rebuilds. `full` forces a rebuild; `sourceVersion`
-   * refreshes to that source version instead of the latest.
-   *
-   * Concurrent refreshes of one view do not duplicate its rows. Two that
-   * plan the same source rows conflict on commit, and the loser throws
-   * rather than writing them a second time.
+   * Every refresh rebuilds the complete view. For local views,
+   * `sourceVersion` refreshes to that source version instead of the latest;
+   * remote SQL refreshes do not support source-version pinning.
    */
   async refresh(options?: {
-    full?: boolean;
     sourceVersion?: number;
   }): Promise<RefreshMaterializedViewResult> {
     validateNonNegativeInteger(options?.sourceVersion, "sourceVersion");
-    return await this.inner.refreshMaterializedView(
-      options?.full,
-      options?.sourceVersion,
-    );
+    return await this.inner.refreshMaterializedView(options?.sourceVersion);
   }
 }
diff --git a/nodejs/lancedb/merge.ts b/nodejs/lancedb/merge.ts
index 5f1d8704a..0c6cdaa29 100644
--- a/nodejs/lancedb/merge.ts
+++ b/nodejs/lancedb/merge.ts
@@ -90,25 +90,25 @@ export class MergeInsertBuilder {
   /**
    * Control MemWAL routing for this merge.
    *
-   * By default (unset), a `mergeInsert` on a table with an LSM write spec is
+   * By default (unset), a `mergeInsert` on a table with a MemWAL write spec is
    * routed through Lance's MemWAL shard writer, and a table without one uses the
    * standard path.
    *
    * @param enable - `true` forces MemWAL routing and errors if the table has no
-   * LSM write spec. `false` forces the standard write path even when a spec is set.
+   * MemWAL write spec. `false` forces the standard write path even when a spec is set.
    */
-  useLsm(enable: boolean): MergeInsertBuilder {
-    return new MergeInsertBuilder(this.#native.useLsm(enable), this.#schema);
+  useMemWal(enable: boolean): MergeInsertBuilder {
+    return new MergeInsertBuilder(this.#native.useMemWal(enable), this.#schema);
   }
   /**
    * Controls how an LSM merge checks that its input targets a single shard.
    *
-   * When a table has an LSM write spec, every row in a `mergeInsert` call must
+   * When a table has a MemWAL write spec, every row in a `mergeInsert` call must
    * route to the same shard. When `true` (the default), every row is inspected
    * to verify this. When `false`, only the first row is inspected and the
    * shard it routes to is used for the whole input — a faster path for callers
    * that have already pre-sharded their input. Has no effect on tables without
-   * an LSM write spec.
+   * a MemWAL write spec.
    *
    * @param validateSingleShard - Whether to check every row routes to one shard. Defaults to `true`.
    */
diff --git a/nodejs/lancedb/query.ts b/nodejs/lancedb/query.ts
index 75a787fcd..3583e5992 100644
--- a/nodejs/lancedb/query.ts
+++ b/nodejs/lancedb/query.ts
@@ -41,26 +41,6 @@ export async function* RecordBatchIterator(
   }
 }
 
-class RecordBatchIterable<
-  NativeQueryType extends NativeQuery | NativeVectorQuery | NativeTakeQuery,
-> implements AsyncIterable<RecordBatch>
-{
-  private inner: NativeQueryType;
-  private options?: QueryExecutionOptions;
-
-  constructor(inner: NativeQueryType, options?: QueryExecutionOptions) {
-    this.inner = inner;
-    this.options = options;
-  }
-
-  // biome-ignore lint/suspicious/noExplicitAny: skip
-  [Symbol.asyncIterator](): AsyncIterator<RecordBatch<any>, any, undefined> {
-    return RecordBatchIterator(
-      this.inner.execute(this.options?.maxBatchLength, this.options?.timeoutMs),
-    );
-  }
-}
-
 /**
  * Options that control the behavior of a particular query execution
  */
@@ -271,13 +251,20 @@ export class QueryBase<
     return RecordBatchIterator(this.nativeExecute());
   }
 
-  /** Collect the results as an Arrow @see {@link ArrowTable}. */
+  /**
+   * Collect the results as an Arrow @see {@link ArrowTable}.
+   *
+   * Empty results retain the query's output schema.
+   */
   async toArrow(options?: Partial<QueryExecutionOptions>): Promise<ArrowTable> {
     const batches = [];
-    const inner = await this.getInner();
-    for await (const batch of new RecordBatchIterable(inner, options)) {
+    const iterator = this.nativeExecute(options);
+    for await (const batch of RecordBatchIterator(iterator)) {
       batches.push(batch);
     }
+    if (batches.length === 0) {
+      return tableFromIPC((await iterator).schema());
+    }
     return new ArrowTable(batches);
   }
 
@@ -477,10 +464,10 @@ export class StandardQueryBase<
    * Control MemWAL read routing for this query.
    *
    * By default (unset), when the table carries a MemWAL write spec (see
-   * {@link Table#setLsmWriteSpec}), reads are routed through the LSM scanner so
+   * {@link Table#enableMemWal}), reads are routed through the LSM scanner so
    * they also return data written via the `mergeInsert` LSM path that has not yet
    * been compacted into the base table (the active/frozen in-memory memtables and
-   * the flushed generations), deduplicated by primary key; a table without a spec
+   * the SSTables), deduplicated by primary key; a table without a spec
    * reads the base table.
    *
    * @param enable - `true` forces the LSM scanner and errors if the table has no
@@ -489,11 +476,11 @@ export class StandardQueryBase<
    *
    * Note: the LSM scanner does not support every query shape (e.g. reranking,
    * hybrid search, `orderBy`). On a MemWAL table those shapes error unless
-   * `useLsm(false)` is set, because a base-only read would silently exclude
+   * `useMemWal(false)` is set, because a base-only read would silently exclude
    * un-compacted MemWAL data.
    */
-  useLsm(enable: boolean): this {
-    this.doCall((inner: NativeQueryType) => inner.useLsm(enable));
+  useMemWal(enable: boolean): this {
+    this.doCall((inner: NativeQueryType) => inner.useMemWal(enable));
     return this;
   }
 }
@@ -532,6 +519,8 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   /**
    * Set the number of partitions to search (probe)
    *
+   * The number of probes must be greater than 0.
+   *
    * This argument is only used when the vector column has an IVF PQ index.
    * If there is no index then this value is ignored.
    *
@@ -543,17 +532,16 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
    * partitions should be searched.
    *
    * Increasing this value will increase the recall of your query but will
-   * also increase the latency of your query.  The default value is 20.  This
-   * default is good for many cases but the best value to use will depend on
-   * your data and the recall that you need to achieve.
+   * also increase the latency of your query. If this method is not called,
+   * Lance's adaptive probe defaults are used.
    *
    * For best results we recommend tuning this parameter with a benchmark against
    * your actual data to find the smallest possible value that will still give
    * you the desired recall.
    *
-   * For more fine grained control over behavior when you have a very narrow filter
-   * you can use `minimumNprobes` and `maximumNprobes`.  This method sets both
-   * the minimum and maximum to the same value.
+   * The value is retained as `nprobes` through client and server request
+   * construction. Lance sets both probe bounds to this value. Explicit minimum
+   * or maximum settings can override their respective bounds.
    */
   nprobes(nprobes: number): VectorQuery {
     this.doVectorCall((inner) => inner.nprobes(nprobes));
@@ -561,6 +549,27 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
     return this;
   }
 
+  /**
+   * Set the initial IVF search effort to a finite value in [0, 1].
+   *
+   * The default, null, uses Auto (internally 0.5). Zero starts at the caller
+   * minimum; one searches all partitions within the caller maximum. Later
+   * probing may continue when too few candidates are found.
+   * Any explicit effort, including 0.5, overrides nprobes in either call order.
+   * Explicit minimum and maximum bounds still apply. This is not a recall
+   * guarantee and does not change refinement or HNSW ef. The MemWAL LSM
+   * scanner does not yet support this option.
+   *
+   * @example
+   * ```ts
+   * table.vectorSearch([0.1, 0.2]).searchEffort(0.75).limit(10);
+   * ```
+   */
+  searchEffort(searchEffort: number | null): VectorQuery {
+    this.doVectorCall((inner) => inner.searchEffort(searchEffort));
+    return this;
+  }
+
   /**
    * Set the minimum number of probes used.
    *
@@ -862,8 +871,8 @@ export class TakeQuery extends QueryBase<NativeTakeQuery> {
    *
    * @param enable - `false` reads the base table only.
    */
-  useLsm(enable: boolean): this {
-    this.doCall((inner: NativeTakeQuery) => inner.useLsm(enable));
+  useMemWal(enable: boolean): this {
+    this.doCall((inner: NativeTakeQuery) => inner.useMemWal(enable));
     return this;
   }
 }
diff --git a/nodejs/lancedb/sanitize.ts b/nodejs/lancedb/sanitize.ts
index 3e5d583c4..6ba4cc881 100644
--- a/nodejs/lancedb/sanitize.ts
+++ b/nodejs/lancedb/sanitize.ts
@@ -9,6 +9,7 @@
 // comes from the exact same library instance.  This is not always the case
 // and so we must sanitize the input to ensure that it is compatible.
 
+import * as arrowModule from "apache-arrow";
 import { BufferType, Data, Vector } from "apache-arrow";
 import type { IntBitWidth, TKeys, TimeBitWidth } from "apache-arrow/type";
 import {
@@ -43,6 +44,8 @@ import {
   Interval,
   IntervalDayTime,
   IntervalYearMonth,
+  LargeBinary,
+  LargeUtf8,
   List,
   Map_,
   Null,
@@ -74,6 +77,66 @@ import {
   Utf8,
 } from "./arrow";
 
+/**
+ * Whether `value` was created by *this* copy of apache-arrow's `ctor`.
+ *
+ * Arrow 21 overrides `Symbol.hasInstance` on `Schema`, `Field`, `Table`,
+ * `RecordBatch`, `Vector` and `Data` so that `instanceof` also matches objects
+ * built by a different copy of the library.  Those are exactly the objects
+ * this module has to rebuild: the `DataType` instances nested inside them are
+ * deliberately *not* given that treatment upstream, so Arrow's own builders and
+ * writers reject them.  Walk the real prototype chain instead of trusting
+ * `instanceof`.
+ */
+function isOwnInstance<T>(
+  value: unknown,
+  // biome-ignore lint/suspicious/noExplicitAny: matches any class constructor
+  ctor: abstract new (...args: any[]) => T,
+): value is T {
+  return (
+    typeof value === "object" &&
+    value !== null &&
+    Object.prototype.isPrototypeOf.call(ctor.prototype, value)
+  );
+}
+
+/**
+ * Types added to Arrow JS after release 18, keyed by the `Type` enum value
+ * they use.  The library compiles and runs against every Arrow release in the
+ * peer range, so these are looked up by name at runtime instead of imported:
+ * an older Arrow simply does not have the class.
+ */
+type NewerTypeName =
+  | "IntervalMonthDayNano"
+  | "LargeList"
+  | "BinaryView"
+  | "Utf8View";
+const NEWER_TYPES: ReadonlyArray<{
+  name: NewerTypeName;
+  typeId: number;
+  since: number;
+}> = [
+  { name: "IntervalMonthDayNano", typeId: -31, since: 20 },
+  { name: "LargeList", typeId: 21, since: 21 },
+  { name: "BinaryView", typeId: 23, since: 21 },
+  { name: "Utf8View", typeId: 24, since: 21 },
+];
+
+// biome-ignore lint/suspicious/noExplicitAny: constructor arity varies by type
+type DataTypeConstructor = new (...args: any[]) => DataType;
+
+function newerTypeConstructor(name: NewerTypeName): DataTypeConstructor {
+  const ctor = (arrowModule as unknown as Record<string, unknown>)[name];
+  if (typeof ctor !== "function") {
+    const since = NEWER_TYPES.find((t) => t.name === name)?.since;
+    throw new Error(
+      `The Arrow type ${name} requires apache-arrow ${since} or newer, ` +
+        "but the installed apache-arrow does not provide it",
+    );
+  }
+  return ctor as DataTypeConstructor;
+}
+
 type SanitizationContext = {
   types: WeakMap<object, DataType>;
   vectors: WeakMap<object, Vector>;
@@ -210,19 +273,37 @@ export function sanitizeList(typeLike: object) {
   return sanitizeListWithContext(typeLike, createSanitizationContext());
 }
 
+function sanitizeListChildWithContext(
+  typeLike: object,
+  context: SanitizationContext,
+  typeName: string,
+): Field {
+  if (!("children" in typeLike) || !Array.isArray(typeLike.children)) {
+    throw Error(
+      `Expected a ${typeName} type to have an array-like \`children\` property`,
+    );
+  }
+  if (typeLike.children.length !== 1) {
+    throw Error(`Expected a ${typeName} type to have exactly one child`);
+  }
+  return sanitizeFieldWithContext(typeLike.children[0], context);
+}
+
 function sanitizeListWithContext(
   typeLike: object,
   context: SanitizationContext,
 ) {
-  if (!("children" in typeLike) || !Array.isArray(typeLike.children)) {
-    throw Error(
-      "Expected a List type to have an array-like `children` property",
-    );
-  }
-  if (typeLike.children.length !== 1) {
-    throw Error("Expected a List type to have exactly one child");
-  }
-  return new List(sanitizeFieldWithContext(typeLike.children[0], context));
+  return new List(sanitizeListChildWithContext(typeLike, context, "List"));
+}
+
+function sanitizeLargeListWithContext(
+  typeLike: object,
+  context: SanitizationContext,
+) {
+  const LargeList = newerTypeConstructor("LargeList");
+  return new LargeList(
+    sanitizeListChildWithContext(typeLike, context, "LargeList"),
+  );
 }
 
 export function sanitizeStruct(typeLike: object) {
@@ -449,11 +530,30 @@ function sanitizeTypeWithContext(
   return type;
 }
 
+function sanitizeNewerTypeById(
+  typeLike: object,
+  typeId: number,
+  context: SanitizationContext,
+): DataType | undefined {
+  const entry = NEWER_TYPES.find((t) => t.typeId === typeId);
+  if (entry === undefined) {
+    return undefined;
+  }
+  if (entry.name === "LargeList") {
+    return sanitizeLargeListWithContext(typeLike, context);
+  }
+  return new (newerTypeConstructor(entry.name))();
+}
+
 function sanitizeTypeById(
   typeLike: object,
   typeId: Type,
   context: SanitizationContext,
 ): DataType {
+  const newer = sanitizeNewerTypeById(typeLike, typeId, context);
+  if (newer !== undefined) {
+    return newer;
+  }
   switch (typeId) {
     case Type.NONE:
       throw Error("Received a Type with a typeId of NONE");
@@ -465,8 +565,12 @@ function sanitizeTypeById(
       return sanitizeFloat(typeLike);
     case Type.Binary:
       return new Binary();
+    case Type.LargeBinary:
+      return new LargeBinary();
     case Type.Utf8:
       return new Utf8();
+    case Type.LargeUtf8:
+      return new LargeUtf8();
     case Type.Bool:
       return new Bool();
     case Type.Decimal:
@@ -566,7 +670,7 @@ function sanitizeFieldWithContext(
   fieldLike: unknown,
   context: SanitizationContext,
 ): Field {
-  if (fieldLike instanceof Field) {
+  if (isOwnInstance(fieldLike, Field)) {
     return fieldLike;
   }
   if (typeof fieldLike !== "object" || fieldLike === null) {
@@ -620,7 +724,7 @@ function sanitizeSchemaWithContext(
   schemaLike: SchemaLike,
   context: SanitizationContext,
 ): Schema {
-  if (schemaLike instanceof Schema) {
+  if (isOwnInstance(schemaLike, Schema)) {
     return schemaLike;
   }
   if (typeof schemaLike !== "object" || schemaLike === null) {
@@ -647,7 +751,7 @@ function sanitizeSchemaWithContext(
 }
 
 export function sanitizeTable(tableLike: TableLike): Table {
-  if (tableLike instanceof Table) {
+  if (isOwnInstance(tableLike, Table)) {
     return tableLike;
   }
   if (typeof tableLike !== "object" || tableLike === null) {
@@ -675,7 +779,7 @@ function sanitizeRecordBatch(
   batchLike: RecordBatchLike,
   context: SanitizationContext,
 ): RecordBatch {
-  if (batchLike instanceof RecordBatch) {
+  if (isOwnInstance(batchLike, RecordBatch)) {
     return batchLike;
   }
   if (typeof batchLike !== "object" || batchLike === null) {
@@ -704,11 +808,17 @@ type DictionaryDataLike = DataLike & {
   dictionary?: DictionaryVectorLike;
 };
 
+// Arrow 21 added `variadicBuffers` to `Data` for the Utf8View / BinaryView
+// types.  Older Arrow versions do not have the property, so it is optional.
+type VariadicDataLike = DataLike & {
+  variadicBuffers?: ReadonlyArray<Uint8Array>;
+};
+
 function sanitizeData(
   dataLike: DataLike,
   context: SanitizationContext,
 ): Data<DataType> {
-  if (dataLike instanceof Data) {
+  if (isOwnInstance(dataLike, Data)) {
     return dataLike;
   }
   const cachedData = context.data.get(dataLike);
@@ -726,7 +836,12 @@ function sanitizeData(
       context.vectors.set(dictionaryLike, dictionary);
     }
   }
-  const data = new Data(
+  // Arrow 21 added an eighth constructor parameter (`variadicBuffers`); older
+  // releases ignore the extra argument but their typings do not declare it.
+  const DataCtor = Data as unknown as new (
+    ...args: unknown[]
+  ) => Data<DataType>;
+  const data = new DataCtor(
     sanitizeTypeWithContext(dataLike.type, context),
     dataLike.offset,
     dataLike.length,
@@ -739,6 +854,7 @@ function sanitizeData(
     },
     dataLike.children.map((child) => sanitizeData(child, context)),
     dictionary,
+    (dataLike as VariadicDataLike).variadicBuffers,
   );
   context.data.set(dataLike, data);
   return data;
@@ -747,7 +863,11 @@ function sanitizeData(
 const constructorsByTypeName = {
   null: () => new Null(),
   binary: () => new Binary(),
+  largebinary: () => new LargeBinary(),
+  binaryview: () => new (newerTypeConstructor("BinaryView"))(),
   utf8: () => new Utf8(),
+  largeutf8: () => new LargeUtf8(),
+  utf8view: () => new (newerTypeConstructor("Utf8View"))(),
   bool: () => new Bool(),
   int8: () => new Int8(),
   int16: () => new Int16(),
@@ -768,6 +888,8 @@ const constructorsByTypeName = {
   timesecond: () => new TimeSecond(),
   intervaldaytime: () => new IntervalDayTime(),
   intervalyearmonth: () => new IntervalYearMonth(),
+  intervalmonthdaynano: () =>
+    new (newerTypeConstructor("IntervalMonthDayNano"))(),
   durationnanosecond: () => new DurationNanosecond(),
   durationmicrosecond: () => new DurationMicrosecond(),
   durationmillisecond: () => new DurationMillisecond(),
diff --git a/nodejs/lancedb/schema.ts b/nodejs/lancedb/schema.ts
index 724a81fc9..3e225464f 100644
--- a/nodejs/lancedb/schema.ts
+++ b/nodejs/lancedb/schema.ts
@@ -69,12 +69,18 @@ class SchemaInferrer {
       this.addField(path, value, row);
     } else if (this.providedSchema === undefined) {
       this.updateInferredField(path, value, row, current);
+    } else if (current instanceof DeferredTypeEvidence) {
+      if (value != null) {
+        this.addSchemaField(this.providedSchema, path, value, row);
+      }
+    } else if (current instanceof FieldTree || DataType.isStruct(current)) {
+      assertStructValue(path, value, row);
     }
   }
 
   private addField(path: string[], value: unknown, row: number): void {
     if (this.providedSchema !== undefined) {
-      this.addSchemaField(this.providedSchema, path, row);
+      this.addSchemaField(this.providedSchema, path, value, row);
       return;
     }
 
@@ -95,15 +101,31 @@ class SchemaInferrer {
     }
   }
 
-  private addSchemaField(schema: Schema, path: string[], row: number): void {
+  private addSchemaField(
+    schema: Schema,
+    path: string[],
+    value: unknown,
+    row: number,
+  ): void {
     const field = fieldAtPath(schema, path);
     if (field === undefined) {
       throw new Error(
         `Found field not in schema: ${path.join(".")} at row ${row}`,
       );
     }
+    if (DataType.isStruct(field.type)) {
+      assertStructValue(path, value, row);
+    }
 
-    const conflict = this.fields.set(path, field.type);
+    // Nulls are kept as deferred evidence so that a struct field seen first as
+    // null can be replaced by a record in a later row, just as a record seen
+    // first already ignores later nulls.
+    const conflict = this.fields.set(
+      path,
+      DeferredTypeEvidence.from(value, row) ?? field.type,
+      (existing) =>
+        existing instanceof DeferredTypeEvidence && existing.isOnlyNulls(),
+    );
     if (conflict !== undefined) {
       throw branchConflictError(conflict, row, "Struct");
     }
@@ -483,6 +505,26 @@ function assertSupportedValue(
   }
 }
 
+/**
+ * Rejects primitives, arrays, and typed arrays given for a struct field, which
+ * would otherwise be written as a struct with every child null.
+ */
+function assertStructValue(path: string[], value: unknown, row: number): void {
+  if (
+    value == null ||
+    (typeof value === "object" &&
+      !Array.isArray(value) &&
+      !ArrayBuffer.isView(value))
+  ) {
+    return;
+  }
+  const kind =
+    typeof value === "object" ? value.constructor.name : typeof value;
+  throw new Error(
+    `Expected a struct value for field ${path.join(".")} at row ${row}, got ${kind}.`,
+  );
+}
+
 function fieldAtPath(schema: Schema, path: string[]): Field | undefined {
   let fields = schema.fields;
   let field: Field | undefined;
diff --git a/nodejs/lancedb/table.ts b/nodejs/lancedb/table.ts
index 132e8bc20..4d6500a5f 100644
--- a/nodejs/lancedb/table.ts
+++ b/nodejs/lancedb/table.ts
@@ -17,7 +17,7 @@ import {
   tableFromIPC,
 } from "./arrow";
 
-import { BlobFile } from "./blob";
+import { BlobFile, BlobReadOptions, runWithSignal } from "./blob";
 import { EmbeddingFunctionConfig, getRegistry } from "./embedding/registry";
 import { IndexOptions } from "./indices";
 import { Job } from "./job";
@@ -35,7 +35,7 @@ import {
   FunctionErrorsOptions,
   IndexConfig,
   IndexStatistics,
-  LsmStats,
+  MemWalStats,
   Branches as NativeBranches,
   OptimizeStats,
   RefreshColumnResult,
@@ -59,9 +59,9 @@ import { sanitizeType } from "./sanitize";
 import { IntoSql, toSQL } from "./util";
 export { IndexConfig } from "./native";
 export {
-  BucketStats,
-  GenerationStats,
-  LsmStats,
+  TableShardStats,
+  SsTableStats,
+  MemWalStats,
   MemtableStats,
 } from "./native";
 
@@ -217,7 +217,7 @@ export type TokenizeTableOptions =
  * `column` value, or upserts of that key can land in different shards and a
  * stale version can win).
  */
-export interface LsmWriteSpec {
+export interface MemWalWriteSpec {
   /** One of `"bucket"`, `"identity"`, or `"unsharded"`. */
   specType: "bucket" | "identity" | "unsharded";
   /** Bucket and identity variants: the sharding column. */
@@ -225,9 +225,8 @@ export interface LsmWriteSpec {
   /** Bucket variant: the number of buckets, in `[1, 1024]`. */
   numBuckets?: number;
   /**
-   * Indexes the MemWAL keeps up to date. Omit to maintain every supported
-   * index, resolved on install — a snapshot, so indexes created later are not
-   * maintained. Pass `[]` for none.
+   * Indexes the MemWAL keeps up to date. Omit for every index the table has,
+   * including ones created later. Pass `[]` for none.
    */
   maintainedIndexes?: string[];
   /** Default `ShardWriter` configuration recorded in the MemWAL index. */
@@ -527,7 +526,8 @@ export abstract class Table {
   abstract takeRowIds(rowIds: readonly (bigint | number)[]): TakeQuery;
 
   /**
-   * Blob v2 columns, including nested dotted paths.
+   * Blob v2 columns addressable by row ID, including struct-nested dotted paths.
+   * Blobs inside lists are omitted because a row ID does not identify an element.
    */
   abstract blobColumns(): Promise<string[]>;
 
@@ -537,6 +537,10 @@ export abstract class Table {
    * Reads the table's current checkout. IDs from another version can fail after
    * compaction unless stable row ids are enabled. Results keep input order and
    * duplicates. Null blobs are `null`. Empty blobs are empty buffers.
+   * One missing or deleted row ID rejects the entire batch, including on remote
+   * tables; no partial results are returned.
+   * Blobs inside lists cannot be fetched by row ID.
+   *
    * Remote servers limit each request to 1024 row IDs and 64 MiB of blob bytes.
    * The client splits requests automatically and reads an individual larger
    * blob through the Range route. This method still materializes all bytes in
@@ -545,6 +549,7 @@ export abstract class Table {
   abstract fetchBlobs(
     column: string,
     rowIds: readonly (bigint | number)[],
+    options?: BlobReadOptions,
   ): Promise<(Buffer | null)[]>;
 
   /**
@@ -553,10 +558,13 @@ export abstract class Table {
    *
    * Preserves input order, duplicates, and nulls. Use this for large payloads.
    * See {@link Table.fetchBlobs} for row-ID validity across versions.
+   * One missing or deleted row ID rejects the entire batch, including on remote
+   * tables; no partial handles are returned.
    */
   abstract fetchBlobFiles(
     column: string,
     rowIds: readonly (bigint | number)[],
+    options?: BlobReadOptions,
   ): Promise<(BlobFile | null)[]>;
 
   /**
@@ -683,7 +691,6 @@ export abstract class Table {
    * @ignore
    */
   abstract refreshMaterializedView(
-    full?: boolean,
     sourceVersion?: number,
   ): Promise<RefreshMaterializedViewResult>;
 
@@ -751,10 +758,10 @@ export abstract class Table {
    */
   abstract setUnenforcedPrimaryKey(columns: string | string[]): Promise<void>;
   /**
-   * Install an {@link LsmWriteSpec} on this table, selecting Lance's MemWAL
+   * Install an {@link MemWalWriteSpec} on this table, selecting Lance's MemWAL
    * LSM-style write path for future `mergeInsert` calls.
    *
-   * `LsmWriteSpec` chooses one of three sharding strategies via `specType`:
+   * `MemWalWriteSpec` chooses one of three sharding strategies via `specType`:
    *
    * - `"bucket"` — hash-bucket writes by the single-column unenforced primary
    *   key (`column` and `numBuckets` required).
@@ -765,16 +772,16 @@ export abstract class Table {
    * ({@link Table#setUnenforcedPrimaryKey}); bucket sharding additionally
    * requires it to be the single column being bucketed.
    *
-   * Omitting `maintainedIndexes` maintains every index on the table, resolved
-   * here, failing if one cannot be maintained — name them to install anyway.
+   * Omitting `maintainedIndexes` maintains every index the table has,
+   * including ones created later, and skips a kind the MemWAL cannot maintain.
    * Naming them pins an exact set, and a still-building index is rejected
    * rather than quietly omitted.
-   * @param {LsmWriteSpec} spec The sharding spec to install.
+   * @param {MemWalWriteSpec} spec The sharding spec to install.
    * @returns {Promise<void>}
    * @example
    * ```ts
    * await table.setUnenforcedPrimaryKey("id");
-   * await table.setLsmWriteSpec({
+   * await table.enableMemWal({
    *   specType: "bucket",
    *   column: "id",
    *   numBuckets: 16,
@@ -782,60 +789,59 @@ export abstract class Table {
    * });
    * ```
    */
-  abstract setLsmWriteSpec(spec: LsmWriteSpec): Promise<void>;
+  abstract enableMemWal(spec: MemWalWriteSpec): Promise<void>;
   /**
-   * Remove the {@link LsmWriteSpec} from this table, reverting to the standard
+   * Remove the {@link MemWalWriteSpec} from this table, reverting to the standard
    * `mergeInsert` write path.
    *
    * Errors if no spec is currently set.
    * @returns {Promise<void>}
    */
-  abstract unsetLsmWriteSpec(): Promise<void>;
+  abstract disableMemWal(): Promise<void>;
   /**
-   * Read the {@link LsmWriteSpec} currently installed on this table.
+   * Read the {@link MemWalWriteSpec} currently installed on this table.
    *
-   * Resolves to `undefined` when the MemWAL LSM write path is not enabled (no
-   * spec has been set, or it was removed with {@link Table#unsetLsmWriteSpec}).
-   * The returned spec mirrors what was passed to
-   * {@link Table#setLsmWriteSpec}, except that `maintainedIndexes` always
-   * reports the concrete list resolved when the spec was set — `undefined`
-   * never round-trips.
-   * @returns {Promise<LsmWriteSpec | undefined>}
+   * Resolves to `undefined` when the MemWAL write path is not enabled (no
+   * spec has been set, or it was removed with {@link Table#disableMemWal}).
+   * The spec is the one installed, including its maintained-index selection:
+   * an absent `maintainedIndexes` for every index the table has, an empty
+   * array for none.
+   * @returns {Promise<MemWalWriteSpec | undefined>}
    */
-  abstract getLsmWriteSpec(): Promise<LsmWriteSpec | undefined>;
+  abstract getMemWalSpec(): Promise<MemWalWriteSpec | undefined>;
   /**
    * Drain and close any cached MemWAL shard writers held for this table.
    *
-   * When an {@link LsmWriteSpec} is installed, `mergeInsert` opens MemWAL
+   * When an {@link MemWalWriteSpec} is installed, `mergeInsert` opens MemWAL
    * shard writers and caches them for reuse across calls. This closes them,
    * flushing pending data; writers reopen lazily on the next `mergeInsert`.
    * It is a no-op when no writers are cached.
    * @returns {Promise<void>}
    */
-  abstract closeLsmWriters(): Promise<void>;
+  abstract closeMemWalWriters(): Promise<void>;
   /**
-   * Seal every bucket's active memtable into a new L0 generation.
+   * Freeze every table shard's active memtable into a new SSTable.
    *
-   * Returns once the seal is committed. Sealing an empty memtable is a no-op,
+   * Returns once the freeze is committed. Freezing an empty memtable is a no-op,
    * so this is safe to call repeatedly.
    * @returns {Promise<void>}
    */
-  abstract flushLsm(): Promise<void>;
+  abstract flushMemWal(): Promise<void>;
   /**
-   * Trigger a background L0 → base compaction pass per bucket.
+   * Trigger a background SSTable compaction pass per table shard.
    *
    * Returns once the passes are *dispatched*, not once they finish — watch
-   * {@link Table#getLsmStats} for progress, or use
-   * {@link Table#checkpointLsm} to wait for convergence.
+   * {@link Table#getMemWalStats} for progress, or use
+   * {@link Table#checkpointMemWal} to wait for convergence.
    * @returns {Promise<void>}
    */
-  abstract compactLsm(): Promise<void>;
+  abstract compactMemWal(): Promise<void>;
   /**
-   * Converge this table's LSM write path into its base table.
+   * Converge this table's MemWAL write path into its base table.
    *
-   * Seals once, then triggers compaction and polls until the L0 that existed
+   * Freezes once, then triggers compaction and polls until the SSTables that existed
    * at the start is gone. The target set is fixed at the start, so
-   * generations created *during* the checkpoint are ignored — that is what
+   * SSTables created *during* the checkpoint are ignored — that is what
    * lets it terminate under write load, and what makes it best-effort: it
    * converges the fresh tier as of some instant. Idempotent, abandonable at
    * any point, and safe to run on a cadence.
@@ -846,26 +852,26 @@ export abstract class Table {
    * @returns {Promise<void>}
    * @example
    * ```ts
-   * const before = await table.getLsmStats();
-   * await table.checkpointLsm();
-   * const after = await table.getLsmStats();
+   * const before = await table.getMemWalStats();
+   * await table.checkpointMemWal();
+   * const after = await table.getMemWalStats();
    * ```
    */
-  abstract checkpointLsm(): Promise<void>;
+  abstract checkpointMemWal(): Promise<void>;
   /**
-   * Read live per-bucket LSM state.
+   * Read live per-table-shard MemWAL state.
    *
-   * Answers "how far behind is my fresh tier", "which bucket is hot", and
+   * Answers "how far behind is my fresh tier", "which table shard is hot", and
    * "why is my fresh-tier vector search brute-force". Mutates no table state.
    *
-   * Resolves to `undefined` only when the LSM write path is not enabled.
-   * @param {boolean} includeGenerationRows Also count rows per L0 generation.
+   * Resolves to `undefined` only when the MemWAL write path is not enabled.
+   * @param {boolean} includeSstableRows Also count rows per SSTable.
    *   Off by default because each count opens an uncached Lance dataset.
-   * @returns {Promise<LsmStats | undefined>}
+   * @returns {Promise<MemWalStats | undefined>}
    */
-  abstract getLsmStats(
-    includeGenerationRows?: boolean,
-  ): Promise<LsmStats | undefined>;
+  abstract getMemWalStats(
+    includeSstableRows?: boolean,
+  ): Promise<MemWalStats | undefined>;
   /** Retrieve the version of the table */
 
   abstract version(): Promise<number>;
@@ -1248,8 +1254,11 @@ export class LocalTable extends Table {
   async fetchBlobs(
     column: string,
     rowIds: readonly (bigint | number)[],
+    options?: BlobReadOptions,
   ): Promise<(Buffer | null)[]> {
-    const values = await this.inner.fetchBlobs(column, rowIdsToBigInts(rowIds));
+    const values = await runWithSignal(options?.signal, (signal) =>
+      this.inner.fetchBlobs(column, rowIdsToBigInts(rowIds), signal),
+    );
     // N-API Option maps missing values to undefined. Collapse those to null.
     return values.map((value) => value ?? null);
   }
@@ -1257,10 +1266,10 @@ export class LocalTable extends Table {
   async fetchBlobFiles(
     column: string,
     rowIds: readonly (bigint | number)[],
+    options?: BlobReadOptions,
   ): Promise<(BlobFile | null)[]> {
-    const files = await this.inner.fetchBlobFiles(
-      column,
-      rowIdsToBigInts(rowIds),
+    const files = await runWithSignal(options?.signal, (signal) =>
+      this.inner.fetchBlobFiles(column, rowIdsToBigInts(rowIds), signal),
     );
     // N-API Option maps missing values to undefined. Collapse those to null.
     return files.map((file) =>
@@ -1415,10 +1424,9 @@ export class LocalTable extends Table {
   }
 
   async refreshMaterializedView(
-    full?: boolean,
     sourceVersion?: number,
   ): Promise<RefreshMaterializedViewResult> {
-    return await this.inner.refreshMaterializedView(full, sourceVersion);
+    return await this.inner.refreshMaterializedView(sourceVersion);
   }
 
   async materializedViewDefinition(): Promise<string> {
@@ -1466,43 +1474,43 @@ export class LocalTable extends Table {
     return await this.inner.setUnenforcedPrimaryKey(cols);
   }
 
-  async setLsmWriteSpec(spec: LsmWriteSpec): Promise<void> {
-    return await this.inner.setLsmWriteSpec(spec);
+  async enableMemWal(spec: MemWalWriteSpec): Promise<void> {
+    return await this.inner.enableMemWal(spec);
   }
 
-  async unsetLsmWriteSpec(): Promise<void> {
-    return await this.inner.unsetLsmWriteSpec();
+  async disableMemWal(): Promise<void> {
+    return await this.inner.disableMemWal();
   }
 
-  async getLsmWriteSpec(): Promise<LsmWriteSpec | undefined> {
+  async getMemWalSpec(): Promise<MemWalWriteSpec | undefined> {
     // The native binding types `specType` as a plain `string`; narrow it back
     // to the public union. The Rust `From` impl only ever emits one of the
     // three valid values, so the cast is safe.
-    return ((await this.inner.getLsmWriteSpec()) ?? undefined) as
-      | LsmWriteSpec
+    return ((await this.inner.getMemWalSpec()) ?? undefined) as
+      | MemWalWriteSpec
       | undefined;
   }
 
-  async closeLsmWriters(): Promise<void> {
-    return await this.inner.closeLsmWriters();
+  async closeMemWalWriters(): Promise<void> {
+    return await this.inner.closeMemWalWriters();
   }
 
-  async flushLsm(): Promise<void> {
-    return await this.inner.flushLsm();
+  async flushMemWal(): Promise<void> {
+    return await this.inner.flushMemWal();
   }
 
-  async compactLsm(): Promise<void> {
-    return await this.inner.compactLsm();
+  async compactMemWal(): Promise<void> {
+    return await this.inner.compactMemWal();
   }
 
-  async checkpointLsm(): Promise<void> {
-    return await this.inner.checkpointLsm();
+  async checkpointMemWal(): Promise<void> {
+    return await this.inner.checkpointMemWal();
   }
 
-  async getLsmStats(
-    includeGenerationRows: boolean = false,
-  ): Promise<LsmStats | undefined> {
-    return (await this.inner.getLsmStats(includeGenerationRows)) ?? undefined;
+  async getMemWalStats(
+    includeSstableRows: boolean = false,
+  ): Promise<MemWalStats | undefined> {
+    return (await this.inner.getMemWalStats(includeSstableRows)) ?? undefined;
   }
 
   async version(): Promise<number> {
diff --git a/nodejs/npm/darwin-arm64/package.json b/nodejs/npm/darwin-arm64/package.json
index 0e9d131b0..2b95f8f3e 100644
--- a/nodejs/npm/darwin-arm64/package.json
+++ b/nodejs/npm/darwin-arm64/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.40.0-beta.11",
+	"version": "0.41.0-beta.7",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",
diff --git a/nodejs/npm/linux-arm64-gnu/package.json b/nodejs/npm/linux-arm64-gnu/package.json
index 39002b700..567766ad1 100644
--- a/nodejs/npm/linux-arm64-gnu/package.json
+++ b/nodejs/npm/linux-arm64-gnu/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.40.0-beta.11",
+	"version": "0.41.0-beta.7",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",
diff --git a/nodejs/npm/linux-arm64-musl/package.json b/nodejs/npm/linux-arm64-musl/package.json
index 232796091..ab4224bef 100644
--- a/nodejs/npm/linux-arm64-musl/package.json
+++ b/nodejs/npm/linux-arm64-musl/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-musl",
-	"version": "0.40.0-beta.11",
+	"version": "0.41.0-beta.7",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-musl.node",
diff --git a/nodejs/npm/linux-x64-gnu/package.json b/nodejs/npm/linux-x64-gnu/package.json
index cc9c003e0..bef819b77 100644
--- a/nodejs/npm/linux-x64-gnu/package.json
+++ b/nodejs/npm/linux-x64-gnu/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.40.0-beta.11",
+	"version": "0.41.0-beta.7",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",
diff --git a/nodejs/npm/linux-x64-musl/package.json b/nodejs/npm/linux-x64-musl/package.json
index 2f0356da2..7df785698 100644
--- a/nodejs/npm/linux-x64-musl/package.json
+++ b/nodejs/npm/linux-x64-musl/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-musl",
-	"version": "0.40.0-beta.11",
+	"version": "0.41.0-beta.7",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-musl.node",
diff --git a/nodejs/npm/win32-arm64-msvc/package.json b/nodejs/npm/win32-arm64-msvc/package.json
index 2f96d1019..dd155262e 100644
--- a/nodejs/npm/win32-arm64-msvc/package.json
+++ b/nodejs/npm/win32-arm64-msvc/package.json
@@ -1,6 +1,6 @@
 {
   "name": "@lancedb/lancedb-win32-arm64-msvc",
-  "version": "0.40.0-beta.11",
+  "version": "0.41.0-beta.7",
   "os": [
     "win32"
   ],
diff --git a/nodejs/npm/win32-x64-msvc/package.json b/nodejs/npm/win32-x64-msvc/package.json
index c941d597f..85b72ee02 100644
--- a/nodejs/npm/win32-x64-msvc/package.json
+++ b/nodejs/npm/win32-x64-msvc/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.40.0-beta.11",
+	"version": "0.41.0-beta.7",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",
diff --git a/nodejs/package.json b/nodejs/package.json
index bd37d97e2..6aefb4ba5 100644
--- a/nodejs/package.json
+++ b/nodejs/package.json
@@ -11,7 +11,7 @@
     "ann"
   ],
   "private": false,
-  "version": "0.40.0-beta.11",
+  "version": "0.41.0-beta.7",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",
@@ -49,10 +49,14 @@
     "@types/jest": "^29.1.2",
     "@types/node": "22.7.4",
     "@types/tmp": "^0.2.6",
+    "apache-arrow": "21.2.0",
     "apache-arrow-15": "npm:apache-arrow@15.0.0",
     "apache-arrow-16": "npm:apache-arrow@16.0.0",
     "apache-arrow-17": "npm:apache-arrow@17.0.0",
     "apache-arrow-18": "npm:apache-arrow@18.0.0",
+    "apache-arrow-19": "npm:apache-arrow@19.0.1",
+    "apache-arrow-20": "npm:apache-arrow@20.0.0",
+    "apache-arrow-21": "npm:apache-arrow@21.2.0",
     "eslint": "^8.57.0",
     "jest": "^29.7.0",
     "shx": "^0.3.4",
@@ -102,7 +106,7 @@
   },
   "peerDependencies": {
     "@types/node": ">=22",
-    "apache-arrow": ">=15.0.0 <=18.1.0"
+    "apache-arrow": ">=15.0.0 <22.0.0"
   },
   "peerDependenciesMeta": {
     "@types/node": {
diff --git a/nodejs/pnpm-lock.yaml b/nodejs/pnpm-lock.yaml
index a03838999..4c19305c8 100644
--- a/nodejs/pnpm-lock.yaml
+++ b/nodejs/pnpm-lock.yaml
@@ -14,9 +14,6 @@ importers:
       '@opentelemetry/api':
         specifier: ^1.9.0
         version: 1.9.1
-      apache-arrow:
-        specifier: '>=15.0.0 <=18.1.0'
-        version: 18.1.0
       reflect-metadata:
         specifier: ^0.2.2
         version: 0.2.2
@@ -54,6 +51,9 @@ importers:
       '@types/tmp':
         specifier: ^0.2.6
         version: 0.2.6
+      apache-arrow:
+        specifier: 21.2.0
+        version: 21.2.0
       apache-arrow-15:
         specifier: npm:apache-arrow@15.0.0
         version: apache-arrow@15.0.0
@@ -66,6 +66,15 @@ importers:
       apache-arrow-18:
         specifier: npm:apache-arrow@18.0.0
         version: apache-arrow@18.0.0
+      apache-arrow-19:
+        specifier: npm:apache-arrow@19.0.1
+        version: apache-arrow@19.0.1
+      apache-arrow-20:
+        specifier: npm:apache-arrow@20.0.0
+        version: apache-arrow@20.0.0
+      apache-arrow-21:
+        specifier: npm:apache-arrow@21.2.0
+        version: apache-arrow@21.2.0
       eslint:
         specifier: ^8.57.0
         version: 8.57.1
@@ -1703,6 +1712,9 @@ packages:
   '@types/node@22.7.4':
     resolution: {integrity: sha512-y+NPi1rFzDs1NdQHHToqeiX2TIS79SWEAw9GYhkkx8bD0ChpfqC+n2j5OXOCpzfojBEBt6DnEnnG9MY0zk1XLg==}
 
+  '@types/node@25.9.8':
+    resolution: {integrity: sha512-VfMrScDmMhUJQmd5hArdQnFvK0OIeD36uN2Va1FcpYaLB/BgkgM8Ulc50XtYISPMz7APJ30+N0T5EM0jlcdfRw==}
+
   '@types/stack-utils@2.0.3':
     resolution: {integrity: sha512-9aEbYZ3TbYMznPdcdr3SmIrLXwC/AKZXQeCf9Pgao5CKb8CyHuEX5jzWPTkvregvhRJHcpRO6BFoGW9ycaOkYw==}
 
@@ -1840,8 +1852,16 @@ packages:
     resolution: {integrity: sha512-gFlPaqN9osetbB83zC29AbbZqGiCuFH1vyyPseJ+B7SIbfBtESV62mMT/CkiIt77W6ykC/nTWFzTXFs0Uldg4g==}
     hasBin: true
 
-  apache-arrow@18.1.0:
-    resolution: {integrity: sha512-v/ShMp57iBnBp4lDgV8Jx3d3Q5/Hac25FWmQ98eMahUiHPXcvwIMKJD0hBIgclm/FCG+LwPkAKtkRO1O/W0YGg==}
+  apache-arrow@19.0.1:
+    resolution: {integrity: sha512-APmMLzS4qbTivLrPdQXexGM4JRr+0g62QDaobzEvip/FdQIrv2qLy0mD5Qdmw4buydtVJgbFeKR8f59I6PPGDg==}
+    hasBin: true
+
+  apache-arrow@20.0.0:
+    resolution: {integrity: sha512-JUeK0jFRUd7rbmrhhzR3O2KXjLaZ4YYYFOptyUfxOsMIoZCPi6bZR58gVi/xi3HTBMPseXm9PXyQ2V916930pA==}
+    hasBin: true
+
+  apache-arrow@21.2.0:
+    resolution: {integrity: sha512-Hxe6Agq26gQOM954qpzYSllJBPJl+e16U5CkfuMUhLrNba+5nKkttIVlflaovN6oaTratqMGAO8H5u/aNhmHWQ==}
     hasBin: true
 
   argparse@1.0.10:
@@ -2033,6 +2053,15 @@ packages:
     resolution: {integrity: sha512-H4UfQhZyakIjC74I9d34fGYDwk3XpSr17QhEd0Q3I9Xq1CETHo4Hcuo87WyWHpAF1aSLjLRf5lD9ZGX2qStUvg==}
     engines: {node: '>=4.0.0'}
 
+  command-line-args@6.0.2:
+    resolution: {integrity: sha512-AIjYVxrV9X752LmPDLbVYv8aMCuHPSLZJXEo2qo/xJfv+NYhaZ4sMSF01rM+gHPaMgvPM0l5D/F+Qx+i2WfSmQ==}
+    engines: {node: '>=12.20'}
+    peerDependencies:
+      '@75lb/nature': latest
+    peerDependenciesMeta:
+      '@75lb/nature':
+        optional: true
+
   command-line-usage@7.0.4:
     resolution: {integrity: sha512-85UdvzTNx/+s5CkSgBm/0hzP80RFHAa7PsfeADE5ezZF3uHz3/Tqj9gIKGT9PTtpycc3Ua64T0oVulGfKxzfqg==}
     engines: {node: '>=12.20.0'}
@@ -2281,6 +2310,15 @@ packages:
     resolution: {integrity: sha512-6Tb2myMioCAgv5kfvP5/PkZZ/ntTpVK39fHY7WkWBgvbeE+VHd/tZuZ4mrC+bxh4cfOZeYKVPaJIZtZXV7GNCQ==}
     engines: {node: '>=4.0.0'}
 
+  find-replace@5.0.2:
+    resolution: {integrity: sha512-Y45BAiE3mz2QsrN2fb5QEtO4qb44NcS7en/0y9PEVsg351HsLeVclP8QPMH79Le9sH3rs5RSwJu99W0WPZO43Q==}
+    engines: {node: '>=14'}
+    peerDependencies:
+      '@75lb/nature': latest
+    peerDependenciesMeta:
+      '@75lb/nature':
+        optional: true
+
   find-up@4.1.0:
     resolution: {integrity: sha512-PpOwAdQ/YlXQ2vj8a3h8IipDuYRi3wceVQQGYWxNINccq40Anw7BlsEXCMbt1Zt+OLA6Fq9suIpIWD0OsnISlw==}
     engines: {node: '>=8'}
@@ -2302,6 +2340,9 @@ packages:
   flatbuffers@24.12.23:
     resolution: {integrity: sha512-dLVCAISd5mhls514keQzmEG6QHmUUsNuWsb4tFafIUwvvgDjXhtfAYSKOzt5SWOy+qByV5pbsDZ+Vb7HUOBEdA==}
 
+  flatbuffers@25.9.23:
+    resolution: {integrity: sha512-MI1qs7Lo4Syw0EOzUl0xjs2lsoeqFku44KpngfIduHBYvzm8h2+7K8YMQh1JtVVVrUvhLpNwqVi4DERegUJhPQ==}
+
   flatted@3.4.2:
     resolution: {integrity: sha512-PjDse7RzhcPkIJwy5t7KPWQSZ9cAbzQXcafsetQoD7sOJRQlGikNbx7yZp2OotDnJyrDcbyRq3Ttb18iYOqkxA==}
 
@@ -3345,6 +3386,9 @@ packages:
   undici-types@6.21.0:
     resolution: {integrity: sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==}
 
+  undici-types@7.24.6:
+    resolution: {integrity: sha512-WRNW+sJgj5OBN4/0JpHFqtqzhpbnV0GuB+OozA9gCL7a993SmU+1JBZCzLNxYsbMfIeDL+lTsphD5jN5N+n0zg==}
+
   unist-util-is@6.0.1:
     resolution: {integrity: sha512-LsiILbtBETkDz8I9p1dQ0uyRUWuaQzd/cuEeS1hoRSyW5E5XGmTzlwY1OrNzzakGowI9Dr/I8HVaw4hTtnxy8g==}
 
@@ -5505,6 +5549,10 @@ snapshots:
     dependencies:
       undici-types: 6.19.8
 
+  '@types/node@25.9.8':
+    dependencies:
+      undici-types: 7.24.6
+
   '@types/stack-utils@2.0.3': {}
 
   '@types/tmp@0.2.6': {}
@@ -5694,17 +5742,40 @@ snapshots:
       json-bignum: 0.0.3
       tslib: 2.8.1
 
-  apache-arrow@18.1.0:
+  apache-arrow@19.0.1:
     dependencies:
       '@swc/helpers': 0.5.21
       '@types/command-line-args': 5.2.3
       '@types/command-line-usage': 5.0.4
       '@types/node': 20.19.41
-      command-line-args: 5.2.1
+      command-line-args: 6.0.2
       command-line-usage: 7.0.4
       flatbuffers: 24.12.23
       json-bignum: 0.0.3
       tslib: 2.8.1
+    transitivePeerDependencies:
+      - '@75lb/nature'
+
+  apache-arrow@20.0.0:
+    dependencies:
+      '@swc/helpers': 0.5.21
+      '@types/command-line-args': 5.2.3
+      '@types/command-line-usage': 5.0.4
+      '@types/node': 20.19.41
+      command-line-args: 6.0.2
+      command-line-usage: 7.0.4
+      flatbuffers: 25.9.23
+      json-bignum: 0.0.3
+      tslib: 2.8.1
+    transitivePeerDependencies:
+      - '@75lb/nature'
+
+  apache-arrow@21.2.0:
+    dependencies:
+      '@types/node': 25.9.8
+      flatbuffers: 25.9.23
+      json-with-bigint: 3.5.8
+      tslib: 2.8.1
 
   argparse@1.0.10:
     dependencies:
@@ -5906,6 +5977,13 @@ snapshots:
       lodash.camelcase: 4.3.0
       typical: 4.0.0
 
+  command-line-args@6.0.2:
+    dependencies:
+      array-back: 6.2.3
+      find-replace: 5.0.2
+      lodash.camelcase: 4.3.0
+      typical: 7.3.0
+
   command-line-usage@7.0.4:
     dependencies:
       array-back: 6.2.3
@@ -6178,6 +6256,8 @@ snapshots:
     dependencies:
       array-back: 3.1.0
 
+  find-replace@5.0.2: {}
+
   find-up@4.1.0:
     dependencies:
       locate-path: 5.0.0
@@ -6201,6 +6281,8 @@ snapshots:
 
   flatbuffers@24.12.23: {}
 
+  flatbuffers@25.9.23: {}
+
   flatted@3.4.2: {}
 
   follow-redirects@1.16.0: {}
@@ -7405,6 +7487,8 @@ snapshots:
 
   undici-types@6.21.0: {}
 
+  undici-types@7.24.6: {}
+
   unist-util-is@6.0.1:
     dependencies:
       '@types/unist': 3.0.3
diff --git a/nodejs/src/blob.rs b/nodejs/src/blob.rs
index 230a11bae..59cf61af6 100644
--- a/nodejs/src/blob.rs
+++ b/nodejs/src/blob.rs
@@ -1,25 +1,45 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
+use std::future::Future;
 use std::ops::Range;
 use std::sync::Arc;
+use std::sync::atomic::{AtomicBool, Ordering};
 
 use arrow_array::{Array, LargeBinaryArray};
+use futures::future::abortable;
 use lancedb::blob::BlobFile as LanceBlobFile;
+use napi::Env;
 use napi::bindgen_prelude::*;
 use napi_derive::napi;
 
 use crate::error::convert_error;
 
+#[napi(object)]
+pub struct BlobRange {
+    pub start: BigInt,
+    pub end: BigInt,
+}
+
 #[napi]
 pub struct BlobFile {
     inner: Arc<LanceBlobFile>,
+    closed: AtomicBool,
 }
 
 impl BlobFile {
     pub(crate) fn new(inner: LanceBlobFile) -> Self {
         Self {
             inner: Arc::new(inner),
+            closed: AtomicBool::new(false),
+        }
+    }
+
+    fn ensure_open(&self) -> napi::Result<()> {
+        if self.closed.load(Ordering::Acquire) {
+            Err(napi::Error::from_reason("blob file is already closed"))
+        } else {
+            Ok(())
         }
     }
 }
@@ -32,21 +52,124 @@ impl BlobFile {
     }
 
     #[napi]
-    pub async fn read(&self) -> napi::Result<Buffer> {
-        let bytes = self.inner.read().await.map_err(|err| convert_error(&err))?;
-        Ok(Buffer::from(bytes.as_ref()))
+    pub fn read<'env>(
+        &self,
+        env: &'env Env,
+        max_bytes: Option<BigInt>,
+        signal: Option<AbortSignal>,
+    ) -> napi::Result<PromiseRaw<'env, Buffer>> {
+        self.ensure_open()?;
+        let max_bytes = max_bytes
+            .map(|max_bytes| {
+                usize::try_from(parse_u64(max_bytes, "maxBytes")?)
+                    .map_err(|_| napi::Error::from_reason("maxBytes is too large"))
+            })
+            .transpose()?;
+        let inner = self.inner.clone();
+        spawn_abortable(env, signal, async move {
+            let bytes = match max_bytes {
+                None => inner.read().await,
+                Some(max_bytes) => inner.read_up_to(max_bytes).await,
+            }
+            .map_err(|err| convert_error(&err))?;
+            Ok(Buffer::from(bytes.as_ref()))
+        })
     }
 
     #[napi]
-    pub async fn read_range(&self, start: BigInt, end: BigInt) -> napi::Result<Buffer> {
+    pub fn read_range<'env>(
+        &self,
+        env: &'env Env,
+        start: BigInt,
+        end: BigInt,
+        signal: Option<AbortSignal>,
+    ) -> napi::Result<PromiseRaw<'env, Buffer>> {
+        self.ensure_open()?;
         let range = bigint_range(start, end)?;
-        let bytes = self
-            .inner
-            .read_range(range)
-            .await
-            .map_err(|err| convert_error(&err))?;
-        Ok(Buffer::from(bytes.as_ref()))
+        let inner = self.inner.clone();
+        spawn_abortable(env, signal, async move {
+            let bytes = inner
+                .read_range(range)
+                .await
+                .map_err(|err| convert_error(&err))?;
+            Ok(Buffer::from(bytes.as_ref()))
+        })
     }
+
+    #[napi]
+    pub fn read_ranges<'env>(
+        &self,
+        env: &'env Env,
+        ranges: Vec<BlobRange>,
+        signal: Option<AbortSignal>,
+    ) -> napi::Result<PromiseRaw<'env, Vec<Buffer>>> {
+        self.ensure_open()?;
+        let ranges = ranges
+            .into_iter()
+            .map(|range| bigint_range(range.start, range.end))
+            .collect::<napi::Result<Vec<_>>>()?;
+        let inner = self.inner.clone();
+        spawn_abortable(env, signal, async move {
+            let buffers = inner
+                .read_ranges(&ranges)
+                .await
+                .map_err(|err| convert_error(&err))?;
+            Ok(buffers
+                .iter()
+                .map(|bytes| Buffer::from(bytes.as_ref()))
+                .collect())
+        })
+    }
+
+    #[napi]
+    pub async fn seek(&self, position: BigInt) -> napi::Result<()> {
+        self.ensure_open()?;
+        let position = parse_u64(position, "position")?;
+        self.inner
+            .seek(position)
+            .await
+            .map_err(|err| convert_error(&err))
+    }
+
+    #[napi]
+    pub async fn tell(&self) -> napi::Result<BigInt> {
+        self.ensure_open()?;
+        let position = self.inner.tell().await.map_err(|err| convert_error(&err))?;
+        Ok(BigInt::from(position))
+    }
+
+    #[napi]
+    pub async fn close(&self) -> napi::Result<()> {
+        if self.closed.swap(true, Ordering::AcqRel) {
+            return Ok(());
+        }
+        self.inner.close().await.map_err(|err| convert_error(&err))
+    }
+
+    #[napi]
+    pub fn is_closed(&self) -> bool {
+        self.closed.load(Ordering::Acquire)
+    }
+}
+
+/// Runs `fut` on the napi runtime. Aborting `signal` drops the future, which
+/// cancels in-flight reads.
+pub fn spawn_abortable<'env, T>(
+    env: &'env Env,
+    signal: Option<AbortSignal>,
+    fut: impl Future<Output = napi::Result<T>> + Send + 'static,
+) -> napi::Result<PromiseRaw<'env, T>>
+where
+    T: ToNapiValue + Send + 'static,
+{
+    let (fut, handle) = abortable(fut);
+    if let Some(signal) = signal {
+        signal.on_abort(move || handle.abort());
+    }
+    env.spawn_future(async move {
+        fut.await
+            .unwrap_or_else(|_| Err(napi::Error::from_reason("blob read aborted")))
+    })
 }
 
 fn bigint_range(start: BigInt, end: BigInt) -> napi::Result<Range<u64>> {
diff --git a/nodejs/src/catalog.rs b/nodejs/src/catalog.rs
index c2288bc4a..82edda5da 100644
--- a/nodejs/src/catalog.rs
+++ b/nodejs/src/catalog.rs
@@ -1,12 +1,12 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-use std::sync::Arc;
+use std::sync::{Arc, Mutex};
 use std::time::Duration;
 
-use lancedb::catalog::{
-    CatalogConnection, CreateDatabaseRequest, DropDatabaseRequest, ListDatabasesRequest,
-};
+use futures::{StreamExt, future::poll_fn};
+
+use lancedb::catalog::{CatalogConnection, CreateDatabaseRequest, DropDatabaseRequest};
 use napi::bindgen_prelude::*;
 use napi_derive::napi;
 
@@ -25,10 +25,37 @@ pub struct CatalogOptions {
     pub oauth_config: Option<OAuthConfig>,
 }
 
-#[napi(object)]
-pub struct ListDatabasesResponse {
-    pub databases: Vec<String>,
-    pub page_token: Option<String>,
+/// A lazy iterator over database names with inspectable pagination state.
+#[napi]
+pub struct DatabaseNames {
+    inner: Mutex<lancedb::catalog::DatabaseNames>,
+    next_lock: futures::lock::Mutex<()>,
+}
+
+#[napi]
+impl DatabaseNames {
+    /// Number of names cached without another REST request.
+    #[napi]
+    pub fn num_page_results(&self) -> u32 {
+        self.inner.lock().unwrap().num_page_results() as u32
+    }
+
+    /// Continuation token for the next REST request.
+    #[napi]
+    pub fn page_token(&self) -> Option<String> {
+        self.inner.lock().unwrap().page_token().map(str::to_owned)
+    }
+
+    /// Fetch the next name, returning None when exhausted.
+    #[napi]
+    pub async fn next(&self) -> Result<Option<String>> {
+        // Serialize advances but allow synchronous inspection between polls.
+        let _guard = self.next_lock.lock().await;
+        poll_fn(|cx| self.inner.lock().unwrap().poll_next_unpin(cx))
+            .await
+            .transpose()
+            .default_error()
+    }
 }
 
 #[napi]
@@ -106,18 +133,14 @@ impl Catalog {
             .default_error()
     }
     #[napi]
-    pub async fn list_databases(
+    pub fn list_databases(
         &self,
-        limit: Option<u32>,
         page_token: Option<String>,
-    ) -> Result<ListDatabasesResponse> {
-        let mut request = ListDatabasesRequest::default();
-        request.limit = limit;
-        request.page_token = page_token;
-        let response = self.inner.list_databases(request).await.default_error()?;
-        Ok(ListDatabasesResponse {
-            databases: response.databases,
-            page_token: response.page_token,
-        })
+        page_limit: Option<u32>,
+    ) -> DatabaseNames {
+        DatabaseNames {
+            inner: Mutex::new(self.inner.list_databases(page_token, page_limit)),
+            next_lock: futures::lock::Mutex::new(()),
+        }
     }
 }
diff --git a/nodejs/src/connection.rs b/nodejs/src/connection.rs
index 4a820af5e..58ec9859a 100644
--- a/nodejs/src/connection.rs
+++ b/nodejs/src/connection.rs
@@ -371,13 +371,15 @@ impl Connection {
     }
 
     #[napi(catch_unwind)]
-    pub async fn list_materialized_views(&self) -> napi::Result<Vec<String>> {
-        let views = self
-            .get_inner()?
-            .list_materialized_views()
-            .await
-            .default_error()?;
-        Ok(views)
+    pub fn list_materialized_views(
+        &self,
+        page_token: Option<String>,
+        page_limit: Option<u32>,
+    ) -> napi::Result<crate::listing::NameListing> {
+        Ok(crate::listing::NameListing::new(
+            self.get_inner()?
+                .list_materialized_views(crate::listing::options(page_token, page_limit)),
+        ))
     }
 
     /// Drop a materialized view.
@@ -460,12 +462,18 @@ impl Connection {
 
     /// The names of the views in one namespace.
     #[napi(catch_unwind)]
-    pub async fn list_views(
+    pub fn list_views(
         &self,
         namespace_path: Option<Vec<String>>,
-    ) -> napi::Result<Vec<String>> {
-        let ns = namespace_path.unwrap_or_default();
-        self.get_inner()?.list_views(&ns).await.default_error()
+        page_token: Option<String>,
+        page_limit: Option<u32>,
+    ) -> napi::Result<crate::listing::NameListing> {
+        Ok(crate::listing::NameListing::new(
+            self.get_inner()?.list_views(
+                &namespace_path.unwrap_or_default(),
+                crate::listing::options(page_token, page_limit),
+            ),
+        ))
     }
 
     /// Start dropping a materialized view and return its cleanup job.
@@ -588,9 +596,15 @@ impl Connection {
 
     /// List server-side jobs across the database's tables.
     #[napi(catch_unwind)]
-    pub async fn list_jobs(&self) -> napi::Result<Vec<crate::job::JobInfo>> {
-        let jobs = self.get_inner()?.list_jobs().await.default_error()?;
-        Ok(jobs.into_iter().map(Into::into).collect())
+    pub fn list_jobs(
+        &self,
+        page_token: Option<String>,
+        page_limit: Option<u32>,
+    ) -> napi::Result<crate::listing::JobListing> {
+        Ok(crate::listing::JobListing::new(
+            self.get_inner()?
+                .list_jobs(crate::listing::options(page_token, page_limit)),
+        ))
     }
 
     /// Request cancellation of a server-side job by id. Returns true if the
diff --git a/nodejs/src/index.rs b/nodejs/src/index.rs
index 762f74870..e5884aa4d 100644
--- a/nodejs/src/index.rs
+++ b/nodejs/src/index.rs
@@ -239,7 +239,12 @@ impl Index {
             opts = opts.base_tokenizer(base_tokenizer);
         }
         if let Some(language) = language {
-            opts = opts.language(&language).unwrap();
+            opts = opts.language(&language).map_err(|_| {
+                napi::Error::from_reason(format!(
+                    "LanceDB does not support the requested language: '{}'",
+                    language
+                ))
+            })?;
         }
         if let Some(max_token_length) = max_token_length {
             opts = opts.max_token_length(Some(max_token_length as usize));
diff --git a/nodejs/src/iterator.rs b/nodejs/src/iterator.rs
index 8d65dfc3f..96d08c765 100644
--- a/nodejs/src/iterator.rs
+++ b/nodejs/src/iterator.rs
@@ -7,6 +7,8 @@ use lancedb::ipc::batches_to_ipc_file;
 use napi::bindgen_prelude::*;
 use napi_derive::napi;
 
+use crate::util::schema_to_buffer;
+
 /** Typescript-style Async Iterator over RecordBatches */
 #[napi]
 pub struct RecordBatchIterator {
@@ -19,6 +21,12 @@ impl RecordBatchIterator {
         Self { inner }
     }
 
+    /// Return the executed stream's output schema as an Arrow IPC file.
+    #[napi]
+    pub fn schema(&self) -> napi::Result<Buffer> {
+        schema_to_buffer(&self.inner.schema())
+    }
+
     #[napi(catch_unwind)]
     pub async unsafe fn next(&mut self) -> napi::Result<Option<Buffer>> {
         if let Some(rst) = self.inner.next().await {
diff --git a/nodejs/src/lib.rs b/nodejs/src/lib.rs
index 1bb5f761d..f47bc113e 100644
--- a/nodejs/src/lib.rs
+++ b/nodejs/src/lib.rs
@@ -18,6 +18,7 @@ mod header;
 mod index;
 mod iterator;
 mod job;
+mod listing;
 pub mod merge;
 pub mod otel;
 pub mod permutation;
diff --git a/nodejs/src/listing.rs b/nodejs/src/listing.rs
new file mode 100644
index 000000000..ed2bce9df
--- /dev/null
+++ b/nodejs/src/listing.rs
@@ -0,0 +1,83 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use crate::error::NapiErrorExt;
+use futures::{StreamExt, future::poll_fn};
+use lancedb::listing::{Listing, ListingOptions};
+use napi_derive::napi;
+use std::sync::Mutex;
+
+pub fn options(page_token: Option<String>, page_limit: Option<u32>) -> ListingOptions {
+    let mut options = ListingOptions::default();
+    options.page_token = page_token;
+    options.page_limit = page_limit;
+    options
+}
+
+#[napi]
+pub struct NameListing {
+    inner: Mutex<Listing<String>>,
+    next_lock: futures::lock::Mutex<()>,
+}
+impl NameListing {
+    pub fn new(inner: Listing<String>) -> Self {
+        Self {
+            inner: Mutex::new(inner),
+            next_lock: futures::lock::Mutex::new(()),
+        }
+    }
+}
+#[napi]
+impl NameListing {
+    #[napi]
+    pub fn num_page_results(&self) -> u32 {
+        self.inner.lock().unwrap().num_page_results() as u32
+    }
+    #[napi]
+    pub fn page_token(&self) -> Option<String> {
+        self.inner.lock().unwrap().page_token().map(str::to_owned)
+    }
+    #[napi]
+    pub async fn next(&self) -> napi::Result<Option<String>> {
+        let _guard = self.next_lock.lock().await;
+        let value = poll_fn(|cx| self.inner.lock().unwrap().poll_next_unpin(cx))
+            .await
+            .transpose()
+            .default_error()?;
+        Ok(value)
+    }
+}
+
+#[napi]
+pub struct JobListing {
+    inner: Mutex<Listing<lancedb::database::JobInfo>>,
+    next_lock: futures::lock::Mutex<()>,
+}
+impl JobListing {
+    pub fn new(inner: Listing<lancedb::database::JobInfo>) -> Self {
+        Self {
+            inner: Mutex::new(inner),
+            next_lock: futures::lock::Mutex::new(()),
+        }
+    }
+}
+#[napi]
+impl JobListing {
+    #[napi]
+    pub fn num_page_results(&self) -> u32 {
+        self.inner.lock().unwrap().num_page_results() as u32
+    }
+    #[napi]
+    pub fn page_token(&self) -> Option<String> {
+        self.inner.lock().unwrap().page_token().map(str::to_owned)
+    }
+    #[napi]
+    pub async fn next(&self) -> napi::Result<Option<crate::job::JobInfo>> {
+        let _guard = self.next_lock.lock().await;
+        let value = poll_fn(|cx| self.inner.lock().unwrap().poll_next_unpin(cx))
+            .await
+            .transpose()
+            .default_error()?;
+        Ok(value.map(|value| value.into()))
+    }
+}
diff --git a/nodejs/src/merge.rs b/nodejs/src/merge.rs
index ac8649419..3d17a037e 100644
--- a/nodejs/src/merge.rs
+++ b/nodejs/src/merge.rs
@@ -50,10 +50,10 @@ impl NativeMergeInsertBuilder {
         this
     }
 
-    #[napi]
-    pub fn use_lsm(&self, enable: bool) -> Self {
+    #[napi(js_name = "useMemWal")]
+    pub fn use_memwal(&self, enable: bool) -> Self {
         let mut this = self.clone();
-        this.inner.use_lsm(enable);
+        this.inner.use_memwal(enable);
         this
     }
 
diff --git a/nodejs/src/query.rs b/nodejs/src/query.rs
index f9fe91d35..68db6f810 100644
--- a/nodejs/src/query.rs
+++ b/nodejs/src/query.rs
@@ -168,9 +168,9 @@ impl Query {
         self.inner = self.inner.clone().with_row_id();
     }
 
-    #[napi]
-    pub fn use_lsm(&mut self, enable: bool) {
-        self.inner = self.inner.clone().use_lsm(enable);
+    #[napi(js_name = "useMemWal")]
+    pub fn use_memwal(&mut self, enable: bool) {
+        self.inner = self.inner.clone().use_memwal(enable);
     }
 
     #[napi]
@@ -289,8 +289,23 @@ impl VectorQuery {
     }
 
     #[napi]
-    pub fn nprobes(&mut self, nprobe: u32) {
-        self.inner = self.inner.clone().nprobes(nprobe as usize);
+    pub fn nprobes(&mut self, nprobe: u32) -> napi::Result<()> {
+        self.inner = self
+            .inner
+            .clone()
+            .nprobes(nprobe as usize)
+            .default_error()?;
+        Ok(())
+    }
+
+    #[napi]
+    pub fn search_effort(&mut self, search_effort: Option<f64>) -> napi::Result<()> {
+        self.inner = self
+            .inner
+            .clone()
+            .search_effort(search_effort)
+            .default_error()?;
+        Ok(())
     }
 
     #[napi]
@@ -379,9 +394,9 @@ impl VectorQuery {
         self.inner = self.inner.clone().with_row_id();
     }
 
-    #[napi]
-    pub fn use_lsm(&mut self, enable: bool) {
-        self.inner = self.inner.clone().use_lsm(enable);
+    #[napi(js_name = "useMemWal")]
+    pub fn use_memwal(&mut self, enable: bool) {
+        self.inner = self.inner.clone().use_memwal(enable);
     }
 
     #[napi]
@@ -489,9 +504,9 @@ impl TakeQuery {
         self.inner = self.inner.clone().with_row_id();
     }
 
-    #[napi]
-    pub fn use_lsm(&mut self, enable: bool) {
-        self.inner = self.inner.clone().use_lsm(enable);
+    #[napi(js_name = "useMemWal")]
+    pub fn use_memwal(&mut self, enable: bool) {
+        self.inner = self.inner.clone().use_memwal(enable);
     }
 
     #[napi(catch_unwind)]
@@ -658,6 +673,7 @@ impl JsFullTextQuery {
             FtsQuery::Phrase(_) => "phrase".to_string(),
             FtsQuery::Boost(_) => "boost".to_string(),
             FtsQuery::MultiMatch(_) => "multi_match".to_string(),
+            FtsQuery::CombinedFields(_) => "combined_fields".to_string(),
             FtsQuery::Boolean(_) => "boolean".to_string(),
         }
     }
diff --git a/nodejs/src/remote.rs b/nodejs/src/remote.rs
index c2d671735..d7e2a21cd 100644
--- a/nodejs/src/remote.rs
+++ b/nodejs/src/remote.rs
@@ -92,6 +92,10 @@ pub struct ClientConfig {
     pub user_agent: Option<String>,
     pub retry_config: Option<RetryConfig>,
     pub timeout_config: Option<TimeoutConfig>,
+    /// Maximum number of concurrent blob HTTP requests across all tables and
+    /// blob handles on this connection. Defaults to 8. Can also be set with
+    /// `LANCE_CLIENT_BLOB_REQUEST_CONCURRENCY`. Must be greater than zero.
+    pub blob_request_concurrency: Option<u32>,
     pub extra_headers: Option<HashMap<String, String>>,
     /// The delimiter joining a namespace path and a name into one object
     /// identifier. `"$"` is the only supported value, and leaving this unset is
@@ -428,6 +432,7 @@ impl From<ClientConfig> for lancedb::remote::ClientConfig {
                 .unwrap_or(concat!("LanceDB-Node-Client/", env!("CARGO_PKG_VERSION")).to_string()),
             retry_config: config.retry_config.map(Into::into).unwrap_or_default(),
             timeout_config: config.timeout_config.map(Into::into).unwrap_or_default(),
+            blob_request_concurrency: config.blob_request_concurrency.map(|value| value as usize),
             extra_headers: config.extra_headers.unwrap_or_default(),
             id_delimiter: config.id_delimiter,
             tls_config: config.tls_config.map(Into::into),
diff --git a/nodejs/src/table.rs b/nodejs/src/table.rs
index d381d98e5..5e29e23b6 100644
--- a/nodejs/src/table.rs
+++ b/nodejs/src/table.rs
@@ -11,11 +11,12 @@ use lancedb::table::{
     FieldMetadataUpdate as LanceFieldMetadataUpdate, FtsToken as LanceDbFtsToken,
     NewColumnTransform, OptimizeAction, OptimizeOptions, Ref, Table as LanceDbTable,
 };
+use napi::Env;
 use napi::bindgen_prelude::*;
 use napi::threadsafe_function::{ThreadsafeFunction, ThreadsafeFunctionCallMode};
 use napi_derive::napi;
 
-use crate::blob::{BlobFile, copy_blob_buffers, parse_row_ids};
+use crate::blob::{BlobFile, copy_blob_buffers, parse_row_ids, spawn_abortable};
 use crate::error::NapiErrorExt;
 use crate::index::Index;
 use crate::merge::NativeMergeInsertBuilder;
@@ -340,36 +341,44 @@ impl Table {
     }
 
     #[napi(catch_unwind)]
-    pub async fn fetch_blobs(
+    pub fn fetch_blobs<'env>(
         &self,
+        env: &'env Env,
         column: String,
         row_ids: Vec<BigInt>,
-    ) -> napi::Result<Vec<Option<Buffer>>> {
+        signal: Option<AbortSignal>,
+    ) -> napi::Result<PromiseRaw<'env, Vec<Option<Buffer>>>> {
         let row_ids = parse_row_ids(row_ids)?;
-        let array = self
-            .inner_ref()?
-            .fetch_blobs(column.as_str(), &row_ids)
-            .await
-            .default_error()?;
-        Ok(copy_blob_buffers(array))
+        let table = self.inner_ref()?.clone();
+        spawn_abortable(env, signal, async move {
+            let array = table
+                .fetch_blobs(column.as_str(), &row_ids)
+                .await
+                .default_error()?;
+            Ok(copy_blob_buffers(array))
+        })
     }
 
     #[napi(catch_unwind)]
-    pub async fn fetch_blob_files(
+    pub fn fetch_blob_files<'env>(
         &self,
+        env: &'env Env,
         column: String,
         row_ids: Vec<BigInt>,
-    ) -> napi::Result<Vec<Option<BlobFile>>> {
+        signal: Option<AbortSignal>,
+    ) -> napi::Result<PromiseRaw<'env, Vec<Option<BlobFile>>>> {
         let row_ids = parse_row_ids(row_ids)?;
-        let files = self
-            .inner_ref()?
-            .fetch_blob_files(column.as_str(), &row_ids)
-            .await
-            .default_error()?;
-        Ok(files
-            .into_iter()
-            .map(|file| file.map(BlobFile::new))
-            .collect())
+        let table = self.inner_ref()?.clone();
+        spawn_abortable(env, signal, async move {
+            let files = table
+                .fetch_blob_files(column.as_str(), &row_ids)
+                .await
+                .default_error()?;
+            Ok(files
+                .into_iter()
+                .map(|file| file.map(BlobFile::new))
+                .collect())
+        })
     }
 
     #[napi(catch_unwind)]
@@ -460,13 +469,12 @@ impl Table {
     #[napi(catch_unwind)]
     pub async fn refresh_materialized_view(
         &self,
-        full: Option<bool>,
         source_version: Option<i64>,
     ) -> napi::Result<RefreshMaterializedViewResult> {
         let view = lancedb::MaterializedView::from_table(self.inner_ref()?.clone())
             .await
             .default_error()?;
-        let mut builder = view.refresh().full(full.unwrap_or(false));
+        let mut builder = view.refresh();
         if let Some(version) = source_version {
             let version = u64::try_from(version).map_err(|_| {
                 napi::Error::from_reason("sourceVersion must be a non-negative integer")
@@ -483,11 +491,9 @@ impl Table {
         let view = lancedb::MaterializedView::from_table(inner)
             .await
             .default_error()?;
-        view.definition().to_json().map_err(|err| {
-            napi::Error::from_reason(format!(
-                "failed to serialize materialized-view definition: {err}"
-            ))
-        })
+        Ok(lancedb::materialized_view::definition_metadata_from_sql(
+            view.definition_sql(),
+        ))
     }
 
     #[napi(catch_unwind)]
@@ -574,64 +580,60 @@ impl Table {
             .default_error()
     }
 
-    #[napi(catch_unwind)]
-    pub async fn set_lsm_write_spec(&self, spec: LsmWriteSpec) -> napi::Result<()> {
-        let native_spec = lancedb::table::LsmWriteSpec::try_from(spec)?;
+    #[napi(catch_unwind, js_name = "enableMemWal")]
+    pub async fn enable_memwal(&self, spec: MemWalWriteSpec) -> napi::Result<()> {
+        let native_spec = lancedb::table::MemWalWriteSpec::try_from(spec)?;
         self.inner_ref()?
-            .set_lsm_write_spec(native_spec)
+            .enable_memwal(native_spec)
             .await
             .default_error()
     }
 
-    #[napi(catch_unwind)]
-    pub async fn unset_lsm_write_spec(&self) -> napi::Result<()> {
+    #[napi(catch_unwind, js_name = "disableMemWal")]
+    pub async fn disable_memwal(&self) -> napi::Result<()> {
+        self.inner_ref()?.disable_memwal().await.default_error()
+    }
+
+    #[napi(catch_unwind, js_name = "getMemWalSpec")]
+    pub async fn get_memwal_spec(&self) -> napi::Result<Option<MemWalWriteSpec>> {
+        let spec = self.inner_ref()?.get_memwal_spec().await.default_error()?;
+        Ok(spec.map(MemWalWriteSpec::from))
+    }
+
+    #[napi(catch_unwind, js_name = "closeMemWalWriters")]
+    pub async fn close_memwal_writers(&self) -> napi::Result<()> {
         self.inner_ref()?
-            .unset_lsm_write_spec()
+            .close_memwal_writers()
             .await
             .default_error()
     }
 
-    #[napi(catch_unwind)]
-    pub async fn get_lsm_write_spec(&self) -> napi::Result<Option<LsmWriteSpec>> {
-        let spec = self
-            .inner_ref()?
-            .get_lsm_write_spec()
-            .await
-            .default_error()?;
-        Ok(spec.map(LsmWriteSpec::from))
+    #[napi(catch_unwind, js_name = "flushMemWal")]
+    pub async fn flush_memwal(&self) -> napi::Result<()> {
+        self.inner_ref()?.flush_memwal().await.default_error()
     }
 
-    #[napi(catch_unwind)]
-    pub async fn close_lsm_writers(&self) -> napi::Result<()> {
-        self.inner_ref()?.close_lsm_writers().await.default_error()
+    #[napi(catch_unwind, js_name = "compactMemWal")]
+    pub async fn compact_memwal(&self) -> napi::Result<()> {
+        self.inner_ref()?.compact_memwal().await.default_error()
     }
 
-    #[napi(catch_unwind)]
-    pub async fn flush_lsm(&self) -> napi::Result<()> {
-        self.inner_ref()?.flush_lsm().await.default_error()
+    #[napi(catch_unwind, js_name = "checkpointMemWal")]
+    pub async fn checkpoint_memwal(&self) -> napi::Result<()> {
+        self.inner_ref()?.checkpoint_memwal().await.default_error()
     }
 
-    #[napi(catch_unwind)]
-    pub async fn compact_lsm(&self) -> napi::Result<()> {
-        self.inner_ref()?.compact_lsm().await.default_error()
-    }
-
-    #[napi(catch_unwind)]
-    pub async fn checkpoint_lsm(&self) -> napi::Result<()> {
-        self.inner_ref()?.checkpoint_lsm().await.default_error()
-    }
-
-    #[napi(catch_unwind)]
-    pub async fn get_lsm_stats(
+    #[napi(catch_unwind, js_name = "getMemWalStats")]
+    pub async fn get_memwal_stats(
         &self,
-        include_generation_rows: bool,
-    ) -> napi::Result<Option<LsmStats>> {
+        include_sstable_rows: bool,
+    ) -> napi::Result<Option<MemWalStats>> {
         let stats = self
             .inner_ref()?
-            .get_lsm_stats(include_generation_rows)
+            .get_memwal_stats(include_sstable_rows)
             .await
             .default_error()?;
-        Ok(stats.map(LsmStats::from))
+        Ok(stats.map(MemWalStats::from))
     }
 
     #[napi(catch_unwind)]
@@ -830,7 +832,10 @@ impl Table {
     pub async fn uses_v2_manifest_paths(&self) -> napi::Result<bool> {
         self.inner_ref()?
             .as_native()
-            .ok_or_else(|| napi::Error::from_reason("This cannot be run on a remote table"))?
+            .ok_or_else(|| lancedb::Error::NotSupported {
+                message: "uses_v2_manifest_paths is not supported for remote tables.".into(),
+            })
+            .default_error()?
             .uses_v2_manifest_paths()
             .await
             .default_error()
@@ -840,7 +845,10 @@ impl Table {
     pub async fn migrate_manifest_paths_v2(&self) -> napi::Result<()> {
         self.inner_ref()?
             .as_native()
-            .ok_or_else(|| napi::Error::from_reason("This cannot be run on a remote table"))?
+            .ok_or_else(|| lancedb::Error::NotSupported {
+                message: "migrate_manifest_paths_v2 is not supported for remote tables.".into(),
+            })
+            .default_error()?
             .migrate_manifest_paths_v2()
             .await
             .default_error()
@@ -946,7 +954,7 @@ impl From<LanceDbFtsToken> for FtsToken {
 /// `column` is required.
 #[napi(object)]
 #[derive(Clone, Debug)]
-pub struct LsmWriteSpec {
+pub struct MemWalWriteSpec {
     /// One of `"bucket"`, `"identity"`, or `"unsharded"`.
     pub spec_type: String,
     /// Bucket and identity variants: the sharding column.
@@ -960,31 +968,31 @@ pub struct LsmWriteSpec {
     pub writer_config_defaults: Option<HashMap<String, String>>,
 }
 
-impl TryFrom<LsmWriteSpec> for lancedb::table::LsmWriteSpec {
+impl TryFrom<MemWalWriteSpec> for lancedb::table::MemWalWriteSpec {
     type Error = napi::Error;
 
-    fn try_from(value: LsmWriteSpec) -> napi::Result<Self> {
+    fn try_from(value: MemWalWriteSpec) -> napi::Result<Self> {
         let writer_config_defaults = value.writer_config_defaults.unwrap_or_default();
         let spec = match value.spec_type.as_str() {
             "bucket" => {
                 let column = value.column.ok_or_else(|| {
-                    napi::Error::from_reason("LsmWriteSpec bucket requires `column`")
+                    napi::Error::from_reason("MemWalWriteSpec bucket requires `column`")
                 })?;
                 let num_buckets = value.num_buckets.ok_or_else(|| {
-                    napi::Error::from_reason("LsmWriteSpec bucket requires `numBuckets`")
+                    napi::Error::from_reason("MemWalWriteSpec bucket requires `numBuckets`")
                 })?;
                 Self::bucket(column, num_buckets)
             }
             "identity" => {
                 let column = value.column.ok_or_else(|| {
-                    napi::Error::from_reason("LsmWriteSpec identity requires `column`")
+                    napi::Error::from_reason("MemWalWriteSpec identity requires `column`")
                 })?;
                 Self::identity(column)
             }
             "unsharded" => Self::unsharded(),
             other => {
                 return Err(napi::Error::from_reason(format!(
-                    "LsmWriteSpec `specType` must be 'bucket', 'identity', or 'unsharded', got '{}'",
+                    "MemWalWriteSpec `specType` must be 'bucket', 'identity', or 'unsharded', got '{}'",
                     other
                 )));
             }
@@ -995,9 +1003,9 @@ impl TryFrom<LsmWriteSpec> for lancedb::table::LsmWriteSpec {
     }
 }
 
-impl From<lancedb::table::LsmWriteSpec> for LsmWriteSpec {
-    fn from(spec: lancedb::table::LsmWriteSpec) -> Self {
-        use lancedb::table::LsmWriteSpec as Native;
+impl From<lancedb::table::MemWalWriteSpec> for MemWalWriteSpec {
+    fn from(spec: lancedb::table::MemWalWriteSpec) -> Self {
+        use lancedb::table::MemWalWriteSpec as Native;
         match spec {
             Native::Bucket {
                 column,
@@ -1036,21 +1044,21 @@ impl From<lancedb::table::LsmWriteSpec> for LsmWriteSpec {
     }
 }
 
-/// One flushed L0 generation.
+/// One SSTable.
 #[napi(object)]
 #[derive(Clone, Debug)]
-pub struct GenerationStats {
-    /// The generation number. Increases as memtables are sealed into L0.
+pub struct SsTableStats {
+    /// The generation number. Increases as memtables are frozen into SSTables.
     pub generation: i64,
-    /// On-disk size of the generation.
+    /// On-disk size of the SSTable.
     pub bytes: i64,
-    /// Present only when `includeGenerationRows` was requested. Off by default
+    /// Present only when `includeSstableRows` was requested. Off by default
     /// because each count opens an uncached Lance dataset.
     pub rows: Option<i64>,
 }
 
-impl From<lancedb::table::GenerationStats> for GenerationStats {
-    fn from(g: lancedb::table::GenerationStats) -> Self {
+impl From<lancedb::table::SsTableStats> for SsTableStats {
+    fn from(g: lancedb::table::SsTableStats) -> Self {
         Self {
             generation: g.generation as i64,
             bytes: g.bytes as i64,
@@ -1063,7 +1071,7 @@ impl From<lancedb::table::GenerationStats> for GenerationStats {
 #[napi(object)]
 #[derive(Clone, Debug)]
 pub struct MemtableStats {
-    /// The generation this memtable will become once sealed.
+    /// The generation this memtable will become once frozen.
     pub generation: i64,
     /// Rows currently buffered.
     pub rows: i64,
@@ -1088,13 +1096,13 @@ impl From<lancedb::table::MemtableStats> for MemtableStats {
     }
 }
 
-/// Live state of one bucket. A table is N buckets on one node; flattening to a
-/// single number hides the one hot bucket that is usually why someone opened
+/// Live state of one table shard. A table is N table shards on one node; flattening to a
+/// single number hides the one hot table shard that is usually why someone opened
 /// this endpoint.
 #[napi(object)]
 #[derive(Clone, Debug)]
-pub struct BucketStats {
-    /// The shard this bucket writes.
+pub struct TableShardStats {
+    /// The id of this table shard.
     pub shard_id: String,
     /// `"Active"` or `"Sealed"` (drop-table 2PC in flight).
     pub status: String,
@@ -1109,20 +1117,20 @@ pub struct BucketStats {
     /// Highest WAL position the writer has seen. The difference against
     /// `replayAfterWalEntryPosition` is the WAL lag.
     pub wal_entry_position_last_seen: i64,
-    /// Flushed L0 generations not yet merged into the base table.
-    pub generations: Vec<GenerationStats>,
-    /// Whether a pass owns this bucket's compaction latch right now. Says *a*
+    /// SSTables not yet merged into the base table.
+    pub sstables: Vec<SsTableStats>,
+    /// Whether a pass owns this table shard's compaction latch right now. Says *a*
     /// driver is running, not *whose*, and the latch is held from dispatch —
     /// including while the pass queues for a pod-wide compactor permit. Read it
     /// as "do not pile on", never as "mine is progressing".
     pub compacting: bool,
-    /// Oldest first, active last. Absent for a `"Sealed"` bucket, whose
+    /// Oldest first, active last. Absent for a `"Sealed"` table shard, whose
     /// in-memory state is torn down.
     pub memtables: Option<Vec<MemtableStats>>,
 }
 
-impl From<lancedb::table::BucketStats> for BucketStats {
-    fn from(b: lancedb::table::BucketStats) -> Self {
+impl From<lancedb::table::TableShardStats> for TableShardStats {
+    fn from(b: lancedb::table::TableShardStats) -> Self {
         Self {
             shard_id: b.shard_id,
             status: b.status,
@@ -1131,7 +1139,7 @@ impl From<lancedb::table::BucketStats> for BucketStats {
             current_generation: b.current_generation as i64,
             replay_after_wal_entry_position: b.replay_after_wal_entry_position as i64,
             wal_entry_position_last_seen: b.wal_entry_position_last_seen as i64,
-            generations: b.generations.into_iter().map(Into::into).collect(),
+            sstables: b.sstables.into_iter().map(Into::into).collect(),
             compacting: b.compacting,
             memtables: b
                 .memtables
@@ -1140,21 +1148,21 @@ impl From<lancedb::table::BucketStats> for BucketStats {
     }
 }
 
-/// Live per-bucket LSM state, as returned by `Table#getLsmStats`.
+/// Live per-table-shard MemWAL state, as returned by `Table#getMemWalStats`.
 ///
-/// Nothing here is derived: sums and differences (total L0 bytes, WAL lag) are
+/// Nothing here is derived: sums and differences (total SSTable bytes, WAL lag) are
 /// the caller's to compute.
 #[napi(object)]
 #[derive(Clone, Debug)]
-pub struct LsmStats {
-    /// One entry per bucket backing this table.
-    pub buckets: Vec<BucketStats>,
+pub struct MemWalStats {
+    /// One entry per table shard backing this table.
+    pub table_shards: Vec<TableShardStats>,
 }
 
-impl From<lancedb::table::LsmStats> for LsmStats {
-    fn from(stats: lancedb::table::LsmStats) -> Self {
+impl From<lancedb::table::MemWalStats> for MemWalStats {
+    fn from(stats: lancedb::table::MemWalStats) -> Self {
         Self {
-            buckets: stats.buckets.into_iter().map(Into::into).collect(),
+            table_shards: stats.table_shards.into_iter().map(Into::into).collect(),
         }
     }
 }
@@ -1356,6 +1364,16 @@ pub struct TableStatistics {
     /// The number of rows in the table
     pub num_rows: i64,
 
+    /// The number of rows marked as deleted across all fragments of the table
+    ///
+    /// These rows are not included in `numRows`, but still occupy space on disk
+    /// until the table is compacted, so a large value here indicates that the
+    /// table should be optimized. Fragments in which every row was deleted are
+    /// dropped outright, so their rows are not counted here.
+    ///
+    /// Absent (`undefined`) when the backend does not report deletion counts.
+    pub num_deleted_rows: Option<i64>,
+
     /// The number of indices in the table
     pub num_indices: i64,
 
@@ -1404,6 +1422,7 @@ impl From<lancedb::table::TableStatistics> for TableStatistics {
         Self {
             total_bytes: v.total_bytes as i64,
             num_rows: v.num_rows as i64,
+            num_deleted_rows: v.num_deleted_rows.map(|n| n as i64),
             num_indices: v.num_indices as i64,
             fragment_stats: FragmentStatistics {
                 num_fragments: v.fragment_stats.num_fragments as i64,
@@ -1599,7 +1618,7 @@ impl From<lancedb::function::FunctionErrors> for FunctionErrors {
 
 #[napi(object)]
 pub struct RefreshMaterializedViewResult {
-    /// How the view was brought up to date: "rebuild", "incremental" or "no_op".
+    /// How the view was brought up to date: "rebuild".
     pub mode: String,
     pub rows_written: i64,
     pub source_version: i64,
@@ -1610,8 +1629,6 @@ impl From<lancedb::RefreshMaterializedViewResult> for RefreshMaterializedViewRes
     fn from(value: lancedb::RefreshMaterializedViewResult) -> Self {
         let mode = match value.mode {
             lancedb::RefreshMode::Rebuild => "rebuild",
-            lancedb::RefreshMode::Incremental => "incremental",
-            lancedb::RefreshMode::NoOp => "no_op",
         };
         Self {
             mode: mode.to_string(),
diff --git a/nodejs/tsconfig.json b/nodejs/tsconfig.json
index 9b83da5ec..094fe5003 100644
--- a/nodejs/tsconfig.json
+++ b/nodejs/tsconfig.json
@@ -10,7 +10,10 @@
     "resolveJsonModule": true,
     "emitDecoratorMetadata": true,
     "experimentalDecorators": true,
-    "moduleResolution": "Node"
+    "moduleResolution": "Node",
+    // flatbuffers 25 (via apache-arrow 21) ships declarations that need
+    // TypeScript 5.7+ generic typed arrays; skip checking third-party .d.ts.
+    "skipLibCheck": true
   },
   "exclude": ["./dist/*", "./examples/*"],
   "typedocOptions": {
diff --git a/python/Cargo.toml b/python/Cargo.toml
index 17ff3f4fc..368b3b24e 100644
--- a/python/Cargo.toml
+++ b/python/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.40.0-beta.11"
+version = "0.41.0-beta.7"
 publish = false
 edition.workspace = true
 description = "Python bindings for LanceDB"
@@ -8,7 +8,7 @@ license.workspace = true
 repository.workspace = true
 keywords.workspace = true
 categories.workspace = true
-rust-version = "1.91.0"
+rust-version = "1.94.0"
 
 [lib]
 name = "_lancedb"
diff --git a/python/pyproject.toml b/python/pyproject.toml
index de84367a8..ed54ae536 100644
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -10,7 +10,7 @@ dependencies = [
     "pyarrow>=16",
     "pydantic>=2.7.4,<3",
     "tqdm>=4.27.0",
-    "lance-namespace>=0.3.2"
+    "lance-namespace>=0.13.0"
 ]
 description = "lancedb"
 authors = [{ name = "LanceDB Devs", email = "dev@lancedb.com" }]
@@ -63,7 +63,7 @@ tests = [
     "polars>=0.19, <=1.32.3",
     "pyarrow<25",
     "pyarrow-stubs>=16.0",
-    "pylance==9.0.0",
+    "pylance==14.0.0b12",
     "requests>=2.31.0",
     "datafusion>=54,<55",
     "opentelemetry-sdk>=1.30.0",
@@ -73,6 +73,7 @@ dev = [
     "pre-commit>=3.5.0",
     "pyright>=1.1.350",
     'typing-extensions>=4.0.0; python_version < "3.11"',
+    "maturin>=1.10",
 ]
 docs = ["mkdocs", "mkdocs-jupyter", "mkdocs-material", "mkdocstrings-python"]
 clip = ["torch", "pillow>=12.1.1", "open-clip-torch"]
diff --git a/python/python/lancedb/__init__.py b/python/python/lancedb/__init__.py
index bef178b9e..1ba0c410d 100644
--- a/python/python/lancedb/__init__.py
+++ b/python/python/lancedb/__init__.py
@@ -15,7 +15,7 @@ from ._lancedb import FtsToken
 from ._lancedb import FunctionErrorFragment as FunctionErrorFragment
 from ._lancedb import FunctionErrorRecord as FunctionErrorRecord
 from ._lancedb import FunctionErrors as FunctionErrors
-from ._lancedb import LsmWriteSpec
+from ._lancedb import MemWalWriteSpec
 from ._lancedb import tokenize as _tokenize
 from .common import URI, sanitize_uri
 from urllib.parse import urlparse
@@ -46,7 +46,6 @@ from .materialized_view import (
     AsyncMaterializedView,
     MaterializedView,
     MaterializedViewDefinition,
-    vector_dedup as vector_dedup,
 )
 from .view import ViewDescription as ViewDescription
 from .table import AsyncTable, CompactionOptions, Table
@@ -62,7 +61,6 @@ from .namespace import (
 from .catalog import (
     AsyncCatalog,
     Catalog,
-    ListDatabasesResponse,
     connect_catalog,
     connect_catalog_async,
 )
@@ -323,8 +321,8 @@ def tokenize(
     language: str = "English",
     max_token_length: Optional[int] = 40,
     lower_case: bool = True,
-    stem: bool = True,
-    remove_stop_words: bool = True,
+    stem: Optional[bool] = None,
+    remove_stop_words: Optional[bool] = None,
     custom_stop_words: Optional[List[str]] = None,
     ascii_folding: bool = True,
     ngram_min_length: int = 3,
@@ -335,6 +333,8 @@ def tokenize(
 
     This does not require an FTS index. The tokenizer options match
     :class:`lancedb.index.FTS`. ``custom_stop_words`` accepts a list of strings.
+    When ``stem`` or ``remove_stop_words`` is None, the base tokenizer's default
+    applies: False for ``code`` and ``ngram``, True otherwise.
     """
 
     return _tokenize(
@@ -578,7 +578,6 @@ async def connect_async(
 __all__ = [
     "Catalog",
     "AsyncCatalog",
-    "ListDatabasesResponse",
     "connect_catalog",
     "connect_catalog_async",
     "AsyncMaterializedView",
@@ -612,7 +611,7 @@ __all__ = [
     "SqlQuery",
     "LanceDBConnection",
     "LanceNamespaceDBConnection",
-    "LsmWriteSpec",
+    "MemWalWriteSpec",
     "RemoteDBConnection",
     "Session",
     "Table",
diff --git a/python/python/lancedb/_blob.py b/python/python/lancedb/_blob.py
index dc4ed37df..1e9110dd0 100644
--- a/python/python/lancedb/_blob.py
+++ b/python/python/lancedb/_blob.py
@@ -6,13 +6,14 @@
 from __future__ import annotations
 
 import io
+import operator
 from collections.abc import Awaitable, Callable, Iterable
 from typing import TYPE_CHECKING, Optional, Union
 
 import pyarrow as pa
 
 from .expr import Expr
-from .schema import row_addressable_blob_v2_paths
+from .schema import blob_column_paths, row_addressable_blob_v2_paths
 from .types import BlobMode, QueryProjection, QueryProjectionSpec
 
 if TYPE_CHECKING:
@@ -26,21 +27,24 @@ BLOB_MODE_TO_HANDLING = {
 
 ROW_ID_FIELD_NAME = "_lance_row_id"
 
-FetchBlobsSync = Callable[[str, pa.Table], pa.Array | pa.ChunkedArray]
-FetchBlobsAsync = Callable[[str, pa.Table], Awaitable[pa.Array | pa.ChunkedArray]]
+FetchBlobsSync = Callable[[str, list[int]], pa.Array | pa.ChunkedArray]
+FetchBlobsAsync = Callable[[str, list[int]], Awaitable[pa.Array | pa.ChunkedArray]]
 
 
 class BlobFile(io.RawIOBase):
     """Seekable lazy handle from :meth:`~lancedb.table.Table.fetch_blob_files`.
 
-    Bytes load on ``read`` or ``read_range``, not when the handle is opened.
-    Use :meth:`aread` from async code.
+    Bytes load on ``read``, ``read1``, ``read_range``, or ``read_ranges``, not
+    when the handle is opened. Range reads leave the cursor unchanged. Use
+    :meth:`aread` from async code.
     """
 
     def __init__(self, inner) -> None:
         self._inner = inner
 
     async def aread(self) -> bytes:
+        if self.closed:
+            raise ValueError("read of closed file")
         return await self._inner.read()
 
     def close(self) -> None:
@@ -58,13 +62,16 @@ class BlobFile(io.RawIOBase):
 
     def seek(self, offset: int, whence: int = io.SEEK_SET) -> int:
         if whence == io.SEEK_SET:
-            self._inner.seek(offset)
+            position = offset
         elif whence == io.SEEK_CUR:
-            self._inner.seek(self._inner.tell() + offset)
+            position = self._inner.tell() + offset
         elif whence == io.SEEK_END:
-            self._inner.seek(self._inner.size() + offset)
+            position = self._inner.size() + offset
         else:
             raise ValueError(f"invalid whence: {whence}")
+        if position < 0:
+            raise ValueError(f"negative seek value {position}")
+        self._inner.seek(position)
         return self._inner.tell()
 
     def tell(self) -> int:
@@ -74,17 +81,39 @@ class BlobFile(io.RawIOBase):
         return self._inner.size()
 
     def readall(self) -> bytes:
+        if self.closed:
+            raise ValueError("read of closed file")
         return self._inner.read_bytes()
 
-    def read(self, size: int = -1) -> bytes:
-        if size == -1:
+    def read(self, size: Optional[int] = -1) -> bytes:
+        if self.closed:
+            raise ValueError("read of closed file")
+        if size is None or size < 0:
             return self._inner.read_bytes()
         return super().read(size)
 
+    def read1(self, size: Optional[int] = -1) -> bytes:
+        """Read up to ``size`` bytes with one underlying read."""
+        if self.closed:
+            raise ValueError("read of closed file")
+        if size is None or size < 0:
+            size = io.DEFAULT_BUFFER_SIZE
+        return self._inner.read_up_to(size)
+
     def read_range(self, offset: int, length: int) -> bytes:
+        if self.closed:
+            raise ValueError("read of closed file")
         return self._inner.read_range(offset, length)
 
+    def read_ranges(self, ranges: list[tuple[int, int]]) -> list[bytes]:
+        """Read ``(offset, length)`` pairs without moving the cursor."""
+        if self.closed:
+            raise ValueError("read of closed file")
+        return self._inner.read_ranges(ranges)
+
     def readinto(self, b: WriteableBuffer) -> int:
+        if self.closed:
+            raise ValueError("readinto of closed file")
         view = memoryview(b).cast("B")
         chunk = self._inner.read_up_to(len(view))
         view[: len(chunk)] = chunk
@@ -107,10 +136,12 @@ def projection_includes_blob_column(
     columns = set(blob_columns)
     if not columns:
         return False
-    if projection is None:
+    if not projection:
         return True
     for output, source in _iter_projection_pairs(projection):
-        if output in columns or source in columns:
+        if output in columns or any(
+            column == source or column.startswith(f"{source}.") for column in columns
+        ):
             return True
     return False
 
@@ -119,17 +150,69 @@ def blob_v2_projection_sources(
     schema: pa.Schema,
     projection: QueryProjection,
 ) -> dict[str, str]:
-    blob_columns = row_addressable_blob_v2_paths(schema)
-    if not blob_columns:
-        return {}
-    columns = set(blob_columns)
-    if projection is None:
+    return _project_blob_paths(row_addressable_blob_v2_paths(schema), projection)
+
+
+def reject_unfetchable_blob_columns(
+    schema: pa.Schema, projection: QueryProjection, blob_sources: dict[str, str]
+) -> None:
+    # Rerankers can drop field metadata, so validate against the source schema.
+    unfetchable = [
+        output
+        for output in _project_blob_paths(blob_column_paths(schema), projection)
+        if output not in blob_sources
+    ]
+    if unfetchable:
+        raise ValueError(
+            f"blob_mode='bytes' cannot fetch blob columns {unfetchable}. Bytes are "
+            "fetched by row id, which covers blob v2 columns with one blob per "
+            "row but not legacy blob columns or blobs inside lists. Use "
+            "blob_mode='descriptions' or remove these columns from the projection."
+        )
+
+
+def reject_unmatched_blob_outputs(
+    output_schema: pa.Schema, blob_sources: dict[str, str]
+) -> None:
+    # Another spelling of a blob column, such as a quoted or recased name, is a
+    # blob in the query's output schema but maps to no source by name.
+    unmatched = [
+        path for path in blob_column_paths(output_schema) if path not in blob_sources
+    ]
+    if unmatched:
+        raise ValueError(
+            f"blob_mode='bytes' could not match result columns {unmatched} to a blob "
+            "column of the table. Select blob columns by their plain names, or "
+            "use blob_mode='descriptions'."
+        )
+
+
+def _project_blob_paths(
+    blob_columns: list[str], projection: QueryProjection
+) -> dict[str, str]:
+    """Map each projected output path to the blob column path it reads."""
+    # An empty projection selects all columns.
+    if not projection:
         return {column: column for column in blob_columns}
-    return {
-        output: source
-        for output, source in _iter_projection_pairs(projection)
-        if source in columns
-    }
+    sources: dict[str, str] = {}
+    entries: dict[str, str] = {}
+    for output, source in _iter_projection_pairs(projection):
+        for column in blob_columns:
+            if column == source:
+                path = output
+            elif column.startswith(f"{source}."):
+                path = f"{output}{column[len(source) :]}"
+            else:
+                continue
+            if path in sources:
+                raise ValueError(
+                    f"projection entries {entries[path]!r} and {output!r} both "
+                    f"produce blob column '{path}'. Rename one of them or drop "
+                    "the redundant selection."
+                )
+            sources[path] = column
+            entries[path] = output
+    return sources
 
 
 def v2_projection_needs_row_id(
@@ -179,11 +262,14 @@ async def replace_v2_blob_columns_with_bytes(
     blob_sources: dict[str, str],
     fetch_blobs: FetchBlobsAsync,
 ) -> pa.Table:
-    for output_name, source_name in blob_sources.items():
-        if output_name not in tbl.column_names:
-            continue
-        blobs = await fetch_blobs(source_name, tbl)
-        tbl = _set_blob_column(tbl, output_name, blobs)
+    present = _present_blob_sources(tbl, blob_sources)
+    if not present:
+        return tbl
+    row_ids = _hit_row_ids(tbl, present)
+    for output_name, source_name in present.items():
+        tbl = _set_blob_column(
+            tbl, output_name, await fetch_blobs(source_name, row_ids)
+        )
     return tbl
 
 
@@ -192,19 +278,48 @@ def replace_v2_blob_columns_with_bytes_sync(
     blob_sources: dict[str, str],
     fetch_blobs: FetchBlobsSync,
 ) -> pa.Table:
-    for output_name, source_name in blob_sources.items():
-        if output_name not in tbl.column_names:
-            continue
-        blobs = fetch_blobs(source_name, tbl)
-        tbl = _set_blob_column(tbl, output_name, blobs)
+    present = _present_blob_sources(tbl, blob_sources)
+    if not present:
+        return tbl
+    row_ids = _hit_row_ids(tbl, present)
+    for output_name, source_name in present.items():
+        tbl = _set_blob_column(tbl, output_name, fetch_blobs(source_name, row_ids))
     return tbl
 
 
+def _present_blob_sources(
+    tbl: pa.Table, blob_sources: dict[str, str]
+) -> dict[str, str]:
+    return {
+        output: source
+        for output, source in blob_sources.items()
+        if _has_column_path(tbl.schema, output)
+    }
+
+
+def _hit_row_ids(tbl: pa.Table, blob_sources: dict[str, str]) -> list[int]:
+    """Read row ids before replacing columns, since a swapped alias can overwrite
+    the lookup source."""
+    if "_rowid" in tbl.column_names:
+        return tbl["_rowid"].to_pylist()
+    for output in blob_sources:
+        descriptor = _leaf_struct_column(tbl, output)
+        if pa.types.is_struct(descriptor.type) and (
+            ROW_ID_FIELD_NAME in descriptor.type.names
+        ):
+            return descriptor.field(ROW_ID_FIELD_NAME).to_pylist()
+    raise ValueError(
+        f"query result has no '_rowid' column and no '{ROW_ID_FIELD_NAME}' "
+        f"field on blob columns {list(blob_sources)}. Call .with_row_id(True) "
+        "to fetch blob bytes for this query."
+    )
+
+
 def stash_auto_row_ids(tbl: pa.Table, blob_paths: Iterable[str]) -> pa.Table:
     if "_rowid" not in tbl.column_names:
         raise ValueError("query result has no '_rowid' column to hide")
 
-    present_paths = [p for p in blob_paths if p.split(".")[0] in tbl.column_names]
+    present_paths = [p for p in blob_paths if _has_column_path(tbl.schema, p)]
     if not present_paths:
         raise ValueError("query result has no blob v2 column to carry a row id")
 
@@ -224,7 +339,7 @@ def read_row_ids_from_hits(hits: pa.Table, blob_column: str) -> list[int]:
 
     try:
         leaf = _leaf_struct_column(hits, blob_column)
-        if ROW_ID_FIELD_NAME in leaf.type.names:
+        if pa.types.is_struct(leaf.type) and ROW_ID_FIELD_NAME in leaf.type.names:
             return leaf.field(ROW_ID_FIELD_NAME).to_pylist()
     except KeyError:
         pass
@@ -287,9 +402,37 @@ def _iter_projection_pairs(
                 yield name, source if source is not None else expr.to_sql()
 
 
-def _set_blob_column(tbl: pa.Table, output_name: str, blobs: pa.Array) -> pa.Table:
-    index = tbl.schema.get_field_index(output_name)
-    return tbl.set_column(index, pa.field(output_name, blobs.type), [blobs])
+def _has_column_path(schema: pa.Schema, path: str) -> bool:
+    top_name, rest = _split_column_path(schema, path)
+    index = schema.get_field_index(top_name)
+    return (
+        index >= 0 and _walk_struct_fields(schema.field(index).type, rest) is not None
+    )
+
+
+def _set_blob_column(
+    tbl: pa.Table, output_name: str, blobs: pa.Array | pa.ChunkedArray
+) -> pa.Table:
+    top_name, rest = _split_column_path(tbl.schema, output_name)
+    if not rest:
+        index = tbl.schema.get_field_index(top_name)
+        return tbl.set_column(index, pa.field(top_name, blobs.type), [blobs])
+
+    leaf_name = rest[-1]
+    if isinstance(blobs, pa.ChunkedArray):
+        blobs = blobs.combine_chunks()
+
+    def replace_blob(children: list, child_fields: list) -> None:
+        for i, field in enumerate(child_fields):
+            if field.name == leaf_name:
+                children[i] = blobs
+                child_fields[i] = pa.field(
+                    leaf_name, blobs.type, nullable=field.nullable
+                )
+                return
+        raise KeyError(f"Blob field '{leaf_name}' not found in '{output_name}'")
+
+    return _transform_struct_column(tbl, top_name, rest[:-1], replace_blob)
 
 
 def _embed_row_id_in_column(tbl: pa.Table, path: str, row_ids: pa.Array) -> pa.Table:
@@ -297,7 +440,9 @@ def _embed_row_id_in_column(tbl: pa.Table, path: str, row_ids: pa.Array) -> pa.T
         children.append(row_ids)
         child_fields.append(pa.field(ROW_ID_FIELD_NAME, pa.uint64(), nullable=False))
 
-    return _transform_struct_column(tbl, path, add_row_id)
+    return _transform_struct_column(
+        tbl, *_split_column_path(tbl.schema, path), add_row_id
+    )
 
 
 def strip_auto_row_ids(tbl: pa.Table, blob_paths: Iterable[str]) -> pa.Table:
@@ -314,16 +459,20 @@ def strip_auto_row_ids(tbl: pa.Table, blob_paths: Iterable[str]) -> pa.Table:
                 return
 
     for path in blob_paths:
-        if path.split(".")[0] not in tbl.column_names:
+        if not _has_column_path(tbl.schema, path):
             continue
-        tbl = _transform_struct_column(tbl, path, drop_row_id)
+        tbl = _transform_struct_column(
+            tbl, *_split_column_path(tbl.schema, path), drop_row_id
+        )
     return tbl
 
 
 def _transform_struct_column(
-    tbl: pa.Table, path: str, leaf_transform: Callable[[list, list], None]
+    tbl: pa.Table,
+    top_name: str,
+    rest: list[str],
+    leaf_transform: Callable[[list, list], None],
 ) -> pa.Table:
-    top_name, *rest = path.split(".")
     top_index = tbl.schema.get_field_index(top_name)
     top_field = tbl.schema.field(top_index)
     top_array = tbl.column(top_name)
@@ -381,26 +530,74 @@ def _rebuild_struct(
 
 
 def _leaf_struct_column(tbl: pa.Table, path: str) -> pa.StructArray:
-    parts = path.split(".")
-    column = tbl.column(parts[0])
+    top_name, rest = _split_column_path(tbl.schema, path)
+    column = tbl.column(top_name)
     if isinstance(column, pa.ChunkedArray):
         column = column.combine_chunks()
-    for part in parts[1:]:
+    for part in rest:
+        if not pa.types.is_struct(column.type):
+            raise KeyError(f"blob path '{path}' crosses non-struct column")
         column = column.field(part)
     return column
 
 
+def _split_column_path(schema: pa.Schema, path: str) -> tuple[str, list[str]]:
+    """Prefer a path ending in a struct, since a flat alias can shadow a nested
+    blob path."""
+    parts = path.split(".")
+    fallback = None
+    for cut in range(len(parts), 0, -1):
+        top_name, rest = ".".join(parts[:cut]), parts[cut:]
+        index = schema.get_field_index(top_name)
+        if index < 0:
+            continue
+        leaf_type = _walk_struct_fields(schema.field(index).type, rest)
+        if leaf_type is None:
+            continue
+        if pa.types.is_struct(leaf_type):
+            return top_name, rest
+        fallback = fallback or (top_name, rest)
+    return fallback or (parts[0], parts[1:])
+
+
+def _walk_struct_fields(
+    data_type: pa.DataType, names: list[str]
+) -> Optional[pa.DataType]:
+    for name in names:
+        if not pa.types.is_struct(data_type):
+            return None
+        index = data_type.get_field_index(name)
+        if index < 0:
+            return None
+        data_type = data_type.field(index).type
+    return data_type
+
+
 def _normalize_blob_row_ids(
-    row_ids: Union[list[int], pa.Table], blob_column: str
+    row_ids: Union[list[int], pa.Array, pa.ChunkedArray, pa.Table], blob_column: str
 ) -> list[int]:
     if isinstance(row_ids, pa.Table):
-        return read_row_ids_from_hits(row_ids, blob_column)
-    if isinstance(row_ids, (pa.Array, pa.ChunkedArray)):
-        raise ValueError(
-            "pass a query table with _rowid, not a column array "
-            "(use fetch_blobs('image', hits), not fetch_blobs('image', hits['image']))"
-        )
-    return list(row_ids)
+        values = read_row_ids_from_hits(row_ids, blob_column)
+    elif isinstance(row_ids, (pa.Array, pa.ChunkedArray)):
+        if not pa.types.is_integer(row_ids.type):
+            raise ValueError(
+                "row id arrays must have an integer type; pass a query table with "
+                "_rowid when using a blob descriptor column"
+            )
+        values = row_ids.to_pylist()
+    else:
+        values = list(row_ids)
+
+    normalized = []
+    for value in values:
+        try:
+            row_id = operator.index(value)
+        except TypeError as exc:
+            raise ValueError("row ids must be integers without nulls") from exc
+        if row_id < 0:
+            raise ValueError("row ids must be non-negative")
+        normalized.append(row_id)
+    return normalized
 
 
 def _wrap_blob_files(handles: Iterable[object]) -> list[Optional[BlobFile]]:
diff --git a/python/python/lancedb/_lancedb.pyi b/python/python/lancedb/_lancedb.pyi
index 9b6ef036d..3cdfed3f3 100644
--- a/python/python/lancedb/_lancedb.pyi
+++ b/python/python/lancedb/_lancedb.pyi
@@ -1,6 +1,6 @@
 from datetime import date, datetime, timedelta
 from decimal import Decimal
-from typing import Dict, List, Optional, Tuple, Any, TypedDict, Union, Literal
+from typing import Dict, List, Optional, Sequence, Tuple, Any, TypedDict, Union, Literal
 from uuid import UUID
 
 import pyarrow as pa
@@ -58,8 +58,8 @@ def tokenize(
     language: str = "English",
     max_token_length: Optional[int] = 40,
     lower_case: bool = True,
-    stem: bool = True,
-    remove_stop_words: bool = True,
+    stem: Optional[bool] = None,
+    remove_stop_words: Optional[bool] = None,
     custom_stop_words: Optional[List[str]] = None,
     ascii_folding: bool = True,
     ngram_min_length: int = 3,
@@ -149,12 +149,24 @@ class Connection(object):
         limit: Optional[int],
     ) -> list[str]: ...  # Deprecated: Use list_tables instead
     async def open_job(self, job_id: str) -> Job: ...
-    async def create_function_async(self, request_json: str) -> Job: ...
-    async def get_function(self, name: str, version: str) -> str: ...
-    async def list_functions(self) -> List[str]: ...
-    async def drop_function(self, name: str, version: str) -> bool: ...
+    async def create_function_async(
+        self, request_json: str, namespace_path: Optional[List[str]] = None
+    ) -> Job: ...
+    async def get_function(
+        self, name: str, version: str, namespace_path: Optional[List[str]] = None
+    ) -> str: ...
+    def list_functions(
+        self,
+        namespace_path: Optional[List[str]] = None,
+        *,
+        page_token: Optional[str] = None,
+        page_limit: Optional[int] = None,
+    ) -> FunctionListing: ...
+    async def drop_function(
+        self, name: str, version: str, namespace_path: Optional[List[str]] = None
+    ) -> bool: ...
     async def drop_function_async(
-        self, name: str, version: str
+        self, name: str, version: str, namespace_path: Optional[List[str]] = None
     ) -> Tuple[bool, Job]: ...
     async def create_secret(
         self, name: str, value: str, namespace_path: Optional[List[str]] = None
@@ -162,9 +174,13 @@ class Connection(object):
     async def alter_secret(
         self, name: str, value: str, namespace_path: Optional[List[str]] = None
     ) -> None: ...
-    async def list_secrets(
-        self, namespace_path: Optional[List[str]] = None
-    ) -> List[str]: ...
+    def list_secrets(
+        self,
+        namespace_path: Optional[List[str]] = None,
+        *,
+        page_token: Optional[str] = None,
+        page_limit: Optional[int] = None,
+    ) -> NameListing: ...
     async def drop_secret(
         self, name: str, namespace_path: Optional[List[str]] = None
     ) -> None: ...
@@ -183,10 +199,16 @@ class Connection(object):
     async def drop_view_async(
         self, name: str, namespace_path: Optional[List[str]] = None
     ) -> Job: ...
-    async def list_views(
-        self, namespace_path: Optional[List[str]] = None
-    ) -> List[str]: ...
-    async def list_jobs(self) -> List[JobInfo]: ...
+    def list_views(
+        self,
+        namespace_path: Optional[List[str]] = None,
+        *,
+        page_token: Optional[str] = None,
+        page_limit: Optional[int] = None,
+    ) -> NameListing: ...
+    def list_jobs(
+        self, *, page_token: Optional[str] = None, page_limit: Optional[int] = None
+    ) -> JobListing: ...
     async def cancel_job(self, job_id: str) -> bool: ...
     async def pause_job(self, job_id: str) -> str: ...
     async def resume_job(self, job_id: str) -> str: ...
@@ -195,6 +217,7 @@ class Connection(object):
         query: str,
         *,
         default_namespace_path: Optional[List[str]] = None,
+        parameters: Optional[pa.RecordBatch] = None,
     ) -> SqlQuery: ...
     async def describe_query(self, query_id: UUID) -> QueryDescription: ...
     async def create_table(
@@ -247,8 +270,6 @@ class Connection(object):
         filter: Optional[str] = None,
         limit: Optional[int] = None,
         with_no_data: bool = False,
-        *,
-        vector_source_json: Optional[str] = None,
     ) -> Table: ...
     async def create_materialized_view_async(
         self,
@@ -258,10 +279,10 @@ class Connection(object):
         filter: Optional[str] = None,
         limit: Optional[int] = None,
         with_no_data: bool = False,
-        *,
-        vector_source_json: Optional[str] = None,
     ) -> Job: ...
-    async def list_materialized_views(self) -> List[str]: ...
+    def list_materialized_views(
+        self, *, page_token: Optional[str] = None, page_limit: Optional[int] = None
+    ) -> NameListing: ...
     async def drop_materialized_view(
         self, name: str, namespace_path: Optional[List[str]] = None
     ) -> None: ...
@@ -290,6 +311,7 @@ class BlobFile:
     def tell(self) -> int: ...
     def size(self) -> int: ...
     def read_range(self, offset: int, length: int) -> bytes: ...
+    def read_ranges(self, ranges: list[tuple[int, int]]) -> list[bytes]: ...
     def read_up_to(self, length: int) -> bytes: ...
 
 class Job:
@@ -402,6 +424,7 @@ class Table:
     def name(self) -> str: ...
     def __repr__(self) -> str: ...
     def is_open(self) -> bool: ...
+    def _is_native(self) -> bool: ...
     def close(self) -> None: ...
     async def schema(self) -> pa.Schema: ...
     async def add(
@@ -492,10 +515,10 @@ class Table:
         limit: Optional[int] = None,
     ) -> FunctionErrors: ...
     async def refresh_materialized_view(
-        self, full: bool = False, source_version: Optional[int] = None
+        self, source_version: Optional[int] = None
     ) -> RefreshMaterializedViewResult: ...
     async def refresh_materialized_view_async(
-        self, full: bool = False, source_version: Optional[int] = None
+        self, source_version: Optional[int] = None
     ) -> Job: ...
     async def materialized_view_definition(self) -> str: ...
     async def add_columns_with_schema(self, schema: pa.Schema) -> AddColumnsResult: ...
@@ -516,17 +539,17 @@ class Table:
     async def initial_storage_options(self) -> Optional[Dict[str, str]]: ...
     async def latest_storage_options(self) -> Optional[Dict[str, str]]: ...
     async def set_unenforced_primary_key(self, columns: List[str]) -> None: ...
-    async def set_lsm_write_spec(self, spec: LsmWriteSpec) -> None: ...
-    async def unset_lsm_write_spec(self) -> None: ...
-    async def get_lsm_write_spec(self) -> Optional[LsmWriteSpec]: ...
+    async def enable_memwal(self, spec: MemWalWriteSpec) -> None: ...
+    async def disable_memwal(self) -> None: ...
+    async def get_memwal_spec(self) -> Optional[MemWalWriteSpec]: ...
     # WAL-PK-FUSION: delete both.
     def hybrid_pk_fusion_learned(self) -> bool: ...
     def note_hybrid_pk_fusion(self) -> None: ...
-    async def checkpoint_lsm(self) -> None: ...
-    async def flush_lsm(self) -> None: ...
-    async def compact_lsm(self) -> None: ...
-    async def get_lsm_stats(self, include_generation_rows: bool) -> Optional[dict]: ...
-    async def close_lsm_writers(self) -> None: ...
+    async def checkpoint_memwal(self) -> None: ...
+    async def flush_memwal(self) -> None: ...
+    async def compact_memwal(self) -> None: ...
+    async def get_memwal_stats(self, include_sstable_rows: bool) -> Optional[dict]: ...
+    async def close_memwal_writers(self) -> None: ...
     @property
     def tags(self) -> Tags: ...
     @property
@@ -542,7 +565,7 @@ class Table:
     async def fetch_blob_ranges(
         self,
         column: str,
-        requests: List[Tuple[int, int, int]],
+        requests: Sequence[Sequence[int]],
     ) -> pa.LargeBinaryArray: ...
     async def fetch_blob_files(
         self, column: str, row_ids: list[int]
@@ -639,7 +662,7 @@ class Query:
     def fast_search(self): ...
     def with_row_id(self): ...
     def postfilter(self): ...
-    def use_lsm(self, enable: bool): ...
+    def use_memwal(self, enable: bool): ...
     def nearest_to(self, query_vec: pa.Array) -> VectorQuery: ...
     def nearest_to_text(self, query: dict) -> FTSQuery: ...
     def order_by(self, ordering: Optional[List[ColumnOrdering]]): ...
@@ -656,7 +679,7 @@ class Query:
 class TakeQuery:
     def select(self, columns: List[str]): ...
     def with_row_id(self): ...
-    def use_lsm(self, enable: bool): ...
+    def use_memwal(self, enable: bool): ...
     async def output_schema(self) -> pa.Schema: ...
     async def execute(self) -> RecordBatchStream: ...
     async def explain_plan(self, verbose: Optional[bool]) -> str: ...
@@ -675,7 +698,7 @@ class FTSQuery:
     def fast_search(self): ...
     def with_row_id(self): ...
     def postfilter(self): ...
-    def use_lsm(self, enable: bool): ...
+    def use_memwal(self, enable: bool): ...
     def get_query(self) -> str: ...
     def add_query_vector(self, query_vec: pa.Array) -> None: ...
     def nearest_to(self, query_vec: pa.Array) -> HybridQuery: ...
@@ -703,9 +726,10 @@ class VectorQuery:
     def column(self, column: str): ...
     def distance_type(self, distance_type: str): ...
     def postfilter(self): ...
-    def use_lsm(self, enable: bool): ...
+    def use_memwal(self, enable: bool): ...
     def refine_factor(self, refine_factor: int): ...
     def nprobes(self, nprobes: int): ...
+    def search_effort(self, search_effort: Optional[float]): ...
     def minimum_nprobes(self, minimum_nprobes: int): ...
     def maximum_nprobes(self, maximum_nprobes: int): ...
     def bypass_vector_index(self): ...
@@ -727,10 +751,11 @@ class HybridQuery:
     def fast_search(self): ...
     def with_row_id(self): ...
     def postfilter(self): ...
-    def use_lsm(self, enable: bool): ...
+    def use_memwal(self, enable: bool): ...
     def distance_type(self, distance_type: str): ...
     def refine_factor(self, refine_factor: int): ...
     def nprobes(self, nprobes: int): ...
+    def search_effort(self, search_effort: Optional[float]): ...
     def minimum_nprobes(self, minimum_nprobes: int): ...
     def maximum_nprobes(self, maximum_nprobes: int): ...
     def bypass_vector_index(self): ...
@@ -754,11 +779,13 @@ class PyQueryRequest:
     select_source_columns: Optional[Dict[str, str]]
     fast_search: Optional[bool]
     with_row_id: Optional[bool]
-    use_lsm: Optional[bool]
+    use_memwal: Optional[bool]
     column: Optional[str]
     query_vector: Optional[List[pa.Array]]
+    nprobes: Optional[int]
     minimum_nprobes: Optional[int]
     maximum_nprobes: Optional[int]
+    search_effort: Optional[float]
     lower_bound: Optional[float]
     upper_bound: Optional[float]
     ef: Optional[int]
@@ -813,22 +840,26 @@ class FtsToken:
     text: str
     position: int
 
-class LsmWriteSpec:
+class MemWalWriteSpec:
     """Specification selecting Lance's MemWAL LSM-style write path for
     `merge_insert`."""
 
     @staticmethod
-    def bucket(column: str, num_buckets: int) -> "LsmWriteSpec": ...
+    def bucket(column: str, num_buckets: int) -> "MemWalWriteSpec": ...
     @staticmethod
-    def identity(column: str) -> "LsmWriteSpec": ...
+    def identity(column: str) -> "MemWalWriteSpec": ...
     @staticmethod
-    def unsharded() -> "LsmWriteSpec": ...
-    def with_maintained_indexes(self, indexes: Optional[List[str]]) -> "LsmWriteSpec":
+    def unsharded() -> "MemWalWriteSpec": ...
+    def with_maintained_indexes(
+        self, indexes: Optional[List[str]]
+    ) -> "MemWalWriteSpec":
         """Set which indexes the MemWAL keeps up to date. None resolves every
         index on the table at install, failing if one cannot be maintained;
         a list is verbatim, empty means none."""
         ...
-    def with_writer_config_defaults(self, defaults: Dict[str, str]) -> "LsmWriteSpec":
+    def with_writer_config_defaults(
+        self, defaults: Dict[str, str]
+    ) -> "MemWalWriteSpec":
         """Return a copy of this spec recording the given default
         `ShardWriter` configuration in the MemWAL index."""
         ...
@@ -930,6 +961,8 @@ class PermutationReader:
     def __init__(self, base_table: Table, permutation_table: Table): ...
 
 class Catalog:
+    @property
+    def authz(self) -> AuthorizationClient: ...
     @property
     def uri(self) -> str: ...
     async def create_database(
@@ -939,9 +972,15 @@ class Catalog:
     async def drop_database(
         self, name: str, *, ignore_missing: bool = False
     ) -> None: ...
-    async def list_databases(
-        self, *, limit: Optional[int] = None, page_token: Optional[str] = None
-    ) -> tuple[list[str], Optional[str]]: ...
+    def list_databases(
+        self, *, page_token: Optional[str] = None, page_limit: Optional[int] = None
+    ) -> DatabaseNames: ...
+
+class DatabaseNames:
+    def num_page_results(self) -> int: ...
+    def page_token(self) -> Optional[str]: ...
+    def __aiter__(self) -> DatabaseNames: ...
+    async def __anext__(self) -> str: ...
 
 async def connect_catalog(
     endpoint: str,
@@ -952,3 +991,78 @@ async def connect_catalog(
     read_consistency_interval: Optional[float] = None,
     oauth_config: Optional[Any] = None,
 ) -> Catalog: ...
+
+class NameListing:
+    def num_page_results(self) -> int: ...
+    def page_token(self) -> Optional[str]: ...
+    def __aiter__(self) -> NameListing: ...
+    async def __anext__(self) -> str: ...
+
+class FunctionListing:
+    def num_page_results(self) -> int: ...
+    def page_token(self) -> Optional[str]: ...
+    def __aiter__(self) -> FunctionListing: ...
+    async def __anext__(self) -> str: ...
+
+class JobListing:
+    def num_page_results(self) -> int: ...
+    def page_token(self) -> Optional[str]: ...
+    def __aiter__(self) -> JobListing: ...
+    async def __anext__(self) -> JobInfo: ...
+
+class AuthorizationClient:
+    async def list_acls(self, request: str) -> str: ...
+    async def add_acl(self, request: str) -> None: ...
+    async def delete_acl(self, request: str) -> None: ...
+    async def list_role_bindings(self, request: str) -> str: ...
+    async def add_role_binding(self, request: str) -> None: ...
+    async def delete_role_binding(self, request: str) -> None: ...
+    async def get_principal(self, request: str) -> str: ...
+    async def get_group(self, request: str) -> str: ...
+
+class AuthzSubject:
+    def __init__(self, kind: str, value: str): ...
+    @staticmethod
+    def from_canonical(value: str) -> AuthzSubject: ...
+    @property
+    def kind(self) -> str: ...
+    @property
+    def value(self) -> str: ...
+    @property
+    def display_value(self) -> str: ...
+    def to_wire(self, context: Optional[str] = None) -> str: ...
+    def __str__(self) -> str: ...
+    def __repr__(self) -> str: ...
+
+class AuthzPrivilege:
+    def __init__(self, value: str): ...
+    @staticmethod
+    def known() -> list[str]: ...
+    def __str__(self) -> str: ...
+
+class AuthzObject:
+    def __init__(self, value: str): ...
+    @staticmethod
+    def system() -> AuthzObject: ...
+    @staticmethod
+    def database(name: str) -> AuthzObject: ...
+    @staticmethod
+    def namespace(database: str, namespace_path: Sequence[str]) -> AuthzObject: ...
+    @staticmethod
+    def table(
+        database: str, namespace_path: Sequence[str], name: str
+    ) -> AuthzObject: ...
+    @staticmethod
+    def view(
+        database: str, namespace_path: Sequence[str], name: str
+    ) -> AuthzObject: ...
+    @staticmethod
+    def secret(
+        database: str, namespace_path: Sequence[str], name: str
+    ) -> AuthzObject: ...
+    @staticmethod
+    def function(
+        database: str, namespace_path: Sequence[str], name: str
+    ) -> AuthzObject: ...
+    def __str__(self) -> str: ...
+    def __repr__(self) -> str: ...
diff --git a/python/python/lancedb/_wal_hybrid.py b/python/python/lancedb/_wal_hybrid.py
index 67597950d..8e5d6ae17 100644
--- a/python/python/lancedb/_wal_hybrid.py
+++ b/python/python/lancedb/_wal_hybrid.py
@@ -76,7 +76,7 @@ _WAL_ROW_ID_REFUSAL = "does not support with_row_id"
 WAL_ROW_ID_UNSUPPORTED = (
     "hybrid search on a MemWAL table cannot return _rowid: the fresh tier has "
     "no stable row id, and the ids the fusion joins on are synthesized from "
-    "the primary key. Set use_lsm(False) to read the base table only (results "
+    "the primary key. Set use_memwal(False) to read the base table only (results "
     "will exclude un-compacted MemWAL data)"
 )
 
@@ -151,13 +151,13 @@ def _stamp_surrogate_row_ids(
     return stamped
 
 
-def pk_fusion_learned(table, use_lsm: Optional[bool]) -> bool:
+def pk_fusion_learned(table, use_memwal: Optional[bool]) -> bool:
     """Whether a previous hybrid query on `table` was refused `_rowid`, so this
     one should go straight to the primary key.
 
     Learned from a refusal, never probed, so asking is free.
     """
-    if table is None or use_lsm is False:
+    if table is None or use_memwal is False:
         return False
     # Only an unambiguous True forks to the key: a table type that answers the
     # question loosely keeps the `_rowid` path.
@@ -192,7 +192,7 @@ class PkFusion:
             raise NotImplementedError(
                 "hybrid search cannot project a blob column on a MemWAL table: "
                 "fetching blobs needs a real _rowid, and the fresh tier has no "
-                "stable one. Set use_lsm(False) to read the base table only "
+                "stable one. Set use_memwal(False) to read the base table only "
                 "(results will exclude un-compacted MemWAL data)"
             )
         return cls(_pk_columns(schema))
diff --git a/python/python/lancedb/authz.py b/python/python/lancedb/authz.py
new file mode 100644
index 000000000..e62a276cf
--- /dev/null
+++ b/python/python/lancedb/authz.py
@@ -0,0 +1,588 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+"""Deployment-wide authorization through ``catalog.authz``.
+
+ACLs grant one privilege on an object to a principal, group, or role. Role
+bindings grant a role to a principal or group. Identity-provider group membership
+is read-only. Name resolution and permission checks are performed by the server.
+"""
+
+from collections.abc import AsyncIterator, Iterator, Sequence
+from dataclasses import dataclass, field
+from enum import Enum
+import json
+from typing import Literal, Optional, TypeVar, Union, cast
+
+from . import _lancedb
+from .background_loop import LOOP
+
+__all__ = [
+    "Authorization",
+    "AsyncAuthorization",
+    "Subject",
+    "Object",
+    "Privilege",
+    "AccessControlEntry",
+    "RoleBinding",
+    "IdentityInfo",
+    "PrincipalInfo",
+    "GroupInfo",
+]
+
+_PAGE_SIZE = 100
+_T = TypeVar("_T")
+_SubjectKind = Literal[
+    "principal_id",
+    "principal_name",
+    "group_id",
+    "group_name",
+    "role",
+    "principal_api_key",
+    "unknown",
+]
+
+
+@dataclass(frozen=True)
+class Subject:
+    """An immutable identity selector. Prefer the named factory methods.
+
+    IDs are stable; names must resolve unambiguously on the server. API-key
+    selectors identify the subject of an operation, not its authenticated caller.
+    Secret values are omitted from representations. Unknown canonical subject
+    kinds returned by newer servers use ``kind="unknown"`` and preserve the full
+    wire string in ``value``; they can be passed back to ACL methods.
+    """
+
+    kind: _SubjectKind
+    _value: str = field(repr=False)
+
+    _inner: _lancedb.AuthzSubject = field(init=False, repr=False, compare=False)
+
+    def __post_init__(self):
+        object.__setattr__(
+            self, "_inner", _lancedb.AuthzSubject(self.kind, self._value)
+        )
+
+    @classmethod
+    def principal_id(cls, id: str) -> "Subject":
+        """Select a principal by canonical identity-provider ID."""
+        return cls("principal_id", id)
+
+    @classmethod
+    def principal_name(cls, name: str) -> "Subject":
+        """Select a principal by an unambiguous provider name."""
+        return cls("principal_name", name)
+
+    @classmethod
+    def group_id(cls, id: str) -> "Subject":
+        """Select a group by canonical identity-provider ID."""
+        return cls("group_id", id)
+
+    @classmethod
+    def group_name(cls, name: str) -> "Subject":
+        """Select a group by an unambiguous provider name."""
+        return cls("group_name", name)
+
+    @classmethod
+    def role(cls, name: str) -> "Subject":
+        """Select a LanceDB-managed role, without an ``r:`` prefix."""
+        return cls("role", name)
+
+    @classmethod
+    def principal_api_key(cls, api_key: str) -> "Subject":
+        """Select the principal holding this secret API key."""
+        return cls("principal_api_key", api_key)
+
+    def __repr__(self) -> str:
+        value = self._inner.display_value
+        return f"Subject.{self.kind}({value!r})"
+
+    @property
+    def value(self) -> str:
+        """The selected value, or full wire string for unknown subject kinds.
+
+        API-key values are secrets.
+        """
+        return self._value
+
+    def __reduce__(self):
+        return type(self), (self.kind, self._value)
+
+    def _wire(self) -> str:
+        return self._inner.to_wire()
+
+    @classmethod
+    def _canonical(cls, value: str) -> "Subject":
+        inner = _lancedb.AuthzSubject.from_canonical(value)
+        return cls(cast(_SubjectKind, inner.kind), inner.value)
+
+
+@dataclass(frozen=True)
+class Object:
+    """An immutable resource name in the authorization syntax.
+
+    Use the builders to avoid interpreting separators as part of a name.
+    Methods also accept raw object strings, validated by the same Rust parser.
+    Unknown resource types are preserved for compatibility with newer servers.
+    Omitted or empty namespace paths select the database's public namespace.
+    """
+
+    _value: str
+    _inner: _lancedb.AuthzObject = field(init=False, repr=False, compare=False)
+
+    def __post_init__(self):
+        object.__setattr__(self, "_inner", _lancedb.AuthzObject(self._value))
+
+    @classmethod
+    def system(cls) -> "Object":
+        """The deployment-wide system object."""
+        return cls(str(_lancedb.AuthzObject.system()))
+
+    @classmethod
+    def database(cls, name: str) -> "Object":
+        """A database; slash-containing names remain one logical database."""
+        return cls(str(_lancedb.AuthzObject.database(name)))
+
+    @classmethod
+    def namespace(
+        cls, *, database: str, namespace_path: Sequence[str] = ("public",)
+    ) -> "Object":
+        """A namespace path, defaulting to the database's public namespace."""
+        return cls(str(_lancedb.AuthzObject.namespace(database, namespace_path)))
+
+    @classmethod
+    def table(
+        cls, *, database: str, name: str, namespace_path: Sequence[str] = ("public",)
+    ) -> "Object":
+        """A table, defaulting to the database's public namespace."""
+        return cls(str(_lancedb.AuthzObject.table(database, namespace_path, name)))
+
+    @classmethod
+    def view(
+        cls, *, database: str, name: str, namespace_path: Sequence[str] = ("public",)
+    ) -> "Object":
+        """A view, defaulting to the database's public namespace."""
+        return cls(str(_lancedb.AuthzObject.view(database, namespace_path, name)))
+
+    @classmethod
+    def secret(
+        cls, *, database: str, name: str, namespace_path: Sequence[str] = ("public",)
+    ) -> "Object":
+        """A secret, defaulting to the database's public namespace."""
+        return cls(str(_lancedb.AuthzObject.secret(database, namespace_path, name)))
+
+    @classmethod
+    def function(
+        cls, *, database: str, name: str, namespace_path: Sequence[str] = ("public",)
+    ) -> "Object":
+        """A function, defaulting to the database's public namespace."""
+        return cls(str(_lancedb.AuthzObject.function(database, namespace_path, name)))
+
+    def __str__(self) -> str:
+        return str(self._inner)
+
+    def __reduce__(self):
+        return type(self), (self._value,)
+
+
+class Privilege(str, Enum):
+    """Known privileges, validated and serialized by Rust.
+
+    ACL methods also accept strings such as ``"SELECT"`` and future privilege
+    names such as ``"PRIVILEGE20"``. Future names are preserved verbatim;
+    their meaning and validity are determined by the server.
+
+    Grant ``CREATE_SECRET`` or ``CREATE_FUNCTION`` on a namespace to delegate
+    creation of those resources without transferring namespace ownership.
+    """
+
+    def __new__(cls, value: str):
+        canonical = str(_lancedb.AuthzPrivilege(value))
+        member = str.__new__(cls, canonical)
+        member._value_ = canonical
+        return member
+
+    OWNERSHIP = "OWNERSHIP"
+    USAGE = "USAGE"
+    SELECT = "SELECT"
+    INSERT = "INSERT"
+    UPDATE = "UPDATE"
+    DELETE = "DELETE"
+    CREATE_DATABASE = "CREATE_DATABASE"
+    CREATE_NAMESPACE = "CREATE_NAMESPACE"
+    CREATE_TABLE = "CREATE_TABLE"
+    CREATE_MATERIALIZED_VIEW = "CREATE_MATERIALIZED_VIEW"
+    CREATE_VIEW = "CREATE_VIEW"
+    CREATE_SECRET = "CREATE_SECRET"
+    CREATE_FUNCTION = "CREATE_FUNCTION"
+    OPERATE = "OPERATE"
+
+
+@dataclass(frozen=True)
+class AccessControlEntry:
+    """One object/subject/privilege grant; subject contains a canonical ID or role."""
+
+    object: str
+    subject: Subject
+    subject_name: Optional[str]
+    privilege: str
+
+
+@dataclass(frozen=True)
+class RoleBinding:
+    """A direct principal/group role binding, not an expanded effective role."""
+
+    subject: Subject
+    subject_name: Optional[str]
+    role: str
+
+
+@dataclass(frozen=True)
+class IdentityInfo:
+    """A canonical identity-provider ID and its optional display name."""
+
+    id: str
+    name: Optional[str] = None
+
+
+@dataclass(frozen=True)
+class PrincipalInfo:
+    """A principal's groups and effective roles, including group-inherited roles."""
+
+    principal_id: str
+    principal_name: Optional[str]
+    groups: list[IdentityInfo]
+    roles: list[str]
+
+
+@dataclass(frozen=True)
+class GroupInfo:
+    """A group's provider-managed members and LanceDB-managed direct roles."""
+
+    group_id: str
+    group_name: Optional[str]
+    principals: list[IdentityInfo]
+    roles: list[str]
+
+
+def _subject(subject: Subject, context: Optional[str] = None) -> str:
+    if not isinstance(subject, Subject):
+        raise ValueError("subject must be a Subject selector")
+    return subject._inner.to_wire(context)
+
+
+def _object(value: Union[str, Object]) -> str:
+    if not isinstance(value, (str, Object)) or not str(value):
+        raise ValueError("object must be a nonempty string or Object")
+    return str(value) if isinstance(value, Object) else str(_lancedb.AuthzObject(value))
+
+
+def _limit(limit: Optional[int]) -> None:
+    if limit is not None and (
+        isinstance(limit, bool) or not isinstance(limit, int) or limit < 0
+    ):
+        raise ValueError("limit must be a nonnegative integer or None")
+
+
+def _privilege(value: Union[str, Privilege]) -> str:
+    if not isinstance(value, str):
+        raise ValueError("privilege must be a string or Privilege")
+    return str(_lancedb.AuthzPrivilege(value))
+
+
+def _entry(value: dict) -> AccessControlEntry:
+    return AccessControlEntry(
+        value["object"],
+        Subject._canonical(value["subject_id"]),
+        value.get("subject_name"),
+        value["privilege"],
+    )
+
+
+def _binding(value: dict) -> RoleBinding:
+    return RoleBinding(
+        Subject._canonical(value["subject_id"]),
+        value.get("subject_name"),
+        value["role"],
+    )
+
+
+class AsyncAuthorization:
+    """Async authorization returned by ``AsyncCatalog.authz``.
+
+    HTTP failures raise [HttpError][lancedb.remote.errors.HttpError], preserving
+    status and request ID. Invalid local arguments raise ValueError. Lists are
+    lazy async iterators: failures can occur during iteration after some results
+    have been yielded. Iteration does not provide a consistent snapshot.
+    """
+
+    def __init__(self, inner: _lancedb.AuthorizationClient):
+        self._inner = inner
+
+    async def _pages(self, method, request: dict, key: str, limit: Optional[int]):
+        yielded = 0
+        while limit is None or yielded < limit:
+            request["max_results"] = (
+                _PAGE_SIZE if limit is None else min(_PAGE_SIZE, limit - yielded)
+            )
+            response = json.loads(await method(json.dumps(request)))
+            for value in response[key]:
+                yield value
+                yielded += 1
+                if limit is not None and yielded >= limit:
+                    return
+            token = response.get("continuation_token")
+            if not token:
+                return
+            if token == request.get("continuation_token"):
+                raise RuntimeError(
+                    "Authorization listing returned a repeated continuation token"
+                )
+            request["continuation_token"] = token
+
+    def list_acls(
+        self,
+        *,
+        object: Optional[Union[str, Object]] = None,
+        subject: Optional[Subject] = None,
+        limit: Optional[int] = None,
+    ) -> AsyncIterator[AccessControlEntry]:
+        """Iterate grants, optionally filtered by exact object and/or subject.
+
+        Requires USAGE on the supplied object, or on system if object is omitted.
+        ``limit`` bounds yielded entries across all pages; None lists all and zero
+        makes no requests. Missing matches produce an empty iterator.
+        """
+        _limit(limit)
+        request = {}
+        if object is not None:
+            request["object"] = _object(object)
+        if subject is not None:
+            request["subject"] = _subject(subject)
+
+        async def entries():
+            async for value in self._pages(
+                self._inner.list_acls, request, "entries", limit
+            ):
+                yield _entry(value)
+
+        return entries()
+
+    async def add_acl(
+        self,
+        *,
+        object: Union[str, Object],
+        subject: Subject,
+        privilege: Union[str, Privilege],
+    ) -> None:
+        """Grant one privilege; requires OWNERSHIP on the object.
+
+        A duplicate grant raises HTTP 409. Granting OWNERSHIP transfers ownership.
+        Writes are not automatically retried after ambiguous failures.
+        """
+        await self._inner.add_acl(
+            json.dumps(
+                {
+                    "object": _object(object),
+                    "subject": _subject(subject),
+                    "privilege": _privilege(privilege),
+                }
+            )
+        )
+
+    async def delete_acl(
+        self,
+        *,
+        object: Union[str, Object],
+        subject: Subject,
+        privilege: Union[str, Privilege],
+    ) -> None:
+        """Revoke one privilege; requires OWNERSHIP on the object.
+
+        A missing grant raises HTTP 404. OWNERSHIP itself cannot be deleted.
+        """
+        await self._inner.delete_acl(
+            json.dumps(
+                {
+                    "object": _object(object),
+                    "subject": _subject(subject),
+                    "privilege": _privilege(privilege),
+                }
+            )
+        )
+
+    def list_role_bindings(
+        self,
+        *,
+        subject: Optional[Subject] = None,
+        role: Optional[str] = None,
+        limit: Optional[int] = None,
+    ) -> AsyncIterator[RoleBinding]:
+        """Iterate direct bindings, optionally filtered by principal/group and role.
+
+        Requires USAGE on system. Limit semantics match ``list_acls``. Principals'
+        roles inherited through groups are available through ``get_principal``.
+        """
+        _limit(limit)
+        request = {}
+        if subject is not None:
+            request["subject"] = _subject(subject, "role_member")
+        if role is not None:
+            request["role"] = role
+
+        async def bindings():
+            async for value in self._pages(
+                self._inner.list_role_bindings, request, "bindings", limit
+            ):
+                yield _binding(value)
+
+        return bindings()
+
+    async def add_role_binding(self, *, subject: Subject, role: str) -> None:
+        """Grant a role idempotently; requires OPERATE on system."""
+        await self._inner.add_role_binding(
+            json.dumps({"subject": _subject(subject, "role_member"), "role": role})
+        )
+
+    async def delete_role_binding(self, *, subject: Subject, role: str) -> None:
+        """Idempotently revoke a role; requires OPERATE on system."""
+        await self._inner.delete_role_binding(
+            json.dumps({"subject": _subject(subject, "role_member"), "role": role})
+        )
+
+    async def get_principal(self, subject: Subject) -> PrincipalInfo:
+        """Look up groups and effective roles; requires USAGE on system.
+
+        Select by ID, name, or API key. Missing/ambiguous identities raise HTTP
+        404/409; unavailable or unconfigured resolution raises HTTP 503/501.
+        """
+        value = json.loads(
+            await self._inner.get_principal(
+                json.dumps({"principal": _subject(subject, "principal")})
+            )
+        )
+        return PrincipalInfo(
+            value["principal_id"],
+            value.get("principal_name"),
+            [IdentityInfo(**group) for group in value["groups"]],
+            value["roles"],
+        )
+
+    async def get_group(self, subject: Subject) -> GroupInfo:
+        """Look up a group's members and roles by ID/name; requires USAGE on system.
+
+        Membership is read-only and comes from the identity provider. Lookup
+        errors have the same HTTP semantics as ``get_principal``.
+        """
+        value = json.loads(
+            await self._inner.get_group(
+                json.dumps({"group": _subject(subject, "group")})
+            )
+        )
+        return GroupInfo(
+            value["group_id"],
+            value.get("group_name"),
+            [IdentityInfo(**principal) for principal in value["principals"]],
+            value["roles"],
+        )
+
+
+async def _next(iterator: AsyncIterator[_T]) -> _T:
+    return await iterator.__anext__()
+
+
+def _sync_iter(iterator: AsyncIterator[_T]) -> Iterator[_T]:
+    try:
+        while True:
+            yield LOOP.run(_next(iterator))
+    except StopAsyncIteration:
+        return
+    finally:
+        close = getattr(iterator, "aclose", None)
+        if close is not None:
+            LOOP.run(close())
+
+
+class Authorization:
+    """Synchronous authorization returned by ``Catalog.authz``.
+
+    Shares the async API's permissions, errors, and lazy pagination behavior.
+    Group membership comes from the identity provider and cannot be edited here.
+    """
+
+    def __init__(self, inner: AsyncAuthorization):
+        self._inner = inner
+
+    def list_acls(
+        self,
+        *,
+        object: Optional[Union[str, Object]] = None,
+        subject: Optional[Subject] = None,
+        limit: Optional[int] = None,
+    ) -> Iterator[AccessControlEntry]:
+        """Iterate grants.
+
+        See [list_acls][lancedb.authz.AsyncAuthorization.list_acls] for permissions.
+        """
+        return _sync_iter(
+            self._inner.list_acls(object=object, subject=subject, limit=limit)
+        )
+
+    def add_acl(
+        self,
+        *,
+        object: Union[str, Object],
+        subject: Subject,
+        privilege: Union[str, Privilege],
+    ) -> None:
+        """Grant a privilege; duplicates raise HTTP 409.
+
+        OWNERSHIP transfers ownership.
+        """
+        LOOP.run(
+            self._inner.add_acl(object=object, subject=subject, privilege=privilege)
+        )
+
+    def delete_acl(
+        self,
+        *,
+        object: Union[str, Object],
+        subject: Subject,
+        privilege: Union[str, Privilege],
+    ) -> None:
+        """Revoke a privilege. Missing grants raise HTTP 404.
+
+        OWNERSHIP cannot be deleted.
+        """
+        LOOP.run(
+            self._inner.delete_acl(object=object, subject=subject, privilege=privilege)
+        )
+
+    def list_role_bindings(
+        self,
+        *,
+        subject: Optional[Subject] = None,
+        role: Optional[str] = None,
+        limit: Optional[int] = None,
+    ) -> Iterator[RoleBinding]:
+        """Iterate direct role bindings, using the same limit semantics as list_acls."""
+        return _sync_iter(
+            self._inner.list_role_bindings(subject=subject, role=role, limit=limit)
+        )
+
+    def add_role_binding(self, *, subject: Subject, role: str) -> None:
+        """Grant a role idempotently; requires OPERATE on system."""
+        LOOP.run(self._inner.add_role_binding(subject=subject, role=role))
+
+    def delete_role_binding(self, *, subject: Subject, role: str) -> None:
+        """Idempotently revoke a role; requires OPERATE on system."""
+        LOOP.run(self._inner.delete_role_binding(subject=subject, role=role))
+
+    def get_principal(self, subject: Subject) -> PrincipalInfo:
+        """Look up groups and effective roles; requires USAGE on system."""
+        return LOOP.run(self._inner.get_principal(subject))
+
+    def get_group(self, subject: Subject) -> GroupInfo:
+        """Look up a group's members and roles; requires USAGE on system."""
+        return LOOP.run(self._inner.get_group(subject))
diff --git a/python/python/lancedb/catalog.py b/python/python/lancedb/catalog.py
index 4fd941ddd..666d414a4 100644
--- a/python/python/lancedb/catalog.py
+++ b/python/python/lancedb/catalog.py
@@ -3,23 +3,78 @@
 
 """Remote catalogs manage databases through a server's root namespace."""
 
-from dataclasses import dataclass
+from functools import cached_property
 from datetime import timedelta
-from typing import Any, Optional, Union
+from typing import Any, AsyncIterator, Iterator, Optional, Union
 
 from . import _lancedb
 from .background_loop import LOOP
+from .authz import AsyncAuthorization, Authorization
 from .db import AsyncConnection, DBConnection
 from .remote import ClientConfig, OAuthConfig
 from .remote.db import RemoteDBConnection
 
 
-@dataclass
-class ListDatabasesResponse:
-    """A page of database names and an optional continuation token."""
+class AsyncDatabaseNames(AsyncIterator[str]):
+    """An async iterator of database names with pagination state.
 
-    databases: list[str]
-    page_token: Optional[str] = None
+    Returned by [list_databases][lancedb.catalog.AsyncCatalog.list_databases].
+    """
+
+    def __init__(self, inner: _lancedb.DatabaseNames):
+        self._inner = inner
+
+    def __aiter__(self) -> "AsyncDatabaseNames":
+        return self
+
+    async def __anext__(self) -> str:
+        return await self._inner.__anext__()
+
+    def num_page_results(self) -> int:
+        """Return the number of names available without another REST request."""
+        return self._inner.num_page_results()
+
+    def page_token(self) -> Optional[str]:
+        """Return the token for the next REST request.
+
+        Before the first request, this is the supplied starting token (``None``
+        starts at the beginning). After the final page is fetched, it is ``None``,
+        even if names remain cached. Drain the cache before saving a token to
+        avoid skipping those names when resuming. A failed request terminates
+        iteration and leaves its token available for resuming a new iterator.
+        """
+        return self._inner.page_token()
+
+
+class DatabaseNames(Iterator[str]):
+    """A synchronous iterator of database names with pagination state.
+
+    Returned by [Catalog.list_databases][lancedb.catalog.Catalog.list_databases].
+    """
+
+    def __init__(self, inner: AsyncDatabaseNames):
+        self._inner = inner
+
+    def __iter__(self) -> "DatabaseNames":
+        return self
+
+    def __next__(self) -> str:
+        try:
+            return LOOP.run(self._inner.__anext__())
+        except StopAsyncIteration:
+            raise StopIteration from None
+
+    def num_page_results(self) -> int:
+        """Return the number of names available without another REST request."""
+        return self._inner.num_page_results()
+
+    def page_token(self) -> Optional[str]:
+        """Return the token for the next REST request.
+
+        See [page_token][lancedb.catalog.AsyncDatabaseNames.page_token]
+        for initial, cached, final-page, and error behavior.
+        """
+        return self._inner.page_token()
 
 
 class AsyncCatalog:
@@ -33,6 +88,11 @@ class AsyncCatalog:
     def __init__(self, inner: _lancedb.Catalog):
         self._inner = inner
 
+    @cached_property
+    def authz(self) -> AsyncAuthorization:
+        """Authorization sharing this catalog's scope and credentials."""
+        return AsyncAuthorization(self._inner.authz)
+
     @property
     def uri(self) -> str:
         """The catalog's root namespace endpoint."""
@@ -50,14 +110,25 @@ class AsyncCatalog:
         """Connect to an existing database by its logical name."""
         return AsyncConnection(await self._inner.connect_database(name))
 
-    async def list_databases(
-        self, *, limit: Optional[int] = None, page_token: Optional[str] = None
-    ) -> ListDatabasesResponse:
-        """List a page of databases. Pass the returned token for the next page."""
-        names, token = await self._inner.list_databases(
-            limit=limit, page_token=page_token
+    def list_databases(
+        self, *, page_token: Optional[str] = None, page_limit: Optional[int] = None
+    ) -> AsyncDatabaseNames:
+        """Iterate lazily over all database names using ``async for``.
+
+        ``page_token`` resumes from a saved token; ``None`` starts at the beginning.
+        ``page_limit`` sets the maximum names per REST response (not the total);
+        ``None`` uses the server default. Request errors are raised during iteration.
+
+        Examples
+        --------
+        ```python
+        async for name in catalog.list_databases():
+            print(name)
+        ```
+        """
+        return AsyncDatabaseNames(
+            self._inner.list_databases(page_token=page_token, page_limit=page_limit)
         )
-        return ListDatabasesResponse(names, token)
 
     async def drop_database(self, name: str, *, ignore_missing: bool = False) -> None:
         """Drop an empty database. A nonempty database is an error."""
@@ -73,7 +144,8 @@ class Catalog:
     ```python
     catalog = lancedb.connect_catalog("https://my-server.example", api_key="secret")
     db = catalog.create_database("analytics", exist_ok=True)
-    page = catalog.list_databases(limit=20)
+    for name in catalog.list_databases():
+        print(name)
     ```
     """
 
@@ -92,6 +164,11 @@ class Catalog:
         self._sql_host_override = sql_host_override
         self._oauth_config = oauth_config
 
+    @cached_property
+    def authz(self) -> Authorization:
+        """Authorization sharing this catalog's scope and credentials."""
+        return Authorization(self._inner.authz)
+
     @property
     def uri(self) -> str:
         """The catalog's root namespace endpoint."""
@@ -118,10 +195,17 @@ class Catalog:
         )
 
     def list_databases(
-        self, *, limit: Optional[int] = None, page_token: Optional[str] = None
-    ) -> ListDatabasesResponse:
-        """List a page of databases. Pass the returned token for the next page."""
-        return LOOP.run(self._inner.list_databases(limit=limit, page_token=page_token))
+        self, *, page_token: Optional[str] = None, page_limit: Optional[int] = None
+    ) -> DatabaseNames:
+        """Iterate lazily over database names, fetching more results as needed.
+
+        ``page_token`` resumes from a saved token; ``None`` starts at the beginning.
+        ``page_limit`` sets the maximum names per REST response (not the total);
+        ``None`` uses the server default. Request errors are raised during iteration.
+        """
+        return DatabaseNames(
+            self._inner.list_databases(page_token=page_token, page_limit=page_limit)
+        )
 
     def drop_database(self, name: str, *, ignore_missing: bool = False) -> None:
         """Drop an empty database. A nonempty database is an error."""
diff --git a/python/python/lancedb/db.py b/python/python/lancedb/db.py
index 490987c3c..47d24b762 100644
--- a/python/python/lancedb/db.py
+++ b/python/python/lancedb/db.py
@@ -31,6 +31,7 @@ if sys.version_info >= (3, 12):
 else:
     from overrides import EnforceOverrides, override  # type: ignore
 
+from lancedb.listing import AsyncListing, Listing
 from lancedb.embeddings.registry import EmbeddingFunctionRegistry
 
 from lancedb.common import data_to_reader, sanitize_uri, validate_schema
@@ -52,12 +53,11 @@ from .functions import FunctionVersion, UdfDefinition
 from .job import AsyncJob, Job, _typed_job
 from .sql import AsyncQuery as AsyncSqlQuery
 from .sql import Query as SqlQuery
-from .sql import QueryDescription
+from .sql import QueryDescription, QueryParameters, to_parameter_batch
 from .materialized_view import (
     AsyncMaterializedView,
     MaterializedView,
     MaterializedViewSource,
-    VectorDedupSource,
     SelectArg,
     normalize_select,
 )
@@ -122,7 +122,48 @@ def _view_description(
 
 
 class DBConnection(EnforceOverrides):
-    """An active LanceDB connection interface."""
+    """An active LanceDB connection interface.
+
+    Use [close][lancedb.db.DBConnection.close] to release the connection's
+    underlying resources, or use the connection as a context manager to close it
+    automatically when leaving the block, including when an exception is raised.
+
+    Examples
+    --------
+    >>> import lancedb
+    >>> with lancedb.connect("memory://") as db:
+    ...     assert db.is_open()
+    >>> db.is_open()
+    False
+    """
+
+    def __enter__(self) -> DBConnection:
+        return self
+
+    def __exit__(self, *_) -> None:
+        self.close()
+
+    @abstractmethod
+    def is_open(self) -> bool:
+        """Return True if the connection is open."""
+        pass
+
+    @abstractmethod
+    def close(self) -> None:
+        """Close the connection, releasing any underlying resources.
+
+        It is safe to call this method multiple times. Database operations on a
+        closed connection raise ``RuntimeError: Connection is closed``.
+
+        Examples
+        --------
+        >>> import lancedb
+        >>> db = lancedb.connect("memory://")
+        >>> db.close()
+        >>> db.is_open()
+        False
+        """
+        pass
 
     def list_namespaces(
         self,
@@ -276,7 +317,7 @@ class DBConnection(EnforceOverrides):
         page_token: Optional[str] = None,
         limit: Optional[int] = None,
     ) -> ListTablesResponse:
-        """List all tables in this database with pagination support.
+        """List a page of tables in this database.
 
         Parameters
         ----------
@@ -287,7 +328,8 @@ class DBConnection(EnforceOverrides):
             Token for pagination. Use the token from a previous response
             to get the next page of results.
         limit: int, optional
-            The maximum number of results to return.
+            The maximum number of results to return, default 100.
+            Zero returns an empty page without a continuation token.
 
         Returns
         -------
@@ -302,7 +344,7 @@ class DBConnection(EnforceOverrides):
     def table_names(
         self,
         page_token: Optional[str] = None,
-        limit: int = 10,
+        limit: Optional[int] = None,
         *,
         namespace_path: Optional[List[str]] = None,
     ) -> Iterable[str]:
@@ -316,8 +358,9 @@ class DBConnection(EnforceOverrides):
         page_token: str, optional
             The token to use for pagination. If not present, start from the beginning.
             Typically, this token is last table name from the previous page.
-        limit: int, default 10
-            The size of the page to return.
+        limit: int, optional
+            The maximum number of tables to return. None returns all tables;
+            zero returns an empty list.
 
         Returns
         -------
@@ -368,9 +411,10 @@ class DBConnection(EnforceOverrides):
             - [LanceModel][lancedb.pydantic.LanceModel]
         mode: str; default "create"
             The mode to use when creating the table.
-            Can be either "create" or "overwrite".
+            Can be "create", "overwrite", or "exist_ok".
             By default, if the table already exists, an exception is raised.
             If you want to overwrite the table, use mode="overwrite".
+            To open an existing table without adding data, use mode="exist_ok".
         exist_ok: bool, default False
             If a table by the same name already exists, then raise an exception
             if exist_ok=False. If exist_ok=True, then open the existing table;
@@ -571,21 +615,12 @@ class DBConnection(EnforceOverrides):
         table. The view is a normal table: it can be queried, indexed and
         searched, and it appears in ``table_names``.
 
-        Ordinary source tables must have stable row ids (create them with the
-        ``new_table_enable_stable_row_ids`` storage option): they keep the
-        view's provenance valid across source compactions, and cannot be
-        enabled after a table exists. Native dedup sources also support physical
-        row IDs interpreted against their pinned snapshot.
-
         Parameters
         ----------
         name: str
             The name of the view.
-        source: str or VectorDedupSource
-            The name of the source table, or an indexed dedup source declared
-            with [vector_dedup][lancedb.vector_dedup]. Dedup sources capture a
-            fixed snapshot and preserve the source table. They accept no
-            additional select, where or limit options.
+        source: str
+            The name of the source table.
         select: list or dict, optional
             The view's columns: column names, ``(alias, SQL expression)``
             pairs, or a dict of the same. Omitting it selects every source
@@ -636,11 +671,16 @@ class DBConnection(EnforceOverrides):
             "materialized views are not supported on this connection type"
         )
 
-    def list_materialized_views(self) -> List[str]:
+    def list_materialized_views(
+        self, *, page_token: Optional[str] = None, page_limit: Optional[int] = None
+    ) -> Listing[str]:
         """The names of the materialized views in this database.
 
-        Found by reading every table's schema, so this costs an open per
-        table.
+        Local listings inspect table schemas one page at a time.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
         """
         raise NotImplementedError(
             "materialized views are not supported on this connection type"
@@ -784,6 +824,7 @@ class DBConnection(EnforceOverrides):
         definition: UdfDefinition,
         *,
         secrets: Optional[Sequence[EnvVarSecret]] = None,
+        namespace_path: Optional[List[str]] = None,
     ) -> FunctionVersion:
         """Build and register a scalar Python UDF, then return its version.
 
@@ -800,6 +841,9 @@ class DBConnection(EnforceOverrides):
             Function needs, each naming a Secret and the environment variable
             its value arrives in. The Function's source is unchanged by this;
             it reads the variable the way it already did.
+        namespace_path : list of str, optional
+            The namespace holding the Function. None or an empty list is the
+            root namespace.
 
         Examples
         --------
@@ -815,13 +859,16 @@ class DBConnection(EnforceOverrides):
         )
         ```
         """
-        return self.create_function_async(definition, secrets=secrets).wait()
+        return self.create_function_async(
+            definition, secrets=secrets, namespace_path=namespace_path
+        ).wait()
 
     def create_function_async(
         self,
         definition: UdfDefinition,
         *,
         secrets: Optional[Sequence[EnvVarSecret]] = None,
+        namespace_path: Optional[List[str]] = None,
     ) -> Job[FunctionVersion]:
         """Submit a scalar Python UDF for building and registration.
 
@@ -833,17 +880,30 @@ class DBConnection(EnforceOverrides):
             "Function catalog operations are not supported for this connection type"
         )
 
-    def get_function(self, name: str, *, version: str) -> FunctionVersion:
+    def get_function(
+        self,
+        name: str,
+        *,
+        version: str,
+        namespace_path: Optional[List[str]] = None,
+    ) -> FunctionVersion:
         """Open one exact immutable Function version from the remote catalog."""
         raise NotImplementedError(
             "Function catalog operations are not supported for this connection type"
         )
 
-    def list_functions(self) -> List[FunctionVersion]:
-        """List every published immutable Function version.
+    def list_functions(
+        self,
+        *,
+        namespace_path: Optional[List[str]] = None,
+        page_token: Optional[str] = None,
+        page_limit: Optional[int] = None,
+    ) -> Listing[FunctionVersion]:
+        """List every published immutable Function version in a namespace.
 
-        Results are ordered by Function name then version. Local connections
-        raise ``NotImplementedError``.
+        Functions in child namespaces are not included. Results are ordered by
+        Function name then version. Local connections raise
+        ``NotImplementedError``.
 
         Examples
         --------
@@ -852,12 +912,22 @@ class DBConnection(EnforceOverrides):
         ```python
         [(function.name, function.version) for function in db.list_functions()]
         ```
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
         """
         raise NotImplementedError(
             "Function catalog operations are not supported for this connection type"
         )
 
-    def drop_function(self, name: str, *, version: str) -> bool:
+    def drop_function(
+        self,
+        name: str,
+        *,
+        version: str,
+        namespace_path: Optional[List[str]] = None,
+    ) -> bool:
         """Drop a Function name and the object it was bound to.
 
         The requested version must exist in the currently named object. Returns
@@ -870,7 +940,13 @@ class DBConnection(EnforceOverrides):
             "Function catalog operations are not supported for this connection type"
         )
 
-    def drop_function_async(self, name: str, *, version: str) -> Tuple[bool, Job]:
+    def drop_function_async(
+        self,
+        name: str,
+        *,
+        version: str,
+        namespace_path: Optional[List[str]] = None,
+    ) -> Tuple[bool, Job]:
         """Drop a Function name and return its cleanup job.
 
         The name is unbound before this returns; the object's content may still
@@ -911,11 +987,21 @@ class DBConnection(EnforceOverrides):
             "Secret operations are not supported for this connection type"
         )
 
-    def list_secrets(self, *, namespace_path: Optional[List[str]] = None) -> List[str]:
+    def list_secrets(
+        self,
+        *,
+        namespace_path: Optional[List[str]] = None,
+        page_token: Optional[str] = None,
+        page_limit: Optional[int] = None,
+    ) -> Listing[str]:
         """The names of every Secret in this database.
 
         Names only. No method returns a stored credential, by construction
         rather than by policy. Local connections raise ``NotImplementedError``.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
         """
         raise NotImplementedError(
             "Secret operations are not supported for this connection type"
@@ -1013,12 +1099,22 @@ class DBConnection(EnforceOverrides):
             "View operations are not supported for this connection type"
         )
 
-    def list_views(self, *, namespace_path: Optional[List[str]] = None) -> List[str]:
+    def list_views(
+        self,
+        *,
+        namespace_path: Optional[List[str]] = None,
+        page_token: Optional[str] = None,
+        page_limit: Optional[int] = None,
+    ) -> Listing[str]:
         """The names of the views in one namespace.
 
         Names only; a definition comes from
         [describe_view][lancedb.db.DBConnection.describe_view]. Local
         connections raise ``NotImplementedError``.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
         """
         raise NotImplementedError(
             "View operations are not supported for this connection type"
@@ -1037,8 +1133,15 @@ class DBConnection(EnforceOverrides):
         """
         raise NotImplementedError("open_job is not supported for this connection type")
 
-    def list_jobs(self) -> List[JobInfo]:
-        """List server-side jobs across the database's tables."""
+    def list_jobs(
+        self, *, page_token: Optional[str] = None, page_limit: Optional[int] = None
+    ) -> Listing[JobInfo]:
+        """List server-side jobs across the database's tables.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
+        """
         raise NotImplementedError("list_jobs is not supported for this connection type")
 
     def cancel_job(self, job_id: str) -> bool:
@@ -1077,6 +1180,7 @@ class DBConnection(EnforceOverrides):
         query: str,
         *,
         default_namespace_path: Optional[List[str]] = None,
+        parameters: Optional[QueryParameters] = None,
     ) -> pa.RecordBatchReader:
         """Execute SQL and return a blocking Arrow reader.
 
@@ -1087,6 +1191,7 @@ class DBConnection(EnforceOverrides):
         return self.execute_query_async(
             query,
             default_namespace_path=default_namespace_path,
+            parameters=parameters,
         ).reader()
 
     def execute_query_async(
@@ -1094,9 +1199,32 @@ class DBConnection(EnforceOverrides):
         query: str,
         *,
         default_namespace_path: Optional[List[str]] = None,
+        parameters: Optional[QueryParameters] = None,
     ) -> SqlQuery:
         """Start executing SQL and return its query handle.
 
+        ``parameters`` binds values to the statement's ``$1`` / ``$name``
+        placeholders. They travel as Arrow rather than as SQL text, so a
+        float keeps its exact value and type and a vector stays a compact
+        fixed-size list. A list binds by position, a dict binds by name, and
+        a one-row ``pyarrow.RecordBatch`` or ``pyarrow.Table`` binds both
+        ways. A 1-D numpy or pyarrow array becomes a fixed-size list of its
+        element type, the shape of a vector column:
+
+        .. code-block:: python
+
+            db.execute_query(
+                "SELECT id FROM docs ORDER BY distance(vector, $1) LIMIT $2",
+                parameters=[np.array([0.1, 0.2, 0.3], dtype=np.float32), 10],
+            )
+
+        Parameters are supported in queries, not in DDL or DML. A
+        parameterized query runs on the call that returns its rows:
+        cancelling it, or dropping its handle before reading, stops it on the
+        server. Its status follows that stream as well: ``describe()``
+        reports ``running`` until the reader has received every row, so open
+        ``reader()`` directly rather than waiting for ``finished``.
+
         Local connections do not support SQL.
         """
         raise NotImplementedError("SQL is not supported for this connection type")
@@ -1247,6 +1375,14 @@ class LanceDBConnection(DBConnection):
     def __repr__(self) -> str:
         return f"{self.__class__.__name__}(uri={self._conn.uri!r})"
 
+    @override
+    def is_open(self) -> bool:
+        return self._conn.is_open()
+
+    @override
+    def close(self) -> None:
+        self._conn.close()
+
     @override
     def serialize(self) -> str:
         import json
@@ -1356,7 +1492,7 @@ class LanceDBConnection(DBConnection):
         page_token: Optional[str] = None,
         limit: Optional[int] = None,
     ) -> ListTablesResponse:
-        """List all tables in this database with pagination support.
+        """List a page of tables in this database.
 
         Parameters
         ----------
@@ -1367,7 +1503,8 @@ class LanceDBConnection(DBConnection):
             Token for pagination. Use the token from a previous response
             to get the next page of results.
         limit: int, optional
-            The maximum number of results to return.
+            The maximum number of results to return, default 100.
+            Zero returns an empty page without a continuation token.
 
         Returns
         -------
@@ -1386,7 +1523,7 @@ class LanceDBConnection(DBConnection):
     def table_names(
         self,
         page_token: Optional[str] = None,
-        limit: int = 10,
+        limit: Optional[int] = None,
         *,
         namespace_path: Optional[List[str]] = None,
     ) -> Iterable[str]:
@@ -1401,8 +1538,9 @@ class LanceDBConnection(DBConnection):
             The namespace to list tables in.
         page_token: str, optional
             The token to use for pagination.
-        limit: int, default 10
-            The maximum number of tables to return.
+        limit: int, optional
+            The maximum number of tables to return. None returns all tables;
+            zero returns an empty list.
 
         Returns
         -------
@@ -1469,8 +1607,6 @@ class LanceDBConnection(DBConnection):
         """
         if namespace_path is None:
             namespace_path = []
-        if mode.lower() not in ["create", "overwrite"]:
-            raise ValueError("mode must be either 'create' or 'overwrite'")
         validate_table_name(name)
 
         tbl = LanceTable.create(
@@ -1578,10 +1714,7 @@ class LanceDBConnection(DBConnection):
         Examples
         --------
         >>> import lancedb
-        >>> db = lancedb.connect(
-        ...     "./.lancedb",
-        ...     storage_options={"new_table_enable_stable_row_ids": "true"},
-        ... )
+        >>> db = lancedb.connect("./.lancedb")
         >>> data = [{"name": "ada", "age": 36}, {"name": "kid", "age": 7}]
         >>> table = db.create_table("people", data)
         >>> view = db.create_materialized_view(
@@ -1636,9 +1769,20 @@ class LanceDBConnection(DBConnection):
         return view
 
     @override
-    def list_materialized_views(self) -> List[str]:
-        """The names of the materialized views in this database."""
-        return LOOP.run(self._conn.list_materialized_views())
+    def list_materialized_views(
+        self, *, page_token: Optional[str] = None, page_limit: Optional[int] = None
+    ) -> Listing[str]:
+        """The names of the materialized views in this database.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
+        """
+        return Listing(
+            self._conn.list_materialized_views(
+                page_token=page_token, page_limit=page_limit
+            )
+        )
 
     @override
     def drop_materialized_view(
@@ -1808,25 +1952,78 @@ class LanceDBConnection(DBConnection):
         definition: UdfDefinition,
         *,
         secrets: Optional[Sequence[EnvVarSecret]] = None,
+        namespace_path: Optional[List[str]] = None,
     ) -> Job[FunctionVersion]:
-        job = LOOP.run(self._conn.create_function_async(definition, secrets=secrets))
+        job = LOOP.run(
+            self._conn.create_function_async(
+                definition, secrets=secrets, namespace_path=namespace_path
+            )
+        )
         return Job(job)
 
     @override
-    def get_function(self, name: str, *, version: str) -> FunctionVersion:
-        return LOOP.run(self._conn.get_function(name, version=version))
+    def get_function(
+        self,
+        name: str,
+        *,
+        version: str,
+        namespace_path: Optional[List[str]] = None,
+    ) -> FunctionVersion:
+        return LOOP.run(
+            self._conn.get_function(
+                name, version=version, namespace_path=namespace_path
+            )
+        )
 
     @override
-    def list_functions(self) -> List[FunctionVersion]:
-        return LOOP.run(self._conn.list_functions())
+    def list_functions(
+        self,
+        *,
+        namespace_path: Optional[List[str]] = None,
+        page_token: Optional[str] = None,
+        page_limit: Optional[int] = None,
+    ) -> Listing[FunctionVersion]:
+        """List functions.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
+        """
+        return Listing(
+            self._conn.list_functions(
+                namespace_path=namespace_path,
+                page_token=page_token,
+                page_limit=page_limit,
+            )
+        )
 
     @override
-    def drop_function(self, name: str, *, version: str) -> bool:
-        return LOOP.run(self._conn.drop_function(name, version=version))
+    def drop_function(
+        self,
+        name: str,
+        *,
+        version: str,
+        namespace_path: Optional[List[str]] = None,
+    ) -> bool:
+        return LOOP.run(
+            self._conn.drop_function(
+                name, version=version, namespace_path=namespace_path
+            )
+        )
 
     @override
-    def drop_function_async(self, name: str, *, version: str) -> Tuple[bool, Job]:
-        dropped, job = LOOP.run(self._conn.drop_function_async(name, version=version))
+    def drop_function_async(
+        self,
+        name: str,
+        *,
+        version: str,
+        namespace_path: Optional[List[str]] = None,
+    ) -> Tuple[bool, Job]:
+        dropped, job = LOOP.run(
+            self._conn.drop_function_async(
+                name, version=version, namespace_path=namespace_path
+            )
+        )
         return dropped, Job(job)
 
     @override
@@ -1842,8 +2039,26 @@ class LanceDBConnection(DBConnection):
         LOOP.run(self._conn.alter_secret(name, value, namespace_path=namespace_path))
 
     @override
-    def list_secrets(self, *, namespace_path: Optional[List[str]] = None) -> List[str]:
-        return LOOP.run(self._conn.list_secrets(namespace_path=namespace_path))
+    def list_secrets(
+        self,
+        *,
+        namespace_path: Optional[List[str]] = None,
+        page_token: Optional[str] = None,
+        page_limit: Optional[int] = None,
+    ) -> Listing[str]:
+        """List secrets.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
+        """
+        return Listing(
+            self._conn.list_secrets(
+                namespace_path=namespace_path,
+                page_token=page_token,
+                page_limit=page_limit,
+            )
+        )
 
     @override
     def drop_secret(
@@ -1886,13 +2101,40 @@ class LanceDBConnection(DBConnection):
         )
 
     @override
-    def list_views(self, *, namespace_path: Optional[List[str]] = None) -> List[str]:
-        return LOOP.run(self._conn.list_views(namespace_path=namespace_path))
+    def list_views(
+        self,
+        *,
+        namespace_path: Optional[List[str]] = None,
+        page_token: Optional[str] = None,
+        page_limit: Optional[int] = None,
+    ) -> Listing[str]:
+        """List views.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
+        """
+        return Listing(
+            self._conn.list_views(
+                namespace_path=namespace_path,
+                page_token=page_token,
+                page_limit=page_limit,
+            )
+        )
 
     @override
-    def list_jobs(self) -> List[JobInfo]:
-        """List server-side jobs across the database's tables."""
-        return LOOP.run(self._conn.list_jobs())
+    def list_jobs(
+        self, *, page_token: Optional[str] = None, page_limit: Optional[int] = None
+    ) -> Listing[JobInfo]:
+        """List server-side jobs across the database's tables.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
+        """
+        return Listing(
+            self._conn.list_jobs(page_token=page_token, page_limit=page_limit)
+        )
 
     @override
     def cancel_job(self, job_id: str) -> bool:
@@ -2128,7 +2370,7 @@ class AsyncConnection(object):
         page_token: Optional[str] = None,
         limit: Optional[int] = None,
     ) -> ListTablesResponse:
-        """List all tables in this database with pagination support.
+        """List a page of tables in this database.
 
         Parameters
         ----------
@@ -2139,7 +2381,8 @@ class AsyncConnection(object):
             Token for pagination. Use the token from a previous response
             to get the next page of results.
         limit: int, optional
-            The maximum number of results to return.
+            The maximum number of results to return, default 100.
+            Zero returns an empty page without a continuation token.
 
         Returns
         -------
@@ -2176,8 +2419,9 @@ class AsyncConnection(object):
 
             This can be combined with limit to implement pagination by setting this to
             the last table name from the previous page.
-        limit: int, default 10
-            The number of results to return.
+        limit: int, optional
+            The maximum number of tables to return. None returns all tables;
+            zero returns an empty list.
 
         Returns
         -------
@@ -2236,11 +2480,12 @@ class AsyncConnection(object):
             - pyarrow.Schema
 
             - [LanceModel][lancedb.pydantic.LanceModel]
-        mode: Literal["create", "overwrite"]; default "create"
+        mode: Literal["create", "overwrite", "exist_ok"]; default "create"
             The mode to use when creating the table.
-            Can be either "create" or "overwrite".
+            Can be "create", "overwrite", or "exist_ok".
             By default, if the table already exists, an exception is raised.
             If you want to overwrite the table, use mode="overwrite".
+            To open an existing table without adding data, use mode="exist_ok".
         exist_ok: bool, default False
             If a table by the same name already exists, then raise an exception
             if exist_ok=False. If exist_ok=True, then open the existing table;
@@ -2511,19 +2756,7 @@ class AsyncConnection(object):
         where: Optional[str],
         limit: Optional[int],
     ) -> Tuple[str, Dict[str, str]]:
-        if not isinstance(source, VectorDedupSource):
-            return source, {}
-        if select is not None or where is not None or limit is not None:
-            raise ValueError(
-                "vector_dedup cannot be combined with select, where or limit"
-            )
-        version = source.dataset_version
-        if version is None:
-            table = await self.open_table(source.source)
-            version = await table.version()
-        return source.source, {
-            "vector_source_json": source._native_source_json(version)
-        }
+        return source, {}
 
     async def create_materialized_view(
         self,
@@ -2591,13 +2824,22 @@ class AsyncConnection(object):
         await view.definition()
         return view
 
-    async def list_materialized_views(self) -> List[str]:
+    def list_materialized_views(
+        self, *, page_token: Optional[str] = None, page_limit: Optional[int] = None
+    ) -> AsyncListing[str]:
         """The names of the materialized views in this database.
 
-        Found by reading every table's schema, so this costs an open per
-        table.
+        Local listings inspect table schemas one page at a time.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
         """
-        return await self._inner.list_materialized_views()
+        return AsyncListing(
+            self._inner.list_materialized_views(
+                page_token=page_token, page_limit=page_limit
+            )
+        )
 
     async def drop_materialized_view(
         self,
@@ -2787,6 +3029,7 @@ class AsyncConnection(object):
         definition: UdfDefinition,
         *,
         secrets: Optional[Sequence[EnvVarSecret]] = None,
+        namespace_path: Optional[List[str]] = None,
     ) -> AsyncJob[FunctionVersion]:
         """Submit a scalar Python UDF for building and registration.
 
@@ -2794,36 +3037,72 @@ class AsyncConnection(object):
         artifact. Waiting on the job returns the immutable Function version.
         ``secrets`` is a sequence of
         [EnvVarSecret][lancedb.secrets.EnvVarSecret], each naming a Secret and
-        the environment variable its value arrives in. Local connections raise
-        ``NotImplementedError``.
+        the environment variable its value arrives in. ``namespace_path`` is
+        the namespace the Function is registered in; None or an empty list is
+        the root. Local connections raise ``NotImplementedError``.
         """
         if not isinstance(definition, UdfDefinition):
             raise TypeError("create_function_async requires a @udf definition")
         request = definition.bind_secrets(secrets)
-        inner = await self._inner.create_function_async(request.to_canonical_json())
+        inner = await self._inner.create_function_async(
+            request.to_canonical_json(), namespace_path
+        )
         return _typed_job(inner, FunctionVersion.from_json)
 
-    async def get_function(self, name: str, *, version: str) -> FunctionVersion:
+    async def get_function(
+        self,
+        name: str,
+        *,
+        version: str,
+        namespace_path: Optional[List[str]] = None,
+    ) -> FunctionVersion:
         """Open one exact immutable Function version from the remote catalog."""
-        return FunctionVersion.from_json(await self._inner.get_function(name, version))
+        return FunctionVersion.from_json(
+            await self._inner.get_function(name, version, namespace_path)
+        )
 
-    async def list_functions(self) -> List[FunctionVersion]:
-        """List every published immutable Function version.
+    def list_functions(
+        self,
+        *,
+        namespace_path: Optional[List[str]] = None,
+        page_token: Optional[str] = None,
+        page_limit: Optional[int] = None,
+    ) -> AsyncListing[FunctionVersion]:
+        """List every published immutable Function version in a namespace.
 
-        Results are ordered by Function name then version. Local connections
-        raise ``NotImplementedError``.
+        Functions in child namespaces are not included. Results are ordered by
+        Function name then version. Local connections raise
+        ``NotImplementedError``.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
         """
-        return [
-            FunctionVersion.from_json(value)
-            for value in await self._inner.list_functions()
-        ]
+        return AsyncListing(
+            self._inner.list_functions(
+                namespace_path=namespace_path,
+                page_token=page_token,
+                page_limit=page_limit,
+            ),
+            FunctionVersion.from_json,
+        )
 
-    async def drop_function(self, name: str, *, version: str) -> bool:
+    async def drop_function(
+        self,
+        name: str,
+        *,
+        version: str,
+        namespace_path: Optional[List[str]] = None,
+    ) -> bool:
         """Drop a Function name and the object it was bound to."""
-        return await self._inner.drop_function(name, version)
+        return await self._inner.drop_function(name, version, namespace_path)
 
     async def drop_function_async(
-        self, name: str, *, version: str
+        self,
+        name: str,
+        *,
+        version: str,
+        namespace_path: Optional[List[str]] = None,
     ) -> Tuple[bool, AsyncJob]:
         """Drop a Function name and return its cleanup job.
 
@@ -2831,7 +3110,9 @@ class AsyncConnection(object):
         be being deleted. Await :meth:`AsyncJob.wait` to wait for that to
         finish.
         """
-        dropped, job = await self._inner.drop_function_async(name, version)
+        dropped, job = await self._inner.drop_function_async(
+            name, version, namespace_path
+        )
         return dropped, AsyncJob(job)
 
     async def create_secret(
@@ -2862,12 +3143,25 @@ class AsyncConnection(object):
             list(validate_namespace_path(namespace_path)),
         )
 
-    async def list_secrets(
-        self, *, namespace_path: Optional[List[str]] = None
-    ) -> List[str]:
-        """The names of every Secret in this database. Names only."""
-        return await self._inner.list_secrets(
-            list(validate_namespace_path(namespace_path))
+    def list_secrets(
+        self,
+        *,
+        namespace_path: Optional[List[str]] = None,
+        page_token: Optional[str] = None,
+        page_limit: Optional[int] = None,
+    ) -> AsyncListing[str]:
+        """The names of every Secret in this database. Names only.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
+        """
+        return AsyncListing(
+            self._inner.list_secrets(
+                namespace_path=namespace_path,
+                page_token=page_token,
+                page_limit=page_limit,
+            )
         )
 
     async def drop_secret(
@@ -2939,15 +3233,39 @@ class AsyncConnection(object):
             await self._inner.drop_view_async(name, namespace_path=namespace_path)
         )
 
-    async def list_views(
-        self, *, namespace_path: Optional[List[str]] = None
-    ) -> List[str]:
-        """The names of the views in one namespace."""
-        return await self._inner.list_views(list(namespace_path or []))
+    def list_views(
+        self,
+        *,
+        namespace_path: Optional[List[str]] = None,
+        page_token: Optional[str] = None,
+        page_limit: Optional[int] = None,
+    ) -> AsyncListing[str]:
+        """The names of the views in one namespace.
 
-    async def list_jobs(self) -> List[JobInfo]:
-        """List server-side jobs across the database's tables."""
-        return await self._inner.list_jobs()
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
+        """
+        return AsyncListing(
+            self._inner.list_views(
+                namespace_path=namespace_path,
+                page_token=page_token,
+                page_limit=page_limit,
+            )
+        )
+
+    def list_jobs(
+        self, *, page_token: Optional[str] = None, page_limit: Optional[int] = None
+    ) -> AsyncListing[JobInfo]:
+        """List server-side jobs across the database's tables.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
+        """
+        return AsyncListing(
+            self._inner.list_jobs(page_token=page_token, page_limit=page_limit)
+        )
 
     async def cancel_job(self, job_id: str) -> bool:
         """Request cancellation of a server-side job by id.
@@ -2980,6 +3298,7 @@ class AsyncConnection(object):
         query: str,
         *,
         default_namespace_path: Optional[List[str]] = None,
+        parameters: Optional[QueryParameters] = None,
     ) -> AsyncRecordBatchReader:
         """Execute SQL and return an asynchronous Arrow reader.
 
@@ -2990,6 +3309,7 @@ class AsyncConnection(object):
         submitted = await self.execute_query_async(
             query,
             default_namespace_path=default_namespace_path,
+            parameters=parameters,
         )
         return await submitted.reader()
 
@@ -2998,17 +3318,21 @@ class AsyncConnection(object):
         query: str,
         *,
         default_namespace_path: Optional[List[str]] = None,
+        parameters: Optional[QueryParameters] = None,
     ) -> AsyncSqlQuery:
         """Start executing SQL and return its query handle.
 
         The database from ``connect_async`` is used for unqualified database
-        references. The namespace defaults to ``["public"]``. Local
-        connections raise ``NotImplementedError``.
+        references. The namespace defaults to ``["public"]``. ``parameters``
+        binds values to ``$1`` / ``$name`` placeholders; see
+        [DBConnection.execute_query_async][lancedb.db.DBConnection.execute_query_async].
+        Local connections raise ``NotImplementedError``.
         """
         return AsyncSqlQuery(
             await self._inner.execute_query_async(
                 query,
                 default_namespace_path=default_namespace_path,
+                parameters=to_parameter_batch(parameters),
             )
         )
 
diff --git a/python/python/lancedb/embeddings/base.py b/python/python/lancedb/embeddings/base.py
index 149b9c089..fbe43860d 100644
--- a/python/python/lancedb/embeddings/base.py
+++ b/python/python/lancedb/embeddings/base.py
@@ -182,7 +182,11 @@ class EmbeddingFunction(BaseModel, ABC):
     def VectorField(self, **kwargs):
         """
         Creates a pydantic Field that can automatically annotate
-        the target vector column for this embedding function
+        the target vector column for this embedding function.
+
+        The field can be annotated as ``list[float]`` for compatibility with
+        static type checkers. LanceDB will infer the fixed vector dimension from
+        this embedding function.
         """
         return Field(json_schema_extra={"vector_column_for": self}, **kwargs)
 
diff --git a/python/python/lancedb/functions.py b/python/python/lancedb/functions.py
index 5f26b590e..445d31027 100644
--- a/python/python/lancedb/functions.py
+++ b/python/python/lancedb/functions.py
@@ -356,16 +356,23 @@ _ObjectVersion = Annotated[
 
 
 class FunctionVersion(_RemoteValue):
-    """A pinned object revision, independent of its executable image digest."""
+    """A pinned object revision, independent of its executable image digest.
+
+    ``name`` and ``namespace_path`` are the Function's parts, as a
+    [SecretReference][lancedb.functions.SecretReference] carries a Secret's;
+    they are what [get_function][lancedb.db.DBConnection.get_function] takes.
+    An empty ``namespace_path`` is the root namespace.
+    """
 
     name: str
+    namespace_path: tuple[str, ...] = ()
     object_id: str
     location: str
     version: _ObjectVersion
     image: FunctionImage
     signature: FunctionSignature
     secret_bindings: tuple[SecretBinding, ...] = ()
-    created_at: str
+    created_at_millis: int
     metadata: Mapping[str, str]
     disabled: bool
 
@@ -444,6 +451,7 @@ class FunctionVersion(_RemoteValue):
         return FunctionApplication(
             function=FunctionVersionRef(
                 name=self.name,
+                namespace_path=self.namespace_path,
                 object_id=self.object_id,
                 location=self.location,
                 version=self.version,
@@ -502,6 +510,7 @@ class FunctionRegistrationRequest(_RemoteValue):
 
 class FunctionVersionRef(_OpenRemoteValue):
     name: str
+    namespace_path: tuple[str, ...] = ()
     object_id: str
     location: str
     version: _ObjectVersion
diff --git a/python/python/lancedb/index.py b/python/python/lancedb/index.py
index 78ee72e57..cf3bf8e4f 100644
--- a/python/python/lancedb/index.py
+++ b/python/python/lancedb/index.py
@@ -112,9 +112,42 @@ class Fm:
 class FTS:
     """Describe a FTS index configuration.
 
-    `FTS` is a full-text search index that can be used on `String` columns
+    `FTS` supports string columns, lists of strings, and JSON columns created
+    from PyArrow ``pa.json_()``. JSON columns use Lance's JSON-aware tokenizer
+    and are stored as ``LargeBinary`` with the ``lance.json`` logical type.
+    Arbitrary binary columns are not supported.
 
-    For example, it works with `title`, `description`, `content`, etc.
+    JSON queries use ``path,str,value`` triples. Paths are relative to the JSON
+    column and use dots for nested object keys. For example,
+    ``meta.tag,str,nature`` matches the string value at ``$.meta.tag``;
+    it does not search other paths or JSON key names. The value is processed
+    with the configured text tokenizer. Use ``str``, not ``string``.
+
+    Create and query a JSON index through the modern API::
+
+        import pyarrow as pa
+        from lancedb.index import FTS
+
+        table = db.create_table(
+            "documents",
+            pa.table({"doc": pa.array(
+                ['{"meta":{"tag":"nature"}}'], type=pa.json_()
+            )}),
+        )
+        table.create_index("doc", config=FTS())
+        table.search(
+            "meta.tag,str,nature", query_type="fts", fts_columns="doc"
+        ).to_list()
+
+    Create the index on the JSON column itself, not a JSON subpath such as
+    ``doc.meta.tag``. JSON-path scalar indexes do not provide FTS path scoping.
+    Plain text such as ``nature`` is not a valid JSON FTS query. The current
+    Lance tokenizer can panic on malformed triples; see
+    https://github.com/lance-format/lance/issues/8812.
+
+    Remote tables use the same ``FTS()`` configuration and require a server
+    that supports JSON FTS. Wait for ``table.wait_for_index(["doc_idx"])``
+    before searching a newly created remote index with its default name.
 
     Examples
     --------
@@ -138,6 +171,7 @@ class FTS:
         - "simple": Splits text by whitespace and punctuation.
         - "whitespace": Split text by whitespace, but not punctuation.
         - "raw": No tokenization. The entire text is treated as a single token.
+        - "code": Tokenizes source code and identifiers.
         - "ngram": N-gram tokenizer for substring-style matching.
         - "icu": ICU dictionary-based word segmentation.
         - "icu/split": ICU segmentation with simple-style delimiter splitting.
@@ -151,12 +185,16 @@ class FTS:
         ignored.
     lower_case : bool, default True
         Whether to convert the token to lower case. This makes queries case-insensitive.
-    stem : bool, default True
+    stem : bool, optional
         Whether to stem the token. Stemming reduces words to their root form.
         For example, in English "running" and "runs" would both be reduced to "run".
-    remove_stop_words : bool, default True
+        ``None`` uses the base tokenizer's default: False for ``code`` and
+        ``ngram``, True otherwise.
+    remove_stop_words : bool, optional
         Whether to remove stop words. Stop words are common words that are often
         removed from text before indexing. For example, in English "the" and "and".
+        ``None`` uses the base tokenizer's default: False for ``code`` and
+        ``ngram``, True otherwise.
     custom_stop_words : list of str, optional
         Custom words replace the built-in language stop words
         and only take effect when ``remove_stop_words`` is True. ``None`` uses
@@ -197,8 +235,8 @@ class FTS:
     language: str = "English"
     max_token_length: Optional[int] = 40
     lower_case: bool = True
-    stem: bool = True
-    remove_stop_words: bool = True
+    stem: Optional[bool] = None
+    remove_stop_words: Optional[bool] = None
     ascii_folding: bool = True
     ngram_min_length: int = 3
     ngram_max_length: int = 3
diff --git a/python/python/lancedb/listing.py b/python/python/lancedb/listing.py
new file mode 100644
index 000000000..0620aa7d8
--- /dev/null
+++ b/python/python/lancedb/listing.py
@@ -0,0 +1,85 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+"""Lazy, resumable resource listings."""
+
+from __future__ import annotations
+
+from typing import Any, AsyncIterator, Callable, Generic, Iterator, Optional, TypeVar
+
+from .background_loop import LOOP
+
+T = TypeVar("T")
+
+
+class AsyncListing(AsyncIterator[T], Generic[T]):
+    """A lazy async iterator with inspectable pagination state.
+
+    Returned by connection resource-listing methods. For example::
+
+        async for name in connection.list_views(page_limit=20):
+            print(name)
+
+    Requests happen during iteration; request errors terminate the iterator.
+    """
+
+    def __init__(self, inner: Any, convert: Callable[[Any], T] = lambda value: value):
+        self._inner = inner
+        self._convert = convert
+
+    def __aiter__(self) -> AsyncListing[T]:
+        return self
+
+    async def __anext__(self) -> T:
+        return self._convert(await self._inner.__anext__())
+
+    def num_page_results(self) -> int:
+        """Return the number of cached items available without another request."""
+        return self._inner.num_page_results()
+
+    def page_token(self) -> Optional[str]:
+        """Return the token for the next request.
+
+        Initially this is the supplied starting token (``None`` starts at the
+        beginning). After fetching the final page it is ``None``, even if items
+        remain cached. Drain the cache before saving a token to avoid skipping
+        items when resuming. A failed request terminates iteration and retains
+        its token for retrying with a new iterator.
+        """
+        return self._inner.page_token()
+
+
+class Listing(Iterator[T], Generic[T]):
+    """A lazy synchronous iterator with inspectable pagination state.
+
+    For example::
+
+        for name in connection.list_views(page_limit=20):
+            print(name)
+
+    Use ``list(connection.list_views())`` to collect all results.
+    """
+
+    def __init__(self, inner: AsyncListing[T]):
+        self._inner = inner
+
+    def __iter__(self) -> Listing[T]:
+        return self
+
+    def __next__(self) -> T:
+        try:
+            return LOOP.run(self._inner.__anext__())
+        except StopAsyncIteration:
+            raise StopIteration from None
+
+    def num_page_results(self) -> int:
+        """Return the number of cached items available without another request."""
+        return self._inner.num_page_results()
+
+    def page_token(self) -> Optional[str]:
+        """Return the next request's token.
+
+        See [AsyncListing.page_token][lancedb.listing.AsyncListing.page_token]
+        for initial, cached, final-page, and error behavior.
+        """
+        return self._inner.page_token()
diff --git a/python/python/lancedb/materialized_view.py b/python/python/lancedb/materialized_view.py
index b4d455870..6342641ff 100644
--- a/python/python/lancedb/materialized_view.py
+++ b/python/python/lancedb/materialized_view.py
@@ -6,11 +6,11 @@ maintained by refresh. See ``DBConnection.create_materialized_view``."""
 
 from __future__ import annotations
 
-import json
-import math
-from dataclasses import dataclass
 from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
 
+import json
+from dataclasses import dataclass
+
 from .background_loop import LOOP
 from .job import AsyncJob, Job, _typed_job
 
@@ -21,7 +21,6 @@ if TYPE_CHECKING:
     from .table import AsyncTable, LanceTable
 
 DEFINITION_META_KEY = b"mv.definition"
-_MAX_FLOAT32 = float.fromhex("0x1.fffffep+127")
 
 SelectArg = Union[
     str,
@@ -31,104 +30,12 @@ SelectArg = Union[
 ]
 
 
-@dataclass(frozen=True)
-class VectorDedupSource:
-    """A declarative source of retained original rows from an indexed snapshot.
-
-    Construct with [vector_dedup][lancedb.vector_dedup] and pass it as the
-    ``source`` of ``create_materialized_view`` or
-    ``create_materialized_view_async``. An omitted ``dataset_version`` is
-    captured once when creation is submitted, not when this object is made.
-    Refreshes keep that captured version. The source table is never modified.
-    """
-
-    source: str
-    column: str
-    distance_threshold: float
-    dataset_version: Optional[int] = None
-
-    def __post_init__(self):
-        for name in ("source", "column"):
-            value = getattr(self, name)
-            if not isinstance(value, str) or not value:
-                raise ValueError(f"{name} must be a non-empty string")
-        if self.dataset_version is not None and (
-            type(self.dataset_version) is not int
-            or not 0 < self.dataset_version < 2**64
-        ):
-            raise ValueError("dataset_version must be a positive uint64 integer")
-        value = self.distance_threshold
-        if isinstance(value, bool) or not isinstance(value, (int, float)):
-            raise TypeError("distance_threshold must be a number")
-        try:
-            value = float(value)
-        except OverflowError as exc:
-            raise ValueError("distance_threshold must fit a finite float32") from exc
-        if not math.isfinite(value) or abs(value) > _MAX_FLOAT32:
-            raise ValueError("distance_threshold must fit a finite float32")
-        object.__setattr__(self, "distance_threshold", value)
-
-    def _native_source_json(self, version: int) -> str:
-        return json.dumps(
-            {
-                "kind": "Dedup",
-                "dataset_version": version,
-                "column": self.column,
-                "distance_threshold": repr(self.distance_threshold),
-            },
-            allow_nan=False,
-        )
+MaterializedViewSource = str
 
 
-def vector_dedup(
-    source: str,
-    *,
-    column: str,
-    distance_threshold: float,
-    dataset_version: Optional[int] = None,
-) -> VectorDedupSource:
-    """Declare an indexed dedup source for a materialized view.
-
-    The default policy keeps the smallest unassigned snapshot row ID and removes
-    only its direct qualifying neighbors: an A-B-C chain without an A-C edge
-    retains A and C. Rows with no pair, including null vectors, survive.
-    Only same-segment/same-IVF-partition pairs are considered; quantized distances
-    use reconstructed index vectors. The index must cover the selected snapshot.
-
-    ``source`` is an exact table name in the connection's root namespace, just
-    as for ``create_materialized_view``. ``dataset_version=None`` captures the
-    source's version when creation is submitted. Pass an explicit positive
-    version to reproduce a previous snapshot. The result contains original
-    source columns and rows. Do not combine this source with ``select``,
-    ``where`` or ``limit``; query the resulting table for further transforms.
-
-    Creation follows the usual MV contract: by default it waits for the result.
-    Use ``with_no_data=True`` to declare only, or
-    ``create_materialized_view_async`` to get a job handle. On Cloud/Enterprise,
-    execution uses server-side MV registry jobs; local connections run in process.
-
-    Examples
-    --------
-    Given a connected ``db`` and an indexed ``images.phash`` column::
-
-        from lancedb import vector_dedup
-
-        view = db.create_materialized_view(
-            "images_clean",
-            vector_dedup("images", column="phash", distance_threshold=4),
-        )
-        cleaned = view.table
-    """
-    return VectorDedupSource(source, column, distance_threshold, dataset_version)
-
-
-MaterializedViewSource = Union[str, VectorDedupSource]
-
-
-DEFINITION_FORMAT = 4
+DEFINITION_FORMAT = 2
 """The newest stored layout this version reads: ``{"format": N, "query": "<SQL>"}``,
-format 2 being a query with ``GROUP BY``, format 3 native pairs and format 4
-native dedup results. A ``kind`` key beside it is for
+format 2 being a query with ``GROUP BY``. A ``kind`` key beside it is for
 readers older than the format number."""
 
 
@@ -272,38 +179,31 @@ class AsyncMaterializedView:
         return _definition_from_json(raw)
 
     async def refresh(
-        self, *, full: bool = False, source_version: Optional[int] = None
+        self, *, source_version: Optional[int] = None
     ) -> "RefreshMaterializedViewResult":
         """Recompute the view from its source.
 
-        The refresh is incremental when the source's changes can be
-        reconciled into the view -- rows added, changed or removed since the
-        last one -- and otherwise rebuilds. ``full=True`` forces a rebuild;
+        Every refresh rebuilds the complete view. For local views,
         ``source_version`` refreshes to that source version instead of the
-        latest.
-
-        Concurrent refreshes of one view do not duplicate its rows. Two that
-        plan the same source rows conflict on commit, and the loser raises
-        rather than writing them a second time.
+        latest; remote SQL refreshes do not support source-version pinning.
         """
         return await self._table._inner.refresh_materialized_view(
-            full=full, source_version=source_version
+            source_version=source_version
         )
 
     async def refresh_async(
-        self, *, full: bool = False, source_version: Optional[int] = None
+        self, *, source_version: Optional[int] = None
     ) -> "AsyncJob[RefreshMaterializedViewResult]":
         """Submit a refresh and return its job without waiting.
 
-        The job may already be complete for a local view. On LanceDB Cloud
-        and Enterprise, its ``id`` is the server job identifier returned by
-        the refresh endpoint.
+        The job may already be complete for a local view. Remote refreshes are
+        submitted through the SQL service.
         """
         from ._lancedb import RefreshMaterializedViewResult
 
         return _typed_job(
             await self._table._inner.refresh_materialized_view_async(
-                full=full, source_version=source_version
+                source_version=source_version
             ),
             RefreshMaterializedViewResult.from_json,
         )
@@ -335,22 +235,18 @@ class MaterializedView:
         return LOOP.run(self._async.definition())
 
     def refresh(
-        self, *, full: bool = False, source_version: Optional[int] = None
+        self, *, source_version: Optional[int] = None
     ) -> "RefreshMaterializedViewResult":
         """Recompute the view from its source. See
         [AsyncMaterializedView.refresh][lancedb.materialized_view.AsyncMaterializedView.refresh]."""
-        return LOOP.run(self._async.refresh(full=full, source_version=source_version))
+        return LOOP.run(self._async.refresh(source_version=source_version))
 
     def refresh_async(
-        self, *, full: bool = False, source_version: Optional[int] = None
+        self, *, source_version: Optional[int] = None
     ) -> "Job[RefreshMaterializedViewResult]":
         """Submit a refresh and return its job without waiting.
 
         See
         [AsyncMaterializedView.refresh_async][lancedb.materialized_view.AsyncMaterializedView.refresh_async].
         """
-        return Job(
-            LOOP.run(
-                self._async.refresh_async(full=full, source_version=source_version)
-            )
-        )
+        return Job(LOOP.run(self._async.refresh_async(source_version=source_version)))
diff --git a/python/python/lancedb/merge.py b/python/python/lancedb/merge.py
index 115a47e2d..d7c25349e 100644
--- a/python/python/lancedb/merge.py
+++ b/python/python/lancedb/merge.py
@@ -37,7 +37,7 @@ class LanceMergeInsertBuilder(object):
         self._when_not_matched_by_source_condition_expr = None
         self._timeout = None
         self._use_index = True
-        self._use_lsm = None
+        self._use_memwal = None
         self._validate_single_shard = None
 
     def when_matched_update_all(
@@ -115,22 +115,22 @@ class LanceMergeInsertBuilder(object):
         self._use_index = use_index
         return self
 
-    def use_lsm(self, enable: bool) -> LanceMergeInsertBuilder:
+    def use_memwal(self, enable: bool) -> LanceMergeInsertBuilder:
         """
         Control MemWAL routing for this merge.
 
-        By default (unset), a `merge_insert` on a table with an LSM write spec is
+        By default (unset), a `merge_insert` on a table with a MemWAL write spec is
         routed through Lance's MemWAL shard writer, and a table without one uses
         the standard path.
 
         Parameters
         ----------
         enable: bool
-            ``True`` forces MemWAL routing and errors if the table has no LSM
+            ``True`` forces MemWAL routing and errors if the table has no MemWAL
             write spec. ``False`` forces the standard write path even when a spec
             is set.
         """
-        self._use_lsm = enable
+        self._use_memwal = enable
         return self
 
     def validate_single_shard(
@@ -139,13 +139,13 @@ class LanceMergeInsertBuilder(object):
         """
         Controls how an LSM merge checks that its input targets a single shard.
 
-        When a table has an LSM write spec, every row in a `merge_insert` call
+        When a table has a MemWAL write spec, every row in a `merge_insert` call
         must route to the same shard. When `True` (the default), every row is
         inspected to verify this. When `False`, only the first row is inspected
         and the shard it routes to is used for the whole input — a faster path
         for callers that have already pre-sharded their input.
 
-        Has no effect on tables without an LSM write spec.
+        Has no effect on tables without a MemWAL write spec.
 
         Parameters
         ----------
diff --git a/python/python/lancedb/namespace.py b/python/python/lancedb/namespace.py
index cf1910c68..26c62925f 100644
--- a/python/python/lancedb/namespace.py
+++ b/python/python/lancedb/namespace.py
@@ -11,7 +11,7 @@ through a namespace abstraction.
 from __future__ import annotations
 
 import sys
-from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Union
+from typing import Any, Dict, Iterable, List, Optional, Union
 from uuid import UUID
 
 if sys.version_info >= (3, 12):
@@ -19,8 +19,7 @@ if sys.version_info >= (3, 12):
 else:
     from overrides import override
 
-if TYPE_CHECKING:
-    from lancedb.query import Query
+from lancedb.query import Query
 
 from datetime import timedelta
 import pyarrow as pa
@@ -52,8 +51,9 @@ from lancedb.background_loop import LOOP
 from lancedb.arrow import AsyncRecordBatchReader
 from lancedb.db import AsyncConnection, DBConnection
 from lancedb.job import AsyncJob, Job
+from lancedb.listing import AsyncListing, Listing
 from lancedb.sql import AsyncQuery as AsyncSqlQuery
-from lancedb.sql import QueryDescription
+from lancedb.sql import QueryDescription, QueryParameters
 from lance_namespace import (
     LanceNamespace,
     connect as namespace_connect,
@@ -105,6 +105,11 @@ def _query_to_namespace_request(
 
     if not isinstance(query, Query):
         raise TypeError(f"Expected Query, got {type(query)}")
+    if query.search_effort is not None:
+        raise ValueError(
+            "namespace QueryTable does not support search_effort; "
+            "execute the query locally"
+        )
 
     vector_request = None
     if query.vector is not None:
@@ -190,8 +195,12 @@ def _query_to_namespace_request(
         kwargs["with_row_id"] = query.with_row_id
     if query.ef is not None:
         kwargs["ef"] = query.ef
+    if query.nprobes is not None:
+        kwargs["nprobes"] = query.nprobes
     if query.minimum_nprobes is not None:
-        kwargs["nprobes"] = query.minimum_nprobes
+        kwargs["minimum_nprobes"] = query.minimum_nprobes
+    if query.maximum_nprobes is not None:
+        kwargs["maximum_nprobes"] = query.maximum_nprobes
     if query.refine_factor is not None:
         kwargs["refine_factor"] = query.refine_factor
     if query.lower_bound is not None:
@@ -205,6 +214,13 @@ def _query_to_namespace_request(
     if query.distance_type is not None:
         kwargs["distance_type"] = query.distance_type
 
+    if kwargs.get("maximum_nprobes") == 0:
+        # The generated 0.13 model rejects LanceDB's unbounded sentinel;
+        # model_copy preserves direct wire pass-through to the Lance receiver.
+        maximum_nprobes = kwargs.pop("maximum_nprobes")
+        return QueryTableRequest(**kwargs).model_copy(
+            update={"maximum_nprobes": maximum_nprobes}
+        )
     return QueryTableRequest(**kwargs)
 
 
@@ -489,6 +505,14 @@ class LanceNamespaceDBConnection(DBConnection):
             )
         self._uri = self._inner.uri
 
+    @override
+    def is_open(self) -> bool:
+        return self._inner.is_open()
+
+    @override
+    def close(self) -> None:
+        self._inner.close()
+
     @override
     def serialize(self) -> str:
         import json
@@ -514,13 +538,15 @@ class LanceNamespaceDBConnection(DBConnection):
     def table_names(
         self,
         page_token: Optional[str] = None,
-        limit: int = 10,
+        limit: Optional[int] = None,
         *,
         namespace_path: Optional[List[str]] = None,
     ) -> Iterable[str]:
         """
         List table names in the database.
 
+        If limit is None, return all table names; zero returns an empty list.
+
         .. deprecated::
             Use :meth:`list_tables` instead, which provides proper pagination support.
         """
@@ -690,9 +716,20 @@ class LanceNamespaceDBConnection(DBConnection):
         return view
 
     @override
-    def list_materialized_views(self) -> List[str]:
-        """The names of the materialized views in the root namespace."""
-        return LOOP.run(self._inner.list_materialized_views())
+    def list_materialized_views(
+        self, *, page_token: Optional[str] = None, page_limit: Optional[int] = None
+    ) -> Listing[str]:
+        """The names of the materialized views in the root namespace.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
+        """
+        return Listing(
+            self._inner.list_materialized_views(
+                page_token=page_token, page_limit=page_limit
+            )
+        )
 
     @override
     def drop_materialized_view(
@@ -1138,13 +1175,15 @@ class AsyncLanceNamespaceDBConnection:
     async def table_names(
         self,
         page_token: Optional[str] = None,
-        limit: int = 10,
+        limit: Optional[int] = None,
         *,
         namespace_path: Optional[List[str]] = None,
     ) -> Iterable[str]:
         """
         List table names in the namespace.
 
+        If limit is None, return all table names; zero returns an empty list.
+
         .. deprecated::
             Use :meth:`list_tables` instead, which provides proper pagination support.
         """
@@ -1289,9 +1328,20 @@ class AsyncLanceNamespaceDBConnection:
         await view.definition()
         return view
 
-    async def list_materialized_views(self) -> List[str]:
-        """The names of the materialized views in the root namespace."""
-        return await self._inner.list_materialized_views()
+    def list_materialized_views(
+        self, *, page_token: Optional[str] = None, page_limit: Optional[int] = None
+    ) -> AsyncListing[str]:
+        """The names of the materialized views in the root namespace.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
+        """
+        return AsyncListing(
+            self._inner.list_materialized_views(
+                page_token=page_token, page_limit=page_limit
+            )
+        )
 
     async def drop_materialized_view(
         self, name: str, namespace_path: Optional[List[str]] = None
@@ -1554,11 +1604,13 @@ class AsyncLanceNamespaceDBConnection:
         query: str,
         *,
         default_namespace_path: Optional[List[str]] = None,
+        parameters: Optional[QueryParameters] = None,
     ) -> AsyncRecordBatchReader:
         """Execute SQL when supported by the underlying connection."""
         return await self._inner.execute_query(
             query,
             default_namespace_path=default_namespace_path,
+            parameters=parameters,
         )
 
     async def execute_query_async(
@@ -1566,6 +1618,7 @@ class AsyncLanceNamespaceDBConnection:
         query: str,
         *,
         default_namespace_path: Optional[List[str]] = None,
+        parameters: Optional[QueryParameters] = None,
     ) -> AsyncSqlQuery:
         """Start executing SQL when supported by the underlying connection.
 
@@ -1574,6 +1627,7 @@ class AsyncLanceNamespaceDBConnection:
         return await self._inner.execute_query_async(
             query,
             default_namespace_path=default_namespace_path,
+            parameters=parameters,
         )
 
     async def describe_query(self, query_id: UUID) -> QueryDescription:
diff --git a/python/python/lancedb/permutation.py b/python/python/lancedb/permutation.py
index 8287ef2fe..d97eccc07 100644
--- a/python/python/lancedb/permutation.py
+++ b/python/python/lancedb/permutation.py
@@ -48,7 +48,7 @@ class PermutationBuilder:
         By default, the permutation builder will create a single split that contains all
         rows in the same order as the base table.
 
-        Tables with an LSM write spec are rejected: unflushed rows have no row id.
+        Tables with a MemWAL write spec are rejected: unflushed rows have no row id.
         """
         self._async = async_permutation_builder(table)
 
diff --git a/python/python/lancedb/pydantic.py b/python/python/lancedb/pydantic.py
index 528d865d7..313c514c3 100644
--- a/python/python/lancedb/pydantic.py
+++ b/python/python/lancedb/pydantic.py
@@ -88,6 +88,17 @@ def Vector(
     ...     pa.field("url", pa.utf8(), False),
     ...     pa.field("embeddings", pa.list_(pa.float32(), 768))
     ... ])
+
+    Notes
+    -----
+    ``Vector`` creates a type dynamically, so calls such as ``Vector(768)`` are
+    not valid static type annotations. For an embedding field, use the standard
+    ``list[float]`` annotation when running mypy; ``VectorField`` supplies the
+    fixed dimension to LanceDB::
+
+        class MyModel(LanceModel):
+            text: str = embeddings.SourceField()
+            vector: list[float] = embeddings.VectorField()
     """
 
     # TODO: make a public parameterized type.
@@ -313,6 +324,10 @@ def _unwrap_optional_annotation(annotation: Any) -> Any | None:
 
 def _pydantic_to_arrow_type(field: FieldInfo) -> pa.DataType:
     """Convert a Pydantic FieldInfo to Arrow DataType"""
+    embedding_vector_type = _embedding_vector_to_arrow_type(field)
+    if embedding_vector_type is not None:
+        return embedding_vector_type
+
     unwrapped = _unwrap_optional_annotation(field.annotation)
     if unwrapped is not None:
         return _pydantic_type_to_arrow_type(unwrapped, field)
@@ -326,8 +341,32 @@ def _pydantic_to_arrow_type(field: FieldInfo) -> pa.DataType:
     return _pydantic_type_to_arrow_type(field.annotation, field)
 
 
+def _embedding_vector_to_arrow_type(field: FieldInfo) -> pa.DataType | None:
+    """Infer a fixed-size vector type from ``VectorField`` metadata."""
+    if not _is_embedding_vector_annotation(field):
+        return None
+
+    function = get_extras(field, "vector_column_for")
+    return pa.list_(pa.float32(), function.ndims())
+
+
+def _is_embedding_vector_annotation(field: FieldInfo) -> bool:
+    if get_extras(field, "vector_column_for") is None:
+        return False
+
+    annotation = _unwrap_optional_annotation(field.annotation)
+    if annotation is None:
+        annotation = field.annotation
+
+    origin = getattr(annotation, "__origin__", None)
+    args = getattr(annotation, "__args__", ())
+    return origin is list and args == (float,)
+
+
 def is_nullable(field: FieldInfo) -> bool:
     """Check if a Pydantic FieldInfo is nullable."""
+    if _is_embedding_vector_annotation(field):
+        return True
     if _unwrap_optional_annotation(field.annotation) is not None:
         return True
     if isinstance(field.annotation, (_GenericAlias, GenericAlias)):
diff --git a/python/python/lancedb/query.py b/python/python/lancedb/query.py
index db213ac71..ebb4fdf16 100644
--- a/python/python/lancedb/query.py
+++ b/python/python/lancedb/query.py
@@ -12,6 +12,7 @@ from typing import (
     TYPE_CHECKING,
     Any,
     Dict,
+    Iterable,
     List,
     Literal,
     Optional,
@@ -39,8 +40,8 @@ from .expr import Expr
 from .rerankers.base import Reranker
 from .rerankers.rrf import RRFReranker
 from .rerankers.util import check_reranker_result
-from .schema import is_blob_like_field, schema_has_blob_field
-from .util import flatten_columns
+from .schema import blob_column_paths, is_blob_like_field, schema_has_blob_field
+from .util import _validate_query_vector, flatten_columns, get_uri_scheme
 from . import _wal_hybrid  # WAL-PK-FUSION: delete.
 from ._blob import (
     BLOB_MODE_TO_HANDLING,
@@ -49,8 +50,11 @@ from ._blob import (
     blob_auto_row_id_for_scan,
     blob_v2_projection_sources,
     finalize_blob_query_table,
+    reject_unfetchable_blob_columns,
+    reject_unmatched_blob_outputs,
     replace_v2_blob_columns_with_bytes,
     replace_v2_blob_columns_with_bytes_sync,
+    strip_auto_row_ids,
     validate_blob_mode,
 )
 from .types import BlobMode, QueryProjection
@@ -98,15 +102,50 @@ class _LanceScanner(Protocol):
     def to_reader(self): ...
 
 
-def _blob_mode_requires_native_pandas(blob_mode: BlobMode, schema: pa.Schema) -> bool:
-    return blob_mode in BLOB_MODE_TO_HANDLING and schema_has_blob_field(schema)
-
-
 def _unsupported_blob_pandas_error(reason: str) -> RuntimeError:
     return RuntimeError(
         "blob columns require Lance native scanner conversion for query "
-        f"to_pandas(), but {reason}. Use a plain scan query or remove blob "
-        "columns from the projection."
+        f"to_pandas(), but {reason}. Use a plain scan query, pass "
+        "blob_mode='bytes' or blob_mode='descriptions', or remove blob columns "
+        "from the projection."
+    )
+
+
+def _supports_local_blob_fetch(
+    async_table: "AsyncTable", pushdown_operations: Iterable[str]
+) -> bool:
+    # Remote tables and QueryTable pushdown answer queries on a server, so a
+    # fetch through this handle could read another version.
+    return async_table._inner._is_native() and "QueryTable" not in pushdown_operations
+
+
+def _blob_bytes_without_snapshot_error() -> NotImplementedError:
+    return NotImplementedError(
+        "blob_mode='bytes' cannot safely materialize blob columns without a "
+        "stable table snapshot. Remote tables and namespace QueryTable pushdown "
+        "run the query on a server, so the blob fetch could read another table "
+        "version. Use blob_mode='descriptions'."
+    )
+
+
+def _is_remote_query_table(table: Any) -> bool:
+    async_table = getattr(table, "_table", table)
+    return not async_table._inner._is_native()
+
+
+def _query_needs_lance_scanner(builder: Any) -> bool:
+    """Whether the query sets options that to_arrow ignores."""
+    to_query_object = getattr(builder, "to_query_object", None)
+    if to_query_object is None:
+        return False
+    try:
+        query = to_query_object()
+    except NotImplementedError:
+        return False
+    return (
+        query.fragments is not None
+        or query.fragment_ids is not None
+        or bool(query.with_row_address)
     )
 
 
@@ -150,7 +189,8 @@ def _combine_where(
 
 
 def _projection_to_scanner_kwargs(columns: QueryProjection) -> Dict[str, Any]:
-    if columns is None:
+    # An empty projection selects all columns.
+    if not columns:
         return {}
     if isinstance(columns, list):
         if all(isinstance(column, str) for column in columns):
@@ -174,7 +214,8 @@ def _projection_to_scanner_kwargs(columns: QueryProjection) -> Dict[str, Any]:
 
 
 def _query_request_projection(req: "PyQueryRequest") -> QueryProjection:
-    if req.select_source_columns is not None:
+    # Treat empty map as a calculated select
+    if req.select_source_columns:
         return req.select_source_columns
     return req.select
 
@@ -311,6 +352,28 @@ def _finish_plain_scan_pandas(
     return df
 
 
+def _finish_remote_pandas(
+    tbl: pa.Table,
+    *,
+    blob_mode: BlobMode,
+    blob_paths: Iterable[str],
+    flatten: Optional[Union[int, bool]],
+    **kwargs,
+) -> pd.DataFrame:
+    # A live remote table can be replaced between query and blob fetch.
+    # Row ids and version numbers do not identify the producing table.
+    if not blob_column_paths(tbl.schema):
+        return flatten_columns(tbl, flatten).to_pandas(**kwargs)
+    if blob_mode == "descriptions":
+        tbl = strip_auto_row_ids(tbl, blob_paths)
+        return flatten_columns(tbl, flatten).to_pandas(**kwargs)
+    raise NotImplementedError(
+        f"remote to_pandas(blob_mode={blob_mode!r}) cannot safely materialize "
+        "blob columns without a stable table snapshot; "
+        "use blob_mode='descriptions'"
+    )
+
+
 async def _finish_plain_scan_pandas_async(
     scanner: _LanceScanner,
     *,
@@ -474,12 +537,12 @@ class MatchQuery(FullTextQuery):
     ----------
     query : str
         The query string to match against.
-    column : str
-        The name of the column to match against.
+    column : str, optional
+        The column to match against. None leaves column selection to query planning.
     boost : float, default 1.0
         The boost factor for the query.
         The score of each matching document is multiplied by this value.
-    fuzziness : int, optional
+    fuzziness : int or None, default 0
         The maximum edit distance for each term in the match query.
         Defaults to 0 (exact match).
         If None, fuzziness is applied automatically by the rules:
@@ -504,9 +567,9 @@ class MatchQuery(FullTextQuery):
     """
 
     query: str
-    column: str
+    column: Optional[str] = None
     boost: float = pydantic.Field(1.0, kw_only=True)
-    fuzziness: int = pydantic.Field(0, kw_only=True)
+    fuzziness: Optional[int] = pydantic.Field(0, kw_only=True)
     max_expansions: int = pydantic.Field(50, kw_only=True)
     operator: FullTextOperator = pydantic.Field(FullTextOperator.OR, kw_only=True)
     prefix_length: int = pydantic.Field(0, kw_only=True)
@@ -692,8 +755,9 @@ class Query(pydantic.BaseModel):
         if True then apply the filter after vector / FTS search.  This is ignored for
         plain SQL filtering.
     nprobes : Optional[int]
-        The number of IVF partitions to search.  If this is None then a default
-        number of partitions will be used.
+        The legacy number of IVF partitions to search. Lance sets both probe
+        bounds to this value. If this is None then Lance's default probe settings
+        will be used.
 
         - A higher number makes search more accurate but also slower.
 
@@ -772,22 +836,28 @@ class Query(pydantic.BaseModel):
     # distance type to use for vector search
     distance_type: Optional[str] = None
 
+    # legacy number of IVF partitions to search
+    #
+    # Lance sets both probe bounds to this value. Explicit bounds can override it.
+    nprobes: Optional[int] = None
+
     # which columns to return in the results (dict values may be str or Expr)
     columns: QueryProjection = None
 
     # minimum number of IVF partitions to search
     #
-    # If None then a default value (20) will be used.
+    # If None then Lance's default will be used.
     minimum_nprobes: Optional[int] = None
 
     # maximum number of IVF partitions to search
     #
-    # If None then a default value (20) will be used.
-    #
-    # If 0 then no limit will be applied and all partitions could be searched
-    # if needed to satisfy the limit.
+    # If None then Lance's default will be used. If 0 then no limit will be applied
+    # and all partitions could be searched if needed to satisfy the limit.
     maximum_nprobes: Optional[int] = None
 
+    # Optional initial IVF effort; None preserves Auto and explicit nprobes.
+    search_effort: Optional[float] = None
+
     # lower bound for distance search
     lower_bound: Optional[float] = None
 
@@ -820,7 +890,7 @@ class Query(pydantic.BaseModel):
     # MemWAL LSM read routing: None auto-routes when the table carries a write
     # spec, True forces the LSM scanner (errors without a spec), False reads the
     # base table only
-    use_lsm: Optional[bool] = None
+    use_memwal: Optional[bool] = None
 
     # size of the nearest neighbor list maintained during HNSW search
     ef: Optional[int] = None
@@ -840,14 +910,16 @@ class Query(pydantic.BaseModel):
         query.full_text_query = req.full_text_search
         query.columns = req.select
         query.with_row_id = req.with_row_id
-        # use_lsm is a genuine tri-state (None / True / False); preserve it as-is
+        # use_memwal is a genuine tri-state (None / True / False); preserve it as-is
         # so a round-tripped query keeps an explicit False.
-        query.use_lsm = req.use_lsm
+        query.use_memwal = req.use_memwal
         query.vector_column = req.column
         query.vector = req.query_vector
         query.distance_type = req.distance_type
+        query.nprobes = req.nprobes
         query.minimum_nprobes = req.minimum_nprobes
         query.maximum_nprobes = req.maximum_nprobes
+        query.search_effort = req.search_effort
         query.lower_bound = req.lower_bound
         query.upper_bound = req.upper_bound
         query.ef = req.ef
@@ -907,6 +979,9 @@ class LanceQueryBuilder(ABC):
         fast_search: bool
             Skip flat search of unindexed data.
         """
+        if query_type != "fts":
+            _validate_query_vector(query)
+
         if ordering_field_name is not None:
             import warnings
 
@@ -1010,7 +1085,7 @@ class LanceQueryBuilder(ABC):
         self._with_row_address = None
         self._fragments = None
         self._fragment_ids = None
-        self._use_lsm = None
+        self._use_memwal = None
         self._vector = None
         self._text = None
         self._ef = None
@@ -1059,44 +1134,58 @@ class LanceQueryBuilder(ABC):
             The maximum time to wait for the query to complete.
             If None, wait indefinitely.
         blob_mode: str, default "lazy"
-            Controls how blob columns are returned for plain scan queries.
-            Vector, FTS, hybrid, and other non-native query shapes keep the
-            existing Arrow conversion path and only support blob descriptions.
+            Controls how blob columns are returned. Eligible local plain scans
+            use Lance's scanner, which returns lazy blob handles in ``"lazy"``
+            mode and also supports legacy blob columns. Other local blob v2
+            queries return descriptors in ``"lazy"`` and ``"descriptions"``
+            modes, or payloads in ``"bytes"`` mode, through :meth:`to_arrow`.
+            Remote blob queries support only ``"descriptions"`` until a stable
+            table snapshot can bind the query and blob fetches.
         **kwargs
             Forwarded to pyarrow.Table.to_pandas after query execution and
             optional flattening.
         """
         validate_blob_mode(blob_mode)
-        output_schema = getattr(self, "output_schema", None)
-        if output_schema is not None:
-            schema = output_schema()
-            if _blob_mode_requires_native_pandas(blob_mode, schema):
-                native_error = None
-                if (flatten is None or blob_mode == "descriptions") and timeout is None:
-                    try:
-                        df = self._plain_scan_to_pandas(
-                            blob_mode, flatten=flatten, **kwargs
-                        )
-                        if df is not None:
-                            return df
-                    except Exception as err:
-                        native_error = err
-                reason = (
-                    "this query shape cannot use Lance native pandas conversion"
-                    if native_error is None
-                    else str(native_error)
-                )
-                raise _unsupported_blob_pandas_error(reason) from native_error
-
-        tbl = flatten_columns(self.to_arrow(timeout=timeout), flatten)
-        if _blob_mode_requires_native_pandas(blob_mode, tbl.schema):
-            raise _unsupported_blob_pandas_error(
-                "this query shape cannot use Lance native pandas conversion"
+        if _is_remote_query_table(self._table):
+            blob_sources = self._validated_blob_sources(blob_mode)
+            return _finish_remote_pandas(
+                self.to_arrow(timeout=timeout),
+                blob_mode=blob_mode,
+                blob_paths=blob_sources,
+                flatten=flatten,
+                **kwargs,
             )
-        return tbl.to_pandas(**kwargs)
+
+        output_schema = getattr(self, "output_schema", None)
+        if output_schema is not None and schema_has_blob_field(output_schema()):
+            if (flatten is None or blob_mode != "lazy") and timeout is None:
+                df = self._plain_scan_to_pandas(blob_mode, flatten=flatten, **kwargs)
+                if df is not None:
+                    return df
+            if _query_needs_lance_scanner(self):
+                raise _unsupported_blob_pandas_error(
+                    "this query shape cannot use Lance native pandas conversion"
+                )
+
+        arrow_mode = "descriptions" if blob_mode == "lazy" else blob_mode
+        tbl = self._to_arrow_in_mode(timeout, arrow_mode)
+        if (
+            blob_mode == "lazy"
+            and schema_has_blob_field(tbl.schema)
+            and not blob_v2_projection_sources(self._table.schema, self._columns)
+        ):
+            raise _unsupported_blob_pandas_error(
+                "the query has no fetchable blob v2 source"
+            )
+        return flatten_columns(tbl, flatten).to_pandas(**kwargs)
 
     @abstractmethod
-    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
+    def to_arrow(
+        self,
+        *,
+        timeout: Optional[timedelta] = None,
+        blob_mode: BlobMode = "lazy",
+    ) -> pa.Table:
         """
         Execute the query and return the results as an
         [Apache Arrow Table](https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table).
@@ -1110,6 +1199,11 @@ class LanceQueryBuilder(ABC):
         timeout: Optional[timedelta]
             The maximum time to wait for the query to complete.
             If None, wait indefinitely.
+        blob_mode: str, default "lazy"
+            ``"lazy"`` returns blob v2 descriptors that carry the row id
+            ``fetch_blobs`` reads. ``"descriptions"`` returns them without it.
+            ``"bytes"`` returns payload bytes and raises for legacy blob columns
+            and blobs inside lists.
         """
         raise NotImplementedError
 
@@ -1140,7 +1234,12 @@ class LanceQueryBuilder(ABC):
         """
         raise NotImplementedError
 
-    def to_list(self, *, timeout: Optional[timedelta] = None) -> List[dict]:
+    def to_list(
+        self,
+        *,
+        timeout: Optional[timedelta] = None,
+        blob_mode: BlobMode = "lazy",
+    ) -> List[dict]:
         """
         Execute the query and return the results as a list of dictionaries.
 
@@ -1153,8 +1252,19 @@ class LanceQueryBuilder(ABC):
         timeout: Optional[timedelta]
             The maximum time to wait for the query to complete.
             If None, wait indefinitely.
+        blob_mode: str, default "lazy"
+            Passed through to :meth:`to_arrow`.
         """
-        return self.to_arrow(timeout=timeout).to_pylist()
+        validate_blob_mode(blob_mode)
+        return self._to_arrow_in_mode(timeout, blob_mode).to_pylist()
+
+    def _to_arrow_in_mode(
+        self, timeout: Optional[timedelta], blob_mode: BlobMode
+    ) -> pa.Table:
+        # Older subclasses may not accept blob_mode. Omit it when it has no effect.
+        if blob_mode == "lazy" or not schema_has_blob_field(self._table.schema):
+            return self.to_arrow(timeout=timeout)
+        return self.to_arrow(timeout=timeout, blob_mode=blob_mode)
 
     def to_pydantic(
         self, model: type[T], *, timeout: Optional[timedelta] = None
@@ -1327,19 +1437,62 @@ class LanceQueryBuilder(ABC):
             query.with_row_id = True
         return query
 
-    def _finalize_blob_query_table(self, tbl: pa.Table) -> pa.Table:
-        blob_auto_row_id = self._blob_auto_row_id_enabled()
-        blob_paths = (
-            blob_v2_projection_sources(self._table.schema, self._columns).keys()
-            if blob_auto_row_id
-            else ()
+    def _validated_blob_sources(self, blob_mode: BlobMode) -> dict[str, str]:
+        """Map projected blob v2 outputs to their source columns.
+
+        Bytes mode is checked here, before the query runs.
+        """
+        validate_blob_mode(blob_mode)
+        schema = self._table.schema
+        blob_sources = blob_v2_projection_sources(schema, self._columns)
+        if blob_mode != "bytes":
+            return blob_sources
+        reject_unfetchable_blob_columns(schema, self._columns, blob_sources)
+        if blob_sources and not _supports_local_blob_fetch(
+            self._table._table, getattr(self._table, "_pushdown_operations", ())
+        ):
+            raise _blob_bytes_without_snapshot_error()
+        reject_unmatched_blob_outputs(self._blob_output_schema(), blob_sources)
+        return blob_sources
+
+    def _blob_output_schema(self) -> pa.Schema:
+        return self.output_schema()
+
+    def _apply_arrow_blob_mode(
+        self, tbl: pa.Table, blob_mode: BlobMode, blob_sources: dict[str, str]
+    ) -> pa.Table:
+        if blob_mode == "lazy":
+            return tbl
+        if blob_mode == "descriptions":
+            return strip_auto_row_ids(tbl, blob_sources)
+        return replace_v2_blob_columns_with_bytes_sync(
+            tbl, blob_sources, self._table.fetch_blobs
         )
-        return finalize_blob_query_table(
+
+    def _finalize_blob_query_table(
+        self,
+        tbl: pa.Table,
+        *,
+        blob_mode: BlobMode,
+        blob_sources: dict[str, str],
+    ) -> pa.Table:
+        blob_auto_row_id = self._blob_auto_row_id_enabled()
+        finalized = finalize_blob_query_table(
             tbl,
             user_requested_row_id=self._user_requested_row_id(),
             blob_auto_row_id=blob_auto_row_id,
-            blob_paths=blob_paths,
+            blob_paths=blob_sources.keys() if blob_auto_row_id else (),
         )
+        finalized = self._apply_arrow_blob_mode(finalized, blob_mode, blob_sources)
+        # with_row_id(False) does not reach the scan, which adds _rowid anyway.
+        # Lazy keeps it because fetch_blobs(hits) reads it there.
+        if (
+            blob_mode != "lazy"
+            and self._with_row_id is False
+            and "_rowid" in finalized.column_names
+        ):
+            finalized = finalized.drop_columns(["_rowid"])
+        return finalized
 
     def with_row_address(self, with_row_address: bool = True) -> Self:
         """Set whether to return row addresses.
@@ -1367,19 +1520,19 @@ class LanceQueryBuilder(ABC):
         self._fragment_ids = fragment_ids
         return self
 
-    def use_lsm(self, enable: bool) -> Self:
+    def use_memwal(self, enable: bool) -> Self:
         """Control MemWAL LSM read routing for this query.
 
-        By default (unset), a query against a table with an LSM write spec is
+        By default (unset), a query against a table with a MemWAL write spec is
         routed through the LSM scanner so it also returns data written via the
         ``merge_insert`` LSM path that has not yet been compacted into the base
-        table (active/frozen memtables + flushed generations); a table without a
+        table (active/frozen memtables + SSTables); a table without a
         spec reads the base table.
 
         Parameters
         ----------
         enable : bool
-            ``True`` forces the LSM scanner and errors if the table has no LSM
+            ``True`` forces the LSM scanner and errors if the table has no MemWAL
             write spec. ``False`` bypasses the MemWAL and reads the base table
             only, even when a spec is present.
 
@@ -1388,7 +1541,7 @@ class LanceQueryBuilder(ABC):
         LanceQueryBuilder
             The LanceQueryBuilder object.
         """
-        self._use_lsm = enable
+        self._use_memwal = enable
         return self
 
     def explain_plan(self, verbose: Optional[bool] = False) -> str:
@@ -1552,6 +1705,11 @@ class LanceQueryBuilder(ABC):
         if not _query_is_plain_scan(query):
             return None
 
+        # Remote and in-memory tables have no Lance dataset to scan.
+        if not self._table._table._inner._is_native() or (
+            get_uri_scheme(self._table._dataset_path) == "memory"
+        ):
+            return None
         dataset = self._table.to_lance()
         blob_auto_row_id = self._blob_auto_row_id_enabled()
         blob_sources = (
@@ -1559,6 +1717,10 @@ class LanceQueryBuilder(ABC):
             if blob_mode == "bytes"
             else {}
         )
+        if blob_sources:
+            reject_unfetchable_blob_columns(
+                self._table.schema, query.columns, blob_sources
+            )
         scanner = dataset.scanner(
             **_scanner_kwargs_for_query(
                 query,
@@ -1572,7 +1734,7 @@ class LanceQueryBuilder(ABC):
             blob_mode=blob_mode,
             blob_sources=blob_sources,
             fetch_blobs=self._table.fetch_blobs,
-            strip_auto_row_id=blob_auto_row_id,
+            strip_auto_row_id=not self._user_requested_row_id(),
             flatten=flatten,
             **kwargs,
         )
@@ -1619,11 +1781,14 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         str_query: Optional[str] = None,
         fast_search: bool = None,
     ):
+        _validate_query_vector(query)
         super().__init__(table)
         self._query = query
         self._distance_type = None
+        self._nprobes = None
         self._minimum_nprobes = None
         self._maximum_nprobes = None
+        self._search_effort = None
         self._lower_bound = None
         self._upper_bound = None
         self._refine_factor = None
@@ -1686,22 +1851,44 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         See discussion in [Querying an ANN Index](https://lancedb.com/docs/indexing/)
         for tuning advice.
 
-        This method sets both the minimum and maximum number of probes to the same
-        value. See `minimum_nprobes` and `maximum_nprobes` for more fine-grained
-        control.
+        The value is retained as `nprobes` through client and server request
+        construction. Lance sets both probe bounds to this value. Explicit minimum
+        or maximum settings can override their respective bounds.
 
         Parameters
         ----------
         nprobes: int
-            The number of probes to use.
+            The number of probes to use. Must be greater than 0.
 
         Returns
         -------
         LanceVectorQueryBuilder
             The LanceQueryBuilder object.
         """
-        self._minimum_nprobes = nprobes
-        self._maximum_nprobes = nprobes
+        if nprobes <= 0:
+            raise ValueError("Invalid input, nprobes must be greater than 0")
+        self._nprobes = nprobes
+        return self
+
+    def search_effort(self, search_effort: Optional[float]) -> LanceVectorQueryBuilder:
+        """Set the initial IVF search effort to a finite value in [0, 1].
+
+        None (the default) uses Auto, internally 0.5. Zero starts at the caller
+        minimum; one searches all partitions within the caller maximum. Later
+        probing may continue when too few candidates are found. Any explicit
+        effort, including 0.5, overrides nprobes in either call order. Explicit
+        minimum and maximum bounds still apply. This is not a recall guarantee
+        and does not change refinement or HNSW ef. MemWAL LSM queries do not
+        yet support this option.
+
+        Examples
+        --------
+        >>> import lancedb
+        >>> db = lancedb.connect("memory://")
+        >>> table = db.create_table("effort_vectors", [{"vector": [0.1, 0.2]}])
+        >>> query = table.search([0.1, 0.2]).search_effort(0.75)
+        """
+        self._search_effort = search_effort
         return self
 
     def minimum_nprobes(self, minimum_nprobes: int) -> LanceVectorQueryBuilder:
@@ -1808,7 +1995,12 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         """
         return self._table._output_schema(self.to_query_object())
 
-    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
+    def to_arrow(
+        self,
+        *,
+        timeout: Optional[timedelta] = None,
+        blob_mode: BlobMode = "lazy",
+    ) -> pa.Table:
         """
         Execute the query and return the results as an
         [Apache Arrow Table](https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table).
@@ -1822,9 +2014,17 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         timeout: Optional[timedelta]
             The maximum time to wait for the query to complete.
             If None, wait indefinitely.
+        blob_mode: str, default "lazy"
+            ``"lazy"`` returns blob v2 descriptors that carry the row id
+            ``fetch_blobs`` reads. ``"descriptions"`` returns them without it.
+            ``"bytes"`` returns payload bytes and raises for legacy blob columns
+            and blobs inside lists.
         """
+        blob_sources = self._validated_blob_sources(blob_mode)
         return self._finalize_blob_query_table(
-            self.to_batches(timeout=timeout).read_all()
+            self.to_batches(timeout=timeout).read_all(),
+            blob_mode=blob_mode,
+            blob_sources=blob_sources,
         )
 
     def to_query_object(self) -> Query:
@@ -1843,8 +2043,10 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
             limit=self._limit,
             distance_type=self._distance_type,
             columns=self._columns,
+            nprobes=self._nprobes,
             minimum_nprobes=self._minimum_nprobes,
             maximum_nprobes=self._maximum_nprobes,
+            search_effort=self._search_effort,
             lower_bound=self._lower_bound,
             upper_bound=self._upper_bound,
             refine_factor=self._refine_factor,
@@ -1853,7 +2055,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
             with_row_address=self._with_row_address,
             fragments=self._fragments,
             fragment_ids=self._fragment_ids,
-            use_lsm=self._use_lsm,
+            use_memwal=self._use_memwal,
             offset=self._offset,
             fast_search=self._fast_search,
             ef=self._ef,
@@ -2078,7 +2280,7 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
             with_row_address=self._with_row_address,
             fragments=self._fragments,
             fragment_ids=self._fragment_ids,
-            use_lsm=self._use_lsm,
+            use_memwal=self._use_memwal,
             full_text_query=FullTextSearchQuery(
                 query=self._query_with_phrase_semantics(), columns=self._fts_columns
             ),
@@ -2095,16 +2297,23 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
         """
         return self._table._output_schema(self.to_query_object())
 
-    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
+    def to_arrow(
+        self,
+        *,
+        timeout: Optional[timedelta] = None,
+        blob_mode: BlobMode = "lazy",
+    ) -> pa.Table:
+        blob_sources = self._validated_blob_sources(blob_mode)
         self._table._ensure_no_legacy_fts_index()
 
         query = self._query_for_scan()
-        results = self._table._execute_query(query, timeout=timeout)
-        results = results.read_all()
+        results = self._table._execute_query(query, timeout=timeout).read_all()
         if self._reranker is not None:
             results = self._reranker.rerank_fts(self._query, results)
             check_reranker_result(results)
-        return self._finalize_blob_query_table(results)
+        return self._finalize_blob_query_table(
+            results, blob_mode=blob_mode, blob_sources=blob_sources
+        )
 
     def to_batches(
         self, /, batch_size: Optional[int] = None, timeout: Optional[timedelta] = None
@@ -2131,9 +2340,17 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
 
 
 class LanceEmptyQueryBuilder(LanceQueryBuilder):
-    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
+    def to_arrow(
+        self,
+        *,
+        timeout: Optional[timedelta] = None,
+        blob_mode: BlobMode = "lazy",
+    ) -> pa.Table:
+        blob_sources = self._validated_blob_sources(blob_mode)
         return self._finalize_blob_query_table(
-            self.to_batches(timeout=timeout).read_all()
+            self.to_batches(timeout=timeout).read_all(),
+            blob_mode=blob_mode,
+            blob_sources=blob_sources,
         )
 
     def to_query_object(self) -> Query:
@@ -2145,7 +2362,7 @@ class LanceEmptyQueryBuilder(LanceQueryBuilder):
             with_row_address=self._with_row_address,
             fragments=self._fragments,
             fragment_ids=self._fragment_ids,
-            use_lsm=self._use_lsm,
+            use_memwal=self._use_memwal,
             offset=self._offset,
             order_by=self._order_by,
         )
@@ -2201,8 +2418,10 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
         self._fts_columns = fts_columns
         self._norm = None
         self._reranker = None
+        self._nprobes = None
         self._minimum_nprobes = None
         self._maximum_nprobes = None
+        self._search_effort = None
         self._refine_factor = None
         self._distance_type = None
         self._phrase_query = None
@@ -2264,16 +2483,30 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
     def to_query_object(self) -> Query:
         raise NotImplementedError("to_query_object not yet supported on a hybrid query")
 
-    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
+    def to_arrow(
+        self,
+        *,
+        timeout: Optional[timedelta] = None,
+        blob_mode: BlobMode = "lazy",
+    ) -> pa.Table:
+        blob_sources = self._validated_blob_sources(blob_mode)
         # WAL-PK-FUSION: without the fallback, this body is `_run_hybrid`'s.
         return _wal_hybrid.with_pk_fallback(
-            lambda: self._run_hybrid(timeout=timeout),
+            lambda: self._run_hybrid(
+                timeout=timeout, blob_mode=blob_mode, blob_sources=blob_sources
+            ),
             self._table,
             fused=lambda: self._pk_fusion is not None,
             caller_requested_row_id=self._caller_requested_row_id,
         )
 
-    def _run_hybrid(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
+    def _run_hybrid(
+        self,
+        *,
+        timeout: Optional[timedelta] = None,
+        blob_mode: BlobMode,
+        blob_sources: dict[str, str],
+    ) -> pa.Table:
         self._create_query_builders()
         fts_query, vector_query = self._fts_query, self._vector_query
         # WAL-PK-FUSION: without the fallback, both legs always ask for row ids.
@@ -2300,18 +2533,33 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
             with_row_ids=True,
             offset=self._offset,
         )
-        return self._finish_hybrid_results(results)
+        return self._finish_hybrid_results(
+            results, blob_mode=blob_mode, blob_sources=blob_sources
+        )
 
-    def _finish_hybrid_results(self, results: pa.Table) -> pa.Table:
+    def _finish_hybrid_results(
+        self,
+        results: pa.Table,
+        *,
+        blob_mode: BlobMode,
+        blob_sources: dict[str, str],
+    ) -> pa.Table:
         if self._pk_fusion is not None:  # WAL-PK-FUSION: delete.
-            return self._pk_fusion.strip(results)
+            return self._apply_arrow_blob_mode(
+                self._pk_fusion.strip(results), blob_mode, blob_sources
+            )
         if self._user_requested_row_id():
-            return results
+            return self._apply_arrow_blob_mode(results, blob_mode, blob_sources)
         if self._blob_auto_row_id_enabled():
-            return self._finalize_blob_query_table(results)
+            return self._finalize_blob_query_table(
+                results, blob_mode=blob_mode, blob_sources=blob_sources
+            )
         if "_rowid" in results.column_names:
-            return results.drop(["_rowid"])
-        return results
+            # Fetch bytes before dropping _rowid.
+            return self._apply_arrow_blob_mode(results, blob_mode, blob_sources).drop(
+                ["_rowid"]
+            )
+        return self._apply_arrow_blob_mode(results, blob_mode, blob_sources)
 
     @staticmethod
     def _combine_hybrid_results(
@@ -2492,18 +2740,48 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
         Higher values will yield better recall (more likely to find vectors if
         they exist) at the expense of latency.
 
+        The value is retained as `nprobes` until Lance sets both probe bounds to it.
+        Explicit minimum or maximum settings can override their respective bounds.
+
         Parameters
         ----------
         nprobes: int
-            The number of probes to use.
+            The number of probes to use. Must be greater than 0.
 
         Returns
         -------
         LanceHybridQueryBuilder
             The LanceHybridQueryBuilder object.
         """
-        self._minimum_nprobes = nprobes
-        self._maximum_nprobes = nprobes
+        if nprobes <= 0:
+            raise ValueError("Invalid input, nprobes must be greater than 0")
+        self._nprobes = nprobes
+        return self
+
+    def search_effort(self, search_effort: Optional[float]) -> LanceHybridQueryBuilder:
+        """Set the initial IVF search effort to a finite value in [0, 1].
+
+        None (the default) uses Auto, internally 0.5. Zero starts at the caller
+        minimum; one searches all partitions within the caller maximum. Later
+        probing may continue when too few candidates are found. Any explicit
+        effort, including 0.5, overrides nprobes in either call order. Explicit
+        minimum and maximum bounds still apply. This is not a recall guarantee
+        and does not change refinement or HNSW ef. MemWAL LSM queries do not
+        yet support this option.
+
+        Examples
+        --------
+        >>> import lancedb
+        >>> db = lancedb.connect("memory://")
+        >>> table = db.create_table(
+        ...     "effort_hybrid", [{"vector": [0.1, 0.2], "text": "dog"}]
+        ... )
+        >>> query = (
+        ...     table.search(query_type="hybrid").vector([0.1, 0.2])
+        ...     .text("dog").search_effort(0.75)
+        ... )
+        """
+        self._search_effort = search_effort
         return self
 
     def minimum_nprobes(self, minimum_nprobes: int) -> LanceHybridQueryBuilder:
@@ -2732,6 +3010,10 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
         )
         return "\n".join(results)
 
+    def _blob_output_schema(self) -> pa.Schema:
+        self._create_query_builders()
+        return self._vector_query.output_schema()
+
     def _create_query_builders(self):
         """Set up and configure the vector and FTS query builders."""
         vector_query, fts_query = self._validate_query(
@@ -2758,7 +3040,7 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
         # WAL-PK-FUSION: without the fallback, select `self._columns` as is.
         self._pk_fusion = None
         columns = self._columns
-        if _wal_hybrid.pk_fusion_learned(self._table, self._use_lsm):
+        if _wal_hybrid.pk_fusion_learned(self._table, self._use_memwal):
             self._pk_fusion = _wal_hybrid.PkFusion.for_query(
                 self._table.schema,
                 self._columns,
@@ -2775,13 +3057,17 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
         if self._with_row_id and self._pk_fusion is None:
             self._vector_query.with_row_id(True)
             self._fts_query.with_row_id(True)
-        if self._use_lsm is not None:
-            self._vector_query.use_lsm(self._use_lsm)
-            self._fts_query.use_lsm(self._use_lsm)
+        if self._use_memwal is not None:
+            self._vector_query.use_memwal(self._use_memwal)
+            self._fts_query.use_memwal(self._use_memwal)
         if self._phrase_query:
             self._fts_query.phrase_query(True)
         if self._distance_type:
             self._vector_query.metric(self._distance_type)
+        if self._nprobes is not None:
+            self._vector_query.nprobes(self._nprobes)
+        if self._search_effort is not None:
+            self._vector_query.search_effort(self._search_effort)
         if self._minimum_nprobes is not None:
             self._vector_query.minimum_nprobes(self._minimum_nprobes)
         if self._maximum_nprobes is not None:
@@ -2988,7 +3274,48 @@ class AsyncQueryBase(object):
         """
         return await self._inner.output_schema()
 
-    async def to_arrow(self, timeout: Optional[timedelta] = None) -> pa.Table:
+    async def _validated_blob_sources(self, blob_mode: BlobMode) -> dict[str, str]:
+        """Map projected blob v2 outputs to their source columns.
+
+        Bytes mode is checked here, before the query runs.
+        """
+        validate_blob_mode(blob_mode)
+        # Lazy needs no mapping, and skipping it saves remote tables a request.
+        if blob_mode == "lazy" or self._table is None:
+            return {}
+        schema = await self._table.schema()
+        projection = _query_request_projection(self._inner.to_query_request())
+        blob_sources = blob_v2_projection_sources(schema, projection)
+        if blob_mode != "bytes":
+            return blob_sources
+        reject_unfetchable_blob_columns(schema, projection, blob_sources)
+        if blob_sources and not _supports_local_blob_fetch(
+            self._table, self._table._pushdown_operations
+        ):
+            raise _blob_bytes_without_snapshot_error()
+        reject_unmatched_blob_outputs(await self._blob_output_schema(), blob_sources)
+        return blob_sources
+
+    async def _blob_output_schema(self) -> pa.Schema:
+        return await self.output_schema()
+
+    async def _apply_arrow_blob_mode(
+        self, tbl: pa.Table, blob_mode: BlobMode, blob_sources: dict[str, str]
+    ) -> pa.Table:
+        if blob_mode == "lazy":
+            return tbl
+        if blob_mode == "descriptions":
+            return strip_auto_row_ids(tbl, blob_sources)
+        return await replace_v2_blob_columns_with_bytes(
+            tbl, blob_sources, self._table.fetch_blobs
+        )
+
+    async def to_arrow(
+        self,
+        timeout: Optional[timedelta] = None,
+        *,
+        blob_mode: BlobMode = "lazy",
+    ) -> pa.Table:
         """
         Execute the query and collect the results into an Apache Arrow Table.
 
@@ -3002,13 +3329,26 @@ class AsyncQueryBase(object):
             The maximum time to wait for the query to complete.
             If not specified, no timeout is applied. If the query does not
             complete within the specified time, an error will be raised.
+        blob_mode: str, default "lazy"
+            ``"lazy"`` returns blob v2 descriptors that carry the row id
+            ``fetch_blobs`` reads. ``"descriptions"`` returns them without it.
+            ``"bytes"`` returns payload bytes and raises for legacy blob columns
+            and blobs inside lists.
+            Remote tables and namespace query pushdown refuse ``"bytes"``.
         """
+        blob_sources = await self._validated_blob_sources(blob_mode)
         batch_iter = await self.to_batches(timeout=timeout)
-        return self._finalize_blob_query_table(
+        tbl = self._finalize_blob_query_table(
             pa.Table.from_batches(await batch_iter.read_all(), schema=batch_iter.schema)
         )
+        return await self._apply_arrow_blob_mode(tbl, blob_mode, blob_sources)
 
-    async def to_list(self, timeout: Optional[timedelta] = None) -> List[dict]:
+    async def to_list(
+        self,
+        timeout: Optional[timedelta] = None,
+        *,
+        blob_mode: BlobMode = "lazy",
+    ) -> List[dict]:
         """
         Execute the query and return the results as a list of dictionaries.
 
@@ -3022,8 +3362,10 @@ class AsyncQueryBase(object):
             The maximum time to wait for the query to complete.
             If not specified, no timeout is applied. If the query does not
             complete within the specified time, an error will be raised.
+        blob_mode: str, default "lazy"
+            Passed through to :meth:`to_arrow`.
         """
-        return (await self.to_arrow(timeout=timeout)).to_pylist()
+        return (await self.to_arrow(timeout=timeout, blob_mode=blob_mode)).to_pylist()
 
     async def to_pandas(
         self,
@@ -3065,40 +3407,66 @@ class AsyncQueryBase(object):
             If not specified, no timeout is applied. If the query does not
             complete within the specified time, an error will be raised.
         blob_mode: str, default "lazy"
-            Controls how blob columns are returned for plain scan queries.
-            Vector, FTS, hybrid, and other non-native query shapes keep the
-            existing Arrow conversion path and only support blob descriptions.
+            Controls how blob columns are returned. Eligible local plain scans
+            use Lance's scanner, which returns lazy blob handles in ``"lazy"``
+            mode and also supports legacy blob columns. Other local blob v2
+            queries return descriptors in ``"lazy"`` and ``"descriptions"``
+            modes, or payloads in ``"bytes"`` mode, through :meth:`to_arrow`.
+            Remote blob queries support only ``"descriptions"`` until a stable
+            table snapshot can bind the query and blob fetches.
         **kwargs
             Forwarded to pyarrow.Table.to_pandas after query execution and
             optional flattening.
         """
         validate_blob_mode(blob_mode)
-        if hasattr(self._inner, "output_schema"):
-            schema = await self.output_schema()
-            if _blob_mode_requires_native_pandas(blob_mode, schema):
-                native_error = None
-                if (flatten is None or blob_mode == "descriptions") and timeout is None:
-                    try:
-                        df = await self._plain_scan_to_pandas(
-                            blob_mode, flatten=flatten, **kwargs
-                        )
-                        if df is not None:
-                            return df
-                    except Exception as err:
-                        native_error = err
-                reason = (
-                    "this query shape cannot use Lance native pandas conversion"
-                    if native_error is None
-                    else str(native_error)
-                )
-                raise _unsupported_blob_pandas_error(reason) from native_error
-
-        tbl = flatten_columns(await self.to_arrow(timeout=timeout), flatten)
-        if _blob_mode_requires_native_pandas(blob_mode, tbl.schema):
-            raise _unsupported_blob_pandas_error(
-                "this query shape cannot use Lance native pandas conversion"
+        if self._table is not None and not self._table._inner._is_native():
+            await self._validated_blob_sources(blob_mode)
+            return await self._remote_to_pandas(
+                flatten=flatten, timeout=timeout, blob_mode=blob_mode, **kwargs
             )
-        return tbl.to_pandas(**kwargs)
+
+        if hasattr(self._inner, "output_schema") and schema_has_blob_field(
+            await self.output_schema()
+        ):
+            if (flatten is None or blob_mode != "lazy") and timeout is None:
+                df = await self._plain_scan_to_pandas(
+                    blob_mode, flatten=flatten, **kwargs
+                )
+                if df is not None:
+                    return df
+            if _query_needs_lance_scanner(self):
+                raise _unsupported_blob_pandas_error(
+                    "this query shape cannot use Lance native pandas conversion"
+                )
+
+        arrow_mode = "descriptions" if blob_mode == "lazy" else blob_mode
+        tbl = await self.to_arrow(timeout=timeout, blob_mode=arrow_mode)
+        if (
+            blob_mode == "lazy"
+            and schema_has_blob_field(tbl.schema)
+            and not await self._validated_blob_sources("descriptions")
+        ):
+            raise _unsupported_blob_pandas_error(
+                "the query has no fetchable blob v2 source"
+            )
+        return flatten_columns(tbl, flatten).to_pandas(**kwargs)
+
+    async def _remote_to_pandas(
+        self,
+        *,
+        flatten: Optional[Union[int, bool]],
+        timeout: Optional[timedelta],
+        blob_mode: BlobMode,
+        **kwargs,
+    ) -> "pd.DataFrame":
+        tbl = await self.to_arrow(timeout=timeout)
+        return _finish_remote_pandas(
+            tbl,
+            blob_mode=blob_mode,
+            blob_paths=self._blob_paths,
+            flatten=flatten,
+            **kwargs,
+        )
 
     async def _plain_scan_to_pandas(
         self,
@@ -3124,6 +3492,13 @@ class AsyncQueryBase(object):
             if blob_mode == "bytes"
             else {}
         )
+        if blob_sources:
+            reject_unfetchable_blob_columns(schema, query.columns, blob_sources)
+        # Remote and in-memory tables have no Lance dataset to scan.
+        if not self._table._inner._is_native() or (
+            get_uri_scheme(await self._table.uri()) == "memory"
+        ):
+            return None
         dataset = await self._table.to_lance()
         scanner = dataset.scanner(
             **_scanner_kwargs_for_query(
@@ -3138,7 +3513,7 @@ class AsyncQueryBase(object):
             blob_mode=blob_mode,
             blob_sources=blob_sources,
             fetch_blobs=self._table.fetch_blobs,
-            strip_auto_row_id=blob_auto_row_id,
+            strip_auto_row_id=self._with_row_id is not True,
             flatten=flatten,
             **kwargs,
         )
@@ -3348,25 +3723,25 @@ class AsyncStandardQuery(AsyncQueryBase):
         self._inner.fast_search()
         return self
 
-    def use_lsm(self, enable: bool) -> Self:
+    def use_memwal(self, enable: bool) -> Self:
         """
         Control MemWAL LSM read routing for this query.
 
-        By default (unset), a query against a table with an LSM write spec (see
-        [AsyncTable.set_lsm_write_spec][lancedb.table.AsyncTable.set_lsm_write_spec])
+        By default (unset), a query against a table with a MemWAL write spec (see
+        [AsyncTable.enable_memwal][lancedb.table.AsyncTable.enable_memwal])
         is routed through the LSM scanner so it also returns data written via the
         ``merge_insert`` LSM path that has not yet been compacted into the base
-        table (the active/frozen in-memory memtables and the flushed generations),
+        table (the active/frozen in-memory memtables and the SSTables),
         deduplicated by primary key; a table without a spec reads the base table.
 
         Parameters
         ----------
         enable : bool
-            ``True`` forces the LSM scanner and errors if the table has no LSM
+            ``True`` forces the LSM scanner and errors if the table has no MemWAL
             write spec. ``False`` bypasses the MemWAL and reads the base table
             only, even when a spec is present.
         """
-        self._inner.use_lsm(enable)
+        self._inner.use_memwal(enable)
         return self
 
     def postfilter(self) -> Self:
@@ -3652,7 +4027,9 @@ class AsyncVectorQueryBase:
 
     def nprobes(self, nprobes: int) -> Self:
         """
-        Set the number of partitions to search (probe)
+        Set the legacy IVF probe parameter
+
+        The number of probes must be greater than 0.
 
         This argument is only used when the vector column has an IVF-based index.
         If there is no index then this value is ignored.
@@ -3662,20 +4039,49 @@ class AsyncVectorQueryBase:
 
         The partition whose centroids are closest to the query vector will be
         exhaustiely searched to find matches.  This parameter controls how many
-        partitions should be searched.
+        partitions will be searched.
 
         Increasing this value will increase the recall of your query but will
-        also increase the latency of your query.  The default value is 20.  This
-        default is good for many cases but the best value to use will depend on
-        your data and the recall that you need to achieve.
+        also increase the latency of your query. If this method is not called,
+        Lance's adaptive probe defaults are used.
 
         For best results we recommend tuning this parameter with a benchmark against
         your actual data to find the smallest possible value that will still give
         you the desired recall.
+
+        LanceDB retains this as `nprobes` through local and remote request
+        construction. Lance sets both probe bounds to this value. Explicit minimum
+        or maximum settings can override their respective bounds.
         """
         self._inner.nprobes(nprobes)
         return self
 
+    def search_effort(self, search_effort: Optional[float]) -> Self:
+        """Set the initial IVF search effort to a finite value in [0, 1].
+
+        None preserves Auto (internally 0.5) and fixed nprobes. Any explicit
+        effort, including 0.5, overrides nprobes in either call order; explicit
+        minimum and maximum bounds remain effective. Zero starts at the caller
+        minimum and one searches all partitions within the maximum. Later
+        probing may continue to obtain enough candidates. This is not a recall
+        guarantee and does not change refinement or HNSW ef. MemWAL LSM queries
+        do not yet support this option.
+
+        Examples
+        --------
+        >>> import asyncio
+        >>> import lancedb
+        >>> async def build_query():
+        ...     db = await lancedb.connect_async("memory://")
+        ...     table = await db.create_table(
+        ...         "effort_async", [{"vector": [0.1, 0.2]}]
+        ...     )
+        ...     return table.vector_search([0.1, 0.2]).search_effort(0.75)
+        >>> query = asyncio.run(build_query())
+        """
+        self._inner.search_effort(search_effort)
+        return self
+
     def minimum_nprobes(self, minimum_nprobes: int) -> Self:
         """Set the minimum number of probes to use.
 
@@ -3947,6 +4353,10 @@ class AsyncHybridQuery(AsyncStandardQuery, AsyncVectorQueryBase):
 
         return self
 
+    async def _blob_output_schema(self) -> pa.Schema:
+        vector_query = AsyncVectorQuery(self._inner.to_vector_query(), self._table)
+        return await vector_query.output_schema()
+
     async def _create_child_queries(
         self,
     ) -> Tuple["AsyncFTSQuery", "AsyncVectorQuery", int, int]:
@@ -3978,7 +4388,7 @@ class AsyncHybridQuery(AsyncStandardQuery, AsyncVectorQueryBase):
         # WAL-PK-FUSION: without the fallback, keep only the two `with_row_id`
         # calls.
         self._pk_fusion = None
-        if _wal_hybrid.pk_fusion_learned(self._table, fts_req.use_lsm):
+        if _wal_hybrid.pk_fusion_learned(self._table, fts_req.use_memwal):
             self._pk_fusion = _wal_hybrid.PkFusion.for_query(
                 await self._table.schema(),
                 _query_request_projection(fts_req),
@@ -4167,13 +4577,13 @@ class AsyncTakeQuery(AsyncQueryBase):
     def __init__(self, inner: LanceTakeQuery, table: Optional["AsyncTable"] = None):
         super().__init__(inner, table)
 
-    def use_lsm(self, enable: bool) -> "AsyncTakeQuery":
+    def use_memwal(self, enable: bool) -> "AsyncTakeQuery":
         """Control MemWAL LSM read routing for this take query.
 
         ``False`` bypasses the MemWAL and reads the base table only — the escape
         hatch, since take-by-row-id/offset is not supported on the LSM scanner.
         """
-        self._inner.use_lsm(enable)
+        self._inner.use_memwal(enable)
         return self
 
     async def _plain_scan_to_pandas(
@@ -4234,14 +4644,14 @@ class BaseQueryBuilder(object):
         self._inner.with_row_id()
         return self
 
-    def use_lsm(self, enable: bool) -> Self:
+    def use_memwal(self, enable: bool) -> Self:
         """
         Control MemWAL LSM read routing for this query.
 
         ``False`` bypasses the MemWAL and reads the base table only, the escape
         hatch for shapes the LSM scanner cannot honor (e.g. take-by-row-id).
         """
-        self._inner.use_lsm(enable)
+        self._inner.use_memwal(enable)
         return self
 
     def with_row_address(self, with_row_address: bool = True) -> Self:
@@ -4308,7 +4718,12 @@ class BaseQueryBuilder(object):
 
         return pa.RecordBatchReader.from_batches(async_reader.schema, iter_sync())
 
-    def to_arrow(self, timeout: Optional[timedelta] = None) -> pa.Table:
+    def to_arrow(
+        self,
+        timeout: Optional[timedelta] = None,
+        *,
+        blob_mode: BlobMode = "lazy",
+    ) -> pa.Table:
         """
         Execute the query and collect the results into an Apache Arrow Table.
 
@@ -4322,10 +4737,20 @@ class BaseQueryBuilder(object):
             The maximum time to wait for the query to complete.
             If not specified, no timeout is applied. If the query does not
             complete within the specified time, an error will be raised.
+        blob_mode: str, default "lazy"
+            ``"lazy"`` returns blob v2 descriptors that carry the row id
+            ``fetch_blobs`` reads. ``"descriptions"`` returns them without it.
+            ``"bytes"`` returns payload bytes and raises for legacy blob columns
+            and blobs inside lists.
         """
-        return LOOP.run(self._inner.to_arrow(timeout))
+        return LOOP.run(self._inner.to_arrow(timeout, blob_mode=blob_mode))
 
-    def to_list(self, timeout: Optional[timedelta] = None) -> List[dict]:
+    def to_list(
+        self,
+        timeout: Optional[timedelta] = None,
+        *,
+        blob_mode: BlobMode = "lazy",
+    ) -> List[dict]:
         """
         Execute the query and return the results as a list of dictionaries.
 
@@ -4339,8 +4764,10 @@ class BaseQueryBuilder(object):
             The maximum time to wait for the query to complete.
             If not specified, no timeout is applied. If the query does not
             complete within the specified time, an error will be raised.
+        blob_mode: str, default "lazy"
+            Passed through to :meth:`to_arrow`.
         """
-        return LOOP.run(self._inner.to_list(timeout))
+        return LOOP.run(self._inner.to_list(timeout, blob_mode=blob_mode))
 
     def to_pandas(
         self,
@@ -4382,7 +4809,7 @@ class BaseQueryBuilder(object):
             If not specified, no timeout is applied. If the query does not
             complete within the specified time, an error will be raised.
         blob_mode: str, default "lazy"
-            Controls how blob columns are returned for plain scan queries.
+            Passed through to :meth:`AsyncQueryBase.to_pandas`.
         **kwargs
             Forwarded to pyarrow.Table.to_pandas after query execution and
             optional flattening.
diff --git a/python/python/lancedb/remote/__init__.py b/python/python/lancedb/remote/__init__.py
index dbcaa1b64..a1c8ebee5 100644
--- a/python/python/lancedb/remote/__init__.py
+++ b/python/python/lancedb/remote/__init__.py
@@ -170,6 +170,10 @@ class ClientConfig:
         Configuration for retrying failed requests.
     timeout_config: Optional[TimeoutConfig]
         Configuration for request timeouts.
+    blob_request_concurrency: Optional[int]
+        Maximum concurrent blob HTTP requests across all handles on this
+        connection. Defaults to 8. Set a positive integer here or via
+        `LANCE_CLIENT_BLOB_REQUEST_CONCURRENCY`.
     extra_headers: Optional[dict]
         Additional headers to include in requests.
     id_delimiter: Optional[str]
@@ -193,6 +197,7 @@ class ClientConfig:
     user_agent: str = f"LanceDB-Python-Client/{__version__}"
     retry_config: RetryConfig = field(default_factory=RetryConfig)
     timeout_config: Optional[TimeoutConfig] = field(default_factory=TimeoutConfig)
+    blob_request_concurrency: Optional[int] = None
     extra_headers: Optional[dict] = None
     id_delimiter: Optional[str] = None
     tls_config: Optional[TlsConfig] = None
diff --git a/python/python/lancedb/remote/db.py b/python/python/lancedb/remote/db.py
index a1c71440c..ac6b8b99d 100644
--- a/python/python/lancedb/remote/db.py
+++ b/python/python/lancedb/remote/db.py
@@ -2,6 +2,8 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 
+from __future__ import annotations
+
 from dataclasses import replace
 from datetime import timedelta
 import json
@@ -34,11 +36,12 @@ from lancedb.remote import ClientConfig, RetryConfig, TimeoutConfig, TlsConfig
 import pyarrow as pa
 
 from ..common import DATA
+from lancedb.listing import Listing
 from ..db import DBConnection, LOOP
 from ..functions import FunctionVersion, UdfDefinition
 from ..job import AsyncJob, Job
 from ..sql import Query as SqlQuery
-from ..sql import QueryDescription
+from ..sql import QueryDescription, QueryParameters
 from ..materialized_view import MaterializedView, MaterializedViewSource, SelectArg
 from ..secrets import EnvVarSecret, SecretInfo
 from ..view import ViewDescription
@@ -109,6 +112,7 @@ def _client_config_to_dict(config: ClientConfig) -> dict[str, Any]:
         "user_agent": config.user_agent,
         "retry_config": _retry_config_to_dict(config.retry_config),
         "timeout_config": _timeout_config_to_dict(config.timeout_config),
+        "blob_request_concurrency": config.blob_request_concurrency,
         "extra_headers": config.extra_headers,
         "id_delimiter": config.id_delimiter,
         "tls_config": _tls_config_to_dict(config.tls_config),
@@ -376,7 +380,7 @@ class RemoteDBConnection(DBConnection):
         page_token: Optional[str] = None,
         limit: Optional[int] = None,
     ) -> ListTablesResponse:
-        """List all tables in this database with pagination support.
+        """List a page of tables in this database.
 
         Parameters
         ----------
@@ -387,7 +391,8 @@ class RemoteDBConnection(DBConnection):
             Token for pagination. Use the token from a previous response
             to get the next page of results.
         limit: int, optional
-            The maximum number of results to return.
+            The maximum number of results to return, default 100.
+            Zero returns an empty page without a continuation token.
 
         Returns
         -------
@@ -406,7 +411,7 @@ class RemoteDBConnection(DBConnection):
     def table_names(
         self,
         page_token: Optional[str] = None,
-        limit: int = 10,
+        limit: Optional[int] = None,
         *,
         namespace_path: Optional[List[str]] = None,
     ) -> Iterable[str]:
@@ -422,8 +427,9 @@ class RemoteDBConnection(DBConnection):
             Empty list represents root namespace.
         page_token: str
             The last token to start the new page.
-        limit: int, default 10
-            The maximum number of tables to return for each page.
+        limit: int, optional
+            The maximum number of tables to return. None returns all tables;
+            zero returns an empty list.
 
         Returns
         -------
@@ -483,12 +489,12 @@ class RemoteDBConnection(DBConnection):
             namespace_path = []
         if storage_options is not None:
             logging.info(
-                "storage_options is ignored in LanceDb Cloud"
+                "storage_options is ignored for remote tables"
                 " (storage is managed; set storage_options on connect() instead)"
             )
         if index_cache_size is not None:
             logging.info(
-                "index_cache_size is ignored in LanceDb Cloud"
+                "index_cache_size is ignored for remote tables"
                 " (there is no local cache to configure)"
             )
 
@@ -608,6 +614,9 @@ class RemoteDBConnection(DBConnection):
             One of "error", "drop", "fill".
         fill_value: float
             The value to use when filling vectors. Only used if on_bad_vectors="fill".
+        embedding_functions: list of EmbeddingFunctionConfig, optional
+            The embedding functions to store in the table schema and use to
+            generate vectors when creating the table or adding data.
 
         Returns
         -------
@@ -682,12 +691,6 @@ class RemoteDBConnection(DBConnection):
         if namespace_path is None:
             namespace_path = []
         validate_table_name(name)
-        if embedding_functions is not None:
-            logging.warning(
-                "embedding_functions is not yet supported on LanceDB Cloud."
-                "Please vote https://github.com/lancedb/lancedb/issues/626 "
-                "for this feature."
-            )
 
         from .table import RemoteTable
 
@@ -700,6 +703,7 @@ class RemoteDBConnection(DBConnection):
                 schema=schema,
                 on_bad_vectors=on_bad_vectors,
                 fill_value=fill_value,
+                embedding_functions=embedding_functions,
             )
         )
         return RemoteTable(
@@ -771,8 +775,20 @@ class RemoteDBConnection(DBConnection):
         return view
 
     @override
-    def list_materialized_views(self) -> List[str]:
-        return LOOP.run(self._conn.list_materialized_views())
+    def list_materialized_views(
+        self, *, page_token: Optional[str] = None, page_limit: Optional[int] = None
+    ) -> Listing[str]:
+        """List materialized views.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
+        """
+        return Listing(
+            self._conn.list_materialized_views(
+                page_token=page_token, page_limit=page_limit
+            )
+        )
 
     @override
     def drop_materialized_view(
@@ -862,25 +878,78 @@ class RemoteDBConnection(DBConnection):
         definition: UdfDefinition,
         *,
         secrets: Optional[Sequence[EnvVarSecret]] = None,
+        namespace_path: Optional[List[str]] = None,
     ) -> Job[FunctionVersion]:
-        job = LOOP.run(self._conn.create_function_async(definition, secrets=secrets))
+        job = LOOP.run(
+            self._conn.create_function_async(
+                definition, secrets=secrets, namespace_path=namespace_path
+            )
+        )
         return Job(job)
 
     @override
-    def get_function(self, name: str, *, version: str) -> FunctionVersion:
-        return LOOP.run(self._conn.get_function(name, version=version))
+    def get_function(
+        self,
+        name: str,
+        *,
+        version: str,
+        namespace_path: Optional[List[str]] = None,
+    ) -> FunctionVersion:
+        return LOOP.run(
+            self._conn.get_function(
+                name, version=version, namespace_path=namespace_path
+            )
+        )
 
     @override
-    def list_functions(self) -> List[FunctionVersion]:
-        return LOOP.run(self._conn.list_functions())
+    def list_functions(
+        self,
+        *,
+        namespace_path: Optional[List[str]] = None,
+        page_token: Optional[str] = None,
+        page_limit: Optional[int] = None,
+    ) -> Listing[FunctionVersion]:
+        """List functions.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
+        """
+        return Listing(
+            self._conn.list_functions(
+                namespace_path=namespace_path,
+                page_token=page_token,
+                page_limit=page_limit,
+            )
+        )
 
     @override
-    def drop_function(self, name: str, *, version: str) -> bool:
-        return LOOP.run(self._conn.drop_function(name, version=version))
+    def drop_function(
+        self,
+        name: str,
+        *,
+        version: str,
+        namespace_path: Optional[List[str]] = None,
+    ) -> bool:
+        return LOOP.run(
+            self._conn.drop_function(
+                name, version=version, namespace_path=namespace_path
+            )
+        )
 
     @override
-    def drop_function_async(self, name: str, *, version: str) -> Tuple[bool, Job]:
-        dropped, job = LOOP.run(self._conn.drop_function_async(name, version=version))
+    def drop_function_async(
+        self,
+        name: str,
+        *,
+        version: str,
+        namespace_path: Optional[List[str]] = None,
+    ) -> Tuple[bool, Job]:
+        dropped, job = LOOP.run(
+            self._conn.drop_function_async(
+                name, version=version, namespace_path=namespace_path
+            )
+        )
         return dropped, Job(job)
 
     @override
@@ -902,8 +971,26 @@ class RemoteDBConnection(DBConnection):
         return LOOP.run(self._conn.describe_secret(name, namespace_path=namespace_path))
 
     @override
-    def list_secrets(self, *, namespace_path: Optional[List[str]] = None) -> List[str]:
-        return LOOP.run(self._conn.list_secrets(namespace_path=namespace_path))
+    def list_secrets(
+        self,
+        *,
+        namespace_path: Optional[List[str]] = None,
+        page_token: Optional[str] = None,
+        page_limit: Optional[int] = None,
+    ) -> Listing[str]:
+        """List secrets.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
+        """
+        return Listing(
+            self._conn.list_secrets(
+                namespace_path=namespace_path,
+                page_token=page_token,
+                page_limit=page_limit,
+            )
+        )
 
     @override
     def drop_secret(
@@ -939,13 +1026,40 @@ class RemoteDBConnection(DBConnection):
         return Job(job)
 
     @override
-    def list_views(self, *, namespace_path: Optional[List[str]] = None) -> List[str]:
-        return LOOP.run(self._conn.list_views(namespace_path=namespace_path))
+    def list_views(
+        self,
+        *,
+        namespace_path: Optional[List[str]] = None,
+        page_token: Optional[str] = None,
+        page_limit: Optional[int] = None,
+    ) -> Listing[str]:
+        """List views.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
+        """
+        return Listing(
+            self._conn.list_views(
+                namespace_path=namespace_path,
+                page_token=page_token,
+                page_limit=page_limit,
+            )
+        )
 
     @override
-    def list_jobs(self) -> List["JobInfo"]:
-        """List server-side jobs across the database's tables."""
-        return LOOP.run(self._conn.list_jobs())
+    def list_jobs(
+        self, *, page_token: Optional[str] = None, page_limit: Optional[int] = None
+    ) -> Listing[JobInfo]:
+        """List server-side jobs across the database's tables.
+
+        Returns a lazy iterator. ``page_limit`` limits each request, not the total;
+        ``page_token`` resumes from a saved token. Requests and errors occur during
+        iteration. See [pagination state][lancedb.listing.AsyncListing].
+        """
+        return Listing(
+            self._conn.list_jobs(page_token=page_token, page_limit=page_limit)
+        )
 
     @override
     def cancel_job(self, job_id: str) -> bool:
@@ -979,18 +1093,22 @@ class RemoteDBConnection(DBConnection):
         query: str,
         *,
         default_namespace_path: Optional[List[str]] = None,
+        parameters: Optional[QueryParameters] = None,
     ) -> SqlQuery:
         """Start executing SQL through this remote connection.
 
         Unqualified tables use this connection's database and the
         ``["public"]`` namespace by default. Fully qualified table names may
         reference other databases available to the same deployment.
+        ``parameters`` binds values to ``$1`` / ``$name`` placeholders; see
+        [DBConnection.execute_query_async][lancedb.db.DBConnection.execute_query_async].
         """
         return SqlQuery(
             LOOP.run(
                 self._conn.execute_query_async(
                     query,
                     default_namespace_path=default_namespace_path,
+                    parameters=parameters,
                 )
             )
         )
@@ -1017,6 +1135,10 @@ class RemoteDBConnection(DBConnection):
         """
         return LOOP.run(self._conn.namespace_client())
 
-    async def close(self):
-        """Close the connection to the database."""
+    @override
+    def is_open(self) -> bool:
+        return self._conn.is_open()
+
+    @override
+    def close(self) -> None:
         self._conn.close()
diff --git a/python/python/lancedb/remote/table.py b/python/python/lancedb/remote/table.py
index f8740cc25..b2e5753d8 100644
--- a/python/python/lancedb/remote/table.py
+++ b/python/python/lancedb/remote/table.py
@@ -31,7 +31,7 @@ from lancedb._lancedb import (
     DropColumnsResult,
     FtsToken,
     IndexConfig,
-    LsmWriteSpec,
+    MemWalWriteSpec,
     MergeResult,
     UpdateResult,
 )
@@ -42,6 +42,7 @@ from lancedb.index import (
     BTree,
     Bitmap,
     HnswFlat,
+    HnswPq,
     HnswSq,
     IvfFlat,
     IvfPq,
@@ -56,6 +57,7 @@ from lancedb.table import IndexConfigType, KNOWN_METRICS
 import pyarrow as pa
 
 from lancedb.common import DATA, VEC, VECTOR_COLUMN_NAME
+from lancedb._blob import validate_blob_mode
 from lancedb.merge import LanceMergeInsertBuilder
 from lancedb.embeddings import EmbeddingFunctionRegistry
 from lancedb.table import _normalize_progress
@@ -63,6 +65,7 @@ from lancedb.table import _normalize_progress
 from ..query import (
     AnalyzePlanDistributedMetrics,
     DocumentGranularity,
+    FullTextQuery,
     LanceQueryBuilder,
     LanceTakeQueryBuilder,
     LanceVectorQueryBuilder,
@@ -78,6 +81,7 @@ from ..table import (
     Tags,
 )
 from ..types import BaseTokenizerType
+from ..util import infer_vector_column_name
 
 
 class RemoteTable(Table):
@@ -237,13 +241,15 @@ class RemoteTable(Table):
         """List all versions of the table"""
         return LOOP.run(self._table.list_versions())
 
-    def to_arrow(self) -> pa.Table:
-        """to_arrow() is not yet supported on LanceDB cloud."""
-        raise NotImplementedError("to_arrow() is not yet supported on LanceDB cloud.")
+    def to_arrow(self, *, blob_mode: BlobMode = "lazy") -> pa.Table:
+        """to_arrow() is not supported for remote tables."""
+        validate_blob_mode(blob_mode)
+        raise NotImplementedError("to_arrow() is not supported for remote tables.")
 
     def to_pandas(self, blob_mode: BlobMode = "lazy", **kwargs):
-        """to_pandas() is not yet supported on LanceDB cloud."""
-        raise NotImplementedError("to_pandas() is not yet supported on LanceDB cloud.")
+        """to_pandas() is not supported for remote tables."""
+        validate_blob_mode(blob_mode)
+        raise NotImplementedError("to_pandas() is not supported for remote tables.")
 
     def checkout(self, version: Union[int, str]):
         result = LOOP.run(self._table.checkout(version))
@@ -352,8 +358,8 @@ class RemoteTable(Table):
         language: str = "English",
         max_token_length: Optional[int] = 40,
         lower_case: bool = True,
-        stem: bool = True,
-        remove_stop_words: bool = True,
+        stem: Optional[bool] = None,
+        remove_stop_words: Optional[bool] = None,
         custom_stop_words: Optional[List[str]] = None,
         ascii_folding: bool = True,
         ngram_min_length: int = 3,
@@ -500,14 +506,8 @@ class RemoteTable(Table):
 
             if accelerator is not None:
                 logging.warning(
-                    "GPU accelerator is not yet supported on LanceDB cloud."
-                    "If you have 100M+ vectors to index,"
-                    "please contact us at contact@lancedb.com"
-                )
-            if replace is not None:
-                logging.warning(
-                    "replace is not supported on LanceDB cloud."
-                    "Existing indexes will always be replaced."
+                    "GPU accelerator is not supported for remote tables "
+                    "and will be ignored."
                 )
 
             idx_type = index_type.upper()
@@ -527,9 +527,11 @@ class RemoteTable(Table):
             elif idx_type == "IVF_SQ":
                 config = IvfSq(distance_type=metric, num_partitions=num_partitions)
             elif idx_type == "IVF_HNSW_PQ":
-                raise ValueError(
-                    "IVF_HNSW_PQ is not supported on LanceDB cloud."
-                    "Please use IVF_HNSW_SQ instead."
+                config = HnswPq(
+                    distance_type=metric,
+                    num_partitions=num_partitions,
+                    num_sub_vectors=num_sub_vectors,
+                    num_bits=num_bits,
                 )
             elif idx_type == "IVF_HNSW_SQ":
                 config = HnswSq(distance_type=metric, num_partitions=num_partitions)
@@ -658,7 +660,7 @@ class RemoteTable(Table):
             capped at the number of CPU cores. Lower this if bulk ingestion is
             using too much memory.
         allow_external_blob_outside_bases: bool, default False
-            Not supported on LanceDB Cloud. Setting this raises.
+            Not supported for remote tables. Setting this raises.
 
         Returns
         -------
@@ -688,7 +690,7 @@ class RemoteTable(Table):
         vector_column_name: Optional[str] = None,
         query_type="auto",
         fts_columns: Optional[Union[str, List[str]]] = None,
-        fast_search: bool = False,
+        fast_search: Optional[bool] = None,
     ) -> LanceVectorQueryBuilder:
         """Create a search query to find the nearest neighbors
         of the given query vector. We currently support
@@ -735,11 +737,17 @@ class RemoteTable(Table):
             - If the table has multiple vector columns then the *vector_column_name*
             needs to be specified. Otherwise, an error is raised.
 
+        query_type: str, default "auto"
+            The type of search: "auto", "vector", "fts", or "hybrid".
+            With "auto", a string query uses the embedding function for the
+            inferred or specified vector column if available, otherwise it uses
+            full-text search.
+
         fast_search: bool, optional
             Skip a flat search of unindexed data. This may improve
             search performance but search results will not include unindexed data.
 
-            - *default False*.
+            - *default None*, which leaves fast search disabled.
 
         Returns
         -------
@@ -754,12 +762,27 @@ class RemoteTable(Table):
             - and also the "_distance" column which is the distance between the query
             vector and the returned vector.
         """
+        if isinstance(query, FullTextQuery):
+            query_type = "fts"
+        elif (
+            query_type == "auto"
+            and isinstance(query, str)
+            and not self.embedding_functions
+        ):
+            query_type = "fts"
+        vector_column_name = infer_vector_column_name(
+            schema=self.schema,
+            query_type=query_type,
+            query=query,
+            vector_column_name=vector_column_name,
+        )
+
         return LanceQueryBuilder.create(
             self,
             query,
             query_type,
             vector_column_name=vector_column_name,
-            fts_columns=fts_columns,
+            fts_columns=fts_columns or [],
             fast_search=fast_search,
         )
 
@@ -934,26 +957,20 @@ class RemoteTable(Table):
 
     def cleanup_old_versions(self, *_):
         """
-        cleanup_old_versions() is a no-op on LanceDB Cloud.
-
-        Tables are automatically cleaned up and optimized.
+        cleanup_old_versions() is a no-op for remote tables.
         """
         warnings.warn(
-            "cleanup_old_versions() is a no-op on LanceDB Cloud. "
-            "Tables are automatically cleaned up and optimized.",
+            "cleanup_old_versions() is a no-op for remote tables.",
             stacklevel=2,
         )
         pass
 
     def compact_files(self, *_):
         """
-        compact_files() is a no-op on LanceDB Cloud.
-
-        Tables are automatically compacted and optimized.
+        compact_files() is a no-op for remote tables.
         """
         warnings.warn(
-            "compact_files() is a no-op on LanceDB Cloud. "
-            "Tables are automatically compacted and optimized.",
+            "compact_files() is a no-op for remote tables.",
             stacklevel=2,
         )
         pass
@@ -965,17 +982,13 @@ class RemoteTable(Table):
         delete_unverified: bool = False,
         compaction_options: Optional[CompactionOptions] = None,
     ):
-        """
-        optimize() is a no-op on LanceDB Cloud.
-
-        Indices are optimized automatically.
-        """
-        warnings.warn(
-            "optimize() is a no-op on LanceDB Cloud. "
-            "Indices are optimized automatically.",
-            stacklevel=2,
+        """optimize() is not supported for remote tables."""
+        return LOOP.run(
+            self._table.optimize(
+                cleanup_older_than=cleanup_older_than,
+                delete_unverified=delete_unverified,
+            )
         )
-        pass
 
     def count_rows(self, filter: Optional[str] = None) -> int:
         return LOOP.run(self._table.count_rows(filter))
@@ -1023,20 +1036,20 @@ class RemoteTable(Table):
         return LOOP.run(self._table.drop_columns(columns))
 
     def set_unenforced_primary_key(self, columns: Union[str, Iterable[str]]) -> None:
-        """Not supported on LanceDB Cloud."""
+        """Set the unenforced primary key for this table to a single column."""
         return LOOP.run(self._table.set_unenforced_primary_key(columns))
 
-    def set_lsm_write_spec(self, spec: "LsmWriteSpec") -> None:
-        """Install an LsmWriteSpec."""
-        return LOOP.run(self._table.set_lsm_write_spec(spec))
+    def enable_memwal(self, spec: "MemWalWriteSpec") -> None:
+        """Install an MemWalWriteSpec."""
+        return LOOP.run(self._table.enable_memwal(spec))
 
-    def unset_lsm_write_spec(self) -> None:
-        """Remove the LsmWriteSpec."""
-        return LOOP.run(self._table.unset_lsm_write_spec())
+    def disable_memwal(self) -> None:
+        """Remove the MemWalWriteSpec."""
+        return LOOP.run(self._table.disable_memwal())
 
-    def get_lsm_write_spec(self) -> Optional["LsmWriteSpec"]:
-        """Read the installed LsmWriteSpec, or ``None``."""
-        return LOOP.run(self._table.get_lsm_write_spec())
+    def get_memwal_spec(self) -> Optional["MemWalWriteSpec"]:
+        """Read the installed MemWalWriteSpec, or ``None``."""
+        return LOOP.run(self._table.get_memwal_spec())
 
     # WAL-PK-FUSION: delete both hooks.
     def _hybrid_pk_fusion_learned(self) -> bool:
@@ -1046,31 +1059,31 @@ class RemoteTable(Table):
     def _note_hybrid_pk_fusion(self) -> None:
         self._table._note_hybrid_pk_fusion()
 
-    def checkpoint_lsm(self) -> None:
+    def checkpoint_memwal(self) -> None:
         """Synchronous version of
-        [`AsyncTable.checkpoint_lsm`][lancedb.AsyncTable.checkpoint_lsm]."""
-        return LOOP.run(self._table.checkpoint_lsm())
+        [`AsyncTable.checkpoint_memwal`][lancedb.AsyncTable.checkpoint_memwal]."""
+        return LOOP.run(self._table.checkpoint_memwal())
 
-    def flush_lsm(self) -> None:
+    def flush_memwal(self) -> None:
         """Synchronous version of
-        [`AsyncTable.flush_lsm`][lancedb.AsyncTable.flush_lsm]."""
-        return LOOP.run(self._table.flush_lsm())
+        [`AsyncTable.flush_memwal`][lancedb.AsyncTable.flush_memwal]."""
+        return LOOP.run(self._table.flush_memwal())
 
-    def compact_lsm(self) -> None:
+    def compact_memwal(self) -> None:
         """Synchronous version of
-        [`AsyncTable.compact_lsm`][lancedb.AsyncTable.compact_lsm]."""
-        return LOOP.run(self._table.compact_lsm())
+        [`AsyncTable.compact_memwal`][lancedb.AsyncTable.compact_memwal]."""
+        return LOOP.run(self._table.compact_memwal())
 
-    def get_lsm_stats(self, *, include_generation_rows: bool = False) -> Optional[dict]:
+    def get_memwal_stats(self, *, include_sstable_rows: bool = False) -> Optional[dict]:
         """Synchronous version of
-        [`AsyncTable.get_lsm_stats`][lancedb.AsyncTable.get_lsm_stats]."""
+        [`AsyncTable.get_memwal_stats`][lancedb.AsyncTable.get_memwal_stats]."""
         return LOOP.run(
-            self._table.get_lsm_stats(include_generation_rows=include_generation_rows)
+            self._table.get_memwal_stats(include_sstable_rows=include_sstable_rows)
         )
 
-    def close_lsm_writers(self) -> None:
-        """No-op on LanceDB Cloud (no local shard writers)."""
-        return LOOP.run(self._table.close_lsm_writers())
+    def close_memwal_writers(self) -> None:
+        """No-op for remote tables (no local shard writers)."""
+        return LOOP.run(self._table.close_memwal_writers())
 
     def drop_index(self, index_name: str):
         return LOOP.run(self._table.drop_index(index_name))
@@ -1137,30 +1150,28 @@ class RemoteTable(Table):
         return LanceTakeQueryBuilder(self._table.take_row_ids(row_ids))
 
     def uses_v2_manifest_paths(self) -> bool:
-        raise NotImplementedError(
-            "uses_v2_manifest_paths() is not supported on the LanceDB Cloud"
-        )
+        return LOOP.run(self._table.uses_v2_manifest_paths())
 
     def migrate_v2_manifest_paths(self):
-        raise NotImplementedError(
-            "migrate_v2_manifest_paths() is not supported on the LanceDB Cloud"
-        )
+        return LOOP.run(self._table.migrate_manifest_paths_v2())
 
     def blob_columns(self) -> list[str]:
         return LOOP.run(self._table.blob_columns())
 
     def fetch_blobs(
-        self, column: str, row_ids: Union[list[int], pa.Table]
+        self,
+        column: str,
+        row_ids: Union[list[int], pa.Array, pa.ChunkedArray, pa.Table],
     ) -> pa.LargeBinaryArray:
         return LOOP.run(self._table.fetch_blobs(column, row_ids))
 
     def fetch_blob_ranges(self, column: str, requests) -> pa.LargeBinaryArray:
-        raise NotImplementedError(
-            "fetch_blob_ranges() is not supported on LanceDB Cloud"
-        )
+        return LOOP.run(self._table.fetch_blob_ranges(column, list(requests)))
 
     def fetch_blob_files(
-        self, column: str, row_ids: Union[list[int], pa.Table]
+        self,
+        column: str,
+        row_ids: Union[list[int], pa.Array, pa.ChunkedArray, pa.Table],
     ) -> "list[Optional[BlobFile]]":
         return LOOP.run(self._table.fetch_blob_files(column, row_ids))
 
diff --git a/python/python/lancedb/schema.py b/python/python/lancedb/schema.py
index 4dce09f0f..5edda5610 100644
--- a/python/python/lancedb/schema.py
+++ b/python/python/lancedb/schema.py
@@ -5,7 +5,7 @@
 """Schema helpers for Lance blob columns."""
 
 import importlib
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Optional
 
 import pyarrow as pa
 import pyarrow.ipc
@@ -181,13 +181,60 @@ def _resolve_blob_type():
     return _resolved_blob_type
 
 
-def blob(name: str, nullable: bool = True) -> pa.Field:
+def blob(
+    name: str,
+    nullable: bool = True,
+    *,
+    inline_size_threshold: Optional[int] = None,
+    dedicated_size_threshold: Optional[int] = None,
+    pack_file_size_threshold: Optional[int] = None,
+) -> pa.Field:
     """Create a Lance blob v2 column field.
 
     When pylance is installed this is ``lance.blob.BlobType``.
+
+    Parameters
+    ----------
+    name:
+        Field name.
+    nullable:
+        Whether the column accepts null values.
+    inline_size_threshold:
+        Maximum payload size in bytes to keep inline in the data file
+        before using packed blob storage. Zero is allowed.
+    dedicated_size_threshold:
+        Maximum payload size in bytes to store in packed blob storage
+        before using dedicated blob storage. Must be positive.
+    pack_file_size_threshold:
+        Maximum size in bytes of a single packed blob sidecar file before
+        a new one is started. Must be positive.
+
+    The thresholds are forwarded to ``lance.blob_field``, which validates
+    them and stores them as field metadata, so pylance is required whenever
+    any threshold is set. When all thresholds are omitted the behavior is
+    unchanged.
     """
-    blob_type = _resolve_blob_type()
-    return pa.field(name, blob_type(), nullable=nullable)
+    if (
+        inline_size_threshold is None
+        and dedicated_size_threshold is None
+        and pack_file_size_threshold is None
+    ):
+        blob_type = _resolve_blob_type()
+        return pa.field(name, blob_type(), nullable=nullable)
+    try:
+        lance = importlib.import_module("lance")
+    except ModuleNotFoundError as err:
+        raise ValueError(
+            "Setting blob tier thresholds requires pylance to be installed"
+        ) from err
+    _resolve_blob_type()
+    return lance.blob_field(
+        name,
+        nullable=nullable,
+        inline_size_threshold=inline_size_threshold,
+        dedicated_size_threshold=dedicated_size_threshold,
+        pack_file_size_threshold=pack_file_size_threshold,
+    )
 
 
 def vector(dimension: int, value_type: pa.DataType = pa.float32()) -> pa.DataType:
diff --git a/python/python/lancedb/sql.py b/python/python/lancedb/sql.py
index 41bbb5328..05b82b0d3 100644
--- a/python/python/lancedb/sql.py
+++ b/python/python/lancedb/sql.py
@@ -3,8 +3,10 @@
 
 """Handles to SQL queries running on a remote database."""
 
+from typing import Any, Mapping, Optional, Sequence, Union
 from uuid import UUID
 
+import numpy as np
 import pyarrow as pa
 
 from lancedb.background_loop import LOOP
@@ -14,6 +16,82 @@ from .arrow import AsyncRecordBatchReader
 
 QueryDescription = _lancedb.QueryDescription
 
+QueryParameters = Union[pa.RecordBatch, pa.Table, Sequence[Any], Mapping[str, Any]]
+"""Values for a statement's ``$1`` / ``$name`` placeholders.
+
+A sequence binds by position (``$1``, ``$2``, ...), a mapping binds by name
+(``$name``), and a one-row ``pyarrow.RecordBatch`` or ``pyarrow.Table`` binds
+both ways: column ``i`` is ``$<i + 1>`` and, when its name is not a number,
+also ``$<name>``.
+"""
+
+
+def _parameter_array(value: Any) -> pa.Array:
+    """One parameter as a one-element array of its own type."""
+    if isinstance(value, pa.Scalar):
+        return pa.array([value.as_py()], type=value.type)
+    # A 1-D array is a vector: keep its element type and make it the
+    # fixed-size list a vector column stores, rather than a list of Python
+    # floats that would come back as float64.
+    if isinstance(value, pa.ChunkedArray):
+        value = value.combine_chunks()
+    if isinstance(value, np.ndarray):
+        if value.ndim == 0:
+            return pa.array(value.reshape(1))
+        if value.ndim != 1:
+            raise ValueError(
+                "a numpy query parameter must be a scalar or a 1-D vector, "
+                f"got {value.ndim} dimensions"
+            )
+        value = pa.array(value)
+    if isinstance(value, pa.Array):
+        return pa.FixedSizeListArray.from_arrays(value, len(value))
+    return pa.array([value])
+
+
+def to_parameter_batch(
+    parameters: Optional[QueryParameters],
+) -> Optional[pa.RecordBatch]:
+    """Turn ``parameters`` into the one-row batch a parameterized query sends.
+
+    Scalars keep the type pyarrow infers for them (numpy scalars keep their
+    dtype), a ``pyarrow.Scalar`` keeps its own type, and a 1-D numpy or
+    pyarrow array becomes a ``FixedSizeList`` of its element type, which is
+    what a vector column stores.
+    """
+    if parameters is None:
+        return None
+    batch: pa.RecordBatch
+    if isinstance(parameters, pa.RecordBatch):
+        batch = parameters
+    elif isinstance(parameters, pa.Table):
+        batches = parameters.combine_chunks().to_batches()
+        batch = (
+            batches[0]
+            if len(batches) == 1
+            else pa.RecordBatch.from_pylist([], schema=parameters.schema)
+        )
+    elif isinstance(parameters, Mapping):
+        batch = pa.RecordBatch.from_arrays(
+            [_parameter_array(value) for value in parameters.values()],
+            names=[str(name) for name in parameters.keys()],
+        )
+    elif isinstance(parameters, Sequence) and not isinstance(parameters, (str, bytes)):
+        batch = pa.RecordBatch.from_arrays(
+            [_parameter_array(value) for value in parameters],
+            names=[str(position + 1) for position in range(len(parameters))],
+        )
+    else:
+        raise TypeError(
+            "query parameters must be a sequence, a mapping, or a one-row "
+            f"pyarrow RecordBatch or Table, not {type(parameters).__name__}"
+        )
+    if batch.num_rows != 1:
+        raise ValueError(
+            f"query parameters must be exactly one row, got {batch.num_rows}"
+        )
+    return batch
+
 
 class AsyncQuery:
     """A handle to a submitted SQL query on an asynchronous connection."""
@@ -85,4 +163,4 @@ class Query:
         LOOP.run(self._inner.cancel())
 
 
-__all__ = ["AsyncQuery", "Query", "QueryDescription"]
+__all__ = ["AsyncQuery", "Query", "QueryDescription", "QueryParameters"]
diff --git a/python/python/lancedb/table.py b/python/python/lancedb/table.py
index 661fff75c..02f7f3dec 100644
--- a/python/python/lancedb/table.py
+++ b/python/python/lancedb/table.py
@@ -224,7 +224,7 @@ if TYPE_CHECKING:
         DeleteResult,
         DropColumnsResult,
         FtsToken,
-        LsmWriteSpec,
+        MemWalWriteSpec,
         MergeResult,
         UpdateResult,
     )
@@ -347,6 +347,101 @@ class CompactionOptions(TypedDict, total=False):
 KNOWN_METRICS = {"l2", "cosine", "dot", "hamming"}
 
 
+def _blob_value_to_storage(value: Any) -> Optional[dict]:
+    """Keep a Python blob's inline data or external URI before Arrow infers it."""
+    if value is None:
+        return None
+    if isinstance(value, (bytes, bytearray, memoryview)):
+        return {"data": bytes(value)}
+    if isinstance(value, str):
+        if not value:
+            raise ValueError("Blob uri cannot be empty")
+        return {"uri": value}
+    if isinstance(value, dict):
+        unknown = value.keys() - {"data", "uri", "position", "size"}
+        if unknown:
+            raise ValueError(f"Unknown blob fields: {sorted(unknown)}")
+        return value
+
+    try:
+        from lance.blob import Blob
+    except ModuleNotFoundError as err:
+        if err.name not in ("lance", "lance.blob"):
+            raise
+    else:
+        if isinstance(value, Blob):
+            return {
+                "data": value.data,
+                "uri": value.uri,
+                "position": value.position,
+                "size": value.size,
+            }
+    raise TypeError(f"Unsupported blob value: {type(value).__name__}")
+
+
+def _blob_input_to_arrow(data: Any, schema: Optional[pa.Schema]) -> Optional[pa.Table]:
+    """Convert Python rows with blob fields before Arrow loses their value types."""
+    if schema is None:
+        return None
+
+    if isinstance(data, list) and data and isinstance(data[0], dict):
+        names = {
+            field.name
+            for field in schema
+            if is_blob_v2_field(field)
+            and any(isinstance(row, dict) and field.name in row for row in data)
+        }
+        if not names:
+            return None
+        values = {name: [row.get(name) for row in data] for name in names}
+        rows = [{**row, **{name: None for name in names}} for row in data]
+        table = pa.Table.from_pylist(rows)
+    elif _check_for_pandas(data) and isinstance(data, pd.DataFrame):
+        names = {
+            field.name
+            for field in schema
+            if is_blob_v2_field(field) and field.name in data.columns
+        }
+        if not names:
+            return None
+        values = {
+            name: [
+                None
+                if value is pd.NA or isinstance(value, float) and np.isnan(value)
+                else value
+                for value in data[name]
+            ]
+            for name in names
+        }
+        table = pa.Table.from_pandas(
+            data.assign(**{name: None for name in names}), preserve_index=False
+        ).replace_schema_metadata(None)
+    else:
+        return None
+
+    for field in schema:
+        if field.name not in names:
+            continue
+        storage_type = (
+            field.type.storage_type
+            if isinstance(field.type, pa.ExtensionType)
+            else field.type
+        )
+        storage = pa.array(
+            [_blob_value_to_storage(value) for value in values[field.name]],
+            type=storage_type,
+        )
+        column = (
+            pa.ExtensionArray.from_storage(field.type, storage)
+            if isinstance(field.type, pa.ExtensionType)
+            else storage
+        )
+        table = table.set_column(
+            table.schema.get_field_index(field.name), field, column
+        )
+    return table
+
+
 def _into_pyarrow_reader(
     data, schema: Optional[pa.Schema] = None
 ) -> pa.RecordBatchReader:
@@ -389,9 +484,14 @@ def _into_pyarrow_reader(
             return pa.Table.from_batches(data).to_reader()
         else:
             data = _serialize_json_values(data, schema)
-            return pa.Table.from_pylist(data).to_reader()
+            table = _blob_input_to_arrow(data, schema)
+            return (
+                table if table is not None else pa.Table.from_pylist(data)
+            ).to_reader()
     elif _check_for_pandas(data) and isinstance(data, pd.DataFrame):
-        table = pa.Table.from_pandas(data, preserve_index=False)
+        table = _blob_input_to_arrow(data, schema)
+        if table is None:
+            table = pa.Table.from_pandas(data, preserve_index=False)
         # Do not serialize Pandas metadata
         meta = table.schema.metadata if table.schema.metadata is not None else {}
         meta = {k: v for k, v in meta.items() if k != b"pandas"}
@@ -562,6 +662,55 @@ def _cast_to_target_schema(
     return pa.RecordBatchReader.from_batches(reordered_schema, gen())
 
 
+def _preserve_blob_v2_fields(
+    inferred_schema: pa.Schema, existing_schema: pa.Schema
+) -> pa.Schema:
+    """Keep existing blob fields when an overwrite infers a replacement schema."""
+    existing_fields = {field.name: field for field in existing_schema}
+    fields = [
+        _preserve_blob_v2_field(field, existing_fields[field.name])
+        if field.name in existing_fields
+        else field
+        for field in inferred_schema
+    ]
+    return pa.schema(fields, metadata=inferred_schema.metadata)
+
+
+def _preserve_blob_v2_field(inferred: pa.Field, existing: pa.Field) -> pa.Field:
+    if is_blob_v2_field(existing):
+        return existing
+
+    inferred_type, existing_type = inferred.type, existing.type
+    if pa.types.is_struct(inferred_type) and pa.types.is_struct(existing_type):
+        existing_children = {field.name: field for field in existing_type}
+        children = [
+            _preserve_blob_v2_field(field, existing_children[field.name])
+            if field.name in existing_children
+            else field
+            for field in inferred_type
+        ]
+        return inferred.with_type(pa.struct(children))
+
+    if _is_list_like(inferred_type) and _is_list_like(existing_type):
+        value_field = _preserve_blob_v2_field(
+            inferred_type.value_field, existing_type.value_field
+        )
+        if value_field == inferred_type.value_field:
+            return inferred
+        value_field = value_field.with_name(existing_type.value_field.name)
+        if pa.types.is_fixed_size_list(inferred_type):
+            value_type = pa.list_(value_field, inferred_type.list_size)
+        elif pa.types.is_large_list(inferred_type):
+            value_type = pa.large_list(value_field)
+        else:
+            value_type = pa.list_(value_field)
+        return inferred.with_type(value_type)
+
+    if blob_v2_column_paths(pa.schema([existing])):
+        return existing
+    return inferred
+
+
 def _coerce_blob_write_columns(
     batch: pa.RecordBatch, target_schema: pa.Schema
 ) -> pa.RecordBatch:
@@ -677,12 +826,7 @@ def _coerce_blob_list_values(
 
 
 def _coerce_value_to_blob(values: pa.Array, target_field: pa.Field) -> pa.Array:
-    if pa.types.is_null(values.type):
-        data = pa.nulls(len(values), type=pa.large_binary())
-    elif pa.types.is_large_binary(values.type):
-        data = values
-    else:
-        data = values.cast(pa.large_binary())
+    value_field = "uri" if _is_string_like(values.type) else "data"
     length = len(values)
     storage_type = target_field.type
     if isinstance(storage_type, pa.ExtensionType):
@@ -690,8 +834,11 @@ def _coerce_value_to_blob(values: pa.Array, target_field: pa.Field) -> pa.Array:
     storage_fields = list(storage_type)
     children = []
     for storage_field in storage_fields:
-        if storage_field.name == "data":
-            children.append(data)
+        if storage_field.name == value_field and not pa.types.is_null(values.type):
+            if values.type == storage_field.type:
+                children.append(values)
+            else:
+                children.append(values.cast(storage_field.type))
         else:
             children.append(pa.nulls(length, type=storage_field.type))
     storage = pa.StructArray.from_arrays(
@@ -711,7 +858,19 @@ def _physical_array_and_type(array: pa.Array) -> tuple[pa.Array, pa.DataType]:
 
 
 def _can_coerce_to_blob(data_type: pa.DataType) -> bool:
-    return _is_binary_like(data_type) or pa.types.is_null(data_type)
+    return (
+        _is_binary_like(data_type)
+        or _is_string_like(data_type)
+        or pa.types.is_null(data_type)
+    )
+
+
+def _is_string_like(data_type: pa.DataType) -> bool:
+    return (
+        pa.types.is_string(data_type)
+        or pa.types.is_large_string(data_type)
+        or pa.types.is_string_view(data_type)
+    )
 
 
 def _is_binary_like(data_type: pa.DataType) -> bool:
@@ -873,6 +1032,24 @@ def _arrow_json_storage_type(input_type: pa.DataType) -> Optional[pa.DataType]:
 
 
 def _align_field(field: pa.Field, target_field: pa.Field) -> pa.Field:
+    if is_blob_v2_field(target_field):
+        source_type = field.type
+        if pa.types.is_struct(source_type):
+            if not any(child.name in ("data", "uri") for child in source_type):
+                raise ValueError(
+                    f"blob struct input for column '{field.name}' must contain "
+                    "a 'data' or 'uri' child"
+                )
+        elif not _can_coerce_to_blob(source_type) and not isinstance(
+            source_type, pa.ExtensionType
+        ):
+            raise ValueError(
+                f"cannot coerce column '{field.name}' with type {source_type} "
+                "into a blob v2 struct. expected binary bytes "
+                "(Binary, LargeBinary, BinaryView), strings "
+                "(Utf8, LargeUtf8, Utf8View), or a Struct with a "
+                "'data' or 'uri' child"
+            )
     # LanceDB exposes stored JSON columns as lance.json (JSONB-backed LargeBinary), but
     # casting the input to that storage type here merely relabels the raw JSON bytes as
     # JSONB. Lance must see arrow.json so it can perform the JSONB encoding.
@@ -1308,9 +1485,17 @@ class Table(ABC):
         return self.to_arrow().to_pandas(**kwargs)
 
     @abstractmethod
-    def to_arrow(self) -> pa.Table:
+    def to_arrow(self, *, blob_mode: BlobMode = "lazy") -> pa.Table:
         """Return the table as a pyarrow Table.
 
+        Parameters
+        ----------
+        blob_mode: str, default "lazy"
+            ``"lazy"`` returns blob v2 descriptors that carry the row id
+            ``fetch_blobs`` reads. ``"descriptions"`` returns them without it.
+            ``"bytes"`` returns payload bytes and raises for legacy blob columns
+            and blobs inside lists.
+
         Returns
         -------
         pa.Table
@@ -1588,8 +1773,8 @@ class Table(ABC):
         language: str = "English",
         max_token_length: Optional[int] = 40,
         lower_case: bool = True,
-        stem: bool = True,
-        remove_stop_words: bool = True,
+        stem: Optional[bool] = None,
+        remove_stop_words: Optional[bool] = None,
         custom_stop_words: Optional[List[str]] = None,
         ascii_folding: bool = True,
         ngram_min_length: int = 3,
@@ -1639,6 +1824,7 @@ class Table(ABC):
             - "simple": Splits text by whitespace and punctuation.
             - "whitespace": Split text by whitespace, but not punctuation.
             - "raw": No tokenization. The entire text is treated as a single token.
+            - "code": Tokenizes source code and identifiers.
             - "ngram": N-Gram tokenizer.
             - "icu": ICU dictionary-based word segmentation.
             - "icu/split": ICU segmentation with simple-style delimiter splitting.
@@ -1653,12 +1839,16 @@ class Table(ABC):
         lower_case : bool, default True
             Whether to convert the token to lower case. This makes queries
             case-insensitive.
-        stem : bool, default True
+        stem : bool, optional
             Whether to stem the token. Stemming reduces words to their root form.
             For example, in English "running" and "runs" would both be reduced to "run".
-        remove_stop_words : bool, default True
+            ``None`` uses the base tokenizer's default: False for ``code`` and
+            ``ngram``, True otherwise.
+        remove_stop_words : bool, optional
             Whether to remove stop words. Stop words are common words that are often
             removed from text before indexing. For example, in English "the" and "and".
+            ``None`` uses the base tokenizer's default: False for ``code`` and
+            ``ngram``, True otherwise.
         custom_stop_words : list of str, optional
             Custom words that replace the built-in language stop words. ``None``
             uses the built-in list; an empty list explicitly uses no stop words.
@@ -1719,7 +1909,8 @@ class Table(ABC):
             - pyarrow.Table or pyarrow.RecordBatch
         mode: str
             The mode to use when writing the data. Valid values are
-            "append" and "overwrite".
+            "append" and "overwrite". Overwrite infers a replacement schema
+            but preserves existing blob v2 fields present in the new data.
         on_bad_vectors: str, default "error"
             What to do if any of the vectors are not the same size or contains NaNs.
             One of "error", "drop", "fill".
@@ -2035,19 +2226,30 @@ class Table(ABC):
 
     @abstractmethod
     def blob_columns(self) -> list[str]:
-        """Names of the blob v2 columns declared on this table."""
+        """Names of blob v2 columns that can be fetched by row id.
+
+        Struct-nested blobs use dotted paths. Blobs inside lists are omitted
+        because a row id does not identify an individual list element.
+        """
 
     @abstractmethod
     def fetch_blobs(
-        self, column: str, row_ids: Union[list[int], pa.Table]
+        self,
+        column: str,
+        row_ids: Union[list[int], pa.Array, pa.ChunkedArray, pa.Table],
     ) -> pa.LargeBinaryArray:
         """Materialize full blob bytes for ``column`` at the given rows.
 
         The result has the same length and order as ``row_ids``. Null blobs
         produce null slots; valid empty blobs produce ``b""``.
 
-        ``_rowid`` values stay valid after compaction when the table has stable
-        row ids.
+        ``row_ids`` may be a list, an integer Arrow array or chunked array,
+        or a query table with row ids. ``_rowid`` values stay valid after
+        compaction when the table has stable row ids.
+        One missing or deleted row ID rejects the entire batch; no partial
+        results are returned.
+
+        Blobs inside lists cannot be fetched by row id yet.
 
         Remote servers limit each request to 1024 row IDs and 64 MiB of blob
         bytes. The client splits requests automatically and reads an individual
@@ -2059,17 +2261,20 @@ class Table(ABC):
     def fetch_blob_ranges(
         self,
         column: str,
-        requests: Sequence[Tuple[int, int, int]],
+        requests: Sequence[Sequence[int]],
     ) -> pa.LargeBinaryArray:
         """Materialize row-specific byte ranges from a blob v2 column.
 
-        Each request is a ``(row_id, offset, length)`` tuple. Requests may be
-        repeated or reordered, including multiple ranges for the same blob.
+        Each request is a three-item ``(row_id, offset, length)`` sequence.
+        Requests may be repeated or reordered, including multiple ranges for
+        the same blob.
         The result has the same length and order as ``requests``; null blobs
         produce null slots and empty ranges on non-null blobs produce ``b""``.
 
         ``_rowid`` values stay valid after compaction when the table has stable
         row ids.
+        One missing or deleted row ID rejects the entire batch; no partial
+        results are returned.
 
         Row IDs can be obtained from a query with ``with_row_id(True)``. This
         API is currently supported only by local tables.
@@ -2077,18 +2282,22 @@ class Table(ABC):
 
     @abstractmethod
     def fetch_blob_files(
-        self, column: str, row_ids: Union[list[int], pa.Table]
+        self,
+        column: str,
+        row_ids: Union[list[int], pa.Array, pa.ChunkedArray, pa.Table],
     ) -> "list[Optional[BlobFile]]":
         """Open lazy, seekable :class:`~lancedb._blob.BlobFile` handles.
 
         Prefer this over :meth:`fetch_blobs` for large payloads. ``row_ids`` is
-        a ``list[int]`` or a query ``pyarrow.Table`` carrying row identity via
-        ``_rowid`` or a ``_lance_row_id`` field on the blob descriptor. Null
-        rows are ``None``. Remote tables require LanceDB Cloud server 0.5.0 or
-        newer.
+        a ``list[int]``, an integer Arrow array or chunked array, or a query
+        ``pyarrow.Table`` carrying row identity via ``_rowid`` or a
+        ``_lance_row_id`` field on the blob descriptor. Null blob values return
+        ``None``. Remote tables require LanceDB Cloud server 0.5.0 or newer.
 
         ``_rowid`` values stay valid after compaction when the table has stable
         row ids.
+        One missing or deleted row ID rejects the entire batch; no partial
+        handles are returned.
         """
 
     @abstractmethod
@@ -2609,6 +2818,10 @@ class Table(ABC):
                 to nullable. Currently, you cannot change a nullable column to
                 non-nullable.
 
+            Each alteration must specify at least one of "rename", "data_type",
+            or "nullable". The legacy key "name" is also accepted for renaming.
+            Unknown keys raise ValueError before any alterations are applied.
+
         Returns
         -------
         AlterColumnsResult
@@ -2848,6 +3061,8 @@ class LanceTable(Table):
                     managed_versioning=managed_versioning,
                 )
             )
+        # Take queries read the pushdown set from the inner table.
+        self._table._pushdown_operations = self._pushdown_operations
 
     @property
     def name(self) -> str:
@@ -2996,19 +3211,23 @@ class LanceTable(Table):
         return LOOP.run(self._table.blob_columns())
 
     def fetch_blobs(
-        self, column: str, row_ids: Union[list[int], pa.Table]
+        self,
+        column: str,
+        row_ids: Union[list[int], pa.Array, pa.ChunkedArray, pa.Table],
     ) -> pa.LargeBinaryArray:
         return LOOP.run(self._table.fetch_blobs(column, row_ids))
 
     def fetch_blob_ranges(
         self,
         column: str,
-        requests: Sequence[Tuple[int, int, int]],
+        requests: Sequence[Sequence[int]],
     ) -> pa.LargeBinaryArray:
         return LOOP.run(self._table.fetch_blob_ranges(column, list(requests)))
 
     def fetch_blob_files(
-        self, column: str, row_ids: Union[list[int], pa.Table]
+        self,
+        column: str,
+        row_ids: Union[list[int], pa.Array, pa.ChunkedArray, pa.Table],
     ) -> "list[Optional[BlobFile]]":
         return LOOP.run(self._table.fetch_blob_files(column, row_ids))
 
@@ -3222,12 +3441,23 @@ class LanceTable(Table):
 
         return self.to_lance().to_pandas(blob_mode=blob_mode, **kwargs)
 
-    def to_arrow(self) -> pa.Table:
+    def to_arrow(self, *, blob_mode: BlobMode = "lazy") -> pa.Table:
         """Return the table as a pyarrow Table.
 
+        Parameters
+        ----------
+        blob_mode: str, default "lazy"
+            ``"lazy"`` returns blob v2 descriptors that carry the row id
+            ``fetch_blobs`` reads. ``"descriptions"`` returns them without it.
+            ``"bytes"`` returns payload bytes and raises for legacy blob columns
+            and blobs inside lists.
+
         Returns
         -------
         pa.Table"""
+        validate_blob_mode(blob_mode)
+        if blob_mode != "lazy" and schema_has_blob_field(self.schema):
+            return self.search().to_arrow(blob_mode=blob_mode)
         if (
             _should_push_down_query_table(
                 self._namespace_client, self._pushdown_operations
@@ -3796,8 +4026,8 @@ class LanceTable(Table):
         language: str = "English",
         max_token_length: Optional[int] = 40,
         lower_case: bool = True,
-        stem: bool = True,
-        remove_stop_words: bool = True,
+        stem: Optional[bool] = None,
+        remove_stop_words: Optional[bool] = None,
         custom_stop_words: Optional[List[str]] = None,
         ascii_folding: bool = True,
         ngram_min_length: int = 3,
@@ -3963,7 +4193,8 @@ class LanceTable(Table):
             The data to insert into the table.
         mode: str
             The mode to use when writing the data. Valid values are
-            "append" and "overwrite".
+            "append" and "overwrite". Overwrite infers a replacement schema
+            but preserves existing blob v2 fields present in the new data.
         on_bad_vectors: str, default "error"
             What to do if any of the vectors are not the same size or contains NaNs.
             One of "error", "drop", "fill", "null".
@@ -4324,9 +4555,9 @@ class LanceTable(Table):
             )
             if storage_options is None:
                 storage_options = {}
-            storage_options["new_table_enable_v2_manifest_paths"] = (
+            storage_options["new_table_enable_v2_manifest_paths"] = str(
                 enable_v2_manifest_paths
-            )
+            ).lower()
 
         self._table = LOOP.run(
             self._conn._conn.create_table(
@@ -4423,6 +4654,7 @@ class LanceTable(Table):
             and not self._route_pushdown_to_rust
             and self.current_branch() is None
             and query.take_offsets is None
+            and query.search_effort is None
         ):
             from lancedb.namespace import _execute_server_side_query
 
@@ -4694,47 +4926,47 @@ class LanceTable(Table):
         [`AsyncTable.set_unenforced_primary_key`][lancedb.AsyncTable.set_unenforced_primary_key]."""
         return LOOP.run(self._table.set_unenforced_primary_key(columns))
 
-    def set_lsm_write_spec(self, spec: "LsmWriteSpec") -> None:
-        """Install an LsmWriteSpec. See
-        [`AsyncTable.set_lsm_write_spec`][lancedb.AsyncTable.set_lsm_write_spec]."""
-        return LOOP.run(self._table.set_lsm_write_spec(spec))
+    def enable_memwal(self, spec: "MemWalWriteSpec") -> None:
+        """Install an MemWalWriteSpec. See
+        [`AsyncTable.enable_memwal`][lancedb.AsyncTable.enable_memwal]."""
+        return LOOP.run(self._table.enable_memwal(spec))
 
-    def unset_lsm_write_spec(self) -> None:
-        """Remove the LsmWriteSpec. See
-        [`AsyncTable.unset_lsm_write_spec`][lancedb.AsyncTable.unset_lsm_write_spec]."""
-        return LOOP.run(self._table.unset_lsm_write_spec())
+    def disable_memwal(self) -> None:
+        """Remove the MemWalWriteSpec. See
+        [`AsyncTable.disable_memwal`][lancedb.AsyncTable.disable_memwal]."""
+        return LOOP.run(self._table.disable_memwal())
 
-    def get_lsm_write_spec(self) -> Optional["LsmWriteSpec"]:
-        """Read the installed LsmWriteSpec, or ``None``. See
-        [`AsyncTable.get_lsm_write_spec`][lancedb.AsyncTable.get_lsm_write_spec]."""
-        return LOOP.run(self._table.get_lsm_write_spec())
+    def get_memwal_spec(self) -> Optional["MemWalWriteSpec"]:
+        """Read the installed MemWalWriteSpec, or ``None``. See
+        [`AsyncTable.get_memwal_spec`][lancedb.AsyncTable.get_memwal_spec]."""
+        return LOOP.run(self._table.get_memwal_spec())
 
-    def checkpoint_lsm(self) -> None:
+    def checkpoint_memwal(self) -> None:
         """Synchronous version of
-        [`AsyncTable.checkpoint_lsm`][lancedb.AsyncTable.checkpoint_lsm]."""
-        return LOOP.run(self._table.checkpoint_lsm())
+        [`AsyncTable.checkpoint_memwal`][lancedb.AsyncTable.checkpoint_memwal]."""
+        return LOOP.run(self._table.checkpoint_memwal())
 
-    def flush_lsm(self) -> None:
+    def flush_memwal(self) -> None:
         """Synchronous version of
-        [`AsyncTable.flush_lsm`][lancedb.AsyncTable.flush_lsm]."""
-        return LOOP.run(self._table.flush_lsm())
+        [`AsyncTable.flush_memwal`][lancedb.AsyncTable.flush_memwal]."""
+        return LOOP.run(self._table.flush_memwal())
 
-    def compact_lsm(self) -> None:
+    def compact_memwal(self) -> None:
         """Synchronous version of
-        [`AsyncTable.compact_lsm`][lancedb.AsyncTable.compact_lsm]."""
-        return LOOP.run(self._table.compact_lsm())
+        [`AsyncTable.compact_memwal`][lancedb.AsyncTable.compact_memwal]."""
+        return LOOP.run(self._table.compact_memwal())
 
-    def get_lsm_stats(self, *, include_generation_rows: bool = False) -> Optional[dict]:
+    def get_memwal_stats(self, *, include_sstable_rows: bool = False) -> Optional[dict]:
         """Synchronous version of
-        [`AsyncTable.get_lsm_stats`][lancedb.AsyncTable.get_lsm_stats]."""
+        [`AsyncTable.get_memwal_stats`][lancedb.AsyncTable.get_memwal_stats]."""
         return LOOP.run(
-            self._table.get_lsm_stats(include_generation_rows=include_generation_rows)
+            self._table.get_memwal_stats(include_sstable_rows=include_sstable_rows)
         )
 
-    def close_lsm_writers(self) -> None:
+    def close_memwal_writers(self) -> None:
         """Close cached MemWAL shard writers. See
-        [`AsyncTable.close_lsm_writers`][lancedb.AsyncTable.close_lsm_writers]."""
-        return LOOP.run(self._table.close_lsm_writers())
+        [`AsyncTable.close_memwal_writers`][lancedb.AsyncTable.close_memwal_writers]."""
+        return LOOP.run(self._table.close_memwal_writers())
 
     def uses_v2_manifest_paths(self) -> bool:
         """
@@ -4765,7 +4997,7 @@ class LanceTable(Table):
         [LanceTable.uses_v2_manifest_paths][lancedb.table.LanceTable.uses_v2_manifest_paths]
         to check if the table is already using the new path style.
         """
-        LOOP.run(self._table.migrate_v2_manifest_paths())
+        LOOP.run(self._table.migrate_manifest_paths_v2())
 
     @deprecation.deprecated(
         deprecated_in="0.33.1",
@@ -5392,18 +5624,18 @@ class AsyncTable:
             columns = list(columns)
         await self._inner.set_unenforced_primary_key(columns)
 
-    async def set_lsm_write_spec(self, spec: "LsmWriteSpec") -> None:
-        """Install an LsmWriteSpec on this table.
+    async def enable_memwal(self, spec: "MemWalWriteSpec") -> None:
+        """Install an MemWalWriteSpec on this table.
 
         The spec selects Lance's MemWAL LSM-style write path for future
-        `merge_insert` calls. ``LsmWriteSpec`` chooses one of three sharding
+        `merge_insert` calls. ``MemWalWriteSpec`` chooses one of three sharding
         strategies:
 
-        - ``LsmWriteSpec.bucket(column, num_buckets)`` — hash-bucket writes by
+        - ``MemWalWriteSpec.bucket(column, num_buckets)`` — hash-bucket writes by
           the single-column unenforced primary key.
-        - ``LsmWriteSpec.identity(column)`` — shard by the raw value of a
+        - ``MemWalWriteSpec.identity(column)`` — shard by the raw value of a
           scalar column.
-        - ``LsmWriteSpec.unsharded()`` — route every write to a single shard.
+        - ``MemWalWriteSpec.unsharded()`` — route every write to a single shard.
 
         All variants require the table to have an unenforced primary key set
         via [`set_unenforced_primary_key`]; bucket sharding additionally
@@ -5418,35 +5650,40 @@ class AsyncTable:
 
         Parameters
         ----------
-        spec : LsmWriteSpec
+        spec : MemWalWriteSpec
             The sharding spec to install.
 
         Examples
         --------
-        >>> from lancedb import LsmWriteSpec
+        >>> from lancedb import MemWalWriteSpec
         >>> # table.set_unenforced_primary_key("id")
-        >>> # table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 16))
+        >>> # table.enable_memwal(MemWalWriteSpec.bucket("id", 16))
         """
-        await self._inner.set_lsm_write_spec(spec)
+        await self._inner.enable_memwal(spec)
 
-    async def unset_lsm_write_spec(self) -> None:
-        """Remove the LsmWriteSpec from this table.
+    async def disable_memwal(self) -> None:
+        """Remove the MemWalWriteSpec from this table.
 
         Reverts to the standard `merge_insert` write path. Errors if no spec
         is currently set.
         """
-        await self._inner.unset_lsm_write_spec()
+        await self._inner.disable_memwal()
 
-    async def get_lsm_write_spec(self) -> Optional["LsmWriteSpec"]:
-        """Read the LsmWriteSpec currently installed on this table.
+    async def get_memwal_spec(self) -> Optional["MemWalWriteSpec"]:
+        """Read the MemWalWriteSpec currently installed on this table.
 
-        Returns ``None`` when the MemWAL LSM write path is not enabled (no
-        spec has been set, or it was removed with `unset_lsm_write_spec`).
-        The returned spec mirrors what was passed to `set_lsm_write_spec`,
-        except that ``maintained_indexes`` always reports the concrete list
-        resolved when the spec was set — ``None`` never round-trips.
+        Returns ``None`` when the MemWAL write path is not enabled at all — no
+        spec has been set, or one was removed with `disable_memwal`.
+        That is a different answer from a spec whose ``maintained_indexes``
+        is ``None``, which is an installed spec selecting indexes
+        automatically.
+
+        The spec read back is the one that was installed, selection
+        included: ``None`` maintains every supported index the table has now
+        or gains later, ``[]`` maintains none, and a non-empty list maintains
+        exactly those. All three round-trip.
         """
-        return await self._inner.get_lsm_write_spec()
+        return await self._inner.get_memwal_spec()
 
     # WAL-PK-FUSION: delete both hooks.
     def _hybrid_pk_fusion_learned(self) -> bool:
@@ -5457,19 +5694,19 @@ class AsyncTable:
         """See [`Table._note_hybrid_pk_fusion`][lancedb.table.Table]."""
         self._inner.note_hybrid_pk_fusion()
 
-    async def checkpoint_lsm(self) -> None:
-        """Converge this table's LSM write path into its base table.
+    async def checkpoint_memwal(self) -> None:
+        """Converge this table's MemWAL write path into its base table.
 
-        One flush, sealing every memtable into L0, then compaction triggers
-        until every generation that existed at that moment has reached base.
-        The loop runs client-side, reading progress from ``get_lsm_stats``.
+        One flush, freezing every memtable into an SSTable, then compaction triggers
+        until every SSTable that existed at that moment has reached base.
+        The loop runs client-side, reading progress from ``get_memwal_stats``.
 
-        Best-effort: generations created *while* it runs are deliberately not
+        Best-effort: SSTables created *while* it runs are deliberately not
         waited on, which is what lets it terminate on a table taking writes.
         Idempotent and safe on a cadence.
 
         There is no deadline, and the caller owns that. It returns when the
-        target generations are gone, raises on a terminal server fault, and
+        target SSTables are gone, raises on a terminal server fault, and
         otherwise waits however long the server takes. A slow table and a
         stuck one are the same picture from the client: the compactor pool is
         shared across every table on the node, so a checkpoint queued behind
@@ -5477,56 +5714,56 @@ class AsyncTable:
         ``asyncio.wait_for`` for a wall-clock bound; abandoning it partway
         costs nothing.
         """
-        await self._inner.checkpoint_lsm()
+        await self._inner.checkpoint_memwal()
 
-    async def flush_lsm(self) -> None:
-        """Seal every bucket's active memtable into L0.
+    async def flush_memwal(self) -> None:
+        """Freeze every table shard's active memtable into an SSTable.
 
-        Does not touch the base table — moving L0 into base is
-        `compact_lsm`. On a node that has not claimed this table, this claims
+        Does not touch the base table — compacting SSTables into base is
+        `compact_memwal`. On a node that has not claimed this table, this claims
         it and replays its WAL log first.
         """
-        await self._inner.flush_lsm()
+        await self._inner.flush_memwal()
 
-    async def compact_lsm(self) -> None:
-        """Trigger a background L0 to base compaction pass per bucket.
+    async def compact_memwal(self) -> None:
+        """Trigger a background SSTable compaction pass per table shard.
 
         Returns once the passes are dispatched, not once they finish: watch
-        ``get_lsm_stats`` for progress, or use ``checkpoint_lsm`` to loop
-        until the current L0 has reached base.
+        ``get_memwal_stats`` for progress, or use ``checkpoint_memwal`` to loop
+        until the current SSTables have reached base.
         """
-        await self._inner.compact_lsm()
+        await self._inner.compact_memwal()
 
-    async def get_lsm_stats(
-        self, *, include_generation_rows: bool = False
+    async def get_memwal_stats(
+        self, *, include_sstable_rows: bool = False
     ) -> Optional[dict]:
-        """Read live per-bucket LSM state.
+        """Read live per-table-shard MemWAL state.
 
-        Answers "how far behind is my fresh tier", "which bucket is hot", and
+        Answers "how far behind is my fresh tier", "which table shard is hot", and
         "why is my fresh-tier vector search brute-force". Mutates no table
         state, though on a node that has not claimed this table it claims it,
         exactly as a read would.
 
-        Returns ``None`` only when the LSM write path is not enabled.
+        Returns ``None`` only when the MemWAL write path is not enabled.
 
         Parameters
         ----------
-        include_generation_rows
-            Report a row count per L0 generation. Off by default: each count
-            opens an uncached Lance dataset, and ``checkpoint_lsm`` polls this
+        include_sstable_rows
+            Report a row count per SSTable. Off by default: each count
+            opens an uncached Lance dataset, and ``checkpoint_memwal`` polls this
             needing only generation numbers.
         """
-        return await self._inner.get_lsm_stats(include_generation_rows)
+        return await self._inner.get_memwal_stats(include_sstable_rows)
 
-    async def close_lsm_writers(self) -> None:
+    async def close_memwal_writers(self) -> None:
         """Drain and close any cached MemWAL shard writers for this table.
 
-        When an LSM write spec is installed, `merge_insert` opens MemWAL shard
+        When a MemWAL write spec is installed, `merge_insert` opens MemWAL shard
         writers and caches them for reuse across calls. This closes them,
         flushing pending data; writers reopen lazily on the next
         `merge_insert`. It is a no-op when no writers are cached.
         """
-        await self._inner.close_lsm_writers()
+        await self._inner.close_memwal_writers()
 
     @property
     def name(self) -> str:
@@ -5587,7 +5824,11 @@ class AsyncTable:
         return AsyncQuery(self._inner.query(), self)
 
     async def to_lance(self, **kwargs) -> lance.LanceDataset:
-        """Return the Lance dataset backing this table.
+        """Return the Lance dataset backing a local table.
+
+        Remote tables cannot be opened as Lance datasets by the client. Use
+        [to_arrow][lancedb.table.AsyncTable.to_arrow] or
+        [query][lancedb.table.AsyncTable.query] to read them through the server.
 
         Parameters
         ----------
@@ -5604,6 +5845,12 @@ class AsyncTable:
         >>> async def get_lance_dataset(table):
         ...     return await table.to_lance()
         """
+        if not self._inner._is_native():
+            raise NotImplementedError(
+                "to_lance() is not supported for remote tables; "
+                "query the server instead"
+            )
+
         try:
             import lance
         except ImportError:
@@ -5633,7 +5880,8 @@ class AsyncTable:
         Parameters
         ----------
         blob_mode: str, default "lazy"
-            Controls how Lance blob columns are returned.
+            Controls how Lance blob columns are returned. Remote tables support
+            "descriptions"; "bytes" and "lazy" are not yet supported.
         **kwargs
             Forwarded to PyArrow / Lance pandas conversion.
 
@@ -5651,28 +5899,46 @@ class AsyncTable:
                 )
             return arrow_tbl.to_pandas(**kwargs)
 
+        if not self._inner._is_native():
+            return await self.query().to_pandas(blob_mode=blob_mode, **kwargs)
+
         if blob_mode == "lazy" and get_uri_scheme(await self.uri()) == "memory":
             return (await self.to_arrow()).to_pandas(**kwargs)
         if blob_mode == "bytes" and blob_v2_column_paths(schema):
             return await self.query().to_pandas(blob_mode=blob_mode, **kwargs)
         return (await self.to_lance()).to_pandas(blob_mode=blob_mode, **kwargs)
 
-    async def to_arrow(self) -> pa.Table:
+    async def to_arrow(self, *, blob_mode: BlobMode = "lazy") -> pa.Table:
         """Return the table as a pyarrow Table.
 
+        Parameters
+        ----------
+        blob_mode: str, default "lazy"
+            ``"lazy"`` returns blob v2 descriptors that carry the row id
+            ``fetch_blobs`` reads. ``"descriptions"`` returns them without it.
+            ``"bytes"`` returns payload bytes and raises for legacy blob columns
+            and blobs inside lists.
+
         Returns
         -------
         pa.Table
         """
+        validate_blob_mode(blob_mode)
+        schema = await self.schema()
+        fetches_bytes = blob_mode == "bytes" and schema_has_blob_field(schema)
         if (
-            _should_push_down_query_table(
+            not fetches_bytes
+            and _should_push_down_query_table(
                 self._namespace_client, self._pushdown_operations
             )
             and not self._route_pushdown_to_rust
         ):
-            return (await self._execute_query(Query())).read_all()
-
-        return await self.query().to_arrow()
+            tbl = (await self._execute_query(Query())).read_all()
+            if blob_mode == "descriptions":
+                tbl = strip_auto_row_ids(tbl, row_addressable_blob_v2_paths(schema))
+            return tbl
+        # The query builder refuses bytes under pushdown before running the query.
+        return await self.query().to_arrow(blob_mode=blob_mode)
 
     async def create_index(
         self,
@@ -5968,7 +6234,8 @@ class AsyncTable:
             - pyarrow.Table or pyarrow.RecordBatch
         mode: str
             The mode to use when writing the data. Valid values are
-            "append" and "overwrite".
+            "append" and "overwrite". Overwrite infers a replacement schema
+            but preserves existing blob v2 fields present in the new data.
         on_bad_vectors: str, default "error"
             What to do if any of the vectors are not the same size or contains NaNs.
             One of "error", "drop", "fill", "null".
@@ -5990,6 +6257,9 @@ class AsyncTable:
         """
         schema = await self.schema()
         data = _serialize_json_values(data, schema)
+        blob_table = _blob_input_to_arrow(data, schema)
+        if blob_table is not None:
+            data = blob_table
         if on_bad_vectors is None:
             on_bad_vectors = "error"
         if fill_value is None:
@@ -6003,6 +6273,11 @@ class AsyncTable:
             data, _ = sanitize_create_table(
                 data, None, on_bad_vectors=on_bad_vectors, fill_value=fill_value
             )
+            if blob_v2_column_paths(schema):
+                # Plain bytes cannot be inferred as blob v2. Keep matching blob
+                # fields while allowing other columns to use the inferred schema.
+                target_schema = _preserve_blob_v2_fields(data.schema, schema)
+                data = _cast_to_target_schema(data, target_schema)
         elif on_bad_vectors != "error" or (
             schema.metadata is not None and b"embedding_functions" in schema.metadata
         ):
@@ -6103,7 +6378,17 @@ class AsyncTable:
     @overload
     async def search(
         self,
-        query: Optional[str] = None,
+        query: None = None,
+        vector_column_name: Optional[str] = None,
+        query_type: QueryType = "auto",
+        ordering_field_name: Optional[str] = None,
+        fts_columns: Optional[Union[str, List[str]]] = None,
+    ) -> AsyncQuery: ...
+
+    @overload
+    async def search(
+        self,
+        query: str,
         vector_column_name: Optional[str] = None,
         query_type: Literal["auto"] = ...,
         ordering_field_name: Optional[str] = None,
@@ -6113,7 +6398,7 @@ class AsyncTable:
     @overload
     async def search(
         self,
-        query: Optional[str] = None,
+        query: str,
         vector_column_name: Optional[str] = None,
         query_type: Literal["hybrid"] = ...,
         ordering_field_name: Optional[str] = None,
@@ -6123,7 +6408,7 @@ class AsyncTable:
     @overload
     async def search(
         self,
-        query: Optional[Union[VEC, "PIL.Image.Image", Tuple]] = None,
+        query: Union[VEC, "PIL.Image.Image", Tuple],
         vector_column_name: Optional[str] = None,
         query_type: Literal["auto"] = ...,
         ordering_field_name: Optional[str] = None,
@@ -6133,7 +6418,7 @@ class AsyncTable:
     @overload
     async def search(
         self,
-        query: Optional[str] = None,
+        query: str,
         vector_column_name: Optional[str] = None,
         query_type: Literal["fts"] = ...,
         ordering_field_name: Optional[str] = None,
@@ -6143,9 +6428,7 @@ class AsyncTable:
     @overload
     async def search(
         self,
-        query: Optional[
-            Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]
-        ] = None,
+        query: Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery],
         vector_column_name: Optional[str] = None,
         query_type: Literal["vector"] = ...,
         ordering_field_name: Optional[str] = None,
@@ -6161,7 +6444,7 @@ class AsyncTable:
         query_type: QueryType = "auto",
         ordering_field_name: Optional[str] = None,
         fts_columns: Optional[Union[str, List[str]]] = None,
-    ) -> Union[AsyncHybridQuery, AsyncFTSQuery, AsyncVectorQuery]:
+    ) -> Union[AsyncQuery, AsyncHybridQuery, AsyncFTSQuery, AsyncVectorQuery]:
         """Create a search query to find the nearest neighbors
         of the given query vector. We currently support [vector search](https://lancedb.com/docs/search/vector-search/)
         and [full-text search](https://lancedb.com/docs/search/full-text-search/).
@@ -6176,8 +6459,9 @@ class AsyncTable:
             - *default None*.
             Acceptable types are: list, np.ndarray, PIL.Image.Image
 
-            - If None then the select/where/limit clauses are applied to filter
-            the table
+            - If None then a plain [AsyncQuery][lancedb.query.AsyncQuery] is
+            returned, equivalent to calling [query][lancedb.table.AsyncTable.query].
+            The select/where/limit clauses are applied to filter the table.
         vector_column_name: str, optional
             The name of the vector column to search.
 
@@ -6205,10 +6489,13 @@ class AsyncTable:
 
         Returns
         -------
-        LanceQueryBuilder
+        AsyncQuery, AsyncHybridQuery, AsyncFTSQuery, or AsyncVectorQuery
             A query builder object representing the query.
         """
 
+        if query is None:
+            return self.query()
+
         def is_embedding(query):
             return isinstance(query, (list, np.ndarray, pa.Array, pa.ChunkedArray))
 
@@ -6365,8 +6652,8 @@ class AsyncTable:
             take_query = self.take_offsets(query.take_offsets)
             if query.columns:
                 take_query = take_query.select(query.columns)
-            if query.use_lsm is not None:
-                take_query = take_query.use_lsm(query.use_lsm)
+            if query.use_memwal is not None:
+                take_query = take_query.use_memwal(query.use_memwal)
             if query.with_row_id:
                 take_query = take_query.with_row_id()
             return take_query
@@ -6382,8 +6669,8 @@ class AsyncTable:
             async_query = async_query.where(query.filter)
         if query.fast_search:
             async_query = async_query.fast_search()
-        if query.use_lsm is not None:
-            async_query = async_query.use_lsm(query.use_lsm)
+        if query.use_memwal is not None:
+            async_query = async_query.use_memwal(query.use_memwal)
         if query.with_row_id:
             async_query = async_query.with_row_id()
         if query.order_by:
@@ -6395,9 +6682,12 @@ class AsyncTable:
             )
             if query.distance_type is not None:
                 async_query = async_query.distance_type(query.distance_type)
+            if query.nprobes is not None:
+                async_query = async_query.nprobes(query.nprobes)
+            if query.search_effort is not None:
+                async_query = async_query.search_effort(query.search_effort)
             if query.minimum_nprobes is not None and query.maximum_nprobes is not None:
-                # Set both to the minimum first to avoid min > max error.
-                async_query = async_query.nprobes(
+                async_query = async_query.minimum_nprobes(
                     query.minimum_nprobes
                 ).maximum_nprobes(query.maximum_nprobes)
             elif query.minimum_nprobes is not None:
@@ -6436,6 +6726,7 @@ class AsyncTable:
             )
             and not self._route_pushdown_to_rust
             and query.take_offsets is None
+            and query.search_effort is None
         ):
             from lancedb.namespace import _execute_server_side_query
 
@@ -6505,7 +6796,7 @@ class AsyncTable:
                 when_not_matched_by_source_condition_expr=merge._when_not_matched_by_source_condition_expr,
                 timeout=merge._timeout,
                 use_index=merge._use_index,
-                use_lsm=merge._use_lsm,
+                use_memwal=merge._use_memwal,
                 validate_single_shard=merge._validate_single_shard,
             ),
         )
@@ -6858,6 +7149,10 @@ class AsyncTable:
                 to nullable. Currently, you cannot change a nullable column to
                 non-nullable.
 
+            Each alteration must specify at least one of "rename", "data_type",
+            or "nullable". The legacy key "name" is also accepted for renaming.
+            Unknown keys raise ValueError before any alterations are applied.
+
         Returns
         -------
         AlterColumnsResult
@@ -6901,9 +7196,11 @@ class AsyncTable:
         """
         versions = await self._inner.list_versions()
         for v in versions:
-            ts_nanos = v["timestamp"]
-            v["timestamp"] = datetime.fromtimestamp(ts_nanos // 1e9) + timedelta(
-                microseconds=(ts_nanos % 1e9) // 1e3
+            # Use integer math: float division on ~1e18 nanosecond
+            # values loses sub-millisecond precision.
+            seconds, nanos = divmod(v["timestamp"], 1_000_000_000)
+            v["timestamp"] = datetime.fromtimestamp(seconds) + timedelta(
+                microseconds=nanos // 1000
             )
 
         return versions
@@ -7024,7 +7321,9 @@ class AsyncTable:
         return await self._inner.blob_columns()
 
     async def fetch_blobs(
-        self, column: str, row_ids: Union[list[int], pa.Table]
+        self,
+        column: str,
+        row_ids: Union[list[int], pa.Array, pa.ChunkedArray, pa.Table],
     ) -> pa.LargeBinaryArray:
         return await self._inner.fetch_blobs(
             column, _normalize_blob_row_ids(row_ids, column)
@@ -7033,12 +7332,14 @@ class AsyncTable:
     async def fetch_blob_ranges(
         self,
         column: str,
-        requests: Sequence[Tuple[int, int, int]],
+        requests: Sequence[Sequence[int]],
     ) -> pa.LargeBinaryArray:
         return await self._inner.fetch_blob_ranges(column, list(requests))
 
     async def fetch_blob_files(
-        self, column: str, row_ids: Union[list[int], pa.Table]
+        self,
+        column: str,
+        row_ids: Union[list[int], pa.Array, pa.ChunkedArray, pa.Table],
     ) -> "list[Optional[BlobFile]]":
         handles = await self._inner.fetch_blob_files(
             column, _normalize_blob_row_ids(row_ids, column)
@@ -7309,6 +7610,13 @@ class TableStatistics:
         and manifests.
     num_rows: int
         The total number of rows in the table.
+    num_deleted_rows: Optional[int]
+        The total number of rows marked as deleted across all fragments of the
+        table. These rows are not counted in ``num_rows``, but still occupy space
+        on disk until the table is compacted, so a large value here indicates
+        that the table should be optimized. Fragments in which every row was
+        deleted are dropped outright, so their rows are not counted here.
+        ``None`` when the backend does not report deletion counts.
     num_indices: int
         The total number of indices in the table.
     fragment_stats: FragmentStatistics
@@ -7317,6 +7625,7 @@ class TableStatistics:
 
     total_bytes: int
     num_rows: int
+    num_deleted_rows: Optional[int]
     num_indices: int
     fragment_stats: FragmentStatistics
 
diff --git a/python/python/lancedb/util.py b/python/python/lancedb/util.py
index 3cf5460a5..d4533887b 100644
--- a/python/python/lancedb/util.py
+++ b/python/python/lancedb/util.py
@@ -10,6 +10,7 @@ import os
 import pathlib
 import warnings
 from datetime import date, datetime
+from decimal import Decimal
 from functools import singledispatch
 from typing import Tuple, Union, Optional, Any, List
 from urllib.parse import urlparse
@@ -235,6 +236,19 @@ def inf_vector_column_query(schema: pa.Schema, dim: Optional[int] = None) -> str
     vector_col_names = []
     for field in schema:
         vector_col_names.extend(_iter_vector_columns(field, [], dim))
+
+    if len(vector_col_names) == 0 and dim is not None:
+        # The query's dimension matched no column. If the schema is
+        # unambiguous without that filter, the single vector column is still
+        # the right one to search: a dimension mismatch is a query-execution
+        # error, not a missing-column error.
+        unfiltered = []
+        for field in schema:
+            unfiltered.extend(_iter_vector_columns(field, []))
+        if len(unfiltered) == 1:
+            return unfiltered[0]
+        vector_col_names = unfiltered
+
     if len(vector_col_names) > 1:
         raise ValueError(
             "Schema has more than one vector column. "
@@ -280,6 +294,14 @@ def infer_vector_column_dim(data_type: pa.DataType) -> Optional[int]:
     return None
 
 
+def _validate_query_vector(query: Any) -> None:
+    """Reject empty vector inputs before vector-column inference or execution."""
+    if (isinstance(query, list) and not query) or (
+        isinstance(query, np.ndarray) and query.size == 0
+    ):
+        raise ValueError("Query vector must not be empty")
+
+
 def _query_vector_dim(query: Optional[Any]) -> Optional[int]:
     if query is None:
         return None
@@ -301,6 +323,9 @@ def infer_vector_column_name(
     query: Optional[Any],  # inferred later in query builder
     vector_column_name: Optional[str],
 ):
+    if query_type != "fts":
+        _validate_query_vector(query)
+
     if vector_column_name is not None:
         return vector_column_name
 
@@ -362,6 +387,14 @@ def _(value: float):
     return str(value)
 
 
+@value_to_sql.register(Decimal)
+def _(value: Decimal):
+    if not value.is_finite():
+        raise ValueError("Non-finite Decimal values cannot be converted to SQL")
+    # Quote exact decimal text so the target-column cast avoids float rounding.
+    return value_to_sql(format(value, "f"))
+
+
 @value_to_sql.register(bool)
 def _(value: bool):
     return str(value).upper()
diff --git a/python/python/tests/test_authz.py b/python/python/tests/test_authz.py
new file mode 100644
index 000000000..b4e145317
--- /dev/null
+++ b/python/python/tests/test_authz.py
@@ -0,0 +1,821 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import asyncio
+from dataclasses import FrozenInstanceError
+import json
+from unittest.mock import AsyncMock
+
+import pytest
+
+import lancedb
+from lancedb.authz import (
+    AccessControlEntry,
+    AsyncAuthorization,
+    Authorization,
+    GroupInfo,
+    IdentityInfo,
+    Object,
+    PrincipalInfo,
+    Privilege,
+    RoleBinding,
+    Subject,
+)
+from lancedb.remote import HeaderProvider
+from lancedb.remote.errors import HttpError
+from test_catalog import catalog_server as _catalog_server
+
+catalog_server = _catalog_server
+
+
+class DynamicHeaders(HeaderProvider):
+    def get_headers(self):
+        return {
+            "Authorization": "Bearer rotating-token",
+            "X-LanceDB-Database": "wrong-dynamic-db",
+            "X-LanceDB-Database-Prefix": "wrong-prefix",
+        }
+
+
+@pytest.mark.parametrize("asynchronous", [False, True])
+def test_authz_operations_and_scope(catalog_server, asynchronous):
+    endpoint, requests, responses = catalog_server
+    responses.extend(
+        [
+            (200, {}),
+            (200, {}),
+            (200, {}),
+            (200, {}),
+            (
+                200,
+                {
+                    "entries": [
+                        {
+                            "subject_id": "g:team-id",
+                            "object": "table:tenant/db:a$b$events",
+                            "privilege": "PRIVILEGE20",
+                        }
+                    ]
+                },
+            ),
+            (
+                200,
+                {
+                    "bindings": [
+                        {
+                            "subject_id": "p:alice-id",
+                            "subject_name": "Alice",
+                            "role": "reader",
+                        }
+                    ]
+                },
+            ),
+            (
+                200,
+                {
+                    "principal_id": "alice-id",
+                    "groups": [{"id": "team-id"}],
+                    "roles": ["reader"],
+                },
+            ),
+            (
+                200,
+                {
+                    "group_id": "team-id",
+                    "group_name": "Engineering",
+                    "principals": [{"id": "alice-id", "name": "Alice"}],
+                    "roles": ["reader"],
+                },
+            ),
+        ]
+    )
+    config = {
+        "header_provider": DynamicHeaders(),
+        "extra_headers": {"x-lancedb-database": "wrong-static-db"},
+    }
+    obj = Object.table(database="tenant/db", namespace_path=["a", "b"], name="events")
+
+    async def exercise():
+        if asynchronous:
+            catalog = await lancedb.connect_catalog_async(
+                endpoint, client_config=config
+            )
+        else:
+            catalog = lancedb.connect_catalog(endpoint, client_config=config)
+        authz = catalog.authz
+        assert authz is catalog.authz
+        assert requests == []  # Constructing handles does not probe a database.
+
+        async def call(method, *args, **kwargs):
+            value = method(*args, **kwargs)
+            return await value if asynchronous else value
+
+        await call(
+            authz.add_acl,
+            object=obj,
+            subject=Subject.group_name("Engineering"),
+            privilege=Privilege.SELECT,
+        )
+        await call(
+            authz.delete_acl,
+            object=obj,
+            subject=Subject.group_id("team-id"),
+            privilege="SELECT",
+        )
+        await call(
+            authz.add_role_binding,
+            subject=Subject.principal_name("Alice"),
+            role="reader",
+        )
+        await call(
+            authz.delete_role_binding,
+            subject=Subject.principal_api_key("subject-secret"),
+            role="reader",
+        )
+        iterator = authz.list_acls(object=obj, subject=Subject.role("reader"))
+        entries = (
+            [entry async for entry in iterator] if asynchronous else list(iterator)
+        )
+        assert entries == [
+            AccessControlEntry(
+                str(obj), Subject.group_id("team-id"), None, "PRIVILEGE20"
+            )
+        ]
+        iterator = authz.list_role_bindings(
+            subject=Subject.group_id("team-id"), role="reader"
+        )
+        bindings = (
+            [binding async for binding in iterator] if asynchronous else list(iterator)
+        )
+        assert bindings == [
+            RoleBinding(Subject.principal_id("alice-id"), "Alice", "reader")
+        ]
+        assert await call(
+            authz.get_principal, Subject.principal_id("alice-id")
+        ) == PrincipalInfo("alice-id", None, [IdentityInfo("team-id")], ["reader"])
+        assert await call(
+            authz.get_group, Subject.group_name("Engineering")
+        ) == GroupInfo(
+            "team-id", "Engineering", [IdentityInfo("alice-id", "Alice")], ["reader"]
+        )
+
+    asyncio.run(exercise())
+    assert [path for path, _, _ in requests] == [
+        "/admin/authz/acl/add",
+        "/admin/authz/acl/delete",
+        "/admin/authz/role_binding/add",
+        "/admin/authz/role_binding/delete",
+        "/admin/authz/acl/list",
+        "/admin/authz/role_binding/list",
+        "/admin/authz/principal",
+        "/admin/authz/group",
+    ]
+    assert [body for _, _, body in requests] == [
+        {"object": str(obj), "subject": "G:Engineering", "privilege": "SELECT"},
+        {"object": str(obj), "subject": "g:team-id", "privilege": "SELECT"},
+        {"subject": "P:Alice", "role": "reader"},
+        {"subject": "a:subject-secret", "role": "reader"},
+        {"object": str(obj), "subject": "r:reader", "max_results": 100},
+        {"subject": "g:team-id", "role": "reader", "max_results": 100},
+        {"principal": "p:alice-id"},
+        {"group": "G:Engineering"},
+    ]
+    for _, headers, _ in requests:
+        headers = {key.lower(): value for key, value in headers.items()}
+        assert "x-lancedb-database" not in headers
+        assert "x-lancedb-database-prefix" not in headers
+        assert headers["authorization"] == "Bearer rotating-token"
+
+
+@pytest.mark.parametrize("asynchronous", [False, True])
+@pytest.mark.parametrize("limit", [None, 0, 1, 3])
+def test_lazy_expanded_acl_pagination(catalog_server, asynchronous, limit):
+    endpoint, requests, responses = catalog_server
+
+    def entry(privilege):
+        return {
+            "object": "database:db",
+            "subject_id": "r:reader",
+            "privilege": privilege,
+        }
+
+    responses.extend(
+        [
+            (
+                200,
+                {
+                    "entries": [entry("SELECT"), entry("INSERT")],
+                    "continuation_token": "opaque/first",
+                },
+            ),
+            (200, {"entries": [], "continuation_token": "opaque/second"}),
+            (200, {"entries": [entry("UPDATE"), entry("DELETE")]}),
+        ]
+    )
+
+    async def exercise():
+        catalog = (
+            await lancedb.connect_catalog_async(endpoint, api_key="caller-secret")
+            if asynchronous
+            else lancedb.connect_catalog(endpoint, api_key="caller-secret")
+        )
+        iterator = catalog.authz.list_acls(subject=Subject.role("reader"), limit=limit)
+        assert requests == []
+        return [entry async for entry in iterator] if asynchronous else list(iterator)
+
+    values = asyncio.run(exercise())
+    expected = ["SELECT", "INSERT", "UPDATE", "DELETE"]
+    assert [entry.privilege for entry in values] == (
+        expected if limit is None else expected[:limit]
+    )
+    assert len(requests) == (0 if limit == 0 else 1 if limit == 1 else 3)
+    for _, headers, body in requests:
+        assert headers["x-api-key"] == "caller-secret"
+        assert body["subject"] == "r:reader"
+    if len(requests) == 3:
+        assert requests[1][2]["continuation_token"] == "opaque/first"
+        assert requests[2][2]["continuation_token"] == "opaque/second"
+
+
+@pytest.mark.parametrize("status", [400, 401, 403, 404, 409, 501, 503])
+def test_http_errors_preserve_status_without_replaying_writes(catalog_server, status):
+    endpoint, requests, responses = catalog_server
+    responses.append((status, {"error": "authorization failure"}))
+    authz = lancedb.connect_catalog(endpoint).authz
+    with pytest.raises(HttpError) as error:
+        authz.add_acl(
+            object="system", subject=Subject.role("operator"), privilege="OPERATE"
+        )
+    assert error.value.status_code == status
+    assert error.value.request_id
+    assert len(requests) == 1
+
+
+@pytest.mark.parametrize("asynchronous", [False, True])
+def test_role_pagination_and_late_failure(catalog_server, asynchronous):
+    endpoint, requests, responses = catalog_server
+    responses.extend(
+        [
+            (
+                200,
+                {
+                    "bindings": [{"subject_id": "g:team", "role": "reader"}],
+                    "continuation_token": "next",
+                },
+            ),
+            (403, {"error": "denied"}),
+        ]
+    )
+
+    async def exercise():
+        catalog = (
+            await lancedb.connect_catalog_async(endpoint)
+            if asynchronous
+            else lancedb.connect_catalog(endpoint)
+        )
+        iterator = catalog.authz.list_role_bindings(role="reader")
+        first = await anext(iterator) if asynchronous else next(iterator)
+        assert first.subject == Subject.group_id("team")
+        assert len(requests) == 1
+        with pytest.raises(HttpError) as error:
+            if asynchronous:
+                await anext(iterator)
+            else:
+                next(iterator)
+        assert error.value.status_code == 403
+
+    asyncio.run(exercise())
+    assert requests[1][2] == {
+        "role": "reader",
+        "max_results": 100,
+        "continuation_token": "next",
+    }
+
+
+def test_early_close_does_not_fetch_next_page(catalog_server):
+    endpoint, requests, responses = catalog_server
+    responses.append(
+        (
+            200,
+            {
+                "bindings": [{"subject_id": "g:team", "role": "reader"}],
+                "continuation_token": "next",
+            },
+        )
+    )
+    iterator = lancedb.connect_catalog(endpoint).authz.list_role_bindings()
+    next(iterator)
+    iterator.close()
+    assert len(requests) == 1
+
+
+def test_selectors_objects_and_redaction():
+    secret = Subject.principal_api_key("do-not-print-this")
+    assert "do-not-print-this" not in repr(secret)
+    assert "do-not-print-this" not in str(secret)
+    with pytest.raises(FrozenInstanceError):
+        secret.kind = "role"
+    assert str(Object.system()) == "system"
+    assert str(Object.database("tenant/db")) == "database:tenant/db"
+    assert (
+        str(Object.namespace(database="db", namespace_path=["a", "b"]))
+        == "namespace:db:a$b"
+    )
+    assert str(Object.table(database="db", name="events")) == "table:db:public$events"
+    assert str(Object.view(database="db", name="recent")) == "view:db:public$recent"
+    assert Subject.principal_id("p:id")._wire() == "p:p:id"
+    assert Subject.group_id("team-id").value == "team-id"
+    for namespace in (["a$b"], [""], "public", [".."]):
+        with pytest.raises(ValueError):
+            Object.table(database="db", namespace_path=namespace, name="events")
+    for database in ("", "a//b", "a/../b", "a:b", "a%2Fb", "a$b"):
+        with pytest.raises(ValueError):
+            Object.database(database)
+
+
+@pytest.mark.asyncio
+async def test_validation_before_io_and_empty_lists():
+    inner = AsyncMock()
+    inner.list_acls.return_value = json.dumps({"entries": []})
+    inner.list_role_bindings.return_value = json.dumps({"bindings": []})
+    authz = AsyncAuthorization(inner)
+    for limit in (-1, 1.5, True):
+        with pytest.raises(ValueError):
+            authz.list_acls(limit=limit)
+        with pytest.raises(ValueError):
+            Authorization(authz).list_role_bindings(limit=limit)
+    with pytest.raises(ValueError):
+        await authz.get_group(Subject.principal_id("alice"))
+    with pytest.raises(ValueError):
+        await authz.get_principal(Subject.group_id("team"))
+    for method in (authz.add_role_binding, authz.delete_role_binding):
+        with pytest.raises(ValueError):
+            await method(subject=Subject.role("reader"), role="writer")
+    with pytest.raises(ValueError):
+        authz.list_role_bindings(subject=Subject.role("reader"))
+    assert [entry async for entry in authz.list_acls(limit=0)] == []
+    assert inner.mock_calls == []
+    assert [entry async for entry in authz.list_acls()] == []
+    assert [entry async for entry in authz.list_role_bindings()] == []
+
+
+def test_api_key_subject_is_not_logged(catalog_server):
+    import os
+    import subprocess
+    import sys
+
+    endpoint, requests, responses = catalog_server
+    responses.append((200, {"principal_id": "public-id", "groups": [], "roles": []}))
+    result = subprocess.run(
+        [
+            sys.executable,
+            "-c",
+            "import lancedb, sys; from lancedb.authz import Subject; "
+            "lancedb.connect_catalog(sys.argv[1]).authz.get_principal("
+            "Subject.principal_api_key('test-subject-secret-never-log'))",
+            endpoint,
+        ],
+        env={**os.environ, "LANCEDB_LOG": "debug"},
+        capture_output=True,
+        text=True,
+        check=True,
+        timeout=30,
+    )
+    assert requests[0][2] == {"principal": "a:test-subject-secret-never-log"}
+    assert "test-subject-secret-never-log" not in result.stdout + result.stderr
+
+
+@pytest.mark.parametrize(
+    "builder, kwargs, wire",
+    [
+        ("system", {}, "system"),
+        ("secret", {"database": "db", "name": "api-key"}, "secret:db:public$api-key"),
+        (
+            "function",
+            {"database": "db", "name": "caption"},
+            "function:db:public$caption",
+        ),
+        (
+            "secret",
+            {"database": "tenant/db", "namespace_path": ["a", "b"], "name": "api-key"},
+            "secret:tenant/db:a$b$api-key",
+        ),
+        (
+            "function",
+            {"database": "tenant/db", "namespace_path": ["a", "b"], "name": "caption"},
+            "function:tenant/db:a$b$caption",
+        ),
+        ("database", {"name": "tenant/db"}, "database:tenant/db"),
+        (
+            "namespace",
+            {"database": "tenant/db", "namespace_path": ["a", "b"]},
+            "namespace:tenant/db:a$b",
+        ),
+        (
+            "table",
+            {"database": "tenant/db", "namespace_path": ["a", "b"], "name": "events"},
+            "table:tenant/db:a$b$events",
+        ),
+        (
+            "view",
+            {"database": "db", "namespace_path": ["public"], "name": "recent"},
+            "view:db:public$recent",
+        ),
+    ],
+)
+def test_rust_backed_object_builders(builder, kwargs, wire):
+    import pickle
+    from lancedb import _lancedb
+
+    value = getattr(Object, builder)(**kwargs)
+    assert str(value) == wire
+    assert str(value._inner) == wire
+    assert str(_lancedb.AuthzObject(wire)) == wire
+    assert value == Object(wire)
+    assert hash(value) == hash(Object(wire))
+    assert pickle.loads(pickle.dumps(value)) == value
+
+
+@pytest.mark.parametrize(
+    "kind, prefix",
+    [
+        ("principal_id", "p:"),
+        ("principal_name", "P:"),
+        ("group_id", "g:"),
+        ("group_name", "G:"),
+        ("role", "r:"),
+        ("principal_api_key", "a:"),
+    ],
+)
+def test_rust_backed_subject_selectors(kind, prefix):
+    import pickle
+    from lancedb import _lancedb
+
+    value = "p:literal@example.com / 名前"
+    subject = getattr(Subject, kind)(value)
+    assert subject.kind == kind
+    assert subject.value == value
+    assert subject._wire() == prefix + value
+    assert _lancedb.AuthzSubject(kind, value).to_wire() == prefix + value
+    assert pickle.loads(pickle.dumps(subject)) == subject
+    assert hash(subject) == hash(getattr(Subject, kind)(value))
+    with pytest.raises(ValueError):
+        getattr(Subject, kind)("")
+    if kind == "principal_api_key":
+        assert value not in repr(subject._inner)
+        assert value not in str(subject._inner)
+    if kind in ("principal_id", "group_id", "role"):
+        assert Subject._canonical(prefix + value) == subject
+    else:
+        with pytest.raises(ValueError) as error:
+            Subject._canonical(prefix + value)
+        assert value not in str(error.value)
+
+
+@pytest.mark.parametrize(
+    "wire",
+    [
+        "",
+        "database:",
+        "database:a//b",
+        "database:a/../b",
+        "database:a%2Fb",
+        "namespace:db:a$$b",
+        "table:db:events",
+        "table:db:public$",
+        "table:db:public$..",
+        "table:db:a/b$events",
+        "view:db:public$café",
+        "secret:db:public$",
+        "function:db:public$..",
+        "secret:db:a$$key",
+        "function:db:caption",
+        "secret:db:public$bad/name",
+        "function:db:public$bad:name",
+        "system:db",
+    ],
+)
+def test_invalid_raw_objects_fail_before_io(catalog_server, wire):
+    endpoint, requests, _ = catalog_server
+    authz = lancedb.connect_catalog(endpoint).authz
+    with pytest.raises(ValueError):
+        Object(wire)
+    with pytest.raises(ValueError):
+        authz.list_acls(object=wire)
+    with pytest.raises(ValueError):
+        authz.add_acl(object=wire, subject=Subject.role("reader"), privilege="SELECT")
+    assert requests == []
+
+
+@pytest.mark.parametrize(
+    "wire",
+    [
+        "future:db:resource",
+        "table_extension:db:resource",
+        "secret:tenant/db:a$b$key",
+        "function:tenant/db:a$b$caption",
+    ],
+)
+def test_objects_round_trip_through_acl_requests(catalog_server, wire):
+    endpoint, requests, responses = catalog_server
+    responses.extend(
+        [
+            (200, {}),
+            (
+                200,
+                {
+                    "entries": [
+                        {
+                            "object": wire,
+                            "subject_id": "r:reader",
+                            "privilege": "SELECT",
+                        }
+                    ]
+                },
+            ),
+            (200, {}),
+        ]
+    )
+    authz = lancedb.connect_catalog(endpoint).authz
+    obj = Object(wire)
+    assert str(obj) == wire
+    authz.add_acl(object=obj, subject=Subject.role("reader"), privilege="SELECT")
+    entries = list(authz.list_acls(object=wire))
+    assert entries[0].object == wire
+    authz.delete_acl(
+        object=entries[0].object, subject=Subject.role("reader"), privilege="SELECT"
+    )
+    assert len(requests) == 3
+    for _, _, body in requests:
+        assert body["object"] == wire
+
+
+@pytest.mark.asyncio
+async def test_native_requests_validate_selector_context_before_io(catalog_server):
+    endpoint, requests, _ = catalog_server
+    catalog = await lancedb.connect_catalog_async(endpoint)
+    native = catalog.authz._inner
+    for method, request in [
+        (native.list_role_bindings, {"subject": "r:reader"}),
+        (native.add_role_binding, {"subject": "r:reader", "role": "writer"}),
+        (native.delete_role_binding, {"subject": "r:reader", "role": "writer"}),
+        (native.get_principal, {"principal": "g:group"}),
+        (native.get_group, {"group": "a:never-log-this-key"}),
+    ]:
+        with pytest.raises(ValueError) as error:
+            await method(json.dumps(request))
+        assert "never-log-this-key" not in str(error.value)
+    assert requests == []
+
+
+def test_privilege_values_use_rust_serialization():
+    import pickle
+    from lancedb import _lancedb
+
+    names = [value.value for value in Privilege]
+    assert names == _lancedb.AuthzPrivilege.known()
+    for privilege in Privilege:
+        assert json.loads(json.dumps(privilege)) == privilege.value
+        assert str(_lancedb.AuthzPrivilege(privilege)) == privilege.value
+        assert pickle.loads(pickle.dumps(privilege)) is privilege
+    for index in range(64):
+        alias = f"PRIVILEGE{index}"
+        expected = names[index] if index < len(names) else alias
+        assert str(_lancedb.AuthzPrivilege(alias)) == expected
+
+
+@pytest.mark.parametrize("asynchronous", [False, True])
+@pytest.mark.parametrize(
+    "creation_privilege", [None, "CREATE_SECRET", "CREATE_FUNCTION"]
+)
+def test_privilege_names_and_future_values_round_trip(
+    catalog_server, asynchronous, creation_privilege
+):
+    endpoint, requests, responses = catalog_server
+    inputs = [
+        Privilege.SELECT,
+        "SELECT",
+        "PRIVILEGE2",
+        "PRIVILEGE20",
+        "PRIVILEGE63",
+        "PRIVILEGE64",
+        "PRIVILEGE256",
+        "PRIVILEGE18446744073709551616",
+    ]
+    expected = ["SELECT", "SELECT", "SELECT", *inputs[3:]]
+    object_id = "table:db:public$t"
+    if creation_privilege is not None:
+        inputs = [Privilege[creation_privilege], creation_privilege]
+        expected = [creation_privilege, creation_privilege]
+        object_id = "namespace:db:public"
+    for privilege in inputs:
+        responses.extend(
+            [
+                (200, {}),
+                (
+                    200,
+                    {
+                        "entries": [
+                            {
+                                "object": object_id,
+                                "subject_id": "r:reader",
+                                "privilege": privilege,
+                            }
+                        ]
+                    },
+                ),
+                (200, {}),
+            ]
+        )
+
+    async def exercise():
+        catalog = (
+            await lancedb.connect_catalog_async(endpoint)
+            if asynchronous
+            else lancedb.connect_catalog(endpoint)
+        )
+        authz = catalog.authz
+        for privilege, canonical in zip(inputs, expected):
+            added = authz.add_acl(
+                object=object_id,
+                subject=Subject.role("reader"),
+                privilege=privilege,
+            )
+            if asynchronous:
+                await added
+            iterator = authz.list_acls()
+            entries = (
+                [entry async for entry in iterator] if asynchronous else list(iterator)
+            )
+            assert entries[0].privilege == canonical
+            deleted = authz.delete_acl(
+                object=entries[0].object,
+                subject=entries[0].subject,
+                privilege=entries[0].privilege,
+            )
+            if asynchronous:
+                await deleted
+
+    asyncio.run(exercise())
+    assert len(requests) == 3 * len(inputs)
+    for index, canonical in enumerate(expected):
+        assert requests[index * 3][2]["object"] == object_id
+        assert requests[index * 3][2]["privilege"] == canonical
+        assert requests[index * 3 + 2][2]["object"] == object_id
+        assert requests[index * 3 + 2][2]["privilege"] == canonical
+
+
+@pytest.mark.parametrize("asynchronous", [False, True])
+def test_invalid_privileges_fail_before_io(catalog_server, asynchronous):
+    endpoint, requests, _ = catalog_server
+    invalid = [
+        "",
+        "select",
+        "SELECT ",
+        "UNKNOWN",
+        "PRIVILEGE",
+        "PRIVILEGE-1",
+        "PRIVILEGE+1",
+        "PRIVILEGE1x",
+        None,
+        2,
+        True,
+        ["SELECT"],
+    ]
+
+    async def exercise():
+        catalog = (
+            await lancedb.connect_catalog_async(endpoint)
+            if asynchronous
+            else lancedb.connect_catalog(endpoint)
+        )
+        async_authz = catalog.authz if asynchronous else catalog.authz._inner
+        native = async_authz._inner
+        for privilege in invalid:
+            for method in [catalog.authz.add_acl, catalog.authz.delete_acl]:
+                with pytest.raises(ValueError, match="privilege"):
+                    result = method(
+                        object="system",
+                        subject=Subject.role("reader"),
+                        privilege=privilege,
+                    )
+                    if asynchronous:
+                        await result
+            for method in [
+                native.add_acl,
+                native.delete_acl,
+            ]:
+                with pytest.raises(ValueError):
+                    await method(
+                        json.dumps(
+                            {
+                                "object": "system",
+                                "subject": "r:reader",
+                                "privilege": privilege,
+                            }
+                        )
+                    )
+
+    asyncio.run(exercise())
+    assert requests == []
+
+
+@pytest.mark.parametrize("asynchronous", [False, True])
+def test_acl_page_preserves_unknown_subjects(catalog_server, asynchronous):
+    import pickle
+
+    endpoint, requests, responses = catalog_server
+    responses.extend(
+        [
+            (
+                200,
+                {
+                    "entries": [
+                        {
+                            "subject_id": "p:known",
+                            "object": "system",
+                            "privilege": "USAGE",
+                        },
+                        {
+                            "subject_id": "x:future-subject",
+                            "object": "system",
+                            "privilege": "USAGE",
+                        },
+                    ]
+                },
+            ),
+            (200, {}),
+        ]
+    )
+
+    async def exercise():
+        catalog = (
+            await lancedb.connect_catalog_async(endpoint)
+            if asynchronous
+            else lancedb.connect_catalog(endpoint)
+        )
+        iterator = catalog.authz.list_acls()
+        entries = (
+            [entry async for entry in iterator] if asynchronous else list(iterator)
+        )
+        assert len(entries) == 2
+        assert entries[0].subject == Subject.principal_id("known")
+        subject = entries[1].subject
+        assert subject.kind == "unknown"
+        assert subject.value == "x:future-subject"
+        assert subject._wire() == "x:future-subject"
+        assert pickle.loads(pickle.dumps(subject)) == subject
+        result = catalog.authz.delete_acl(
+            object=entries[1].object, subject=subject, privilege=entries[1].privilege
+        )
+        if asynchronous:
+            await result
+
+    asyncio.run(exercise())
+    assert len(requests) == 2
+    assert requests[1][2]["subject"] == "x:future-subject"
+
+
+@pytest.mark.parametrize("builder", ["secret", "function"])
+def test_secret_and_function_builders_validate_components(builder):
+    from lancedb import _lancedb
+
+    for database, namespace, name in [
+        ("a//b", ["public"], "valid"),
+        ("db", ["a$b"], "valid"),
+        ("db", "public", "valid"),
+        ("db", ["public"], "bad$name"),
+        ("db", ["public"], ".."),
+    ]:
+        with pytest.raises(ValueError):
+            getattr(Object, builder)(
+                database=database, namespace_path=namespace, name=name
+            )
+        with pytest.raises(ValueError):
+            getattr(_lancedb.AuthzObject, builder)(database, namespace, name)
+
+
+@pytest.mark.parametrize(
+    "builder", ["namespace", "table", "view", "secret", "function"]
+)
+def test_empty_and_omitted_namespace_paths_select_public(builder):
+    from lancedb import _lancedb
+
+    kwargs = {"database": "db"}
+    native_args = ()
+    wire = "namespace:db:public"
+    if builder != "namespace":
+        kwargs["name"] = "item"
+        native_args = ("item",)
+        wire = f"{builder}:db:public$item"
+
+    build = getattr(Object, builder)
+    expected = build(**kwargs, namespace_path=["public"])
+    assert str(expected) == wire
+    assert build(**kwargs) == expected
+    for namespace in [[], ()]:
+        assert build(**kwargs, namespace_path=namespace) == expected
+        assert (
+            str(getattr(_lancedb.AuthzObject, builder)("db", namespace, *native_args))
+            == wire
+        )
diff --git a/python/python/tests/test_blob.py b/python/python/tests/test_blob.py
index cada6e83e..8be90115c 100644
--- a/python/python/tests/test_blob.py
+++ b/python/python/tests/test_blob.py
@@ -7,19 +7,22 @@ import sys
 import textwrap
 
 import lance
+import pandas as pd
 import pyarrow as pa
 import pyarrow.compute as pc
 import pytest
-from lance.blob import BlobType as LanceBlobType
+from lance.blob import Blob, BlobType as LanceBlobType
 
 import lancedb
 from lancedb._blob import (
+    _set_blob_column,
     blob_v2_projection_sources,
     read_row_ids_from_hits,
     stash_auto_row_ids,
 )
 from lancedb.expr import col
 from lancedb.index import FTS
+from lancedb.rerankers import Reranker
 from lancedb.schema import blob_column_paths, blob_v2_column_paths
 
 
@@ -68,21 +71,25 @@ def _row_ids_by_id(table):
 
 def _assert_missing_blob_row_ids(exc_info):
     message = str(exc_info.value)
-    assert "row ids" in message
+    assert "row ids but some do not exist in the table" in message
     assert "rowaddr" not in message
     assert "fragment" not in message
 
 
 def _assert_fetch_apis_reject_missing_row_ids(table, row_ids):
+    expected = f"first missing row ids: [{row_ids[0]}]"
     with pytest.raises(ValueError) as exc_info:
         table.fetch_blobs("image", row_ids)
     _assert_missing_blob_row_ids(exc_info)
+    assert expected in str(exc_info.value)
     with pytest.raises(ValueError) as exc_info:
         table.fetch_blob_files("image", row_ids)
     _assert_missing_blob_row_ids(exc_info)
+    assert expected in str(exc_info.value)
     with pytest.raises(ValueError) as exc_info:
         table.fetch_blob_ranges("image", [(row_id, 0, 1) for row_id in row_ids])
     _assert_missing_blob_row_ids(exc_info)
+    assert expected in str(exc_info.value)
 
 
 def test_blob_factory_declares_v2_field():
@@ -93,6 +100,37 @@ def test_blob_factory_declares_v2_field():
     assert type(field.type) is LanceBlobType
 
 
+def test_blob_thresholds_land_in_field_metadata():
+    field = lancedb.blob(
+        "payload",
+        inline_size_threshold=8 * 1024,
+        dedicated_size_threshold=1 << 20,
+        pack_file_size_threshold=1 << 26,
+    )
+    assert field.type.extension_name == "lance.blob.v2"
+    metadata = field.metadata
+    assert metadata[b"lance-encoding:blob-inline-size-threshold"] == b"8192"
+    assert metadata[b"lance-encoding:blob-dedicated-size-threshold"] == b"1048576"
+    assert metadata[b"lance-encoding:blob-pack-file-size-threshold"] == b"67108864"
+
+
+def test_blob_without_thresholds_has_no_threshold_metadata():
+    field = lancedb.blob("image")
+    metadata = field.metadata or {}
+    assert not any(b"threshold" in key for key in metadata)
+
+
+def test_blob_thresholds_are_validated():
+    with pytest.raises(ValueError, match="inline_size_threshold must be non-negative"):
+        lancedb.blob("payload", inline_size_threshold=-1)
+    with pytest.raises(ValueError, match="dedicated_size_threshold must be positive"):
+        lancedb.blob("payload", dedicated_size_threshold=0)
+    with pytest.raises(ValueError, match="pack_file_size_threshold must be positive"):
+        lancedb.blob("payload", pack_file_size_threshold=0)
+    with pytest.raises(TypeError, match="inline_size_threshold must be an int"):
+        lancedb.blob("payload", inline_size_threshold="8192")
+
+
 def test_blob_type_works_without_pylance():
     script = _HIDE_LANCE_BLOB + textwrap.dedent(
         """\
@@ -296,6 +334,273 @@ def test_blob_v2_projection_sources_use_typed_column_name():
     }
 
 
+def test_blob_v2_projection_sources_include_selected_struct():
+    schema = pa.schema(
+        [
+            pa.field(
+                "info", pa.struct([pa.field("name", pa.string()), lancedb.blob("blob")])
+            )
+        ]
+    )
+
+    assert blob_v2_projection_sources(schema, ["info"]) == {"info.blob": "info.blob"}
+    assert blob_v2_projection_sources(schema, {"renamed": "info"}) == {
+        "renamed.blob": "info.blob"
+    }
+
+
+@pytest.mark.parametrize("parent_path", ["info", "outer.info"])
+def test_set_blob_column_missing_nested_field_raises(parent_path):
+    row = {"name": "first"}
+    for name in reversed(parent_path.split(".")):
+        row = {name: row}
+    table = pa.Table.from_pylist([row])
+    blobs = pa.array([b"hello"], type=pa.large_binary())
+
+    with pytest.raises(KeyError) as exc_info:
+        _set_blob_column(table, f"{parent_path}.blob", blobs)
+
+    assert parent_path in str(exc_info.value)
+    assert "'blob'" in str(exc_info.value)
+
+
+def _nested_blob_table(db, name):
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int64()),
+            pa.field(
+                "info", pa.struct([pa.field("name", pa.string()), lancedb.blob("blob")])
+            ),
+        ]
+    )
+    table = db.create_table(name, schema=schema)
+    info = pa.StructArray.from_arrays(
+        [
+            pa.array(["first", "second", "third"]),
+            pa.array([b"hello", None, b"hidden"], type=pa.large_binary()),
+        ],
+        names=["name", "blob"],
+        mask=pa.array([False, False, True]),
+    )
+    table.add(pa.table({"id": pa.array([0, 1, 2]), "info": info}))
+    return table
+
+
+def test_nested_blob_to_pandas_bytes(tmp_path):
+    table = _nested_blob_table(lancedb.connect(tmp_path), "nested_pandas_bytes")
+    expected = [
+        {"name": "first", "blob": b"hello"},
+        {"name": "second", "blob": None},
+        None,
+    ]
+
+    assert table.to_pandas(blob_mode="bytes")["info"].tolist() == expected
+    assert table.search().to_pandas(blob_mode="bytes")["info"].tolist() == expected
+    assert (
+        table.search().select(["info"]).to_pandas(blob_mode="bytes")["info"].tolist()
+        == expected
+    )
+    assert (
+        table.search()
+        .select({"renamed": "info"})
+        .to_pandas(blob_mode="bytes")["renamed"]
+        .tolist()
+        == expected
+    )
+    assert table.search().select({"b": "info.blob"}).to_pandas(blob_mode="bytes")[
+        "b"
+    ].tolist() == [b"hello", None, None]
+
+
+def test_to_arrow_and_to_list_blob_mode(tmp_path):
+    table = _nested_blob_table(lancedb.connect(tmp_path), "arrow_blob_mode")
+    expected = [
+        {"name": "first", "blob": b"hello"},
+        {"name": "second", "blob": None},
+        None,
+    ]
+
+    assert table.to_arrow(blob_mode="bytes")["info"].to_pylist() == expected
+    assert table.search().to_arrow(blob_mode="bytes")["info"].to_pylist() == expected
+    assert table.search().to_list(blob_mode="bytes") == [
+        {"id": 0, "info": expected[0]},
+        {"id": 1, "info": expected[1]},
+        {"id": 2, "info": expected[2]},
+    ]
+    assert table.search().select({"b": "info.blob"}).to_list(blob_mode="bytes") == [
+        {"b": b"hello"},
+        {"b": None},
+        {"b": None},
+    ]
+    assert table.search().select(["id", "info.blob"]).to_list(blob_mode="bytes") == [
+        {"id": 0, "info.blob": b"hello"},
+        {"id": 1, "info.blob": None},
+        {"id": 2, "info.blob": None},
+    ]
+    assert table.search().select({"x.y": "info"}).to_list(blob_mode="bytes") == [
+        {"x.y": expected[0]},
+        {"x.y": expected[1]},
+        {"x.y": expected[2]},
+    ]
+    assert [
+        row["info"] for row in table.search().select([]).to_list(blob_mode="bytes")
+    ] == expected
+    assert table.search().select([]).to_pandas(blob_mode="bytes")["info"].tolist() == (
+        table.search().select([]).to_arrow(blob_mode="bytes")["info"].to_pylist()
+    )
+    assert "_rowid" not in table.search().select([]).to_arrow().column_names
+    # A flat non-blob column named like the nested blob path keeps its values.
+    shadowed = table.search().select({"info": "info", "info.blob": "id"})
+    assert [row["info.blob"] for row in shadowed.to_list()] == [0, 1, 2]
+    assert [row["info"] for row in shadowed.to_list(blob_mode="bytes")] == expected
+    with pytest.raises(ValueError, match="both produce blob column 'info.blob'"):
+        table.search().select(["info", "info.blob"]).to_list(blob_mode="bytes")
+
+    default_blob = table.search().select({"b": "info.blob"}).to_list()[0]["b"]
+    assert isinstance(default_blob, dict)
+    lazy = table.to_arrow(blob_mode="lazy")["info"].to_pylist()
+    assert table.to_arrow()["info"].to_pylist() == lazy
+    lazy_descriptor = lazy[0]["blob"]
+    assert "_lance_row_id" in lazy_descriptor
+    lazy_descriptor.pop("_lance_row_id")
+    descriptions = table.to_arrow(blob_mode="descriptions")["info"].to_pylist()
+    assert descriptions[0]["blob"] == lazy_descriptor
+    assert (
+        table.search().to_list(blob_mode="descriptions")[0]["info"]["blob"]
+        == lazy_descriptor
+    )
+
+    with pytest.raises(ValueError, match="blob_mode must be one of"):
+        table.to_arrow(blob_mode="handles")
+    with pytest.raises(ValueError, match="blob_mode must be one of"):
+        table.search().to_list(blob_mode="handles")
+
+
+@pytest.mark.asyncio
+async def test_async_to_arrow_and_to_list_blob_mode(tmp_path):
+    db = await lancedb.connect_async(tmp_path)
+    table = await db.create_table(
+        "async_arrow_blob_mode",
+        schema=pa.schema(
+            [
+                pa.field("id", pa.int64()),
+                pa.field(
+                    "info",
+                    pa.struct([pa.field("name", pa.string()), lancedb.blob("blob")]),
+                ),
+            ]
+        ),
+    )
+    await table.add(
+        pa.table(
+            {
+                "id": pa.array([0, 1]),
+                "info": pa.StructArray.from_arrays(
+                    [
+                        pa.array(["first", "second"]),
+                        pa.array([b"hello", None], type=pa.large_binary()),
+                    ],
+                    names=["name", "blob"],
+                ),
+            }
+        )
+    )
+    expected = [
+        {"name": "first", "blob": b"hello"},
+        {"name": "second", "blob": None},
+    ]
+
+    assert (await table.to_arrow(blob_mode="bytes"))["info"].to_pylist() == expected
+    assert (await table.query().to_arrow(blob_mode="bytes"))[
+        "info"
+    ].to_pylist() == expected
+    assert await table.query().to_list(blob_mode="bytes") == [
+        {"id": 0, "info": expected[0]},
+        {"id": 1, "info": expected[1]},
+    ]
+    default_blob = (await table.query().select({"b": "info.blob"}).to_list())[0]["b"]
+    assert isinstance(default_blob, dict)
+    descriptions = await table.to_arrow(blob_mode="descriptions")
+    assert (
+        "_lance_row_id"
+        not in descriptions.schema.field("info").type.field("blob").type.names
+    )
+
+
+def test_vector_query_to_arrow_blob_mode_bytes(tmp_path):
+    db = lancedb.connect(tmp_path)
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int64()),
+            pa.field("vector", pa.list_(pa.float32(), 2)),
+            lancedb.blob("blob"),
+        ]
+    )
+    table = db.create_table("vector_arrow_blob", schema=schema)
+    table.add(
+        [
+            {"id": 1, "vector": [1.0, 0.0], "blob": b"one"},
+            {"id": 2, "vector": [0.0, 1.0], "blob": b"two"},
+        ]
+    )
+    table.add([{"id": 3, "vector": [0.9, 0.1], "blob": b"three"}])
+    table.add([{"id": 4, "vector": [0.1, 0.9], "blob": None}])
+
+    hits = table.search([0.0, 1.0]).limit(4).to_arrow(blob_mode="bytes").to_pylist()
+    assert [(hit["id"], hit["blob"]) for hit in hits] == [
+        (2, b"two"),
+        (4, None),
+        (3, b"three"),
+        (1, b"one"),
+    ]
+    arrow = table.search([1.0, 0.0]).limit(1).to_arrow(blob_mode="bytes")
+    assert arrow["blob"].to_pylist() == [b"one"]
+    assert table.search([1.0, 0.0]).limit(1).to_list(blob_mode="bytes")[0]["blob"] == (
+        b"one"
+    )
+    assert isinstance(
+        table.search([1.0, 0.0]).limit(1).to_list()[0]["blob"],
+        dict,
+    )
+
+
+@pytest.mark.asyncio
+async def test_nested_blob_to_pandas_bytes_async(tmp_path):
+    db = await lancedb.connect_async(tmp_path)
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int64()),
+            pa.field(
+                "info", pa.struct([pa.field("name", pa.string()), lancedb.blob("blob")])
+            ),
+        ]
+    )
+    table = await db.create_table("nested_pandas_bytes_async", schema=schema)
+    await table.add(
+        pa.table(
+            {
+                "id": pa.array([0, 1]),
+                "info": pa.StructArray.from_arrays(
+                    [
+                        pa.array(["first", "second"]),
+                        pa.array([b"hello", None], type=pa.large_binary()),
+                    ],
+                    names=["name", "blob"],
+                ),
+            }
+        )
+    )
+    expected = [
+        {"name": "first", "blob": b"hello"},
+        {"name": "second", "blob": None},
+    ]
+
+    assert (await table.to_pandas(blob_mode="bytes"))["info"].tolist() == expected
+    assert (await table.query().to_pandas(blob_mode="bytes"))[
+        "info"
+    ].tolist() == expected
+
+
 def _legacy_v1_table(name):
     # Legacy v1 blob columns are only writable at file version <= 2.1.
     db = lancedb.connect(
@@ -395,6 +700,19 @@ async def test_async_table_to_pandas_descriptions_mode_omits_row_id():
     assert set(descriptor.keys()) == {"kind", "position", "size", "blob_id", "blob_uri"}
 
 
+@pytest.mark.asyncio
+async def test_async_calculated_select_does_not_read_every_blob_column():
+    db = await lancedb.connect_async("memory:///calculated_select")
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    table = await db.create_table("calculated_select", schema=schema)
+    await table.add([{"id": 1, "image": b"hello"}])
+
+    hits = await table.query().select({"next_id": col("id") + 1}).to_arrow()
+
+    assert hits.column_names == ["next_id"]
+    assert hits["next_id"].to_pylist() == [2]
+
+
 @pytest.mark.asyncio
 async def test_async_typed_blob_projection_preserves_source_column():
     db = await lancedb.connect_async("memory:///typed_blob_projection")
@@ -409,6 +727,185 @@ async def test_async_typed_blob_projection_preserves_source_column():
     assert blobs.to_pylist() == [b"alpha"]
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize("shape", ["scan", "hybrid"])
+async def test_async_bytes_mode_rejects_blob_spelling_it_cannot_match(shape):
+    db = await lancedb.connect_async("memory:///")
+    table = await _hybrid_blob_table_async(db)
+    query = table.query()
+    if shape == "hybrid":
+        query = query.nearest_to([1.0, 0.0]).nearest_to_text("hello")
+
+    with pytest.raises(ValueError, match=r"could not match result columns \['pic'\]"):
+        await query.select({"id": "id", "pic": "`image`"}).to_arrow(blob_mode="bytes")
+
+
+def test_bytes_mode_keeps_struct_with_blob_descriptor_field_names():
+    descriptor_like = pa.struct(
+        [
+            pa.field("kind", pa.uint8()),
+            pa.field("position", pa.uint64()),
+            pa.field("size", pa.uint64()),
+            pa.field("blob_id", pa.uint32()),
+            pa.field("blob_uri", pa.utf8()),
+        ]
+    )
+    meta = {"kind": 1, "position": 2, "size": 3, "blob_id": 4, "blob_uri": "u"}
+    table = lancedb.connect("memory:///").create_table(
+        "descriptor_like",
+        schema=pa.schema(
+            [
+                pa.field("id", pa.int64()),
+                lancedb.blob("image"),
+                pa.field("meta", descriptor_like),
+            ]
+        ),
+    )
+    table.add([{"id": 1, "image": b"one", "meta": meta}])
+
+    assert table.search().to_list(blob_mode="bytes") == [
+        {"id": 1, "image": b"one", "meta": meta}
+    ]
+
+
+@pytest.mark.parametrize("source", ["`image`", "IMAGE"])
+def test_bytes_mode_rejects_blob_spelling_it_cannot_match(source):
+    table = _blob_table("blob_spelling", [{"id": 1, "image": b"one"}])
+
+    with pytest.raises(ValueError, match="could not match result columns"):
+        table.search().select({"id": "id", "pic": source}).to_arrow(blob_mode="bytes")
+
+
+def test_to_pandas_does_not_drop_fragment_selection_on_fallback():
+    table = _blob_table("blob_fragments", [{"id": 1, "image": b"one"}])
+    table.add([{"id": 2, "image": b"two"}])
+
+    query = table.search().fragment_ids([0])
+    with pytest.raises(RuntimeError, match="native pandas conversion"):
+        query.to_pandas(blob_mode="bytes")
+    with pytest.raises(RuntimeError, match="native pandas conversion"):
+        table.search().with_row_address(True).to_pandas(blob_mode="descriptions")
+
+
+@pytest.mark.parametrize("blob_mode", ["lazy", "descriptions", "bytes"])
+def test_to_pandas_reports_lance_dataset_failures(tmp_path, blob_mode):
+    table = lancedb.connect(tmp_path).create_table(
+        "dataset_failure",
+        schema=pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")]),
+    )
+    table.add([{"id": 1, "image": b"one"}])
+
+    def reject_credentials():
+        raise PermissionError("storage credentials rejected")
+
+    table.to_lance = reject_credentials
+    with pytest.raises(PermissionError, match="storage credentials rejected"):
+        table.search().to_pandas(blob_mode=blob_mode)
+
+
+def test_to_pandas_keeps_to_arrow_overrides_without_blob_mode():
+    from lancedb.query import LanceEmptyQueryBuilder
+
+    class ToArrowWithoutBlobMode(LanceEmptyQueryBuilder):
+        def to_arrow(self, *, timeout=None):
+            return super().to_arrow(timeout=timeout)
+
+    table = lancedb.connect("memory:///").create_table("no_blob", data=[{"id": 1}])
+    query = ToArrowWithoutBlobMode(table)
+    assert query.to_list(blob_mode="bytes") == [{"id": 1}]
+    assert query.to_pandas(blob_mode="descriptions")["id"].tolist() == [1]
+    with pytest.raises(ValueError, match="blob_mode must be one of"):
+        query.to_list(blob_mode="handles")
+    with pytest.raises(ValueError, match="blob_mode must be one of"):
+        table.search().to_list(blob_mode="handles")
+
+
+def test_take_queries_honor_blob_mode():
+    table = _blob_table(
+        "take_blob_mode", [{"id": 1, "image": b"one"}, {"id": 2, "image": b"two"}]
+    )
+
+    hits = table.take_offsets([1, 0]).to_arrow(blob_mode="bytes")
+    assert dict(zip(hits["id"].to_pylist(), hits["image"].to_pylist())) == {
+        1: b"one",
+        2: b"two",
+    }
+    assert [
+        row["image"] for row in table.take_row_ids([0]).to_list(blob_mode="bytes")
+    ] == [b"one"]
+    assert table.take_offsets([0]).to_pandas(blob_mode="bytes")["image"].tolist() == [
+        b"one"
+    ]
+    description = table.take_row_ids([0]).to_list(blob_mode="descriptions")[0]["image"]
+    assert "_lance_row_id" not in description
+
+
+def test_bytes_mode_follows_swapped_blob_aliases():
+    db = lancedb.connect("memory:///")
+    table = db.create_table(
+        "swapped_aliases",
+        schema=pa.schema(
+            [
+                pa.field("id", pa.int64()),
+                pa.field("caption", pa.utf8()),
+                lancedb.blob("image"),
+                lancedb.blob("thumb"),
+            ]
+        ),
+    )
+    table.add([{"id": 0, "caption": "cap", "image": b"img", "thumb": b"th"}])
+
+    swapped = table.search().select({"id": "id", "image": "thumb", "thumb": "image"})
+    assert swapped.to_list(blob_mode="bytes") == [
+        {"id": 0, "image": b"th", "thumb": b"img"}
+    ]
+    over_text = table.search().select({"id": "id", "image": "caption", "pic": "image"})
+    assert over_text.to_list(blob_mode="bytes") == [
+        {"id": 0, "image": "cap", "pic": b"img"}
+    ]
+
+
+def test_bytes_mode_refused_under_namespace_query_pushdown(tmp_path):
+    db = lancedb.connect_namespace(
+        "dir",
+        {"root": str(tmp_path)},
+        namespace_client_pushdown_operations=["QueryTable"],
+    )
+    table = db.create_table(
+        "pushdown_blob",
+        schema=pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")]),
+    )
+    table.add([{"id": 1, "image": b"one"}])
+
+    with pytest.raises(NotImplementedError, match="stable table snapshot"):
+        table.to_arrow(blob_mode="bytes")
+    with pytest.raises(NotImplementedError, match="stable table snapshot"):
+        table.search().to_list(blob_mode="bytes")
+    with pytest.raises(NotImplementedError, match="stable table snapshot"):
+        table.take_row_ids([0]).to_list(blob_mode="bytes")
+    descriptor = table.to_arrow(blob_mode="descriptions")["image"].to_pylist()[0]
+    assert "_lance_row_id" not in descriptor
+
+
+@pytest.mark.asyncio
+async def test_async_bytes_mode_refused_under_namespace_query_pushdown(tmp_path):
+    db = lancedb.connect_namespace_async(
+        "dir",
+        {"root": str(tmp_path)},
+        namespace_client_pushdown_operations=["QueryTable"],
+    )
+    table = await db.create_table(
+        "pushdown_blob",
+        schema=pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")]),
+    )
+    await table.add([{"id": 1, "image": b"one"}])
+
+    with pytest.raises(NotImplementedError, match="stable table snapshot"):
+        await table.to_arrow(blob_mode="bytes")
+    with pytest.raises(NotImplementedError, match="stable table snapshot"):
+        await table.query().to_list(blob_mode="bytes")
+
+
 def test_fetch_blobs_round_trip():
     table = _blob_table(
         "round_trip",
@@ -419,6 +916,73 @@ def test_fetch_blobs_round_trip():
     assert [blobs[0].as_py(), blobs[1].as_py()] == [b"alpha", b"beta"]
 
 
+def test_add_overwrite_preserves_blob_v2(tmp_path):
+    db = lancedb.connect(tmp_path)
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    table = db.create_table("overwrite_blob", schema=schema)
+    table.add([{"id": 1, "image": b"old"}])
+
+    payload = b"x" * 200_000
+    table.add([{"id": 2, "image": payload}], mode="overwrite")
+
+    assert table.count_rows() == 1
+    assert blob_v2_column_paths(table.schema) == ["image"]
+    assert table.blob_columns() == ["image"]
+    assert table.fetch_blobs("image", [_row_ids_by_id(table)[2]]).to_pylist() == [
+        payload
+    ]
+    assert list(tmp_path.rglob("*.blob"))
+
+
+def test_add_overwrite_with_blob_still_infers_vector_schema():
+    db = lancedb.connect("memory:///overwrite_blob_vector")
+    schema = pa.schema(
+        [
+            pa.field("vector", pa.list_(pa.float32(), 2)),
+            lancedb.blob("image"),
+        ]
+    )
+    table = db.create_table("overwrite_blob_vector", schema=schema)
+
+    table.add([{"vector": [1.0, 2.0, 3.0], "image": b"data"}], mode="overwrite")
+
+    assert table.schema.field("vector").type == pa.list_(pa.float32(), 3)
+    assert table.blob_columns() == ["image"]
+
+
+@pytest.mark.asyncio
+async def test_add_overwrite_preserves_blob_v2_async():
+    db = await lancedb.connect_async("memory:///overwrite_blob_async")
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    table = await db.create_table("overwrite_blob_async", schema=schema)
+
+    await table.add([{"id": 1, "image": b"updated"}], mode="overwrite")
+
+    assert blob_v2_column_paths(await table.schema()) == ["image"]
+    assert await table.blob_columns() == ["image"]
+    hits = await table.query().with_row_id().to_arrow()
+    assert (await table.fetch_blobs("image", hits)).to_pylist() == [b"updated"]
+
+
+def test_add_overwrite_preserves_nested_blob_v2():
+    db = lancedb.connect("memory:///overwrite_nested_blob")
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int64()),
+            pa.field("info", pa.struct([lancedb.blob("image")])),
+        ]
+    )
+    table = db.create_table("overwrite_nested_blob", schema=schema)
+
+    table.add([{"id": 1, "info": {"image": b"nested"}}], mode="overwrite")
+
+    assert blob_v2_column_paths(table.schema) == ["info.image"]
+    assert table.blob_columns() == ["info.image"]
+    assert table.fetch_blobs("info.image", [_row_ids_by_id(table)[1]]).to_pylist() == [
+        b"nested"
+    ]
+
+
 def test_merge_insert_writes_python_bytes():
     table = _blob_table("merge_bytes", [{"id": 1, "image": b"before"}])
     result = (
@@ -434,6 +998,50 @@ def test_merge_insert_writes_python_bytes():
     assert blobs.to_pylist() == [b"updated", b"inserted"]
 
 
+@pytest.mark.parametrize("write", ["merge_insert", "create_table"])
+def test_uri_string_blob_write_reaches_external_base_validation(tmp_path, write):
+    blob_path = tmp_path / "payload.bin"
+    blob_path.write_bytes(b"payload")
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    rows = pa.table(
+        {"id": pa.array([1], pa.int64()), "image": pa.array([blob_path.as_uri()])}
+    )
+    db = lancedb.connect(tmp_path)
+
+    # These APIs do not expose add's outside-base opt-in. The URI should reach
+    # the blob policy check instead of failing at PyArrow's string-to-struct cast.
+    with pytest.raises(ValueError, match="outside registered external bases"):
+        if write == "merge_insert":
+            table = db.create_table("merge_uri", schema=schema)
+            table.merge_insert("id").when_not_matched_insert_all().execute(rows)
+        else:
+            db.create_table("create_uri", data=rows, schema=schema)
+
+
+@pytest.mark.parametrize("write", ["merge_insert", "create_table"])
+@pytest.mark.parametrize(
+    "source_type, value",
+    [
+        (pa.binary(4), b"abcd"),
+        (pa.list_(pa.uint8()), [1, 2]),
+        (pa.int64(), 42),
+    ],
+)
+def test_blob_write_rejects_unsupported_input(write, source_type, value):
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    rows = pa.table(
+        {"id": pa.array([1], pa.int64()), "image": pa.array([value], source_type)}
+    )
+    db = lancedb.connect("memory:///")
+
+    with pytest.raises(ValueError, match="cannot coerce column 'image' with type"):
+        if write == "merge_insert":
+            table = db.create_table("merge_invalid", schema=schema)
+            table.merge_insert("id").when_not_matched_insert_all().execute(rows)
+        else:
+            db.create_table("create_invalid", data=rows, schema=schema)
+
+
 def test_merge_insert_bytes_after_reopen_without_touching_blob_type(tmp_path):
     db = lancedb.connect(tmp_path)
     schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
@@ -660,12 +1268,29 @@ def test_merge_insert_list_blob_dicts():
 
 def test_list_blob_column_queries_as_raw_descriptors():
     table = _list_blob_table("list_query")
+    assert table.blob_columns() == []
     hits = table.search().limit(10).to_arrow()
     element = hits.schema.field("images").type.value_type
     assert pa.types.is_struct(element)
     assert "_lance_row_id" not in element.names
-    with pytest.raises(ValueError, match="expected struct before segment"):
-        table.fetch_blobs("images.image", [0])
+    for column in ("images", "images.image"):
+        with pytest.raises(ValueError, match="blobs inside lists cannot be fetched"):
+            table.fetch_blobs(column, [0])
+        with pytest.raises(ValueError, match="blobs inside lists cannot be fetched"):
+            table.fetch_blob_files(column, [0])
+        with pytest.raises(ValueError, match="blobs inside lists cannot be fetched"):
+            table.fetch_blob_ranges(column, [(0, 0, 1)])
+
+
+def test_bytes_mode_rejects_blobs_inside_lists():
+    table = _list_blob_table("list_blob_bytes")
+
+    with pytest.raises(ValueError, match=r"\['images.image'\].*blobs inside lists"):
+        table.to_arrow(blob_mode="bytes")
+    with pytest.raises(ValueError, match="blobs inside lists"):
+        table.search().select({"pics": "images"}).to_list(blob_mode="bytes")
+    descriptions = table.to_arrow(blob_mode="descriptions")["images"].to_pylist()
+    assert descriptions[0][0]["size"] == len(b"before")
 
 
 def test_row_addressable_paths_exclude_list_children():
@@ -713,6 +1338,38 @@ def test_fetch_blobs_accepts_query_result():
     assert {blobs[i].as_py() for i in range(len(blobs))} == {b"gamma"}
 
 
+def test_fetch_blob_apis_accept_integer_arrow_row_ids():
+    table = _blob_table(
+        "arrow_row_ids",
+        [{"id": 1, "image": b"alpha"}, {"id": 2, "image": b"beta"}],
+    )
+    by_id = _row_ids_by_id(table)
+    ids = [by_id[2], by_id[1]]
+
+    for row_ids in (
+        pa.array(ids, type=pa.uint64()),
+        pa.chunked_array([ids[:1], ids[1:]], type=pa.int64()),
+    ):
+        assert table.fetch_blobs("image", row_ids).to_pylist() == [b"beta", b"alpha"]
+        files = table.fetch_blob_files("image", row_ids)
+        assert [file.read() for file in files] == [b"beta", b"alpha"]
+
+
+def test_fetch_blob_apis_validate_arrow_and_negative_row_ids():
+    table = _blob_table("invalid_arrow_row_ids", [{"id": 1, "image": b"alpha"}])
+    hits = table.search().with_row_id(True).to_arrow()
+
+    for fetch in (table.fetch_blobs, table.fetch_blob_files):
+        with pytest.raises(ValueError, match="non-negative"):
+            fetch("image", [-1])
+        with pytest.raises(ValueError, match="non-negative"):
+            fetch("image", pa.array([-1], type=pa.int64()))
+        with pytest.raises(ValueError, match="without nulls"):
+            fetch("image", pa.array([None], type=pa.int64()))
+        with pytest.raises(ValueError, match="integer type"):
+            fetch("image", hits["image"])
+
+
 def test_fetch_blobs_after_compact_with_stable_row_ids(tmp_path):
     db = lancedb.connect(
         tmp_path, storage_options={"new_table_enable_stable_row_ids": "true"}
@@ -815,13 +1472,14 @@ def test_add_list_of_dicts_to_blob_list_column(large_list):
     table = db.create_table(f"blob_{large_list}_list_add", data=seed)
 
     table.add([{"id": 1, "items": [None]}])
+    table.add([{"id": 2, "items": [b"a", None, b""]}])
     table.add(
-        [{"id": 2, "items": [b"a", None]}],
+        [{"id": 3, "items": [b"b", None]}],
         on_bad_vectors="fill",
     )
 
     ids = table.search().select(["id"]).to_arrow()["id"].to_pylist()
-    assert sorted(ids) == [0, 1, 2]
+    assert sorted(ids) == [0, 1, 2, 3]
     assert pa.types.is_large_list(table.schema.field("items").type) is large_list
 
 
@@ -844,6 +1502,15 @@ def test_fetch_blob_ranges_aligns_repeated_ranges_and_nulls():
     assert ranges.to_pylist() == [b"cde", None, b"ab", b"cde", b""]
 
 
+def test_fetch_blob_ranges_accepts_three_item_sequences():
+    table = _blob_table("range_sequences", [{"id": 1, "image": b"abcdef"}])
+    row_id = _row_ids_by_id(table)[1]
+
+    ranges = table.fetch_blob_ranges("image", [[row_id, 1, 2], (row_id, 4, 2)])
+
+    assert ranges.to_pylist() == [b"bc", b"ef"]
+
+
 def test_fetch_blob_ranges_validates_requests():
     table = _blob_table("range_validation", [{"id": 1, "image": b"abc"}])
     row_id = _row_ids_by_id(table)[1]
@@ -858,6 +1525,30 @@ def test_fetch_blob_ranges_validates_requests():
         table.fetch_blob_ranges("image", [(2**64 - 1, 0, 1)])
     _assert_missing_blob_row_ids(exc_info)
 
+    for index, name in enumerate(("row id", "offset", "length")):
+        request = [row_id, 0, 1]
+        request[index] = -1
+        with pytest.raises(
+            ValueError, match=f"blob range {name} must be a non-negative"
+        ):
+            table.fetch_blob_ranges("image", [request])
+
+    with pytest.raises(ValueError, match="three values"):
+        table.fetch_blob_ranges("image", [[row_id, 0]])
+
+
+def test_fetch_blob_apis_reject_row_past_fragment_end():
+    table = _blob_table(
+        "past_fragment_end",
+        [
+            {"id": 1, "image": b"a"},
+            {"id": 2, "image": b"b"},
+            {"id": 3, "image": b"c"},
+        ],
+    )
+    _assert_fetch_apis_reject_missing_row_ids(table, [3])
+    _assert_fetch_apis_reject_missing_row_ids(table, [3, 0])
+
 
 def test_fetch_blob_apis_reject_missing_fragment_row_addr():
     table = _blob_table("missing_frag", [{"id": 1, "image": b"x"}])
@@ -875,6 +1566,19 @@ def test_fetch_blob_apis_reject_deleted_row_ids():
     _assert_fetch_apis_reject_missing_row_ids(table, [by_id[2], by_id[1]])
 
 
+def test_fetch_blob_apis_reject_deleted_stable_row_ids(tmp_path):
+    db = lancedb.connect(
+        tmp_path, storage_options={"new_table_enable_stable_row_ids": "true"}
+    )
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    table = db.create_table("stable_deleted", schema=schema)
+    table.add([{"id": 1, "image": b"one"}, {"id": 2, "image": b"two"}])
+    by_id = _row_ids_by_id(table)
+    table.delete("id = 2")
+
+    _assert_fetch_apis_reject_missing_row_ids(table, [by_id[2], by_id[1]])
+
+
 def test_fetch_blob_ranges_empty_requests_returns_empty_array():
     table = _blob_table("range_empty", [{"id": 1, "image": b"x"}])
     assert table.fetch_blob_ranges("image", []).to_pylist() == []
@@ -913,6 +1617,11 @@ def test_fetch_blobs_nested_path():
     blobs = table.fetch_blobs("info.blob", [by_id[1], by_id[2]])
     assert [blobs[0].as_py(), blobs[1].as_py()] == [b"nested-alpha", b"nested-beta"]
 
+    # The source path's parent name now holds a string column in the hits.
+    hits = table.search().select({"id": "id", "info": "info.name", "b": "info.blob"})
+    blobs = table.fetch_blobs("info.blob", hits.to_arrow())
+    assert blobs.to_pylist() == [b"nested-alpha", b"nested-beta"]
+
 
 def test_fetch_blob_files_lazy_read():
     payload = b"lazy-read" * 100
@@ -923,7 +1632,7 @@ def test_fetch_blob_files_lazy_read():
     assert handles[0].read() == payload
     handles[0].close()
     assert handles[0].closed
-    with pytest.raises(RuntimeError, match="already closed"):
+    with pytest.raises(ValueError, match="read of closed file"):
         handles[0].read()
 
 
@@ -972,8 +1681,8 @@ def test_blob_file_finalizer_and_close_on_runtime_worker():
         explicit_handle.close()  # Close remains idempotent after the worker call.
         try:
             explicit_handle.read()
-        except RuntimeError as error:
-            assert "already closed" in str(error)
+        except ValueError as error:
+            assert "read of closed file" in str(error)
         else:
             raise AssertionError("a closed blob file was readable")
         print("worker close completed")
@@ -1031,6 +1740,9 @@ async def test_async_fetch_blob_files_lazy_read():
     handles = await table.fetch_blob_files("image", hits)
     assert len(handles) == 1
     assert await handles[0].aread() == payload
+    handles[0].close()
+    with pytest.raises(ValueError, match="read of closed file"):
+        await handles[0].aread()
 
 
 def test_fetch_blobs_from_query_result_without_row_id_raises():
@@ -1087,6 +1799,122 @@ def test_blob_v2_hybrid_fetch_blobs():
     assert {blobs[i].as_py() for i in range(len(blobs))} == {b"alpha", b"beta"}
 
 
+def _blob_query_shape(table, shape):
+    if shape == "scan":
+        return table.search().where("id < 3")
+    if shape == "vector":
+        return table.search([1.0, 0.0])
+    if shape == "fts":
+        return table.search("hello", query_type="fts")
+    return table.search(query_type="hybrid").vector([1.0, 0.0]).text("hello")
+
+
+@pytest.mark.parametrize("with_row_id", [None, True, False])
+@pytest.mark.parametrize("blob_mode", ["bytes", "descriptions"])
+@pytest.mark.parametrize("shape", ["scan", "vector", "fts", "hybrid"])
+def test_blob_mode_for_every_query_shape(shape, blob_mode, with_row_id):
+    table = _hybrid_blob_table(lancedb.connect("memory:///"))
+    query = _blob_query_shape(table, shape).select(["id", "image"]).limit(2)
+    if with_row_id is not None:
+        query = query.with_row_id(with_row_id)
+
+    hits = query.to_arrow(blob_mode=blob_mode)
+
+    assert ("_rowid" in hits.column_names) == (with_row_id is True)
+    images = {row["id"]: row["image"] for row in hits.to_pylist()}
+    if blob_mode == "bytes":
+        assert images == {1: b"alpha", 2: b"beta"}
+    else:
+        assert sorted(images) == [1, 2]
+        assert all("_lance_row_id" not in image for image in images.values())
+    df = query.to_pandas(blob_mode=blob_mode)
+    assert dict(zip(df["id"], df["image"])) == images
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("shape", ["scan", "vector", "fts", "hybrid"])
+async def test_async_blob_bytes_for_every_query_shape(shape):
+    db = await lancedb.connect_async("memory:///")
+    table = await _hybrid_blob_table_async(db)
+    query = table.query()
+    if shape in ("vector", "hybrid"):
+        query = query.nearest_to([1.0, 0.0])
+    if shape in ("fts", "hybrid"):
+        query = query.nearest_to_text("hello")
+    if shape == "scan":
+        query = query.where("id < 3")
+    query = query.select(["id", "image"]).limit(2)
+
+    hits = await query.to_arrow(blob_mode="bytes")
+
+    assert {row["id"]: row["image"] for row in hits.to_pylist()} == {
+        1: b"alpha",
+        2: b"beta",
+    }
+    df = await query.to_pandas(blob_mode="bytes")
+    assert dict(zip(df["id"], df["image"])) == {1: b"alpha", 2: b"beta"}
+
+
+class _KeepAllScoresReranker(Reranker):
+    """Rebuilds the merged table like model rerankers with return_score="all"."""
+
+    def __init__(self):
+        super().__init__(return_score="all")
+
+    def rerank_hybrid(self, query, vector_results, fts_results):
+        combined = self._merge_and_keep_scores(vector_results, fts_results)
+        scores = pa.array([1.0] * len(combined), type=pa.float32())
+        return combined.append_column("_relevance_score", scores)
+
+
+def test_bytes_mode_rejects_legacy_blob_after_reranker_drops_metadata(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table(
+        "legacy_rerank",
+        pa.table(
+            {
+                "id": pa.array([1, 2], pa.int64()),
+                "text": ["hello alpha", "hello beta"],
+                "vector": pa.array(
+                    [[1.0, 0.0], [0.9, 0.1]], type=pa.list_(pa.float32(), 2)
+                ),
+                "blob": pa.array([b"one", b"two"], pa.large_binary()),
+            },
+            schema=_HYBRID_BLOB_SCHEMA.remove(3).append(
+                pa.field(
+                    "blob", pa.large_binary(), metadata={"lance-encoding:blob": "true"}
+                )
+            ),
+        ),
+        storage_options={"new_table_data_storage_version": "2.1"},
+    )
+    table.create_index("text", config=FTS(with_position=False))
+    query = (
+        table.search(query_type="hybrid")
+        .vector([1.0, 0.0])
+        .text("hello")
+        .rerank(_KeepAllScoresReranker())
+    )
+
+    with pytest.raises(ValueError, match=r"\['blob'\].*legacy blob columns"):
+        query.to_arrow(blob_mode="bytes")
+
+
+@pytest.mark.parametrize("source", ["`image`", "IMAGE"])
+def test_bytes_mode_rejects_blob_spelling_after_reranker_drops_metadata(source):
+    table = _hybrid_blob_table(lancedb.connect("memory:///"))
+    query = (
+        table.search(query_type="hybrid")
+        .vector([1.0, 0.0])
+        .text("hello")
+        .select({"id": "id", "pic": source})
+        .rerank(_KeepAllScoresReranker())
+    )
+
+    with pytest.raises(ValueError, match=r"could not match result columns \['pic'\]"):
+        query.to_arrow(blob_mode="bytes")
+
+
 @pytest.mark.asyncio
 async def test_blob_v2_hybrid_fetch_blobs_async():
     db = await lancedb.connect_async("memory:///hybrid_blob_fetch_async")
@@ -1166,6 +1994,49 @@ def test_blob_file_seek_read_and_read_range():
         handle.seek(0, 99)
 
 
+def test_blob_file_matches_file_error_and_range_contract():
+    payload = b"abcdef"
+    table = _blob_table("file_contract", [{"id": 1, "image": payload}])
+    row_id = _row_ids_by_id(table)[1]
+    handle = table.fetch_blob_files("image", [row_id])[0]
+
+    handle.seek(2)
+    for offset, whence in [(-1, io.SEEK_SET), (-3, io.SEEK_CUR), (-7, io.SEEK_END)]:
+        with pytest.raises(ValueError, match="negative seek value -1"):
+            handle.seek(offset, whence)
+        assert handle.tell() == 2
+
+    assert handle.read(None) == payload[2:]
+    handle.seek(2)
+    assert handle.read1(2) == payload[2:4]
+    assert handle.read1() == payload[4:]
+    handle.seek(2)
+    assert handle.read_ranges([(0, 2), (4, 2), (2, 0)]) == [b"ab", b"ef", b""]
+    assert handle.tell() == 2
+
+    with pytest.raises(ValueError):
+        handle.read_range(5, 2)
+    with pytest.raises(ValueError):
+        handle.read_ranges([(0, 1), (5, 2)])
+    assert handle.tell() == 2
+
+    handle.close()
+    with pytest.raises(ValueError, match="read of closed file"):
+        handle.read(1)
+    with pytest.raises(ValueError, match="read of closed file"):
+        handle.read(None)
+    with pytest.raises(ValueError, match="read of closed file"):
+        handle.read1(1)
+    with pytest.raises(ValueError, match="read of closed file"):
+        handle.readall()
+    with pytest.raises(ValueError, match="readinto of closed file"):
+        handle.readinto(bytearray(1))
+    with pytest.raises(ValueError):
+        handle.read_range(0, 1)
+    with pytest.raises(ValueError):
+        handle.read_ranges([])
+
+
 def test_fetch_blob_files_from_query_partial_read():
     payload = _identifiable_payload(65536)
     table = _blob_table("query_partial", [{"id": 1, "image": payload}])
@@ -1432,3 +2303,129 @@ def test_add_external_uri_string_round_trips_with_flag(tmp_path):
     hits = table.search().to_arrow()
     blobs = table.fetch_blobs("image", hits)
     assert blobs[0].as_py() == payload
+
+
+@pytest.mark.parametrize("as_pandas", [False, True])
+def test_add_bytes_and_uri_in_one_batch_preserves_external_uri(tmp_path, as_pandas):
+    payload = b"external-payload"
+    blob_path = tmp_path / "payload.bin"
+    blob_path.write_bytes(payload)
+    rows = [
+        {"id": 1, "image": b"inline"},
+        {"id": 2, "image": blob_path.as_uri()},
+    ]
+    db = lancedb.connect(tmp_path / "db")
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    table = db.create_table("mixed", schema=schema)
+
+    table.add(
+        pd.DataFrame(rows) if as_pandas else rows,
+        allow_external_blob_outside_bases=True,
+    )
+
+    by_id = _row_ids_by_id(table)
+    assert table.fetch_blobs("image", [by_id[1], by_id[2]]).to_pylist() == [
+        b"inline",
+        payload,
+    ]
+
+
+@pytest.mark.parametrize("as_pandas", [False, True])
+def test_add_mixed_python_blob_values_preserves_external_uris(tmp_path, as_pandas):
+    payload = b"external-payload"
+    blob_path = tmp_path / "payload.bin"
+    blob_path.write_bytes(payload)
+    uri = blob_path.as_uri()
+    rows = [
+        {"id": 1, "image": b"inline"},
+        {"id": 2, "image": uri},
+        {"id": 3, "image": {"data": b"from-dict"}},
+        {"id": 4, "image": Blob.from_bytes(b"from-blob")},
+        {"id": 5, "image": Blob.from_uri(uri)},
+        {"id": 6, "image": {"uri": uri}},
+        {"id": 7, "image": None},
+    ]
+    db = lancedb.connect(tmp_path / "db")
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    table = db.create_table("mixed", schema=schema)
+
+    table.add(
+        pd.DataFrame(rows) if as_pandas else rows,
+        allow_external_blob_outside_bases=True,
+    )
+
+    by_id = _row_ids_by_id(table)
+    assert table.fetch_blobs("image", [by_id[i] for i in range(1, 8)]).to_pylist() == [
+        b"inline",
+        payload,
+        b"from-dict",
+        b"from-blob",
+        payload,
+        payload,
+        None,
+    ]
+
+
+@pytest.mark.parametrize("as_pandas", [False, True])
+def test_merge_insert_mixed_python_blobs_validates_external_uri(tmp_path, as_pandas):
+    blob_path = tmp_path / "payload.bin"
+    blob_path.write_bytes(b"external-payload")
+    table = _blob_table("merge_mixed_uri", [{"id": 1, "image": b"before"}])
+    rows = [
+        {"id": 1, "image": b"updated"},
+        {"id": 2, "image": blob_path.as_uri()},
+    ]
+
+    with pytest.raises(ValueError, match="outside registered external bases"):
+        (
+            table.merge_insert("id")
+            .when_matched_update_all()
+            .when_not_matched_insert_all()
+            .execute(pd.DataFrame(rows) if as_pandas else rows)
+        )
+    assert table.count_rows() == 1
+
+
+@pytest.mark.parametrize("as_pandas", [False, True])
+def test_merge_insert_python_blob_dicts_and_objects(as_pandas):
+    table = _blob_table("merge_python_blobs", [{"id": 1, "image": b"before"}])
+    rows = [
+        {"id": 1, "image": b"updated"},
+        {"id": 2, "image": {"data": b"from-dict"}},
+        {"id": 3, "image": Blob.from_bytes(b"from-blob")},
+    ]
+
+    result = (
+        table.merge_insert("id")
+        .when_matched_update_all()
+        .when_not_matched_insert_all()
+        .execute(pd.DataFrame(rows) if as_pandas else rows)
+    )
+
+    assert result.num_updated_rows == 1
+    assert result.num_inserted_rows == 2
+    by_id = _row_ids_by_id(table)
+    assert table.fetch_blobs("image", [by_id[i] for i in range(1, 4)]).to_pylist() == [
+        b"updated",
+        b"from-dict",
+        b"from-blob",
+    ]
+
+
+def test_create_table_with_python_blob_dict_and_object():
+    db = lancedb.connect("memory:///")
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    table = db.create_table(
+        "create_python_blobs",
+        data=[
+            {"id": 1, "image": {"data": b"from-dict"}},
+            {"id": 2, "image": Blob.from_bytes(b"from-blob")},
+        ],
+        schema=schema,
+    )
+
+    by_id = _row_ids_by_id(table)
+    assert table.fetch_blobs("image", [by_id[1], by_id[2]]).to_pylist() == [
+        b"from-dict",
+        b"from-blob",
+    ]
diff --git a/python/python/tests/test_catalog.py b/python/python/tests/test_catalog.py
index ca5d68fdf..b2036b7a6 100644
--- a/python/python/tests/test_catalog.py
+++ b/python/python/tests/test_catalog.py
@@ -55,7 +55,7 @@ def test_catalog_sync_scope_and_serialization(catalog_server):
             (200, {}),
             (200, {"tables": []}),
             (200, {"tables": []}),
-            (200, {"namespaces": ["team/search"], "page_token": "next"}),
+            (200, {"namespaces": ["team/search"]}),
             (204, None),
         ]
     )
@@ -85,13 +85,12 @@ def test_catalog_sync_scope_and_serialization(catalog_server):
         with pytest.raises(ValueError, match="sql_host_override must use"):
             connection.execute_query_async("SELECT 1")
     assert restored.table_names() == []
-    page = catalog.list_databases(limit=1, page_token="a/b")
-    assert page == lancedb.ListDatabasesResponse(["team/search"], "next")
+    assert list(catalog.list_databases()) == ["team/search"]
     catalog.drop_database("team/search", ignore_missing=True)
     assert requests[0][0] == "/v1/namespace/team%2Fsearch/create"
     assert requests[0][2] == {"mode": "ExistOk"}
     assert requests[1][0] == "/v1/namespace/team%2Fsearch/describe"
-    assert requests[4][0] == "/v1/namespace/$/list?limit=1&page_token=a%2Fb"
+    assert requests[4][0] == "/v1/namespace/$/list"
     assert requests[5][2] == {"mode": "Skip", "behavior": "Restrict"}
     for i, (_, headers, _) in enumerate(requests):
         headers = {key.lower(): value for key, value in headers.items()}
@@ -138,3 +137,171 @@ async def test_catalog_async_and_errors(catalog_server):
 def test_catalog_requires_remote_endpoint(endpoint):
     with pytest.raises(ValueError, match="endpoint"):
         lancedb.connect_catalog(endpoint)
+
+
+@pytest.mark.parametrize("asynchronous", [False, True])
+@pytest.mark.asyncio
+@pytest.mark.parametrize("page_token,page_limit", [(None, None), ("start/token", 2)])
+async def test_database_names_iteration(
+    catalog_server, asynchronous, page_token, page_limit
+):
+    endpoint, requests, responses = catalog_server
+    responses.extend(
+        [
+            (200, {"namespaces": ["first", "second"], "page_token": "a/b"}),
+            (200, {"namespaces": [], "page_token": "next"}),
+            (200, {"namespaces": ["last", "final"], "page_token": ""}),
+        ]
+    )
+    catalog = (
+        await lancedb.connect_catalog_async(endpoint)
+        if asynchronous
+        else lancedb.connect_catalog(endpoint)
+    )
+    names = catalog.list_databases(page_token=page_token, page_limit=page_limit)
+    assert requests == []
+    assert names.num_page_results() == 0
+    assert names.page_token() == page_token
+    for cached, expected in [(1, "first"), (0, "second")]:
+        assert (await names.__anext__() if asynchronous else next(names)) == expected
+        assert names.num_page_results() == cached
+        assert names.page_token() == "a/b"
+        assert len(requests) == 1
+    assert (await names.__anext__() if asynchronous else next(names)) == "last"
+    assert names.num_page_results() == 1
+    assert names.page_token() is None
+    assert (await names.__anext__() if asynchronous else next(names)) == "final"
+    assert names.num_page_results() == 0
+    assert names.page_token() is None
+    for _ in range(2):
+        with pytest.raises(StopAsyncIteration if asynchronous else StopIteration):
+            if asynchronous:
+                await names.__anext__()
+            else:
+                next(names)
+    from urllib.parse import parse_qs, urlsplit
+
+    for request, token in zip(requests, [page_token, "a/b", "next"]):
+        url = urlsplit(request[0])
+        assert url.path == "/v1/namespace/$/list"
+        expected = {}
+        if token is not None:
+            expected["page_token"] = [token]
+        if page_limit is not None:
+            expected["limit"] = [str(page_limit)]
+        assert parse_qs(url.query) == expected
+
+
+@pytest.mark.parametrize("asynchronous", [False, True])
+@pytest.mark.parametrize("fail", [False, True])
+@pytest.mark.asyncio
+async def test_database_names_empty_or_error(catalog_server, asynchronous, fail):
+    endpoint, requests, responses = catalog_server
+    responses.append(
+        (401, {"error": "unauthorized"}) if fail else (200, {"namespaces": []})
+    )
+    catalog = (
+        await lancedb.connect_catalog_async(endpoint)
+        if asynchronous
+        else lancedb.connect_catalog(endpoint)
+    )
+    names = catalog.list_databases()
+    if fail:
+        with pytest.raises(HttpError):
+            if asynchronous:
+                await names.__anext__()
+            else:
+                next(names)
+    assert ([name async for name in names] if asynchronous else list(names)) == []
+    assert len(requests) == 1
+
+
+@pytest.mark.parametrize("asynchronous", [False, True])
+@pytest.mark.asyncio
+async def test_database_names_resume(catalog_server, asynchronous):
+    endpoint, requests, responses = catalog_server
+    responses.extend(
+        [
+            (200, {"namespaces": ["first"], "page_token": "resume/token"}),
+            (200, {"namespaces": ["second"]}),
+        ]
+    )
+    catalog = (
+        await lancedb.connect_catalog_async(endpoint)
+        if asynchronous
+        else lancedb.connect_catalog(endpoint)
+    )
+    names = catalog.list_databases(page_limit=1)
+    assert (await names.__anext__() if asynchronous else next(names)) == "first"
+    assert names.num_page_results() == 0
+    resumed = catalog.list_databases(page_token=names.page_token(), page_limit=1)
+    assert resumed.page_token() == "resume/token"
+    assert len(requests) == 1
+    assert ([name async for name in resumed] if asynchronous else list(resumed)) == [
+        "second"
+    ]
+    assert resumed.page_token() is None
+    assert resumed.num_page_results() == 0
+    assert requests[1][0] == "/v1/namespace/$/list?limit=1&page_token=resume%2Ftoken"
+
+
+@pytest.mark.parametrize("asynchronous", [False, True])
+@pytest.mark.parametrize("page_limit", [0, 2**32 - 1])
+@pytest.mark.asyncio
+async def test_database_names_invalid_limit(catalog_server, asynchronous, page_limit):
+    endpoint, requests, _ = catalog_server
+    catalog = (
+        await lancedb.connect_catalog_async(endpoint)
+        if asynchronous
+        else lancedb.connect_catalog(endpoint)
+    )
+    names = catalog.list_databases(page_limit=page_limit)
+    with pytest.raises(ValueError, match="limit"):
+        if asynchronous:
+            await names.__anext__()
+        else:
+            next(names)
+    assert names.num_page_results() == 0
+    assert requests == []
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("asynchronous", [False, True])
+async def test_catalog_rejects_invalid_database_names_before_io(
+    catalog_server, asynchronous
+):
+    endpoint, requests, _ = catalog_server
+    catalog = (
+        await lancedb.connect_catalog_async(endpoint)
+        if asynchronous
+        else lancedb.connect_catalog(endpoint)
+    )
+    for name in [
+        "",
+        ".",
+        "..",
+        "/db",
+        "db/",
+        "a//b",
+        "a/./b",
+        "a/../b",
+        "a b",
+        "a:b",
+        "a$b",
+        "a%b",
+        "a?b",
+        "a#b",
+        "a\\b",
+        "café",
+        "a\nb",
+    ]:
+        for method in [
+            catalog.create_database,
+            catalog.connect_database,
+            catalog.drop_database,
+        ]:
+            with pytest.raises(ValueError, match="Invalid database name"):
+                result = method(name)
+                if asynchronous:
+                    await result
+    assert requests == []
diff --git a/python/python/tests/test_db.py b/python/python/tests/test_db.py
index aeb9feeb8..7d37910a3 100644
--- a/python/python/tests/test_db.py
+++ b/python/python/tests/test_db.py
@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 
+import contextlib
 import inspect
 import re
 import sys
@@ -244,6 +245,40 @@ def test_db_contains_and_len_include_all_table_name_pages(tmp_db: lancedb.DBConn
     assert "does_not_exist" not in tmp_db
 
 
+@pytest.mark.asyncio
+async def test_table_listing_defaults_and_zero_limit(tmp_path):
+    names = [f"t{i:03}" for i in range(130)]
+    for name in names:
+        (tmp_path / f"{name}.lance").mkdir()
+
+    db = lancedb.connect(tmp_path)
+    assert db.table_names() == names
+    assert db.table_names(limit=None) == names
+    assert db.table_names(limit=0) == []
+    first = db.list_tables()
+    assert first.tables == names[:100]
+    assert first.page_token
+    second = db.list_tables(page_token=first.page_token)
+    assert second.tables == names[100:]
+    assert second.page_token is None
+    assert db.list_tables(limit=200).tables == names
+    empty = db.list_tables(limit=0)
+    assert empty.tables == []
+    assert empty.page_token is None
+
+    adb = await lancedb.connect_async(tmp_path)
+    assert await adb.table_names() == names
+    assert await adb.table_names(limit=0) == []
+    first = await adb.list_tables()
+    assert first.tables == names[:100]
+    second = await adb.list_tables(page_token=first.page_token)
+    assert second.tables == names[100:]
+    assert second.page_token is None
+    empty = await adb.list_tables(limit=0)
+    assert empty.tables == []
+    assert empty.page_token is None
+
+
 def test_db_contains_stops_after_matching_table_page(
     tmp_db: lancedb.DBConnection, monkeypatch
 ):
@@ -350,6 +385,40 @@ async def test_create_table_from_iterator_async(mem_db_async: lancedb.AsyncConne
     assert await table.count_rows() == 10
 
 
+def test_create_exist_ok_mode(tmp_db: lancedb.DBConnection):
+    db = tmp_db
+    data = [{"vector": [1.1, 1.2]}, {"vector": [0.2, 1.8]}]
+    table = db.create_table("t1", data, mode="exist_ok")
+    assert table.count_rows() == 2
+    original = table.to_arrow()
+
+    reopened = db.create_table("t1", [{"vector": [9.0, 8.0]}], mode="exist_ok")
+    assert reopened.to_arrow().equals(original)
+
+
+def test_create_empty_exist_ok_mode(tmp_db: lancedb.DBConnection):
+    schema = pa.schema([pa.field("id", pa.int64())])
+    table = tmp_db.create_table("empty", schema=schema, mode="exist_ok")
+    assert table.schema == schema
+    assert table.count_rows() == 0
+    table.add([{"id": 7}])
+
+    reopened = tmp_db.create_table("empty", schema=schema, mode="exist_ok")
+    assert reopened.to_arrow().to_pylist() == [{"id": 7}]
+
+    bad_schema = pa.schema([pa.field("other", pa.int64())])
+    with pytest.raises(ValueError):
+        tmp_db.create_table("empty", schema=bad_schema, mode="exist_ok")
+
+
+def test_create_table_invalid_mode(tmp_db: lancedb.DBConnection):
+    db = tmp_db
+    data = [{"vector": [1.1, 1.2]}, {"vector": [0.2, 1.8]}]
+    with pytest.raises(ValueError, match="Invalid mode bogus"):
+        db.create_table("tb", data, mode="bogus")
+    assert "tb" not in db
+
+
 def test_create_exist_ok(tmp_db: lancedb.DBConnection):
     from conftest import pandas_string_type
 
@@ -405,6 +474,49 @@ async def test_connect(tmp_path):
     assert str(db) == f"ListingDatabase(uri={tmp_path}, read_consistency_interval=5s)"
 
 
+@pytest.fixture(params=["memory", "local", "namespace", "from_inner"])
+def sync_db(request, tmp_path):
+    if request.param == "memory":
+        return lancedb.connect("memory://")
+    if request.param == "namespace":
+        return lancedb.connect_namespace("dir", {"root": str(tmp_path)})
+    db = lancedb.connect(tmp_path)
+    if request.param == "from_inner":
+        return lancedb.db.LanceDBConnection.from_inner(db._inner, None)
+    return db
+
+
+def test_sync_close(sync_db):
+    assert sync_db.is_open()
+    assert sync_db.close() is None
+    assert not sync_db.is_open()
+    assert sync_db.close() is None
+
+    with pytest.warns(DeprecationWarning, match="table_names"):
+        with pytest.raises(RuntimeError, match="Connection is closed"):
+            sync_db.table_names()
+    with pytest.raises(RuntimeError, match="Connection is closed"):
+        sync_db.list_tables()
+    with pytest.raises(RuntimeError, match="Connection is closed"):
+        sync_db.open_table("test")
+
+
+@pytest.mark.parametrize("raise_error", [False, True])
+def test_sync_context_manager(sync_db, raise_error):
+    with contextlib.ExitStack() as stack:
+        if raise_error:
+            stack.enter_context(pytest.raises(ValueError, match="test error"))
+        with sync_db as db:
+            assert db is sync_db
+            assert db.is_open()
+            if raise_error:
+                raise ValueError("test error")
+
+    assert not sync_db.is_open()
+    with pytest.raises(RuntimeError, match="Connection is closed"):
+        sync_db.list_tables()
+
+
 @pytest.mark.asyncio
 async def test_close(mem_db_async: lancedb.AsyncConnection):
     assert mem_db_async.is_open()
@@ -495,6 +607,27 @@ async def test_create_exist_ok_async(tmp_db_async: lancedb.AsyncConnection):
     #     await db.create_table("test", schema=bad_schema, exist_ok=True)
 
 
+@pytest.mark.parametrize("enable_v2_manifest_paths", [False, True])
+@pytest.mark.parametrize("empty", [False, True])
+def test_create_table_deprecated_v2_manifest_paths(
+    tmp_path, enable_v2_manifest_paths, empty
+):
+    db = lancedb.connect(tmp_path)
+    data = None if empty else [{"id": 1}]
+
+    with pytest.warns(DeprecationWarning, match="enable_v2_manifest_paths"):
+        table = db.create_table(
+            "test",
+            data=data,
+            schema=pa.schema([("id", pa.int64())]),
+            enable_v2_manifest_paths=enable_v2_manifest_paths,
+        )
+
+    assert table.uses_v2_manifest_paths() == enable_v2_manifest_paths
+    assert table.to_arrow().to_pylist() == ([] if empty else data)
+    assert db.open_table("test").uses_v2_manifest_paths() == enable_v2_manifest_paths
+
+
 @pytest.mark.asyncio
 async def test_create_table_v2_manifest_paths_async(tmp_path):
     db_with_v2_paths = await lancedb.connect_async(
diff --git a/python/python/tests/test_e2e_remote_db.py b/python/python/tests/test_e2e_remote_db.py
index d4364bc43..74d5cd7de 100644
--- a/python/python/tests/test_e2e_remote_db.py
+++ b/python/python/tests/test_e2e_remote_db.py
@@ -2,13 +2,78 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 
+import os
+from datetime import timedelta
+from uuid import uuid4
+
+import lancedb
 import numpy as np
+import pyarrow as pa
 import pytest
 from lancedb import LanceDBConnection
+from lancedb.index import FTS
 
 # TODO: setup integ test mark and script
 
 
+@pytest.mark.skipif(
+    not os.environ.get("LANCEDB_URI"), reason="Set LANCEDB_URI to a JSON FTS server"
+)
+def test_json_fts_against_server():
+    """Requires a writable database and capacity to run the distributed indexer.
+
+    Configure LANCEDB_URI, LANCEDB_API_KEY, and optionally LANCEDB_REGION and
+    LANCEDB_HOST_OVERRIDE before running this test.
+    """
+    uri = os.environ["LANCEDB_URI"]
+    assert uri.startswith("db://"), "LANCEDB_URI must identify a remote database"
+    conn = lancedb.connect(
+        uri,
+        api_key=os.environ.get("LANCEDB_API_KEY"),
+        region=os.environ.get("LANCEDB_REGION", "us-east-1"),
+        host_override=os.environ.get("LANCEDB_HOST_OVERRIDE"),
+    )
+    name = f"json_fts_{uuid4().hex}"
+    table = conn.create_table(
+        name,
+        pa.table(
+            {
+                "id": [0, 1, 2],
+                "doc": pa.array(
+                    [
+                        '{"meta":{"tag":"nature"},"author":"alice"}',
+                        '{"meta":{"tag":"city"},"author":"nature"}',
+                        '{"meta":{"tag":"nature"},"author":"bob"}',
+                    ],
+                    type=pa.json_(),
+                ),
+                "raw": pa.array([b"nature"] * 3, type=pa.large_binary()),
+            }
+        ),
+    )
+    try:
+        table.create_index("doc", config=FTS(), name="doc_fts")
+        table.wait_for_index(["doc_fts"], timeout=timedelta(minutes=5))
+        stats = table.index_stats("doc_fts")
+        assert stats.num_indexed_rows == 3
+        assert stats.num_unindexed_rows == 0
+        assert stats.index_type == "FTS"
+        for query, expected in [
+            ("meta.tag,str,nature", [0, 2]),
+            ("meta.tag,str,alice", []),
+            ("meta.tag,str,tag", []),
+        ]:
+            search = table.search(query, query_type="fts", fts_columns="doc")
+            assert sorted(row["id"] for row in search.to_list()) == expected
+            plan = search.explain_plan(True)
+            assert "MatchQuery" in plan, plan
+            assert "FlatMatchQuery" not in plan, plan
+        with pytest.raises(ValueError, match="FTS.*lance.json.*raw binary"):
+            table.create_index("raw", config=FTS())
+    finally:
+        conn.drop_table(name)
+
+
 @pytest.mark.skip(reason="Need to set up a local server")
 def test_against_local_server():
     conn = LanceDBConnection("lancedb+http://localhost:10024")
diff --git a/python/python/tests/test_first_class_function_slice1.py b/python/python/tests/test_first_class_function_slice1.py
index 1abd42e56..b08dadf36 100644
--- a/python/python/tests/test_first_class_function_slice1.py
+++ b/python/python/tests/test_first_class_function_slice1.py
@@ -162,6 +162,29 @@ def test_function_version_binds_named_columns_as_one_immutable_application():
     ] == [("text", "column", "documents.body")]
 
 
+def test_function_version_names_its_namespace_as_a_separate_field():
+    """A Function is named by its parts, as a Secret is; a root one states no
+    namespace."""
+    value = job_result("remote_function_job.json")
+    root = FunctionVersion.from_json(json.dumps(value))
+    assert root.namespace_path == ()
+    assert "namespace_path" not in json.loads(root.to_canonical_json())
+
+    nested = FunctionVersion(**{**value, "namespace_path": ["analytics", "features"]})
+    assert nested.name == "embed"
+    assert nested.namespace_path == ("analytics", "features")
+    assert json.loads(nested.to_canonical_json())["namespace_path"] == [
+        "analytics",
+        "features",
+    ]
+    application = nested(text=col("documents.body"))
+    assert application.function.name == "embed"
+    assert application.function.namespace_path == ("analytics", "features")
+    assert json.loads(application.to_canonical_json())["function"][
+        "namespace_path"
+    ] == ["analytics", "features"]
+
+
 def test_function_version_binding_validates_names_and_direct_columns():
     version = FunctionVersion.from_json(
         json.dumps(job_result("remote_function_job.json"))
diff --git a/python/python/tests/test_first_class_function_slice2.py b/python/python/tests/test_first_class_function_slice2.py
index 1cd8d4cf5..975310ed4 100644
--- a/python/python/tests/test_first_class_function_slice2.py
+++ b/python/python/tests/test_first_class_function_slice2.py
@@ -1499,7 +1499,7 @@ def test_local_function_catalog_operations_are_not_supported(tmp_path):
     with pytest.raises(NotImplementedError, match=message):
         db.get_function("normalize_score", version=FUNCTION_VERSION)
     with pytest.raises(NotImplementedError, match=message):
-        db.list_functions()
+        list(db.list_functions())
     with pytest.raises(NotImplementedError, match=message):
         db.drop_function("normalize_score", version=FUNCTION_VERSION)
 
@@ -1539,8 +1539,10 @@ def _mock_remote_function_catalog():
                 else (None, None)
             )
             if function_action[1] == "create":
+                *namespace_path, name = function_action[0].split("$")
                 state["version"] = {
-                    "name": "normalize_score",
+                    "name": name,
+                    **({"namespace_path": namespace_path} if namespace_path else {}),
                     "version": FUNCTION_VERSION,
                     "object_id": "fixture",
                     "location": "memory:///fixture",
@@ -1553,7 +1555,7 @@ def _mock_remote_function_catalog():
                     )["image"],
                     "signature": body["signature"],
                     "secret_bindings": body.get("secret_bindings", []),
-                    "created_at": "2026-08-21T00:00:00Z",
+                    "created_at_millis": 1787270400000,  # 2026-08-21T00:00:00Z
                 }
                 response = {"job_id": "job-register"}
                 status = 202
@@ -1565,10 +1567,18 @@ def _mock_remote_function_catalog():
                     "job_state": "DONE",
                     "result": state["version"],
                 }
-            elif self.path == "/v1/function/normalize_score/describe":
+            # A namespaced Function's id is its namespace path and name joined
+            # by `$`, so the name is the last segment of it.
+            elif (
+                function_action[1] == "describe"
+                and function_action[0].split("$")[-1] == "normalize_score"
+            ):
                 assert body == {"version": FUNCTION_VERSION}
                 response = state["version"]
-            elif self.path == "/v1/function/normalize_score/drop":
+            elif (
+                function_action[1] == "drop"
+                and function_action[0].split("$")[-1] == "normalize_score"
+            ):
                 assert body == {"version": FUNCTION_VERSION}
                 response = {"dropped": True}
             elif secret_action[1] in ("create", "alter"):
@@ -1602,7 +1612,8 @@ def _mock_remote_function_catalog():
                     assert query["page_token"] == "next"
                     self._write_response(200, {"secrets": [{"name": "hf-prod"}]})
                 return
-            if url.path != "/v1/namespace/$/function/list":
+            parts = url.path.strip("/").split("/")
+            if parts[:2] != ["v1", "namespace"] or parts[3:] != ["function", "list"]:
                 self._write_response(404, {"error": "not found"})
                 return
             assert query["include_definition"] == "true"
@@ -1709,7 +1720,7 @@ def test_remote_secret_verbs_round_trip():
         )
         assert db.create_secret("openai-prod", "sk-live-0001") is None
         assert db.alter_secret("openai-prod", "sk-live-0002") is None
-        assert db.list_secrets() == ["openai-prod", "hf-prod"]
+        assert list(db.list_secrets()) == ["openai-prod", "hf-prod"]
         assert db.drop_secret("openai-prod") is None
 
     routes = [path for path, _ in state["requests"]]
@@ -1770,7 +1781,7 @@ def test_remote_list_functions_paginates_and_returns_typed_versions():
         )
         created = db.create_function(normalize_score)
         state["requests"].clear()
-        functions = db.list_functions()
+        functions = list(db.list_functions())
 
     assert functions == [created]
     assert state["requests"] == [
@@ -1794,7 +1805,7 @@ async def test_async_remote_list_functions_returns_typed_versions():
         registration = await db.create_function_async(normalize_score)
         created = await registration.wait()
         state["requests"].clear()
-        functions = await db.list_functions()
+        functions = [item async for item in db.list_functions()]
 
     assert functions == [created]
     assert [path for path, _ in state["requests"]] == [
@@ -1840,3 +1851,102 @@ async def test_async_remote_drop_function_sends_exact_version():
             {"version": FUNCTION_VERSION},
         )
     ]
+
+
+def test_remote_function_verbs_address_a_namespace_in_the_path():
+    """A Function's namespace is part of the identifier each route addresses;
+    no body carries it, so a namespaced request differs only in its path."""
+    namespace = ["analytics", "features"]
+    with _mock_remote_function_catalog() as (host, state):
+        db = lancedb.connect(
+            "db://dev",
+            api_key="fake",
+            host_override=host,
+            client_config={"retry_config": {"retries": 0}},
+        )
+        created = db.create_function(normalize_score, namespace_path=namespace)
+        # The returned parts are what the other verbs take, so a created
+        # Function reopens from its own fields.
+        assert created.name == "normalize_score"
+        assert created.namespace_path == tuple(namespace)
+        reopened = db.get_function(
+            created.name,
+            version=created.version,
+            namespace_path=list(created.namespace_path),
+        )
+        assert reopened == created
+        assert reopened(value=lancedb.col("score")).function.namespace_path == tuple(
+            namespace
+        )
+        list(db.list_functions(namespace_path=namespace))
+        assert (
+            db.drop_function(
+                "normalize_score", version=FUNCTION_VERSION, namespace_path=namespace
+            )
+            is True
+        )
+        dropped, _ = db.drop_function_async(
+            "normalize_score", version=FUNCTION_VERSION, namespace_path=namespace
+        )
+        assert dropped is True
+
+    assert [path for path, _ in state["requests"]] == [
+        "/v1/function/analytics$features$normalize_score/create",
+        "/v1/jobs/describe",
+        "/v1/function/analytics$features$normalize_score/describe",
+        "/v1/namespace/analytics$features/function/list",
+        "/v1/namespace/analytics$features/function/list",
+        "/v1/function/analytics$features$normalize_score/drop",
+        "/v1/function/analytics$features$normalize_score/drop",
+    ]
+
+
+@pytest.mark.asyncio
+async def test_async_remote_function_verbs_address_a_namespace_in_the_path():
+    namespace = ["analytics", "features"]
+    with _mock_remote_function_catalog() as (host, state):
+        db = await lancedb.connect_async(
+            "db://dev",
+            api_key="fake",
+            host_override=host,
+            client_config={"retry_config": {"retries": 0}},
+        )
+        registration = await db.create_function_async(
+            normalize_score, namespace_path=namespace
+        )
+        await registration.wait()
+        await db.get_function(
+            "normalize_score", version=FUNCTION_VERSION, namespace_path=namespace
+        )
+        [item async for item in db.list_functions(namespace_path=namespace)]
+        assert (
+            await db.drop_function(
+                "normalize_score", version=FUNCTION_VERSION, namespace_path=namespace
+            )
+            is True
+        )
+
+    assert [path for path, _ in state["requests"]] == [
+        "/v1/function/analytics$features$normalize_score/create",
+        "/v1/jobs/describe",
+        "/v1/function/analytics$features$normalize_score/describe",
+        "/v1/namespace/analytics$features/function/list",
+        "/v1/namespace/analytics$features/function/list",
+        "/v1/function/analytics$features$normalize_score/drop",
+    ]
+
+
+def test_a_function_namespace_path_must_be_a_list_of_segments():
+    """A bare string is refused rather than read as a sequence of one-character
+    segments, and nothing reaches the service."""
+    with _mock_remote_function_catalog() as (host, state):
+        db = lancedb.connect(
+            "db://dev",
+            api_key="fake",
+            host_override=host,
+            client_config={"retry_config": {"retries": 0}},
+        )
+        with pytest.raises(TypeError):
+            list(db.list_functions(namespace_path="analytics"))
+
+    assert state["requests"] == []
diff --git a/python/python/tests/test_fts.py b/python/python/tests/test_fts.py
index e7b0a83ce..d7fa2e0ec 100644
--- a/python/python/tests/test_fts.py
+++ b/python/python/tests/test_fts.py
@@ -13,6 +13,7 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
+import json
 import os
 import random
 import shutil
@@ -44,6 +45,75 @@ from utils import exception_output
 TEST_LANGUAGE_MODEL_HOME = Path(__file__).parent / "models"
 
 
+@pytest.mark.skipif(not hasattr(pa, "json_"), reason="requires PyArrow JSON type")
+@pytest.mark.parametrize("block_size", [128, 256])
+def test_json_fts(tmp_path, block_size):
+    db = ldb.connect(tmp_path)
+    data = pa.table(
+        {
+            "id": [0, 1, 2, 3],
+            "doc": pa.array(
+                [
+                    '{"title":"brown fox","meta":{"tag":"nature"},"author":"alice"}',
+                    '{"title":"blue car","meta":{"tag":"city"}}',
+                    '{"title":"brown bear","meta":{"tag":"nature"}}',
+                    '{"title":"red fox","author":"alice"}',
+                ],
+                type=pa.json_(),
+            ),
+        }
+    )
+    table = db.create_table("json_fts", data)
+    table.create_index("doc", config=FTS(block_size=block_size), name="doc_fts")
+    table = db.open_table("json_fts")
+    index = table.list_indices()[0]
+    assert index.name == "doc_fts"
+    assert index.index_type == "FTS"
+    assert index.columns == ["doc"]
+    stats = table.index_stats("doc_fts")
+    assert stats.num_indexed_rows == 4
+    assert stats.num_unindexed_rows == 0
+
+    for query, expected in [
+        ("title,str,brown", [0, 2]),
+        ("meta.tag,str,nature", [0, 2]),
+        ("author,str,alice", [0, 3]),
+        ("title,str,alice", []),
+        ("title,str,title", []),
+    ]:
+        search = table.search(query, query_type="fts", fts_columns="doc")
+        assert sorted(row["id"] for row in search.to_list()) == expected
+        plan = search.explain_plan(True)
+        assert "MatchQuery" in plan, plan
+        assert "FlatMatchQuery" not in plan, plan
+
+
+@pytest.mark.skipif(not hasattr(pa, "json_"), reason="requires PyArrow JSON type")
+@pytest.mark.asyncio
+async def test_json_fts_async(tmp_path):
+    db = await ldb.connect_async(tmp_path)
+    table = await db.create_table(
+        "json_fts",
+        pa.table({"doc": pa.array(['{"meta":{"tag":"nature"}}'], type=pa.json_())}),
+    )
+    await table.create_index("doc", config=FTS())
+    search = table.query().nearest_to_text(MatchQuery("meta.tag,str,nature", "doc"))
+    assert len(await search.to_list()) == 1
+    plan = await search.explain_plan(True)
+    assert "MatchQuery" in plan, plan
+    assert "FlatMatchQuery" not in plan, plan
+
+
+@pytest.mark.parametrize("dtype", [pa.binary(), pa.large_binary()])
+def test_fts_rejects_raw_binary(tmp_path, dtype):
+    table = ldb.connect(tmp_path).create_table(
+        "binary_fts", pa.table({"doc": pa.array([b'{"title":"brown"}'], type=dtype)})
+    )
+    with pytest.raises(ValueError, match="FTS.*lance.json.*raw binary"):
+        table.create_index("doc", config=FTS())
+    assert table.list_indices() == []
+
+
 @pytest.fixture
 def table(tmp_path) -> ldb.table.LanceTable:
     # Use local random state to avoid affecting other tests
@@ -894,6 +964,78 @@ def test_tokenize_uses_simple_index_tokenizer(mem_db: DBConnection):
     ]
 
 
+@pytest.mark.parametrize("legacy", [False, True])
+@pytest.mark.parametrize(
+    ("base_tokenizer", "options", "expected_filters", "expected_tokens"),
+    [
+        (
+            "code",
+            {},
+            (False, False),
+            [("running", 0), ("in", 1), ("cafe", 2)],
+        ),
+        (
+            "code",
+            {"stem": None, "remove_stop_words": None},
+            (False, False),
+            [("running", 0), ("in", 1), ("cafe", 2)],
+        ),
+        (
+            "code",
+            {"stem": True},
+            (True, False),
+            [("run", 0), ("in", 1), ("cafe", 2)],
+        ),
+        (
+            "code",
+            {"remove_stop_words": True},
+            (False, True),
+            [("running", 0), ("cafe", 2)],
+        ),
+        (
+            "code",
+            {"stem": True, "remove_stop_words": True},
+            (True, True),
+            [("run", 0), ("cafe", 2)],
+        ),
+        (
+            "simple",
+            {},
+            (True, True),
+            [("run", 0), ("cafe", 2)],
+        ),
+        (
+            "simple",
+            {"stem": False, "remove_stop_words": False},
+            (False, False),
+            [("running", 0), ("in", 1), ("cafe", 2)],
+        ),
+    ],
+)
+def test_fts_tokenizer_filter_defaults(
+    mem_db, legacy, base_tokenizer, options, expected_filters, expected_tokens
+):
+    text = "Running in café"
+    table = mem_db.create_table("test_filter_defaults", data=[{"text": text}])
+    if legacy:
+        with pytest.warns(DeprecationWarning, match="create_fts_index"):
+            table.create_fts_index("text", base_tokenizer=base_tokenizer, **options)
+    else:
+        table.create_index("text", config=FTS(base_tokenizer=base_tokenizer, **options))
+
+    details = table.list_indices()[0].index_details
+    assert (details["stem"], details["remove_stop_words"]) == expected_filters
+    assert [
+        (token.text, token.position) for token in table.tokenize(text, column="text")
+    ] == expected_tokens
+    assert [
+        (token.text, token.position)
+        for token in ldb.tokenize(text, base_tokenizer=base_tokenizer, **options)
+    ] == expected_tokens
+    if base_tokenizer == "code" and not expected_filters[1]:
+        assert table.search("in", query_type="fts").to_list()[0]["text"] == text
+
+
 def test_tokenize_uses_icu_index_tokenizer_by_name(mem_db: DBConnection):
     data = pa.table({"text": ["Hello, こんにちは世界!"]})
     table = mem_db.create_table("test_tokenize_icu", data=data)
@@ -1137,6 +1279,34 @@ def test_fts_lindera_tokenizer(
     assert [row["text"] for row in results] == ["成田国際空港"]
 
 
+@pytest.mark.parametrize("fuzziness", [None, 0, 1, 2])
+def test_match_query_fuzziness_to_json(fuzziness):
+    query = MatchQuery("puppy", "text", fuzziness=fuzziness)
+
+    assert query.fuzziness == fuzziness
+    assert json.loads(query.to_json())["match"]["fuzziness"] == fuzziness
+
+
+@pytest.mark.parametrize(
+    "options, expected",
+    [
+        pytest.param({}, ["ab"], id="default-exact"),
+        pytest.param({"fuzziness": 0}, ["ab"], id="explicit-exact"),
+        pytest.param({"fuzziness": None}, ["ab"], id="automatic"),
+        pytest.param({"fuzziness": 1}, ["ab", "ac"], id="one-edit"),
+        pytest.param({"fuzziness": 2}, ["ab", "ac", "zz"], id="two-edits"),
+    ],
+)
+def test_match_query_fuzziness_search(tmp_db, options, expected):
+    # Automatic fuzziness is zero for two-character terms.
+    table = tmp_db.create_table("fuzzy", pa.table({"text": ["ab", "ac", "zz"]}))
+    table.create_index("text", config=FTS(stem=False, remove_stop_words=False))
+
+    results = table.search(MatchQuery("ab", "text", **options)).to_list()
+
+    assert sorted(row["text"] for row in results) == sorted(expected)
+
+
 def test_fts_query_to_json():
     """Test that FTS query to_json() produces valid JSON strings with exact format."""
 
@@ -1267,7 +1437,10 @@ def test_fts_query_to_json():
     json_str = multi_match.to_json()
     expected = (
         '{"multi_match":{"query":"python","columns":["tags","title"],'
-        '"boost":[1.0,1.0]}}'
+        '"boost":[1.0,1.0],"match_queries":[{"column":"tags","terms":"python",'
+        '"boost":1.0,"fuzziness":0,"max_expansions":50,"operator":"Or",'
+        '"prefix_length":0},{"column":"title","terms":"python","boost":1.0,'
+        '"fuzziness":0,"max_expansions":50,"operator":"Or","prefix_length":0}]}}'
     )
     assert json_str == expected
 
diff --git a/python/python/tests/test_hybrid_query.py b/python/python/tests/test_hybrid_query.py
index c5f58308d..8d670a136 100644
--- a/python/python/tests/test_hybrid_query.py
+++ b/python/python/tests/test_hybrid_query.py
@@ -355,6 +355,20 @@ def test_hybrid_query_minimum_nprobes_zero_raises(sync_table: Table):
         )
 
 
+def test_hybrid_query_nprobes_remains_independent(sync_table: Table):
+    query = sync_table.search(query_type="hybrid").nprobes(20)
+
+    assert query._nprobes == 20
+    assert query._minimum_nprobes is None
+    assert query._maximum_nprobes is None
+
+    query = query.minimum_nprobes(5).nprobes(20)
+
+    assert query._nprobes == 20
+    assert query._minimum_nprobes == 5
+    assert query._maximum_nprobes is None
+
+
 def test_hybrid_query_distance_range(sync_table: Table):
     reranker = RRFReranker(return_score="all")
     result = (
diff --git a/python/python/tests/test_listing.py b/python/python/tests/test_listing.py
new file mode 100644
index 000000000..4a2745eb8
--- /dev/null
+++ b/python/python/tests/test_listing.py
@@ -0,0 +1,194 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import asyncio
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+import json
+from pathlib import Path
+from threading import Thread
+from urllib.parse import parse_qs, urlsplit
+
+import lancedb
+from lancedb.remote.errors import HttpError
+import pytest
+
+
+@pytest.fixture
+def listing_server():
+    requests, responses = [], []
+
+    class Handler(BaseHTTPRequestHandler):
+        def handle_request(self):
+            body = self.rfile.read(int(self.headers.get("Content-Length", "0")))
+            requests.append((self.command, self.path, json.loads(body) if body else {}))
+            status, response = responses.pop(0)
+            self.send_response(status)
+            self.send_header("Content-Type", "application/json")
+            self.end_headers()
+            self.wfile.write(json.dumps(response).encode())
+
+        do_GET = handle_request
+        do_POST = handle_request
+
+    with ThreadingHTTPServer(("127.0.0.1", 0), Handler) as server:
+        thread = Thread(target=server.serve_forever, daemon=True)
+        thread.start()
+        try:
+            yield f"http://127.0.0.1:{server.server_port}", requests, responses
+        finally:
+            server.shutdown()
+            thread.join()
+
+
+def response_page(kind, names, token):
+    if kind == "secrets":
+        items = [{"name": name} for name in names]
+    elif kind == "jobs":
+        items = [
+            {
+                "job_id": name,
+                "table": "t",
+                "job_type": "create_index",
+                "state": "in_progress",
+                "created_at_millis": 1,
+            }
+            for name in names
+        ]
+    elif kind == "functions":
+        fixture = (
+            Path(__file__).resolve().parents[3]
+            / "rust/lancedb/tests/fixtures/first_class_functions/v1"
+            / "remote_function_version.canonical.json"
+        )
+        definition = json.loads(fixture.read_text())
+        items = [
+            {"name": "embed", "version": "1", "definition": definition} for _ in names
+        ]
+    else:
+        items = names
+    key = "views" if kind == "materialized_views" else kind
+    return {key: items, "page_token": token}
+
+
+async def connection(endpoint, asynchronous):
+    kwargs = dict(
+        api_key="fake",
+        host_override=endpoint,
+        client_config={"retry_config": {"retries": 0}},
+    )
+    if asynchronous:
+        return await lancedb.connect_async("db://dev", **kwargs)
+    return lancedb.connect("db://dev", **kwargs)
+
+
+async def advance(items, asynchronous):
+    return await items.__anext__() if asynchronous else next(items)
+
+
+KINDS = ["secrets", "views", "jobs", "functions", "materialized_views"]
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("kind", KINDS)
+@pytest.mark.parametrize("asynchronous", [False, True])
+async def test_listing_lazy_pages_and_resume(listing_server, kind, asynchronous):
+    endpoint, requests, responses = listing_server
+    responses.extend(
+        (200, response_page(kind, names, token))
+        for names, token in [(["a", "b"], "empty"), ([], "last"), (["c", "d"], "")]
+    )
+    db = await connection(endpoint, asynchronous)
+    method = getattr(db, "list_" + kind)
+    kwargs = (
+        {} if kind in ("jobs", "materialized_views") else {"namespace_path": ["team"]}
+    )
+    items = method(page_token="start/token", page_limit=2, **kwargs)
+    assert requests == []
+    assert items.page_token() == "start/token"
+    assert items.num_page_results() == 0
+    first = await advance(items, asynchronous)
+    if kind == "functions":
+        assert first.name == "embed"
+        assert first.version == "1"
+    elif kind == "jobs":
+        assert first.job_id == "a"
+        assert first.state == "running"
+    else:
+        assert first == "a"
+    assert items.num_page_results() == 1
+    assert items.page_token() == "empty"
+    await advance(items, asynchronous)
+    assert items.num_page_results() == 0
+    assert len(requests) == 1
+    resumed = method(page_token=items.page_token(), page_limit=2, **kwargs)
+    await advance(resumed, asynchronous)
+    assert len(requests) == 3
+    assert resumed.page_token() is None
+    assert resumed.num_page_results() == 1
+    remaining = [item async for item in resumed] if asynchronous else list(resumed)
+    assert len(remaining) == 1
+    assert resumed.num_page_results() == 0
+    assert ([item async for item in resumed] if asynchronous else list(resumed)) == []
+    for (verb, url, body), token in zip(requests, ["start/token", "empty", "last"]):
+        path = urlsplit(url)
+        if kind == "jobs":
+            assert verb == "POST"
+            assert path.path == "/v1/jobs/list"
+            assert body == {"page_token": token, "limit": 2}
+        else:
+            namespace = "$" if kind == "materialized_views" else "team"
+            resource = kind.removesuffix("s")
+            assert path.path == f"/v1/namespace/{namespace}/{resource}/list"
+            expected = {"page_token": [token], "limit": ["2"]}
+            if kind == "functions":
+                expected["include_definition"] = ["true"]
+            assert parse_qs(path.query) == expected
+    assert responses == []
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("kind", KINDS)
+@pytest.mark.parametrize("asynchronous", [False, True])
+async def test_listing_error_retains_token(listing_server, kind, asynchronous):
+    endpoint, requests, responses = listing_server
+    responses.extend([(401, {}), (200, response_page(kind, ["a"], None))])
+    db = await connection(endpoint, asynchronous)
+    method = getattr(db, "list_" + kind)
+    items = method(page_token="retry")
+    with pytest.raises(HttpError):
+        await advance(items, asynchronous)
+    assert items.page_token() == "retry"
+    assert items.num_page_results() == 0
+    assert ([item async for item in items] if asynchronous else list(items)) == []
+    assert len(requests) == 1
+    resumed = method(page_token=items.page_token())
+    assert len([item async for item in resumed] if asynchronous else list(resumed)) == 1
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("kind", KINDS)
+async def test_listing_serializes_concurrent_advances(listing_server, kind):
+    endpoint, requests, responses = listing_server
+    responses.append((200, response_page(kind, ["a", "b"], None)))
+    db = await connection(endpoint, True)
+    items = getattr(db, "list_" + kind)()
+    results = await asyncio.gather(
+        items.__anext__(), items.__anext__(), items.__anext__(), return_exceptions=True
+    )
+    assert (
+        len([result for result in results if isinstance(result, StopAsyncIteration)])
+        == 1
+    )
+    assert len(requests) == 1
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("kind", KINDS)
+@pytest.mark.parametrize("page_limit", [0, 2**32 - 1])
+async def test_listing_invalid_limit_does_not_fetch(listing_server, kind, page_limit):
+    endpoint, requests, _ = listing_server
+    db = await connection(endpoint, True)
+    items = getattr(db, "list_" + kind)(page_limit=page_limit)
+    with pytest.raises(ValueError, match="limit"):
+        await items.__anext__()
+    assert requests == []
diff --git a/python/python/tests/test_materialized_views.py b/python/python/tests/test_materialized_views.py
index 1ee21fbac..bd1b2a0bb 100644
--- a/python/python/tests/test_materialized_views.py
+++ b/python/python/tests/test_materialized_views.py
@@ -93,28 +93,10 @@ def test_remote_list_uses_namespace_route():
             host_override=host,
             client_config={"retry_config": {"retries": 0}},
         )
-        assert db.list_materialized_views() == ["daily_sales"]
+        assert list(db.list_materialized_views()) == ["daily_sales"]
     assert requests == ["/v1/namespace/$/materialized_view/list"]
 
 
-def test_remote_create_async_returns_server_job():
-    with mock_remote_materialized_view_create() as (host, requests):
-        db = lancedb.connect(
-            "db://dev",
-            api_key="fake",
-            host_override=host,
-            client_config={"retry_config": {"retries": 0}},
-        )
-        job = db.create_materialized_view_async("adults", "people", where="age >= 18")
-        assert job.id == "mv-create-123"
-    assert requests == [
-        (
-            "/v1/materialized_view/adults/create",
-            {"query": 'SELECT * FROM "people" WHERE age >= 18', "with_no_data": False},
-        )
-    ]
-
-
 def test_remote_drop_async_returns_server_job():
     with mock_remote_materialized_view_create() as (host, requests):
         db = lancedb.connect(
@@ -208,7 +190,7 @@ def test_create_and_refresh_jobs(tmp_path):
     drop_job = db.drop_materialized_view_async("adults")
     assert drop_job.id is None
     assert drop_job.wait() is None
-    assert "adults" not in db.list_materialized_views()
+    assert "adults" not in list(db.list_materialized_views())
 
 
 def test_definition_round_trips(tmp_path):
@@ -221,29 +203,29 @@ def test_definition_round_trips(tmp_path):
     )
 
 
-def test_incremental_refresh_after_append(tmp_path):
+def test_full_refresh_after_append(tmp_path):
     db = make_db(tmp_path)
     view = db.create_materialized_view("copy", "people", with_no_data=True)
     view.refresh()
 
     db.open_table("people").add([{"name": "alan", "age": 41}])
     result = view.refresh()
-    assert result.mode == "incremental"
-    assert result.rows_written == 1
+    assert result.mode == "rebuild"
+    assert result.rows_written == 4
     assert view.table.count_rows() == 4
 
-    assert view.refresh().mode == "no_op"
+    assert view.refresh().mode == "rebuild"
 
 
-def test_incremental_refresh_after_update(tmp_path):
+def test_full_refresh_after_update(tmp_path):
     db = make_db(tmp_path)
     view = db.create_materialized_view("copy", "people", with_no_data=True)
     view.refresh()
 
     db.open_table("people").update(where="name = 'kid'", values={"age": 8})
     result = view.refresh()
-    assert result.mode == "incremental"
-    assert result.rows_written == 1
+    assert result.mode == "rebuild"
+    assert result.rows_written == 3
     rows = view.table.search().to_list()
     assert sorted(row["age"] for row in rows) == [8, 36, 85]
 
@@ -264,18 +246,43 @@ def test_legacy_storage_source_update_rebuilds(tmp_path):
     assert sorted(row["age"] for row in rows) == [8, 36]
 
 
+def test_direct_writes_to_a_view_are_refused(tmp_path):
+    db = make_db(tmp_path)
+    view = db.create_materialized_view("copy", "people")
+    table = view.table
+    row = [{"name": "eve", "age": 1}]
+    refused = pytest.raises(NotImplementedError, match="is a materialized view")
+
+    with refused:
+        table.add(row)
+    with refused:
+        table.update(where="name = 'kid'", values={"age": 8})
+    with refused:
+        table.delete("age < 18")
+    with refused:
+        table.merge_insert("name").when_not_matched_insert_all().execute(row)
+    with refused:
+        table.add_columns({"older": "age + 1"})
+    with refused:
+        table.drop_columns(["age"])
+    assert table.count_rows() == 3
+
+    db.open_table("people").add(row)
+    assert view.refresh().rows_written == 4
+
+
 def test_list_and_not_a_view(tmp_path):
     db = make_db(tmp_path)
     db.create_materialized_view("adults", "people", where="age >= 18")
 
-    assert db.list_materialized_views() == ["adults"]
+    assert list(db.list_materialized_views()) == ["adults"]
     with pytest.raises(ValueError, match="not a materialized view"):
         db.open_materialized_view("people")
     with pytest.raises(ValueError, match="not a materialized view"):
         db.drop_materialized_view("people")
 
     db.drop_materialized_view("adults")
-    assert db.list_materialized_views() == []
+    assert list(db.list_materialized_views()) == []
 
 
 def test_invalid_expression_fails_at_create(tmp_path):
@@ -303,7 +310,7 @@ async def test_async_create_refresh_and_open(tmp_path):
     reopened = await db.open_materialized_view("shouts")
     definition = await reopened.definition()
     assert definition.query == "SELECT upper(name) AS shout FROM people"
-    assert await db.list_materialized_views() == ["shouts"]
+    assert [item async for item in db.list_materialized_views()] == ["shouts"]
 
 
 @pytest.mark.asyncio
@@ -327,12 +334,12 @@ async def test_async_create_and_refresh_jobs(tmp_path):
     drop_job = await db.drop_materialized_view_async("adults")
     assert drop_job.id is None
     assert await drop_job.wait() is None
-    assert "adults" not in await db.list_materialized_views()
+    assert "adults" not in [item async for item in db.list_materialized_views()]
 
 
 @pytest.mark.asyncio
-async def test_async_incremental(tmp_path):
-    db = await lancedb.connect_async(tmp_path, storage_options=STABLE_ROW_IDS)
+async def test_async_refresh_rebuilds(tmp_path):
+    db = await lancedb.connect_async(tmp_path)
     await db.create_table("people", [{"name": "ada", "age": 36}])
     view = await db.create_materialized_view("copy", "people", with_no_data=True)
     await view.refresh()
@@ -340,15 +347,15 @@ async def test_async_incremental(tmp_path):
     table = await db.open_table("people")
     await table.add([{"name": "alan", "age": 41}])
     result = await view.refresh()
-    assert result.mode == "incremental"
-    assert result.rows_written == 1
+    assert result.mode == "rebuild"
+    assert result.rows_written == 2
 
 
-def test_source_requires_stable_row_ids(tmp_path):
+def test_source_does_not_require_stable_row_ids(tmp_path):
     db = lancedb.connect(tmp_path)
     db.create_table("plain", [{"x": 1}])
-    with pytest.raises(Exception, match="stable row ids"):
-        db.create_materialized_view("v", "plain")
+    view = db.create_materialized_view("v", "plain")
+    assert view.table.count_rows() == 1
 
 
 def test_bare_select_names_are_quoted(tmp_path):
@@ -373,7 +380,7 @@ def test_scalar_select_is_one_column(tmp_path):
     view = db.create_materialized_view("just_name", "people", select="name")
     view.refresh()
     rows = view.table.search().to_list()
-    assert set(rows[0]) - {"__source_row_id"} == {"name"}
+    assert set(rows[0]) == {"name"}
     assert sorted(row["name"] for row in rows) == ["ada", "grace", "kid"]
 
 
@@ -384,7 +391,7 @@ async def test_async_scalar_select_is_one_column(tmp_path):
     view = await db.create_materialized_view("just_name", "people", select="name")
     await view.refresh()
     rows = await view.table.query().to_list()
-    assert set(rows[0]) - {"__source_row_id"} == {"name"}
+    assert set(rows[0]) == {"name"}
 
 
 def test_limit_above_i64_max_is_refused(tmp_path):
@@ -417,7 +424,7 @@ def test_namespace_connection_materialized_views(tmp_path):
     view = db.create_materialized_view("adults", "people", where="age >= 18")
     view.refresh()
     assert view.table.count_rows() == 1
-    assert db.list_materialized_views() == ["adults"]
+    assert list(db.list_materialized_views()) == ["adults"]
 
     reopened = db.open_materialized_view("adults")
     assert reopened.definition.query.startswith("SELECT name, age FROM ")
@@ -433,7 +440,7 @@ def test_namespace_connection_materialized_views(tmp_path):
 
     assert db.drop_materialized_view_async("job_view").wait() is None
     db.drop_materialized_view("adults")
-    assert db.list_materialized_views() == []
+    assert list(db.list_materialized_views()) == []
 
 
 @pytest.mark.asyncio
@@ -452,7 +459,7 @@ async def test_async_namespace_connection_materialized_views(tmp_path):
     view = await db.create_materialized_view("adults", "people", where="age >= 18")
     await view.refresh()
     assert await view.table.count_rows() == 1
-    assert await db.list_materialized_views() == ["adults"]
+    assert [item async for item in db.list_materialized_views()] == ["adults"]
 
     reopened = await db.open_materialized_view("adults")
     assert (await reopened.definition()).query.startswith("SELECT name, age FROM ")
@@ -478,7 +485,7 @@ async def test_async_namespace_connection_materialized_views(tmp_path):
     drop_job = await db.drop_materialized_view_async("job_view")
     assert await drop_job.wait() is None
     await db.drop_materialized_view("adults")
-    assert await db.list_materialized_views() == []
+    assert [item async for item in db.list_materialized_views()] == []
 
 
 def test_stored_queries_and_legacy_layouts_are_read():
@@ -520,166 +527,26 @@ def test_stored_queries_and_legacy_layouts_are_read():
         "SELECT * FROM people"
     )
 
-    for fmt, function in [(3, "vector_duplicate_pairs"), (4, "vector_dedup")]:
-        native = f"SELECT * FROM {function}('images', 3, 'phash', 4)"
-        assert read({"format": fmt, "query": native}).query == native
-
     # A newer writer's layout is reported, never guessed at.
     for newer in (
-        {"format": 5, "query": query},
+        {"format": 3, "query": query},
         {"kind": "select_v3", "source_table": "people"},
     ):
         with pytest.raises(NotImplementedError, match="cannot refresh"):
             read(newer)
 
 
-def indexed_dedup_source(db, name="images"):
-    import pyarrow as pa
-    from lancedb.index import IvfFlat
-
-    vectors = pa.FixedSizeListArray.from_arrays(
-        pa.array(
-            [v for i in range(256) for v in (float(i if i < 3 else i * 10), 0.0)],
-            type=pa.float32(),
-        ),
-        2,
-    )
-    table = db.create_table(name, pa.table({"id": range(256), "vector": vectors}))
-    table.create_index("vector", config=IvfFlat(num_partitions=1))
-    return table
-
-
-@pytest.mark.parametrize("stable", [False, True])
-def test_vector_dedup_source_captures_snapshot_and_preserves_source(tmp_path, stable):
-    db = lancedb.connect(
-        tmp_path,
-        storage_options={"new_table_enable_stable_row_ids": str(stable).lower()},
-    )
-    source = indexed_dedup_source(db, "images.with.dot")
-    version = source.version
-    expression = lancedb.vector_dedup(
-        source.name, column="vector", distance_threshold=1
-    )
-    view = db.create_materialized_view("clean", expression)
-    assert "vector_dedup(" in view.definition.query
-    assert f", {version}, 'vector', 1.0)" in view.definition.query
-    assert view.table.count_rows() == 255
-    assert [r["id"] for r in view.table.search().where("id < 3").to_list()] == [0, 2]
-    assert source.version == version
-    assert source.count_rows() == 256
-
-    source.delete("id = 0")
-    view.refresh_async().wait()
-    assert view.table.search().where("id = 0").to_list() == [
-        {"id": 0, "vector": [0.0, 0.0]}
-    ]
-    assert expression.dataset_version is None  # reusable immutable declaration
-
-
-def test_vector_dedup_create_job_and_explicit_snapshot(tmp_path):
-    db = lancedb.connect(tmp_path)
-    source = indexed_dedup_source(db)
-    version = source.version
-    source.delete("id = 0")
-    job = db.create_materialized_view_async(
-        "clean",
-        lancedb.vector_dedup(
-            "images", column="vector", distance_threshold=1, dataset_version=version
-        ),
-    )
-    assert job.wait() is None
-    view = db.open_materialized_view("clean")
-    assert view.table.count_rows() == 255
-    assert len(view.table.search().where("id = 0").to_list()) == 1
-
-
-@pytest.mark.asyncio
-async def test_async_vector_dedup_declaration_and_creation(tmp_path):
-    sync_db = lancedb.connect(tmp_path)
-    source = indexed_dedup_source(sync_db)
-    db = await lancedb.connect_async(tmp_path)
-    expression = lancedb.vector_dedup("images", column="vector", distance_threshold=1)
-    view = await db.create_materialized_view("clean", expression, with_no_data=True)
-    assert await view.table.count_rows() == 0
-    # The snapshot is captured at declaration even if refresh comes later.
-    source.delete("id = 0")
-    job = await view.refresh_async()
-    await job.wait()
-    assert await view.table.count_rows() == 255
-    another = await db.create_materialized_view_async(
-        "later", expression, with_no_data=True
-    )
-    await another.wait()
-    later = await db.open_materialized_view("later")
-    assert (await later.definition()).query != (await view.definition()).query
-    assert await later.table.count_rows() == 0
-
-
-@pytest.mark.parametrize(
-    "options", [{"select": ["id"]}, {"select": []}, {"where": "id > 0"}, {"limit": 1}]
-)
-def test_vector_dedup_rejects_extra_clauses_before_opening_source(tmp_path, options):
-    db = lancedb.connect(tmp_path)
-    expression = lancedb.vector_dedup("missing", column="vector", distance_threshold=1)
-    with pytest.raises(ValueError, match="cannot be combined"):
-        db.create_materialized_view("bad", expression, **options)
-    assert db.list_tables().tables == []
-
-
-@pytest.mark.parametrize("version", [0, -1, True, 1.5, "2", 2**64])
-def test_vector_dedup_rejects_invalid_snapshot(version):
-    with pytest.raises(ValueError, match="dataset_version"):
-        lancedb.vector_dedup(
-            "images", column="vector", distance_threshold=1, dataset_version=version
-        )
-
-
-@pytest.mark.parametrize(
-    "threshold", [float("nan"), float("inf"), -float("inf"), 1e100, 10**1000, True, "4"]
-)
-def test_vector_dedup_rejects_invalid_threshold(threshold):
-    with pytest.raises((ValueError, TypeError), match="distance_threshold"):
-        lancedb.vector_dedup("images", column="vector", distance_threshold=threshold)
-
-
-def test_vector_dedup_descriptor_is_frozen():
-    from dataclasses import FrozenInstanceError
-
-    expression = lancedb.vector_dedup("images", column="vector", distance_threshold=1)
-    with pytest.raises(FrozenInstanceError):
-        expression.column = "other"
-
-
-@pytest.mark.parametrize("with_no_data", [False, True])
-def test_remote_vector_dedup_uses_mv_endpoint_without_sql_client(with_no_data):
-    with mock_remote_materialized_view_create() as (host, requests):
-        db = lancedb.connect(
-            "db://dev",
-            api_key="fake",
-            host_override=host,
-            sql_host_override="grpc://127.0.0.1:1",
-            client_config={"retry_config": {"retries": 0}},
-        )
-        job = db.create_materialized_view_async(
-            "clean",
-            lancedb.vector_dedup(
-                "images.with'dot",
-                column="vector'field",
-                distance_threshold=4,
-                dataset_version=7,
-            ),
-            with_no_data=with_no_data,
-        )
-        assert job.id == "mv-create-123"
-    assert requests == [
-        (
-            "/v1/materialized_view/clean/create",
-            {
-                "query": (
-                    "SELECT * FROM vector_dedup('`images.with''dot`', "
-                    "7, 'vector''field', 4.0)"
-                ),
-                "with_no_data": with_no_data,
-            },
-        )
-    ]
+def test_local_materialized_view_listing_pages_and_resume(tmp_path):
+    db = make_db(tmp_path)
+    db.create_materialized_view("a_view", "people", with_no_data=True)
+    db.create_materialized_view("z_view", "people", with_no_data=True)
+    db.create_table("ordinary", [{"id": 1}])
+    names = db.list_materialized_views(page_limit=1)
+    assert names.num_page_results() == 0
+    first = next(names)
+    assert names.num_page_results() == 0
+    assert names.page_token() is not None
+    resumed = db.list_materialized_views(page_token=names.page_token(), page_limit=1)
+    assert sorted([first, *resumed]) == ["a_view", "z_view"]
+    assert resumed.page_token() is None
+    assert list(resumed) == []
diff --git a/python/python/tests/test_lsm_write_spec.py b/python/python/tests/test_memwal_spec.py
similarity index 60%
rename from python/python/tests/test_lsm_write_spec.py
rename to python/python/tests/test_memwal_spec.py
index d43cb7532..a1ec6a90d 100644
--- a/python/python/tests/test_lsm_write_spec.py
+++ b/python/python/tests/test_memwal_spec.py
@@ -1,8 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-"""Tests for installing and clearing an LsmWriteSpec via
-`Table.set_lsm_write_spec` / `Table.unset_lsm_write_spec`.
+"""Tests for installing and clearing an MemWalWriteSpec via
+`Table.enable_memwal` / `Table.disable_memwal`.
 """
 
 from datetime import timedelta
@@ -10,7 +10,7 @@ from datetime import timedelta
 import lancedb
 import pyarrow as pa
 import pytest
-from lancedb._lancedb import LsmWriteSpec
+from lancedb._lancedb import MemWalWriteSpec
 from lancedb.index import BTree
 
 SCHEMA = pa.schema(
@@ -21,9 +21,9 @@ SCHEMA = pa.schema(
 )
 
 
-def test_lsm_write_spec_module_metadata():
-    assert lancedb.LsmWriteSpec is LsmWriteSpec
-    assert LsmWriteSpec.__module__ == "lancedb._lancedb"
+def test_memwal_spec_module_metadata():
+    assert lancedb.MemWalWriteSpec is MemWalWriteSpec
+    assert MemWalWriteSpec.__module__ == "lancedb._lancedb"
 
 
 def _batch(ids, vs):
@@ -43,35 +43,35 @@ def _make_table(tmp_path):
     return db, table
 
 
-def test_set_lsm_write_spec_validates(tmp_path):
+def test_enable_memwal_validates(tmp_path):
     _db, table = _make_table(tmp_path)
 
     # Out-of-range num_buckets.
     with pytest.raises(Exception, match="num_buckets"):
-        table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 0))
+        table.enable_memwal(MemWalWriteSpec.bucket("id", 0))
     with pytest.raises(Exception, match="num_buckets"):
-        table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 1025))
+        table.enable_memwal(MemWalWriteSpec.bucket("id", 1025))
 
-    # Happy path then mutation rejected.
-    table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 4))
-    with pytest.raises(Exception, match="mutation"):
-        table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 8))
+    # Happy path, then a set over the installed spec is refused.
+    table.enable_memwal(MemWalWriteSpec.bucket("id", 4))
+    with pytest.raises(Exception, match="already set"):
+        table.enable_memwal(MemWalWriteSpec.bucket("id", 8))
 
 
-def test_unset_lsm_write_spec(tmp_path):
+def test_disable_memwal(tmp_path):
     _db, table = _make_table(tmp_path)
 
     # unset errors when no spec is set.
-    with pytest.raises(Exception, match="no LSM write spec"):
-        table.unset_lsm_write_spec()
+    with pytest.raises(Exception, match="no MemWAL write spec"):
+        table.disable_memwal()
 
     # Install a spec, then remove it; afterwards a fresh spec can be set.
-    table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 4))
-    table.unset_lsm_write_spec()
+    table.enable_memwal(MemWalWriteSpec.bucket("id", 4))
+    table.disable_memwal()
     # A second unset errors — there is no spec left to remove.
-    with pytest.raises(Exception, match="no LSM write spec"):
-        table.unset_lsm_write_spec()
-    table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 8))
+    with pytest.raises(Exception, match="no MemWAL write spec"):
+        table.disable_memwal()
+    table.enable_memwal(MemWalWriteSpec.bucket("id", 8))
 
 
 def test_set_unsharded_spec(tmp_path):
@@ -79,12 +79,12 @@ def test_set_unsharded_spec(tmp_path):
     # Lance MemWAL still requires a primary key on the dataset; Unsharded
     # just skips per-row hashing.
     table.set_unenforced_primary_key("id")
-    table.set_lsm_write_spec(LsmWriteSpec.unsharded())
-    table.unset_lsm_write_spec()
+    table.enable_memwal(MemWalWriteSpec.unsharded())
+    table.disable_memwal()
 
 
-def test_lsm_write_spec_repr():
-    s = LsmWriteSpec.bucket("id", 4)
+def test_memwal_spec_repr():
+    s = MemWalWriteSpec.bucket("id", 4)
     assert s.spec_type == "bucket"
     assert s.column == "id"
     assert s.num_buckets == 4
@@ -95,20 +95,20 @@ def test_lsm_write_spec_repr():
     assert "id" in repr(s)
     assert "4" in repr(s)
 
-    u = LsmWriteSpec.unsharded()
+    u = MemWalWriteSpec.unsharded()
     assert u.spec_type == "unsharded"
     assert u.column is None
     assert u.num_buckets is None
     assert "unsharded" in repr(u)
 
 
-def test_lsm_write_spec_with_maintained_indexes():
-    s = LsmWriteSpec.bucket("id", 4).with_maintained_indexes(["idx_a", "idx_b"])
+def test_memwal_spec_with_maintained_indexes():
+    s = MemWalWriteSpec.bucket("id", 4).with_maintained_indexes(["idx_a", "idx_b"])
     assert s.maintained_indexes == ["idx_a", "idx_b"]
 
 
 @pytest.mark.asyncio
-async def test_async_set_unset_lsm_write_spec(tmp_path):
+async def test_async_set_disable_memwal(tmp_path):
     db = await lancedb.connect_async(
         tmp_path, read_consistency_interval=timedelta(seconds=0)
     )
@@ -118,11 +118,11 @@ async def test_async_set_unset_lsm_write_spec(tmp_path):
     )
 
     await table.set_unenforced_primary_key("id")
-    await table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 4))
-    await table.unset_lsm_write_spec()
+    await table.enable_memwal(MemWalWriteSpec.bucket("id", 4))
+    await table.disable_memwal()
     # A second unset errors.
-    with pytest.raises(Exception, match="no LSM write spec"):
-        await table.unset_lsm_write_spec()
+    with pytest.raises(Exception, match="no MemWAL write spec"):
+        await table.disable_memwal()
 
 
 def test_set_identity_spec(tmp_path):
@@ -130,12 +130,12 @@ def test_set_identity_spec(tmp_path):
     # Identity sharding still requires an unenforced primary key on the
     # table; it shards by the raw value of the given column.
     table.set_unenforced_primary_key("id")
-    table.set_lsm_write_spec(LsmWriteSpec.identity("v"))
-    table.unset_lsm_write_spec()
+    table.enable_memwal(MemWalWriteSpec.identity("v"))
+    table.disable_memwal()
 
 
-def test_lsm_write_spec_identity_and_writer_config_defaults():
-    s = LsmWriteSpec.identity("v")
+def test_memwal_spec_identity_and_writer_config_defaults():
+    s = MemWalWriteSpec.identity("v")
     assert s.spec_type == "identity"
     assert s.column == "v"
     assert s.num_buckets is None
@@ -146,12 +146,12 @@ def test_lsm_write_spec_identity_and_writer_config_defaults():
     assert "durable_write" in repr(s)
 
 
-def test_get_lsm_write_spec(tmp_path):
+def test_get_memwal_spec(tmp_path):
     _db, table = _make_table(tmp_path)
     table.set_unenforced_primary_key("id")
 
     # None when nothing is installed.
-    assert table.get_lsm_write_spec() is None
+    assert table.get_memwal_spec() is None
 
     # A real scalar index is needed to name it as a maintained index.
     table.create_index("id", config=BTree())
@@ -159,12 +159,12 @@ def test_get_lsm_write_spec(tmp_path):
 
     # Bucket spec round-trips, including maintained indexes and writer config
     # defaults.
-    table.set_lsm_write_spec(
-        LsmWriteSpec.bucket("id", 4)
+    table.enable_memwal(
+        MemWalWriteSpec.bucket("id", 4)
         .with_maintained_indexes([idx_name])
         .with_writer_config_defaults({"durable_write": "false"})
     )
-    spec = table.get_lsm_write_spec()
+    spec = table.get_memwal_spec()
     assert spec is not None
     assert spec.spec_type == "bucket"
     assert spec.column == "id"
@@ -173,30 +173,30 @@ def test_get_lsm_write_spec(tmp_path):
     assert spec.writer_config_defaults == {"durable_write": "false"}
 
     # After unset, None again.
-    table.unset_lsm_write_spec()
-    assert table.get_lsm_write_spec() is None
+    table.disable_memwal()
+    assert table.get_memwal_spec() is None
 
-    # Identity round-trips (column recovered from the schema). Leaving the
-    # maintained set to be inferred picks up the index on the table, so the
-    # spec reads back naming it rather than as "infer".
-    table.set_lsm_write_spec(LsmWriteSpec.identity("id"))
-    spec = table.get_lsm_write_spec()
+    # Identity round-trips (column recovered from the schema). An unnamed
+    # maintained set reads back as None: what is stored is the intent to
+    # maintain every index, not the set it resolves to now.
+    table.enable_memwal(MemWalWriteSpec.identity("id"))
+    spec = table.get_memwal_spec()
     assert spec.spec_type == "identity"
     assert spec.column == "id"
-    assert spec.maintained_indexes == [idx_name]
-    table.unset_lsm_write_spec()
+    assert spec.maintained_indexes is None
+    table.disable_memwal()
 
     # Unsharded round-trips (no routing column). Opting out is distinct from
-    # the inferred default.
-    table.set_lsm_write_spec(LsmWriteSpec.unsharded().with_maintained_indexes([]))
-    spec = table.get_lsm_write_spec()
+    # the unnamed default.
+    table.enable_memwal(MemWalWriteSpec.unsharded().with_maintained_indexes([]))
+    spec = table.get_memwal_spec()
     assert spec.spec_type == "unsharded"
     assert spec.column is None
     assert spec.maintained_indexes == []
 
 
 @pytest.mark.asyncio
-async def test_async_get_lsm_write_spec(tmp_path):
+async def test_async_get_memwal_spec(tmp_path):
     db = await lancedb.connect_async(
         tmp_path, read_consistency_interval=timedelta(seconds=0)
     )
@@ -205,20 +205,20 @@ async def test_async_get_lsm_write_spec(tmp_path):
         pa.RecordBatchReader.from_batches(SCHEMA, [_batch(["seed"], [0])]),
     )
 
-    assert await table.get_lsm_write_spec() is None
+    assert await table.get_memwal_spec() is None
 
     # A real scalar index is needed to name it as a maintained index.
     await table.create_index("id", config=BTree())
     idx_name = (await table.list_indices())[0].name
 
-    await table.set_lsm_write_spec(
-        LsmWriteSpec.bucket("id", 8).with_maintained_indexes([idx_name])
+    await table.enable_memwal(
+        MemWalWriteSpec.bucket("id", 8).with_maintained_indexes([idx_name])
     )
-    spec = await table.get_lsm_write_spec()
+    spec = await table.get_memwal_spec()
     assert spec is not None
     assert spec.spec_type == "bucket"
     assert spec.column == "id"
     assert spec.num_buckets == 8
     assert spec.maintained_indexes == [idx_name]
-    await table.unset_lsm_write_spec()
-    assert await table.get_lsm_write_spec() is None
+    await table.disable_memwal()
+    assert await table.get_memwal_spec() is None
diff --git a/python/python/tests/test_merge_insert_lsm.py b/python/python/tests/test_merge_insert_lsm.py
index e74c21589..aec25993a 100644
--- a/python/python/tests/test_merge_insert_lsm.py
+++ b/python/python/tests/test_merge_insert_lsm.py
@@ -8,7 +8,7 @@ from datetime import timedelta
 import lancedb
 import pyarrow as pa
 import pytest
-from lancedb._lancedb import LsmWriteSpec
+from lancedb._lancedb import MemWalWriteSpec
 from lancedb.index import FTS, IvfPq
 
 SCHEMA = pa.schema(
@@ -49,12 +49,12 @@ def _region_reader(rows):
 
 
 def _bucket_table(tmp_path):
-    """A table with ``id`` as the primary key and a single-bucket LSM spec."""
+    """A table with ``id`` as the primary key and a single-bucket MemWAL spec."""
     db = lancedb.connect(tmp_path, read_consistency_interval=timedelta(seconds=0))
     table = db.create_table("t", _reader([1, 2, 3]))
     table.set_unenforced_primary_key("id")
     # num_buckets = 1: every row routes to the single bucket.
-    table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 1))
+    table.enable_memwal(MemWalWriteSpec.bucket("id", 1))
     return table
 
 
@@ -78,7 +78,7 @@ def test_lsm_merge_insert_unsharded(tmp_path):
     db = lancedb.connect(tmp_path, read_consistency_interval=timedelta(seconds=0))
     table = db.create_table("t", _reader([1, 2, 3]))
     table.set_unenforced_primary_key("id")
-    table.set_lsm_write_spec(LsmWriteSpec.unsharded())
+    table.enable_memwal(MemWalWriteSpec.unsharded())
     result = (
         table.merge_insert("id")
         .when_matched_update_all()
@@ -92,7 +92,7 @@ def test_lsm_merge_insert_identity(tmp_path):
     db = lancedb.connect(tmp_path, read_consistency_interval=timedelta(seconds=0))
     table = db.create_table("t", _region_reader([(1, "us"), (2, "us")]))
     table.set_unenforced_primary_key("id")
-    table.set_lsm_write_spec(LsmWriteSpec.identity("region"))
+    table.enable_memwal(MemWalWriteSpec.identity("region"))
     # All rows share one identity value, so they route to one shard.
     result = (
         table.merge_insert([])
@@ -103,31 +103,31 @@ def test_lsm_merge_insert_identity(tmp_path):
     assert result.num_rows == 2
 
 
-def test_lsm_merge_insert_use_lsm_false(tmp_path):
+def test_lsm_merge_insert_use_memwal_false(tmp_path):
     table = _bucket_table(tmp_path)  # rows id = 1, 2, 3
-    # use_lsm(False) opts out: the standard path runs and commits even with a spec.
+    # use_memwal(False) opts out: the standard path runs and commits even with a spec.
     result = (
         table.merge_insert("id")
         .when_not_matched_insert_all()
-        .use_lsm(False)
+        .use_memwal(False)
         .execute(_reader([3, 4, 5]))
     )
     assert result.num_inserted_rows == 2
     assert table.count_rows() == 5
 
 
-def test_lsm_merge_insert_use_lsm_true_without_spec_errors(tmp_path):
-    # A table with a primary key but no LSM write spec installed.
+def test_lsm_merge_insert_use_memwal_true_without_spec_errors(tmp_path):
+    # A table with a primary key but no MemWAL write spec installed.
     db = lancedb.connect(tmp_path, read_consistency_interval=timedelta(seconds=0))
     table = db.create_table("t", _reader([1, 2, 3]))
     table.set_unenforced_primary_key("id")
-    # use_lsm(True) demands MemWAL routing; without a spec it errors.
-    with pytest.raises(Exception, match="use_lsm"):
+    # use_memwal(True) demands MemWAL routing; without a spec it errors.
+    with pytest.raises(Exception, match="use_memwal"):
         (
             table.merge_insert("id")
             .when_matched_update_all()
             .when_not_matched_insert_all()
-            .use_lsm(True)
+            .use_memwal(True)
             .execute(_reader([3, 4, 5]))
         )
 
@@ -145,7 +145,7 @@ def test_lsm_merge_insert_validate_single_shard_off(tmp_path):
 
 
 def test_lsm_merge_insert_no_spec_uses_standard_path(tmp_path):
-    # A table with a primary key but no LSM write spec installed.
+    # A table with a primary key but no MemWAL write spec installed.
     db = lancedb.connect(tmp_path, read_consistency_interval=timedelta(seconds=0))
     table = db.create_table("t", _reader([1, 2, 3]))
     table.set_unenforced_primary_key("id")
@@ -186,7 +186,7 @@ def test_lsm_close_writers(tmp_path):
         .when_not_matched_insert_all()
         .execute(_reader([7, 8]))
     )
-    table.close_lsm_writers()
+    table.close_memwal_writers()
     # The writer reopens lazily on the next merge_insert.
     result = (
         table.merge_insert([])
@@ -204,14 +204,14 @@ async def test_async_lsm_merge_insert(tmp_path):
     )
     table = await db.create_table("t", _reader([1, 2, 3]))
     await table.set_unenforced_primary_key("id")
-    await table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 1))
+    await table.enable_memwal(MemWalWriteSpec.bucket("id", 1))
 
     builder = (
         table.merge_insert([]).when_matched_update_all().when_not_matched_insert_all()
     )
     result = await builder.execute(_reader([3, 4, 5]))
     assert result.num_rows == 3
-    await table.close_lsm_writers()
+    await table.close_memwal_writers()
 
 
 def _lsm_upsert(table, ids):
@@ -228,7 +228,7 @@ def test_lsm_read_sees_active_memtable(tmp_path):
     db = lancedb.connect(tmp_path, read_consistency_interval=timedelta(seconds=0))
     table = db.create_table("t", _reader([1, 2, 3]))  # base ids 1,2,3
     table.set_unenforced_primary_key("id")
-    table.set_lsm_write_spec(LsmWriteSpec.unsharded())
+    table.enable_memwal(MemWalWriteSpec.unsharded())
 
     _lsm_upsert(table, [4, 5])  # active memtable only, not committed to base
 
@@ -236,8 +236,8 @@ def test_lsm_read_sees_active_memtable(tmp_path):
     lsm = table.search().to_arrow()
     assert sorted(lsm["id"].to_pylist()) == [1, 2, 3, 4, 5]
 
-    # use_lsm(False) bypasses the MemWAL and reads the base table only.
-    base_only = table.search().use_lsm(False).to_arrow()
+    # use_memwal(False) bypasses the MemWAL and reads the base table only.
+    base_only = table.search().use_memwal(False).to_arrow()
     assert sorted(base_only["id"].to_pylist()) == [1, 2, 3]
 
 
@@ -245,7 +245,7 @@ def test_lsm_read_dedup_newest_wins(tmp_path):
     db = lancedb.connect(tmp_path, read_consistency_interval=timedelta(seconds=0))
     table = db.create_table("t", _reader([1, 2, 3]))  # id 2 -> value 1
     table.set_unenforced_primary_key("id")
-    table.set_lsm_write_spec(LsmWriteSpec.unsharded())
+    table.enable_memwal(MemWalWriteSpec.unsharded())
 
     _lsm_upsert(table, [2, 3, 4])  # ids 2,3,4 -> values 0,1,2
 
@@ -258,22 +258,22 @@ def test_lsm_read_dedup_newest_wins(tmp_path):
 def test_lsm_read_without_spec_reads_base(tmp_path):
     db = lancedb.connect(tmp_path, read_consistency_interval=timedelta(seconds=0))
     table = db.create_table("t", _reader([1, 2, 3]))
-    table.set_unenforced_primary_key("id")  # no LSM write spec
+    table.set_unenforced_primary_key("id")  # no MemWAL write spec
 
-    # No spec: default read and use_lsm(False) both read the base table, no error.
+    # No spec: default read and use_memwal(False) both read the base table, no error.
     assert sorted(table.search().to_arrow()["id"].to_pylist()) == [1, 2, 3]
-    assert sorted(table.search().use_lsm(False).to_arrow()["id"].to_pylist()) == [
+    assert sorted(table.search().use_memwal(False).to_arrow()["id"].to_pylist()) == [
         1,
         2,
         3,
     ]
 
 
-def test_lsm_read_unsupported_shape_errors_without_use_lsm_false(tmp_path):
+def test_lsm_read_unsupported_shape_errors_without_use_memwal_false(tmp_path):
     db = lancedb.connect(tmp_path, read_consistency_interval=timedelta(seconds=0))
     table = db.create_table("t", _reader([1, 2, 3]))
     table.set_unenforced_primary_key("id")
-    table.set_lsm_write_spec(LsmWriteSpec.unsharded())
+    table.enable_memwal(MemWalWriteSpec.unsharded())
     _lsm_upsert(table, [4])
 
     # with_row_id is unsupported by the LSM scanner; on a MemWAL table the default
@@ -281,8 +281,8 @@ def test_lsm_read_unsupported_shape_errors_without_use_lsm_false(tmp_path):
     with pytest.raises(Exception):
         table.search().with_row_id(True).to_arrow()
 
-    # use_lsm(False) is the escape hatch: it reads the base table only.
-    base = table.search().with_row_id(True).use_lsm(False).to_arrow()
+    # use_memwal(False) is the escape hatch: it reads the base table only.
+    base = table.search().with_row_id(True).use_memwal(False).to_arrow()
     assert sorted(base["id"].to_pylist()) == [1, 2, 3]
 
 
@@ -293,7 +293,7 @@ async def test_async_lsm_read(tmp_path):
     )
     table = await db.create_table("t", _reader([1, 2, 3]))
     await table.set_unenforced_primary_key("id")
-    await table.set_lsm_write_spec(LsmWriteSpec.unsharded())
+    await table.enable_memwal(MemWalWriteSpec.unsharded())
 
     builder = (
         table.merge_insert([]).when_matched_update_all().when_not_matched_insert_all()
@@ -331,7 +331,7 @@ def _vector_reader(rows):
 def _vector_table(tmp_path):
     """Base table whose vector column is indexed so its rows are visible to the LSM
     vector scanner (the base arm uses ``fast_search`` — indexed data only), plus an
-    unsharded LSM spec that maintains that index for the memtable.
+    unsharded MemWAL spec that maintains that index for the memtable.
 
     Rows 1,2 are category ``a``, row 3 is ``b``, and 4..60 are filler ``c`` that
     give the tiny IVF index enough data to train.
@@ -353,8 +353,8 @@ def _vector_table(tmp_path):
         "vector", config=IvfPq(num_partitions=1, num_sub_vectors=2, num_bits=4)
     )
     index_name = table.list_indices()[0].name
-    table.set_lsm_write_spec(
-        LsmWriteSpec.unsharded().with_maintained_indexes([index_name])
+    table.enable_memwal(
+        MemWalWriteSpec.unsharded().with_maintained_indexes([index_name])
     )
     return table
 
@@ -379,9 +379,9 @@ def test_lsm_read_vector_sees_memtable(tmp_path):
     assert {1, 2, 3} <= ids  # indexed base rows
     assert 1000 in ids  # in-flight memtable row
 
-    # use_lsm(False) bypasses the MemWAL, so the in-flight row is not visible.
+    # use_memwal(False) bypasses the MemWAL, so the in-flight row is not visible.
     base_ids = set(
-        table.search(query).use_lsm(False).limit(100).to_arrow()["id"].to_pylist()
+        table.search(query).use_memwal(False).limit(100).to_arrow()["id"].to_pylist()
     )
     assert {1, 2, 3} <= base_ids
     assert 1000 not in base_ids
@@ -453,8 +453,8 @@ def test_lsm_read_fts_sees_memtable(tmp_path):
     # Native FTS index (tantivy is not compatible with the LSM memtable index).
     table.create_index("text", config=FTS())
     index_name = table.list_indices()[0].name
-    table.set_lsm_write_spec(
-        LsmWriteSpec.unsharded().with_maintained_indexes([index_name])
+    table.enable_memwal(
+        MemWalWriteSpec.unsharded().with_maintained_indexes([index_name])
     )
 
     # in-flight doc 4 lands in the memtable's maintained FTS index.
@@ -498,8 +498,8 @@ def test_lsm_read_vector_unsupported_knobs_error(tmp_path):
     with pytest.raises(Exception, match="use_index"):
         table.search(query).bypass_vector_index().to_arrow()
 
-    # use_lsm(False) is the escape hatch: the base-only standard path honors them.
-    base = table.search(query).distance_range(0.0, 100.0).use_lsm(False).to_arrow()
+    # use_memwal(False) is the escape hatch: the base-only standard path honors them.
+    base = table.search(query).distance_range(0.0, 100.0).use_memwal(False).to_arrow()
     assert 1000 not in set(base["id"].to_pylist())
 
 
@@ -544,7 +544,7 @@ def test_lsm_read_fts_unmaintained_index_errors(tmp_path):
     table.create_index("text", config=FTS())
     # No maintained indexes: the active memtable FTS arm cannot serve un-compacted
     # docs, so the search would silently omit them — reject instead.
-    table.set_lsm_write_spec(LsmWriteSpec.unsharded().with_maintained_indexes([]))
+    table.enable_memwal(MemWalWriteSpec.unsharded().with_maintained_indexes([]))
     with pytest.raises(Exception, match="maintained"):
         table.search("fox", query_type="fts", fts_columns="text").to_arrow()
 
@@ -553,7 +553,7 @@ def test_lsm_read_time_travel_errors(tmp_path):
     db = lancedb.connect(tmp_path, read_consistency_interval=timedelta(seconds=0))
     table = db.create_table("t", _reader([1, 2, 3]))
     table.set_unenforced_primary_key("id")
-    table.set_lsm_write_spec(LsmWriteSpec.unsharded())
+    table.enable_memwal(MemWalWriteSpec.unsharded())
     pinned = table.version
     table.add(_reader([4, 5]))  # standard add commits a newer version
     table.checkout(pinned)  # detached head at the historical version
@@ -562,8 +562,8 @@ def test_lsm_read_time_travel_errors(tmp_path):
     # historical version is rejected.
     with pytest.raises(Exception, match="time-travel"):
         table.search().to_arrow()
-    # use_lsm(False) reads the base table at the pinned version.
-    base = table.search().use_lsm(False).to_arrow()
+    # use_memwal(False) reads the base table at the pinned version.
+    base = table.search().use_memwal(False).to_arrow()
     assert sorted(base["id"].to_pylist()) == [1, 2, 3]
 
 
@@ -571,14 +571,14 @@ def test_lsm_read_take_row_ids_errors(tmp_path):
     db = lancedb.connect(tmp_path, read_consistency_interval=timedelta(seconds=0))
     table = db.create_table("t", _reader([1, 2, 3]))
     table.set_unenforced_primary_key("id")
-    table.set_lsm_write_spec(LsmWriteSpec.unsharded())
+    table.enable_memwal(MemWalWriteSpec.unsharded())
     _lsm_upsert(table, [4])
     # take-by-row-id auto-routes through the LSM scanner, which has no stable _rowid,
     # so it hard-errors instead of failing with an opaque column-not-found error.
     with pytest.raises(Exception, match="row id"):
         table.take_row_ids([0, 1]).to_arrow()
-    # use_lsm(False) is the escape hatch: it reads the base table.
-    base = table.take_row_ids([0, 1]).use_lsm(False).to_arrow()
+    # use_memwal(False) is the escape hatch: it reads the base table.
+    base = table.take_row_ids([0, 1]).use_memwal(False).to_arrow()
     assert base.num_rows == 2
 
 
@@ -588,8 +588,8 @@ def test_lsm_read_fts_postfilter_errors(tmp_path):
     table.set_unenforced_primary_key("id")
     table.create_index("text", config=FTS())
     index_name = table.list_indices()[0].name
-    table.set_lsm_write_spec(
-        LsmWriteSpec.unsharded().with_maintained_indexes([index_name])
+    table.enable_memwal(
+        MemWalWriteSpec.unsharded().with_maintained_indexes([index_name])
     )
     # The LSM scanner always prefilters; postfilter on FTS changes result semantics,
     # so it hard-errors (previously only the vector arm rejected it).
@@ -608,9 +608,7 @@ def test_lsm_read_fts_multiple_same_type_indexes_errors(tmp_path):
     table.set_unenforced_primary_key("id")
     table.create_index("text", config=FTS(), name="fts_a")
     table.create_index("text", config=FTS(), name="fts_b", replace=False)
-    table.set_lsm_write_spec(
-        LsmWriteSpec.unsharded().with_maintained_indexes(["fts_a"])
-    )
+    table.enable_memwal(MemWalWriteSpec.unsharded().with_maintained_indexes(["fts_a"]))
     # Two FTS indexes on the column: the base planner's chosen index is ambiguous, so
     # the scanner can't pick a catch-up watermark and rejects rather than risk
     # dropping rows the actually-used index has not caught up to.
@@ -631,7 +629,7 @@ def test_lsm_read_vector_unmaintained_index_errors(tmp_path):
     )
     # Spec with NO maintained indexes: the base vector index's catch-up is untracked,
     # so the scanner rejects rather than risk dropping compacted-but-unindexed rows.
-    table.set_lsm_write_spec(LsmWriteSpec.unsharded().with_maintained_indexes([]))
+    table.enable_memwal(MemWalWriteSpec.unsharded().with_maintained_indexes([]))
     with pytest.raises(Exception, match="maintained"):
         table.search([1.0] * VECTOR_DIM).to_arrow()
 
@@ -644,7 +642,7 @@ def test_lsm_read_fts_optimized_index_not_rejected(tmp_path):
     table.add(_fts_reader([(i, "lazy fox") for i in range(6, 11)]))
     table.optimize()  # may split the FTS index into multiple physical segments
     name = table.list_indices()[0].name
-    table.set_lsm_write_spec(LsmWriteSpec.unsharded().with_maintained_indexes([name]))
+    table.enable_memwal(MemWalWriteSpec.unsharded().with_maintained_indexes([name]))
     # Multiple physical segments of one logical index must not be miscounted as
     # multiple indexes and rejected.
     ids = set(
diff --git a/python/python/tests/test_namespace.py b/python/python/tests/test_namespace.py
index 7f095b71d..520a6992d 100644
--- a/python/python/tests/test_namespace.py
+++ b/python/python/tests/test_namespace.py
@@ -6,11 +6,17 @@
 import tempfile
 import shutil
 import importlib
+import http.server
+import json
+import threading
+from urllib.parse import parse_qs, unquote, urlparse
 import pytest
 import pyarrow as pa
 import lancedb
 from lance_namespace.errors import NamespaceNotEmptyError, TableNotFoundError
-from lancedb.namespace import _MAX_QUERY_K
+from lancedb.listing import AsyncListing, Listing
+from lancedb.namespace import _MAX_QUERY_K, _query_to_namespace_request
+from lancedb.query import Query
 from lancedb.table import AsyncTable, LanceTable
 
 
@@ -72,6 +78,132 @@ def _namespace_lance_table(namespace_client: _NamespaceClient) -> LanceTable:
     return table
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize("namespace_path", [[], ["test_ns"]])
+async def test_namespace_table_names_defaults_and_limits(tmp_path, namespace_path):
+    db = lancedb.connect_namespace("dir", {"root": str(tmp_path)})
+    if namespace_path:
+        db.create_namespace(namespace_path)
+    names = [f"t{idx:03}" for idx in range(12)]
+    schema = pa.schema([pa.field("id", pa.int64())])
+    for name in names:
+        db.create_table(name, schema=schema, namespace_path=namespace_path)
+
+    assert list(db.table_names(namespace_path=namespace_path)) == names
+    assert list(db.table_names(limit=None, namespace_path=namespace_path)) == names
+    assert list(db.table_names(limit=0, namespace_path=namespace_path)) == []
+    assert list(db.table_names(limit=2, namespace_path=namespace_path)) == names[:2]
+    assert (
+        list(
+            db.table_names(page_token=names[1], limit=2, namespace_path=namespace_path)
+        )
+        == names[2:4]
+    )
+
+    adb = lancedb.connect_namespace_async("dir", {"root": str(tmp_path)})
+    assert list(await adb.table_names(namespace_path=namespace_path)) == names
+    assert (
+        list(await adb.table_names(limit=None, namespace_path=namespace_path)) == names
+    )
+    assert list(await adb.table_names(limit=0, namespace_path=namespace_path)) == []
+    assert (
+        list(await adb.table_names(limit=2, namespace_path=namespace_path)) == names[:2]
+    )
+    assert (
+        list(
+            await adb.table_names(
+                page_token=names[1], limit=2, namespace_path=namespace_path
+            )
+        )
+        == names[2:4]
+    )
+
+
+@pytest.fixture
+def paginated_rest_namespace():
+    names = [f"t{idx:03}" for idx in reversed(range(12))]
+    requests = []
+
+    class Handler(http.server.BaseHTTPRequestHandler):
+        def do_GET(self):
+            url = urlparse(self.path)
+            query = parse_qs(url.query)
+            requests.append((unquote(url.path), query))
+            token = query.get("page_token", [None])[0]
+            if token not in {None, "opaque-next"}:
+                self.send_error(400, "Unknown page token")
+                return
+            start = 0 if token is None else 10
+            body = {"tables": names[start : start + 10]}
+            if start == 0:
+                body["page_token"] = "opaque-next"
+            self.send_response(200)
+            self.send_header("Content-Type", "application/json")
+            self.end_headers()
+            self.wfile.write(json.dumps(body).encode())
+
+        def log_message(self, *args):
+            pass
+
+    with http.server.HTTPServer(("127.0.0.1", 0), Handler) as server:
+        thread = threading.Thread(target=server.serve_forever)
+        thread.start()
+        try:
+            yield {"uri": f"http://127.0.0.1:{server.server_port}"}, requests
+        finally:
+            server.shutdown()
+            thread.join()
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("namespace_path", [[], ["n"]])
+async def test_rest_namespace_table_names_collects_pages(
+    paginated_rest_namespace, namespace_path
+):
+    properties, requests = paginated_rest_namespace
+    names = [f"t{idx:03}" for idx in range(12)]
+    db = lancedb.connect_namespace("rest", properties)
+    first = db.list_tables(namespace_path=namespace_path)
+    second = db.list_tables(namespace_path=namespace_path, page_token=first.page_token)
+    assert len(first.tables) == 10
+    assert first.page_token == "opaque-next"
+    assert sorted(first.tables + second.tables) == names
+    assert second.page_token is None
+
+    assert list(db.table_names(namespace_path=namespace_path)) == names
+    assert list(db.table_names(limit=None, namespace_path=namespace_path)) == names
+    assert list(db.table_names(limit=11, namespace_path=namespace_path)) == names[:11]
+    assert (
+        list(db.table_names(page_token="t005", limit=2, namespace_path=namespace_path))
+        == names[6:8]
+    )
+
+    adb = lancedb.connect_namespace_async("rest", properties)
+    assert list(await adb.table_names(namespace_path=namespace_path)) == names
+    assert (
+        list(await adb.table_names(limit=None, namespace_path=namespace_path)) == names
+    )
+    assert (
+        list(await adb.table_names(limit=11, namespace_path=namespace_path))
+        == names[:11]
+    )
+    assert (
+        list(
+            await adb.table_names(
+                page_token="t005", limit=2, namespace_path=namespace_path
+            )
+        )
+        == names[6:8]
+    )
+
+    request_count = len(requests)
+    assert list(db.table_names(limit=0, namespace_path=namespace_path)) == []
+    assert list(await adb.table_names(limit=0, namespace_path=namespace_path)) == []
+    assert len(requests) == request_count
+    identifier = "$" if not namespace_path else "n"
+    assert all(path == f"/v1/namespace/{identifier}/table/list" for path, _ in requests)
+
+
 class TestNamespaceConnection:
     """Test namespace-based LanceDB connection using DirectoryNamespace."""
 
@@ -104,6 +236,18 @@ class TestNamespaceConnection:
         assert isinstance(db, lancedb.LanceNamespaceDBConnection)
         assert len(list(db.table_names())) == 0
 
+    def test_list_materialized_views(self):
+        db = lancedb.connect_namespace("dir", {"root": self.temp_dir})
+        db.create_table("source", [{"id": 1}])
+        db.create_materialized_view("view", "source", with_no_data=True)
+
+        names = db.list_materialized_views(page_limit=1)
+        assert isinstance(names, Listing)
+        assert names.num_page_results() == 0
+        assert list(names) == ["view"]
+        assert names.num_page_results() == 0
+        assert names.page_token() is None
+
     def test_sync_builtin_namespace_uses_rust_without_python_client(self, monkeypatch):
         """Built-in sync namespace connections should not construct or call the
         Python namespace client for normal namespace/table management."""
@@ -605,6 +749,18 @@ class TestAsyncNamespaceConnection:
         table_names = await db.table_names()
         assert len(list(table_names)) == 0
 
+    async def test_list_materialized_views(self):
+        db = lancedb.connect_namespace_async("dir", {"root": self.temp_dir})
+        await db.create_table("source", [{"id": 1}])
+        await db.create_materialized_view("view", "source", with_no_data=True)
+
+        names = db.list_materialized_views(page_limit=1)
+        assert isinstance(names, AsyncListing)
+        assert names.num_page_results() == 0
+        assert [name async for name in names] == ["view"]
+        assert names.num_page_results() == 0
+        assert names.page_token() is None
+
     async def test_async_builtin_namespace_uses_rust_without_python_client(
         self, monkeypatch
     ):
@@ -956,6 +1112,29 @@ class TestPushdownOperations:
         ]
         assert all(r.k <= 2**31 - 1 for r in namespace_client.requests)
 
+    def test_probe_fields_pass_through_independently(self):
+        request = _query_to_namespace_request(
+            ["geneva", "hist"],
+            Query(
+                vector=[1.0, 2.0],
+                nprobes=20,
+                minimum_nprobes=3,
+                maximum_nprobes=10,
+            ),
+        )
+
+        assert request.nprobes == 20
+        assert request.minimum_nprobes == 3
+        assert request.maximum_nprobes == 10
+
+        request = _query_to_namespace_request(
+            ["geneva", "hist"],
+            Query(vector=[1.0, 2.0], nprobes=5, maximum_nprobes=0),
+        )
+        assert request.nprobes == 5
+        assert request.maximum_nprobes == 0
+        assert request.to_dict()["maximum_nprobes"] == 0
+
 
 @pytest.mark.asyncio
 class TestAsyncPushdownOperations:
@@ -1030,3 +1209,15 @@ def test_local_table_to_arrow_and_to_pandas_are_unchanged(tmp_path):
 
     assert table.to_arrow().column("id").to_pylist() == [1, 2]
     assert table.to_pandas()["id"].tolist() == [1, 2]
+
+
+@pytest.mark.parametrize("effort", [0.0, 0.5, 1.0])
+def test_namespace_request_rejects_search_effort(effort):
+    from lancedb.query import Query
+
+    with pytest.raises(
+        ValueError, match="namespace QueryTable does not support search_effort"
+    ):
+        _query_to_namespace_request(
+            ["test"], Query(vector=[1.0, 0.0], search_effort=effort)
+        )
diff --git a/python/python/tests/test_pydantic.py b/python/python/tests/test_pydantic.py
index ca03940f2..81674d735 100644
--- a/python/python/tests/test_pydantic.py
+++ b/python/python/tests/test_pydantic.py
@@ -9,6 +9,7 @@ from typing import List, Optional, Tuple
 import pyarrow as pa
 import pydantic
 import pytest
+from lancedb.conftest import MockTextEmbeddingFunction
 from lancedb.pydantic import (
     LanceModel,
     MultiVector,
@@ -425,6 +426,25 @@ def test_bare_vector_raises_clear_error():
         exec("class TestModel(LanceModel):\n    vector: Vector", namespace)
 
 
+def test_embedding_vector_list_annotation():
+    embedding = MockTextEmbeddingFunction.create()
+
+    class StaticTypingModel(LanceModel):
+        text: str = embedding.SourceField()
+        vector: list[float] = embedding.VectorField()
+
+    schema = pydantic_to_schema(StaticTypingModel)
+    assert schema == pa.schema(
+        [
+            pa.field("text", pa.utf8(), False),
+            pa.field("vector", pa.list_(pa.float32(), embedding.ndims()), True),
+        ]
+    )
+
+    model = StaticTypingModel(text="hello", vector=[0.0] * embedding.ndims())
+    assert model.vector == [0.0] * embedding.ndims()
+
+
 def test_fixed_size_list_field():
     class TestModel(pydantic.BaseModel):
         vec: Vector(16)
diff --git a/python/python/tests/test_query.py b/python/python/tests/test_query.py
index e3d55917d..c8d0ad159 100644
--- a/python/python/tests/test_query.py
+++ b/python/python/tests/test_query.py
@@ -24,6 +24,7 @@ from lancedb.pydantic import LanceModel, Vector
 from lancedb.query import (
     AsyncFTSQuery,
     AsyncHybridQuery,
+    AsyncQuery,
     AsyncQueryBase,
     AsyncVectorQuery,
     ColumnOrdering,
@@ -110,6 +111,26 @@ async def _create_blob_v2_query_table_async(db, name):
     return table
 
 
+def _nested_blob_v2_query_schema():
+    return pa.schema(
+        [
+            pa.field("id", pa.int64()),
+            pa.field("vector", pa.list_(pa.float32(), list_size=2)),
+            pa.field(
+                "info",
+                pa.struct([pa.field("label", pa.string()), lancedb.blob("blob")]),
+            ),
+        ]
+    )
+
+
+def _nested_blob_v2_query_rows():
+    return [
+        {"id": 1, "vector": [1.0, 0.0], "info": {"label": "one", "blob": b"one"}},
+        {"id": 2, "vector": [2.0, 0.0], "info": {"label": "two", "blob": b"two"}},
+    ]
+
+
 def _assert_lazy_blob(value, expected: bytes):
     assert hasattr(value, "readall")
     assert value.readall() == expected
@@ -502,24 +523,223 @@ def test_vector_query_to_pandas_blob_mode_requires_native_path(tmp_db):
         storage_options=LEGACY_BLOB_STORAGE_OPTIONS,
     )
 
-    with pytest.raises(RuntimeError, match="Lance native pandas conversion"):
+    with pytest.raises(RuntimeError, match="no fetchable blob v2 source"):
         table.search([1.0, 0.0]).select(["blob", "vector"]).limit(1).to_pandas(
             blob_mode="lazy"
         )
 
 
-def test_vector_query_to_pandas_blob_descriptions_requires_plain_scan(tmp_db):
+def test_vector_query_to_pandas_legacy_blob_descriptions_and_bytes(tmp_db):
     pytest.importorskip("lance")
     table = tmp_db.create_table(
         "test_vector_query_blob_descriptions",
         _blob_query_data(),
         storage_options=LEGACY_BLOB_STORAGE_OPTIONS,
     )
+    query = table.search([1.0, 0.0]).select(["blob", "vector"]).limit(1)
 
-    with pytest.raises(RuntimeError, match="plain scan query"):
-        table.search([1.0, 0.0]).select(["blob", "vector"]).limit(1).to_pandas(
-            blob_mode="descriptions"
+    df = query.to_pandas(blob_mode="descriptions")
+    assert df["blob"].tolist() == [{"position": 0, "size": 3}]
+
+    with pytest.raises(ValueError, match="legacy blob columns"):
+        query.to_pandas(blob_mode="bytes")
+    with pytest.raises(ValueError, match="legacy blob columns"):
+        query.to_arrow(blob_mode="bytes")
+    with pytest.raises(ValueError, match="legacy blob columns"):
+        table.to_arrow(blob_mode="bytes")
+    assert table.to_pandas(blob_mode="bytes")["blob"].tolist() == [
+        b"one",
+        b"two",
+        b"three",
+        b"four",
+    ]
+
+
+@pytest.mark.parametrize("blob_mode", ["default", "lazy", "bytes", "descriptions"])
+@pytest.mark.parametrize("with_row_id", [None, True, False])
+def test_vector_query_to_pandas_blob_v2_modes(tmp_db, blob_mode, with_row_id):
+    pytest.importorskip("lance")
+    table = _create_blob_v2_query_table(tmp_db, f"test_vector_blob_v2_{blob_mode}")
+
+    query = table.search([1.0, 0.0]).select(["id", "blob", "vector"]).limit(1)
+    if with_row_id is not None:
+        query = query.with_row_id(with_row_id)
+    df = (
+        query.to_pandas()
+        if blob_mode == "default"
+        else query.to_pandas(blob_mode=blob_mode)
+    )
+
+    assert df["id"].tolist() == [1]
+    assert ("_rowid" in df.columns) == (with_row_id is True)
+    if blob_mode == "bytes":
+        assert df["blob"].tolist() == [b"one"]
+    else:
+        descriptor = df["blob"].iloc[0]
+        assert descriptor != b"one"
+        assert not hasattr(descriptor, "readall")
+        assert "_lance_row_id" not in descriptor
+
+
+@pytest.mark.parametrize("blob_mode", ["lazy", "bytes", "descriptions"])
+def test_blob_v2_non_scan_query_shapes_to_pandas(tmp_db, blob_mode):
+    pytest.importorskip("lance")
+    table = _create_blob_v2_query_table(tmp_db, "test_blob_v2_query_shapes")
+    table.create_index("tag", config=FTS())
+
+    queries = [
+        table.search("keep", query_type="fts").limit(2),
+        table.search(query_type="hybrid").vector([1.0, 0.0]).text("keep").limit(2),
+        table.take_offsets([1, 2]).select(["id", "blob"]),
+    ]
+    for query in queries:
+        df = query.to_pandas(blob_mode=blob_mode)
+        assert len(df) == 2
+        if blob_mode == "bytes":
+            assert df["blob"].tolist() == [
+                {1: b"one", 2: b"two", 3: b"three", 4: b"four"}[row_id]
+                for row_id in df["id"]
+            ]
+        else:
+            assert all("_lance_row_id" not in blob for blob in df["blob"])
+        assert "_rowid" not in df.columns
+
+
+@pytest.mark.parametrize("rename_parent", [False, True])
+def test_vector_query_to_pandas_nested_blob_bytes(tmp_db, rename_parent):
+    pytest.importorskip("lance")
+    table = tmp_db.create_table(
+        f"test_vector_nested_blob_{rename_parent}",
+        schema=_nested_blob_v2_query_schema(),
+    )
+    table.add(_nested_blob_v2_query_rows())
+
+    projection = {"id": "id", "renamed": "info"} if rename_parent else ["id", "info"]
+    df = (
+        table.search([1.0, 0.0])
+        .select(projection)
+        .limit(2)
+        .to_pandas(blob_mode="bytes")
+    )
+
+    assert df["id"].tolist() == [1, 2]
+    parent = "renamed" if rename_parent else "info"
+    assert df[parent].tolist() == [
+        {"label": "one", "blob": b"one"},
+        {"label": "two", "blob": b"two"},
+    ]
+    for blob_mode in ("lazy", "descriptions"):
+        descriptors = (
+            table.search([1.0, 0.0])
+            .select(projection)
+            .limit(1)
+            .to_pandas(blob_mode=blob_mode)
         )
+        assert "_lance_row_id" not in descriptors[parent].iloc[0]["blob"]
+
+
+@pytest.mark.asyncio
+async def test_async_vector_query_to_pandas_nested_blob_bytes(tmp_db_async):
+    pytest.importorskip("lance")
+    table = await tmp_db_async.create_table(
+        "test_async_vector_nested_blob",
+        schema=_nested_blob_v2_query_schema(),
+    )
+    await table.add(_nested_blob_v2_query_rows())
+
+    df = await (
+        table.vector_search([1.0, 0.0])
+        .select(["id", "info"])
+        .limit(2)
+        .to_pandas(blob_mode="bytes")
+    )
+
+    assert df["id"].tolist() == [1, 2]
+    assert df["info"].tolist() == [
+        {"label": "one", "blob": b"one"},
+        {"label": "two", "blob": b"two"},
+    ]
+
+
+def test_blob_v2_vector_projection_and_plain_scan_options(tmp_db, monkeypatch):
+    pytest.importorskip("lance")
+    table = _create_blob_v2_query_table(tmp_db, "test_blob_v2_pandas_options")
+
+    df = (
+        table.search([1.0, 0.0])
+        .select({"payload": "blob", "id": "id"})
+        .limit(1)
+        .to_pandas(blob_mode="bytes")
+    )
+    assert df["payload"].tolist() == [b"one"]
+
+    query = table.search().where("id = 1")
+
+    def fail_to_arrow(*args, **kwargs):
+        raise AssertionError("the native scanner should handle bytes with flatten")
+
+    monkeypatch.setattr(query, "to_arrow", fail_to_arrow)
+    assert query.to_pandas(blob_mode="bytes", flatten=True)["blob"].tolist() == [b"one"]
+
+    flattened_lazy = table.search().where("id = 1").to_pandas(flatten=True)
+    assert flattened_lazy["id"].tolist() == [1]
+    assert not any("_lance_row_id" in col for col in flattened_lazy.columns)
+
+    timed = (
+        table.search()
+        .where("id = 1")
+        .to_pandas(blob_mode="bytes", timeout=timedelta(seconds=10))
+    )
+    assert timed["blob"].tolist() == [b"one"]
+
+    timed_lazy = table.search().where("id = 1").to_pandas(timeout=timedelta(seconds=10))
+    assert timed_lazy["id"].tolist() == [1]
+
+
+def test_blob_v2_pandas_preserves_fetch_error(tmp_db, monkeypatch):
+    pytest.importorskip("lance")
+    table = _create_blob_v2_query_table(tmp_db, "test_blob_v2_fetch_error")
+
+    def missing_blob(*args, **kwargs):
+        raise FileNotFoundError("missing obj.bin")
+
+    monkeypatch.setattr(table, "fetch_blobs", missing_blob)
+    with pytest.raises(FileNotFoundError, match="missing obj.bin"):
+        table.search().to_pandas(blob_mode="bytes")
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("blob_mode", ["lazy", "bytes", "descriptions"])
+@pytest.mark.parametrize("with_row_id", [False, True])
+async def test_async_vector_query_to_pandas_blob_v2_modes(
+    tmp_db_async, blob_mode, with_row_id
+):
+    pytest.importorskip("lance")
+    table = await _create_blob_v2_query_table_async(
+        tmp_db_async, f"test_async_vector_blob_v2_{blob_mode}"
+    )
+
+    query = table.vector_search([1.0, 0.0]).limit(1)
+    if with_row_id:
+        query = query.with_row_id()
+    df = await query.to_pandas(blob_mode=blob_mode)
+    assert df["id"].tolist() == [1]
+    assert ("_rowid" in df.columns) == with_row_id
+    if blob_mode == "bytes":
+        assert df["blob"].tolist() == [b"one"]
+    else:
+        assert "_lance_row_id" not in df["blob"].iloc[0]
+
+
+@pytest.mark.asyncio
+async def test_async_take_query_to_pandas_blob_v2_bytes(tmp_db_async):
+    pytest.importorskip("lance")
+    table = await _create_blob_v2_query_table_async(
+        tmp_db_async, "test_async_take_blob_v2"
+    )
+
+    df = await table.take_offsets([1, 2]).to_pandas(blob_mode="bytes")
+    assert set(df["blob"]) == {b"two", b"three"}
 
 
 def test_order_by_plain_query(mem_db):
@@ -974,6 +1194,19 @@ def test_query_builder_with_filter(table):
     assert all(np.array(rs[0]["vector"]) == [3, 4])
 
 
+@pytest.mark.parametrize("query_type", ["vector", "hybrid"])
+@pytest.mark.parametrize("nprobes", [0, -1])
+def test_nprobes_nonpositive_sync(table, query_type, nprobes):
+    if query_type == "hybrid":
+        query = table.search(query_type="hybrid").vector([0, 0]).text("a")
+    else:
+        query = table.search([0, 0])
+    with pytest.raises(
+        ValueError, match="^Invalid input, nprobes must be greater than 0$"
+    ):
+        query.nprobes(nprobes)
+
+
 def test_invalid_nprobes_sync(table):
     with pytest.raises(ValueError, match="minimum_nprobes must be greater than 0"):
         LanceVectorQueryBuilder(table, [0, 0], "vector").minimum_nprobes(0).to_list()
@@ -981,12 +1214,22 @@ def test_invalid_nprobes_sync(table):
         ValueError,
         match="maximum_nprobes must be greater than or equal to minimum_nprobes",
     ):
-        LanceVectorQueryBuilder(table, [0, 0], "vector").maximum_nprobes(5).to_list()
+        (
+            LanceVectorQueryBuilder(table, [0, 0], "vector")
+            .minimum_nprobes(10)
+            .maximum_nprobes(5)
+            .to_list()
+        )
     with pytest.raises(
         ValueError,
-        match="minimum_nprobes must be less than or equal to maximum_nprobes",
+        match="maximum_nprobes must be greater than or equal to minimum_nprobes",
     ):
-        LanceVectorQueryBuilder(table, [0, 0], "vector").minimum_nprobes(100).to_list()
+        (
+            LanceVectorQueryBuilder(table, [0, 0], "vector")
+            .maximum_nprobes(5)
+            .minimum_nprobes(100)
+            .to_list()
+        )
 
 
 def test_nprobes_works_sync(table):
@@ -1005,6 +1248,18 @@ def test_multiple_nprobes_calls_works_sync(table):
     ).minimum_nprobes(20).to_list()
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize("hybrid", [False, True])
+async def test_nprobes_zero_async(table_async: AsyncTable, hybrid):
+    query = table_async.query().nearest_to([0, 0])
+    if hybrid:
+        query = query.nearest_to_text("dog")
+    with pytest.raises(
+        ValueError, match="^Invalid input, nprobes must be greater than 0$"
+    ):
+        query.nprobes(0)
+
+
 @pytest.mark.asyncio
 async def test_invalid_nprobes_async(table_async: AsyncTable):
     with pytest.raises(ValueError, match="minimum_nprobes must be greater than 0"):
@@ -1013,12 +1268,22 @@ async def test_invalid_nprobes_async(table_async: AsyncTable):
         ValueError,
         match="maximum_nprobes must be greater than or equal to minimum_nprobes",
     ):
-        await table_async.vector_search([0, 0]).maximum_nprobes(5).to_list()
+        await (
+            table_async.vector_search([0, 0])
+            .minimum_nprobes(10)
+            .maximum_nprobes(5)
+            .to_list()
+        )
     with pytest.raises(
         ValueError,
         match="minimum_nprobes must be less than or equal to maximum_nprobes",
     ):
-        await table_async.vector_search([0, 0]).minimum_nprobes(100).to_list()
+        await (
+            table_async.vector_search([0, 0])
+            .maximum_nprobes(5)
+            .minimum_nprobes(100)
+            .to_list()
+        )
 
 
 def test_query_builder_with_prefilter(table):
@@ -1279,6 +1544,27 @@ async def test_query_to_polars_async(table_async: AsyncTable):
     assert df.shape == (0, num_columns)
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize("search_kwargs", [{}, {"query": None}])
+@pytest.mark.parametrize("with_vector", [False, True])
+async def test_async_search_without_query(mem_db_async, search_kwargs, with_vector):
+    data = [{"id": i} for i in range(10)]
+    if with_vector:
+        for row in data:
+            row["vector"] = [float(row["id"]), 1.0]
+    table = await mem_db_async.create_table("test", data)
+
+    query = await table.search(**search_kwargs)
+    assert isinstance(query, AsyncQuery)
+    assert await query.limit(3).to_arrow() == await table.query().limit(3).to_arrow()
+
+    query = await table.search(**search_kwargs)
+    assert await query.where("id >= 8").select(["id"]).limit(3).to_list() == [
+        {"id": 8},
+        {"id": 9},
+    ]
+
+
 @pytest.mark.asyncio
 async def test_none_query(table_async: AsyncTable):
     with pytest.raises(ValueError):
@@ -1588,8 +1874,9 @@ def test_query_serialization_sync(table: lancedb.table.Table):
         q,
         vector_column="vector",
         vector=[5.0, 6.0],
-        minimum_nprobes=10,
-        maximum_nprobes=10,
+        nprobes=10,
+        minimum_nprobes=None,
+        maximum_nprobes=None,
         refine_factor=5,
     )
 
@@ -1607,8 +1894,19 @@ def test_query_serialization_sync(table: lancedb.table.Table):
         q,
         vector_column="vector",
         vector=[5.0, 6.0],
-        minimum_nprobes=50,
-        maximum_nprobes=50,
+        nprobes=50,
+        minimum_nprobes=None,
+        maximum_nprobes=None,
+    )
+
+    q = table.search([5.0, 6.0]).minimum_nprobes(5).nprobes(50).to_query_object()
+    check_set_props(
+        q,
+        vector_column="vector",
+        vector=[5.0, 6.0],
+        nprobes=50,
+        minimum_nprobes=5,
+        maximum_nprobes=None,
     )
 
     q = table.search([5.0, 6.0]).maximum_nprobes(10).to_query_object()
@@ -1620,6 +1918,15 @@ def test_query_serialization_sync(table: lancedb.table.Table):
         minimum_nprobes=None,
     )
 
+    q = table.search([5.0, 6.0]).nprobes(5).maximum_nprobes(0).to_query_object()
+    check_set_props(
+        q,
+        vector_column="vector",
+        vector=[5.0, 6.0],
+        nprobes=5,
+        maximum_nprobes=0,
+    )
+
     q = table.search([5.0, 6.0]).distance_range(0.0, 1.0).to_query_object()
     check_set_props(
         q, vector_column="vector", vector=[5.0, 6.0], lower_bound=0.0, upper_bound=1.0
@@ -1669,8 +1976,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         limit=10,
         vector=sample_vector,
         postfilter=False,
-        minimum_nprobes=20,
-        maximum_nprobes=20,
+        minimum_nprobes=None,
+        maximum_nprobes=None,
         with_row_id=False,
         bypass_vector_index=False,
     )
@@ -1680,8 +1987,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         q,
         vector=sample_vector,
         postfilter=False,
-        minimum_nprobes=20,
-        maximum_nprobes=20,
+        minimum_nprobes=None,
+        maximum_nprobes=None,
         with_row_id=False,
         bypass_vector_index=False,
         limit=10,
@@ -1692,8 +1999,9 @@ async def test_query_serialization_async(table_async: AsyncTable):
         q,
         vector=sample_vector,
         postfilter=False,
-        minimum_nprobes=50,
-        maximum_nprobes=50,
+        nprobes=50,
+        minimum_nprobes=None,
+        maximum_nprobes=None,
         with_row_id=False,
         bypass_vector_index=False,
         limit=10,
@@ -1712,8 +2020,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         filter="id = 1",
         postfilter=True,
         vector=sample_vector,
-        minimum_nprobes=20,
-        maximum_nprobes=20,
+        minimum_nprobes=None,
+        maximum_nprobes=None,
         with_row_id=False,
         bypass_vector_index=False,
     )
@@ -1727,8 +2035,9 @@ async def test_query_serialization_async(table_async: AsyncTable):
     check_set_props(
         q,
         vector=sample_vector,
-        minimum_nprobes=10,
-        maximum_nprobes=10,
+        nprobes=10,
+        minimum_nprobes=None,
+        maximum_nprobes=None,
         refine_factor=5,
         postfilter=False,
         with_row_id=False,
@@ -1741,7 +2050,24 @@ async def test_query_serialization_async(table_async: AsyncTable):
         q,
         vector=sample_vector,
         minimum_nprobes=5,
-        maximum_nprobes=20,
+        maximum_nprobes=None,
+        postfilter=False,
+        with_row_id=False,
+        bypass_vector_index=False,
+        limit=10,
+    )
+
+    q = (
+        (await table_async.search([5.0, 6.0]))
+        .nprobes(5)
+        .maximum_nprobes(0)
+        .to_query_object()
+    )
+    check_set_props(
+        q,
+        vector=sample_vector,
+        nprobes=5,
+        maximum_nprobes=0,
         postfilter=False,
         with_row_id=False,
         bypass_vector_index=False,
@@ -1759,8 +2085,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         lower_bound=0.0,
         upper_bound=1.0,
         postfilter=False,
-        minimum_nprobes=20,
-        maximum_nprobes=20,
+        minimum_nprobes=None,
+        maximum_nprobes=None,
         with_row_id=False,
         bypass_vector_index=False,
         limit=10,
@@ -1772,8 +2098,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         distance_type="cosine",
         vector=sample_vector,
         postfilter=False,
-        minimum_nprobes=20,
-        maximum_nprobes=20,
+        minimum_nprobes=None,
+        maximum_nprobes=None,
         with_row_id=False,
         bypass_vector_index=False,
         limit=10,
@@ -1785,8 +2111,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         ef=7,
         vector=sample_vector,
         postfilter=False,
-        minimum_nprobes=20,
-        maximum_nprobes=20,
+        minimum_nprobes=None,
+        maximum_nprobes=None,
         with_row_id=False,
         bypass_vector_index=False,
         limit=10,
@@ -1798,8 +2124,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         bypass_vector_index=True,
         vector=sample_vector,
         postfilter=False,
-        minimum_nprobes=20,
-        maximum_nprobes=20,
+        minimum_nprobes=None,
+        maximum_nprobes=None,
         with_row_id=False,
         limit=10,
     )
@@ -2091,6 +2417,12 @@ def test_ensure_vector_query_nested_empty_list():
         ensure_vector_query([[]])
 
 
+@pytest.mark.parametrize("query", [[], np.array([], dtype=np.float32)])
+def test_vector_query_builder_empty_vector(table, query):
+    with pytest.raises(ValueError, match="^Query vector must not be empty$"):
+        LanceVectorQueryBuilder(table, query, "vector")
+
+
 def test_fast_search(tmp_path):
     db = lancedb.connect(tmp_path)
 
@@ -2152,3 +2484,127 @@ def test_blob_v2_to_batches_row_id(tmp_db):
     assert "_rowid" in hits.column_names
     blobs = table.fetch_blobs("blob", hits)
     assert [blobs[i].as_py() for i in range(len(blobs))] == [b"one", b"two"]
+
+
+@pytest.mark.parametrize("metric", ["l2", "cosine", "dot"])
+@pytest.mark.parametrize(
+    "effort,probes", [(None, 4), (0, 1), (0.25, 2), (0.5, 4), (0.75, 8), (1, 16)]
+)
+def test_search_effort_probe_budget(tmp_path, monkeypatch, metric, effort, probes):
+    import re
+    from lancedb.index import IvfFlat
+
+    monkeypatch.setenv("LANCE_AUTO_PROBE_MARGIN", "0")
+    monkeypatch.setenv("LANCE_AUTO_MIN_INITIAL_NPROBES", "4")
+    monkeypatch.setenv("LANCE_AUTO_MAX_INITIAL_NPROBES", "4")
+    rng = np.random.default_rng(42)
+    vectors = np.repeat(np.eye(16, dtype=np.float32), 32, axis=0)
+    vectors += rng.normal(0, 0.001, vectors.shape).astype(np.float32)
+    data = pa.table(
+        {
+            "id": np.arange(512),
+            "text": ["dog"] * 512,
+            "vector": pa.FixedSizeListArray.from_arrays(pa.array(vectors.ravel()), 16),
+        }
+    )
+    table = lancedb.connect(tmp_path).create_table("effort", data.slice(0, 256))
+    table.add(data.slice(256))
+    table.create_index(
+        "vector", config=IvfFlat(distance_type=metric, num_partitions=16)
+    )
+    vector = np.eye(16, dtype=np.float32)[0]
+
+    def search(value=effort):
+        return table.search(vector).distance_type(metric).search_effort(value).limit(8)
+
+    def partitions(query):
+        vector_plan = query.analyze_plan().split("FTS Search Plan:", 1)[0]
+        values = re.findall(r"partitions_searched=(\d+)", vector_plan)
+        assert values
+        return sum(map(int, values))
+
+    assert partitions(search()) == probes
+    exact = set(
+        table.search(vector)
+        .distance_type(metric)
+        .bypass_vector_index()
+        .limit(8)
+        .to_arrow()["id"]
+        .to_pylist()
+    )
+    actual = set(search().to_arrow()["id"].to_pylist())
+    assert len(actual & exact) / 8 >= 0.5
+    expected = 1 if effort is None else probes
+    assert partitions(search().nprobes(1)) == expected
+    assert (
+        partitions(
+            table.search(vector)
+            .distance_type(metric)
+            .nprobes(1)
+            .search_effort(effort)
+            .limit(8)
+        )
+        == expected
+    )
+    assert partitions(search().nprobes(1).search_effort(None)) == 1
+    assert partitions(search().maximum_nprobes(2)) <= 2
+    assert partitions(search().minimum_nprobes(3).maximum_nprobes(3)) == 3
+    assert partitions(search(0).nprobes(16).maximum_nprobes(0)) == 1
+    if effort is None:
+        assert search().to_arrow() == search(0.5).to_arrow()
+    filtered = search(0).maximum_nprobes(2).where("id % 8 = 0", prefilter=True)
+    assert partitions(filtered) <= 2
+    assert all(row["id"] % 8 == 0 for row in filtered.to_list())
+
+    if metric == "l2" and effort == 0.5:
+        table.create_index("text", config=FTS())
+        hybrid = (
+            table.search(query_type="hybrid")
+            .vector(vector)
+            .text("dog")
+            .nprobes(1)
+            .search_effort(0.5)
+            .limit(8)
+        )
+        assert partitions(hybrid) == 4
+        assert partitions(hybrid.search_effort(None)) == 1
+
+
+@pytest.mark.parametrize(
+    "effort", [-0.1, 1.1, float("nan"), float("inf"), -float("inf")]
+)
+def test_search_effort_invalid_sync(table, effort):
+    with pytest.raises(ValueError, match="search_effort must be finite and in"):
+        table.search([0, 0]).search_effort(effort).to_list()
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("hybrid", [False, True])
+@pytest.mark.parametrize("effort", [None, 0, 0.25, 0.5, 0.75, 1])
+async def test_search_effort_async_roundtrip(table_async, hybrid, effort):
+    query = table_async.vector_search([0, 0])
+    if hybrid:
+        query = query.nearest_to_text("dog")
+    query = query.nprobes(5).search_effort(effort).minimum_nprobes(2).maximum_nprobes(3)
+    request = query.to_query_object()
+    assert request.search_effort == effort
+    assert request.nprobes == 5
+    assert request.minimum_nprobes == 2
+    assert request.maximum_nprobes == 3
+    assert query.search_effort(None).to_query_object().search_effort is None
+    query.search_effort(effort)
+    if not hybrid:
+        assert len(await query.limit(1).to_list()) == 1
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("hybrid", [False, True])
+@pytest.mark.parametrize(
+    "effort", [-0.1, 1.1, float("nan"), float("inf"), -float("inf")]
+)
+async def test_search_effort_invalid_async(table_async, hybrid, effort):
+    query = table_async.vector_search([0, 0])
+    if hybrid:
+        query = query.nearest_to_text("dog")
+    with pytest.raises(ValueError, match="search_effort must be finite and in"):
+        query.search_effort(effort)
diff --git a/python/python/tests/test_remote_db.py b/python/python/tests/test_remote_db.py
index e32dbe8ca..6ee41aa0c 100644
--- a/python/python/tests/test_remote_db.py
+++ b/python/python/tests/test_remote_db.py
@@ -4,6 +4,7 @@ from concurrent.futures import ThreadPoolExecutor
 import contextlib
 from datetime import timedelta
 import http.server
+import io
 import json
 import multiprocessing as mp
 import pickle
@@ -13,11 +14,19 @@ import threading
 import time
 from unittest.mock import MagicMock, patch
 import uuid
+from urllib.parse import parse_qs, urlparse
 from packaging.version import Version
 
 import lancedb
-from lancedb.conftest import MockTextEmbeddingFunction
-from lancedb.query import ColumnOrdering
+import numpy as np
+from lancedb.conftest import MockNonNormTextEmbeddingFunction, MockTextEmbeddingFunction
+from lancedb.embeddings import EmbeddingFunctionConfig, EmbeddingFunctionRegistry
+from lancedb.query import (
+    AsyncQuery,
+    ColumnOrdering,
+    LanceVectorQueryBuilder,
+    MatchQuery,
+)
 from lancedb.remote import ClientConfig
 from lancedb.remote.errors import HttpError, RetryError
 import pytest
@@ -98,6 +107,67 @@ async def mock_lancedb_connection_async(handler, **client_config):
             handle.join()
 
 
+def paginated_table_listing_handler(request):
+    url = urlparse(request.path)
+    assert url.path in {
+        "/v1/table/",
+        "/v1/namespace/$/table/list",
+        "/v1/namespace/n/table/list",
+    }
+    query = parse_qs(url.query)
+    limit = int(query.get("limit", [10])[0])
+    assert limit > 0
+    token = query.get("page_token", ["page-0"])[0]
+    start = int(token.removeprefix("page-"))
+    end = min(start + limit, 130)
+    body = {"tables": [f"t{i:03}" for i in range(start, end)]}
+    if end < 130:
+        body["page_token"] = f"page-{end}"
+    request.send_response(200)
+    request.send_header("Content-Type", "application/json")
+    request.end_headers()
+    request.wfile.write(json.dumps(body).encode())
+
+
+@pytest.mark.parametrize("namespace", [[], ["n"]])
+def test_remote_table_listing_defaults_and_zero_limit(namespace):
+    names = [f"t{i:03}" for i in range(130)]
+    with mock_lancedb_connection(paginated_table_listing_handler) as db:
+        assert db.table_names(namespace_path=namespace) == names
+        assert db.table_names(limit=None, namespace_path=namespace) == names
+        assert db.table_names(limit=0, namespace_path=namespace) == []
+        first = db.list_tables(namespace_path=namespace)
+        assert first.tables == names[:100]
+        assert first.page_token == "page-100"
+        second = db.list_tables(namespace_path=namespace, page_token=first.page_token)
+        assert second.tables == names[100:]
+        assert second.page_token is None
+        assert db.list_tables(namespace_path=namespace, limit=200).tables == names
+        empty = db.list_tables(namespace_path=namespace, limit=0)
+        assert empty.tables == []
+        assert empty.page_token is None
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("namespace", [[], ["n"]])
+async def test_async_remote_table_listing_defaults_and_zero_limit(namespace):
+    names = [f"t{i:03}" for i in range(130)]
+    async with mock_lancedb_connection_async(paginated_table_listing_handler) as db:
+        assert await db.table_names(namespace_path=namespace) == names
+        assert await db.table_names(limit=0, namespace_path=namespace) == []
+        first = await db.list_tables(namespace_path=namespace)
+        assert first.tables == names[:100]
+        assert first.page_token == "page-100"
+        second = await db.list_tables(
+            namespace_path=namespace, page_token=first.page_token
+        )
+        assert second.tables == names[100:]
+        assert second.page_token is None
+        empty = await db.list_tables(namespace_path=namespace, limit=0)
+        assert empty.tables == []
+        assert empty.page_token is None
+
+
 @pytest.mark.asyncio
 async def test_async_remote_db():
     def handler(request):
@@ -119,6 +189,77 @@ async def test_async_remote_db():
         assert table_names == []
 
 
+@pytest.mark.parametrize(
+    "alteration, match",
+    [
+        ({"path": "id"}, "One of rename, nullable or data_type"),
+        (
+            {"path": "id", "nulable": False},  # spellchecker:disable-line
+            "Unknown column alteration key 'nulable'",  # spellchecker:disable-line
+        ),
+        (
+            {
+                "path": "id",
+                "rename": "new_id",
+                "nulable": False,  # spellchecker:disable-line
+            },
+            "Unknown column alteration key 'nulable'",  # spellchecker:disable-line
+        ),
+    ],
+)
+def test_remote_alter_columns_rejects_invalid_before_request(alteration, match):
+    requests = []
+
+    def handler(request):
+        requests.append(request.path)
+        request.send_response(200)
+        request.send_header("Content-Type", "application/json")
+        request.end_headers()
+        request.wfile.write(b'{"version": 1, "schema": {"fields": []}}')
+
+    with mock_lancedb_connection(handler) as db:
+        table = db.open_table("test")
+        requests.clear()
+
+        with pytest.raises(ValueError, match=match):
+            table.alter_columns(alteration)
+
+        assert requests == []
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "alteration, match",
+    [
+        ({"path": "id"}, "One of rename, nullable or data_type"),
+        (
+            {"path": "id", "nulable": False},  # spellchecker:disable-line
+            "Unknown column alteration key 'nulable'",  # spellchecker:disable-line
+        ),
+    ],
+)
+async def test_async_remote_alter_columns_rejects_invalid_before_request(
+    alteration, match
+):
+    requests = []
+
+    def handler(request):
+        requests.append(request.path)
+        request.send_response(200)
+        request.send_header("Content-Type", "application/json")
+        request.end_headers()
+        request.wfile.write(b'{"version": 1, "schema": {"fields": []}}')
+
+    async with mock_lancedb_connection_async(handler) as db:
+        table = await db.open_table("test")
+        requests.clear()
+
+        with pytest.raises(ValueError, match=match):
+            await table.alter_columns(alteration)
+
+        assert requests == []
+
+
 @pytest.mark.asyncio
 async def test_async_checkout():
     def handler(request):
@@ -524,6 +665,106 @@ def test_remote_permutation_is_picklable():
         ]
 
 
+@pytest.mark.parametrize("with_schema", [True, False])
+def test_create_table_embedding_functions(with_schema):
+    func = MockNonNormTextEmbeddingFunction.create()
+    config = EmbeddingFunctionConfig(
+        source_column="text", vector_column="vector", function=func
+    )
+    schema = pa.schema(
+        [pa.field("text", pa.string()), pa.field("vector", pa.list_(pa.float32(), 10))]
+    )
+    received = {}
+
+    def handler(request):
+        if request.path == "/v1/table/test/describe/":
+            # Echo the metadata actually sent by create_table, as the server does.
+            metadata = received["create"].schema.metadata or {}
+            send_json(
+                request,
+                {
+                    "version": 1,
+                    "schema": {
+                        "fields": [
+                            {
+                                "name": "text",
+                                "type": {"type": "string"},
+                                "nullable": True,
+                            },
+                            {
+                                "name": "vector",
+                                "type": {
+                                    "type": "fixed_size_list",
+                                    "fields": [
+                                        {
+                                            "name": "item",
+                                            "type": {"type": "float"},
+                                            "nullable": True,
+                                        }
+                                    ],
+                                    "length": 10,
+                                },
+                                "nullable": True,
+                            },
+                        ],
+                        "metadata": {
+                            k.decode(): v.decode() for k, v in metadata.items()
+                        },
+                    },
+                },
+            )
+        elif request.path in (
+            "/v1/table/test/create/?mode=create",
+            "/v1/table/test/insert/",
+        ):
+            if request.headers.get("Transfer-Encoding") == "chunked":
+                body = bytearray()
+                while True:
+                    size = int(request.rfile.readline(), 16)
+                    if size == 0:
+                        request.rfile.readline()
+                        break
+                    body.extend(request.rfile.read(size))
+                    request.rfile.read(2)
+            else:
+                body = request.rfile.read(int(request.headers["Content-Length"]))
+            operation = "create" if "/create/" in request.path else "insert"
+            received[operation] = pa.ipc.open_stream(body).read_all()
+            send_json(request, {})
+        else:
+            request.send_response(404)
+            request.end_headers()
+
+    with mock_lancedb_connection(handler) as db:
+        table = db.create_table(
+            "test",
+            schema=schema if with_schema else None,
+            data=None if with_schema else [{"text": "hello world"}],
+            embedding_functions=[config],
+        )
+        metadata = pa.schema(
+            [],
+            metadata=EmbeddingFunctionRegistry.get_instance().get_table_metadata(
+                [config]
+            ),
+        ).metadata
+        assert received["create"].schema.metadata == metadata
+        assert table.schema.metadata == metadata
+        assert table.schema.field("vector").type == pa.list_(pa.float32(), 10)
+        assert table.embedding_functions["vector"].source_column == "text"
+        if with_schema:
+            assert received["create"].num_rows == 0
+        else:
+            assert received["create"]["vector"].to_pylist() == [
+                func.compute_source_embeddings(["hello world"])[0].tolist()
+            ]
+
+        table.add([{"text": "goodbye world"}])
+        assert received["insert"]["vector"].to_pylist() == [
+            func.compute_source_embeddings(["goodbye world"])[0].tolist()
+        ]
+
+
 def test_create_table_exist_ok():
     def handler(request):
         if request.path == "/v1/table/test/create/?mode=exist_ok":
@@ -845,6 +1086,77 @@ def test_table_create_indices():
         table.drop_index("custom_fts_idx")
 
 
+@pytest.mark.parametrize("legacy", [False, True])
+@pytest.mark.parametrize(
+    ("base_tokenizer", "options", "expected_filters"),
+    [
+        ("code", {}, (False, False)),
+        ("code", {"stem": None, "remove_stop_words": None}, (False, False)),
+        ("code", {"stem": True}, (True, False)),
+        ("code", {"remove_stop_words": True}, (False, True)),
+        ("code", {"stem": True, "remove_stop_words": True}, (True, True)),
+        ("simple", {}, (True, True)),
+        ("simple", {"stem": False, "remove_stop_words": False}, (False, False)),
+    ],
+)
+def test_remote_fts_tokenizer_filter_defaults(
+    legacy, base_tokenizer, options, expected_filters
+):
+    from lancedb.index import FTS
+
+    received_requests = []
+
+    def handler(request):
+        if request.path == "/v1/table/test/create_index/":
+            content_len = int(request.headers["Content-Length"])
+            received_requests.append(json.loads(request.rfile.read(content_len)))
+            request.send_response(200)
+            request.end_headers()
+        elif request.path == "/v1/table/test/create/?mode=create":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(b"{}")
+        elif request.path == "/v1/table/test/describe/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(
+                json.dumps(
+                    {
+                        "version": 1,
+                        "schema": {
+                            "fields": [
+                                {
+                                    "name": "text",
+                                    "type": {"type": "string"},
+                                    "nullable": False,
+                                }
+                            ]
+                        },
+                    }
+                ).encode()
+            )
+        else:
+            request.send_response(404)
+            request.end_headers()
+
+    with mock_lancedb_connection(handler) as db:
+        table = db.create_table("test", [{"text": "Running in cafes"}])
+        if legacy:
+            with pytest.warns(DeprecationWarning, match="create_fts_index"):
+                table.create_fts_index("text", base_tokenizer=base_tokenizer, **options)
+        else:
+            table.create_index(
+                "text", config=FTS(base_tokenizer=base_tokenizer, **options)
+            )
+
+    assert len(received_requests) == 1
+    params = received_requests[0]
+    assert params["base_tokenizer"] == base_tokenizer
+    assert (params["stem"], params["remove_stop_words"]) == expected_filters
+
+
 def test_remote_create_index_async_returns_job():
     from lancedb.index import BTree
 
@@ -1099,7 +1411,73 @@ def test_remote_job_wait_raises_on_failure():
             job.wait()
 
 
-def test_remote_create_index_new_api():
+@pytest.mark.parametrize("server_version", ["0.2.0", "0.6.0"])
+def test_remote_json_fts_config(server_version):
+    from lancedb.index import FTS
+
+    received_requests = []
+
+    def handler(request):
+        request.send_response(200)
+        request.send_header("Content-Type", "application/json")
+        request.send_header("phalanx-version", server_version)
+        request.end_headers()
+        if request.path == "/v1/table/test/describe/":
+            fields = [
+                {
+                    "name": "doc",
+                    "type": {"type": "large_binary"},
+                    "nullable": True,
+                    "metadata": {"ARROW:extension:name": "lance.json"},
+                },
+                {
+                    "name": "raw",
+                    "type": {"type": "large_binary"},
+                    "nullable": True,
+                },
+            ]
+            request.wfile.write(
+                json.dumps({"version": 1, "schema": {"fields": fields}}).encode()
+            )
+        elif request.path == "/v1/table/test/create_index/":
+            received_requests.append(read_json_body(request))
+            request.wfile.write(b"{}")
+        else:
+            raise AssertionError(f"Unexpected request: {request.path}")
+
+    with mock_lancedb_connection(handler) as db:
+        table = db.open_table("test")
+        table.create_index(
+            "doc",
+            config=FTS(
+                with_position=True,
+                base_tokenizer="whitespace",
+                stem=False,
+                remove_stop_words=False,
+                lower_case=False,
+                block_size=256,
+            ),
+            name="doc_fts",
+            replace=False,
+        )
+        with pytest.raises(ValueError, match="FTS.*lance.json.*raw binary"):
+            table.create_index("raw", config=FTS())
+
+    assert len(received_requests) == 1
+    request = received_requests[0]
+    assert request["column"] == "doc"
+    assert request["index_type"] == "FTS"
+    assert request["name"] == "doc_fts"
+    assert request["replace"] is False
+    assert request["with_position"] is True
+    assert request["base_tokenizer"] == "whitespace"
+    assert request["stem"] is False
+    assert request["remove_stop_words"] is False
+    assert request["lower_case"] is False
+    assert request["block_size"] == 256
+
+
+def test_remote_create_index_new_api(caplog):
     received_requests = []
 
     def handler(request):
@@ -1160,7 +1538,8 @@ def test_remote_create_index_new_api():
     from lancedb.index import BTree, FTS, IvfPq, IvfRq
 
     with mock_lancedb_connection(handler) as db:
-        table = db.create_table("test", [{"id": 1}])
+        table = db.create_table("test", [{"id": 1}], embedding_functions=[])
+        assert "embedding_functions is not yet supported" not in caplog.text
 
         # New API: column-first, config= kwarg. Should NOT emit DeprecationWarning.
         import warnings as _warnings
@@ -1182,6 +1561,8 @@ def test_remote_create_index_new_api():
                 vector_column_name="vector",
                 index_type="IVF_RQ",
                 num_partitions=8,
+                replace=False,
+                accelerator="cuda",
             )
 
         assert len(received_requests) == 6
@@ -1195,6 +1576,159 @@ def test_remote_create_index_new_api():
         ]
         assert received_requests[2]["block_size"] == 256
         assert received_requests[4]["replace"] is False
+        assert received_requests[5]["replace"] is False
+        assert "replace is not supported" not in caplog.text
+        assert caplog.messages == [
+            "GPU accelerator is not supported for remote tables and will be ignored."
+        ]
+
+
+def remote_unsupported_handler(request):
+    assert request.path == "/v1/table/test/describe/"
+    request.send_response(200)
+    request.send_header("Content-Type", "application/json")
+    request.end_headers()
+    request.wfile.write(
+        json.dumps(
+            dict(
+                version=1,
+                schema=dict(
+                    fields=[
+                        dict(name="id", type={"type": "int64"}, nullable=False),
+                        dict(
+                            name="vector",
+                            type={
+                                "type": "fixed_size_list",
+                                "fields": [
+                                    dict(
+                                        name="item",
+                                        type={"type": "float"},
+                                        nullable=True,
+                                    )
+                                ],
+                                "length": 2,
+                            },
+                            nullable=False,
+                        ),
+                    ]
+                ),
+            )
+        ).encode()
+    )
+
+
+def remote_unsupported_operations():
+    from lancedb.index import HnswPq
+
+    return [
+        ("uses_v2_manifest_paths", (), {}, "uses_v2_manifest_paths"),
+        ("migrate_v2_manifest_paths", (), {}, "migrate_manifest_paths_v2"),
+        ("fetch_blob_ranges", ("image", [(1, 0, 1)]), {}, "fetch_blob_ranges"),
+        ("optimize", (), {}, "optimize"),
+        ("create_index", ("vector",), {"config": HnswPq()}, "IVF_HNSW_PQ"),
+    ]
+
+
+@pytest.mark.parametrize(
+    "method,args,kwargs,operation", remote_unsupported_operations()
+)
+def test_remote_unsupported_operations_sync(method, args, kwargs, operation):
+    with mock_lancedb_connection(remote_unsupported_handler) as db:
+        table = db.open_table("test")
+        with pytest.raises(NotImplementedError) as error:
+            getattr(table, method)(*args, **kwargs)
+        expected = f"LanceDBError: not supported: {operation} is not supported "
+        expected += "for remote tables."
+        if operation == "IVF_HNSW_PQ":
+            expected += " Please use IVF_HNSW_SQ instead."
+        assert str(error.value) == expected
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "method,args,kwargs,operation", remote_unsupported_operations()
+)
+async def test_remote_unsupported_operations_async(method, args, kwargs, operation):
+    async with mock_lancedb_connection_async(remote_unsupported_handler) as db:
+        table = await db.open_table("test")
+        if method == "migrate_v2_manifest_paths":
+            method = "migrate_manifest_paths_v2"
+        with pytest.raises(NotImplementedError) as error:
+            await getattr(table, method)(*args, **kwargs)
+        expected = f"LanceDBError: not supported: {operation} is not supported "
+        expected += "for remote tables."
+        if operation == "IVF_HNSW_PQ":
+            expected += " Please use IVF_HNSW_SQ instead."
+        assert str(error.value) == expected
+
+
+def test_remote_unsupported_legacy_hnsw_pq():
+    with mock_lancedb_connection(remote_unsupported_handler) as db:
+        table = db.open_table("test")
+        with pytest.warns(DeprecationWarning, match="create_index"):
+            with pytest.raises(
+                NotImplementedError,
+                match="IVF_HNSW_PQ is not supported for remote tables. "
+                "Please use IVF_HNSW_SQ instead.",
+            ):
+                table.create_index(
+                    vector_column_name="vector", index_type="IVF_HNSW_PQ"
+                )
+
+
+def test_remote_python_only_messages(caplog):
+    with mock_lancedb_connection(remote_unsupported_handler) as db:
+        with caplog.at_level("INFO"):
+            table = db.open_table("test", storage_options={}, index_cache_size=1)
+        assert "storage_options is ignored for remote tables" in caplog.text
+        assert "index_cache_size is ignored for remote tables" in caplog.text
+        for method in ["to_arrow", "to_pandas"]:
+            with pytest.raises(NotImplementedError) as error:
+                getattr(table, method)()
+            assert str(error.value) == f"{method}() is not supported for remote tables."
+        for method in ["compact_files", "cleanup_old_versions"]:
+            with pytest.warns(UserWarning) as warning:
+                getattr(table, method)()
+            assert (
+                str(warning[0].message) == f"{method}() is a no-op for remote tables."
+            )
+
+
+def test_remote_embedding_config_does_not_warn_or_drop_metadata(caplog):
+    received_tables = []
+
+    def handler(request):
+        assert request.path == "/v1/table/test/create/?mode=create"
+        if request.headers.get("Transfer-Encoding") == "chunked":
+            body = bytearray()
+            while size := int(request.rfile.readline().split(b";")[0], 16):
+                body.extend(request.rfile.read(size))
+                assert request.rfile.read(2) == b"\r\n"
+            assert request.rfile.read(2) == b"\r\n"
+        else:
+            body = request.rfile.read(int(request.headers["Content-Length"]))
+        received_tables.append(pa.ipc.open_stream(body).read_all())
+        request.send_response(200)
+        request.send_header("Content-Type", "application/json")
+        request.end_headers()
+        request.wfile.write(b"{}")
+
+    config = EmbeddingFunctionConfig(
+        source_column="text",
+        vector_column="vector",
+        function=MockNonNormTextEmbeddingFunction.create(),
+    )
+    schema = pa.schema(
+        [pa.field("text", pa.string()), pa.field("vector", pa.list_(pa.float32(), 10))]
+    )
+    with mock_lancedb_connection(handler) as db:
+        db.create_table("test", schema=schema, embedding_functions=[config])
+    assert caplog.messages == []
+    assert len(received_tables) == 1
+    metadata = EmbeddingFunctionRegistry.get_instance().get_table_metadata([config])
+    assert (
+        received_tables[0].schema.metadata == pa.schema([], metadata=metadata).metadata
+    )
 
 
 def test_table_wait_for_index_timeout():
@@ -1301,7 +1835,9 @@ def test_stats():
         table = db.create_table("test", [{"id": 1}])
         res = table.stats()
         print(f"{res=}")
-        assert res == stats
+        # The server does not report num_deleted_rows, and that unsupported
+        # state is preserved as None.
+        assert res == {**stats, "num_deleted_rows": None}
 
 
 @contextlib.contextmanager
@@ -1309,10 +1845,10 @@ def lsm_test_table(lsm_handler):
     """A remote table whose LSM routes are served by ``lsm_handler``.
 
     ``lsm_handler(request, route)`` is called for ``/v1/table/test/<route>/``
-    where route is one of flush_lsm, compact_lsm, get_lsm_stats, and is
+    where route is one of flush_memwal, compact_memwal, get_memwal_stats, and is
     responsible for writing the response.
     """
-    routes = ("flush_lsm", "compact_lsm", "get_lsm_stats")
+    routes = ("flush_memwal", "compact_memwal", "get_memwal_stats")
 
     def handler(request):
         match = re.fullmatch(r"/v1/table/test/(\w+)/", request.path)
@@ -1344,9 +1880,9 @@ def send_json(request, payload, status=200):
     request.wfile.write(json.dumps(payload).encode())
 
 
-def test_get_lsm_stats_sync():
+def test_get_memwal_stats_sync():
     """The sync wrapper round-trips the server payload into a dict."""
-    bucket = {
+    table_shard = {
         "shard_id": "b0",
         "status": "Active",
         "writer_epoch": 3,
@@ -1354,7 +1890,7 @@ def test_get_lsm_stats_sync():
         "current_generation": 6,
         "replay_after_wal_entry_position": 40,
         "wal_entry_position_last_seen": 42,
-        "generations": [{"generation": 5, "bytes": 1024, "rows": 7}],
+        "sstables": [{"generation": 5, "bytes": 1024, "rows": 7}],
         "compacting": False,
         "memtables": [
             {
@@ -1369,29 +1905,29 @@ def test_get_lsm_stats_sync():
     seen_bodies = []
 
     def lsm_handler(request, route):
-        assert route == "get_lsm_stats"
+        assert route == "get_memwal_stats"
         seen_bodies.append(read_json_body(request))
-        send_json(request, {"lsm_stats": {"buckets": [bucket]}})
+        send_json(request, {"memwal_stats": {"table_shards": [table_shard]}})
 
     with lsm_test_table(lsm_handler) as table:
-        assert table.get_lsm_stats() == {"buckets": [bucket]}
+        assert table.get_memwal_stats() == {"table_shards": [table_shard]}
         # Off by default, and forwarded when asked for.
-        assert seen_bodies == [{"include_generation_rows": False}]
-        table.get_lsm_stats(include_generation_rows=True)
-        assert seen_bodies[-1] == {"include_generation_rows": True}
+        assert seen_bodies == [{"include_sstable_rows": False}]
+        table.get_memwal_stats(include_sstable_rows=True)
+        assert seen_bodies[-1] == {"include_sstable_rows": True}
 
 
-def test_get_lsm_stats_sync_returns_none_when_lsm_disabled():
-    """A null envelope means the LSM write path is not enabled, not an error."""
+def test_get_memwal_stats_sync_returns_none_when_memwal_disabled():
+    """A null envelope means the MemWAL write path is not enabled, not an error."""
 
     def lsm_handler(request, route):
-        send_json(request, {"lsm_stats": None})
+        send_json(request, {"memwal_stats": None})
 
     with lsm_test_table(lsm_handler) as table:
-        assert table.get_lsm_stats() is None
+        assert table.get_memwal_stats() is None
 
 
-def test_flush_and_compact_lsm_sync():
+def test_flush_and_compact_memwal_sync():
     """Both are one-shot POSTs answered 202 with no body."""
     called = []
 
@@ -1401,13 +1937,13 @@ def test_flush_and_compact_lsm_sync():
         request.end_headers()
 
     with lsm_test_table(lsm_handler) as table:
-        assert table.flush_lsm() is None
-        assert table.compact_lsm() is None
-        assert called == ["flush_lsm", "compact_lsm"]
+        assert table.flush_memwal() is None
+        assert table.compact_memwal() is None
+        assert called == ["flush_memwal", "compact_memwal"]
 
 
-def test_checkpoint_lsm_sync():
-    """Seal, read the watermark, and return once L0 holds nothing.
+def test_checkpoint_memwal_sync():
+    """Freeze, read the watermark, and return once no SSTables remain.
 
     The convergence loop itself is covered in Rust; this pins the sync
     binding to the endpoints it drives.
@@ -1416,28 +1952,65 @@ def test_checkpoint_lsm_sync():
 
     def lsm_handler(request, route):
         called.append(route)
-        if route == "get_lsm_stats":
-            # An empty L0 yields no target watermark, so the loop is done
-            # after the seal without ever polling compaction.
-            send_json(request, {"lsm_stats": {"buckets": []}})
+        if route == "get_memwal_stats":
+            # An empty SSTable tier yields no target watermark, so the loop is done
+            # after the freeze without ever polling compaction.
+            send_json(request, {"memwal_stats": {"table_shards": []}})
         else:
             request.send_response(202)
             request.end_headers()
 
     with lsm_test_table(lsm_handler) as table:
-        assert table.checkpoint_lsm() is None
-        assert called == ["flush_lsm", "get_lsm_stats"]
+        assert table.checkpoint_memwal() is None
+        assert called == ["flush_memwal", "get_memwal_stats"]
 
 
 @contextlib.contextmanager
-def query_test_table(query_handler, *, server_version=Version("0.1.0")):
+def query_test_table(
+    query_handler,
+    *,
+    server_version=Version("0.1.0"),
+    vector_columns=None,
+    embedding_functions=None,
+):
+    if vector_columns is None:
+        vector_columns = {"vector": 3}
+    schema = {
+        "fields": [
+            {"name": "text", "type": {"type": "string"}, "nullable": False},
+            *[
+                {
+                    "name": name,
+                    "type": {
+                        "type": "fixed_size_list",
+                        "fields": [
+                            {
+                                "name": "item",
+                                "type": {"type": "float"},
+                                "nullable": True,
+                            }
+                        ],
+                        "length": dim,
+                    },
+                    "nullable": False,
+                }
+                for name, dim in vector_columns.items()
+            ],
+        ]
+    }
+    if embedding_functions:
+        metadata = EmbeddingFunctionRegistry.get_instance().get_table_metadata(
+            embedding_functions
+        )
+        schema["metadata"] = {key: value.decode() for key, value in metadata.items()}
+
     def handler(request):
         if request.path == "/v1/table/test/describe/":
             request.send_response(200)
             request.send_header("Content-Type", "application/json")
             request.send_header("phalanx-version", str(server_version))
             request.end_headers()
-            request.wfile.write(b'{"version": 1, "schema": {"fields": []}}')
+            request.wfile.write(json.dumps({"version": 1, "schema": schema}).encode())
         elif request.path == "/v1/table/test/query/":
             content_len = int(request.headers.get("Content-Length"))
             body = request.rfile.read(content_len)
@@ -1462,6 +2035,108 @@ def query_test_table(query_handler, *, server_version=Version("0.1.0")):
         yield table
 
 
+@pytest.mark.parametrize(
+    "query,search_kwargs,expected_column",
+    [
+        pytest.param([0.5] * 8, {}, "vector", id="vector-8"),
+        pytest.param([0.5] * 4, {}, "vec2", id="vector-4"),
+        pytest.param(np.array([0.5] * 4), {}, "vec2", id="numpy-vector"),
+        pytest.param([[0.5] * 4, [0.2] * 4], {}, "vec2", id="batch-vectors"),
+        pytest.param(
+            [0.5] * 4, {"vector_column_name": "vec2"}, "vec2", id="explicit-column"
+        ),
+        pytest.param(None, {}, None, id="scan"),
+        pytest.param("hello", {"query_type": "fts"}, None, id="fts"),
+        pytest.param(
+            "hello", {"query_type": "fts", "fts_columns": "text"}, None, id="fts-column"
+        ),
+        pytest.param(MatchQuery("hello", "text"), {}, None, id="structured-fts"),
+    ],
+)
+def test_query_object_sync_matches_local(mem_db, query, search_kwargs, expected_column):
+    vector_columns = {"vector": 8, "vec2": 4}
+    schema = pa.schema(
+        [
+            pa.field(name, pa.list_(pa.float32(), dim))
+            for name, dim in vector_columns.items()
+        ]
+    )
+    local = mem_db.create_table("test", schema=schema)
+    local_query = local.search(query, **search_kwargs).limit(2).to_query_object()
+    assert local_query.vector_column == expected_column
+    assert local_query.fast_search is None
+
+    with query_test_table(None, vector_columns=vector_columns) as remote:
+        remote_query = remote.search(query, **search_kwargs).limit(2).to_query_object()
+        assert remote_query == local_query
+
+
+@pytest.mark.parametrize("fast_search", [None, False, True])
+@pytest.mark.parametrize("query,query_type", [([1, 2, 3], "vector"), ("hello", "fts")])
+def test_query_object_sync_preserves_fast_search(fast_search, query, query_type):
+    vector_columns = {"vector": 3} if query_type == "vector" else {}
+    with query_test_table(None, vector_columns=vector_columns) as table:
+        query_obj = table.search(
+            query, query_type=query_type, fast_search=fast_search
+        ).to_query_object()
+        assert query_obj.fast_search is fast_search
+
+
+def test_query_sync_rejects_ambiguous_vector_column(mem_db):
+    vector_columns = {"vector": 4, "vec2": 4}
+    schema = pa.schema(
+        [
+            pa.field(name, pa.list_(pa.float32(), dim))
+            for name, dim in vector_columns.items()
+        ]
+    )
+    local = mem_db.create_table("test", schema=schema)
+    with pytest.raises(ValueError, match="Candidates:.*vector.*vec2") as local_error:
+        local.search([0.5] * 4)
+
+    with query_test_table(None, vector_columns=vector_columns) as remote:
+        with pytest.raises(ValueError) as remote_error:
+            remote.search([0.5] * 4)
+        assert str(remote_error.value) == str(local_error.value)
+
+
+@pytest.mark.parametrize("vector_columns", [{}, {"v1": 4, "v2": 8}])
+@pytest.mark.parametrize("query_type", ["auto", "fts"])
+def test_query_sync_fts_without_embeddings(vector_columns, query_type):
+    seen = []
+
+    def handler(body):
+        seen.append(body)
+        assert body["full_text_query"] == {"query": "hello", "columns": []}
+        assert body["vector"] == []
+        assert "vector_column" not in body
+        return pa.table({"id": [1]})
+
+    with query_test_table(handler, vector_columns=vector_columns) as table:
+        assert table.search("hello", query_type=query_type).to_list() == [{"id": 1}]
+    assert len(seen) == 1
+
+
+def test_query_sync_auto_with_embedding_function():
+    def handler(body):
+        assert body["vector_column"] == "vector"
+        assert body["vector"] == [0.0] * 10
+        assert "full_text_query" not in body
+        return pa.table({"id": [1]})
+
+    config = EmbeddingFunctionConfig(
+        source_column="text",
+        vector_column="vector",
+        function=MockTextEmbeddingFunction.create(),
+    )
+    with query_test_table(
+        handler, vector_columns={"vector": 10}, embedding_functions=[config]
+    ) as table:
+        query = table.search("hello")
+        assert query.to_query_object().vector_column == "vector"
+        assert query.to_list() == [{"id": 1}]
+
+
 def test_head():
     def handler(body):
         assert body == {
@@ -1488,9 +2163,7 @@ def test_query_sync_minimal():
             "upper_bound": None,
             "ef": None,
             "vector": [1.0, 2.0, 3.0],
-            "nprobes": 20,
-            "minimum_nprobes": 20,
-            "maximum_nprobes": 20,
+            "vector_column": "vector",
             "version": None,
         }
 
@@ -1502,7 +2175,84 @@ def test_query_sync_minimal():
         assert data == expected
 
 
-def test_query_sync_empty_query():
+@pytest.mark.parametrize("query_type", ["auto", "vector", "hybrid"])
+@pytest.mark.parametrize("vector_column_name", [None, "embedding"])
+def test_query_sync_text_embedding(query_type, vector_column_name):
+    embedding_func = MockTextEmbeddingFunction.create()
+    config = EmbeddingFunctionConfig(
+        source_column="text", vector_column="embedding", function=embedding_func
+    )
+    query = "quick brown fox"
+    expected_vector = embedding_func.compute_query_embeddings(query)[0]
+    queries = []
+
+    def handler(body):
+        queries.append(body)
+        if "full_text_query" in body:
+            assert query_type == "hybrid"
+            assert body["full_text_query"]["query"] == query
+            return pa.table({"id": [1], "_rowid": [1], "_score": [1.0]})
+        assert body["vector_column"] == "embedding"
+        assert body["vector"] == pytest.approx(expected_vector)
+        return pa.table({"id": [1], "_rowid": [1], "_distance": [0.0]})
+
+    with query_test_table(
+        handler,
+        vector_columns={
+            "embedding": embedding_func.ndims(),
+            **({"other": embedding_func.ndims()} if vector_column_name else {}),
+        },
+        embedding_functions=[config],
+    ) as table:
+        assert list(table.embedding_functions) == ["embedding"]
+        builder = table.search(
+            query, query_type=query_type, vector_column_name=vector_column_name
+        )
+        if query_type != "hybrid":
+            assert isinstance(builder, LanceVectorQueryBuilder)
+        results = builder.to_list()
+        assert results[0]["id"] == 1
+        if query_type != "hybrid":
+            assert results[0]["_distance"] == 0.0
+    assert len(queries) == (2 if query_type == "hybrid" else 1)
+
+
+@pytest.mark.parametrize("query_type", ["auto", "vector", "hybrid"])
+def test_query_sync_ambiguous_vector_columns(query_type):
+    config = EmbeddingFunctionConfig(
+        source_column="text",
+        vector_column="v1",
+        function=MockTextEmbeddingFunction.create(),
+    )
+    queries = []
+
+    def handler(body):
+        queries.append(body)
+        return pa.table({"id": [1]})
+
+    with query_test_table(
+        handler,
+        vector_columns={"v1": 10, "v2": 10},
+        embedding_functions=[config],
+    ) as table:
+        with pytest.raises(ValueError, match=r"Candidates: \['v1', 'v2'\]"):
+            table.search("puppy", query_type=query_type).to_list()
+    assert queries == []
+
+
+@pytest.mark.parametrize("vector_column, dim", [("v1", 3), ("v2", 4)])
+def test_query_sync_infers_vector_column_by_dimension(vector_column, dim):
+    def handler(body):
+        assert body["vector_column"] == vector_column
+        assert body["vector"] == [1.0] * dim
+        return pa.table({"id": [1]})
+
+    with query_test_table(handler, vector_columns={"v1": 3, "v2": 4}) as table:
+        assert table.search([1.0] * dim).to_list() == [{"id": 1}]
+
+
+@pytest.mark.parametrize("vector_columns", [{}, {"v1": 3, "v2": 3}])
+def test_query_sync_empty_query(vector_columns):
     def handler(body):
         assert body == {
             "k": 10,
@@ -1515,12 +2265,66 @@ def test_query_sync_empty_query():
 
         return pa.table({"id": [1, 2, 3]})
 
-    with query_test_table(handler) as table:
+    with query_test_table(handler, vector_columns=vector_columns) as table:
         data = table.search(None).where("true").select(["id"]).limit(10).to_list()
         expected = [{"id": 1}, {"id": 2}, {"id": 3}]
         assert data == expected
 
 
+@pytest.mark.parametrize("query", [[], np.array([], dtype=np.float32)])
+@pytest.mark.parametrize("vector_column_name", [None, "vector"])
+@pytest.mark.parametrize("query_type", ["auto", "vector"])
+def test_query_sync_empty_vector(query, vector_column_name, query_type):
+    def handler(body):
+        pytest.fail("An empty query vector must be rejected before sending a query")
+
+    with query_test_table(handler) as table:
+        with pytest.raises(ValueError, match="^Query vector must not be empty$"):
+            table.search(
+                query, vector_column_name=vector_column_name, query_type=query_type
+            ).limit(3).to_arrow()
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("search_kwargs", [{}, {"query": None}])
+async def test_async_search_without_query(search_kwargs):
+    expected = pa.table({"id": [7, 8, 9]})
+
+    def handler(request):
+        if request.path == "/v1/table/test/describe/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(b'{"version": 1, "schema": {"fields": []}}')
+        elif request.path == "/v1/table/test/query/":
+            body = json.loads(
+                request.rfile.read(int(request.headers["Content-Length"]))
+            )
+            assert body == {
+                "k": 3,
+                "filter": "id >= 7",
+                "vector": [],
+                "columns": ["id"],
+                "prefilter": True,
+                "version": None,
+            }
+            request.send_response(200)
+            request.send_header("Content-Type", "application/vnd.apache.arrow.file")
+            request.end_headers()
+            with pa.ipc.new_file(request.wfile, schema=expected.schema) as writer:
+                writer.write_table(expected)
+        else:
+            request.send_response(404)
+            request.end_headers()
+
+    async with mock_lancedb_connection_async(handler) as db:
+        table = await db.open_table("test")
+        query = await table.search(**search_kwargs)
+        assert isinstance(query, AsyncQuery)
+        result = await query.where("id >= 7").select(["id"]).limit(3).to_arrow()
+        assert result == expected
+
+
 def test_query_sync_maximal():
     def handler(body):
         assert body == {
@@ -1531,8 +2335,6 @@ def test_query_sync_maximal():
             "refine_factor": 10,
             "vector": [1.0, 2.0, 3.0],
             "nprobes": 5,
-            "minimum_nprobes": 5,
-            "maximum_nprobes": 5,
             "lower_bound": None,
             "upper_bound": None,
             "ef": None,
@@ -1581,7 +2383,54 @@ def test_query_sync_maximal():
         )
 
 
-def test_query_sync_nprobes():
+@pytest.mark.parametrize("hybrid", [False, True])
+def test_query_sync_nprobes_zero(hybrid):
+    query_requests = []
+
+    def handler(body):
+        query_requests.append(body)
+        return pa.table({"id": []})
+
+    with query_test_table(handler) as table:
+        if hybrid:
+            query = table.search(query_type="hybrid").vector([1, 2, 3]).text("dog")
+        else:
+            query = table.search([1, 2, 3])
+        with pytest.raises(
+            ValueError, match="^Invalid input, nprobes must be greater than 0$"
+        ):
+            query.nprobes(0)
+
+    assert query_requests == []
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("hybrid", [False, True])
+async def test_query_async_nprobes_zero(hybrid):
+    requests = []
+
+    def handler(request):
+        requests.append(request.path)
+        if request.path == "/v1/table/test/describe/":
+            send_json(request, {"version": 1, "schema": {"fields": []}})
+        else:
+            request.send_response(404)
+            request.end_headers()
+
+    async with mock_lancedb_connection_async(handler) as db:
+        table = await db.open_table("test")
+        query = table.query().nearest_to([1, 2, 3])
+        if hybrid:
+            query = query.nearest_to_text("dog")
+        with pytest.raises(
+            ValueError, match="^Invalid input, nprobes must be greater than 0$"
+        ):
+            query.nprobes(0)
+
+    assert requests == ["/v1/table/test/describe/"]
+
+
+def test_query_sync_probe_bounds():
     def handler(body):
         assert body == {
             "k": 10,
@@ -1593,7 +2442,6 @@ def test_query_sync_nprobes():
             "upper_bound": None,
             "ef": None,
             "vector": [1.0, 2.0, 3.0],
-            "nprobes": 5,
             "minimum_nprobes": 5,
             "maximum_nprobes": 15,
             "version": None,
@@ -1610,7 +2458,28 @@ def test_query_sync_nprobes():
         )
 
 
-def test_query_sync_no_max_nprobes():
+def test_query_sync_nprobes_passthrough():
+    def handler(body):
+        assert body["nprobes"] == 20
+        assert "minimum_nprobes" not in body
+        assert "maximum_nprobes" not in body
+        return pa.table({"id": [1]})
+
+    with query_test_table(handler) as table:
+        table.search([1, 2, 3]).nprobes(20).to_list()
+
+
+def test_query_sync_nprobes_with_unbounded_maximum():
+    def handler(body):
+        assert body["nprobes"] == 20
+        assert body["maximum_nprobes"] == 0
+        return pa.table({"id": [1]})
+
+    with query_test_table(handler) as table:
+        table.search([1, 2, 3]).nprobes(20).maximum_nprobes(0).to_list()
+
+
+def test_query_sync_minimum_nprobes_only():
     def handler(body):
         assert body == {
             "k": 10,
@@ -1622,9 +2491,7 @@ def test_query_sync_no_max_nprobes():
             "upper_bound": None,
             "ef": None,
             "vector": [1.0, 2.0, 3.0],
-            "nprobes": 5,
             "minimum_nprobes": 5,
-            "maximum_nprobes": 0,
             "version": None,
         }
 
@@ -1634,7 +2501,6 @@ def test_query_sync_no_max_nprobes():
         (
             table.search([1, 2, 3], vector_column_name="vector2", fast_search=True)
             .minimum_nprobes(5)
-            .maximum_nprobes(0)
             .to_list()
         )
 
@@ -1664,7 +2530,9 @@ def test_query_sync_batch_queries(server_version):
         assert results == [{"id": 1, "query_index": 0}, {"id": 1, "query_index": 1}]
 
 
-def test_query_sync_fts():
+@pytest.mark.parametrize("vector_columns", [{}, {"vector": 3}, {"v1": 3, "v2": 3}])
+@pytest.mark.parametrize("query_type", ["auto", "fts"])
+def test_query_sync_fts(vector_columns, query_type):
     def handler(body):
         assert body == {
             "full_text_query": {
@@ -1679,8 +2547,8 @@ def test_query_sync_fts():
 
         return pa.table({"id": [1, 2, 3]})
 
-    with query_test_table(handler) as table:
-        (table.search("puppy", query_type="fts").to_list())
+    with query_test_table(handler, vector_columns=vector_columns) as table:
+        (table.search("puppy", query_type=query_type).to_list())
 
     def handler(body):
         assert body == {
@@ -1707,9 +2575,11 @@ def test_query_sync_fts():
 
         return pa.table({"id": [1, 2, 3]})
 
-    with query_test_table(handler) as table:
+    with query_test_table(handler, vector_columns=vector_columns) as table:
         (
-            table.search("puppy", query_type="fts", fts_columns=["name", "description"])
+            table.search(
+                "puppy", query_type=query_type, fts_columns=["name", "description"]
+            )
             .with_row_id(True)
             .limit(42)
             .to_list()
@@ -1747,7 +2617,9 @@ def test_query_sync_fts_document_granularity():
             }
         )
 
-    with query_test_table(handler, server_version=Version("0.6.0")) as table:
+    with query_test_table(
+        handler, server_version=Version("0.6.0"), vector_columns={}
+    ) as table:
         result = table.search(
             MatchQuery(
                 "alpha",
@@ -1782,9 +2654,7 @@ def test_query_sync_hybrid():
                 "prefilter": True,
                 "refine_factor": None,
                 "vector": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-                "nprobes": 20,
-                "minimum_nprobes": 20,
-                "maximum_nprobes": 20,
+                "vector_column": "vector",
                 "lower_bound": None,
                 "upper_bound": None,
                 "ef": None,
@@ -1793,15 +2663,14 @@ def test_query_sync_hybrid():
             }
             return pa.table({"_rowid": [1, 2, 3], "_distance": [0.1, 0.2, 0.3]})
 
-    with query_test_table(handler) as table:
-        embedding_func = MockTextEmbeddingFunction()
-        embedding_config = MagicMock()
-        embedding_config.function = embedding_func
-
-        embedding_funcs = MagicMock()
-        embedding_funcs.get = MagicMock(return_value=embedding_config)
-        table.embedding_functions = embedding_funcs
-
+    config = EmbeddingFunctionConfig(
+        source_column="text",
+        vector_column="vector",
+        function=MockTextEmbeddingFunction.create(),
+    )
+    with query_test_table(
+        handler, vector_columns={"vector": 10}, embedding_functions=[config]
+    ) as table:
         (table.search("puppy", query_type="hybrid").limit(42).to_list())
 
 
@@ -1859,6 +2728,11 @@ def test_create_client():
     assert isinstance(db.client_config, ClientConfig)
     assert db.client_config.retry_config.retries == 42
 
+    db = lancedb.connect(
+        **mandatory_args, client_config={"blob_request_concurrency": 3}
+    )
+    assert db.client_config.blob_request_concurrency == 3
+
     with pytest.warns(DeprecationWarning):
         db = lancedb.connect(**mandatory_args, connection_timeout=42)
         assert db.client_config.timeout_config.connect_timeout == timedelta(seconds=42)
@@ -2145,15 +3019,46 @@ async def test_header_provider_overrides_static_headers():
 
 
 def test_close():
-    """Test that close() works without AttributeError."""
-    import asyncio
-
     def handler(req):
         req.send_response(200)
         req.end_headers()
 
     with mock_lancedb_connection(handler) as db:
-        asyncio.run(db.close())
+        assert db.close() is None
+        assert not db.is_open()
+        assert db.close() is None
+
+        with pytest.warns(DeprecationWarning, match="table_names"):
+            with pytest.raises(RuntimeError, match="Connection is closed"):
+                db.table_names()
+        with pytest.raises(RuntimeError, match="Connection is closed"):
+            db.list_tables()
+        with pytest.raises(RuntimeError, match="Connection is closed"):
+            db.open_table("test")
+
+
+@pytest.mark.parametrize("raise_error", [False, True])
+def test_sync_context_manager(raise_error):
+    def handler(req):
+        req.send_response(200)
+        req.send_header("Content-Type", "application/json")
+        req.end_headers()
+        req.wfile.write(b'{"tables": []}')
+
+    with mock_lancedb_connection(handler) as db:
+        with contextlib.ExitStack() as stack:
+            if raise_error:
+                stack.enter_context(pytest.raises(ValueError, match="test error"))
+            with db as conn:
+                assert conn is db
+                assert conn.is_open()
+                assert conn.list_tables().tables == []
+                if raise_error:
+                    raise ValueError("test error")
+
+        assert not db.is_open()
+        with pytest.raises(RuntimeError, match="Connection is closed"):
+            db.list_tables()
 
 
 @pytest.mark.parametrize("exception", [KeyboardInterrupt, SystemExit, GeneratorExit])
@@ -2391,6 +3296,27 @@ def blob_query_response_table():
     )
 
 
+def blob_descriptor_take_table():
+    """What `fetch_blob_files` reads for rows 10, 20, 30, and 40."""
+    queried = blob_query_response_table()
+    image_field = queried.schema.field("image")
+    images = pa.StructArray.from_arrays(
+        [
+            pa.array([0, 0, 0, 0], type=pa.uint8()),
+            pa.array([0, 0, 0, 0], type=pa.uint64()),
+            pa.array([5, 0, 5, 0], type=pa.uint64()),
+            pa.array([0, 0, 0, 0], type=pa.uint32()),
+            pa.array(["", "", "", ""], type=pa.string()),
+        ],
+        fields=image_field.type,
+        mask=pa.array([False, True, False, False]),
+    )
+    return pa.Table.from_arrays(
+        [images, pa.array([10, 20, 30, 40], type=pa.uint64())],
+        schema=pa.schema([image_field, pa.field("_rowid", pa.uint64())]),
+    )
+
+
 @contextlib.contextmanager
 def blob_remote_table(*, server_version=Version("0.5.0")):
     def handler(request):
@@ -2403,11 +3329,16 @@ def blob_remote_table(*, server_version=Version("0.5.0")):
         elif request.path.startswith("/v1/table/test/blob/image/"):
             path = request.path.partition("?")[0]
             row_id = int(path.split("/")[-2])
-            payload = {10: b"alpha", 20: None, 30: b"gamma"}[row_id]
+            payload = {10: b"alpha", 20: None, 30: b"gamma", 40: b""}[row_id]
             if payload is None:
                 request.send_response(204)
                 request.end_headers()
                 return
+            if not payload:
+                request.send_response(416)
+                request.send_header("Content-Range", "bytes */0")
+                request.end_headers()
+                return
             byte_range = request.headers["Range"].removeprefix("bytes=")
             start_text, end_text = byte_range.split("-", maxsplit=1)
             start = int(start_text)
@@ -2416,14 +3347,26 @@ def blob_remote_table(*, server_version=Version("0.5.0")):
             request.send_response(206)
             request.send_header("Content-Range", f"bytes {start}-{end}/{len(payload)}")
             request.send_header("Content-Length", str(len(chunk)))
+            request.send_header(
+                "x-lancedb-version", str(BLOB_DESCRIBE_RESPONSE["version"])
+            )
             request.end_headers()
             request.wfile.write(chunk)
         elif request.path == "/v1/table/test/query/":
             content_len = int(request.headers.get("Content-Length", 0))
             body = json.loads(request.rfile.read(content_len))
-            assert body["columns"] == ["id", "image"]
-            assert body["with_row_id"] is True
-            response_table = blob_query_response_table()
+            columns = body.get("columns")
+            assert columns in (None, ["id", "image"], ["id"], ["image"])
+            if columns == ["id"]:
+                response_table = blob_query_response_table().select(["id"])
+            else:
+                assert body["with_row_id"] is True
+                if columns == ["image"]:
+                    # fetch_blob_files sizes its handles from a descriptor take.
+                    assert body["filter"].startswith("_rowid IN")
+                    response_table = blob_descriptor_take_table()
+                else:
+                    response_table = blob_query_response_table()
             request.send_response(200)
             request.send_header("Content-Type", "application/vnd.apache.arrow.file")
             request.end_headers()
@@ -2457,25 +3400,170 @@ def test_remote_blob_columns_and_fetch():
         assert blobs.to_pylist() == [b"alpha", None, b"gamma"]
 
 
+@pytest.mark.asyncio
+async def test_async_remote_to_lance_rejects_storage_access(monkeypatch):
+    with blob_remote_table() as remote_table:
+        table = remote_table._table
+
+        async def fail_uri():
+            raise AssertionError("remote table URI must not be requested")
+
+        monkeypatch.setattr(table, "uri", fail_uri)
+        with pytest.raises(NotImplementedError, match="remote tables"):
+            await table.to_lance()
+
+
+@pytest.mark.asyncio
+async def test_async_remote_blob_descriptions_to_pandas_uses_server(monkeypatch):
+    with blob_remote_table() as remote_table:
+        table = remote_table._table
+
+        async def fail_uri():
+            raise AssertionError("remote table URI must not be requested")
+
+        async def fail_to_lance():
+            raise AssertionError("remote table storage must not be opened")
+
+        monkeypatch.setattr(table, "uri", fail_uri)
+        monkeypatch.setattr(table, "to_lance", fail_to_lance)
+
+        query_df = await (
+            table.query().select(["id", "image"]).to_pandas(blob_mode="descriptions")
+        )
+        table_df = await table.to_pandas(blob_mode="descriptions")
+
+        for df in (query_df, table_df):
+            assert df["id"].tolist() == [1, 2, 3]
+            assert "_rowid" not in df.columns
+            images = df["image"].tolist()
+            assert images[1] is None
+            assert images[0]["size"] == 5
+            assert "_lance_row_id" not in images[0]
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("blob_mode", ["bytes", "lazy"])
+async def test_async_remote_blob_pandas_rejects_unpinned_fetch(monkeypatch, blob_mode):
+    with blob_remote_table() as remote_table:
+        table = remote_table._table
+
+        async def fail_uri():
+            raise AssertionError("remote table URI must not be requested")
+
+        async def fail_to_lance():
+            raise AssertionError("remote table storage must not be opened")
+
+        async def fail_fetch(*args, **kwargs):
+            raise AssertionError("blob fetch must not use an unpinned remote table")
+
+        monkeypatch.setattr(table, "uri", fail_uri)
+        monkeypatch.setattr(table, "to_lance", fail_to_lance)
+        monkeypatch.setattr(table, "fetch_blobs", fail_fetch)
+        monkeypatch.setattr(table, "fetch_blob_files", fail_fetch)
+
+        pandas_kwargs = {} if blob_mode == "lazy" else {"blob_mode": blob_mode}
+        with pytest.raises(NotImplementedError, match="stable table snapshot"):
+            await table.query().select(["id", "image"]).to_pandas(**pandas_kwargs)
+        with pytest.raises(NotImplementedError, match="stable table snapshot"):
+            await table.to_pandas(**pandas_kwargs)
+
+
+@pytest.mark.parametrize("method", ["to_arrow", "to_list"])
+def test_remote_search_bytes_refused_before_query(monkeypatch, method):
+    with blob_remote_table() as table:
+        query = table.search().select(["id", "image"])
+        descriptions = getattr(query, method)(blob_mode="descriptions")
+        images = (
+            descriptions["image"].to_pylist()
+            if method == "to_arrow"
+            else [row["image"] for row in descriptions]
+        )
+        assert "_lance_row_id" not in images[0]
+
+        def fail_query(*args, **kwargs):
+            raise AssertionError("bytes must be refused before the query runs")
+
+        monkeypatch.setattr(table, "_execute_query", fail_query)
+        with pytest.raises(NotImplementedError, match="stable table snapshot"):
+            getattr(query, method)(blob_mode="bytes")
+        with pytest.raises(ValueError, match="blob_mode must be one of"):
+            table.to_pandas(blob_mode="handles")
+
+
+@pytest.mark.asyncio
+async def test_async_remote_to_arrow_bytes_refused_before_query(monkeypatch):
+    with blob_remote_table() as remote_table:
+        table = remote_table._table
+
+        async def fail_fetch(*args, **kwargs):
+            raise AssertionError("blob fetch must not use an unpinned remote table")
+
+        monkeypatch.setattr(table, "fetch_blobs", fail_fetch)
+        query = table.query().select(["id", "image"])
+        monkeypatch.setattr(query, "to_batches", fail_fetch)
+        with pytest.raises(NotImplementedError, match="stable table snapshot"):
+            await query.to_arrow(blob_mode="bytes")
+        with pytest.raises(NotImplementedError, match="stable table snapshot"):
+            await table.to_arrow(blob_mode="bytes")
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("blob_mode", ["bytes", "lazy"])
+async def test_async_remote_nonblob_projection_to_pandas(blob_mode):
+    with blob_remote_table() as remote_table:
+        df = await (
+            remote_table._table.query().select(["id"]).to_pandas(blob_mode=blob_mode)
+        )
+
+    assert df["id"].tolist() == [1, 2, 3]
+    assert list(df.columns) == ["id"]
+
+
 def test_remote_blob_files_are_lazy_seekable_handles():
     with blob_remote_table() as table:
-        files = table.fetch_blob_files("image", [10, 20, 30])
+        files = table.fetch_blob_files("image", [10, 20, 30, 40])
 
-        assert len(files) == 3
-        alpha, null_row, gamma = files
+        assert len(files) == 4
+        alpha, null_row, gamma, empty = files
         assert null_row is None
         assert alpha is not None
         assert gamma is not None
+        assert empty is not None
         assert alpha.size() == 5
         assert alpha.read_range(1, 3) == b"lph"
         gamma.seek(2)
         assert gamma.read() == b"mma"
+        assert empty.size() == 0
+        assert empty.read() == b""
+        assert empty.read_range(0, 0) == b""
         alpha.close()
         assert alpha.closed
-        with pytest.raises(RuntimeError, match="already closed"):
+        with pytest.raises(ValueError, match="read of closed file"):
             alpha.read_range(0, 1)
 
 
+def test_remote_blob_file_matches_file_error_and_range_contract():
+    with blob_remote_table() as table:
+        handle = table.fetch_blob_files("image", [10])[0]
+        handle.seek(2)
+
+        with pytest.raises(ValueError, match="negative seek value -1"):
+            handle.seek(-3, io.SEEK_CUR)
+        assert handle.tell() == 2
+        assert handle.read(None) == b"pha"
+
+        handle.seek(2)
+        assert handle.read1(2) == b"ph"
+        assert handle.read_ranges([(0, 2), (3, 2)]) == [b"al", b"ha"]
+        assert handle.tell() == 4
+        with pytest.raises(ValueError):
+            handle.read_range(4, 2)
+
+        handle.close()
+        with pytest.raises(ValueError, match="read of closed file"):
+            handle.read(1)
+
+
 def test_remote_blob_fetch_accepts_query_table():
     hits = pa.table({"_rowid": pa.array([10, 20, 30], type=pa.uint64())})
 
@@ -2495,6 +3583,56 @@ def test_remote_blob_query_stashes_row_ids_for_fetch():
     assert blobs.to_pylist() == [b"alpha", None, b"gamma"]
 
 
+def test_remote_blob_query_to_pandas():
+    with blob_remote_table() as table:
+        query = table.search().select(["id", "image"]).limit(3)
+        df = query.to_pandas(blob_mode="descriptions", split_blocks=True)
+
+    assert df["id"].tolist() == [1, 2, 3]
+    assert "_rowid" not in df.columns
+    assert df["image"].iloc[1] is None
+    assert "_lance_row_id" not in df["image"].iloc[0]
+
+
+@pytest.mark.parametrize("blob_mode", ["default", "lazy", "bytes"])
+def test_remote_blob_query_to_pandas_rejects_unpinned_fetch(monkeypatch, blob_mode):
+    with blob_remote_table() as table:
+
+        def fail_fetch(*args, **kwargs):
+            raise AssertionError("blob fetch must not use an unpinned remote table")
+
+        monkeypatch.setattr(table, "fetch_blobs", fail_fetch)
+        query = table.search().select(["id", "image"]).limit(3)
+        pandas_kwargs = {} if blob_mode == "default" else {"blob_mode": blob_mode}
+        with pytest.raises(NotImplementedError, match="stable table snapshot"):
+            query.to_pandas(**pandas_kwargs)
+
+
+@pytest.mark.parametrize("blob_mode", ["lazy", "bytes"])
+def test_remote_nonblob_projection_to_pandas(blob_mode):
+    with blob_remote_table() as table:
+        df = table.search().select(["id"]).to_pandas(blob_mode=blob_mode)
+
+    assert df["id"].tolist() == [1, 2, 3]
+    assert list(df.columns) == ["id"]
+
+
+@pytest.mark.asyncio
+async def test_async_remote_blob_query_to_pandas_flatten():
+    with blob_remote_table() as table:
+        df = (
+            await table._table.query()
+            .select(["id", "image"])
+            .limit(3)
+            .to_pandas(blob_mode="descriptions", flatten=True, split_blocks=True)
+        )
+
+    assert df["id"].tolist() == [1, 2, 3]
+    assert df["image.size"].iloc[0] == 5
+    assert "image._lance_row_id" not in df.columns
+    assert "_rowid" not in df.columns
+
+
 def test_remote_blob_query_survives_a_server_that_ignores_the_row_id_request():
     def handler(request):
         if request.path == "/v1/table/test/describe/":
@@ -2524,14 +3662,22 @@ def test_remote_blob_query_survives_a_server_that_ignores_the_row_id_request():
         assert "_lance_row_id" not in hits.schema.field("image").type.names
         with pytest.raises(ValueError, match="pass a list of row ids"):
             table.fetch_blobs("image", hits)
+        with pytest.raises(NotImplementedError, match="stable table snapshot"):
+            table.search().select(["id", "image"]).to_pandas(blob_mode="bytes")
 
 
 def test_remote_blob_byte_apis_not_supported_on_old_server():
     with blob_remote_table(server_version=Version("0.1.0")) as table:
         assert table.blob_columns() == ["image"]
-        with pytest.raises(NotImplementedError, match="not supported"):
+        with pytest.raises(
+            NotImplementedError,
+            match="fetch_blobs is not supported by this LanceDB server",
+        ):
             table.fetch_blobs("image", [1])
-        with pytest.raises(NotImplementedError, match="not supported"):
+        with pytest.raises(
+            NotImplementedError,
+            match="fetch_blob_files requires LanceDB server 0.5.0 or newer",
+        ):
             table.fetch_blob_files("image", [1])
 
 
@@ -2659,7 +3805,7 @@ def test_remote_connection_jobs_surface():
             request.end_headers()
 
     with mock_lancedb_connection(handler) as db:
-        jobs = db.list_jobs()
+        jobs = list(db.list_jobs())
         assert [job.job_id for job in jobs] == ["job-1", "job-2"]
         assert jobs[0].state == "running"
         assert jobs[0].table == "t1"
@@ -2819,7 +3965,7 @@ def test_view_crud_addresses_its_own_routes():
         described = db.describe_view("adults", namespace_path=["analytics"])
         assert described.schema == view.schema
 
-        assert db.list_views(namespace_path=["analytics"]) == ["adults"]
+        assert list(db.list_views(namespace_path=["analytics"])) == ["adults"]
         db.drop_view("adults", namespace_path=["analytics"])
 
     assert paths == [
diff --git a/python/python/tests/test_sql.py b/python/python/tests/test_sql.py
index eeadb3a33..e6f5fd3c7 100644
--- a/python/python/tests/test_sql.py
+++ b/python/python/tests/test_sql.py
@@ -1,8 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
+import threading
 from uuid import UUID
 
+import numpy as np
 import pytest
 import pyarrow as pa
 
@@ -11,7 +13,7 @@ from lancedb import _lancedb
 from lancedb.arrow import AsyncRecordBatchReader
 from lancedb.db import AsyncConnection
 from lancedb.remote.db import RemoteDBConnection
-from lancedb.sql import AsyncQuery, Query
+from lancedb.sql import AsyncQuery, Query, to_parameter_batch
 
 NIL_QUERY_ID = UUID(int=0)
 
@@ -24,12 +26,20 @@ class FakeNativeQuery:
 
 
 class FakeNativeConnection:
-    async def execute_query_async(self, query, *, default_namespace_path=None):
+    def __init__(self):
+        self.parameters = []
+
+    async def execute_query_async(
+        self, query, *, default_namespace_path=None, parameters=None
+    ):
+        self.parameters.append(parameters)
         return FakeNativeQuery()
 
 
 class FakeAsyncConnection:
-    async def execute_query_async(self, query, *, default_namespace_path=None):
+    async def execute_query_async(
+        self, query, *, default_namespace_path=None, parameters=None
+    ):
         return AsyncQuery(FakeNativeQuery())
 
 
@@ -160,3 +170,100 @@ def test_execute_query_async_rejects_invalid_namespace_components(
         remote_connection().execute_query_async(
             "SELECT 1", default_namespace_path=default_namespace_path
         )
+
+
+def test_positional_parameters_keep_their_types():
+    vector = np.array([0.1, 0.2, 0.3], dtype=np.float32)
+    batch = to_parameter_batch(
+        [42, 1.5, "a", np.float32(0.25), vector, pa.scalar(7, pa.int16())]
+    )
+    assert batch.num_rows == 1
+    assert batch.schema.names == ["1", "2", "3", "4", "5", "6"]
+    assert batch.schema.types == [
+        pa.int64(),
+        pa.float64(),
+        pa.string(),
+        pa.float32(),
+        pa.list_(pa.float32(), 3),
+        pa.int16(),
+    ]
+    sent = batch.column(4).flatten().to_numpy()
+    assert sent.dtype == np.float32
+    assert sent.tobytes() == vector.tobytes()
+
+
+def test_named_parameters_and_batches():
+    half = pa.array(np.array([1.0, 2.0], dtype=np.float16))
+    batch = to_parameter_batch({"vector": half, "k": 3})
+    assert batch.schema.names == ["vector", "k"]
+    assert batch.schema.field("vector").type == pa.list_(pa.float16(), 2)
+
+    row = pa.record_batch({"id": [1]})
+    assert to_parameter_batch(row) is row
+    assert to_parameter_batch(pa.table({"id": [1]})).equals(row)
+    assert to_parameter_batch(None) is None
+
+
+@pytest.mark.parametrize(
+    "parameters",
+    [pa.record_batch({"id": [1, 2]}), pa.table({"id": pa.array([], pa.int64())})],
+)
+def test_parameters_are_exactly_one_row(parameters):
+    with pytest.raises(ValueError, match="exactly one row"):
+        to_parameter_batch(parameters)
+
+
+def test_parameters_must_be_a_collection():
+    with pytest.raises(TypeError, match="query parameters"):
+        to_parameter_batch("SELECT 1")
+
+
+@pytest.mark.asyncio
+async def test_async_execute_query_sends_parameters_as_a_batch():
+    native = FakeNativeConnection()
+    connection = AsyncConnection(native)
+    await connection.execute_query("SELECT $1", parameters=[1])
+    await connection.execute_query_async("SELECT 1")
+    sent, unparameterized = native.parameters
+    assert sent.equals(pa.record_batch({"1": [1]}))
+    assert unparameterized is None
+
+
+def test_parameters_reach_the_server_as_the_values_that_were_sent():
+    """A round trip through the bindings against a Flight server that answers
+    each exchange with the parameter row it received."""
+    flight = pytest.importorskip("pyarrow.flight")
+
+    class EchoServer(flight.FlightServerBase):
+        def __init__(self):
+            super().__init__("grpc://127.0.0.1:0")
+            self.commands = []
+            self.lock = threading.Lock()
+
+        def do_exchange(self, context, descriptor, reader, writer):
+            parameters = reader.read_all()
+            with self.lock:
+                self.commands.append(descriptor.command)
+            writer.begin(parameters.schema)
+            writer.write_table(parameters)
+            writer.close()
+
+    vector = np.array([0.1, 1e-45, -0.0], dtype=np.float32)
+    with EchoServer() as server:
+        db = lancedb.connect(
+            "db://analytics",
+            api_key="test-key",
+            host_override="http://localhost:10024",
+            sql_host_override=f"grpc://127.0.0.1:{server.port}",
+        )
+        echoed = db.execute_query(
+            "SELECT echo", parameters={"vector": vector, "k": 10}
+        ).read_all()
+        (command,) = server.commands
+
+    assert b"CommandStatementQuery" in command
+    assert b"SELECT echo" in command
+    assert echoed.schema.field("vector").type == pa.list_(pa.float32(), 3)
+    received = echoed.column("vector").combine_chunks().flatten().to_numpy()
+    assert received.view(np.uint32).tolist() == vector.view(np.uint32).tolist()
+    assert echoed.column("k").to_pylist() == [10]
diff --git a/python/python/tests/test_table.py b/python/python/tests/test_table.py
index 915a32a29..150590c8e 100644
--- a/python/python/tests/test_table.py
+++ b/python/python/tests/test_table.py
@@ -12,7 +12,7 @@ import warnings
 import weakref
 from concurrent.futures import ThreadPoolExecutor
 from datetime import date, datetime, timedelta
-from decimal import Decimal
+from decimal import Decimal, localcontext
 from time import sleep
 from typing import Any, List
 from unittest.mock import patch
@@ -113,6 +113,32 @@ def test_basic(mem_db: DBConnection):
     assert table.to_arrow() == expected_data
 
 
+@pytest.mark.parametrize("enable_v2", [False, True])
+def test_migrate_v2_manifest_paths(tmp_path, enable_v2):
+    db = lancedb.connect(
+        tmp_path,
+        storage_options={"new_table_enable_v2_manifest_paths": str(enable_v2).lower()},
+    )
+    table = db.create_table("calls", [{"id": 1, "vector": [1.0, 1.0]}])
+    table.add([{"id": 2, "vector": [2.0, 2.0]}])
+    expected_data = table.to_arrow()
+    expected_versions = table.list_versions()
+    assert table.uses_v2_manifest_paths() == enable_v2
+
+    # Migration is also safe to repeat on a table already using v2 paths.
+    for _ in range(2):
+        table.migrate_v2_manifest_paths()
+        assert table.uses_v2_manifest_paths()
+        reopened = db.open_table("calls")
+        assert reopened.uses_v2_manifest_paths()
+        assert reopened.to_arrow() == expected_data
+        assert reopened.list_versions() == expected_versions
+
+    manifests = list((tmp_path / "calls.lance" / "_versions").glob("*.manifest"))
+    assert len(manifests) == len(expected_versions)
+    assert all(len(path.stem) == 20 and path.stem.isdigit() for path in manifests)
+
+
 def test_search_preserves_nulls_from_sliced_arrow_table(mem_db: DBConnection):
     data = pa.table(
         {
@@ -967,6 +993,63 @@ def test_polars(mem_db: DBConnection):
     assert len(filtered_result) == 2
 
 
+@pytest.mark.asyncio
+async def test_list_versions_timestamp_precision():
+    # 2026-10-03T01:03:49.274Z in nanoseconds. Float math turns .274000 into
+    # .273999, so make sure the conversion is exact.
+    ts_nanos = 1790989429274000000
+
+    class FakeInner:
+        async def list_versions(self):
+            return [{"version": 1, "timestamp": ts_nanos, "metadata": {}}]
+
+    table = table_module.AsyncTable(FakeInner())
+    versions = await table.list_versions()
+
+    expected = datetime.fromtimestamp(ts_nanos // 1_000_000_000)
+    assert versions[0]["timestamp"] == expected + timedelta(microseconds=274000)
+    assert versions[0]["timestamp"].microsecond == 274000
+
+
+def test_list_versions_timestamp_precision_sync(mem_db: DBConnection):
+    # The sync LanceTable.list_versions delegates to AsyncTable.list_versions,
+    # which is the path the parity report hit.
+    ts_nanos = 1790989429274000000
+
+    class FakeInner:
+        async def list_versions(self):
+            return [{"version": 1, "timestamp": ts_nanos, "metadata": {}}]
+
+    table = mem_db.create_table("ts_precision", data=[{"id": 1}])
+    table._table = table_module.AsyncTable(FakeInner())
+    versions = table.list_versions()
+
+    expected = datetime.fromtimestamp(ts_nanos // 1_000_000_000)
+    assert versions[0]["timestamp"] == expected + timedelta(microseconds=274000)
+    assert versions[0]["timestamp"].microsecond == 274000
+
+
+def test_list_versions_timestamp_precision_remote():
+    # RemoteTable.list_versions also delegates to AsyncTable.list_versions.
+    from lancedb.remote.table import RemoteTable
+
+    ts_nanos = 1790989429274000000
+
+    class FakeInner:
+        def name(self):
+            return "ts_precision"
+
+        async def list_versions(self):
+            return [{"version": 1, "timestamp": ts_nanos, "metadata": {}}]
+
+    table = RemoteTable(table_module.AsyncTable(FakeInner()), "dev")
+    versions = table.list_versions()
+
+    expected = datetime.fromtimestamp(ts_nanos // 1_000_000_000)
+    assert versions[0]["timestamp"] == expected + timedelta(microseconds=274000)
+    assert versions[0]["timestamp"].microsecond == 274000
+
+
 def test_versioning(mem_db: DBConnection):
     table = mem_db.create_table(
         "test",
@@ -2529,6 +2612,51 @@ def test_update_with_arrow_scalar(mem_db: DBConnection):
     assert table.to_arrow()["vector"].to_pylist() == [[1.0, 2.0, 3.0, 4.0]]
 
 
+@pytest.mark.parametrize("use_arrow_scalar", [False, True])
+@pytest.mark.parametrize(
+    "decimal_type, value",
+    [
+        (pa.decimal128(10, 2), "9.99"),
+        (pa.decimal128(10, 2), "-9.99"),
+        (pa.decimal128(10, 2), "0.00"),
+        (pa.decimal128(10, 2), "1.23E+5"),
+        (pa.decimal128(38, 18), "1E-18"),
+        (pa.decimal128(38, 18), "12345678901234567.123456789012345678"),
+        (pa.decimal128(38, 18), "12345678901234567890.123456789012345678"),
+        (pa.decimal128(38, 18), "-12345678901234567890.123456789012345678"),
+        (
+            pa.decimal256(76, 38),
+            "12345678901234567890123456789012345678."
+            "12345678901234567890123456789012345678",
+        ),
+        (
+            pa.decimal256(76, 38),
+            "-12345678901234567890123456789012345678."
+            "12345678901234567890123456789012345678",
+        ),
+    ],
+)
+def test_update_decimal(mem_db: DBConnection, decimal_type, value, use_arrow_scalar):
+    expected = Decimal(value)
+    table = mem_db.create_table(
+        "my_table",
+        pa.table(
+            {
+                "id": [1, 2],
+                "price": pa.array([Decimal("1"), expected], type=decimal_type),
+            }
+        ),
+    )
+    update = table.to_arrow()["price"][1] if use_arrow_scalar else expected
+    with localcontext() as context:
+        context.prec = 6
+        result = table.update(where="id = 1", values={"price": update})
+    assert result.rows_updated == 1
+    actual = table.to_arrow()
+    assert actual.schema.field("price").type == decimal_type
+    assert actual["price"].to_pylist() == [expected, expected]
+
+
 def test_update_types(mem_db: DBConnection):
     table = mem_db.create_table(
         "my_table",
@@ -3751,6 +3879,24 @@ def test_empty_query(mem_db: DBConnection):
     assert df.num_rows == 42
 
 
+@pytest.mark.parametrize("query", [[], np.array([], dtype=np.float32)])
+@pytest.mark.parametrize("vector_column_name", [None, "vector"])
+@pytest.mark.parametrize("query_type", ["auto", "vector"])
+@pytest.mark.parametrize("multiple_vector_columns", [False, True])
+def test_search_empty_vector(
+    mem_db, query, vector_column_name, query_type, multiple_vector_columns
+):
+    fields = [pa.field("vector", pa.list_(pa.float32(), 8))]
+    if multiple_vector_columns:
+        fields.append(pa.field("vec2", pa.list_(pa.float32(), 4)))
+    table = mem_db.create_table("empty_vector_query", schema=pa.schema(fields))
+
+    with pytest.raises(ValueError, match="^Query vector must not be empty$"):
+        table.search(
+            query, vector_column_name=vector_column_name, query_type=query_type
+        ).limit(3).to_arrow()
+
+
 def test_search_with_schema_inf_single_vector(mem_db: DBConnection):
     class MyTable(LanceModel):
         text: str
@@ -3870,6 +4016,26 @@ def test_search_nested_vector_no_candidates(mem_db: DBConnection):
         table.search([0.0, 1.0]).limit(1).to_arrow()
 
 
+def test_search_single_vector_column_wrong_query_dim(mem_db: DBConnection):
+    """A query vector whose dimension doesn't match the (only) vector column
+    must still resolve that column, surfacing a dimension-mismatch error from
+    query execution rather than a misleading "no vector column" error."""
+    schema = pa.schema(
+        [
+            pa.field("id", pa.uint32(), False),
+            pa.field("vector_col", pa.list_(pa.float32(), 32), False),
+        ]
+    )
+    table = mem_db.create_table(
+        "wrong_query_dim",
+        data=[{"id": 1, "vector_col": [0.1] * 32}],
+        schema=schema,
+    )
+
+    with pytest.raises(ValueError, match="dim"):
+        table.search([0.01] * 120).limit(1).to_arrow()
+
+
 def test_compact_cleanup(tmp_db: DBConnection):
     pytest.importorskip("lance")
     table = tmp_db.create_table(
@@ -4144,14 +4310,74 @@ async def test_add_columns_with_schema(mem_db_async: AsyncConnection):
     )
 
 
-def test_alter_columns(mem_db: DBConnection):
+@pytest.mark.parametrize("rename_key", ["rename", "name"])
+def test_alter_columns(mem_db: DBConnection, rename_key):
     data = pa.table({"id": [0, 1]})
     table = mem_db.create_table("my_table", data=data)
-    alter_columns_res = table.alter_columns({"path": "id", "rename": "new_id"})
+    alter_columns_res = table.alter_columns({"path": "id", rename_key: "new_id"})
     assert alter_columns_res.version == 2
     assert table.to_arrow().column_names == ["new_id"]
 
 
+INVALID_COLUMN_ALTERATIONS = [
+    (({"path": "id"},), "One of rename, nullable or data_type"),
+    (
+        ({"path": "id", "nulable": False},),  # spellchecker:disable-line
+        "Unknown column alteration key 'nulable'",  # spellchecker:disable-line
+    ),
+    (
+        (
+            {
+                "path": "id",
+                "rename": "new_id",
+                "nulable": False,  # spellchecker:disable-line
+            },
+        ),
+        "Unknown column alteration key 'nulable'",  # spellchecker:disable-line
+    ),
+    (
+        ({"path": "id", "rename": "new_id"}, {"path": "id"}),
+        "One of rename, nullable or data_type",
+    ),
+]
+
+
+def test_alter_columns_nullable_false(mem_db: DBConnection):
+    table = mem_db.create_table("my_table", data=pa.table({"id": [0, 1]}))
+    result = table.alter_columns({"path": "id", "nullable": False})
+    assert result.version == 2
+    assert not table.schema.field("id").nullable
+
+
+@pytest.mark.parametrize("alterations, match", INVALID_COLUMN_ALTERATIONS)
+def test_alter_columns_rejects_invalid(mem_db: DBConnection, alterations, match):
+    table = mem_db.create_table("my_table", data=pa.table({"id": [0, 1]}))
+    initial_version = table.version
+    initial_schema = table.schema
+
+    with pytest.raises(ValueError, match=match):
+        table.alter_columns(*alterations)
+
+    assert table.version == initial_version
+    assert table.schema == initial_schema
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("alterations, match", INVALID_COLUMN_ALTERATIONS)
+async def test_alter_columns_rejects_invalid_async(
+    mem_db_async: AsyncConnection, alterations, match
+):
+    table = await mem_db_async.create_table("my_table", data=pa.table({"id": [0, 1]}))
+    initial_version = await table.version()
+    initial_schema = await table.schema()
+
+    with pytest.raises(ValueError, match=match):
+        await table.alter_columns(*alterations)
+
+    assert await table.version() == initial_version
+    assert await table.schema() == initial_schema
+
+
 def test_update_field_metadata(mem_db: DBConnection):
     data = pa.table({"id": [0, 1], "category": ["a", "b"]})
     table = mem_db.create_table("my_table", data=data)
@@ -4177,10 +4403,11 @@ def test_update_field_metadata(mem_db: DBConnection):
 
 
 @pytest.mark.asyncio
-async def test_alter_columns_async(mem_db_async: AsyncConnection):
+@pytest.mark.parametrize("rename_key", ["rename", "name"])
+async def test_alter_columns_async(mem_db_async: AsyncConnection, rename_key):
     data = pa.table({"id": [0, 1]})
     table = await mem_db_async.create_table("my_table", data=data)
-    alter_columns_res = await table.alter_columns({"path": "id", "rename": "new_id"})
+    alter_columns_res = await table.alter_columns({"path": "id", rename_key: "new_id"})
     assert alter_columns_res.version == 2
     assert (await table.to_arrow()).column_names == ["new_id"]
     alter_columns_res = await table.alter_columns(
@@ -4507,22 +4734,25 @@ def test_stats(mem_db: DBConnection):
         "my_table",
         data=[{"text": "foo", "id": 0}, {"text": "bar", "id": 1}],
     )
-    assert len(table) == 2
+    table.add([{"text": "baz", "id": 1}])
+    assert len(table) == 3
     stats = table.stats()
     print(f"{stats=}")
     assert stats == {
-        # Full on-disk size of the data file, footer and metadata included.
-        "total_bytes": 637,
-        "num_rows": 2,
+        # Full on-disk size of the data files, footer and metadata included:
+        # 637 bytes for the two-row fragment plus 415 for the one-row fragment.
+        "total_bytes": 1052,
+        "num_rows": 3,
+        "num_deleted_rows": 0,
         "num_indices": 0,
         "fragment_stats": {
-            "num_fragments": 1,
-            "num_small_fragments": 1,
+            "num_fragments": 2,
+            "num_small_fragments": 2,
             "lengths": {
-                "min": 2,
+                "min": 1,
                 "max": 2,
-                "mean": 2,
-                "p25": 2,
+                "mean": 1,
+                "p25": 1,
                 "p50": 2,
                 "p75": 2,
                 "p99": 2,
@@ -4530,12 +4760,19 @@ def test_stats(mem_db: DBConnection):
         },
     }
 
+    # Both rows with id = 1 are deleted, but that empties the second fragment,
+    # which is dropped outright rather than kept with a deletion file, so only
+    # the row marked in the surviving fragment is counted.
+    table.delete("id = 1")
     # Index files count toward total_bytes too (only deletion files and
     # manifests are excluded).
     table.create_index("id", config=BTree())
     stats_with_index = table.stats()
     assert stats_with_index["num_indices"] == 1
     assert stats_with_index["total_bytes"] > stats["total_bytes"]
+    assert stats_with_index["num_rows"] == 1
+    assert stats_with_index["num_deleted_rows"] == 1
+    assert stats_with_index["fragment_stats"]["num_fragments"] == 1
 
 
 def test_create_table_empty_list_with_schema(mem_db: DBConnection):
diff --git a/python/python/tests/test_util.py b/python/python/tests/test_util.py
index d9c574eb9..741214794 100644
--- a/python/python/tests/test_util.py
+++ b/python/python/tests/test_util.py
@@ -4,6 +4,7 @@
 
 import os
 import pathlib
+from decimal import Decimal, localcontext
 from typing import Optional
 
 import lance
@@ -27,7 +28,13 @@ import pandas as pd
 import polars as pl
 import pytest
 import lancedb
-from lancedb.util import flatten_columns, get_uri_scheme, join_uri, value_to_sql
+from lancedb.util import (
+    flatten_columns,
+    get_uri_scheme,
+    inf_vector_column_query,
+    join_uri,
+    value_to_sql,
+)
 from utils import exception_output
 
 
@@ -157,6 +164,41 @@ def test_value_to_sql_string(tmp_path):
         assert table.to_pandas().query("search == @value")["replace"].item() == value
 
 
+@pytest.mark.parametrize(
+    "value, expected",
+    [
+        ("9.99", "'9.99'"),
+        ("-9.99", "'-9.99'"),
+        ("0", "'0'"),
+        ("-0.00", "'-0.00'"),
+        ("1.23E+5", "'123000'"),
+        ("1E-18", "'0.000000000000000001'"),
+        (
+            "12345678901234567890.123456789012345678",
+            "'12345678901234567890.123456789012345678'",
+        ),
+        (
+            "12345678901234567890123456789012345678."
+            "12345678901234567890123456789012345678",
+            "'12345678901234567890123456789012345678."
+            "12345678901234567890123456789012345678'",
+        ),
+    ],
+)
+def test_value_to_sql_decimal(value, expected):
+    with localcontext() as context:
+        context.prec = 6
+        assert value_to_sql(Decimal(value)) == expected
+
+
+@pytest.mark.parametrize("value", ["NaN", "sNaN", "NaN123456", "Infinity", "-Infinity"])
+def test_value_to_sql_decimal_non_finite(value):
+    with pytest.raises(
+        ValueError, match="^Non-finite Decimal values cannot be converted to SQL$"
+    ):
+        value_to_sql(Decimal(value))
+
+
 def test_value_to_sql_dict():
     # Simple flat struct
     assert value_to_sql({"a": 1, "b": "hello"}) == "named_struct('a', 1, 'b', 'hello')"
@@ -951,6 +993,39 @@ def test_cast_to_target_schema_coerces_binary_to_blob_v2():
     ]
 
 
+@pytest.mark.parametrize(
+    "string_type", [pa.string(), pa.large_string(), pa.string_view()]
+)
+def test_cast_to_target_schema_coerces_string_to_blob_v2_uri(string_type):
+    data = pa.table({"image": pa.array(["file:///payload", None], type=string_type)})
+    target = pa.schema([lancedb.blob("image")])
+
+    output = _cast_to_target_schema(data.to_reader(), target).read_all()
+
+    image = output["image"].chunk(0)
+    assert type(image.type) is lancedb.BlobType
+    assert image.storage.to_pylist() == [
+        {"data": None, "uri": "file:///payload", "position": None, "size": None},
+        None,
+    ]
+
+
+@pytest.mark.parametrize(
+    "source_type, value",
+    [
+        (pa.binary(4), b"abcd"),
+        (pa.list_(pa.uint8()), [1, 2]),
+        (pa.int64(), 42),
+    ],
+)
+def test_cast_to_target_schema_rejects_unsupported_blob_input(source_type, value):
+    data = pa.table({"image": pa.array([value], type=source_type)})
+    target = pa.schema([lancedb.blob("image")])
+
+    with pytest.raises(ValueError, match="cannot coerce column 'image' with type"):
+        _cast_to_target_schema(data.to_reader(), target)
+
+
 def test_cast_to_target_schema_coerces_binary_to_metadata_blob_struct():
     storage = lancedb.blob("image").type.storage_type
     target = pa.schema(
@@ -1182,3 +1257,41 @@ def test_infer_vector_column_raises_clear_error(tmp_path):
         # Plain vector search on a table with no vector column should raise
         # a clear ValueError, not a cryptic TypeError.
         table.search([1.0, 2.0]).to_list()
+
+
+def test_inf_vector_column_query_dim_mismatch_still_resolves():
+    """Regression: a query dimension that matches no column must not hide an
+    otherwise-unambiguous vector column behind "no vector column in the data".
+
+    `dim` exists to disambiguate between *multiple* vector columns of
+    different sizes. When the schema has exactly one vector column, that
+    column is still the only sensible one to search; a dimension mismatch is
+    a query-execution concern, not a missing-column one. This previously
+    broke remote search (#4409 wired up client-side inference there), which
+    expects the server to reject the mismatched dimension itself.
+    """
+    schema = pa.schema(
+        [
+            pa.field("id", pa.uint32(), False),
+            pa.field("vector_col", pa.list_(pa.float32(), 32), False),
+            pa.field("s", pa.string(), False),
+        ]
+    )
+
+    assert inf_vector_column_query(schema, dim=32) == "vector_col"
+    assert inf_vector_column_query(schema, dim=120) == "vector_col"
+    assert inf_vector_column_query(schema) == "vector_col"
+
+
+def test_inf_vector_column_query_dim_mismatch_with_multiple_columns():
+    """With more than one vector column, a dimension that matches none of
+    them is still ambiguous and must raise, not silently pick one."""
+    schema = pa.schema(
+        [
+            pa.field("vector1", pa.list_(pa.float32(), 10), False),
+            pa.field("vector2", pa.list_(pa.float32(), 20), False),
+        ]
+    )
+
+    with pytest.raises(ValueError, match="more than one vector column"):
+        inf_vector_column_query(schema, dim=99)
diff --git a/python/python/tests/test_wal_hybrid.py b/python/python/tests/test_wal_hybrid.py
index 11a9755e0..675fc927a 100644
--- a/python/python/tests/test_wal_hybrid.py
+++ b/python/python/tests/test_wal_hybrid.py
@@ -81,15 +81,15 @@ def test_base_table_hybrid_still_joins_on_row_ids():
     assert builder._vector_query._columns is None
 
 
-def test_wal_hybrid_with_use_lsm_false_joins_on_row_ids():
-    builder = _wal_hybrid().use_lsm(False)
+def test_wal_hybrid_with_use_memwal_false_joins_on_row_ids():
+    builder = _wal_hybrid().use_memwal(False)
     builder._create_query_builders()
     assert builder._pk_fusion is None
 
 
 def test_wal_hybrid_refuses_an_explicit_with_row_id():
     builder = _wal_hybrid().with_row_id(True)
-    with pytest.raises(NotImplementedError, match="use_lsm"):
+    with pytest.raises(NotImplementedError, match="use_memwal"):
         builder._create_query_builders()
 
 
@@ -119,7 +119,9 @@ def test_wal_hybrid_fuses_on_the_key_and_drops_it_again():
         limit=10,
         with_row_ids=True,
     )
-    results = builder._finish_hybrid_results(combined)
+    results = builder._finish_hybrid_results(
+        combined, blob_mode="lazy", blob_sources={}
+    )
 
     assert "_rowid" not in results.column_names, "the surrogate is internal"
     assert "id" not in results.column_names, "the caller selected only `text`"
@@ -139,7 +141,7 @@ def test_wal_hybrid_refuses_row_id_even_with_return_score_all(row_id_first: bool
     else:
         builder = builder.rerank(reranker).with_row_id(True)
 
-    with pytest.raises(NotImplementedError, match="use_lsm"):
+    with pytest.raises(NotImplementedError, match="use_memwal"):
         builder._create_query_builders()
 
 
@@ -197,7 +199,7 @@ def test_fallback_explains_the_refusal_to_a_caller_who_asked_for_row_ids():
     def run():
         raise _REFUSAL
 
-    with pytest.raises(NotImplementedError, match="use_lsm") as info:
+    with pytest.raises(NotImplementedError, match="use_memwal") as info:
         with_pk_fallback(
             run, mock.Mock(), fused=lambda: False, caller_requested_row_id=True
         )
diff --git a/python/python/tests/utils.py b/python/python/tests/utils.py
index 4882f0c28..b3548b678 100644
--- a/python/python/tests/utils.py
+++ b/python/python/tests/utils.py
@@ -73,10 +73,10 @@ class MockPermutationServer:
                     },
                 },
             )
-        if path == f"/v1/table/{self.name}/get_lsm_write_spec/":
+        if path == f"/v1/table/{self.name}/get_memwal_spec/":
             self._read_body(request)
-            # Null spec: this table has no LSM write path.
-            return self._json(request, {"lsm_write_spec": None})
+            # Null spec: this table has no MemWAL write path.
+            return self._json(request, {"memwal_spec": None})
         if path == f"/v1/table/{self.name}/count_rows/":
             self._read_body(request)
             return self._json(request, self.num_rows)
diff --git a/python/src/authz.rs b/python/src/authz.rs
new file mode 100644
index 000000000..6ad3e69b1
--- /dev/null
+++ b/python/src/authz.rs
@@ -0,0 +1,280 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use crate::error::PythonErrorExt;
+use crate::runtime::future_into_py;
+use lancedb::authz::*;
+use pyo3::exceptions::{PyRuntimeError, PyValueError};
+use pyo3::types::{PyAnyMethods, PyBytes, PyString};
+use pyo3::{Bound, PyAny, PyRef, PyResult, pyclass, pymethods};
+use std::sync::Arc;
+
+#[pyclass]
+pub struct AuthorizationClient {
+    pub(crate) inner: Arc<dyn Authorization>,
+}
+
+#[pymethods]
+impl AuthorizationClient {
+    fn list_acls<'py>(self_: PyRef<'py, Self>, request: &str) -> PyResult<Bound<'py, PyAny>> {
+        let request: ListAccessControlEntriesRequest = serde_json::from_str(request)
+            .map_err(|_| PyValueError::new_err("Invalid authorization request"))?;
+        let inner = self_.inner.clone();
+        future_into_py(self_.py(), async move {
+            let result = inner.list_acls(request).await.infer_error()?;
+            serde_json::to_string(&result).map_err(|err| PyRuntimeError::new_err(err.to_string()))
+        })
+    }
+    fn add_acl<'py>(self_: PyRef<'py, Self>, request: &str) -> PyResult<Bound<'py, PyAny>> {
+        let request: AccessControlEntryRequest = serde_json::from_str(request)
+            .map_err(|_| PyValueError::new_err("Invalid authorization request"))?;
+        let inner = self_.inner.clone();
+        future_into_py(self_.py(), async move {
+            inner.add_acl(request).await.infer_error()
+        })
+    }
+    fn delete_acl<'py>(self_: PyRef<'py, Self>, request: &str) -> PyResult<Bound<'py, PyAny>> {
+        let request: AccessControlEntryRequest = serde_json::from_str(request)
+            .map_err(|_| PyValueError::new_err("Invalid authorization request"))?;
+        let inner = self_.inner.clone();
+        future_into_py(self_.py(), async move {
+            inner.delete_acl(request).await.infer_error()
+        })
+    }
+    fn list_role_bindings<'py>(
+        self_: PyRef<'py, Self>,
+        request: &str,
+    ) -> PyResult<Bound<'py, PyAny>> {
+        let request: ListRoleBindingsRequest = serde_json::from_str(request)
+            .map_err(|_| PyValueError::new_err("Invalid authorization request"))?;
+        request.validate().infer_error()?;
+        let inner = self_.inner.clone();
+        future_into_py(self_.py(), async move {
+            let result = inner.list_role_bindings(request).await.infer_error()?;
+            serde_json::to_string(&result).map_err(|err| PyRuntimeError::new_err(err.to_string()))
+        })
+    }
+    fn add_role_binding<'py>(
+        self_: PyRef<'py, Self>,
+        request: &str,
+    ) -> PyResult<Bound<'py, PyAny>> {
+        let request: RoleBindingRequest = serde_json::from_str(request)
+            .map_err(|_| PyValueError::new_err("Invalid authorization request"))?;
+        request.validate().infer_error()?;
+        let inner = self_.inner.clone();
+        future_into_py(self_.py(), async move {
+            inner.add_role_binding(request).await.infer_error()
+        })
+    }
+    fn delete_role_binding<'py>(
+        self_: PyRef<'py, Self>,
+        request: &str,
+    ) -> PyResult<Bound<'py, PyAny>> {
+        let request: RoleBindingRequest = serde_json::from_str(request)
+            .map_err(|_| PyValueError::new_err("Invalid authorization request"))?;
+        request.validate().infer_error()?;
+        let inner = self_.inner.clone();
+        future_into_py(self_.py(), async move {
+            inner.delete_role_binding(request).await.infer_error()
+        })
+    }
+    fn get_principal<'py>(self_: PyRef<'py, Self>, request: &str) -> PyResult<Bound<'py, PyAny>> {
+        let request: GetPrincipalRequest = serde_json::from_str(request)
+            .map_err(|_| PyValueError::new_err("Invalid authorization request"))?;
+        request.validate().infer_error()?;
+        let inner = self_.inner.clone();
+        future_into_py(self_.py(), async move {
+            let result = inner.get_principal(request).await.infer_error()?;
+            serde_json::to_string(&result).map_err(|err| PyRuntimeError::new_err(err.to_string()))
+        })
+    }
+    fn get_group<'py>(self_: PyRef<'py, Self>, request: &str) -> PyResult<Bound<'py, PyAny>> {
+        let request: GetGroupRequest = serde_json::from_str(request)
+            .map_err(|_| PyValueError::new_err("Invalid authorization request"))?;
+        request.validate().infer_error()?;
+        let inner = self_.inner.clone();
+        future_into_py(self_.py(), async move {
+            let result = inner.get_group(request).await.infer_error()?;
+            serde_json::to_string(&result).map_err(|err| PyRuntimeError::new_err(err.to_string()))
+        })
+    }
+}
+
+/// The shared privilege parser and canonical wire names.
+#[pyclass(frozen)]
+pub struct AuthzPrivilege {
+    inner: Privilege,
+}
+
+#[pymethods]
+impl AuthzPrivilege {
+    #[new]
+    fn new(value: &str) -> PyResult<Self> {
+        Ok(Self {
+            inner: value.parse().map_err(PyValueError::new_err)?,
+        })
+    }
+
+    #[staticmethod]
+    fn known() -> Vec<String> {
+        Privilege::KNOWN.iter().map(ToString::to_string).collect()
+    }
+
+    fn __str__(&self) -> String {
+        self.inner.to_string()
+    }
+}
+
+#[pyclass(frozen)]
+pub struct AuthzSubject {
+    inner: Subject,
+}
+
+#[pymethods]
+impl AuthzSubject {
+    #[new]
+    fn new(kind: &str, value: &Bound<'_, PyAny>) -> PyResult<Self> {
+        let value: String = value
+            .extract()
+            .map_err(|_| PyValueError::new_err("Subject value must be a nonempty string"))?;
+        Ok(Self {
+            inner: Subject::new(kind.parse().infer_error()?, value).infer_error()?,
+        })
+    }
+
+    #[staticmethod]
+    fn from_canonical(value: &str) -> PyResult<Self> {
+        Ok(Self {
+            inner: Subject::from_canonical(value).infer_error()?,
+        })
+    }
+
+    #[getter]
+    fn kind(&self) -> &str {
+        self.inner.kind().as_str()
+    }
+    #[getter]
+    fn value(&self) -> &str {
+        self.inner.value()
+    }
+    #[getter]
+    fn display_value(&self) -> &str {
+        self.inner.display_value()
+    }
+
+    #[pyo3(signature = (context=None))]
+    fn to_wire(&self, context: Option<&str>) -> PyResult<String> {
+        match context {
+            None => {}
+            Some("principal") => self.inner.validate_principal().infer_error()?,
+            Some("group") => self.inner.validate_group().infer_error()?,
+            Some("role_member") => self.inner.validate_role_member().infer_error()?,
+            Some(_) => return Err(PyValueError::new_err("Invalid subject context")),
+        }
+        Ok(self.inner.to_wire())
+    }
+
+    fn __repr__(&self) -> String {
+        format!("{:?}", self.inner)
+    }
+    fn __str__(&self) -> String {
+        self.inner.to_string()
+    }
+}
+
+#[pyclass(frozen)]
+pub struct AuthzObject {
+    inner: Object,
+}
+
+impl AuthzObject {
+    fn validated(inner: Object) -> PyResult<Self> {
+        inner.validate().map_err(PyValueError::new_err)?;
+        Ok(Self { inner })
+    }
+}
+
+fn namespace_path(value: &Bound<'_, PyAny>) -> PyResult<NamespacePath> {
+    if value.is_instance_of::<PyString>() || value.is_instance_of::<PyBytes>() {
+        return Err(PyValueError::new_err(
+            "namespace must be a sequence of components",
+        ));
+    }
+    let components = value
+        .extract()
+        .map_err(|_| PyValueError::new_err("namespace must be a sequence of string components"))?;
+    NamespacePath::new(components).map_err(PyValueError::new_err)
+}
+
+#[pymethods]
+impl AuthzObject {
+    #[new]
+    fn new(value: &str) -> PyResult<Self> {
+        Ok(Self {
+            inner: value.parse().infer_error()?,
+        })
+    }
+
+    #[staticmethod]
+    fn system() -> Self {
+        Self {
+            inner: Object::System,
+        }
+    }
+
+    #[staticmethod]
+    fn database(name: &str) -> PyResult<Self> {
+        Self::validated(Object::Database(DatabaseObject::new(name.into())))
+    }
+
+    #[staticmethod]
+    fn namespace(database: &str, namespace: &Bound<'_, PyAny>) -> PyResult<Self> {
+        Self::validated(Object::Namespace(NamespaceObject {
+            database: database.into(),
+            namespace: namespace_path(namespace)?,
+        }))
+    }
+
+    #[staticmethod]
+    fn table(database: &str, namespace: &Bound<'_, PyAny>, name: &str) -> PyResult<Self> {
+        Self::validated(Object::Table(TableObject {
+            database: database.into(),
+            namespace: namespace_path(namespace)?,
+            table: name.into(),
+        }))
+    }
+
+    #[staticmethod]
+    fn view(database: &str, namespace: &Bound<'_, PyAny>, name: &str) -> PyResult<Self> {
+        Self::validated(Object::View(ViewObject {
+            database: database.into(),
+            namespace: namespace_path(namespace)?,
+            view: name.into(),
+        }))
+    }
+
+    #[staticmethod]
+    fn secret(database: &str, namespace: &Bound<'_, PyAny>, name: &str) -> PyResult<Self> {
+        Self::validated(Object::Secret(SecretObject {
+            database: database.into(),
+            namespace: namespace_path(namespace)?,
+            secret: name.into(),
+        }))
+    }
+
+    #[staticmethod]
+    fn function(database: &str, namespace: &Bound<'_, PyAny>, name: &str) -> PyResult<Self> {
+        Self::validated(Object::Function(FunctionObject {
+            database: database.into(),
+            namespace: namespace_path(namespace)?,
+            function: name.into(),
+        }))
+    }
+
+    fn __str__(&self) -> String {
+        self.inner.to_string()
+    }
+    fn __repr__(&self) -> String {
+        format!("{:?}", self.inner)
+    }
+}
diff --git a/python/src/catalog.rs b/python/src/catalog.rs
index f61b409ff..9e375b314 100644
--- a/python/src/catalog.rs
+++ b/python/src/catalog.rs
@@ -1,12 +1,13 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
+use std::sync::{Arc, Mutex};
 use std::time::Duration;
 
-use lancedb::catalog::{
-    CatalogConnection, CreateDatabaseRequest, DropDatabaseRequest, ListDatabasesRequest,
-};
-use pyo3::exceptions::PyValueError;
+use futures::{StreamExt, future::poll_fn};
+
+use lancedb::catalog::{CatalogConnection, CreateDatabaseRequest, DropDatabaseRequest};
+use pyo3::exceptions::{PyStopAsyncIteration, PyValueError};
 use pyo3::{Bound, PyAny, PyRef, PyResult, Python, pyclass, pyfunction, pymethods};
 
 use crate::connection::{Connection, PyClientConfig};
@@ -20,6 +21,13 @@ pub struct Catalog {
 
 #[pymethods]
 impl Catalog {
+    #[getter]
+    fn authz(&self) -> PyResult<crate::authz::AuthorizationClient> {
+        Ok(crate::authz::AuthorizationClient {
+            inner: self.inner.authz().infer_error()?,
+        })
+    }
+
     #[getter]
     fn uri(&self) -> &str {
         self.inner.uri()
@@ -67,19 +75,48 @@ impl Catalog {
         })
     }
 
-    #[pyo3(signature = (*, limit=None, page_token=None))]
-    fn list_databases<'py>(
-        self_: PyRef<'py, Self>,
-        limit: Option<u32>,
-        page_token: Option<String>,
-    ) -> PyResult<Bound<'py, PyAny>> {
+    #[pyo3(signature = (*, page_token=None, page_limit=None))]
+    fn list_databases(&self, page_token: Option<String>, page_limit: Option<u32>) -> DatabaseNames {
+        DatabaseNames {
+            inner: Arc::new(Mutex::new(
+                self.inner.list_databases(page_token, page_limit),
+            )),
+            next_lock: Arc::new(tokio::sync::Mutex::new(())),
+        }
+    }
+}
+
+#[pyclass]
+pub struct DatabaseNames {
+    inner: Arc<Mutex<lancedb::catalog::DatabaseNames>>,
+    next_lock: Arc<tokio::sync::Mutex<()>>,
+}
+
+#[pymethods]
+impl DatabaseNames {
+    fn num_page_results(&self, py: Python<'_>) -> usize {
+        py.detach(|| self.inner.lock().unwrap().num_page_results())
+    }
+
+    fn page_token(&self, py: Python<'_>) -> Option<String> {
+        py.detach(|| self.inner.lock().unwrap().page_token().map(str::to_owned))
+    }
+
+    fn __aiter__(self_: PyRef<'_, Self>) -> PyRef<'_, Self> {
+        self_
+    }
+
+    fn __anext__(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.inner.clone();
+        let next_lock = self_.next_lock.clone();
         future_into_py(self_.py(), async move {
-            let mut request = ListDatabasesRequest::default();
-            request.limit = limit;
-            request.page_token = page_token;
-            let response = inner.list_databases(request).await.infer_error()?;
-            Ok((response.databases, response.page_token))
+            // Serialize advances while allowing synchronous state inspection
+            // between polls, including while a REST request is pending.
+            let _guard = next_lock.lock().await;
+            poll_fn(|cx| inner.lock().unwrap().poll_next_unpin(cx))
+                .await
+                .ok_or_else(|| PyStopAsyncIteration::new_err(""))?
+                .infer_error()
         })
     }
 }
diff --git a/python/src/connection.rs b/python/src/connection.rs
index a0fc3b04b..9ac081b9d 100644
--- a/python/src/connection.rs
+++ b/python/src/connection.rs
@@ -14,9 +14,10 @@ use crate::{
     table::Table,
 };
 use arrow::{
+    array::RecordBatch,
     datatypes::Schema,
     ffi_stream::ArrowArrayStreamReader,
-    pyarrow::{FromPyArrow, ToPyArrow},
+    pyarrow::{FromPyArrow, PyArrowType, ToPyArrow},
 };
 use lancedb::{
     connection::Connection as LanceConnection,
@@ -148,18 +149,22 @@ impl Connection {
         self.get_inner().map(|inner| inner.uri().to_string())
     }
 
-    #[pyo3(signature = (query, *, default_namespace_path=None))]
+    #[pyo3(signature = (query, *, default_namespace_path=None, parameters=None))]
     pub fn execute_query_async<'a>(
         self_: PyRef<'a, Self>,
         query: String,
         default_namespace_path: Option<Bound<'_, PyAny>>,
+        parameters: Option<PyArrowType<RecordBatch>>,
     ) -> PyResult<Bound<'a, PyAny>> {
         let inner = self_.get_inner()?.clone();
         let default_namespace_path = parse_default_namespace_path(default_namespace_path)?;
         future_into_py(self_.py(), async move {
-            let operation = inner
+            let mut operation = inner
                 .execute_query_async(query)
                 .default_namespace_path(default_namespace_path);
+            if let Some(PyArrowType(parameters)) = parameters {
+                operation = operation.parameters(parameters);
+            }
             operation
                 .execute()
                 .await
@@ -407,8 +412,7 @@ impl Connection {
         })
     }
 
-    #[allow(clippy::too_many_arguments)]
-    #[pyo3(signature = (name, source, projections=None, filter=None, limit=None, with_no_data=false, *, vector_source_json=None))]
+    #[pyo3(signature = (name, source, projections=None, filter=None, limit=None, with_no_data=false))]
     pub fn create_materialized_view(
         self_: PyRef<'_, Self>,
         name: String,
@@ -417,17 +421,10 @@ impl Connection {
         filter: Option<String>,
         limit: Option<u64>,
         with_no_data: bool,
-        vector_source_json: Option<String>,
     ) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.get_inner()?.clone();
         future_into_py(self_.py(), async move {
             let mut builder = inner.create_materialized_view(name, source);
-            if let Some(source) = vector_source_json {
-                builder = builder.vector_source(
-                    serde_json::from_str(&source)
-                        .map_err(|e| PyValueError::new_err(e.to_string()))?,
-                );
-            }
             if let Some(projections) = projections {
                 builder = builder.select(projections);
             }
@@ -443,8 +440,7 @@ impl Connection {
         })
     }
 
-    #[allow(clippy::too_many_arguments)]
-    #[pyo3(signature = (name, source, projections=None, filter=None, limit=None, with_no_data=false, *, vector_source_json=None))]
+    #[pyo3(signature = (name, source, projections=None, filter=None, limit=None, with_no_data=false))]
     pub fn create_materialized_view_async(
         self_: PyRef<'_, Self>,
         name: String,
@@ -453,17 +449,10 @@ impl Connection {
         filter: Option<String>,
         limit: Option<u64>,
         with_no_data: bool,
-        vector_source_json: Option<String>,
     ) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.get_inner()?.clone();
         future_into_py(self_.py(), async move {
             let mut builder = inner.create_materialized_view(name, source);
-            if let Some(source) = vector_source_json {
-                builder = builder.vector_source(
-                    serde_json::from_str(&source)
-                        .map_err(|e| PyValueError::new_err(e.to_string()))?,
-                );
-            }
             if let Some(projections) = projections {
                 builder = builder.select(projections);
             }
@@ -480,12 +469,16 @@ impl Connection {
         })
     }
 
-    pub fn list_materialized_views(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
-        let inner = self_.get_inner()?.clone();
-        future_into_py(self_.py(), async move {
-            let views = inner.list_materialized_views().await.infer_error()?;
-            Ok(views)
-        })
+    #[pyo3(signature = (*, page_token=None, page_limit=None))]
+    pub fn list_materialized_views(
+        &self,
+        page_token: Option<String>,
+        page_limit: Option<u32>,
+    ) -> PyResult<crate::listing::NameListing> {
+        Ok(crate::listing::NameListing::new(
+            self.get_inner()?
+                .list_materialized_views(crate::listing::options(page_token, page_limit)),
+        ))
     }
 
     #[pyo3(signature = (name, namespace_path=None))]
@@ -754,31 +747,37 @@ impl Connection {
         })
     }
 
+    #[pyo3(signature = (request_json, namespace_path=None))]
     pub fn create_function_async(
         self_: PyRef<'_, Self>,
         request_json: String,
+        namespace_path: Option<Vec<String>>,
     ) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.get_inner()?.clone();
         let request = lancedb::function::FunctionRegistrationRequest::from_json(&request_json)
             .infer_error()?;
+        let namespace_path = namespace_path.unwrap_or_default();
         future_into_py(self_.py(), async move {
             inner
-                .create_function_async(request)
+                .create_function_async(request, &namespace_path)
                 .await
                 .infer_error()
                 .map(crate::job::Job::new_typed)
         })
     }
 
+    #[pyo3(signature = (name, version, namespace_path=None))]
     pub fn get_function(
         self_: PyRef<'_, Self>,
         name: String,
         version: String,
+        namespace_path: Option<Vec<String>>,
     ) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.get_inner()?.clone();
+        let namespace_path = namespace_path.unwrap_or_default();
         future_into_py(self_.py(), async move {
             inner
-                .get_function(name, version)
+                .get_function(name, version, &namespace_path)
                 .await
                 .infer_error()?
                 .to_canonical_json()
@@ -786,39 +785,50 @@ impl Connection {
         })
     }
 
-    pub fn list_functions(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
-        let inner = self_.get_inner()?.clone();
-        future_into_py(self_.py(), async move {
-            inner
-                .list_functions()
-                .await
-                .infer_error()?
-                .into_iter()
-                .map(|function| function.to_canonical_json().infer_error())
-                .collect::<PyResult<Vec<_>>>()
-        })
+    #[pyo3(signature = (namespace_path=None, *, page_token=None, page_limit=None))]
+    pub fn list_functions(
+        &self,
+        namespace_path: Option<Vec<String>>,
+        page_token: Option<String>,
+        page_limit: Option<u32>,
+    ) -> PyResult<crate::listing::FunctionListing> {
+        Ok(crate::listing::FunctionListing::new(
+            self.get_inner()?.list_functions(
+                &namespace_path.unwrap_or_default(),
+                crate::listing::options(page_token, page_limit),
+            ),
+        ))
     }
 
+    #[pyo3(signature = (name, version, namespace_path=None))]
     pub fn drop_function(
         self_: PyRef<'_, Self>,
         name: String,
         version: String,
+        namespace_path: Option<Vec<String>>,
     ) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.get_inner()?.clone();
+        let namespace_path = namespace_path.unwrap_or_default();
         future_into_py(self_.py(), async move {
-            inner.drop_function(name, version).await.infer_error()
+            inner
+                .drop_function(name, version, &namespace_path)
+                .await
+                .infer_error()
         })
     }
 
+    #[pyo3(signature = (name, version, namespace_path=None))]
     pub fn drop_function_async(
         self_: PyRef<'_, Self>,
         name: String,
         version: String,
+        namespace_path: Option<Vec<String>>,
     ) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.get_inner()?.clone();
+        let namespace_path = namespace_path.unwrap_or_default();
         future_into_py(self_.py(), async move {
             inner
-                .drop_function_async(name, version)
+                .drop_function_async(name, version, &namespace_path)
                 .await
                 .infer_error()
                 .map(|(dropped, job)| (dropped, crate::job::Job::new(job)))
@@ -859,16 +869,19 @@ impl Connection {
         })
     }
 
-    #[pyo3(signature = (namespace_path=None))]
+    #[pyo3(signature = (namespace_path=None, *, page_token=None, page_limit=None))]
     pub fn list_secrets(
-        self_: PyRef<'_, Self>,
+        &self,
         namespace_path: Option<Vec<String>>,
-    ) -> PyResult<Bound<'_, PyAny>> {
-        let inner = self_.get_inner()?.clone();
-        let namespace_path = namespace_path.unwrap_or_default();
-        future_into_py(self_.py(), async move {
-            inner.list_secrets(&namespace_path).await.infer_error()
-        })
+        page_token: Option<String>,
+        page_limit: Option<u32>,
+    ) -> PyResult<crate::listing::NameListing> {
+        Ok(crate::listing::NameListing::new(
+            self.get_inner()?.list_secrets(
+                &namespace_path.unwrap_or_default(),
+                crate::listing::options(page_token, page_limit),
+            ),
+        ))
     }
 
     #[pyo3(signature = (name, namespace_path=None))]
@@ -969,27 +982,31 @@ impl Connection {
         })
     }
 
-    #[pyo3(signature = (namespace_path=None))]
+    #[pyo3(signature = (namespace_path=None, *, page_token=None, page_limit=None))]
     pub fn list_views(
-        self_: PyRef<'_, Self>,
+        &self,
         namespace_path: Option<Vec<String>>,
-    ) -> PyResult<Bound<'_, PyAny>> {
-        let inner = self_.get_inner()?.clone();
-        let namespace_path = namespace_path.unwrap_or_default();
-        future_into_py(self_.py(), async move {
-            inner.list_views(&namespace_path).await.infer_error()
-        })
+        page_token: Option<String>,
+        page_limit: Option<u32>,
+    ) -> PyResult<crate::listing::NameListing> {
+        Ok(crate::listing::NameListing::new(
+            self.get_inner()?.list_views(
+                &namespace_path.unwrap_or_default(),
+                crate::listing::options(page_token, page_limit),
+            ),
+        ))
     }
 
-    pub fn list_jobs(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
-        let inner = self_.get_inner()?.clone();
-        future_into_py(self_.py(), async move {
-            let jobs = inner.list_jobs().await.infer_error()?;
-            Ok(jobs
-                .into_iter()
-                .map(crate::job::JobInfo::from)
-                .collect::<Vec<_>>())
-        })
+    #[pyo3(signature = (*, page_token=None, page_limit=None))]
+    pub fn list_jobs(
+        &self,
+        page_token: Option<String>,
+        page_limit: Option<u32>,
+    ) -> PyResult<crate::listing::JobListing> {
+        Ok(crate::listing::JobListing::new(
+            self.get_inner()?
+                .list_jobs(crate::listing::options(page_token, page_limit)),
+        ))
     }
 
     pub fn cancel_job(self_: PyRef<'_, Self>, job_id: String) -> PyResult<Bound<'_, PyAny>> {
@@ -1203,6 +1220,7 @@ pub struct PyClientConfig {
     user_agent: String,
     retry_config: Option<PyClientRetryConfig>,
     timeout_config: Option<PyClientTimeoutConfig>,
+    blob_request_concurrency: Option<usize>,
     extra_headers: Option<HashMap<String, String>>,
     id_delimiter: Option<String>,
     tls_config: Option<PyClientTlsConfig>,
@@ -1288,6 +1306,7 @@ impl From<PyClientConfig> for lancedb::remote::ClientConfig {
             user_agent: value.user_agent,
             retry_config: value.retry_config.map(Into::into).unwrap_or_default(),
             timeout_config: value.timeout_config.map(Into::into).unwrap_or_default(),
+            blob_request_concurrency: value.blob_request_concurrency,
             extra_headers: value.extra_headers.unwrap_or_default(),
             id_delimiter: value.id_delimiter,
             tls_config: value.tls_config.map(Into::into),
diff --git a/python/src/index.rs b/python/src/index.rs
index 54b15f55e..d4e16a67e 100644
--- a/python/src/index.rs
+++ b/python/src/index.rs
@@ -54,8 +54,6 @@ pub fn extract_index_params(source: &Option<Bound<'_, PyAny>>) -> PyResult<Lance
                     .with_position(params.with_position)
                     .lower_case(params.lower_case)
                     .max_token_length(params.max_token_length)
-                    .remove_stop_words(params.remove_stop_words)
-                    .stem(params.stem)
                     .ascii_folding(params.ascii_folding)
                     .ngram_min_length(params.ngram_min_length)
                     .ngram_max_length(params.ngram_max_length)
@@ -65,6 +63,12 @@ pub fn extract_index_params(source: &Option<Bound<'_, PyAny>>) -> PyResult<Lance
                         DocumentGranularity::try_from(params.document_granularity.as_str())
                             .map_err(|err| PyValueError::new_err(err.to_string()))?,
                     );
+                if let Some(stem) = params.stem {
+                    inner_opts = inner_opts.stem(stem);
+                }
+                if let Some(remove_stop_words) = params.remove_stop_words {
+                    inner_opts = inner_opts.remove_stop_words(remove_stop_words);
+                }
                 if let Some(memory_limit) = params.memory_limit {
                     inner_opts = inner_opts.memory_limit_mb(memory_limit);
                 }
@@ -215,8 +219,8 @@ struct FtsParams {
     language: String,
     max_token_length: Option<usize>,
     lower_case: bool,
-    stem: bool,
-    remove_stop_words: bool,
+    stem: Option<bool>,
+    remove_stop_words: Option<bool>,
     custom_stop_words: Option<Vec<String>>,
     ascii_folding: bool,
     ngram_min_length: u32,
diff --git a/python/src/lib.rs b/python/src/lib.rs
index ab04663c0..8dd98d019 100644
--- a/python/src/lib.rs
+++ b/python/src/lib.rs
@@ -16,12 +16,13 @@ use query::{FTSQuery, HybridQuery, Query, VectorQuery};
 use session::Session;
 use table::{
     AddColumnsResult, AddResult, AlterColumnsResult, DeleteResult, DropColumnsResult, FtsToken,
-    FunctionErrorFragment, FunctionErrorRecord, FunctionErrors, LsmWriteSpec, MergeResult,
+    FunctionErrorFragment, FunctionErrorRecord, FunctionErrors, MemWalWriteSpec, MergeResult,
     PyBlobFile, RefreshColumnResult, RefreshMaterializedViewResult, Table,
     UpdateFieldMetadataResult, UpdateResult,
 };
 
 pub mod arrow;
+pub mod authz;
 pub mod catalog;
 pub mod connection;
 pub mod error;
@@ -29,6 +30,7 @@ pub mod expr;
 pub mod header;
 pub mod index;
 pub mod job;
+mod listing;
 pub mod namespace;
 pub mod oauth;
 pub mod otel;
@@ -64,6 +66,15 @@ pub fn _lancedb(py: Python, m: &Bound<'_, PyModule>) -> PyResult<()> {
     env_logger::init_from_env(env);
     m.add_class::<Connection>()?;
     m.add_class::<catalog::Catalog>()?;
+    m.add_class::<catalog::DatabaseNames>()?;
+    m.add_class::<listing::NameListing>()?;
+    m.add_class::<listing::FunctionListing>()?;
+    m.add_class::<listing::JobListing>()?;
+
+    m.add_class::<authz::AuthorizationClient>()?;
+    m.add_class::<authz::AuthzSubject>()?;
+    m.add_class::<authz::AuthzObject>()?;
+    m.add_class::<authz::AuthzPrivilege>()?;
     m.add_function(wrap_pyfunction!(catalog::connect_catalog, m)?)?;
     m.add_class::<Session>()?;
     m.add_class::<Table>()?;
@@ -93,7 +104,7 @@ pub fn _lancedb(py: Python, m: &Bound<'_, PyModule>) -> PyResult<()> {
     m.add_class::<UpdateFieldMetadataResult>()?;
     m.add_class::<AddResult>()?;
     m.add_class::<MergeResult>()?;
-    m.add_class::<LsmWriteSpec>()?;
+    m.add_class::<MemWalWriteSpec>()?;
     m.add_class::<DeleteResult>()?;
     m.add_class::<DropColumnsResult>()?;
     m.add_class::<UpdateResult>()?;
diff --git a/python/src/listing.rs b/python/src/listing.rs
new file mode 100644
index 000000000..2d1a3916e
--- /dev/null
+++ b/python/src/listing.rs
@@ -0,0 +1,131 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use crate::error::PythonErrorExt;
+use crate::runtime::future_into_py;
+use futures::{StreamExt, future::poll_fn};
+use lancedb::listing::{Listing, ListingOptions};
+use pyo3::exceptions::PyStopAsyncIteration;
+use pyo3::{Bound, PyAny, PyRef, PyResult, Python, pyclass, pymethods};
+use std::sync::{Arc, Mutex};
+
+pub fn options(page_token: Option<String>, page_limit: Option<u32>) -> ListingOptions {
+    let mut options = ListingOptions::default();
+    options.page_token = page_token;
+    options.page_limit = page_limit;
+    options
+}
+
+#[pyclass]
+pub struct NameListing {
+    inner: Arc<Mutex<Listing<String>>>,
+    next_lock: Arc<tokio::sync::Mutex<()>>,
+}
+impl NameListing {
+    pub fn new(inner: Listing<String>) -> Self {
+        Self {
+            inner: Arc::new(Mutex::new(inner)),
+            next_lock: Arc::new(tokio::sync::Mutex::new(())),
+        }
+    }
+}
+#[pymethods]
+impl NameListing {
+    fn num_page_results(&self, py: Python<'_>) -> usize {
+        py.detach(|| self.inner.lock().unwrap().num_page_results())
+    }
+    fn page_token(&self, py: Python<'_>) -> Option<String> {
+        py.detach(|| self.inner.lock().unwrap().page_token().map(str::to_owned))
+    }
+    fn __aiter__(self_: PyRef<'_, Self>) -> PyRef<'_, Self> {
+        self_
+    }
+    fn __anext__(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
+        let inner = self_.inner.clone();
+        let next_lock = self_.next_lock.clone();
+        future_into_py(self_.py(), async move {
+            let _guard = next_lock.lock().await;
+            let value = poll_fn(|cx| inner.lock().unwrap().poll_next_unpin(cx))
+                .await
+                .ok_or_else(|| PyStopAsyncIteration::new_err(""))?
+                .infer_error()?;
+            Ok(value)
+        })
+    }
+}
+
+#[pyclass]
+pub struct FunctionListing {
+    inner: Arc<Mutex<Listing<lancedb::function::FunctionVersion>>>,
+    next_lock: Arc<tokio::sync::Mutex<()>>,
+}
+impl FunctionListing {
+    pub fn new(inner: Listing<lancedb::function::FunctionVersion>) -> Self {
+        Self {
+            inner: Arc::new(Mutex::new(inner)),
+            next_lock: Arc::new(tokio::sync::Mutex::new(())),
+        }
+    }
+}
+#[pymethods]
+impl FunctionListing {
+    fn num_page_results(&self, py: Python<'_>) -> usize {
+        py.detach(|| self.inner.lock().unwrap().num_page_results())
+    }
+    fn page_token(&self, py: Python<'_>) -> Option<String> {
+        py.detach(|| self.inner.lock().unwrap().page_token().map(str::to_owned))
+    }
+    fn __aiter__(self_: PyRef<'_, Self>) -> PyRef<'_, Self> {
+        self_
+    }
+    fn __anext__(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
+        let inner = self_.inner.clone();
+        let next_lock = self_.next_lock.clone();
+        future_into_py(self_.py(), async move {
+            let _guard = next_lock.lock().await;
+            let value = poll_fn(|cx| inner.lock().unwrap().poll_next_unpin(cx))
+                .await
+                .ok_or_else(|| PyStopAsyncIteration::new_err(""))?
+                .infer_error()?;
+            value.to_canonical_json().infer_error()
+        })
+    }
+}
+
+#[pyclass]
+pub struct JobListing {
+    inner: Arc<Mutex<Listing<lancedb::database::JobInfo>>>,
+    next_lock: Arc<tokio::sync::Mutex<()>>,
+}
+impl JobListing {
+    pub fn new(inner: Listing<lancedb::database::JobInfo>) -> Self {
+        Self {
+            inner: Arc::new(Mutex::new(inner)),
+            next_lock: Arc::new(tokio::sync::Mutex::new(())),
+        }
+    }
+}
+#[pymethods]
+impl JobListing {
+    fn num_page_results(&self, py: Python<'_>) -> usize {
+        py.detach(|| self.inner.lock().unwrap().num_page_results())
+    }
+    fn page_token(&self, py: Python<'_>) -> Option<String> {
+        py.detach(|| self.inner.lock().unwrap().page_token().map(str::to_owned))
+    }
+    fn __aiter__(self_: PyRef<'_, Self>) -> PyRef<'_, Self> {
+        self_
+    }
+    fn __anext__(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
+        let inner = self_.inner.clone();
+        let next_lock = self_.next_lock.clone();
+        future_into_py(self_.py(), async move {
+            let _guard = next_lock.lock().await;
+            let value = poll_fn(|cx| inner.lock().unwrap().poll_next_unpin(cx))
+                .await
+                .ok_or_else(|| PyStopAsyncIteration::new_err(""))?
+                .infer_error()?;
+            Ok(crate::job::JobInfo::from(value))
+        })
+    }
+}
diff --git a/python/src/query.rs b/python/src/query.rs
index 2398376d5..4f23fa1b4 100644
--- a/python/src/query.rs
+++ b/python/src/query.rs
@@ -71,7 +71,7 @@ impl<'a, 'py> FromPyObject<'a, 'py> for PyLanceDB<FtsQuery> {
         match class_name(&ob)?.as_str() {
             "MatchQuery" => {
                 let query = ob.getattr("query")?.extract()?;
-                let column = ob.getattr("column")?.extract()?;
+                let column = ob.getattr("column")?.extract::<Option<String>>()?;
                 let boost = ob.getattr("boost")?.extract()?;
                 let fuzziness = ob.getattr("fuzziness")?.extract()?;
                 let max_expansions = ob.getattr("max_expansions")?.extract()?;
@@ -88,7 +88,7 @@ impl<'a, 'py> FromPyObject<'a, 'py> for PyLanceDB<FtsQuery> {
 
                 let mut query =
                     MatchQuery::new(query)
-                        .with_column(Some(column))
+                        .with_column(column)
                         .with_boost(boost)
                         .with_fuzziness(fuzziness)
                         .with_max_expansions(max_expansions)
@@ -180,6 +180,9 @@ impl<'py> IntoPyObject<'py> for PyLanceDB<FtsQuery> {
             .expect("Failed to import namespace");
 
         match self.0 {
+            FtsQuery::CombinedFields(_) => Err(PyValueError::new_err(
+                "Combined-fields FTS queries are not yet supported in Python",
+            )),
             FtsQuery::Match(query) => {
                 let kwargs = PyDict::new(py);
                 kwargs.set_item("boost", query.boost)?;
@@ -196,7 +199,7 @@ impl<'py> IntoPyObject<'py> for PyLanceDB<FtsQuery> {
                 }
                 namespace
                     .getattr(intern!(py, "MatchQuery"))?
-                    .call((query.terms, query.column.unwrap()), Some(&kwargs))
+                    .call((query.terms, query.column), Some(&kwargs))
             }
             FtsQuery::Phrase(query) => {
                 let kwargs = PyDict::new(py);
@@ -330,13 +333,13 @@ pub struct PyQueryRequest {
     pub select_source_columns: Option<HashMap<String, String>>,
     pub fast_search: Option<bool>,
     pub with_row_id: Option<bool>,
-    pub use_lsm: Option<bool>,
+    pub use_memwal: Option<bool>,
     pub column: Option<String>,
     pub query_vector: Option<PyQueryVectors>,
+    pub nprobes: Option<usize>,
     pub minimum_nprobes: Option<usize>,
-    // None means user did not set it and default should be used (currently 20)
-    // Some(0) means user set it to None and there is no limit
     pub maximum_nprobes: Option<usize>,
+    pub search_effort: Option<f64>,
     pub lower_bound: Option<f32>,
     pub upper_bound: Option<f32>,
     pub ef: Option<usize>,
@@ -363,11 +366,13 @@ impl From<AnyQuery> for PyQueryRequest {
                 select: PySelect(query_request.select),
                 fast_search: Some(query_request.fast_search),
                 with_row_id: Some(query_request.with_row_id),
-                use_lsm: query_request.use_lsm,
+                use_memwal: query_request.use_memwal,
                 column: None,
                 query_vector: None,
+                nprobes: None,
                 minimum_nprobes: None,
                 maximum_nprobes: None,
+                search_effort: None,
                 lower_bound: None,
                 upper_bound: None,
                 ef: None,
@@ -390,14 +395,13 @@ impl From<AnyQuery> for PyQueryRequest {
                 select: PySelect(vector_query.base.select),
                 fast_search: Some(vector_query.base.fast_search),
                 with_row_id: Some(vector_query.base.with_row_id),
-                use_lsm: vector_query.base.use_lsm,
+                use_memwal: vector_query.base.use_memwal,
                 column: vector_query.column,
                 query_vector: Some(PyQueryVectors(vector_query.query_vector)),
-                minimum_nprobes: Some(vector_query.minimum_nprobes),
-                maximum_nprobes: match vector_query.maximum_nprobes {
-                    None => Some(0),
-                    Some(value) => Some(value),
-                },
+                nprobes: vector_query.nprobes,
+                minimum_nprobes: vector_query.minimum_nprobes,
+                maximum_nprobes: vector_query.maximum_nprobes,
+                search_effort: vector_query.search_effort,
                 lower_bound: vector_query.lower_bound,
                 upper_bound: vector_query.upper_bound,
                 ef: vector_query.ef,
@@ -536,8 +540,8 @@ impl Query {
         self.inner = self.inner.clone().fast_search();
     }
 
-    pub fn use_lsm(&mut self, enable: bool) {
-        self.inner = self.inner.clone().use_lsm(enable);
+    pub fn use_memwal(&mut self, enable: bool) {
+        self.inner = self.inner.clone().use_memwal(enable);
     }
 
     pub fn with_row_id(&mut self) {
@@ -702,8 +706,8 @@ impl TakeQuery {
         self.inner = self.inner.clone().with_row_id();
     }
 
-    pub fn use_lsm(&mut self, enable: bool) {
-        self.inner = self.inner.clone().use_lsm(enable);
+    pub fn use_memwal(&mut self, enable: bool) {
+        self.inner = self.inner.clone().use_memwal(enable);
     }
 
     #[pyo3(signature = ())]
@@ -815,8 +819,8 @@ impl FTSQuery {
         self.inner = self.inner.clone().fast_search();
     }
 
-    pub fn use_lsm(&mut self, enable: bool) {
-        self.inner = self.inner.clone().use_lsm(enable);
+    pub fn use_memwal(&mut self, enable: bool) {
+        self.inner = self.inner.clone().use_memwal(enable);
     }
 
     pub fn with_row_id(&mut self) {
@@ -966,8 +970,8 @@ impl VectorQuery {
         self.inner = self.inner.clone().fast_search();
     }
 
-    pub fn use_lsm(&mut self, enable: bool) {
-        self.inner = self.inner.clone().use_lsm(enable);
+    pub fn use_memwal(&mut self, enable: bool) {
+        self.inner = self.inner.clone().use_memwal(enable);
     }
 
     pub fn with_row_id(&mut self) {
@@ -992,8 +996,18 @@ impl VectorQuery {
         self.inner = self.inner.clone().refine_factor(refine_factor);
     }
 
-    pub fn nprobes(&mut self, nprobe: u32) {
-        self.inner = self.inner.clone().nprobes(nprobe as usize);
+    pub fn nprobes(&mut self, nprobe: u32) -> PyResult<()> {
+        self.inner = self.inner.clone().nprobes(nprobe as usize).infer_error()?;
+        Ok(())
+    }
+
+    pub fn search_effort(&mut self, search_effort: Option<f64>) -> PyResult<()> {
+        self.inner = self
+            .inner
+            .clone()
+            .search_effort(search_effort)
+            .infer_error()?;
+        Ok(())
     }
 
     pub fn minimum_nprobes(&mut self, minimum_nprobes: u32) -> PyResult<()> {
@@ -1164,9 +1178,9 @@ impl HybridQuery {
         self.inner_fts.postfilter();
     }
 
-    pub fn use_lsm(&mut self, enable: bool) {
-        self.inner_vec.use_lsm(enable);
-        self.inner_fts.use_lsm(enable);
+    pub fn use_memwal(&mut self, enable: bool) {
+        self.inner_vec.use_memwal(enable);
+        self.inner_fts.use_memwal(enable);
     }
 
     pub fn add_query_vector(&mut self, vector: Bound<'_, PyAny>) -> PyResult<()> {
@@ -1185,8 +1199,20 @@ impl HybridQuery {
         self.inner_vec.refine_factor(refine_factor);
     }
 
-    pub fn nprobes(&mut self, nprobe: u32) {
-        self.inner_vec.nprobes(nprobe);
+    pub fn nprobes(&mut self, nprobe: u32) -> PyResult<()> {
+        self.inner_vec.nprobes(nprobe)
+    }
+
+    pub fn search_effort(&mut self, search_effort: Option<f64>) -> PyResult<()> {
+        self.inner_vec.search_effort(search_effort)
+    }
+
+    pub fn minimum_nprobes(&mut self, minimum_nprobes: u32) -> PyResult<()> {
+        self.inner_vec.minimum_nprobes(minimum_nprobes)
+    }
+
+    pub fn maximum_nprobes(&mut self, maximum_nprobes: u32) -> PyResult<()> {
+        self.inner_vec.maximum_nprobes(maximum_nprobes)
     }
 
     pub fn ef(&mut self, ef: u32) {
@@ -1233,6 +1259,10 @@ impl HybridQuery {
         req.query_vector = vec_req.query_vector;
         req.column = vec_req.column;
         req.distance_type = vec_req.distance_type;
+        req.nprobes = vec_req.nprobes;
+        req.minimum_nprobes = vec_req.minimum_nprobes;
+        req.maximum_nprobes = vec_req.maximum_nprobes;
+        req.search_effort = vec_req.search_effort;
         req.ef = vec_req.ef;
         req.refine_factor = vec_req.refine_factor;
         req.lower_bound = vec_req.lower_bound;
diff --git a/python/src/table.rs b/python/src/table.rs
index 22743c442..ce1f961be 100644
--- a/python/src/table.rs
+++ b/python/src/table.rs
@@ -41,16 +41,16 @@ use pyo3::{
 
 mod scannable;
 
-/// Convert `LsmStats` to a Python dict, preserving the per-bucket list.
+/// Convert `MemWalStats` to a Python dict, preserving the per-table-shard list.
 ///
 /// Deliberately not flattened to a table-level summary: a table is N
-/// buckets on one node, and the per-bucket detail is the reason the
-/// endpoint exists — flattening hides the single hot bucket someone opened
+/// table shards on one node, and the per-shard detail is the reason the
+/// endpoint exists — flattening hides the single hot table shard someone opened
 /// it to find.
-fn lsm_stats_to_py(py: Python<'_>, stats: &lancedb::table::LsmStats) -> PyResult<Py<PyDict>> {
+fn memwal_stats_to_py(py: Python<'_>, stats: &lancedb::table::MemWalStats) -> PyResult<Py<PyDict>> {
     let out = PyDict::new(py);
-    let buckets = PyList::empty(py);
-    for b in &stats.buckets {
+    let table_shards = PyList::empty(py);
+    for b in &stats.table_shards {
         let e = PyDict::new(py);
         e.set_item("shard_id", &b.shard_id)?;
         e.set_item("status", &b.status)?;
@@ -66,15 +66,15 @@ fn lsm_stats_to_py(py: Python<'_>, stats: &lancedb::table::LsmStats) -> PyResult
             b.wal_entry_position_last_seen,
         )?;
 
-        let generations = PyList::empty(py);
-        for g in &b.generations {
+        let sstables = PyList::empty(py);
+        for g in &b.sstables {
             let ge = PyDict::new(py);
             ge.set_item("generation", g.generation)?;
             ge.set_item("bytes", g.bytes)?;
             ge.set_item("rows", g.rows)?;
-            generations.append(ge)?;
+            sstables.append(ge)?;
         }
-        e.set_item("generations", generations)?;
+        e.set_item("sstables", sstables)?;
         e.set_item("compacting", b.compacting)?;
 
         e.set_item(
@@ -96,9 +96,9 @@ fn lsm_stats_to_py(py: Python<'_>, stats: &lancedb::table::LsmStats) -> PyResult
                 })
                 .transpose()?,
         )?;
-        buckets.append(e)?;
+        table_shards.append(e)?;
     }
-    out.set_item("buckets", buckets)?;
+    out.set_item("table_shards", table_shards)?;
     Ok(out.unbind())
 }
 
@@ -399,20 +399,20 @@ fn fmt_maintained(maintained: &Option<Vec<String>>) -> String {
 /// Constructed via the `bucket(...)`, `identity(...)`, or `unsharded()`
 /// classmethods, then optionally chain `with_maintained_indexes(...)` and
 /// `with_writer_config_defaults(...)`. A fresh spec maintains every index the
-/// MemWAL supports, resolved on install.
+/// table has, including ones created later.
 #[pyclass(module = "lancedb._lancedb", from_py_object)]
 #[derive(Clone, Debug)]
-pub struct LsmWriteSpec {
-    inner: lancedb::table::LsmWriteSpec,
+pub struct MemWalWriteSpec {
+    inner: lancedb::table::MemWalWriteSpec,
 }
 
 #[pymethods]
-impl LsmWriteSpec {
+impl MemWalWriteSpec {
     /// Hash-bucket sharding by the unenforced primary key column.
     #[staticmethod]
     pub fn bucket(column: String, num_buckets: u32) -> Self {
         Self {
-            inner: lancedb::table::LsmWriteSpec::bucket(column, num_buckets),
+            inner: lancedb::table::MemWalWriteSpec::bucket(column, num_buckets),
         }
     }
 
@@ -426,7 +426,7 @@ impl LsmWriteSpec {
     #[staticmethod]
     pub fn identity(column: String) -> Self {
         Self {
-            inner: lancedb::table::LsmWriteSpec::identity(column),
+            inner: lancedb::table::MemWalWriteSpec::identity(column),
         }
     }
 
@@ -435,13 +435,13 @@ impl LsmWriteSpec {
     #[staticmethod]
     pub fn unsharded() -> Self {
         Self {
-            inner: lancedb::table::LsmWriteSpec::unsharded(),
+            inner: lancedb::table::MemWalWriteSpec::unsharded(),
         }
     }
 
-    /// Set which indexes the MemWAL maintains. `None` (the default)
-    /// resolves every supported index on install; a list is verbatim,
-    /// and an empty list maintains nothing.
+    /// Set which indexes the MemWAL maintains. `None` (the default) is
+    /// every index the table has, including ones created later; a list is
+    /// verbatim, and an empty list maintains nothing.
     #[pyo3(signature = (indexes))]
     pub fn with_maintained_indexes(&self, indexes: Option<Vec<String>>) -> Self {
         Self {
@@ -459,33 +459,33 @@ impl LsmWriteSpec {
 
     pub fn __repr__(&self) -> String {
         match &self.inner {
-            lancedb::table::LsmWriteSpec::Bucket {
+            lancedb::table::MemWalWriteSpec::Bucket {
                 column,
                 num_buckets,
                 maintained_indexes,
                 writer_config_defaults,
             } => format!(
-                "LsmWriteSpec.bucket(column={:?}, num_buckets={}, maintained_indexes={}, writer_config_defaults={:?})",
+                "MemWalWriteSpec.bucket(column={:?}, num_buckets={}, maintained_indexes={}, writer_config_defaults={:?})",
                 column,
                 num_buckets,
                 fmt_maintained(maintained_indexes),
                 writer_config_defaults,
             ),
-            lancedb::table::LsmWriteSpec::Identity {
+            lancedb::table::MemWalWriteSpec::Identity {
                 column,
                 maintained_indexes,
                 writer_config_defaults,
             } => format!(
-                "LsmWriteSpec.identity(column={:?}, maintained_indexes={}, writer_config_defaults={:?})",
+                "MemWalWriteSpec.identity(column={:?}, maintained_indexes={}, writer_config_defaults={:?})",
                 column,
                 fmt_maintained(maintained_indexes),
                 writer_config_defaults,
             ),
-            lancedb::table::LsmWriteSpec::Unsharded {
+            lancedb::table::MemWalWriteSpec::Unsharded {
                 maintained_indexes,
                 writer_config_defaults,
             } => format!(
-                "LsmWriteSpec.unsharded(maintained_indexes={}, writer_config_defaults={:?})",
+                "MemWalWriteSpec.unsharded(maintained_indexes={}, writer_config_defaults={:?})",
                 fmt_maintained(maintained_indexes),
                 writer_config_defaults,
             ),
@@ -497,9 +497,9 @@ impl LsmWriteSpec {
     #[getter]
     pub fn spec_type(&self) -> &'static str {
         match &self.inner {
-            lancedb::table::LsmWriteSpec::Bucket { .. } => "bucket",
-            lancedb::table::LsmWriteSpec::Identity { .. } => "identity",
-            lancedb::table::LsmWriteSpec::Unsharded { .. } => "unsharded",
+            lancedb::table::MemWalWriteSpec::Bucket { .. } => "bucket",
+            lancedb::table::MemWalWriteSpec::Identity { .. } => "identity",
+            lancedb::table::MemWalWriteSpec::Unsharded { .. } => "unsharded",
         }
     }
 
@@ -507,9 +507,9 @@ impl LsmWriteSpec {
     #[getter]
     pub fn column(&self) -> Option<String> {
         match &self.inner {
-            lancedb::table::LsmWriteSpec::Bucket { column, .. }
-            | lancedb::table::LsmWriteSpec::Identity { column, .. } => Some(column.clone()),
-            lancedb::table::LsmWriteSpec::Unsharded { .. } => None,
+            lancedb::table::MemWalWriteSpec::Bucket { column, .. }
+            | lancedb::table::MemWalWriteSpec::Identity { column, .. } => Some(column.clone()),
+            lancedb::table::MemWalWriteSpec::Unsharded { .. } => None,
         }
     }
 
@@ -517,7 +517,7 @@ impl LsmWriteSpec {
     #[getter]
     pub fn num_buckets(&self) -> Option<u32> {
         match &self.inner {
-            lancedb::table::LsmWriteSpec::Bucket { num_buckets, .. } => Some(*num_buckets),
+            lancedb::table::MemWalWriteSpec::Bucket { num_buckets, .. } => Some(*num_buckets),
             _ => None,
         }
     }
@@ -535,14 +535,14 @@ impl LsmWriteSpec {
     }
 }
 
-impl From<LsmWriteSpec> for lancedb::table::LsmWriteSpec {
-    fn from(spec: LsmWriteSpec) -> Self {
+impl From<MemWalWriteSpec> for lancedb::table::MemWalWriteSpec {
+    fn from(spec: MemWalWriteSpec) -> Self {
         spec.inner
     }
 }
 
-impl From<lancedb::table::LsmWriteSpec> for LsmWriteSpec {
-    fn from(inner: lancedb::table::LsmWriteSpec) -> Self {
+impl From<lancedb::table::MemWalWriteSpec> for MemWalWriteSpec {
+    fn from(inner: lancedb::table::MemWalWriteSpec) -> Self {
         Self { inner }
     }
 }
@@ -724,8 +724,6 @@ impl From<lancedb::RefreshMaterializedViewResult> for RefreshMaterializedViewRes
     fn from(result: lancedb::RefreshMaterializedViewResult) -> Self {
         let mode = match result.mode {
             lancedb::RefreshMode::Rebuild => "rebuild",
-            lancedb::RefreshMode::Incremental => "incremental",
-            lancedb::RefreshMode::NoOp => "no_op",
         };
         Self {
             mode: mode.to_string(),
@@ -831,6 +829,14 @@ impl PyBlobFile {
     }
 }
 
+fn blob_file_error(operation: &str, error: lance_core::Error) -> pyo3::PyErr {
+    let message = format!("blob {operation} failed: {error}");
+    match error {
+        lance_core::Error::InvalidInput { .. } => PyValueError::new_err(message),
+        _ => PyRuntimeError::new_err(message),
+    }
+}
+
 #[pymethods]
 impl PyBlobFile {
     fn read_bytes(self_: PyRef<'_, Self>) -> PyResult<Py<PyBytes>> {
@@ -839,7 +845,7 @@ impl PyBlobFile {
         let py = self_.py();
         let bytes = py
             .detach(move || block_on(async move { inner.read().await }))
-            .map_err(|e| PyRuntimeError::new_err(format!("blob read failed: {e}")))?;
+            .map_err(|e| blob_file_error("read", e))?;
         Ok(PyBytes::new(py, bytes.as_ref()).unbind())
     }
 
@@ -847,10 +853,7 @@ impl PyBlobFile {
         self_.ensure_open()?;
         let inner = self_.inner.clone();
         future_into_py(self_.py(), async move {
-            let bytes = inner
-                .read()
-                .await
-                .map_err(|e| PyRuntimeError::new_err(format!("blob read failed: {e}")))?;
+            let bytes = inner.read().await.map_err(|e| blob_file_error("read", e))?;
             Python::attach(|py| Ok(PyBytes::new(py, bytes.as_ref()).unbind()))
         })
     }
@@ -873,7 +876,7 @@ impl PyBlobFile {
         self_
             .py()
             .detach(move || block_on(async move { inner.close().await }))
-            .map_err(|e| PyRuntimeError::new_err(format!("blob close failed: {e}")))
+            .map_err(|e| blob_file_error("close", e))
     }
 
     fn is_closed(self_: PyRef<'_, Self>) -> bool {
@@ -886,7 +889,7 @@ impl PyBlobFile {
         self_
             .py()
             .detach(move || block_on(async move { inner.seek(position).await }))
-            .map_err(|e| PyRuntimeError::new_err(format!("blob seek failed: {e}")))
+            .map_err(|e| blob_file_error("seek", e))
     }
 
     fn tell(self_: PyRef<'_, Self>) -> PyResult<u64> {
@@ -895,7 +898,7 @@ impl PyBlobFile {
         self_
             .py()
             .detach(move || block_on(async move { inner.tell().await }))
-            .map_err(|e| PyRuntimeError::new_err(format!("blob tell failed: {e}")))
+            .map_err(|e| blob_file_error("tell", e))
     }
 
     fn size(self_: PyRef<'_, Self>) -> u64 {
@@ -912,17 +915,39 @@ impl PyBlobFile {
         let py = self_.py();
         let bytes = py
             .detach(move || block_on(async move { inner.read_range(offset..end).await }))
-            .map_err(|e| PyRuntimeError::new_err(format!("blob read_range failed: {e}")))?;
+            .map_err(|e| blob_file_error("read_range", e))?;
         Ok(PyBytes::new(py, bytes.as_ref()).unbind())
     }
 
+    /// Read blob-local byte ranges without moving the cursor.
+    fn read_ranges(self_: PyRef<'_, Self>, ranges: Vec<(u64, u64)>) -> PyResult<Vec<Py<PyBytes>>> {
+        let ranges = ranges
+            .into_iter()
+            .map(|(offset, length)| {
+                offset
+                    .checked_add(length)
+                    .map(|end| offset..end)
+                    .ok_or_else(|| PyValueError::new_err("offset + length overflowed"))
+            })
+            .collect::<PyResult<Vec<_>>>()?;
+        let inner = self_.inner.clone();
+        let py = self_.py();
+        let bytes = py
+            .detach(move || block_on(async move { inner.read_ranges(&ranges).await }))
+            .map_err(|e| blob_file_error("read_ranges", e))?;
+        Ok(bytes
+            .iter()
+            .map(|bytes| PyBytes::new(py, bytes.as_ref()).unbind())
+            .collect())
+    }
+
     fn read_up_to(self_: PyRef<'_, Self>, length: usize) -> PyResult<Py<PyBytes>> {
         self_.ensure_open()?;
         let inner = self_.inner.clone();
         let py = self_.py();
         let bytes = py
             .detach(move || block_on(async move { inner.read_up_to(length).await }))
-            .map_err(|e| PyRuntimeError::new_err(format!("blob read_up_to failed: {e}")))?;
+            .map_err(|e| blob_file_error("read_up_to", e))?;
         Ok(PyBytes::new(py, bytes.as_ref()).unbind())
     }
 }
@@ -957,8 +982,8 @@ impl From<LanceDbFtsToken> for FtsToken {
     language = "English".to_string(),
     max_token_length = Some(40),
     lower_case = true,
-    stem = true,
-    remove_stop_words = true,
+    stem = None,
+    remove_stop_words = None,
     custom_stop_words = None,
     ascii_folding = true,
     ngram_min_length = 3,
@@ -972,15 +997,15 @@ pub fn tokenize(
     language: String,
     max_token_length: Option<u32>,
     lower_case: bool,
-    stem: bool,
-    remove_stop_words: bool,
+    stem: Option<bool>,
+    remove_stop_words: Option<bool>,
     custom_stop_words: Option<Vec<String>>,
     ascii_folding: bool,
     ngram_min_length: u32,
     ngram_max_length: u32,
     prefix_only: bool,
 ) -> PyResult<Vec<FtsToken>> {
-    let params = FtsIndexBuilder::default()
+    let mut params = FtsIndexBuilder::default()
         .base_tokenizer(base_tokenizer)
         .language(&language)
         .map_err(|_| {
@@ -991,13 +1016,17 @@ pub fn tokenize(
         })?
         .max_token_length(max_token_length.map(|value| value as usize))
         .lower_case(lower_case)
-        .stem(stem)
-        .remove_stop_words(remove_stop_words)
         .ascii_folding(ascii_folding)
         .ngram_min_length(ngram_min_length)
         .ngram_max_length(ngram_max_length)
         .ngram_prefix_only(prefix_only)
         .custom_stop_words(custom_stop_words);
+    if let Some(stem) = stem {
+        params = params.stem(stem);
+    }
+    if let Some(remove_stop_words) = remove_stop_words {
+        params = params.remove_stop_words(remove_stop_words);
+    }
     let tokens = lancedb_tokenize(&query, &params).infer_error()?;
     Ok(tokens.into_iter().map(FtsToken::from).collect())
 }
@@ -1047,6 +1076,11 @@ impl Table {
         self.inner.is_some()
     }
 
+    /// Whether this table has a local Lance dataset that the client may open.
+    pub fn _is_native(&self) -> PyResult<bool> {
+        Ok(self.inner_ref()?.as_native().is_some())
+    }
+
     /// Closes the table, releasing any resources associated with it.
     pub fn close(&mut self) {
         self.inner.take();
@@ -1400,6 +1434,7 @@ impl Table {
                 let dict = PyDict::new(py);
                 dict.set_item("total_bytes", stats.total_bytes)?;
                 dict.set_item("num_rows", stats.num_rows)?;
+                dict.set_item("num_deleted_rows", stats.num_deleted_rows)?;
                 dict.set_item("num_indices", stats.num_indices)?;
 
                 let fragment_stats = PyDict::new(py);
@@ -1586,14 +1621,30 @@ impl Table {
     pub fn fetch_blob_ranges(
         self_: PyRef<'_, Self>,
         column: String,
-        requests: Vec<(u64, u64, u64)>,
+        requests: Vec<Vec<i128>>,
     ) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.inner_ref()?.clone();
+        let requests = requests
+            .into_iter()
+            .map(|values| {
+                let [row_id, offset, length] = <[i128; 3]>::try_from(values).map_err(|_| {
+                    PyValueError::new_err("each blob range request must have three values")
+                })?;
+                let to_u64 = |value, name| {
+                    u64::try_from(value).map_err(|_| {
+                        PyValueError::new_err(format!(
+                            "blob range {name} must be a non-negative 64-bit integer"
+                        ))
+                    })
+                };
+                Ok(BlobRangeRequest::new(
+                    to_u64(row_id, "row id")?,
+                    to_u64(offset, "offset")?,
+                    to_u64(length, "length")?,
+                ))
+            })
+            .collect::<PyResult<Vec<_>>>()?;
         future_into_py(self_.py(), async move {
-            let requests = requests
-                .into_iter()
-                .map(|(row_id, offset, length)| BlobRangeRequest::new(row_id, offset, length))
-                .collect::<Vec<_>>();
             let blobs: LargeBinaryArray = inner
                 .fetch_blob_ranges(column, requests)
                 .await
@@ -1719,8 +1770,8 @@ impl Table {
         if let Some(use_index) = parameters.use_index {
             builder.use_index(use_index);
         }
-        if let Some(use_lsm) = parameters.use_lsm {
-            builder.use_lsm(use_lsm);
+        if let Some(use_memwal) = parameters.use_memwal {
+            builder.use_memwal(use_memwal);
         }
         if let Some(validate_single_shard) = parameters.validate_single_shard {
             builder.validate_single_shard(validate_single_shard);
@@ -1745,29 +1796,29 @@ impl Table {
         })
     }
 
-    pub fn set_lsm_write_spec<'a>(
+    pub fn enable_memwal<'a>(
         self_: PyRef<'a, Self>,
-        spec: LsmWriteSpec,
+        spec: MemWalWriteSpec,
     ) -> PyResult<Bound<'a, PyAny>> {
         let inner = self_.inner_ref()?.clone();
-        let native_spec = lancedb::table::LsmWriteSpec::from(spec);
+        let native_spec = lancedb::table::MemWalWriteSpec::from(spec);
         future_into_py(self_.py(), async move {
-            inner.set_lsm_write_spec(native_spec).await.infer_error()
+            inner.enable_memwal(native_spec).await.infer_error()
         })
     }
 
-    pub fn unset_lsm_write_spec(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
+    pub fn disable_memwal(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.inner_ref()?.clone();
         future_into_py(self_.py(), async move {
-            inner.unset_lsm_write_spec().await.infer_error()
+            inner.disable_memwal().await.infer_error()
         })
     }
 
-    pub fn get_lsm_write_spec(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
+    pub fn get_memwal_spec(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.inner_ref()?.clone();
         future_into_py(self_.py(), async move {
-            let spec = inner.get_lsm_write_spec().await.infer_error()?;
-            Ok(spec.map(LsmWriteSpec::from))
+            let spec = inner.get_memwal_spec().await.infer_error()?;
+            Ok(spec.map(MemWalWriteSpec::from))
         })
     }
 
@@ -1786,55 +1837,54 @@ impl Table {
         Ok(())
     }
 
-    /// Converge the table's LSM write path into its base table.
+    /// Converge the table's MemWAL write path into its base table.
     ///
     /// Best-effort: with writes flowing, new rows may land after the last
     /// pass. Errors if the table stops making progress.
-    pub fn checkpoint_lsm(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
+    pub fn checkpoint_memwal(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.inner_ref()?.clone();
         future_into_py(self_.py(), async move {
-            inner.checkpoint_lsm().await.infer_error()
+            inner.checkpoint_memwal().await.infer_error()
         })
     }
 
-    /// Seal every bucket's active memtable into L0.
-    pub fn flush_lsm(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
-        let inner = self_.inner_ref()?.clone();
-        future_into_py(
-            self_.py(),
-            async move { inner.flush_lsm().await.infer_error() },
-        )
-    }
-
-    /// Trigger a background L0 → base pass per bucket. Returns once the
-    /// passes are dispatched, not once they finish — watch `get_lsm_stats`.
-    pub fn compact_lsm(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
+    /// Freeze every table shard's active memtable into an SSTable.
+    pub fn flush_memwal(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.inner_ref()?.clone();
         future_into_py(self_.py(), async move {
-            inner.compact_lsm().await.infer_error()
+            inner.flush_memwal().await.infer_error()
         })
     }
 
-    /// Live LSM state, or `None` when the LSM write path is not enabled.
-    #[pyo3(signature = (include_generation_rows=false))]
-    pub fn get_lsm_stats(
+    /// Trigger a background SSTable compaction pass per table shard. Returns once the
+    /// passes are dispatched, not once they finish — watch `get_memwal_stats`.
+    pub fn compact_memwal(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
+        let inner = self_.inner_ref()?.clone();
+        future_into_py(self_.py(), async move {
+            inner.compact_memwal().await.infer_error()
+        })
+    }
+
+    /// Live MemWAL state, or `None` when the MemWAL write path is not enabled.
+    #[pyo3(signature = (include_sstable_rows=false))]
+    pub fn get_memwal_stats(
         self_: PyRef<'_, Self>,
-        include_generation_rows: bool,
+        include_sstable_rows: bool,
     ) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.inner_ref()?.clone();
         future_into_py(self_.py(), async move {
             let stats = inner
-                .get_lsm_stats(include_generation_rows)
+                .get_memwal_stats(include_sstable_rows)
                 .await
                 .infer_error()?;
-            Python::attach(|py| stats.map(|s| lsm_stats_to_py(py, &s)).transpose())
+            Python::attach(|py| stats.map(|s| memwal_stats_to_py(py, &s)).transpose())
         })
     }
 
-    pub fn close_lsm_writers(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
+    pub fn close_memwal_writers(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.inner_ref()?.clone();
         future_into_py(self_.py(), async move {
-            inner.close_lsm_writers().await.infer_error()
+            inner.close_memwal_writers().await.infer_error()
         })
     }
 
@@ -1843,7 +1893,10 @@ impl Table {
         future_into_py(self_.py(), async move {
             inner
                 .as_native()
-                .ok_or_else(|| PyValueError::new_err("This cannot be run on a remote table"))?
+                .ok_or_else(|| lancedb::Error::NotSupported {
+                    message: "uses_v2_manifest_paths is not supported for remote tables.".into(),
+                })
+                .infer_error()?
                 .uses_v2_manifest_paths()
                 .await
                 .infer_error()
@@ -1855,7 +1908,10 @@ impl Table {
         future_into_py(self_.py(), async move {
             inner
                 .as_native()
-                .ok_or_else(|| PyValueError::new_err("This cannot be run on a remote table"))?
+                .ok_or_else(|| lancedb::Error::NotSupported {
+                    message: "migrate_manifest_paths_v2 is not supported for remote tables.".into(),
+                })
+                .infer_error()?
                 .migrate_manifest_paths_v2()
                 .await
                 .infer_error()
@@ -1951,10 +2007,9 @@ impl Table {
         })
     }
 
-    #[pyo3(signature = (full=false, source_version=None))]
+    #[pyo3(signature = (source_version=None))]
     pub fn refresh_materialized_view(
         self_: PyRef<'_, Self>,
-        full: bool,
         source_version: Option<u64>,
     ) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.inner_ref()?.clone();
@@ -1962,7 +2017,7 @@ impl Table {
             let view = lancedb::MaterializedView::from_table(inner)
                 .await
                 .infer_error()?;
-            let mut builder = view.refresh().full(full);
+            let mut builder = view.refresh();
             if let Some(version) = source_version {
                 builder = builder.source_version(version);
             }
@@ -1971,10 +2026,9 @@ impl Table {
         })
     }
 
-    #[pyo3(signature = (full=false, source_version=None))]
+    #[pyo3(signature = (source_version=None))]
     pub fn refresh_materialized_view_async(
         self_: PyRef<'_, Self>,
-        full: bool,
         source_version: Option<u64>,
     ) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.inner_ref()?.clone();
@@ -1982,7 +2036,7 @@ impl Table {
             let view = lancedb::MaterializedView::from_table(inner)
                 .await
                 .infer_error()?;
-            let mut builder = view.refresh().full(full);
+            let mut builder = view.refresh();
             if let Some(version) = source_version {
                 builder = builder.source_version(version);
             }
@@ -1997,11 +2051,9 @@ impl Table {
             let view = lancedb::MaterializedView::from_table(inner)
                 .await
                 .infer_error()?;
-            view.definition().to_json().map_err(|err| {
-                PyRuntimeError::new_err(format!(
-                    "failed to serialize materialized-view definition: {err}"
-                ))
-            })
+            Ok(lancedb::materialized_view::definition_metadata_from_sql(
+                view.definition_sql(),
+            ))
         })
     }
 
@@ -2031,6 +2083,17 @@ impl Table {
         let alterations = alterations
             .iter()
             .map(|alteration| {
+                for key in alteration.keys().iter() {
+                    let key = key.extract::<String>()?;
+                    if !matches!(
+                        key.as_str(),
+                        "path" | "rename" | "name" | "nullable" | "data_type"
+                    ) {
+                        return Err(PyValueError::new_err(format!(
+                            "Unknown column alteration key '{key}'"
+                        )));
+                    }
+                }
                 let path = alteration
                     .get_item("path")?
                     .ok_or_else(|| PyValueError::new_err("Missing path"))?
@@ -2153,7 +2216,7 @@ pub struct MergeInsertParams {
     when_not_matched_by_source_condition_expr: Option<PyExpr>,
     timeout: Option<std::time::Duration>,
     use_index: Option<bool>,
-    use_lsm: Option<bool>,
+    use_memwal: Option<bool>,
     validate_single_shard: Option<bool>,
 }
 
diff --git a/python/uv.lock b/python/uv.lock
index 8366dd397..ace8b98b9 100644
--- a/python/uv.lock
+++ b/python/uv.lock
@@ -1849,19 +1849,19 @@ wheels = [
 
 [[package]]
 name = "lance-namespace"
-version = "0.8.6"
+version = "0.13.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "lance-namespace-urllib3-client" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/af/12/f7ab93b29be3edbf5fc3610714bf2d06088e7f4524bfb38dfd6852458b08/lance_namespace-0.8.6.tar.gz", hash = "sha256:18232e721c8188145f4ec9389cc2dfbeeabf54a619d94885ea1b3375bee9f4af", size = 11529, upload-time = "2026-06-12T17:36:41.651Z" }
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+    { url = "https://files.pythonhosted.org/packages/e7/47/cfa33cca1ba7c749fd2918cfc5c8ded788f378cc6424d23e4fead5a14125/lance_namespace-0.13.0-py3-none-any.whl", hash = "sha256:438c7b17aef421c21c138196e715f2510d62f07e865372047f87c1e75e618c7a", size = 13507, upload-time = "2026-09-14T02:47:01.765655Z" },
 ]
 
 [[package]]
 name = "lance-namespace-urllib3-client"
-version = "0.8.6"
+version = "0.13.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pydantic" },
@@ -1869,9 +1869,9 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "urllib3" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/c7/80/fb224b4a89c1c1638cde949cb6cce6c3aca7759effbfea46a3d9c3960b21/lance_namespace_urllib3_client-0.8.6.tar.gz", hash = "sha256:b6fb1d306e74a7576e5309919020be744527de484a63dbf5eed10f8b368548df", size = 228772, upload-time = "2026-06-12T17:36:42.609Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/69/25/9aaa4a5e8999693fb0f227c2c0c4b97bd8f0539066408cdff0b89d41b2d5/lance_namespace_urllib3_client-0.13.0.tar.gz", hash = "sha256:1e8a79c6e4e6277033597fd76aa0e2f33d909ca1436c935936e9e569221f43ef", size = 238348, upload-time = "2026-09-14T02:47:04.521878Z" }
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 ]
 
 [[package]]
@@ -1900,6 +1900,7 @@ clip = [
     { name = "torch" },
 ]
 dev = [
+    { name = "maturin" },
     { name = "pre-commit" },
     { name = "pyright" },
     { name = "ruff" },
@@ -1980,7 +1981,8 @@ requires-dist = [
     { name = "huggingface-hub", marker = "extra == 'embeddings'", specifier = ">=0.19.0" },
     { name = "ibm-watsonx-ai", marker = "python_full_version >= '3.10' and extra == 'embeddings'", specifier = ">=1.1.2" },
     { name = "instructorembedding", marker = "extra == 'embeddings'", specifier = ">=1.0.1" },
-    { name = "lance-namespace", specifier = ">=0.3.2" },
+    { name = "lance-namespace", specifier = ">=0.13.0" },
+    { name = "maturin", marker = "extra == 'dev'", specifier = ">=1.10" },
     { name = "mkdocs", marker = "extra == 'docs'" },
     { name = "mkdocs-jupyter", marker = "extra == 'docs'" },
     { name = "mkdocs-material", marker = "extra == 'docs'" },
@@ -2005,7 +2007,7 @@ requires-dist = [
     { name = "pyarrow-stubs", marker = "extra == 'tests'", specifier = ">=16.0" },
     { name = "pydantic", specifier = ">=2.7.4,<3" },
     { name = "pylance", marker = "extra == 'pylance'", specifier = ">=5.0.0b5" },
-    { name = "pylance", marker = "extra == 'tests'", specifier = "==9.0.0" },
+    { name = "pylance", marker = "extra == 'tests'", specifier = "==14.0.0b12" },
     { name = "pyright", marker = "extra == 'dev'", specifier = ">=1.1.350" },
     { name = "pytest", marker = "extra == 'tests'", specifier = ">=7.0" },
     { name = "pytest-asyncio", marker = "extra == 'tests'", specifier = ">=0.21" },
@@ -2156,6 +2158,30 @@ wheels = [
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 ]
 
+[[package]]
+name = "maturin"
+version = "1.15.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "tomli", marker = "python_full_version < '3.11'" },
+]
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+wheels = [
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+    { url = "https://files.pythonhosted.org/packages/51/36/e23a21cb34a648b711036b9b2fe1d4f3f4ee24f8db54215d73f1a9a3a3ec/maturin-1.15.0-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:c40b4eae7bf5ef1f4b1af8d623fe4105016f93578fb15b764e741d08ec3b92dd", size = 10014962, upload-time = "2026-08-24T12:10:58.486Z" },
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+    { url = "https://files.pythonhosted.org/packages/fe/91/b495e19e2f5c503b540452b2039115e7b2363867e8c5ad4179eb752fa92c/maturin-1.15.0-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.musllinux_1_1_x86_64.whl", hash = "sha256:653020a63525bb224e5ab0adf02e17a2e08bc86dbea7fc1399c9a56d7529b99e", size = 10541186, upload-time = "2026-08-24T12:11:02.857Z" },
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+    { url = "https://files.pythonhosted.org/packages/14/4d/801379f646cbc6b00998e5289b0630a886be3a4ee4c75b6bc9b87478a7f1/maturin-1.15.0-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4f9d33e6c3f9615c8caceecbbbd440f8eb25a3ddeb687077682cd5eca2e9ae15", size = 10807183, upload-time = "2026-08-24T12:11:11.73Z" },
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+    { url = "https://files.pythonhosted.org/packages/40/dc/4e90da594986ba78dd3bc8a5921ecdcdb11085b22b02a412caab3b225601/maturin-1.15.0-py3-none-win_amd64.whl", hash = "sha256:552c2be4afd43fe8d5c9f3ec8d4c4756d973b8dcbe94c14084390301f50243e1", size = 10335085, upload-time = "2026-08-24T12:11:18.326Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/10/15d4314edf130955edf2dc237aa393a8a7c10f2b9b57b89fa2f61f915659/maturin-1.15.0-py3-none-win_arm64.whl", hash = "sha256:c7dc0c66c78d3debdd9c5aa807e861fbcbf07f3505d34b125df74c03986b0f48", size = 9713795, upload-time = "2026-08-24T12:11:20.83Z" },
+]
+
 [[package]]
 name = "mdit-py-plugins"
 version = "0.6.1"
@@ -3912,8 +3938,8 @@ crypto = [
 
 [[package]]
 name = "pylance"
-version = "9.0.0"
-source = { registry = "https://pypi.org/simple" }
+version = "14.0.0b12"
+source = { registry = "https://pypi.fury.io/lance-format/" }
 dependencies = [
     { name = "lance-namespace" },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
@@ -3922,12 +3948,12 @@ dependencies = [
     { name = "pyarrow" },
 ]
 wheels = [
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+    { url = "https://pypi.fury.io/lance-format/-/ver_1Ixasc/pylance-14.0.0b12-cp310-abi3-macosx_11_0_arm64.whl", hash = "sha256:84350a3c5c1eea3398f4c4fad7cfe85b831d831a5e43b5ca2a783dbdb0e11969" },
+    { url = "https://pypi.fury.io/lance-format/-/ver_sCEkG/pylance-14.0.0b12-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:76b386c182a6a8e7b86fbef28f84d1dd39e4626ac8681a0cf36f40a45dd7cb87" },
+    { url = "https://pypi.fury.io/lance-format/-/ver_1vdT1C/pylance-14.0.0b12-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6aa548c7937fcdfa8f1943a2d59041a42f8b500199880dbb509c7195fa253381" },
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+    { url = "https://pypi.fury.io/lance-format/-/ver_XNynX/pylance-14.0.0b12-cp310-abi3-win_amd64.whl", hash = "sha256:4d0cdb689e898ce71e8e434d00a3b97639cea8fb97da21f1c921543be363268f" },
 ]
 
 [[package]]
diff --git a/rust/lancedb/Cargo.toml b/rust/lancedb/Cargo.toml
index fbc9ab5ee..591fb72de 100644
--- a/rust/lancedb/Cargo.toml
+++ b/rust/lancedb/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "lancedb"
-version = "0.40.0-beta.11"
+version = "0.41.0-beta.7"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true
@@ -66,19 +66,23 @@ serde = { workspace = true }
 serde_json = { workspace = true }
 async-openai = { version = "0.20.0", optional = true }
 serde_with = { version = "3.8.1" }
+strum_macros = "0.28"
 tempfile = { workspace = true }
 aws-smithy-types = { workspace = true, optional = true }
-aws-sdk-bedrockruntime = { version = "1.27.0", optional = true }
+# Default features include the legacy `rustls` feature, which pulls in rustls 0.21.
+aws-sdk-bedrockruntime = { version = "1.77.0", optional = true, default-features = false, features = ["default-https-client", "rt-tokio"] }
 # For remote feature
-reqwest = { version = "0.12.0", default-features = false, features = [
+reqwest = { version = "0.13.0", default-features = false, features = [
     "charset",
     "gzip",
     "http2",
     "json",
-    "macos-system-configuration",
+    "query",
     # Avoid linking OpenSSL into Python wheels, which breaks on FIPS hosts.
-    "rustls-tls-native-roots",
+    # This uses the aws-lc-rs crypto provider.
+    "rustls",
     "stream",
+    "system-proxy",
 ], optional = true }
 tonic = { workspace = true, optional = true }
 http = { version = "1", optional = true } # Matching what is in reqwest
@@ -114,9 +118,10 @@ random_word = { version = "0.4.3", features = ["en"] }
 tokio = { workspace = true, features = ["io-util", "macros", "net", "test-util"] }
 uuid = { workspace = true }
 walkdir = "2"
-aws-sdk-dynamodb = { version = "1.55.0" }
-aws-sdk-s3 = { version = "1.55.0" }
-aws-sdk-kms = { version = "1.48.0" }
+# Default features include the legacy `rustls` feature, which pulls in rustls 0.21.
+aws-sdk-dynamodb = { version = "1.68.0", default-features = false, features = ["default-https-client", "rt-tokio"] }
+aws-sdk-s3 = { version = "1.79.0", default-features = false, features = ["default-https-client", "rt-tokio", "sigv4a", "http-1x"] }
+aws-sdk-kms = { version = "1.63.0", default-features = false, features = ["default-https-client", "rt-tokio"] }
 aws-config = { version = "1.5.10" }
 aws-smithy-runtime = { version = "1.9.1" }
 aws-smithy-types.workspace = true
diff --git a/rust/lancedb/examples/ivf_pq.rs b/rust/lancedb/examples/ivf_pq.rs
index 79f674615..b457558ba 100644
--- a/rust/lancedb/examples/ivf_pq.rs
+++ b/rust/lancedb/examples/ivf_pq.rs
@@ -142,7 +142,7 @@ async fn search_index(table: &Table) -> Result<()> {
         // Override the default of 10 to get more rows
         .limit(15)
         // Override the default of 20 to search more partitions
-        .nprobes(30)
+        .nprobes(30)?
         // Override the default of None to apply a refine step
         .refine_factor(1)
         .execute()
diff --git a/rust/lancedb/src/authz/api.rs b/rust/lancedb/src/authz/api.rs
new file mode 100644
index 000000000..38e7d381b
--- /dev/null
+++ b/rust/lancedb/src/authz/api.rs
@@ -0,0 +1,239 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+//! Authorization requests, responses, and the deployment-wide API.
+
+use super::{Object, Privilege, Subject};
+use crate::Result;
+use serde::{Deserialize, Serialize};
+
+/// Filters and pagination for a page of ACL grants. Omitted filters match all.
+#[derive(Clone, Default, Serialize, Deserialize)]
+#[non_exhaustive]
+pub struct ListAccessControlEntriesRequest {
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub object: Option<Object>,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub subject: Option<Subject>,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub continuation_token: Option<String>,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    /// Maximum stored subject/object pairs, not expanded privilege entries.
+    /// The server caps this at 100; None uses its default. Must be positive.
+    pub max_results: Option<u32>,
+}
+
+/// One privilege granted to a canonical subject on an object.
+#[derive(Clone, Serialize, Deserialize, Debug, PartialEq, Eq)]
+#[non_exhaustive]
+pub struct AccessControlEntry {
+    #[serde(deserialize_with = "Subject::deserialize_canonical")]
+    pub subject_id: Subject,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub subject_name: Option<String>,
+    pub object: Object,
+    pub privilege: Privilege,
+}
+
+/// One page of grants. A stored subject/object pair may expand to several entries.
+#[derive(Clone, Default, Serialize, Deserialize, Debug, PartialEq, Eq)]
+#[non_exhaustive]
+pub struct ListAccessControlEntriesResponse {
+    pub entries: Vec<AccessControlEntry>,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub continuation_token: Option<String>,
+}
+
+/// One grant to add or delete. The subject may be selected by ID, name, or API key.
+#[derive(Clone, Serialize, Deserialize)]
+#[non_exhaustive]
+pub struct AccessControlEntryRequest {
+    pub subject: Subject,
+    pub object: Object,
+    /// A privilege such as [`Privilege::Select`] or [`Privilege::Usage`].
+    /// [`Privilege::Unknown`] preserves future privilege names.
+    pub privilege: Privilege,
+}
+
+/// Filters and pagination for direct principal/group role bindings.
+#[derive(Clone, Default, Serialize, Deserialize)]
+#[non_exhaustive]
+pub struct ListRoleBindingsRequest {
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub subject: Option<Subject>,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub role: Option<String>,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub continuation_token: Option<String>,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    /// Maximum bindings on this page; the server caps this at 100.
+    pub max_results: Option<u32>,
+}
+
+/// A direct principal or group membership in a LanceDB-managed role.
+#[derive(Clone, Serialize, Deserialize, Debug, PartialEq, Eq)]
+#[non_exhaustive]
+pub struct RoleBinding {
+    #[serde(deserialize_with = "Subject::deserialize_canonical")]
+    pub subject_id: Subject,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub subject_name: Option<String>,
+    pub role: String,
+}
+
+/// One page of direct bindings, without expanding group membership.
+#[derive(Clone, Default, Serialize, Deserialize, Debug, PartialEq, Eq)]
+#[non_exhaustive]
+pub struct ListRoleBindingsResponse {
+    pub bindings: Vec<RoleBinding>,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub continuation_token: Option<String>,
+}
+
+/// A role to grant to or revoke from a principal or group. Roles cannot nest.
+#[derive(Clone, Serialize, Deserialize)]
+#[non_exhaustive]
+pub struct RoleBindingRequest {
+    pub subject: Subject,
+    pub role: String,
+}
+
+/// Select a principal by canonical ID, provider name, or API key.
+#[derive(Clone, Serialize, Deserialize)]
+#[non_exhaustive]
+pub struct GetPrincipalRequest {
+    pub principal: Subject,
+}
+
+/// Select a group by canonical ID or provider name.
+#[derive(Clone, Serialize, Deserialize)]
+#[non_exhaustive]
+pub struct GetGroupRequest {
+    pub group: Subject,
+}
+
+/// A canonical identity-provider ID and optional display name.
+#[derive(Clone, Default, Serialize, Deserialize, Debug, PartialEq, Eq)]
+#[non_exhaustive]
+pub struct IdentityInfo {
+    pub id: String,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub name: Option<String>,
+}
+
+/// A principal, its provider-managed groups, and roles held directly or via groups.
+#[derive(Clone, Default, Serialize, Deserialize, Debug, PartialEq, Eq)]
+#[non_exhaustive]
+pub struct PrincipalInfo {
+    pub principal_id: String,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub principal_name: Option<String>,
+    pub groups: Vec<IdentityInfo>,
+    pub roles: Vec<String>,
+}
+
+/// A group, its provider-managed members, and its directly granted roles.
+#[derive(Clone, Default, Serialize, Deserialize, Debug, PartialEq, Eq)]
+#[non_exhaustive]
+pub struct GroupInfo {
+    pub group_id: String,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub group_name: Option<String>,
+    pub principals: Vec<IdentityInfo>,
+    pub roles: Vec<String>,
+}
+
+/// Authorization operations on one deployment, independent of database selection.
+///
+/// ACL writes preserve server conflict/not-found errors and must not be replayed
+/// after ambiguous transport failures. Role-binding writes are idempotent.
+#[async_trait::async_trait]
+pub trait Authorization: Send + Sync + std::fmt::Debug + 'static {
+    /// List a page of ACLs. Requires USAGE on the object, or system if unfiltered.
+    async fn list_acls(
+        &self,
+        request: ListAccessControlEntriesRequest,
+    ) -> Result<ListAccessControlEntriesResponse>;
+    /// Grant a privilege; requires OWNERSHIP. Duplicate grants return HTTP 409.
+    /// Granting OWNERSHIP transfers it from the previous owner.
+    async fn add_acl(&self, request: AccessControlEntryRequest) -> Result<()>;
+    /// Revoke a privilege; requires OWNERSHIP. Missing grants return HTTP 404.
+    /// OWNERSHIP cannot be deleted.
+    async fn delete_acl(&self, request: AccessControlEntryRequest) -> Result<()>;
+    /// List a page of direct bindings; requires USAGE on system.
+    async fn list_role_bindings(
+        &self,
+        request: ListRoleBindingsRequest,
+    ) -> Result<ListRoleBindingsResponse>;
+    /// Idempotently grant a role; requires OPERATE on system.
+    async fn add_role_binding(&self, request: RoleBindingRequest) -> Result<()>;
+    /// Idempotently revoke a role; requires OPERATE on system.
+    async fn delete_role_binding(&self, request: RoleBindingRequest) -> Result<()>;
+    /// Inspect a principal and effective roles; requires USAGE on system.
+    async fn get_principal(&self, request: GetPrincipalRequest) -> Result<PrincipalInfo>;
+    /// Inspect a group and direct roles; requires USAGE on system.
+    async fn get_group(&self, request: GetGroupRequest) -> Result<GroupInfo>;
+}
+
+impl AccessControlEntryRequest {
+    /// Build one grant from validated resource and identity selectors.
+    pub fn new(object: Object, subject: Subject, privilege: Privilege) -> Self {
+        Self {
+            object,
+            subject,
+            privilege,
+        }
+    }
+}
+
+impl ListRoleBindingsRequest {
+    /// Reject role subjects, since roles cannot contain other roles.
+    pub fn validate(&self) -> Result<()> {
+        if let Some(subject) = &self.subject {
+            subject.validate_role_member()?;
+        }
+        Ok(())
+    }
+}
+
+impl RoleBindingRequest {
+    /// Build a role grant/revocation for a principal or group.
+    pub fn new(subject: Subject, role: impl Into<String>) -> Result<Self> {
+        subject.validate_role_member()?;
+        Ok(Self {
+            subject,
+            role: role.into(),
+        })
+    }
+
+    /// Validate the member selector, including deserialized requests.
+    pub fn validate(&self) -> Result<()> {
+        self.subject.validate_role_member()
+    }
+}
+
+impl GetPrincipalRequest {
+    /// Select a principal by ID, name, or API key.
+    pub fn new(principal: Subject) -> Result<Self> {
+        principal.validate_principal()?;
+        Ok(Self { principal })
+    }
+
+    /// Validate the lookup selector, including deserialized requests.
+    pub fn validate(&self) -> Result<()> {
+        self.principal.validate_principal()
+    }
+}
+
+impl GetGroupRequest {
+    /// Select a group by ID or name.
+    pub fn new(group: Subject) -> Result<Self> {
+        group.validate_group()?;
+        Ok(Self { group })
+    }
+
+    /// Validate the lookup selector, including deserialized requests.
+    pub fn validate(&self) -> Result<()> {
+        self.group.validate_group()
+    }
+}
diff --git a/rust/lancedb/src/authz/mod.rs b/rust/lancedb/src/authz/mod.rs
new file mode 100644
index 000000000..3191627e0
--- /dev/null
+++ b/rust/lancedb/src/authz/mod.rs
@@ -0,0 +1,48 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+//! Deployment-wide authorization, accessed through a catalog.
+//!
+//! Subjects use `p:ID`, `P:NAME`, `g:ID`, `G:NAME`, `r:ROLE`, or `a:API_KEY`.
+//! Objects use literal names such as `table:tenant/db:public$events`.
+//! Names are resolved by the server. Request types intentionally do not implement
+//! Debug because their subject selectors may contain secret API keys.
+//!
+//! ```no_run
+//! # #[cfg(feature = "remote")]
+//! # async fn example() -> lancedb::Result<()> {
+//! let catalog = lancedb::connect_catalog("https://example.com")
+//!     .api_key("secret")
+//!     .execute().await?;
+//! let authz = catalog.authz()?;
+//! use lancedb::authz::{AccessControlEntryRequest, Object, TableObject, NamespacePath, Subject, Privilege};
+//! let grant = AccessControlEntryRequest::new(
+//!     Object::Table(TableObject {
+//!         database: "analytics".into(),
+//!         namespace: NamespacePath::from_component("public"),
+//!         table: "foo".into(),
+//!     }),
+//!     Subject::role("reader")?,
+//!     Privilege::Select,
+//! );
+//! authz.add_acl(grant).await?;
+//! let page = authz.list_acls(Default::default()).await?;
+//! for entry in page.entries {
+//!     println!("{} {} {}", entry.object, entry.subject_id, entry.privilege);
+//! }
+//! # Ok(())
+//! # }
+//! ```
+
+pub mod api;
+pub mod object;
+pub mod privilege;
+pub mod subject;
+
+pub use api::*;
+pub use object::{
+    DatabaseObject, FunctionObject, NamespaceObject, NamespacePath, Object, SecretObject,
+    TableObject, ViewObject,
+};
+pub use privilege::*;
+pub use subject::{Subject, SubjectKind};
diff --git a/rust/lancedb/src/authz/object.rs b/rust/lancedb/src/authz/object.rs
new file mode 100644
index 000000000..fd324b748
--- /dev/null
+++ b/rust/lancedb/src/authz/object.rs
@@ -0,0 +1,992 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use crate::utils::{
+    validate_database_name, validate_namespace_component, validate_secret_name,
+    validate_table_name, validate_view_name,
+};
+use serde::{Deserialize, Deserializer, Serialize, Serializer, de::Error as _, ser::Error as _};
+
+/// The default namespace for authorization resources.
+pub const DEFAULT_NAMESPACE: &str = "public";
+
+pub const SYSTEM_TYPE_STRING: &str = "system";
+pub const DATABASE_TYPE_STRING: &str = "database";
+pub const NAMESPACE_TYPE_STRING: &str = "namespace";
+pub const TABLE_TYPE_STRING: &str = "table";
+pub const VIEW_TYPE_STRING: &str = "view";
+pub const SECRET_TYPE_STRING: &str = "secret";
+pub const FUNCTION_TYPE_STRING: &str = "function";
+pub const UNKNOWN_TYPE_STRING: &str = "unknown";
+
+/// A non-empty path through the namespace hierarchy.
+///
+/// The `$` separator is only the canonical wire encoding. Authorization code
+/// should use the components and prefix helpers instead of interpreting the
+/// encoded form.
+#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
+pub struct NamespacePath(Vec<String>);
+
+impl NamespacePath {
+    /// Construct a path, validating each component without splitting separators.
+    /// An empty path selects the default `public` namespace.
+    ///
+    /// ```
+    /// use lancedb::authz::NamespacePath;
+    ///
+    /// let path = NamespacePath::new(vec![]).unwrap();
+    /// assert_eq!(path.components(), &["public"]);
+    /// assert_eq!(path, NamespacePath::default());
+    /// ```
+    pub fn new(components: Vec<String>) -> Result<Self, String> {
+        if components.is_empty() {
+            return Ok(Self::default());
+        }
+        for component in &components {
+            validate_namespace_component(component).map_err(|error| error.to_string())?;
+        }
+        Ok(Self(components))
+    }
+
+    /// Construct a single-component path.
+    ///
+    /// # Panics
+    /// Panics if the component is invalid. Use [`Self::new`] for untrusted input.
+    pub fn from_component(component: impl Into<String>) -> Self {
+        Self::new(vec![component.into()]).expect("namespace components must be valid")
+    }
+
+    pub fn components(&self) -> &[String] {
+        &self.0
+    }
+
+    pub fn parent(&self) -> Option<Self> {
+        (self.0.len() > 1).then(|| Self(self.0[..self.0.len() - 1].to_vec()))
+    }
+
+    pub fn prefixes(&self) -> impl Iterator<Item = Self> + '_ {
+        (1..=self.0.len()).map(|length| Self(self.0[..length].to_vec()))
+    }
+
+    pub fn is_descendant_of(&self, ancestor: &Self) -> bool {
+        self.0.len() > ancestor.0.len() && self.0.starts_with(&ancestor.0)
+    }
+
+    pub fn parse(path: &str) -> Result<Self, String> {
+        Self::new(path.split('$').map(str::to_string).collect())
+    }
+}
+
+impl Default for NamespacePath {
+    /// Select the default `public` namespace.
+    fn default() -> Self {
+        Self(vec![DEFAULT_NAMESPACE.to_string()])
+    }
+}
+
+impl std::fmt::Display for NamespacePath {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        f.write_str(&self.0.join("$"))
+    }
+}
+
+/// An authorization resource, serialized using its canonical wire name.
+///
+/// Unknown resource types are preserved so clients can read rules created by
+/// newer servers. Known resource types validate names during parsing and serialization.
+#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
+pub enum Object {
+    System,
+    Database(DatabaseObject),
+    Namespace(NamespaceObject),
+    Table(TableObject),
+    View(ViewObject),
+    Secret(SecretObject),
+    Function(FunctionObject),
+    Unknown(String),
+}
+
+impl Object {
+    /// Validate resource names, including objects recovered from storage keys.
+    pub fn validate(&self) -> Result<(), String> {
+        match self {
+            Self::System => Ok(()),
+            Self::Database(object) => object.validate(),
+            Self::Namespace(object) => object.validate(),
+            Self::Table(object) => object.validate(),
+            Self::View(object) => object.validate(),
+            Self::Secret(object) => object.validate(),
+            Self::Function(object) => object.validate(),
+            Self::Unknown(value) if value.is_empty() => Err("object must not be empty".into()),
+            Self::Unknown(_) => Ok(()),
+        }
+    }
+
+    /// The database an object belongs to, or None.
+    pub fn database(&self) -> Option<&str> {
+        match self {
+            Self::System => None,
+            Self::Database(object) => Some(&object.database),
+            Self::Namespace(object) => Some(&object.database),
+            Self::Table(object) => Some(&object.database),
+            Self::View(object) => Some(&object.database),
+            Self::Secret(object) => Some(&object.database),
+            Self::Function(object) => Some(&object.database),
+            Self::Unknown(_) => None,
+        }
+    }
+
+    pub fn type_name(&self) -> &'static str {
+        match self {
+            Self::System => SYSTEM_TYPE_STRING,
+            Self::Database(_) => DATABASE_TYPE_STRING,
+            Self::Namespace(_) => NAMESPACE_TYPE_STRING,
+            Self::Table(_) => TABLE_TYPE_STRING,
+            Self::View(_) => VIEW_TYPE_STRING,
+            Self::Secret(_) => SECRET_TYPE_STRING,
+            Self::Function(_) => FUNCTION_TYPE_STRING,
+            Self::Unknown(_) => UNKNOWN_TYPE_STRING,
+        }
+    }
+}
+
+impl std::str::FromStr for Object {
+    type Err = crate::Error;
+
+    fn from_str(value: &str) -> crate::Result<Self> {
+        Self::deserialize(serde::de::value::StrDeserializer::<serde::de::value::Error>::new(value))
+            .map_err(|error| crate::Error::InvalidInput {
+                message: error.to_string(),
+            })
+    }
+}
+
+impl std::fmt::Display for Object {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        match self {
+            Self::System => f.write_str(SYSTEM_TYPE_STRING),
+            Self::Database(object) => object.fmt(f),
+            Self::Namespace(object) => object.fmt(f),
+            Self::Table(object) => object.fmt(f),
+            Self::View(object) => object.fmt(f),
+            Self::Secret(object) => object.fmt(f),
+            Self::Function(object) => object.fmt(f),
+            Self::Unknown(s) => f.write_str(s),
+        }
+    }
+}
+
+impl Serialize for Object {
+    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
+    where
+        S: Serializer,
+    {
+        self.validate().map_err(S::Error::custom)?;
+        self.to_string().serialize(serializer)
+    }
+}
+
+impl<'de> Deserialize<'de> for Object {
+    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
+    where
+        D: Deserializer<'de>,
+    {
+        let s = String::deserialize(deserializer)?;
+        let kind = s.split(':').next().unwrap_or_default();
+        if s == SYSTEM_TYPE_STRING {
+            Ok(Self::System)
+        } else if s.is_empty() || kind == SYSTEM_TYPE_STRING {
+            Err(D::Error::custom(format!("invalid Object {s:?}")))
+        } else if kind == DATABASE_TYPE_STRING {
+            let database = DatabaseObject::deserialize(serde::de::value::StringDeserializer::<
+                D::Error,
+            >::new(s))?;
+            Ok(Self::Database(database))
+        } else if kind == NAMESPACE_TYPE_STRING {
+            let namespace = NamespaceObject::deserialize(serde::de::value::StringDeserializer::<
+                D::Error,
+            >::new(s))?;
+            Ok(Self::Namespace(namespace))
+        } else if kind == TABLE_TYPE_STRING {
+            let table =
+                TableObject::deserialize(serde::de::value::StringDeserializer::<D::Error>::new(s))?;
+            Ok(Self::Table(table))
+        } else if kind == VIEW_TYPE_STRING {
+            let view =
+                ViewObject::deserialize(serde::de::value::StringDeserializer::<D::Error>::new(s))?;
+            Ok(Self::View(view))
+        } else if kind == SECRET_TYPE_STRING {
+            let secret = SecretObject::deserialize(
+                serde::de::value::StringDeserializer::<D::Error>::new(s),
+            )?;
+            Ok(Self::Secret(secret))
+        } else if kind == FUNCTION_TYPE_STRING {
+            let function = FunctionObject::deserialize(serde::de::value::StringDeserializer::<
+                D::Error,
+            >::new(s))?;
+            Ok(Self::Function(function))
+        } else {
+            Ok(Self::Unknown(s))
+        }
+    }
+}
+
+/// A database resource, encoded as `database:NAME`.
+#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
+pub struct DatabaseObject {
+    pub database: String,
+}
+
+impl DatabaseObject {
+    pub fn new(database: String) -> Self {
+        Self { database }
+    }
+
+    fn validate(&self) -> Result<(), String> {
+        validate_database_name(&self.database).map_err(|error| error.to_string())?;
+        Ok(())
+    }
+}
+
+impl std::fmt::Display for DatabaseObject {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(f, "database:{}", self.database)
+    }
+}
+
+impl Serialize for DatabaseObject {
+    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
+    where
+        S: Serializer,
+    {
+        self.validate().map_err(S::Error::custom)?;
+        self.to_string().serialize(serializer)
+    }
+}
+
+impl<'de> Deserialize<'de> for DatabaseObject {
+    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
+    where
+        D: Deserializer<'de>,
+    {
+        let s = String::deserialize(deserializer)?;
+        let database = s
+            .strip_prefix("database:")
+            .ok_or_else(|| D::Error::custom(format!("invalid DatabaseObject {s:?}")))?;
+        let object = Self {
+            database: database.to_string(),
+        };
+        object.validate().map_err(D::Error::custom)?;
+        Ok(object)
+    }
+}
+
+/// A namespace resource, encoded as `namespace:DATABASE:PATH`.
+#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
+pub struct NamespaceObject {
+    pub database: String,
+    pub namespace: NamespacePath,
+}
+
+impl NamespaceObject {
+    fn validate(&self) -> Result<(), String> {
+        validate_database_name(&self.database).map_err(|error| error.to_string())?;
+        Ok(())
+    }
+}
+
+impl std::fmt::Display for NamespaceObject {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(f, "namespace:{}:{}", self.database, self.namespace)
+    }
+}
+
+impl Serialize for NamespaceObject {
+    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
+    where
+        S: Serializer,
+    {
+        self.validate().map_err(S::Error::custom)?;
+        self.to_string().serialize(serializer)
+    }
+}
+
+impl<'de> Deserialize<'de> for NamespaceObject {
+    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
+    where
+        D: Deserializer<'de>,
+    {
+        let s = String::deserialize(deserializer)?;
+        let invalid = || D::Error::custom(format!("invalid NamespaceObject {s:?}"));
+        let rest = s.strip_prefix("namespace:").ok_or_else(invalid)?;
+        let (database, namespace) = rest.split_once(':').ok_or_else(invalid)?;
+        let object = Self {
+            database: database.to_string(),
+            namespace: NamespacePath::parse(namespace).map_err(D::Error::custom)?,
+        };
+        object.validate().map_err(D::Error::custom)?;
+        Ok(object)
+    }
+}
+
+/// A table resource, encoded as `table:DATABASE:NAMESPACE$TABLE`.
+#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
+pub struct TableObject {
+    pub database: String,
+    pub namespace: NamespacePath,
+    pub table: String,
+}
+
+impl TableObject {
+    fn validate(&self) -> Result<(), String> {
+        validate_database_name(&self.database).map_err(|error| error.to_string())?;
+        validate_table_name(&self.table).map_err(|error| error.to_string())?;
+        Ok(())
+    }
+}
+
+impl std::fmt::Display for TableObject {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(
+            f,
+            "table:{}:{}${}",
+            self.database, self.namespace, self.table
+        )
+    }
+}
+
+impl Serialize for TableObject {
+    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
+    where
+        S: Serializer,
+    {
+        self.validate().map_err(S::Error::custom)?;
+        self.to_string().serialize(serializer)
+    }
+}
+
+impl<'de> Deserialize<'de> for TableObject {
+    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
+    where
+        D: Deserializer<'de>,
+    {
+        let s = String::deserialize(deserializer)?;
+        let invalid = || D::Error::custom(format!("invalid TableObject {s:?}"));
+        let rest = s.strip_prefix("table:").ok_or_else(invalid)?;
+        let (database, path) = rest.split_once(':').ok_or_else(invalid)?;
+        let (namespace, table) = path.rsplit_once('$').ok_or_else(invalid)?;
+        let object = Self {
+            database: database.to_string(),
+            namespace: NamespacePath::parse(namespace).map_err(D::Error::custom)?,
+            table: table.to_string(),
+        };
+        object.validate().map_err(D::Error::custom)?;
+        Ok(object)
+    }
+}
+
+/// A view resource, encoded as `view:DATABASE:NAMESPACE$VIEW`.
+#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
+pub struct ViewObject {
+    pub database: String,
+    pub namespace: NamespacePath,
+    pub view: String,
+}
+
+impl ViewObject {
+    fn validate(&self) -> Result<(), String> {
+        validate_database_name(&self.database).map_err(|error| error.to_string())?;
+        validate_view_name(&self.view).map_err(|error| error.to_string())?;
+        Ok(())
+    }
+}
+
+impl std::fmt::Display for ViewObject {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(f, "view:{}:{}${}", self.database, self.namespace, self.view)
+    }
+}
+
+impl Serialize for ViewObject {
+    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
+    where
+        S: Serializer,
+    {
+        self.validate().map_err(S::Error::custom)?;
+        self.to_string().serialize(serializer)
+    }
+}
+
+impl<'de> Deserialize<'de> for ViewObject {
+    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
+    where
+        D: Deserializer<'de>,
+    {
+        let s = String::deserialize(deserializer)?;
+        let invalid = || D::Error::custom(format!("invalid ViewObject {s:?}"));
+        let rest = s.strip_prefix("view:").ok_or_else(invalid)?;
+        let (database, path) = rest.split_once(':').ok_or_else(invalid)?;
+        let (namespace, view) = path.rsplit_once('$').ok_or_else(invalid)?;
+        let object = Self {
+            database: database.to_string(),
+            namespace: NamespacePath::parse(namespace).map_err(D::Error::custom)?,
+            view: view.to_string(),
+        };
+        object.validate().map_err(D::Error::custom)?;
+        Ok(object)
+    }
+}
+/// A secret resource, encoded as `secret:DATABASE:NAMESPACE$SECRET`.
+///
+/// ```
+/// use lancedb::authz::{SecretObject, NamespacePath, Object};
+///
+/// let object = Object::Secret(SecretObject {
+///     database: "analytics".into(),
+///     namespace: NamespacePath::from_component("public"),
+///     secret: "example".into(),
+/// });
+/// assert_eq!(object.to_string(), "secret:analytics:public$example");
+/// ```
+#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
+pub struct SecretObject {
+    pub database: String,
+    pub namespace: NamespacePath,
+    pub secret: String,
+}
+
+impl SecretObject {
+    fn validate(&self) -> Result<(), String> {
+        validate_database_name(&self.database).map_err(|error| error.to_string())?;
+        validate_secret_name(&self.secret).map_err(|error| error.to_string())?;
+        Ok(())
+    }
+}
+
+impl std::fmt::Display for SecretObject {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(
+            f,
+            "secret:{}:{}${}",
+            self.database, self.namespace, self.secret
+        )
+    }
+}
+
+impl Serialize for SecretObject {
+    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
+    where
+        S: Serializer,
+    {
+        self.validate().map_err(S::Error::custom)?;
+        self.to_string().serialize(serializer)
+    }
+}
+
+impl<'de> Deserialize<'de> for SecretObject {
+    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
+    where
+        D: Deserializer<'de>,
+    {
+        let s = String::deserialize(deserializer)?;
+        let invalid = || D::Error::custom(format!("invalid SecretObject {s:?}"));
+        let rest = s.strip_prefix("secret:").ok_or_else(invalid)?;
+        let (database, path) = rest.split_once(':').ok_or_else(invalid)?;
+        let (namespace, secret) = path.rsplit_once('$').ok_or_else(invalid)?;
+        let object = Self {
+            database: database.to_string(),
+            namespace: NamespacePath::parse(namespace).map_err(D::Error::custom)?,
+            secret: secret.to_string(),
+        };
+        object.validate().map_err(D::Error::custom)?;
+        Ok(object)
+    }
+}
+
+/// A function resource, encoded as `function:DATABASE:NAMESPACE$FUNCTION`.
+///
+/// ```
+/// use lancedb::authz::{FunctionObject, NamespacePath, Object};
+///
+/// let object = Object::Function(FunctionObject {
+///     database: "analytics".into(),
+///     namespace: NamespacePath::from_component("public"),
+///     function: "example".into(),
+/// });
+/// assert_eq!(object.to_string(), "function:analytics:public$example");
+/// ```
+#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
+pub struct FunctionObject {
+    pub database: String,
+    pub namespace: NamespacePath,
+    pub function: String,
+}
+
+impl FunctionObject {
+    fn validate(&self) -> Result<(), String> {
+        validate_database_name(&self.database).map_err(|error| error.to_string())?;
+        validate_table_name(&self.function).map_err(|error| error.to_string())?;
+        Ok(())
+    }
+}
+
+impl std::fmt::Display for FunctionObject {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(
+            f,
+            "function:{}:{}${}",
+            self.database, self.namespace, self.function
+        )
+    }
+}
+
+impl Serialize for FunctionObject {
+    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
+    where
+        S: Serializer,
+    {
+        self.validate().map_err(S::Error::custom)?;
+        self.to_string().serialize(serializer)
+    }
+}
+
+impl<'de> Deserialize<'de> for FunctionObject {
+    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
+    where
+        D: Deserializer<'de>,
+    {
+        let s = String::deserialize(deserializer)?;
+        let invalid = || D::Error::custom(format!("invalid FunctionObject {s:?}"));
+        let rest = s.strip_prefix("function:").ok_or_else(invalid)?;
+        let (database, path) = rest.split_once(':').ok_or_else(invalid)?;
+        let (namespace, function) = path.rsplit_once('$').ok_or_else(invalid)?;
+        let object = Self {
+            database: database.to_string(),
+            namespace: NamespacePath::parse(namespace).map_err(D::Error::custom)?,
+            function: function.to_string(),
+        };
+        object.validate().map_err(D::Error::custom)?;
+        Ok(object)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn system_object_round_trip() {
+        let encoded = serde_json::to_string(&Object::System).unwrap();
+        assert_eq!(encoded, r#""system""#);
+        assert_eq!(
+            serde_json::from_str::<Object>(&encoded).unwrap(),
+            Object::System
+        );
+    }
+
+    #[test]
+    fn object_database_round_trip() {
+        let object = Object::Database(DatabaseObject {
+            database: "db".to_string(),
+        });
+        let encoded = serde_json::to_string(&object).unwrap();
+        assert_eq!(encoded, r#""database:db""#);
+        assert_eq!(serde_json::from_str::<Object>(&encoded).unwrap(), object);
+    }
+
+    #[test]
+    fn object_namespace_round_trip() {
+        let object = Object::Namespace(NamespaceObject {
+            database: "db".to_string(),
+            namespace: NamespacePath::from_component("ns"),
+        });
+        let encoded = serde_json::to_string(&object).unwrap();
+        assert_eq!(encoded, r#""namespace:db:ns""#);
+        assert_eq!(serde_json::from_str::<Object>(&encoded).unwrap(), object);
+    }
+
+    #[test]
+    fn object_table_round_trip() {
+        let object = Object::Table(TableObject {
+            database: "db".to_string(),
+            namespace: NamespacePath::from_component("ns"),
+            table: "foo".to_string(),
+        });
+        let encoded = serde_json::to_string(&object).unwrap();
+        assert_eq!(encoded, r#""table:db:ns$foo""#);
+        assert_eq!(serde_json::from_str::<Object>(&encoded).unwrap(), object);
+    }
+
+    #[test]
+    fn object_view_round_trip() {
+        let object = Object::View(ViewObject {
+            database: "db".to_string(),
+            namespace: NamespacePath::from_component("ns"),
+            view: "foo".to_string(),
+        });
+        let encoded = serde_json::to_string(&object).unwrap();
+        assert_eq!(encoded, r#""view:db:ns$foo""#);
+        assert_eq!(serde_json::from_str::<Object>(&encoded).unwrap(), object);
+    }
+
+    #[test]
+    fn secret_objects_round_trip_and_validate() {
+        let resource = SecretObject {
+            database: "tenant/db".into(),
+            namespace: NamespacePath::new(vec!["a".into(), "b".into()]).unwrap(),
+            secret: "example".into(),
+        };
+        let wire = "secret:tenant/db:a$b$example";
+        assert_eq!(serde_json::to_value(&resource).unwrap(), wire);
+        assert_eq!(
+            serde_json::from_value::<SecretObject>(serde_json::json!(wire)).unwrap(),
+            resource
+        );
+        let object = Object::Secret(resource.clone());
+        assert_eq!(object.database(), Some("tenant/db"));
+        assert_eq!(object.type_name(), "secret");
+        assert_eq!(wire.parse::<Object>().unwrap(), object);
+        assert_eq!(serde_json::to_value(object).unwrap(), wire);
+        for name in ["", ".", "..", "bad$name", "bad/name", "bad:name"] {
+            let invalid = SecretObject {
+                secret: name.into(),
+                ..resource.clone()
+            };
+            assert!(serde_json::to_value(&invalid).is_err());
+            assert!(serde_json::to_value(Object::Secret(invalid)).is_err());
+        }
+    }
+
+    #[test]
+    fn function_objects_round_trip_and_validate() {
+        let resource = FunctionObject {
+            database: "tenant/db".into(),
+            namespace: NamespacePath::new(vec!["a".into(), "b".into()]).unwrap(),
+            function: "example".into(),
+        };
+        let wire = "function:tenant/db:a$b$example";
+        assert_eq!(serde_json::to_value(&resource).unwrap(), wire);
+        assert_eq!(
+            serde_json::from_value::<FunctionObject>(serde_json::json!(wire)).unwrap(),
+            resource
+        );
+        let object = Object::Function(resource.clone());
+        assert_eq!(object.database(), Some("tenant/db"));
+        assert_eq!(object.type_name(), "function");
+        assert_eq!(wire.parse::<Object>().unwrap(), object);
+        assert_eq!(serde_json::to_value(object).unwrap(), wire);
+        for name in ["", ".", "..", "bad$name", "bad/name", "bad:name"] {
+            let invalid = FunctionObject {
+                function: name.into(),
+                ..resource.clone()
+            };
+            assert!(serde_json::to_value(&invalid).is_err());
+            assert!(serde_json::to_value(Object::Function(invalid)).is_err());
+        }
+    }
+
+    #[test]
+    fn unknown_objects_round_trip() {
+        for wire in [
+            "future:db:resource",
+            "table_extension:db:resource",
+            "garbage",
+        ] {
+            let object: Object = wire.parse().unwrap();
+            assert_eq!(object, Object::Unknown(wire.into()));
+            assert_eq!(object.database(), None);
+            assert_eq!(object.type_name(), UNKNOWN_TYPE_STRING);
+            assert_eq!(object.to_string(), wire);
+            assert_eq!(serde_json::to_value(&object).unwrap(), wire);
+            assert_eq!(
+                serde_json::from_value::<Object>(serde_json::json!(wire)).unwrap(),
+                object
+            );
+        }
+    }
+
+    #[test]
+    fn database_object_round_trip() {
+        let object = DatabaseObject {
+            database: "db".to_string(),
+        };
+        let encoded = serde_json::to_string(&object).unwrap();
+        assert_eq!(encoded, r#""database:db""#);
+        assert_eq!(
+            serde_json::from_str::<DatabaseObject>(&encoded).unwrap(),
+            object
+        );
+    }
+
+    #[test]
+    fn namespace_object_round_trip() {
+        let object = NamespaceObject {
+            database: "db".to_string(),
+            namespace: NamespacePath::from_component("ns"),
+        };
+        let encoded = serde_json::to_string(&object).unwrap();
+        assert_eq!(encoded, r#""namespace:db:ns""#);
+        assert_eq!(
+            serde_json::from_str::<NamespaceObject>(&encoded).unwrap(),
+            object
+        );
+    }
+
+    #[test]
+    fn namespace_object_rejects_missing_namespace() {
+        assert!(serde_json::from_str::<NamespaceObject>(r#""namespace:db""#).is_err());
+    }
+
+    #[test]
+    fn table_object_round_trip() {
+        let object = TableObject {
+            database: "db".to_string(),
+            namespace: NamespacePath::from_component("ns"),
+            table: "foo".to_string(),
+        };
+        let encoded = serde_json::to_string(&object).unwrap();
+        assert_eq!(encoded, r#""table:db:ns$foo""#);
+        assert_eq!(
+            serde_json::from_str::<TableObject>(&encoded).unwrap(),
+            object
+        );
+    }
+
+    #[test]
+    fn table_object_rejects_missing_prefix() {
+        assert!(serde_json::from_str::<TableObject>(r#""foo""#).is_err());
+    }
+
+    #[test]
+    fn table_object_rejects_missing_table() {
+        assert!(serde_json::from_str::<TableObject>(r#""table:db:ns""#).is_err());
+    }
+
+    #[test]
+    fn objects_order_by_variant_then_path_components() {
+        let database = Object::Database(DatabaseObject::new("z".to_string()));
+        let namespace = Object::Namespace(NamespaceObject {
+            database: "a".to_string(),
+            namespace: NamespacePath::from_component("a"),
+        });
+        assert!(Object::System < database);
+        assert!(database < namespace);
+
+        let first_table = Object::Table(TableObject {
+            database: "db".to_string(),
+            namespace: NamespacePath::from_component("a"),
+            table: "z".to_string(),
+        });
+        let second_table = Object::Table(TableObject {
+            database: "db".to_string(),
+            namespace: NamespacePath::from_component("b"),
+            table: "a".to_string(),
+        });
+        assert!(first_table < second_table);
+    }
+
+    #[test]
+    fn empty_namespace_paths_select_public() {
+        let namespace = NamespacePath::new(vec![]).unwrap();
+        assert_eq!(namespace, NamespacePath::default());
+        assert_eq!(namespace.components(), &["public"]);
+        assert!(namespace.parent().is_none());
+        assert_eq!(
+            namespace.prefixes().collect::<Vec<_>>(),
+            vec![namespace.clone()]
+        );
+
+        let database = "db".to_string();
+        let objects = [
+            Object::Namespace(NamespaceObject {
+                database: database.clone(),
+                namespace: namespace.clone(),
+            }),
+            Object::Table(TableObject {
+                database: database.clone(),
+                namespace: namespace.clone(),
+                table: "item".into(),
+            }),
+            Object::View(ViewObject {
+                database: database.clone(),
+                namespace: namespace.clone(),
+                view: "item".into(),
+            }),
+            Object::Secret(SecretObject {
+                database: database.clone(),
+                namespace: namespace.clone(),
+                secret: "item".into(),
+            }),
+            Object::Function(FunctionObject {
+                database,
+                namespace,
+                function: "item".into(),
+            }),
+        ];
+        for (object, wire) in objects.into_iter().zip([
+            "namespace:db:public",
+            "table:db:public$item",
+            "view:db:public$item",
+            "secret:db:public$item",
+            "function:db:public$item",
+        ]) {
+            assert_eq!(serde_json::to_value(&object).unwrap(), wire);
+            assert_eq!(wire.parse::<Object>().unwrap(), object);
+        }
+    }
+
+    #[test]
+    fn namespace_path_returns_cumulative_prefixes() {
+        let path = NamespacePath::new(vec!["ns1".into(), "ns2".into(), "ns3".into()]).unwrap();
+        assert_eq!(
+            path.prefixes()
+                .map(|prefix| prefix.to_string())
+                .collect::<Vec<_>>(),
+            vec!["ns1", "ns1$ns2", "ns1$ns2$ns3"]
+        );
+        assert_eq!(path.parent().unwrap().to_string(), "ns1$ns2");
+    }
+
+    #[test]
+    fn nested_objects_round_trip_without_escaping() {
+        for wire in [
+            "database:tenant/db",
+            "namespace:tenant/db:a$b",
+            "table:tenant/db:a$b$c",
+            "view:tenant/db:a$b$c",
+            "secret:tenant/db:a$b$c",
+            "function:tenant/db:a$b$c",
+        ] {
+            let object: Object = serde_json::from_value(serde_json::json!(wire)).unwrap();
+            assert_eq!(wire.parse::<Object>().unwrap(), object);
+            assert_eq!(object.database(), Some("tenant/db"));
+            assert_eq!(object.to_string(), wire);
+            assert_eq!(serde_json::to_value(object).unwrap(), wire);
+        }
+    }
+
+    #[test]
+    fn objects_reject_invalid_names_and_legacy_forms() {
+        for database in [
+            "",
+            ".",
+            "..",
+            "db:bad",
+            "db%2Fchild",
+            "db$child",
+            "/db",
+            "db/",
+            "db//child",
+            "db/../child",
+            "db name",
+        ] {
+            for wire in [
+                format!("database:{database}"),
+                format!("namespace:{database}:ns"),
+                format!("table:{database}:ns$t"),
+                format!("view:{database}:ns$v"),
+                format!("secret:{database}:ns$s"),
+                format!("function:{database}:ns$f"),
+            ] {
+                assert!(
+                    serde_json::from_value::<Object>(serde_json::json!(wire)).is_err(),
+                    "{wire}"
+                );
+            }
+        }
+        for name in ["", "bad:name", "bad/name", "bad%24name", "bad name"] {
+            for wire in [
+                format!("namespace:db:{name}"),
+                format!("table:db:{name}$t"),
+                format!("view:db:{name}$v"),
+                format!("table:db:ns${name}"),
+                format!("view:db:ns${name}"),
+                format!("secret:db:{name}$s"),
+                format!("function:db:{name}$f"),
+                format!("secret:db:ns${name}"),
+                format!("function:db:ns${name}"),
+            ] {
+                assert!(
+                    serde_json::from_value::<Object>(serde_json::json!(wire)).is_err(),
+                    "{wire}"
+                );
+            }
+        }
+        for wire in [
+            "",
+            "system:db",
+            "database",
+            "namespace",
+            "table",
+            "view",
+            "namespace:db/ns",
+            "table:db/ns/t",
+            "view:db/ns/v",
+            "table:db:ns",
+            "view:db:ns",
+            "namespace:db:a$$b",
+            "table:db:ns$.",
+            "view:db:ns$..",
+            "table:db:a$$t",
+            "view:db:$v",
+            "secret",
+            "function",
+            "secret:db/ns/s",
+            "function:db/ns/f",
+            "secret:db:ns",
+            "function:db:ns",
+            "secret:db:a$$s",
+            "function:db:$f",
+            "secret:db:ns$.",
+            "function:db:ns$..",
+        ] {
+            assert!(
+                serde_json::from_value::<Object>(serde_json::json!(wire)).is_err(),
+                "{wire}"
+            );
+            assert!(matches!(
+                wire.parse::<Object>(),
+                Err(crate::Error::InvalidInput { .. })
+            ));
+        }
+    }
+
+    #[test]
+    fn namespace_components_use_namespace_validation() {
+        for component in ["a$b", "c/d", "e%f", "a:b", "", ".", ".."] {
+            assert!(NamespacePath::new(vec![component.into()]).is_err());
+        }
+        assert!(NamespacePath::new(vec!["a.b".into(), "c..d".into()]).is_ok());
+    }
+
+    #[test]
+    fn serialization_rejects_invalid_directly_constructed_objects() {
+        let database = DatabaseObject::new("db:bad".into());
+        let namespace = NamespaceObject {
+            database: "db%2Fbad".into(),
+            namespace: NamespacePath::from_component("ns"),
+        };
+        let table = TableObject {
+            database: "db".into(),
+            namespace: NamespacePath::from_component("ns"),
+            table: "bad$name".into(),
+        };
+        let view = ViewObject {
+            database: "db".into(),
+            namespace: NamespacePath::from_component("ns"),
+            view: "..".into(),
+        };
+        assert!(serde_json::to_string(&database).is_err());
+        assert!(serde_json::to_string(&namespace).is_err());
+        assert!(serde_json::to_string(&table).is_err());
+        assert!(serde_json::to_string(&view).is_err());
+        for object in [
+            Object::Database(database),
+            Object::Namespace(namespace),
+            Object::Table(table),
+            Object::View(view),
+        ] {
+            assert!(serde_json::to_string(&object).is_err());
+        }
+    }
+}
diff --git a/rust/lancedb/src/authz/privilege.rs b/rust/lancedb/src/authz/privilege.rs
new file mode 100644
index 000000000..66b07643a
--- /dev/null
+++ b/rust/lancedb/src/authz/privilege.rs
@@ -0,0 +1,514 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use std::{fmt, str::FromStr};
+
+use serde::{Deserialize, Deserializer, Serialize, Serializer, de::Error as _};
+
+/// A LanceDB privilege, serialized by name (for example, `SELECT`).
+/// Numeric aliases use `PRIVILEGE` followed by decimal digits. Numbers 0 through
+/// 13 map to the known privileges in declaration order. Unassigned numbers are
+/// preserved verbatim; their meaning and validity are determined by the server.
+///
+/// ```
+/// use lancedb::authz::Privilege;
+///
+/// assert_eq!("PRIVILEGE0".parse::<Privilege>().unwrap(), Privilege::Ownership);
+/// assert_eq!("PRIVILEGE2".parse::<Privilege>().unwrap(), Privilege::Select);
+///
+/// let privilege: Privilege = "PRIVILEGE256".parse().unwrap();
+/// assert_eq!(privilege.to_string(), "PRIVILEGE256");
+///
+/// // Delegate creation on a namespace without transferring its ownership.
+/// for privilege in [Privilege::CreateSecret, Privilege::CreateFunction] {
+///     assert!(privilege.valid_for_namespace());
+/// }
+/// ```
+#[derive(Debug, Clone, PartialEq, Eq, strum_macros::Display)]
+#[strum(serialize_all = "SCREAMING_SNAKE_CASE")]
+#[non_exhaustive]
+pub enum Privilege {
+    /// A special privilege that conveys ownership.
+    Ownership,
+
+    /// On a database: allows accessing the namespaces inside.
+    /// On a namespace: allows accessing the tables inside.
+    ///
+    /// When looking for Usage on a Database, if there are no ACLs
+    /// found for that database, we assume it doesn't exist yet and
+    /// check for CreateDatabase on System.
+    ///
+    /// When looking for Usage on a public (aka default) Namespace,
+    /// if there are no ACLs found for that Namespace, we assume it
+    /// doesn't exist yet and check for CreateNamespace on the
+    /// containing database instead.
+    Usage,
+
+    /// On a table: allows reading rows.
+    Select,
+
+    /// On a table: allows appending rows.
+    Insert,
+
+    /// On a table: allows modifying existing rows.
+    Update,
+
+    /// On a table: allows deleting rows.
+    Delete,
+
+    /// On the system object: can create a database.
+    CreateDatabase,
+
+    /// On a database or namespace: can create immediate child namespaces.
+    CreateNamespace,
+
+    /// On a namespace: can create a table in that namespace.
+    CreateTable,
+
+    /// On a namespace: can create a materialized view in that namespace.
+    CreateMaterializedView,
+
+    /// On a namespace: can create a view in that namespace.
+    CreateView,
+
+    /// On a namespace: can create a secret without taking ownership of the namespace.
+    CreateSecret,
+
+    /// On a namespace: can create a function without taking ownership of the namespace.
+    CreateFunction,
+
+    /// On the system object:
+    /// - can read server configuration.
+    Operate,
+
+    /// A future privilege name, preserved verbatim.
+    #[strum(to_string = "{0}")]
+    Unknown(UnknownPrivilege),
+}
+
+/// An unknown privilege.
+#[derive(Debug, Clone, PartialEq, Eq)]
+pub struct UnknownPrivilege(String);
+
+impl fmt::Display for UnknownPrivilege {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        self.0.fmt(f)
+    }
+}
+
+impl Privilege {
+    pub const KNOWN: [Self; 14] = [
+        Self::Ownership,
+        Self::Usage,
+        Self::Select,
+        Self::Insert,
+        Self::Update,
+        Self::Delete,
+        Self::CreateDatabase,
+        Self::CreateNamespace,
+        Self::CreateTable,
+        Self::CreateMaterializedView,
+        Self::CreateView,
+        Self::CreateSecret,
+        Self::CreateFunction,
+        Self::Operate,
+    ];
+
+    pub fn valid_for_system(&self) -> bool {
+        matches!(
+            self,
+            Self::Operate | Self::CreateDatabase | Self::Usage | Self::Unknown(_)
+        )
+    }
+
+    pub fn valid_for_database(&self) -> bool {
+        matches!(
+            self,
+            Self::Ownership | Self::Usage | Self::CreateNamespace | Self::Unknown(_)
+        )
+    }
+
+    pub fn valid_for_namespace(&self) -> bool {
+        matches!(
+            self,
+            Self::Ownership
+                | Self::Usage
+                | Self::CreateNamespace
+                | Self::CreateTable
+                | Self::CreateMaterializedView
+                | Self::CreateView
+                | Self::CreateSecret
+                | Self::CreateFunction
+                | Self::Unknown(_)
+        )
+    }
+
+    pub fn valid_for_table(&self) -> bool {
+        matches!(
+            self,
+            Self::Ownership
+                | Self::Select
+                | Self::Insert
+                | Self::Update
+                | Self::Delete
+                | Self::Unknown(_)
+        )
+    }
+
+    pub fn valid_for_view(&self) -> bool {
+        matches!(self, Self::Ownership | Self::Select | Self::Unknown(_))
+    }
+
+    pub fn valid_for_secret(&self) -> bool {
+        matches!(
+            self,
+            Self::Ownership | Self::Usage | Self::Update | Self::Unknown(_)
+        )
+    }
+
+    pub fn valid_for_function(&self) -> bool {
+        matches!(self, Self::Ownership | Self::Usage | Self::Unknown(_))
+    }
+
+    /// Whether this privilege can mutate database contents or schema.
+    /// Unknown privileges are conservatively treated as writes.
+    pub fn is_write(&self) -> bool {
+        !matches!(self, Self::Usage | Self::Select | Self::Operate)
+    }
+}
+
+impl FromStr for Privilege {
+    type Err = String;
+
+    fn from_str(name: &str) -> Result<Self, Self::Err> {
+        if let Some(privilege) = Self::KNOWN.into_iter().find(|p| p.to_string() == name) {
+            return Ok(privilege);
+        }
+        if let Some(digits) = name.strip_prefix("PRIVILEGE")
+            && !digits.is_empty()
+            && digits.bytes().all(|b| b.is_ascii_digit())
+        {
+            return Ok(match digits.parse::<u64>() {
+                Ok(0) => Self::Ownership,
+                Ok(1) => Self::Usage,
+                Ok(2) => Self::Select,
+                Ok(3) => Self::Insert,
+                Ok(4) => Self::Update,
+                Ok(5) => Self::Delete,
+                Ok(6) => Self::CreateDatabase,
+                Ok(7) => Self::CreateNamespace,
+                Ok(8) => Self::CreateTable,
+                Ok(9) => Self::CreateMaterializedView,
+                Ok(10) => Self::CreateView,
+                Ok(11) => Self::CreateSecret,
+                Ok(12) => Self::CreateFunction,
+                Ok(13) => Self::Operate,
+                _ => Self::Unknown(UnknownPrivilege(name.to_owned())),
+            });
+        }
+        Err(format!("invalid privilege {name:?}"))
+    }
+}
+
+/// Converts a privilege to its numeric identifier.
+///
+/// Unknown privileges return an error if their number exceeds [`u8::MAX`].
+///
+/// ```
+/// use lancedb::authz::Privilege;
+///
+/// assert_eq!(u8::try_from(&Privilege::Select).unwrap(), 2);
+/// let unknown: Privilege = "PRIVILEGE255".parse().unwrap();
+/// assert_eq!(u8::try_from(&unknown).unwrap(), 255);
+/// let too_large: Privilege = "PRIVILEGE256".parse().unwrap();
+/// assert!(u8::try_from(&too_large).is_err());
+/// ```
+impl TryFrom<&Privilege> for u8 {
+    type Error = std::num::ParseIntError;
+
+    fn try_from(privilege: &Privilege) -> Result<Self, Self::Error> {
+        Ok(match privilege {
+            Privilege::Ownership => 0,
+            Privilege::Usage => 1,
+            Privilege::Select => 2,
+            Privilege::Insert => 3,
+            Privilege::Update => 4,
+            Privilege::Delete => 5,
+            Privilege::CreateDatabase => 6,
+            Privilege::CreateNamespace => 7,
+            Privilege::CreateTable => 8,
+            Privilege::CreateMaterializedView => 9,
+            Privilege::CreateView => 10,
+            Privilege::CreateSecret => 11,
+            Privilege::CreateFunction => 12,
+            Privilege::Operate => 13,
+            Privilege::Unknown(unknown) => unknown.0["PRIVILEGE".len()..].parse()?,
+        })
+    }
+}
+
+/// Converts an owned privilege to its numeric identifier.
+///
+/// Like the borrowed conversion, this returns an error for numbers above 255.
+///
+/// ```
+/// use lancedb::authz::Privilege;
+///
+/// assert_eq!(u8::try_from(Privilege::Ownership).unwrap(), 0);
+/// ```
+impl TryFrom<Privilege> for u8 {
+    type Error = std::num::ParseIntError;
+
+    fn try_from(privilege: Privilege) -> Result<Self, Self::Error> {
+        Self::try_from(&privilege)
+    }
+}
+
+impl Serialize for Privilege {
+    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
+    where
+        S: Serializer,
+    {
+        serializer.collect_str(self)
+    }
+}
+
+impl<'de> Deserialize<'de> for Privilege {
+    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
+    where
+        D: Deserializer<'de>,
+    {
+        String::deserialize(deserializer)?
+            .parse()
+            .map_err(D::Error::custom)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::Privilege;
+
+    #[test]
+    fn display_matches_serde() {
+        for privilege in Privilege::KNOWN {
+            assert_eq!(
+                serde_json::to_string(&privilege).unwrap(),
+                format!("\"{privilege}\""),
+            );
+        }
+    }
+
+    #[test]
+    fn secret_and_function_creation_privileges() {
+        for (name, privilege) in [
+            ("CREATE_SECRET", Privilege::CreateSecret),
+            ("CREATE_FUNCTION", Privilege::CreateFunction),
+        ] {
+            assert_eq!(name.parse::<Privilege>().unwrap(), privilege);
+            assert_eq!(privilege.to_string(), name);
+            assert_eq!(serde_json::to_value(&privilege).unwrap(), name);
+            assert_eq!(
+                serde_json::from_value::<Privilege>(serde_json::json!(name)).unwrap(),
+                privilege
+            );
+            assert!(privilege.valid_for_namespace());
+            assert!(!privilege.valid_for_system());
+            assert!(!privilege.valid_for_database());
+            assert!(!privilege.valid_for_table());
+            assert!(!privilege.valid_for_view());
+            assert!(privilege.is_write());
+        }
+    }
+
+    #[test]
+    fn numeric_aliases_resolve_to_known_privileges() {
+        for (number, privilege) in [
+            (0, Privilege::Ownership),
+            (1, Privilege::Usage),
+            (2, Privilege::Select),
+            (3, Privilege::Insert),
+            (4, Privilege::Update),
+            (5, Privilege::Delete),
+            (6, Privilege::CreateDatabase),
+            (7, Privilege::CreateNamespace),
+            (8, Privilege::CreateTable),
+            (9, Privilege::CreateMaterializedView),
+            (10, Privilege::CreateView),
+            (11, Privilege::CreateSecret),
+            (12, Privilege::CreateFunction),
+            (13, Privilege::Operate),
+        ] {
+            assert_eq!(u8::try_from(&privilege).unwrap(), number);
+            assert_eq!(u8::try_from(privilege.clone()).unwrap(), number);
+            for name in [
+                format!("PRIVILEGE{number}"),
+                format!("PRIVILEGE{number:03}"),
+            ] {
+                let parsed = name.parse::<Privilege>().unwrap();
+                assert_eq!(parsed, privilege, "{name}");
+                assert_eq!(
+                    serde_json::from_value::<Privilege>(serde_json::json!(name)).unwrap(),
+                    privilege
+                );
+                assert_eq!(
+                    serde_json::to_value(&parsed).unwrap(),
+                    privilege.to_string()
+                );
+                assert_eq!(
+                    privilege.to_string().parse::<Privilege>().unwrap(),
+                    privilege
+                );
+            }
+        }
+    }
+
+    #[test]
+    fn unknown_privileges_convert_to_u8() {
+        for number in 14..=u8::MAX {
+            for name in [
+                format!("PRIVILEGE{number}"),
+                format!("PRIVILEGE{number:03}"),
+            ] {
+                let privilege: Privilege = name.parse().unwrap();
+                assert!(matches!(privilege, Privilege::Unknown(_)));
+                assert_eq!(u8::try_from(&privilege).unwrap(), number);
+                assert_eq!(u8::try_from(privilege).unwrap(), number);
+            }
+        }
+    }
+
+    #[test]
+    fn privileges_outside_u8_range_fail_conversion() {
+        for name in [
+            "PRIVILEGE256",
+            "PRIVILEGE0256",
+            "PRIVILEGE18446744073709551616",
+        ] {
+            let privilege: Privilege = name.parse().unwrap();
+            assert_eq!(
+                u8::try_from(&privilege).unwrap_err().kind(),
+                &std::num::IntErrorKind::PosOverflow
+            );
+            assert_eq!(
+                u8::try_from(privilege).unwrap_err().kind(),
+                &std::num::IntErrorKind::PosOverflow
+            );
+        }
+    }
+
+    #[test]
+    fn future_privilege_names_round_trip() {
+        for name in [
+            "PRIVILEGE14",
+            "PRIVILEGE20",
+            "PRIVILEGE64",
+            "PRIVILEGE256",
+            "PRIVILEGE18446744073709551616",
+            "PRIVILEGE020",
+        ] {
+            let privilege: Privilege = name.parse().unwrap();
+            assert!(matches!(privilege, Privilege::Unknown(_)));
+            assert_eq!(privilege.to_string(), name);
+            assert_eq!(serde_json::to_value(&privilege).unwrap(), name);
+            assert_eq!(
+                serde_json::from_value::<Privilege>(serde_json::json!(name)).unwrap(),
+                privilege
+            );
+        }
+    }
+
+    #[test]
+    fn rejects_invalid_privileges() {
+        for name in [
+            "",
+            "select",
+            " SELECT",
+            "SELECT ",
+            "PRIVILEGE",
+            "PRIVILEGE-1",
+            "PRIVILEGE+1",
+            "PRIVILEGE 1",
+            "PRIVILEGE1x",
+            "privilege12",
+            "UNKNOWN",
+        ] {
+            assert!(name.parse::<Privilege>().is_err(), "{name}");
+            assert!(
+                serde_json::from_value::<Privilege>(serde_json::json!(name)).is_err(),
+                "{name}"
+            );
+        }
+    }
+
+    #[test]
+    fn unknown_privileges_are_valid_for_all_objects_and_treated_as_writes() {
+        for name in ["PRIVILEGE14", "PRIVILEGE20", "PRIVILEGE64", "PRIVILEGE256"] {
+            let privilege: Privilege = name.parse().unwrap();
+            assert!(privilege.valid_for_system());
+            assert!(privilege.valid_for_database());
+            assert!(privilege.valid_for_namespace());
+            assert!(privilege.valid_for_table());
+            assert!(privilege.valid_for_view());
+            assert!(privilege.valid_for_secret());
+            assert!(privilege.valid_for_function());
+            assert!(privilege.is_write());
+        }
+        assert!(!Privilege::Ownership.valid_for_system());
+        assert!(!Privilege::Select.valid_for_database());
+    }
+
+    #[test]
+    fn test_privilege_is_write() {
+        for privilege in Privilege::KNOWN {
+            assert_eq!(
+                privilege.is_write(),
+                matches!(
+                    privilege,
+                    Privilege::Ownership
+                        | Privilege::Insert
+                        | Privilege::Update
+                        | Privilege::Delete
+                        | Privilege::CreateDatabase
+                        | Privilege::CreateNamespace
+                        | Privilege::CreateTable
+                        | Privilege::CreateMaterializedView
+                        | Privilege::CreateView
+                        | Privilege::CreateSecret
+                        | Privilege::CreateFunction
+                ),
+                "unexpected write classification for {privilege}"
+            );
+        }
+    }
+
+    #[test]
+    fn create_secret_and_create_function_are_namespace_scoped() {
+        for privilege in [Privilege::CreateSecret, Privilege::CreateFunction] {
+            assert!(privilege.valid_for_namespace());
+            assert!(!privilege.valid_for_system());
+            assert!(!privilege.valid_for_database());
+            assert!(!privilege.valid_for_table());
+            assert!(!privilege.valid_for_view());
+            assert!(!privilege.valid_for_secret());
+            assert!(!privilege.valid_for_function());
+        }
+    }
+
+    #[test]
+    fn secret_and_function_privileges() {
+        assert_eq!(
+            Privilege::KNOWN
+                .into_iter()
+                .filter(Privilege::valid_for_secret)
+                .collect::<Vec<_>>(),
+            vec![Privilege::Ownership, Privilege::Usage, Privilege::Update]
+        );
+        assert_eq!(
+            Privilege::KNOWN
+                .into_iter()
+                .filter(Privilege::valid_for_function)
+                .collect::<Vec<_>>(),
+            vec![Privilege::Ownership, Privilege::Usage]
+        );
+    }
+}
diff --git a/rust/lancedb/src/authz/subject.rs b/rust/lancedb/src/authz/subject.rs
new file mode 100644
index 000000000..2cc607fc4
--- /dev/null
+++ b/rust/lancedb/src/authz/subject.rs
@@ -0,0 +1,378 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+//! Identity selectors. Provider names resolve on the server; IDs remain literal.
+
+use std::{fmt, str::FromStr};
+
+use serde::{Deserialize, Deserializer, Serialize, Serializer, de::Error as _};
+
+use crate::{Error, Result};
+
+/// The kind of identifier supplied to an authorization operation.
+#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
+#[non_exhaustive]
+pub enum SubjectKind {
+    PrincipalId,
+    PrincipalName,
+    GroupId,
+    GroupName,
+    Role,
+    PrincipalApiKey,
+    /// An unrecognized canonical subject. Its full wire string is stored as the value.
+    Unknown,
+}
+
+impl SubjectKind {
+    /// Stable name used by language bindings.
+    pub fn as_str(self) -> &'static str {
+        match self {
+            Self::PrincipalId => "principal_id",
+            Self::PrincipalName => "principal_name",
+            Self::GroupId => "group_id",
+            Self::GroupName => "group_name",
+            Self::Role => "role",
+            Self::PrincipalApiKey => "principal_api_key",
+            Self::Unknown => "unknown",
+        }
+    }
+
+    fn prefix(self) -> &'static str {
+        match self {
+            Self::PrincipalId => "p:",
+            Self::PrincipalName => "P:",
+            Self::GroupId => "g:",
+            Self::GroupName => "G:",
+            Self::Role => "r:",
+            Self::PrincipalApiKey => "a:",
+            Self::Unknown => "",
+        }
+    }
+}
+
+const KINDS: [SubjectKind; 6] = [
+    SubjectKind::PrincipalId,
+    SubjectKind::PrincipalName,
+    SubjectKind::GroupId,
+    SubjectKind::GroupName,
+    SubjectKind::Role,
+    SubjectKind::PrincipalApiKey,
+];
+
+impl FromStr for SubjectKind {
+    type Err = Error;
+
+    fn from_str(value: &str) -> Result<Self> {
+        if value == "unknown" {
+            return Ok(Self::Unknown);
+        }
+        KINDS
+            .into_iter()
+            .find(|kind| kind.as_str() == value)
+            .ok_or_else(|| invalid("Invalid subject kind"))
+    }
+}
+
+/// An immutable validated principal, group, or role selector.
+///
+/// Unrecognized canonical prefixes are preserved as [`SubjectKind::Unknown`],
+/// with the complete wire string in [`Self::value`], so future subject kinds can
+/// be listed and sent back to the server.
+///
+/// ```
+/// use lancedb::authz::{Subject, SubjectKind};
+/// let subject = Subject::from_canonical("x:future-subject")?;
+/// assert_eq!(subject.kind(), SubjectKind::Unknown);
+/// assert_eq!(subject.to_wire(), "x:future-subject");
+/// # Ok::<(), lancedb::Error>(())
+/// ```
+///
+/// Debug and Display redact API keys. [`Self::to_wire`] and serialization
+/// intentionally expose the key for transport and must not be logged.
+///
+/// ```
+/// use lancedb::authz::Subject;
+/// let subject = Subject::group_name("Engineering")?;
+/// assert_eq!(subject.to_wire(), "G:Engineering");
+/// assert_eq!(subject, "G:Engineering".parse::<Subject>()?);
+/// # Ok::<(), lancedb::Error>(())
+/// ```
+#[derive(Clone, PartialEq, Eq, Hash)]
+pub struct Subject {
+    kind: SubjectKind,
+    value: String,
+}
+
+fn invalid(message: &str) -> Error {
+    // Never include the selector: it may contain an API key.
+    Error::InvalidInput {
+        message: message.into(),
+    }
+}
+
+impl Subject {
+    /// Construct a selector without resolving it against an identity provider.
+    pub fn new(kind: SubjectKind, value: impl Into<String>) -> Result<Self> {
+        let value = value.into();
+        if value.is_empty() {
+            return Err(invalid("Subject value must be a nonempty string"));
+        }
+        if kind == SubjectKind::Unknown
+            && (!value
+                .split_once(':')
+                .is_some_and(|(prefix, value)| !prefix.is_empty() && !value.is_empty())
+                || KINDS.iter().any(|kind| value.starts_with(kind.prefix())))
+        {
+            return Err(invalid("Invalid unknown subject selector"));
+        }
+        Ok(Self { kind, value })
+    }
+
+    /// Select a principal by canonical ID, without stripping any prefix in the ID.
+    pub fn principal_id(id: impl Into<String>) -> Result<Self> {
+        Self::new(SubjectKind::PrincipalId, id)
+    }
+    /// Select a principal by an unambiguous provider name.
+    pub fn principal_name(name: impl Into<String>) -> Result<Self> {
+        Self::new(SubjectKind::PrincipalName, name)
+    }
+    /// Select a group by canonical ID.
+    pub fn group_id(id: impl Into<String>) -> Result<Self> {
+        Self::new(SubjectKind::GroupId, id)
+    }
+    /// Select a group by an unambiguous provider name.
+    pub fn group_name(name: impl Into<String>) -> Result<Self> {
+        Self::new(SubjectKind::GroupName, name)
+    }
+    /// Select a LanceDB-managed role.
+    pub fn role(name: impl Into<String>) -> Result<Self> {
+        Self::new(SubjectKind::Role, name)
+    }
+    /// Select the principal holding this secret API key, not the request's caller.
+    pub fn principal_api_key(key: impl Into<String>) -> Result<Self> {
+        Self::new(SubjectKind::PrincipalApiKey, key)
+    }
+
+    /// The selector kind.
+    pub fn kind(&self) -> SubjectKind {
+        self.kind
+    }
+    /// The selected ID, name, role, or API key. API-key values are secrets.
+    /// For unknown kinds, this is the full canonical wire string.
+    pub fn value(&self) -> &str {
+        &self.value
+    }
+    /// The value safe for display; API keys are replaced with `<redacted>`.
+    pub fn display_value(&self) -> &str {
+        if self.kind == SubjectKind::PrincipalApiKey {
+            "<redacted>"
+        } else {
+            &self.value
+        }
+    }
+    /// Encode the literal wire selector. The result can contain a secret API key.
+    pub fn to_wire(&self) -> String {
+        format!("{}{}", self.kind.prefix(), self.value)
+    }
+
+    /// Parse a returned canonical identity, rejecting names and secret selectors.
+    pub fn from_canonical(value: &str) -> Result<Self> {
+        let subject: Self = value.parse()?;
+        if !matches!(
+            subject.kind,
+            SubjectKind::PrincipalId
+                | SubjectKind::GroupId
+                | SubjectKind::Role
+                | SubjectKind::Unknown
+        ) {
+            return Err(invalid("Server returned a non-canonical subject"));
+        }
+        Ok(subject)
+    }
+
+    /// Deserialize a canonical response subject, rejecting secrets and name selectors.
+    pub fn deserialize_canonical<'de, D: Deserializer<'de>>(
+        deserializer: D,
+    ) -> std::result::Result<Self, D::Error> {
+        Self::from_canonical(&String::deserialize(deserializer)?).map_err(D::Error::custom)
+    }
+
+    /// Require a principal selector for a principal lookup.
+    pub fn validate_principal(&self) -> Result<()> {
+        if matches!(
+            self.kind,
+            SubjectKind::PrincipalId | SubjectKind::PrincipalName | SubjectKind::PrincipalApiKey
+        ) {
+            Ok(())
+        } else {
+            Err(invalid("Subject kind is not valid for a principal lookup"))
+        }
+    }
+    /// Require a group selector for a group lookup.
+    pub fn validate_group(&self) -> Result<()> {
+        if matches!(self.kind, SubjectKind::GroupId | SubjectKind::GroupName) {
+            Ok(())
+        } else {
+            Err(invalid("Subject kind is not valid for a group lookup"))
+        }
+    }
+    /// Require a principal/group selector. Roles cannot contain other roles.
+    pub fn validate_role_member(&self) -> Result<()> {
+        if self.kind == SubjectKind::Role {
+            Err(invalid("Roles cannot be members of other roles"))
+        } else {
+            Ok(())
+        }
+    }
+}
+
+impl FromStr for Subject {
+    type Err = Error;
+    fn from_str(value: &str) -> Result<Self> {
+        for kind in KINDS {
+            if let Some(value) = value.strip_prefix(kind.prefix()) {
+                return Self::new(kind, value);
+            }
+        }
+        Self::new(SubjectKind::Unknown, value)
+    }
+}
+
+impl fmt::Debug for Subject {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        f.debug_struct("Subject")
+            .field("kind", &self.kind)
+            .field("value", &self.display_value())
+            .finish()
+    }
+}
+impl fmt::Display for Subject {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        write!(f, "{}{}", self.kind.prefix(), self.display_value())
+    }
+}
+impl Serialize for Subject {
+    fn serialize<S: Serializer>(&self, serializer: S) -> std::result::Result<S::Ok, S::Error> {
+        serializer.serialize_str(&self.to_wire())
+    }
+}
+impl<'de> Deserialize<'de> for Subject {
+    fn deserialize<D: Deserializer<'de>>(deserializer: D) -> std::result::Result<Self, D::Error> {
+        String::deserialize(deserializer)?
+            .parse()
+            .map_err(D::Error::custom)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn selectors_roundtrip_without_normalizing_values() {
+        for kind in KINDS {
+            for value in [
+                "alice@example.com",
+                "p:literal-id",
+                "Engineering / R&D",
+                "名前",
+            ] {
+                let subject = Subject::new(kind, value).unwrap();
+                assert_eq!(subject.to_wire().parse::<Subject>().unwrap(), subject);
+                let json = serde_json::to_string(&subject).unwrap();
+                assert_eq!(serde_json::from_str::<Subject>(&json).unwrap(), subject);
+                assert_eq!(subject.value(), value);
+                assert!(Subject::new(kind, "").is_err());
+            }
+        }
+    }
+
+    #[test]
+    fn contexts_and_canonical_ids() {
+        for kind in KINDS {
+            let subject = Subject::new(kind, "value").unwrap();
+            assert_eq!(
+                subject.validate_principal().is_ok(),
+                matches!(
+                    kind,
+                    SubjectKind::PrincipalId
+                        | SubjectKind::PrincipalName
+                        | SubjectKind::PrincipalApiKey
+                )
+            );
+            assert_eq!(
+                subject.validate_group().is_ok(),
+                matches!(kind, SubjectKind::GroupId | SubjectKind::GroupName)
+            );
+            assert_eq!(
+                subject.validate_role_member().is_ok(),
+                kind != SubjectKind::Role
+            );
+            assert_eq!(
+                Subject::from_canonical(&subject.to_wire()).is_ok(),
+                matches!(
+                    kind,
+                    SubjectKind::PrincipalId | SubjectKind::GroupId | SubjectKind::Role
+                )
+            );
+        }
+    }
+
+    #[test]
+    fn unknown_subjects_preserve_canonical_wire_strings() {
+        for wire in ["x:future-subject", "future:literal:identifier"] {
+            let subject = Subject::from_canonical(wire).unwrap();
+            assert_eq!(subject.kind(), SubjectKind::Unknown);
+            assert_eq!(subject.value(), wire);
+            assert_eq!(subject.to_wire(), wire);
+            assert_eq!(serde_json::to_value(&subject).unwrap(), wire);
+            assert_eq!(
+                serde_json::from_value::<Subject>(serde_json::json!(wire)).unwrap(),
+                subject
+            );
+            assert!(subject.validate_principal().is_err());
+            assert!(subject.validate_group().is_err());
+        }
+        for wire in [
+            "",
+            "no-prefix",
+            ":value",
+            "x:",
+            "p:value",
+            "P:value",
+            "g:value",
+            "G:value",
+            "r:value",
+            "a:secret",
+        ] {
+            assert!(Subject::new(SubjectKind::Unknown, wire).is_err());
+        }
+    }
+
+    #[test]
+    fn acl_page_preserves_known_and_unknown_subjects() {
+        let payload = serde_json::json!({"entries": [
+            {"subject_id": "p:known", "object": "system", "privilege": "USAGE"},
+            {"subject_id": "x:future-subject", "object": "system", "privilege": "USAGE"}
+        ]});
+        let page: crate::authz::ListAccessControlEntriesResponse =
+            serde_json::from_value(payload.clone()).unwrap();
+        assert_eq!(page.entries.len(), 2);
+        assert_eq!(page.entries[0].subject_id.kind(), SubjectKind::PrincipalId);
+        assert_eq!(page.entries[1].subject_id.kind(), SubjectKind::Unknown);
+        assert_eq!(serde_json::to_value(page).unwrap(), payload);
+    }
+
+    #[test]
+    fn secrets_are_only_exposed_by_explicit_serialization() {
+        let secret = "never-log-this-key";
+        let subject = Subject::principal_api_key(secret).unwrap();
+        assert!(!format!("{subject:?} {subject}").contains(secret));
+        assert!(serde_json::to_string(&subject).unwrap().contains(secret));
+        let error = Subject::from_canonical(&subject.to_wire()).unwrap_err();
+        assert!(!error.to_string().contains(secret));
+        assert!(!format!("{error:?}").contains(secret));
+        let error = secret.parse::<Subject>().unwrap_err();
+        assert!(!error.to_string().contains(secret));
+    }
+}
diff --git a/rust/lancedb/src/blob.rs b/rust/lancedb/src/blob.rs
index cbc0724c6..ca68164e4 100644
--- a/rust/lancedb/src/blob.rs
+++ b/rust/lancedb/src/blob.rs
@@ -247,8 +247,10 @@ impl BlobRangeRequest {
 /// `Struct<data, uri>` with the `lance.blob.v2` marker. Same layout Lance
 /// expects on write.
 ///
-/// A blob column may be top-level or nested inside a struct or list. Nested
-/// blobs are addressed by a dotted path (e.g. `info.blob`) in the read APIs.
+/// A blob column may be top-level or nested inside a struct or list. Blobs
+/// nested inside structs use dotted paths (e.g. `info.blob`) in the read APIs.
+/// Blobs inside lists can be written and queried as descriptors, but cannot
+/// currently be fetched by row id.
 ///
 /// ```
 /// use arrow_schema::{DataType, Field, Schema};
@@ -262,6 +264,37 @@ pub fn blob(name: impl AsRef<str>, nullable: bool) -> Field {
     lance::blob::blob_field(name.as_ref(), nullable)
 }
 
+/// Options for creating a blob v2 column field with tier thresholds.
+///
+/// Re-exported from lance so the thresholds are built exactly the way
+/// [`lance::blob::blob_field_with_options`] builds them. Note that lance does
+/// not currently expose a pack file size threshold option, so only the inline
+/// and dedicated size thresholds are supported here.
+pub use lance::blob::BlobFieldOptions as BlobOptions;
+
+/// Creates an Arrow field for a Lance blob v2 column with tier thresholds.
+///
+/// Same as [`blob`], but forwards `options` to
+/// [`lance::blob::blob_field_with_options`], storing the inline and dedicated
+/// size thresholds as field metadata. With [`BlobOptions::default()`] this is
+/// identical to [`blob`].
+///
+/// ```
+/// use std::num::NonZeroUsize;
+///
+/// use lancedb::blob::BlobOptions;
+///
+/// let options = BlobOptions {
+///     inline_size_threshold: Some(8 * 1024),
+///     dedicated_size_threshold: NonZeroUsize::new(1 << 20),
+/// };
+/// let field = lancedb::blob::blob_with_options("payload", true, options);
+/// assert!(lancedb::blob::is_blob(&field));
+/// ```
+pub fn blob_with_options(name: impl AsRef<str>, nullable: bool, options: BlobOptions) -> Field {
+    lance::blob::blob_field_with_options(name.as_ref(), nullable, options)
+}
+
 /// Returns true if `field` is a blob v2 column.
 ///
 /// ```
@@ -286,7 +319,7 @@ fn field_tree_has_blob_v2(field: &Field) -> bool {
     }
 }
 
-/// Collects the dotted paths of blob v2 columns under `field`, into `paths`.
+/// Collects the dotted paths of row-addressable blob v2 columns under `field`.
 fn collect_blob_paths(field: &Field, prefix: &str, paths: &mut Vec<String>) {
     let path = if prefix.is_empty() {
         field.name().clone()
@@ -297,26 +330,58 @@ fn collect_blob_paths(field: &Field, prefix: &str, paths: &mut Vec<String>) {
         paths.push(path);
         return;
     }
-    match field.data_type() {
-        DataType::Struct(children) => {
-            for child in children {
-                collect_blob_paths(child, &path, paths);
-            }
+    if let DataType::Struct(children) = field.data_type() {
+        for child in children {
+            collect_blob_paths(child, &path, paths);
         }
-        DataType::List(child) | DataType::LargeList(child) | DataType::FixedSizeList(child, _) => {
-            collect_blob_paths(child, &path, paths)
-        }
-        _ => {}
     }
 }
 
+fn is_list_blob_path(field: &Field, prefix: &str, column: &str, inside_list: bool) -> bool {
+    let path = if prefix.is_empty() {
+        field.name().clone()
+    } else {
+        format!("{prefix}.{}", field.name())
+    };
+    if field.is_blob_v2() {
+        return inside_list && column == path;
+    }
+    match field.data_type() {
+        DataType::Struct(children) => children
+            .iter()
+            .any(|child| is_list_blob_path(child, &path, column, inside_list)),
+        DataType::List(child) | DataType::LargeList(child) | DataType::FixedSizeList(child, _) => {
+            (column == path && field_tree_has_blob_v2(child))
+                || is_list_blob_path(child, &path, column, true)
+        }
+        _ => false,
+    }
+}
+
+/// Give list blob paths a useful error before they reach the row-based reader.
+pub(crate) fn reject_list_blob_column(schema: &Schema, column: &str) -> Result<()> {
+    if schema
+        .fields()
+        .iter()
+        .any(|field| is_list_blob_path(field, "", column, false))
+    {
+        return Err(Error::InvalidInput {
+            message: format!(
+                "column '{column}' contains a blob inside a list; blobs inside lists cannot be fetched by row id yet"
+            ),
+        });
+    }
+    Ok(())
+}
+
 /// Returns true if `schema` declares any blob v2 column, including nested ones.
 pub(crate) fn has_blob_columns(schema: &Schema) -> bool {
     schema.fields().iter().any(|f| field_tree_has_blob_v2(f))
 }
 
-/// Blob v2 column paths in `schema`, declaration order preserved. Nested blobs
-/// are dotted paths (e.g. `info.blob`).
+/// Row-addressable blob v2 column paths in `schema`, in declaration order.
+/// Struct-nested blobs use dotted paths (e.g. `info.blob`); list-nested blobs
+/// are omitted because a row id cannot identify an individual list element.
 pub(crate) fn blob_column_names(schema: &Schema) -> Vec<String> {
     let mut paths = Vec::new();
     for field in schema.fields() {
@@ -351,6 +416,11 @@ pub(crate) fn ensure_blob_v2_column(
     schema: &lance_core::datatypes::Schema,
     column: &str,
 ) -> Result<()> {
+    // A top-level blob cannot have a list ancestor. Keep the common read path
+    // from converting the entire schema just to validate that condition.
+    if column.contains('.') || schema.field(column).is_none_or(|field| !field.is_blob_v2()) {
+        reject_list_blob_column(&Schema::from(schema), column)?;
+    }
     match schema.field(column) {
         Some(field) if field.is_blob_v2() => Ok(()),
         Some(field) if field.is_blob() => Err(Error::InvalidInput {
@@ -368,45 +438,115 @@ pub(crate) fn ensure_blob_v2_column(
     }
 }
 
-fn ensure_all_row_ids_resolved(column: &str, requested: usize, resolved: usize) -> Result<()> {
-    if requested == resolved {
+fn missing_blob_row_ids_error(column: &str, requested: usize, missing: &[u64]) -> Error {
+    let detail = if missing.is_empty() {
+        String::new()
+    } else {
+        format!("; first missing row ids: {missing:?}")
+    };
+    Error::InvalidInput {
+        message: format!(
+            "blob read for column '{column}' requested {requested} row ids but some \
+             do not exist in the table{detail}; pass row ids collected from this table"
+        ),
+    }
+}
+
+/// Diagnose only after a failed read. Stable row IDs use Lance's deletion-aware
+/// index; physical row IDs are fragment/offset pairs. Limit the error to three
+/// distinct IDs so a large failed batch stays readable.
+async fn find_missing_blob_row_ids(dataset: &Dataset, row_ids: &[u64]) -> Result<Vec<u64>> {
+    let mut missing = Vec::new();
+    if let Some(index) = lance::dataset::rowids::get_row_id_index(dataset).await? {
+        for (row_id, address) in row_ids.iter().zip(index.get_many(row_ids)?) {
+            if address.is_none() && !missing.contains(row_id) {
+                missing.push(*row_id);
+                if missing.len() == 3 {
+                    break;
+                }
+            }
+        }
+    } else {
+        let mut fragments = std::collections::HashMap::new();
+        for &row_id in row_ids {
+            let fragment_id = row_id >> 32;
+            let offset = row_id as u32;
+            if let std::collections::hash_map::Entry::Vacant(entry) = fragments.entry(fragment_id) {
+                let fragment = dataset.get_fragment(fragment_id as usize);
+                let state = if let Some(fragment) = fragment {
+                    Some((
+                        fragment.physical_rows().await?,
+                        fragment.get_deletion_vector().await?,
+                    ))
+                } else {
+                    None
+                };
+                entry.insert(state);
+            }
+            let is_missing = match fragments.get(&fragment_id) {
+                Some(Some((physical_rows, deletions))) => {
+                    offset as usize >= *physical_rows
+                        || deletions
+                            .as_ref()
+                            .is_some_and(|deletions| deletions.contains(offset))
+                }
+                Some(None) => true,
+                None => unreachable!(),
+            };
+            if is_missing && !missing.contains(&row_id) {
+                missing.push(row_id);
+                if missing.len() == 3 {
+                    break;
+                }
+            }
+        }
+    }
+    Ok(missing)
+}
+
+async fn ensure_all_row_ids_resolved(
+    dataset: &Dataset,
+    column: &str,
+    row_ids: &[u64],
+    resolved: usize,
+) -> Result<()> {
+    if row_ids.len() == resolved {
         return Ok(());
     }
-    if resolved < requested {
-        Err(Error::InvalidInput {
-            message: format!(
-                "blob read for column '{column}' requested {requested} row ids but only {resolved} \
-                 exist in the table; pass row ids collected from this table"
-            ),
-        })
+    if resolved < row_ids.len() {
+        let missing = find_missing_blob_row_ids(dataset, row_ids)
+            .await
+            .unwrap_or_default();
+        Err(missing_blob_row_ids_error(column, row_ids.len(), &missing))
     } else {
         Err(Error::Runtime {
             message: format!(
-                "blob read for column '{column}' returned {resolved} results for {requested} row ids"
+                "blob read for column '{column}' returned {resolved} results for {} row ids",
+                row_ids.len()
             ),
         })
     }
 }
 
-/// Lance take reports a missing physical row address as NotSupported or InvalidInput.
-fn map_blob_take_error(column: &str, requested: usize, err: lance::Error) -> Error {
-    let missing_row_addr = match &err {
-        lance::Error::NotSupported { source, .. } => {
-            source.to_string().contains("must not target deleted rows")
-        }
-        lance::Error::InvalidInput { source, .. } => source
-            .to_string()
-            .contains("belongs to non-existent fragment"),
-        _ => false,
-    };
+/// Lance reports missing physical and stable row ids through several error paths
+/// without a shared typed error.
+async fn map_blob_take_error(
+    dataset: &Dataset,
+    column: &str,
+    row_ids: &[u64],
+    err: lance::Error,
+) -> Error {
+    let message = err.to_string();
+    let missing_row_id = message.contains("must not target deleted rows")
+        || message.contains("belongs to non-existent fragment")
+        || (message.contains("Invalid read params ") && message.contains("addressable rows"))
+        || message.contains("Could not resolve all requested row IDs");
 
-    if missing_row_addr {
-        Error::InvalidInput {
-            message: format!(
-                "blob read for column '{column}' requested {requested} row ids but some \
-                 do not exist in the table; pass row ids collected from this table"
-            ),
-        }
+    if missing_row_id {
+        let missing = find_missing_blob_row_ids(dataset, row_ids)
+            .await
+            .unwrap_or_default();
+        missing_blob_row_ids_error(column, row_ids.len(), &missing)
     } else {
         err.into()
     }
@@ -423,18 +563,25 @@ pub(crate) async fn take_blob_ranges_aligned(
         return Ok(LargeBinaryBuilder::new().finish());
     }
 
+    let row_ids = requests
+        .iter()
+        .map(|request| request.row_id)
+        .collect::<Vec<_>>();
     let lance_requests = requests
         .iter()
         .map(|request| LanceBlobRangeRequest::new(request.row_id, request.offset, request.length))
         .collect::<Vec<_>>();
-    let payloads = dataset
+    let payloads = match dataset
         .read_blob_ranges(column)?
         .with_row_ids(lance_requests)
         .preserve_order(true)
         .execute()
         .await
-        .map_err(|err| map_blob_take_error(column, requests.len(), err))?;
-    ensure_all_row_ids_resolved(column, requests.len(), payloads.len())?;
+    {
+        Ok(payloads) => payloads,
+        Err(err) => return Err(map_blob_take_error(dataset, column, &row_ids, err).await),
+    };
+    ensure_all_row_ids_resolved(dataset, column, &row_ids, payloads.len()).await?;
 
     let mut builder = LargeBinaryBuilder::new();
     for payload in payloads {
@@ -457,14 +604,17 @@ pub(crate) async fn take_blobs_aligned(
         return Ok(LargeBinaryBuilder::new().finish());
     }
 
-    let payloads = dataset
+    let payloads = match dataset
         .read_blobs(column)?
         .with_row_ids(row_ids.to_vec())
         .preserve_order(true)
         .execute()
         .await
-        .map_err(|err| map_blob_take_error(column, row_ids.len(), err))?;
-    ensure_all_row_ids_resolved(column, row_ids.len(), payloads.len())?;
+    {
+        Ok(payloads) => payloads,
+        Err(err) => return Err(map_blob_take_error(dataset, column, row_ids, err).await),
+    };
+    ensure_all_row_ids_resolved(dataset, column, row_ids, payloads.len()).await?;
 
     let mut builder = LargeBinaryBuilder::new();
     for payload in payloads {
@@ -487,11 +637,11 @@ pub(crate) async fn take_blob_files_aligned(
         return Ok(Vec::new());
     }
 
-    let handles = dataset
-        .take_blobs(row_ids, column)
-        .await
-        .map_err(|err| map_blob_take_error(column, row_ids.len(), err))?;
-    ensure_all_row_ids_resolved(column, row_ids.len(), handles.len())?;
+    let handles = match dataset.take_blobs(row_ids, column).await {
+        Ok(handles) => handles,
+        Err(err) => return Err(map_blob_take_error(dataset, column, row_ids, err).await),
+    };
+    ensure_all_row_ids_resolved(dataset, column, row_ids, handles.len()).await?;
     Ok(handles
         .into_iter()
         .map(|handle| handle.map(Into::into))
@@ -501,8 +651,13 @@ pub(crate) async fn take_blob_files_aligned(
 #[cfg(test)]
 mod tests {
     use super::*;
+    use std::num::NonZeroUsize;
+
     use arrow_schema::DataType;
-    use lance_arrow::ARROW_EXT_NAME_KEY;
+    use lance_arrow::{
+        ARROW_EXT_NAME_KEY, BLOB_DEDICATED_SIZE_THRESHOLD_META_KEY,
+        BLOB_INLINE_SIZE_THRESHOLD_META_KEY,
+    };
 
     fn blob_schema() -> Schema {
         Schema::new(vec![
@@ -521,6 +676,38 @@ mod tests {
         assert!(matches!(field.data_type(), DataType::Struct(_)));
     }
 
+    #[test]
+    fn blob_with_options_sets_threshold_metadata() {
+        let options = BlobOptions {
+            inline_size_threshold: Some(8 * 1024),
+            dedicated_size_threshold: NonZeroUsize::new(1 << 20),
+        };
+        let field = blob_with_options("payload", true, options);
+        assert!(is_blob(&field));
+        assert_eq!(
+            field
+                .metadata()
+                .get(BLOB_INLINE_SIZE_THRESHOLD_META_KEY)
+                .map(String::as_str),
+            Some("8192")
+        );
+        assert_eq!(
+            field
+                .metadata()
+                .get(BLOB_DEDICATED_SIZE_THRESHOLD_META_KEY)
+                .map(String::as_str),
+            Some("1048576")
+        );
+    }
+
+    #[test]
+    fn blob_with_default_options_matches_plain_blob() {
+        let field = blob_with_options("payload", true, BlobOptions::default());
+        let plain = blob("payload", true);
+        assert_eq!(field.metadata(), plain.metadata());
+        assert_eq!(field.data_type(), plain.data_type());
+    }
+
     #[test]
     fn has_blob_columns_detects_blob_fields() {
         assert!(has_blob_columns(&blob_schema()));
@@ -619,6 +806,54 @@ mod tests {
         assert_eq!(blob_column_names(&schema), vec!["info.blob"]);
     }
 
+    #[test]
+    fn list_blob_paths_are_not_row_addressable() {
+        let schema = Schema::new(vec![
+            blob("image", true),
+            Field::new(
+                "info",
+                DataType::Struct(vec![blob("thumbnail", true)].into()),
+                true,
+            ),
+            Field::new("images", DataType::List(Arc::new(blob("item", true))), true),
+            Field::new(
+                "groups",
+                DataType::LargeList(Arc::new(Field::new(
+                    "group",
+                    DataType::Struct(vec![blob("photo", true)].into()),
+                    true,
+                ))),
+                true,
+            ),
+            Field::new(
+                "frames",
+                DataType::FixedSizeList(Arc::new(blob("frame", true)), 2),
+                true,
+            ),
+        ]);
+        assert!(has_blob_columns(&schema));
+        assert_eq!(blob_column_names(&schema), vec!["image", "info.thumbnail"]);
+
+        for column in [
+            "images",
+            "images.item",
+            "groups",
+            "groups.group.photo",
+            "frames",
+            "frames.frame",
+        ] {
+            let err = reject_list_blob_column(&schema, column).unwrap_err();
+            assert!(matches!(err, Error::InvalidInput { .. }));
+            assert!(
+                err.to_string()
+                    .contains("blobs inside lists cannot be fetched")
+            );
+        }
+        for column in ["image", "info.thumbnail", "info"] {
+            reject_list_blob_column(&schema, column).unwrap();
+        }
+    }
+
     #[test]
     fn storage_version_noop_without_blob_columns() {
         let schema = Schema::new(vec![Field::new("id", DataType::Int64, false)]);
diff --git a/rust/lancedb/src/catalog.rs b/rust/lancedb/src/catalog.rs
index 5eaa1d49e..ad8674b77 100644
--- a/rust/lancedb/src/catalog.rs
+++ b/rust/lancedb/src/catalog.rs
@@ -15,7 +15,12 @@
 //! ```
 
 use std::fmt;
+use std::pin::Pin;
 use std::sync::Arc;
+use std::task::{Context, Poll};
+
+use futures::future::BoxFuture;
+use futures::{FutureExt, Stream};
 
 use crate::Result;
 use crate::connection::Connection;
@@ -118,9 +123,83 @@ pub struct ListDatabasesResponse {
     pub page_token: Option<String>,
 }
 
+/// A lazy stream of database names with inspectable pagination state.
+///
+/// The continuation token advances when a page is fetched, so resuming from it
+/// skips any names still cached in this stream. Drain the cache before saving a
+/// token if those names must be included. Errors terminate the stream; the token
+/// for the failed request remains available for resuming with a new stream.
+pub struct DatabaseNames {
+    catalog: Arc<dyn Catalog>,
+    names: std::vec::IntoIter<String>,
+    page_token: Option<String>,
+    page_limit: Option<u32>,
+    done: bool,
+    pending: Option<BoxFuture<'static, Result<ListDatabasesResponse>>>,
+}
+
+impl DatabaseNames {
+    /// Number of names available without another REST request.
+    pub fn num_page_results(&self) -> usize {
+        self.names.len()
+    }
+
+    /// Token for the next REST request, or `None` after the last page is fetched.
+    ///
+    /// Before the first request, returns the supplied starting token (`None`
+    /// means start from the beginning). A final page can still have cached names.
+    pub fn page_token(&self) -> Option<&str> {
+        self.page_token.as_deref()
+    }
+}
+
+impl Stream for DatabaseNames {
+    type Item = Result<String>;
+
+    fn poll_next(self: Pin<&mut Self>, cx: &mut Context<'_>) -> Poll<Option<Self::Item>> {
+        let this = self.get_mut();
+        loop {
+            if let Some(name) = this.names.next() {
+                return Poll::Ready(Some(Ok(name)));
+            }
+            if this.done {
+                return Poll::Ready(None);
+            }
+            let pending = this.pending.get_or_insert_with(|| {
+                let catalog = this.catalog.clone();
+                let request = ListDatabasesRequest {
+                    page_token: this.page_token.clone(),
+                    limit: this.page_limit,
+                };
+                async move { catalog.list_databases(request).await }.boxed()
+            });
+            let response = futures::ready!(pending.as_mut().poll(cx));
+            this.pending = None;
+            match response {
+                Ok(response) => {
+                    this.page_token = response.page_token.filter(|token| !token.is_empty());
+                    this.done = this.page_token.is_none();
+                    this.names = response.databases.into_iter();
+                }
+                Err(error) => {
+                    this.done = true;
+                    return Poll::Ready(Some(Err(error)));
+                }
+            }
+        }
+    }
+}
+
 /// A backend that manages databases. Implementations own database lifecycle semantics.
 #[async_trait::async_trait]
 pub trait Catalog: Send + Sync + std::fmt::Debug + 'static {
+    /// Obtain deployment-wide authorization, if supported by this backend.
+    fn authz(&self) -> Result<Arc<dyn crate::authz::Authorization>> {
+        Err(crate::Error::NotSupported {
+            message: "Authorization administration is not supported by this catalog".into(),
+        })
+    }
+
     /// Catalog endpoint or location.
     fn uri(&self) -> &str;
     /// Create a database, or open it when `exist_ok` permits.
@@ -167,6 +246,11 @@ impl CatalogConnection {
     pub fn uri(&self) -> &str {
         self.catalog.uri()
     }
+    /// Obtain a handle sharing this catalog's deployment scope and credentials.
+    pub fn authz(&self) -> Result<Arc<dyn crate::authz::Authorization>> {
+        self.catalog.authz()
+    }
+
     /// Access the underlying backend.
     pub fn catalog(&self) -> &Arc<dyn Catalog> {
         &self.catalog
@@ -188,12 +272,35 @@ impl CatalogConnection {
         self.catalog.drop_database(request.into()).await
     }
 
-    /// List a page of databases. Pass [`ListDatabasesRequest::default`] for the first page.
-    pub async fn list_databases(
+    /// Iterate lazily over all database names, fetching more results as needed.
+    ///
+    /// `page_token` starts at a saved continuation token; `None` starts at the
+    /// beginning. `page_limit` limits each REST response (not the whole stream);
+    /// `None` uses the server default. Errors terminate the listing.
+    ///
+    /// ```
+    /// # async fn example(catalog: &lancedb::catalog::CatalogConnection) -> lancedb::Result<()> {
+    /// use futures::TryStreamExt;
+    /// let mut names = catalog.list_databases(None, None);
+    /// while let Some(name) = names.try_next().await? {
+    ///     println!("{name}");
+    /// }
+    /// # Ok(())
+    /// # }
+    /// ```
+    pub fn list_databases(
         &self,
-        request: ListDatabasesRequest,
-    ) -> Result<ListDatabasesResponse> {
-        self.catalog.list_databases(request).await
+        page_token: Option<String>,
+        page_limit: Option<u32>,
+    ) -> DatabaseNames {
+        DatabaseNames {
+            catalog: self.catalog.clone(),
+            names: Vec::new().into_iter(),
+            page_token,
+            page_limit,
+            done: false,
+            pending: None,
+        }
     }
 
     /// Connect to a database without creating it if it is missing.
diff --git a/rust/lancedb/src/connection.rs b/rust/lancedb/src/connection.rs
index 0e5bbf5da..8a0e489c2 100644
--- a/rust/lancedb/src/connection.rs
+++ b/rust/lancedb/src/connection.rs
@@ -23,11 +23,12 @@ use crate::connection::create_table::CreateTableBuilder;
 use crate::data::scannable::Scannable;
 use crate::database::listing::ListingDatabase;
 use crate::database::{
-    CloneTableRequest, Database, DatabaseOptions, JobInfo, OpenTableRequest, PauseJobStatus,
-    ReadConsistency, ResumeJobStatus, TableNamesRequest,
+    CloneTableRequest, Database, DatabaseOptions, ExecuteQueryRequest, JobInfo, OpenTableRequest,
+    PauseJobStatus, ReadConsistency, ResumeJobStatus, TableNamesRequest,
 };
 use crate::embeddings::{EmbeddingRegistry, MemoryRegistry};
 use crate::error::{Error, Result};
+use crate::listing::{Listing, ListingOptions};
 #[cfg(feature = "remote")]
 use crate::remote::{
     client::ClientConfig,
@@ -37,10 +38,7 @@ use crate::remote::{
     },
 };
 use crate::secrets::SecretInfo;
-use crate::utils::{
-    validate_namespace, validate_secret_component, validate_secret_reference,
-    validate_view_reference,
-};
+use crate::utils::{validate_namespace, validate_secret_reference, validate_view_reference};
 use crate::view::ViewDescription;
 use lance::io::ObjectStoreParams;
 pub use lance_file::version::LanceFileVersion;
@@ -105,7 +103,9 @@ impl TableNamesBuilder {
         self
     }
 
-    /// The maximum number of table names to return
+    /// The maximum number of table names to return.
+    ///
+    /// Without a limit, all names are returned. Zero returns an empty list.
     pub fn limit(mut self, limit: u32) -> Self {
         self.request.limit = Some(limit);
         self
@@ -334,16 +334,14 @@ pub struct CloneTableBuilder {
 /// Builder for asynchronously executing a SQL statement on a remote database.
 pub struct ExecuteQueryAsyncBuilder {
     parent: Arc<dyn Database>,
-    query: String,
-    default_namespace_path: Vec<String>,
+    request: ExecuteQueryRequest,
 }
 
 impl ExecuteQueryAsyncBuilder {
     fn new(parent: Arc<dyn Database>, query: String) -> Self {
         Self {
             parent,
-            query,
-            default_namespace_path: vec!["public".to_string()],
+            request: ExecuteQueryRequest::new(query),
         }
     }
 
@@ -356,15 +354,63 @@ impl ExecuteQueryAsyncBuilder {
         I: IntoIterator<Item = S>,
         S: Into<String>,
     {
-        self.default_namespace_path = path.into_iter().map(Into::into).collect();
+        self.request.default_namespace_path = path.into_iter().map(Into::into).collect();
+        self
+    }
+
+    /// Bind values to the statement's placeholders.
+    ///
+    /// `parameters` is a single-row batch. Column `i` binds `$<i + 1>`, and a
+    /// column whose name is not a number also binds `$<name>`, so
+    /// `WHERE id = $1` and `WHERE id = $id` both work. The values travel as
+    /// Arrow rather than as SQL text, so a float keeps its exact bits and type
+    /// and a vector stays a compact `FixedSizeList`.
+    ///
+    /// Every placeholder needs a value and every parameter must be used by a
+    /// placeholder. Parameters are supported in queries (`SELECT`, and
+    /// `EXPLAIN` of one), not in DDL or DML, nor in table-function arguments.
+    /// Do not follow a named placeholder directly with `$`: `$a$` starts a
+    /// dollar-quoted string.
+    ///
+    /// A parameterized statement runs on the call that returns its rows rather
+    /// than detached from it: cancelling the query, or dropping its handle
+    /// before reading it, stops the statement on the server. Its status follows
+    /// that stream too -- it reports `Running` until the reader has received
+    /// every row -- so open the reader directly rather than waiting for
+    /// `Finished`.
+    ///
+    /// # Example
+    ///
+    /// ```no_run
+    /// # use std::sync::Arc;
+    /// # use arrow_array::{ArrayRef, FixedSizeListArray, Int64Array, RecordBatch};
+    /// # use arrow_array::types::Float32Type;
+    /// # async fn query(db: &lancedb::Connection) -> lancedb::Result<()> {
+    /// let vector = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+    ///     [Some([0.1_f32, 0.2, 0.3].map(Some))],
+    ///     3,
+    /// );
+    /// let parameters = RecordBatch::try_from_iter([
+    ///     ("vector", Arc::new(vector) as ArrayRef),
+    ///     ("k", Arc::new(Int64Array::from(vec![10])) as ArrayRef),
+    /// ])?;
+    /// let query = db
+    ///     .execute_query_async("SELECT id FROM docs ORDER BY distance(vector, $vector) LIMIT $k")
+    ///     .parameters(parameters)
+    ///     .execute()
+    ///     .await?;
+    /// let rows = query.reader().await?;
+    /// # Ok(())
+    /// # }
+    /// ```
+    pub fn parameters(mut self, parameters: RecordBatch) -> Self {
+        self.request.parameters = Some(parameters);
         self
     }
 
     /// Start the statement and return its asynchronous query handle.
     pub async fn execute(self) -> Result<crate::sql::Query> {
-        self.parent
-            .execute_query_async(&self.query, &self.default_namespace_path)
-            .await
+        self.parent.execute_query_async(self.request).await
     }
 }
 
@@ -455,8 +501,9 @@ impl Connection {
     ///
     /// The query can reference tables in other databases with SQL dot notation.
     /// Use [`ExecuteQueryAsyncBuilder::default_namespace_path`] to avoid qualifying
-    /// tables in the default namespace. Local connections return
-    /// [`Error::NotSupported`].
+    /// tables in the default namespace, and [`ExecuteQueryAsyncBuilder::parameters`]
+    /// to bind Arrow values to `$1` / `$name` placeholders instead of writing them
+    /// into the SQL text. Local connections return [`Error::NotSupported`].
     ///
     /// # Example
     ///
@@ -505,7 +552,8 @@ impl Connection {
     /// under creation, may contain only uncommitted storage, or may be concurrently
     /// dropped before it is opened.
     ///
-    /// The parameters `page_token` and `limit` can be used to paginate the results
+    /// Without a limit, all names are returned. The parameters `start_after` and
+    /// `limit` can be used to paginate the results.
     pub fn table_names(&self) -> TableNamesBuilder {
         TableNamesBuilder::new(self.internal.clone())
     }
@@ -568,6 +616,7 @@ impl Connection {
     /// Creates a new table by cloning from an existing source table.
     /// By default, this performs a shallow clone where the new table shares
     /// the underlying data files with the source table.
+    /// The target table name must be unused. An existing table is left unchanged.
     ///
     /// # Parameters
     /// - `target_table_name`: The name of the new table to create
@@ -587,7 +636,8 @@ impl Connection {
         )
     }
 
-    /// Build and register a Python callable as an immutable Function version.
+    /// Build and register a Python callable as an immutable Function version
+    /// in `namespace_path`, which is empty for the root namespace.
     ///
     /// The server-side job builds the OCI image, then registers the completed
     /// artifact. Waiting on the returned typed job yields the durable
@@ -597,8 +647,11 @@ impl Connection {
     pub async fn create_function_async(
         &self,
         request: crate::function::FunctionRegistrationRequest,
+        namespace_path: &[String],
     ) -> Result<crate::job::Job<crate::function::FunctionVersion>> {
-        self.internal.create_function_async(request).await
+        self.internal
+            .create_function_async(request, namespace_path)
+            .await
     }
 
     /// Look up one exact immutable Function version in the remote catalog.
@@ -610,16 +663,17 @@ impl Connection {
         &self,
         name: impl AsRef<str>,
         version: impl AsRef<str>,
+        namespace_path: &[String],
     ) -> Result<crate::function::FunctionVersion> {
         self.internal
-            .get_function(name.as_ref(), version.as_ref())
+            .get_function(name.as_ref(), version.as_ref(), namespace_path)
             .await
     }
 
-    /// List every published immutable Function version in the remote catalog.
+    /// List every published immutable Function version in `namespace_path` of
+    /// the remote catalog. Functions in child namespaces are not included.
     ///
-    /// Results are ordered by Function name then version. The client walks all
-    /// server pages before returning. Local databases return
+    /// Results are ordered by Function name then version. Local databases return
     /// [`Error::NotSupported`].
     ///
     /// # Example
@@ -628,14 +682,30 @@ impl Connection {
     /// # async fn list_functions(
     /// #     connection: &lancedb::Connection,
     /// # ) -> Result<(), Box<dyn std::error::Error>> {
-    /// for function in connection.list_functions().await? {
+    /// use futures::TryStreamExt;
+    /// let mut functions = connection.list_functions(&[], Default::default());
+    /// while let Some(function) = functions.try_next().await? {
     ///     println!("{} {}", function.name(), function.version());
     /// }
     /// # Ok(())
     /// # }
     /// ```
-    pub async fn list_functions(&self) -> Result<Vec<crate::function::FunctionVersion>> {
-        self.internal.list_functions().await
+    ///
+    /// Results are fetched lazily, one page at a time. See [`Listing`]
+    /// for cached-result and continuation-token semantics. Errors terminate iteration.
+    ///
+    pub fn list_functions(
+        &self,
+        namespace_path: &[String],
+        options: ListingOptions,
+    ) -> Listing<crate::function::FunctionVersion> {
+        let database = self.database().clone();
+        let namespace_path = namespace_path.to_vec();
+        Listing::new(options, move |options| {
+            let database = database.clone();
+            let namespace_path = namespace_path.clone();
+            async move { database.list_functions(&namespace_path, options).await }
+        })
     }
 
     /// Remove the current Function name binding, retaining the object history.
@@ -647,9 +717,10 @@ impl Connection {
         &self,
         name: impl AsRef<str>,
         version: impl AsRef<str>,
+        namespace_path: &[String],
     ) -> Result<bool> {
         self.internal
-            .drop_function(name.as_ref(), version.as_ref())
+            .drop_function(name.as_ref(), version.as_ref(), namespace_path)
             .await
     }
 
@@ -663,9 +734,10 @@ impl Connection {
         &self,
         name: impl AsRef<str>,
         version: impl AsRef<str>,
+        namespace_path: &[String],
     ) -> Result<(bool, crate::job::Job)> {
         self.internal
-            .drop_function_async(name.as_ref(), version.as_ref())
+            .drop_function_async(name.as_ref(), version.as_ref(), namespace_path)
             .await
     }
 
@@ -711,11 +783,35 @@ impl Connection {
     /// Names only. No path in this API returns a stored credential, by
     /// construction rather than by policy. Local databases return
     /// [`Error::NotSupported`].
-    pub async fn list_secrets(&self, namespace_path: &[String]) -> Result<Vec<String>> {
-        for segment in namespace_path {
-            validate_secret_component("Secret namespace path segment", segment)?;
-        }
-        self.internal.list_secrets(namespace_path).await
+    ///
+    /// Results are fetched lazily, one page at a time. See [`Listing`]
+    /// for cached-result and continuation-token semantics. Errors terminate iteration.
+    ///
+    /// ```
+    /// # async fn example(connection: &lancedb::Connection) -> lancedb::Result<()> {
+    /// use futures::TryStreamExt;
+    /// let mut items = connection.list_secrets(&[], Default::default());
+    /// while let Some(item) = items.try_next().await? {
+    ///     println!("{item:?}");
+    /// }
+    /// # Ok(())
+    /// # }
+    /// ```
+    pub fn list_secrets(
+        &self,
+        namespace_path: &[String],
+        options: ListingOptions,
+    ) -> Listing<String> {
+        let database = self.database().clone();
+        let namespace_path = namespace_path.to_vec();
+        Listing::new(options, move |options| {
+            let database = database.clone();
+            let namespace_path = namespace_path.clone();
+            async move {
+                validate_namespace(&namespace_path)?;
+                database.list_secrets(&namespace_path, options).await
+            }
+        })
     }
 
     /// Drop a Secret.
@@ -783,7 +879,8 @@ impl Connection {
     /// let described = connection.describe_view("recent_orders", &namespace).await?;
     /// println!("{} in {:?}", described.query, described.default_namespace_path);
     ///
-    /// let names = connection.list_views(&namespace).await?;
+    /// use futures::TryStreamExt;
+    /// let names: Vec<_> = connection.list_views(&namespace, Default::default()).try_collect().await?;
     /// assert!(names.iter().any(|name| name == "recent_orders"));
     ///
     /// // Dropping the view leaves `orders` untouched.
@@ -859,11 +956,36 @@ impl Connection {
     /// The names of the views in one namespace.
     ///
     /// Names only; a definition is query metadata and comes from
-    /// [`Self::describe_view`]. The client walks all server pages before
-    /// returning. Local databases return [`Error::NotSupported`].
-    pub async fn list_views(&self, namespace_path: &[String]) -> Result<Vec<String>> {
-        validate_namespace(namespace_path)?;
-        self.internal.list_views(namespace_path).await
+    /// [`Self::describe_view`]. Local databases return [`Error::NotSupported`].
+    ///
+    /// Results are fetched lazily, one page at a time. See [`Listing`]
+    /// for cached-result and continuation-token semantics. Errors terminate iteration.
+    ///
+    /// ```
+    /// # async fn example(connection: &lancedb::Connection) -> lancedb::Result<()> {
+    /// use futures::TryStreamExt;
+    /// let mut items = connection.list_views(&[], Default::default());
+    /// while let Some(item) = items.try_next().await? {
+    ///     println!("{item:?}");
+    /// }
+    /// # Ok(())
+    /// # }
+    /// ```
+    pub fn list_views(
+        &self,
+        namespace_path: &[String],
+        options: ListingOptions,
+    ) -> Listing<String> {
+        let database = self.database().clone();
+        let namespace_path = namespace_path.to_vec();
+        Listing::new(options, move |options| {
+            let database = database.clone();
+            let namespace_path = namespace_path.clone();
+            async move {
+                validate_namespace(&namespace_path)?;
+                database.list_views(&namespace_path, options).await
+            }
+        })
     }
 
     /// Rename a table in the database.
@@ -922,8 +1044,26 @@ impl Connection {
     }
 
     /// List server-side jobs across the database's tables.
-    pub async fn list_jobs(&self) -> Result<Vec<JobInfo>> {
-        self.internal.list_jobs().await
+    ///
+    /// Results are fetched lazily, one page at a time. See [`Listing`]
+    /// for cached-result and continuation-token semantics. Errors terminate iteration.
+    ///
+    /// ```
+    /// # async fn example(connection: &lancedb::Connection) -> lancedb::Result<()> {
+    /// use futures::TryStreamExt;
+    /// let mut items = connection.list_jobs(Default::default());
+    /// while let Some(item) = items.try_next().await? {
+    ///     println!("{item:?}");
+    /// }
+    /// # Ok(())
+    /// # }
+    /// ```
+    pub fn list_jobs(&self, options: ListingOptions) -> Listing<JobInfo> {
+        let database = self.database().clone();
+        Listing::new(options, move |options| {
+            let database = database.clone();
+            async move { database.list_jobs(options).await }
+        })
     }
 
     /// Request cancellation of a server-side job by id. Returns true if the
@@ -1043,7 +1183,21 @@ impl Connection {
     }
 
     /// List tables with pagination support
-    pub async fn list_tables(&self, request: ListTablesRequest) -> Result<ListTablesResponse> {
+    ///
+    /// The default limit is 100 tables per page for both local and remote connections,
+    /// including namespaces. Zero returns an empty page without a continuation token.
+    /// Follow the response's opaque `page_token` until it is absent to retrieve every table;
+    /// a page can contain fewer than the limit even when more tables remain.
+    pub async fn list_tables(&self, mut request: ListTablesRequest) -> Result<ListTablesResponse> {
+        // Apply the SDK's page size here so every backend receives the same request.
+        let limit = request.limit.get_or_insert(100);
+        if *limit <= 0 {
+            return Ok(ListTablesResponse {
+                context: None,
+                tables: Vec::new(),
+                page_token: None,
+            });
+        }
         self.internal.list_tables(request).await
     }
 
@@ -2129,6 +2283,61 @@ mod tests {
         assert_eq!(seen, names);
     }
 
+    #[tokio::test]
+    async fn test_table_listing_defaults_and_zero_limit() {
+        let tempdir = tempfile::tempdir().unwrap();
+        // Listing discovers physical entries without opening the datasets.
+        let names: Vec<_> = (0..130).map(|i| format!("t{i:03}")).collect();
+        for name in &names {
+            std::fs::create_dir(tempdir.path().join(format!("{name}.lance"))).unwrap();
+        }
+        let db = connect(tempdir.path().to_str().unwrap())
+            .execute()
+            .await
+            .unwrap();
+        assert_eq!(db.table_names().execute().await.unwrap(), names);
+        assert!(
+            db.table_names()
+                .limit(0)
+                .execute()
+                .await
+                .unwrap()
+                .is_empty()
+        );
+
+        let first = db.list_tables(ListTablesRequest::default()).await.unwrap();
+        assert_eq!(first.tables, names[..100]);
+        assert!(first.page_token.is_some());
+        let second = db
+            .list_tables(ListTablesRequest {
+                page_token: first.page_token,
+                ..Default::default()
+            })
+            .await
+            .unwrap();
+        assert_eq!(second.tables, names[100..]);
+        assert!(second.page_token.is_none());
+
+        let all = db
+            .list_tables(ListTablesRequest {
+                limit: Some(200),
+                ..Default::default()
+            })
+            .await
+            .unwrap();
+        assert_eq!(all.tables, names);
+        assert!(all.page_token.is_none());
+        let empty = db
+            .list_tables(ListTablesRequest {
+                limit: Some(0),
+                ..Default::default()
+            })
+            .await
+            .unwrap();
+        assert!(empty.tables.is_empty());
+        assert!(empty.page_token.is_none());
+    }
+
     #[tokio::test]
     async fn test_open_table() {
         let tc = new_test_connection().await.unwrap();
@@ -2182,6 +2391,63 @@ mod tests {
         assert_eq!(tables.len(), 0);
     }
 
+    #[rstest::rstest]
+    #[tokio::test]
+    async fn test_clone_table_target_already_exists(
+        #[values("source", "target")] target: &str,
+        #[values(false, true)] enable_v2_manifest_paths: bool,
+    ) {
+        use crate::query::ExecutableQuery;
+        use futures::TryStreamExt;
+
+        let tmp_dir = tempdir().unwrap();
+        let options = ListingDatabaseOptions::builder()
+            .enable_v2_manifest_paths(enable_v2_manifest_paths)
+            .build();
+        let db = connect(tmp_dir.path().to_str().unwrap())
+            .database_options(&options)
+            .execute()
+            .await
+            .unwrap();
+        let source_data = arrow_array::record_batch!(("id", Int32, [0, 1, 2])).unwrap();
+        let target_data = arrow_array::record_batch!(("id", Int32, [10, 11])).unwrap();
+        db.create_table("source", source_data.clone())
+            .execute()
+            .await
+            .unwrap();
+        db.create_table("target", target_data.clone())
+            .execute()
+            .await
+            .unwrap();
+
+        let source_uri = tmp_dir.path().join("source.lance");
+        let err = db
+            .clone_table(target, source_uri.to_str().unwrap())
+            .execute()
+            .await
+            .unwrap_err();
+        assert!(
+            matches!(&err, Error::TableAlreadyExists { name } if name == target),
+            "unexpected clone error: {err:?}"
+        );
+
+        // Reopen both tables to verify that the failed clone left their data and versions intact.
+        for (name, expected_data) in [("source", source_data), ("target", target_data)] {
+            let table = db.open_table(name).execute().await.unwrap();
+            assert_eq!(table.version().await.unwrap(), 1);
+            assert_eq!(table.list_versions().await.unwrap().len(), 1);
+            let batches = table
+                .query()
+                .execute()
+                .await
+                .unwrap()
+                .try_collect::<Vec<_>>()
+                .await
+                .unwrap();
+            assert_eq!(batches, vec![expected_data]);
+        }
+    }
+
     #[tokio::test]
     async fn test_clone_table() {
         let tmp_dir = tempdir().unwrap();
diff --git a/rust/lancedb/src/database.rs b/rust/lancedb/src/database.rs
index 224fd8770..05386e0b7 100644
--- a/rust/lancedb/src/database.rs
+++ b/rust/lancedb/src/database.rs
@@ -14,10 +14,11 @@
 //!  * Tables may be managed by a database system (e.g. Postgres)
 //!  * A custom table implementation (e.g. remote table, etc.) may be used
 
-use std::collections::{HashMap, HashSet};
+use std::collections::HashMap;
 use std::sync::Arc;
 use std::time::Duration;
 
+use arrow_array::RecordBatch;
 use lance::dataset::ReadParams;
 use lance_namespace::LanceNamespace;
 use lance_namespace::models::{
@@ -29,6 +30,7 @@ use lance_namespace::models::{
 use crate::data::scannable::Scannable;
 use crate::error::Result;
 use crate::job::Job;
+use crate::listing::{ListingOptions, ListingPage};
 use crate::materialized_view::CreateMaterializedViewRequest;
 use crate::secrets::SecretInfo;
 use crate::table::{BaseTable, WriteOptions};
@@ -190,6 +192,32 @@ impl CloneTableRequest {
     }
 }
 
+/// A request to start a SQL statement on a remote database.
+#[derive(Clone, Debug)]
+pub struct ExecuteQueryRequest {
+    /// The SQL text.
+    pub query: String,
+    /// The namespace unqualified table names resolve in. Empty is `public`,
+    /// the SQL name of the root namespace.
+    pub default_namespace_path: Vec<String>,
+    /// Values for the statement's placeholders, as a single-row batch.
+    ///
+    /// Column `i` binds `$<i + 1>`, and a column whose name is not a number
+    /// also binds `$<name>`. See
+    /// [`ExecuteQueryAsyncBuilder::parameters`](crate::connection::ExecuteQueryAsyncBuilder::parameters).
+    pub parameters: Option<RecordBatch>,
+}
+
+impl ExecuteQueryRequest {
+    pub fn new(query: impl Into<String>) -> Self {
+        Self {
+            query: query.into(),
+            default_namespace_path: vec!["public".to_string()],
+            parameters: None,
+        }
+    }
+}
+
 /// How long until a change is reflected from one Table instance to another
 ///
 /// Tables are always internally consistent.  If a write method is called on
@@ -333,10 +361,12 @@ pub trait Database:
     ///
     /// See [`CloneTableRequest`] for detailed documentation and examples.
     async fn clone_table(&self, request: CloneTableRequest) -> Result<Arc<dyn BaseTable>>;
-    /// Submit a Function creation job that builds an image and registers it.
+    /// Submit a Function creation job that builds an image and registers it
+    /// in `namespace_path`.
     async fn create_function_async(
         &self,
         _request: crate::function::FunctionRegistrationRequest,
+        _namespace_path: &[String],
     ) -> Result<crate::job::Job<crate::function::FunctionVersion>> {
         function_catalog_not_supported()
     }
@@ -360,67 +390,74 @@ pub trait Database:
     ) -> Result<Job> {
         job_op_not_supported("remote materialized-view drop")
     }
-    /// List materialized-view names in a namespace.
+    /// Fetch one page of materialized-view names in a namespace.
+    /// Local backends filter a page of table names, so an empty result may
+    /// still have a continuation token.
     #[doc(hidden)]
-    async fn list_materialized_views(&self, namespace_path: &[String]) -> Result<Vec<String>> {
+    async fn list_materialized_views(
+        &self,
+        namespace_path: &[String],
+        options: ListingOptions,
+    ) -> Result<ListingPage<String>> {
+        let response = self
+            .list_tables(ListTablesRequest {
+                id: Some(namespace_path.to_vec()),
+                page_token: options.page_token,
+                limit: options.page_limit.map(|limit| limit as i32),
+                ..Default::default()
+            })
+            .await?;
         let mut names = Vec::new();
-        let mut page_token = None;
-        let mut seen_page_tokens = HashSet::new();
-        loop {
-            let response = self
-                .list_tables(ListTablesRequest {
-                    id: Some(namespace_path.to_vec()),
-                    page_token: page_token.clone(),
-                    ..Default::default()
+        for name in response.tables {
+            let Ok(table) = self
+                .open_table(OpenTableRequest {
+                    name: name.clone(),
+                    namespace_path: namespace_path.to_vec(),
+                    index_cache_size: None,
+                    lance_read_params: None,
+                    location: None,
+                    namespace_client: None,
+                    managed_versioning: None,
                 })
-                .await?;
-            for name in response.tables {
-                let Ok(table) = self
-                    .open_table(OpenTableRequest {
-                        name: name.clone(),
-                        namespace_path: namespace_path.to_vec(),
-                        index_cache_size: None,
-                        lance_read_params: None,
-                        location: None,
-                        namespace_client: None,
-                        managed_versioning: None,
-                    })
-                    .await
-                else {
-                    continue;
-                };
-                let schema = table.schema().await?;
-                if crate::materialized_view::read_definition(schema.metadata())?.is_some() {
-                    names.push(name);
-                }
-            }
-            let Some(next_page_token) = response.page_token.filter(|token| !token.is_empty())
+                .await
             else {
-                break;
+                continue;
             };
-            if !seen_page_tokens.insert(next_page_token.clone()) {
-                return Err(crate::Error::Runtime {
-                    message: "materialized-view listing repeated a page token".into(),
-                });
+            let schema = table.schema().await?;
+            if crate::materialized_view::read_definition(schema.metadata())?.is_some() {
+                names.push(name);
             }
-            page_token = Some(next_page_token);
         }
-        Ok(names)
+
+        Ok(ListingPage {
+            items: names,
+            page_token: response.page_token,
+        })
     }
     /// Look up one exact immutable Function version.
     async fn get_function(
         &self,
         _name: &str,
         _version: &str,
+        _namespace_path: &[String],
     ) -> Result<crate::function::FunctionVersion> {
         function_catalog_not_supported()
     }
-    /// List every published immutable Function version in the remote catalog.
-    async fn list_functions(&self) -> Result<Vec<crate::function::FunctionVersion>> {
+    /// Fetch one page of published immutable Function versions in one namespace.
+    async fn list_functions(
+        &self,
+        _namespace_path: &[String],
+        _options: ListingOptions,
+    ) -> Result<ListingPage<crate::function::FunctionVersion>> {
         function_catalog_not_supported()
     }
     /// Remove the current Function name binding, retaining the object history.
-    async fn drop_function(&self, _name: &str, _version: &str) -> Result<bool> {
+    async fn drop_function(
+        &self,
+        _name: &str,
+        _version: &str,
+        _namespace_path: &[String],
+    ) -> Result<bool> {
         function_catalog_not_supported()
     }
     /// Start dropping a Function and return a handle to the cleanup job.
@@ -431,8 +468,9 @@ pub trait Database:
         &self,
         name: &str,
         version: &str,
+        namespace_path: &[String],
     ) -> Result<(bool, crate::job::Job)> {
-        let dropped = self.drop_function(name, version).await?;
+        let dropped = self.drop_function(name, version, namespace_path).await?;
         Ok((dropped, crate::job::Job::new_done()))
     }
     /// Create a named Secret in this database. Fails if the name is taken, so
@@ -456,11 +494,15 @@ pub trait Database:
     ) -> Result<()> {
         secret_catalog_not_supported()
     }
-    /// The names of every Secret in this database.
+    /// Fetch one page of Secret names in this database.
     ///
     /// Names only. No API path returns a stored credential, by construction
     /// rather than by policy.
-    async fn list_secrets(&self, _namespace_path: &[String]) -> Result<Vec<String>> {
+    async fn list_secrets(
+        &self,
+        _namespace_path: &[String],
+        _options: ListingOptions,
+    ) -> Result<ListingPage<String>> {
         secret_catalog_not_supported()
     }
     /// Drop a Secret. Functions bound to it fail at their next job, which is
@@ -503,8 +545,12 @@ pub trait Database:
     async fn drop_view_async(&self, _name: &str, _namespace_path: &[String]) -> Result<Job> {
         view_ops_not_supported()
     }
-    /// The names of the views in one namespace.
-    async fn list_views(&self, _namespace_path: &[String]) -> Result<Vec<String>> {
+    /// Fetch one page of view names in one namespace.
+    async fn list_views(
+        &self,
+        _namespace_path: &[String],
+        _options: ListingOptions,
+    ) -> Result<ListingPage<String>> {
         view_ops_not_supported()
     }
     /// Open a job by id, returning a handle with its record already
@@ -513,8 +559,8 @@ pub trait Database:
     async fn open_job(&self, _job_id: &str) -> Result<crate::job::Job> {
         job_op_not_supported("open_job")
     }
-    /// List server-side jobs across the database's tables.
-    async fn list_jobs(&self) -> Result<Vec<JobInfo>> {
+    /// Fetch one page of server-side jobs across the database's tables.
+    async fn list_jobs(&self, _options: ListingOptions) -> Result<ListingPage<JobInfo>> {
         job_op_not_supported("list_jobs")
     }
     /// Request cancellation of a job by id. Returns true if the server
@@ -536,8 +582,7 @@ pub trait Database:
     /// Start executing a SQL statement on a remote database.
     async fn execute_query_async(
         &self,
-        _query: &str,
-        _default_namespace_path: &[String],
+        _request: ExecuteQueryRequest,
     ) -> Result<crate::sql::Query> {
         Err(crate::error::Error::NotSupported {
             message: "SQL is not supported by this database".to_string(),
diff --git a/rust/lancedb/src/database/listing.rs b/rust/lancedb/src/database/listing.rs
index dc1627116..8255fef4b 100644
--- a/rust/lancedb/src/database/listing.rs
+++ b/rust/lancedb/src/database/listing.rs
@@ -1107,6 +1107,21 @@ impl Database for ListingDatabase {
             ..Default::default()
         };
 
+        let target_uri = self.table_uri(&request.target_table_name)?;
+        match DatasetBuilder::from_uri(&target_uri)
+            .with_read_params(read_params.clone())
+            .load()
+            .await
+        {
+            Ok(_) => {
+                return Err(Error::TableAlreadyExists {
+                    name: request.target_table_name,
+                });
+            }
+            Err(lance::Error::DatasetNotFound { .. }) => {}
+            Err(err) => return Err(err.into()),
+        }
+
         let mut source_dataset = DatasetBuilder::from_uri(&request.source_uri)
             .with_read_params(read_params.clone())
             .load()
@@ -1122,7 +1137,6 @@ impl Database for ListingDatabase {
             }),
         }?;
 
-        let target_uri = self.table_uri(&request.target_table_name)?;
         source_dataset
             .shallow_clone(&target_uri, version_ref, Some(storage_params))
             .await
diff --git a/rust/lancedb/src/database/namespace.rs b/rust/lancedb/src/database/namespace.rs
index 6bca29476..d93f73c64 100644
--- a/rust/lancedb/src/database/namespace.rs
+++ b/rust/lancedb/src/database/namespace.rs
@@ -288,16 +288,44 @@ impl Database for LanceNamespaceDatabase {
     }
 
     async fn table_names(&self, request: TableNamesRequest) -> Result<Vec<String>> {
-        let ns_request = ListTablesRequest {
-            id: Some(request.namespace_path),
-            page_token: request.start_after,
-            limit: request.limit.map(|l| l as i32),
-            ..Default::default()
-        };
+        if request.limit == Some(0) {
+            return Ok(Vec::new());
+        }
 
-        let response = self.namespace.list_tables(ns_request).await?;
+        // Namespace tokens are opaque, and pages need not arrive in name order.
+        // Collect the listing before applying the name cursor and overall limit.
+        let mut names = Vec::new();
+        let mut page_token = None;
+        let mut seen_tokens = HashSet::new();
+        loop {
+            let response = self
+                .namespace
+                .list_tables(ListTablesRequest {
+                    id: Some(request.namespace_path.clone()),
+                    page_token,
+                    ..Default::default()
+                })
+                .await?;
+            names.extend(response.tables);
+            let Some(token) = response.page_token.filter(|token| !token.is_empty()) else {
+                break;
+            };
+            if !seen_tokens.insert(token.clone()) {
+                return Err(Error::Runtime {
+                    message: "Namespace table listing response repeated a page_token".into(),
+                });
+            }
+            page_token = Some(token);
+        }
 
-        Ok(response.tables)
+        names.sort();
+        if let Some(ref start_after) = request.start_after {
+            names.retain(|name| name > start_after);
+        }
+        if let Some(limit) = request.limit {
+            names.truncate(limit as usize);
+        }
+        Ok(names)
     }
 
     async fn list_tables(&self, request: ListTablesRequest) -> Result<ListTablesResponse> {
@@ -589,6 +617,135 @@ mod tests {
     use futures::TryStreamExt;
     use tempfile::tempdir;
 
+    #[derive(Debug)]
+    struct ScriptedNamespace {
+        pages: Vec<ListTablesResponse>,
+        requests: Mutex<Vec<ListTablesRequest>>,
+    }
+
+    #[async_trait]
+    impl LanceNamespace for ScriptedNamespace {
+        fn namespace_id(&self) -> String {
+            "scripted".into()
+        }
+
+        async fn list_tables(
+            &self,
+            request: ListTablesRequest,
+        ) -> lance_core::Result<ListTablesResponse> {
+            let mut requests = self.requests.lock().unwrap();
+            let page = self.pages[requests.len()].clone();
+            requests.push(request);
+            Ok(page)
+        }
+    }
+
+    fn namespace_with_pages(
+        pages: Vec<ListTablesResponse>,
+    ) -> (LanceNamespaceDatabase, Arc<ScriptedNamespace>) {
+        let namespace = Arc::new(ScriptedNamespace {
+            pages,
+            requests: Mutex::new(Vec::new()),
+        });
+        let db = LanceNamespaceDatabase::from_namespace_client(
+            namespace.clone(),
+            "scripted".into(),
+            HashMap::new(),
+            HashMap::new(),
+            None,
+            None,
+            HashSet::new(),
+        );
+        (db, namespace)
+    }
+
+    fn listing_page(names: &[&str], token: Option<&str>) -> ListTablesResponse {
+        ListTablesResponse {
+            tables: names.iter().map(|name| (*name).into()).collect(),
+            page_token: token.map(String::from),
+            ..Default::default()
+        }
+    }
+
+    #[tokio::test]
+    #[allow(deprecated)]
+    async fn test_table_names_collects_namespace_pages() {
+        for namespace_path in [vec![], vec!["child".into()]] {
+            for (start_after, limit, expected) in [
+                (None, None, vec!["a", "b", "c", "d"]),
+                (Some("a".into()), Some(2), vec!["b", "c"]),
+            ] {
+                let (db, namespace) = namespace_with_pages(vec![
+                    listing_page(&["d", "b"], Some("opaque-1")),
+                    listing_page(&[], Some("opaque-2")),
+                    listing_page(&["c", "a"], None),
+                ]);
+                let names = db
+                    .table_names(TableNamesRequest {
+                        namespace_path: namespace_path.clone(),
+                        start_after,
+                        limit,
+                    })
+                    .await
+                    .unwrap();
+                assert_eq!(names, expected);
+
+                let requests = namespace.requests.lock().unwrap();
+                assert_eq!(requests.len(), 3);
+                for request in requests.iter() {
+                    assert_eq!(request.id.as_ref(), Some(&namespace_path));
+                    assert_eq!(request.limit, None);
+                }
+                assert_eq!(requests[0].page_token, None);
+                assert_eq!(requests[1].page_token.as_deref(), Some("opaque-1"));
+                assert_eq!(requests[2].page_token.as_deref(), Some("opaque-2"));
+            }
+        }
+    }
+
+    #[tokio::test]
+    #[allow(deprecated)]
+    async fn test_table_names_zero_limit_skips_namespace_request() {
+        let (db, namespace) = namespace_with_pages(vec![]);
+        assert!(
+            db.table_names(TableNamesRequest {
+                limit: Some(0),
+                ..Default::default()
+            })
+            .await
+            .unwrap()
+            .is_empty()
+        );
+        assert!(namespace.requests.lock().unwrap().is_empty());
+    }
+
+    #[tokio::test]
+    #[allow(deprecated)]
+    async fn test_table_names_stops_on_empty_namespace_token() {
+        let (db, namespace) = namespace_with_pages(vec![listing_page(&["a"], Some(""))]);
+        assert_eq!(
+            db.table_names(TableNamesRequest::default()).await.unwrap(),
+            vec!["a"]
+        );
+        assert_eq!(namespace.requests.lock().unwrap().len(), 1);
+    }
+
+    #[tokio::test]
+    #[allow(deprecated)]
+    async fn test_table_names_rejects_namespace_token_cycle() {
+        let (db, namespace) = namespace_with_pages(vec![
+            listing_page(&["a"], Some("one")),
+            listing_page(&["b"], Some("two")),
+            listing_page(&["c"], Some("one")),
+        ]);
+        let error = db
+            .table_names(TableNamesRequest::default())
+            .await
+            .unwrap_err();
+        assert!(error.to_string().contains("repeated a page_token"));
+        assert_eq!(namespace.requests.lock().unwrap().len(), 3);
+    }
+
     /// Helper function to create test data
     fn create_test_data() -> RecordBatch {
         let schema = Arc::new(Schema::new(vec![
diff --git a/rust/lancedb/src/dataloader/permutation/builder.rs b/rust/lancedb/src/dataloader/permutation/builder.rs
index ba0d0e180..e08f3eddc 100644
--- a/rust/lancedb/src/dataloader/permutation/builder.rs
+++ b/rust/lancedb/src/dataloader/permutation/builder.rs
@@ -250,17 +250,17 @@ impl PermutationBuilder {
         }
 
         // Unflushed rows have no row id, so a permutation cannot address them.
-        match self.base_table.base_table().get_lsm_write_spec().await {
+        match self.base_table.base_table().get_memwal_spec().await {
             Ok(Some(_)) => {
                 return Err(Error::NotSupported {
-                    message: "the data loader does not support tables with an LSM write \
+                    message: "the data loader does not support tables with a MemWAL write \
                               spec: rows that have not been flushed to the base table \
                               have no row id, so a permutation cannot reference them"
                         .to_string(),
                 });
             }
             Ok(None) => {}
-            // No LSM write path means no spec.
+            // No MemWAL write path means no spec.
             Err(Error::NotSupported { .. }) => {}
             Err(err) => return Err(err),
         }
@@ -489,9 +489,9 @@ mod tests {
                         )
                         .unwrap()
                 }
-                "/v1/table/remote_base/get_lsm_write_spec/" => http::Response::builder()
+                "/v1/table/remote_base/get_memwal_spec/" => http::Response::builder()
                     .status(200)
-                    .body(br#"{"lsm_write_spec":null}"#.to_vec())
+                    .body(br#"{"memwal_spec":null}"#.to_vec())
                     .unwrap(),
                 "/v1/table/remote_base/count_rows/" => {
                     let body = body.unwrap();
@@ -737,8 +737,8 @@ mod tests {
     /// permutation cannot reference them.  Reading the base table alone would drop
     /// them from training without saying so, so the table is refused instead.
     #[tokio::test]
-    async fn test_permutation_rejects_lsm_write_spec() {
-        use crate::table::LsmWriteSpec;
+    async fn test_permutation_rejects_memwal_spec() {
+        use crate::table::MemWalWriteSpec;
         use arrow_array::{Int32Array, RecordBatchIterator};
         use arrow_schema::{DataType, Field, Schema};
 
@@ -766,13 +766,13 @@ mod tests {
 
         table.set_unenforced_primary_key(["idx"]).await.unwrap();
         table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .enable_memwal(MemWalWriteSpec::unsharded())
             .await
             .unwrap();
 
         let err = PermutationBuilder::new(table).build().await.unwrap_err();
         assert!(
-            err.to_string().contains("LSM write spec"),
+            err.to_string().contains("MemWAL write spec"),
             "expected the pre-check to refuse the table, got: {err}"
         );
     }
diff --git a/rust/lancedb/src/error.rs b/rust/lancedb/src/error.rs
index f6a2df9a6..6052b67d8 100644
--- a/rust/lancedb/src/error.rs
+++ b/rust/lancedb/src/error.rs
@@ -127,7 +127,7 @@ pub enum Error {
         "Hit retry limit for request_id={request_id} (\
         request_failures={request_failures}/{max_request_failures}, \
         connect_failures={connect_failures}/{max_connect_failures}, \
-        read_failures={read_failures}/{max_read_failures})"
+        read_failures={read_failures}/{max_read_failures}): {source}"
     ))]
     Retry {
         request_id: String,
diff --git a/rust/lancedb/src/function.rs b/rust/lancedb/src/function.rs
index 3b7209bd5..98d915118 100644
--- a/rust/lancedb/src/function.rs
+++ b/rust/lancedb/src/function.rs
@@ -85,22 +85,25 @@ fn has_unknown_keys(value: &Value, allowed: &[&str]) -> bool {
         .is_some_and(|object| object.keys().any(|key| !allowed.contains(&key.as_str())))
 }
 
+/// Every field a [`FunctionVersionRef`] document may carry. An application or
+/// binding with any other key comes from a newer contract.
+pub(crate) const FUNCTION_VERSION_REF_FIELDS: &[&str] = &[
+    "name",
+    "namespace_path",
+    "object_id",
+    "location",
+    "version",
+    "manifest_digest",
+];
+
 fn application_has_unknown_nested_fields(value: &Value) -> bool {
     let Some(application) = value.as_object() else {
         return false;
     };
-    if application.get("function").is_some_and(|value| {
-        has_unknown_keys(
-            value,
-            &[
-                "name",
-                "object_id",
-                "location",
-                "version",
-                "manifest_digest",
-            ],
-        )
-    }) {
+    if application
+        .get("function")
+        .is_some_and(|value| has_unknown_keys(value, FUNCTION_VERSION_REF_FIELDS))
+    {
         return true;
     }
     if application
@@ -431,9 +434,17 @@ fn deserialize_object_version<'de, D: Deserializer<'de>>(
 }
 
 /// One immutable Function object revision and its executable artifact.
+///
+/// The Function is named by its parts, `name` and `namespace_path`, the way a
+/// [`SecretReference`](crate::secrets::SecretReference) names a Secret.
 #[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
 pub struct FunctionVersion {
     name: String,
+    /// The namespace holding the Function. Empty is the root, and is absent
+    /// from the wire, as a [`SecretReference`](crate::secrets::SecretReference)
+    /// omits it.
+    #[serde(default, skip_serializing_if = "Vec::is_empty")]
+    namespace_path: Vec<String>,
     object_id: String,
     location: String,
     #[serde(deserialize_with = "deserialize_object_version")]
@@ -442,7 +453,8 @@ pub struct FunctionVersion {
     signature: FunctionSignature,
     #[serde(default, skip_serializing_if = "Vec::is_empty")]
     secret_bindings: Vec<SecretBinding>,
-    created_at: String,
+    /// When this version was created, in milliseconds since the epoch.
+    created_at_millis: i64,
     metadata: BTreeMap<String, String>,
     disabled: bool,
 }
@@ -463,6 +475,7 @@ impl FunctionVersion {
     pub fn reference(&self) -> FunctionVersionRef {
         FunctionVersionRef {
             name: self.name.clone(),
+            namespace_path: self.namespace_path.clone(),
             object_id: self.object_id.clone(),
             location: self.location.clone(),
             version: self.version.clone(),
@@ -472,6 +485,9 @@ impl FunctionVersion {
     pub fn name(&self) -> &str {
         &self.name
     }
+    pub fn namespace_path(&self) -> &[String] {
+        &self.namespace_path
+    }
     pub fn version(&self) -> &str {
         &self.version
     }
@@ -492,8 +508,9 @@ impl FunctionVersion {
         &self.secret_bindings
     }
 
-    pub fn created_at(&self) -> &str {
-        &self.created_at
+    /// When this version was created, in milliseconds since the epoch.
+    pub fn created_at_millis(&self) -> i64 {
+        self.created_at_millis
     }
 }
 
@@ -556,6 +573,10 @@ impl_json!(FunctionRegistrationRequest);
 #[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
 pub struct FunctionVersionRef {
     pub name: String,
+    /// The namespace holding the Function; empty is the root and is absent
+    /// from the wire.
+    #[serde(default, skip_serializing_if = "Vec::is_empty")]
+    pub namespace_path: Vec<String>,
     pub object_id: String,
     pub location: String,
     #[serde(deserialize_with = "deserialize_object_version")]
@@ -954,6 +975,46 @@ mod tests {
         );
     }
 
+    /// A Function is named by its parts, and a root one states no namespace.
+    #[test]
+    fn function_version_carries_its_namespace_as_a_separate_field() {
+        const VERSION: &str = include_str!(
+            "../tests/fixtures/first_class_functions/v1/remote_function_version.canonical.json"
+        );
+        let root = FunctionVersion::from_json(VERSION).unwrap();
+        assert!(root.namespace_path().is_empty());
+        assert_eq!(root.to_canonical_json().unwrap(), VERSION.trim_end());
+
+        let mut document: Value = serde_json::from_str(VERSION).unwrap();
+        document["namespace_path"] = serde_json::json!(["analytics", "features"]);
+        let nested = FunctionVersion::from_json(&document.to_string()).unwrap();
+        assert_eq!(nested.name(), "embed");
+        assert_eq!(nested.namespace_path(), ["analytics", "features"]);
+        let reference = nested.reference();
+        assert_eq!(reference.name, "embed");
+        assert_eq!(reference.namespace_path, ["analytics", "features"]);
+        let reference: Value = serde_json::to_value(&reference).unwrap();
+        assert_eq!(
+            reference["namespace_path"],
+            serde_json::json!(["analytics", "features"])
+        );
+        let root_reference = serde_json::to_value(root.reference()).unwrap();
+        assert!(root_reference.get("namespace_path").is_none());
+
+        // An application carrying the namespace is not from a newer contract.
+        let application = serde_json::json!({
+            "function": reference,
+            "inputs": [],
+            "output": serde_json::to_value(nested.signature().output.clone()).unwrap(),
+        });
+        let application = FunctionApplication::from_json(&application.to_string()).unwrap();
+        assert!(!application.has_unknown_fields());
+        assert_eq!(
+            application.function().namespace_path,
+            ["analytics", "features"]
+        );
+    }
+
     /// Arrays are ordered by the caller, so canonicalization must leave them
     /// alone -- sorting them would change what a signature means.
     #[test]
diff --git a/rust/lancedb/src/index.rs b/rust/lancedb/src/index.rs
index cdc973044..82f8ad845 100644
--- a/rust/lancedb/src/index.rs
+++ b/rust/lancedb/src/index.rs
@@ -75,6 +75,17 @@ pub enum Index {
 
     /// Full text search index using BM25.
     ///
+    /// Supports strings, lists of strings, and `lance.json` logical fields
+    /// stored as `LargeBinary`. Raw binary fields are not supported. Lance
+    /// selects its JSON-aware tokenizer from the field metadata.
+    ///
+    /// JSON queries use `path,str,value` triples, where the dotted path is
+    /// relative to the indexed column. For example, `meta.tag,str,nature`
+    /// searches the string value at `$.meta.tag`, not other paths or key names.
+    /// Create the index on the JSON column, not on a JSON subpath. Plain text
+    /// queries are invalid for this tokenizer; malformed triples can panic in
+    /// the current Lance dependency (see [lance#8812](https://github.com/lance-format/lance/issues/8812)).
+    ///
     /// The posting block size defaults to 128. Supported values are 128 and 256;
     /// a value of 256 uses the experimental FTS V3 format and may introduce
     /// breaking changes.
diff --git a/rust/lancedb/src/job.rs b/rust/lancedb/src/job.rs
index 54b46fc11..711a73033 100644
--- a/rust/lancedb/src/job.rs
+++ b/rust/lancedb/src/job.rs
@@ -3,6 +3,8 @@
 
 //! Handles to operations a server may run asynchronously.
 
+use std::future::Future;
+use std::pin::Pin;
 use std::sync::{Arc, RwLock, RwLockReadGuard, RwLockWriteGuard};
 
 use arrow_array::RecordBatch;
@@ -250,6 +252,18 @@ where
     pub(crate) fn spawned(task: JoinHandle<Result<T>>) -> Self {
         Self::new_typed(Box::new(SpawnedJob::new(task)))
     }
+
+    pub(crate) fn spawned_with_cancellation<F, Fut>(task: JoinHandle<Result<T>>, cancel: F) -> Self
+    where
+        F: Fn() -> Fut + Send + Sync + 'static,
+        Fut: Future<Output = Result<()>> + Send + 'static,
+    {
+        let cancellation: Cancellation = Arc::new(move || Box::pin(cancel()));
+        Self::new_typed(Box::new(SpawnedJob::new_with_cancellation(
+            task,
+            cancellation,
+        )))
+    }
 }
 
 impl<T> Job<T>
@@ -501,10 +515,28 @@ impl Outcome {
 struct SpawnedJob {
     outcome: watch::Receiver<Option<Outcome>>,
     abort: AbortHandle,
+    cancellation: Option<Cancellation>,
 }
 
+type CancellationFuture = Pin<Box<dyn Future<Output = Result<()>> + Send>>;
+type Cancellation = Arc<dyn Fn() -> CancellationFuture + Send + Sync>;
+
 impl SpawnedJob {
     fn new<T>(task: JoinHandle<Result<T>>) -> Self
+    where
+        T: Serialize + Send + 'static,
+    {
+        Self::new_inner(task, None)
+    }
+
+    fn new_with_cancellation<T>(task: JoinHandle<Result<T>>, cancellation: Cancellation) -> Self
+    where
+        T: Serialize + Send + 'static,
+    {
+        Self::new_inner(task, Some(cancellation))
+    }
+
+    fn new_inner<T>(task: JoinHandle<Result<T>>, cancellation: Option<Cancellation>) -> Self
     where
         T: Serialize + Send + 'static,
     {
@@ -526,7 +558,11 @@ impl SpawnedJob {
             };
             let _ = tx.send(Some(outcome));
         });
-        Self { outcome, abort }
+        Self {
+            outcome,
+            abort,
+            cancellation,
+        }
     }
 }
 
@@ -556,6 +592,19 @@ impl JobHandle for SpawnedJob {
     }
 
     async fn cancel(&self) -> Result<()> {
+        if let Some(cancellation) = &self.cancellation {
+            let mut outcome = self.outcome.clone();
+            tokio::select! {
+                biased;
+                settled = outcome.wait_for(Option::is_some) => {
+                    settled.map_err(|_| Error::Runtime {
+                        message: "job outcome was dropped before cancellation completed".to_string(),
+                    })?;
+                    return Ok(());
+                }
+                result = cancellation() => result?,
+            }
+        }
         self.abort.abort();
         Ok(())
     }
diff --git a/rust/lancedb/src/lib.rs b/rust/lancedb/src/lib.rs
index 530328cbb..68b05c531 100644
--- a/rust/lancedb/src/lib.rs
+++ b/rust/lancedb/src/lib.rs
@@ -167,12 +167,13 @@
 //! # }
 //! ```
 
-// The MemWAL LSM read path (`table::query::lsm`) deepens the `create_plan` future's
+// The MemWAL read path (`table::query::lsm`) deepens the `create_plan` future's
 // type graph enough to overflow the default trait-recursion limit while evaluating
 // auto-traits (`Send`) through the Linux io_uring build's moka cache. Raise it.
 #![recursion_limit = "256"]
 
 pub mod arrow;
+pub mod authz;
 pub mod blob;
 pub mod catalog;
 pub mod connection;
@@ -187,6 +188,7 @@ pub mod index;
 pub mod io;
 pub mod ipc;
 pub mod job;
+pub mod listing;
 pub mod materialized_view;
 #[cfg(feature = "metrics-otel")]
 pub mod metrics_otel;
diff --git a/rust/lancedb/src/listing.rs b/rust/lancedb/src/listing.rs
new file mode 100644
index 000000000..710202d9d
--- /dev/null
+++ b/rust/lancedb/src/listing.rs
@@ -0,0 +1,261 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+//! Lazy, resumable listings of database resources.
+
+use std::collections::HashSet;
+use std::pin::Pin;
+use std::task::{Context, Poll};
+
+use futures::future::BoxFuture;
+use futures::{Future, FutureExt, Stream};
+
+use crate::{Error, Result};
+
+/// Pagination controls for a resource listing.
+#[derive(Clone, Debug, Default)]
+#[non_exhaustive]
+pub struct ListingOptions {
+    /// Starting continuation token; `None` starts at the beginning.
+    pub page_token: Option<String>,
+    /// Maximum results per request, not a limit on the entire listing.
+    /// `None` uses the backend default. Must be between 1 and `i32::MAX`.
+    pub page_limit: Option<u32>,
+}
+
+impl ListingOptions {
+    /// Resume at a saved continuation token.
+    pub fn page_token(mut self, token: impl Into<String>) -> Self {
+        self.page_token = Some(token.into());
+        self
+    }
+
+    /// Set the maximum number of results requested per page.
+    pub fn page_limit(mut self, limit: u32) -> Self {
+        self.page_limit = Some(limit);
+        self
+    }
+}
+
+/// One backend page of a resource listing.
+#[derive(Debug)]
+pub struct ListingPage<T> {
+    /// Items returned on this page, possibly empty even when more pages exist.
+    pub items: Vec<T>,
+    /// Token for the next page; `None` or an empty string ends the listing.
+    pub page_token: Option<String>,
+}
+
+type FetchPage<T> =
+    Box<dyn Fn(ListingOptions) -> BoxFuture<'static, Result<ListingPage<T>>> + Send + Sync>;
+
+/// A lazy stream of resources with inspectable pagination state.
+///
+/// No requests are made until the stream is polled. Empty pages with continuation
+/// tokens are skipped. Errors terminate the stream and retain the failed request's
+/// token, allowing a new stream to retry it. Repeated tokens are rejected.
+///
+/// A token advances when its page is fetched. Drain [`Self::num_page_results`]
+/// before saving [`Self::page_token`] to avoid skipping cached items on resumption.
+/// After the final page is fetched the token is `None`, even with cached items.
+/// Use [`futures::TryStreamExt::try_collect`] to collect a listing into a vector.
+pub struct Listing<T> {
+    fetch: FetchPage<T>,
+    items: std::vec::IntoIter<T>,
+    options: ListingOptions,
+    done: bool,
+    seen_tokens: HashSet<String>,
+    pending: Option<BoxFuture<'static, Result<ListingPage<T>>>>,
+}
+
+impl<T> Listing<T> {
+    pub(crate) fn new<F, Fut>(options: ListingOptions, fetch: F) -> Self
+    where
+        F: Fn(ListingOptions) -> Fut + Send + Sync + 'static,
+        Fut: Future<Output = Result<ListingPage<T>>> + Send + 'static,
+    {
+        let seen_tokens = options
+            .page_token
+            .iter()
+            .filter(|t| !t.is_empty())
+            .cloned()
+            .collect();
+        Self {
+            fetch: Box::new(move |options| fetch(options).boxed()),
+            items: Vec::new().into_iter(),
+            options,
+            done: false,
+            seen_tokens,
+            pending: None,
+        }
+    }
+
+    /// Number of items available without another backend request.
+    pub fn num_page_results(&self) -> usize {
+        self.items.len()
+    }
+
+    /// Token for the next request. Initially the supplied token; `None` after
+    /// fetching the final page. Drain cached items before saving this token.
+    pub fn page_token(&self) -> Option<&str> {
+        self.options.page_token.as_deref()
+    }
+}
+
+impl<T: Unpin> Stream for Listing<T> {
+    type Item = Result<T>;
+
+    fn poll_next(self: Pin<&mut Self>, cx: &mut Context<'_>) -> Poll<Option<Self::Item>> {
+        let this = self.get_mut();
+        loop {
+            if let Some(item) = this.items.next() {
+                return Poll::Ready(Some(Ok(item)));
+            }
+            if this.done {
+                return Poll::Ready(None);
+            }
+            if this
+                .options
+                .page_limit
+                .is_some_and(|limit| limit == 0 || limit > i32::MAX as u32)
+            {
+                this.done = true;
+                return Poll::Ready(Some(Err(Error::InvalidInput {
+                    message: "Listing page limit must be between 1 and 2147483647".into(),
+                })));
+            }
+            let pending = this
+                .pending
+                .get_or_insert_with(|| (this.fetch)(this.options.clone()));
+            let response = futures::ready!(pending.as_mut().poll(cx));
+            this.pending = None;
+            match response {
+                Ok(response) => {
+                    let token = response.page_token.filter(|token| !token.is_empty());
+                    if token
+                        .as_ref()
+                        .is_some_and(|token| !this.seen_tokens.insert(token.clone()))
+                    {
+                        this.done = true;
+                        return Poll::Ready(Some(Err(Error::Runtime {
+                            message: "Listing response repeated a page_token".into(),
+                        })));
+                    }
+                    this.done = token.is_none();
+                    this.options.page_token = token;
+                    this.items = response.items.into_iter();
+                }
+                Err(error) => {
+                    this.done = true;
+                    return Poll::Ready(Some(Err(error)));
+                }
+            }
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use futures::{StreamExt, TryStreamExt};
+    use std::sync::{Arc, Mutex};
+
+    #[tokio::test]
+    async fn lazy_pages_empty_pages_and_resume() {
+        let requests = Arc::new(Mutex::new(Vec::new()));
+        let fetch = {
+            let requests = requests.clone();
+            move |options: ListingOptions| {
+                requests.lock().unwrap().push(options.clone());
+                async move {
+                    let (items, token) = match options.page_token.as_deref() {
+                        Some("start") => (vec![1, 2], Some("empty")),
+                        Some("empty") => (vec![], Some("last")),
+                        Some("last") => (vec![3, 4], Some("")),
+                        _ => panic!("unexpected request"),
+                    };
+                    Ok(ListingPage {
+                        items,
+                        page_token: token.map(str::to_owned),
+                    })
+                }
+            }
+        };
+        let mut listing = Listing::new(
+            ListingOptions::default().page_token("start").page_limit(2),
+            fetch.clone(),
+        );
+        assert!(requests.lock().unwrap().is_empty());
+        assert_eq!(listing.page_token(), Some("start"));
+        assert_eq!(listing.num_page_results(), 0);
+        assert_eq!(listing.try_next().await.unwrap(), Some(1));
+        assert_eq!(listing.num_page_results(), 1);
+        assert_eq!(listing.page_token(), Some("empty"));
+        assert_eq!(listing.try_next().await.unwrap(), Some(2));
+        assert_eq!(requests.lock().unwrap().len(), 1);
+        let mut resumed = Listing::new(
+            ListingOptions::default()
+                .page_token(listing.page_token().unwrap())
+                .page_limit(2),
+            fetch,
+        );
+        assert_eq!(resumed.try_next().await.unwrap(), Some(3));
+        assert_eq!(resumed.page_token(), None);
+        assert_eq!(resumed.num_page_results(), 1);
+        assert_eq!(resumed.try_next().await.unwrap(), Some(4));
+        assert_eq!(resumed.try_next().await.unwrap(), None);
+        assert_eq!(resumed.try_next().await.unwrap(), None);
+        let requests = requests.lock().unwrap();
+        assert_eq!(requests.len(), 3);
+        assert!(requests.iter().all(|request| request.page_limit == Some(2)));
+    }
+
+    #[tokio::test]
+    async fn failure_retains_token_and_terminates() {
+        let mut listing =
+            Listing::<String>::new(ListingOptions::default().page_token("retry"), |_| async {
+                Err(Error::Runtime {
+                    message: "failed".into(),
+                })
+            });
+        assert!(listing.next().await.unwrap().is_err());
+        assert_eq!(listing.page_token(), Some("retry"));
+        assert_eq!(listing.num_page_results(), 0);
+        assert!(listing.next().await.is_none());
+    }
+
+    #[tokio::test]
+    async fn invalid_limits_do_not_fetch() {
+        for limit in [0, u32::MAX] {
+            let mut listing =
+                Listing::<String>::new(ListingOptions::default().page_limit(limit), |_| async {
+                    panic!("invalid limits must not reach the backend")
+                });
+            assert!(matches!(
+                listing.next().await,
+                Some(Err(Error::InvalidInput { .. }))
+            ));
+            assert!(listing.next().await.is_none());
+        }
+    }
+
+    #[tokio::test]
+    async fn jobs_can_exceed_the_old_page_cap() {
+        let listing = Listing::new(ListingOptions::default(), |options| async move {
+            let n: usize = options
+                .page_token
+                .as_deref()
+                .unwrap_or("0")
+                .parse()
+                .unwrap();
+            Ok(ListingPage {
+                items: vec![n],
+                page_token: (n < 110).then(|| (n + 1).to_string()),
+            })
+        });
+        assert_eq!(
+            listing.try_collect::<Vec<_>>().await.unwrap(),
+            (0..=110).collect::<Vec<_>>()
+        );
+    }
+}
diff --git a/rust/lancedb/src/materialized_view.rs b/rust/lancedb/src/materialized_view.rs
index 616bd42d4..c04a6cbbe 100644
--- a/rust/lancedb/src/materialized_view.rs
+++ b/rust/lancedb/src/materialized_view.rs
@@ -7,102 +7,28 @@
 //! one source table and maintained by refresh rather than by writes. Creation
 //! commits an empty table carrying the defining query in schema metadata; a
 //! query this version cannot maintain reads back as unrefreshable, not as a
-//! plain table. Queries, indexes and search work on the view unchanged.
+//! plain table. Queries, indexes and search work on the view unchanged;
+//! writes to its rows or stored columns are refused.
 
-mod duplicate_pairs;
 mod grouped;
-mod grouped_units;
-mod partitioned;
-mod vector_dedup;
-pub use duplicate_pairs::{VectorSource, VectorSourceKind};
 pub use grouped::IVF_PARTITION;
-/// Refreshing a view grouped by [`IVF_PARTITION`] in units, one per index
-/// partition, so the work can be spread over several processes. The caller
-/// plans once, computes every unit of that plan wherever it likes, and
-/// commits the whole set; a unit is bound to the view and plan it was
-/// computed for, and the commit publishes all of them or none.
-///
-/// ```no_run
-/// # #![recursion_limit = "256"]
-/// # use lancedb::materialized_view::{
-/// #     commit_grouped_refresh, plan_grouped_refresh, write_grouped_unit, WrittenUnit,
-/// # };
-/// # use lancedb::materialized_view::MaterializedView;
-/// # async fn refresh_in_units(view: &MaterializedView) -> Result<(), Box<dyn std::error::Error>> {
-/// // The view this refresh was requested for. Every path below carries it,
-/// // including the fallbacks: a view dropped and recreated meanwhile is a
-/// // different view, and refreshing it in place of this one is the identity
-/// // crossing the units path refuses.
-/// let incarnation = view.incarnation().map(str::to_string);
-///
-/// async fn in_one_pass(view: &MaterializedView, incarnation: Option<&str>) -> lancedb::Result<()> {
-///     let mut refresh = view.refresh();
-///     if let Some(token) = incarnation {
-///         refresh = refresh.expect_incarnation(token);
-///     }
-///     refresh.execute().await?;
-///     Ok(())
-/// }
-///
-/// // No plan means this view cannot be refreshed in units: it is not
-/// // grouped by the partition of an indexed column, its index no longer
-/// // says which fragments it covers, or it carries no incarnation to bind
-/// // the units to.
-/// let Some(plan) = plan_grouped_refresh(view.table(), None).await? else {
-///     in_one_pass(view, incarnation.as_deref()).await?;
-///     return Ok(());
-/// };
-///
-/// // Each unit is independent; this loop stands in for dispatching them.
-/// // A unit whose view was dropped and recreated, or whose plan is stale,
-/// // fails here rather than producing rows for the wrong view.
-/// let mut units: Vec<WrittenUnit> = Vec::new();
-/// for unit in 0..plan.units {
-///     units.push(write_grouped_unit(view.table(), unit, &plan).await?);
-/// }
-///
-/// match commit_grouped_refresh(view.table(), &plan, units, incarnation.as_deref()).await {
-///     Ok(result) => println!("{} rows at version {}", result.rows_written, result.version),
-///     Err(err) => {
-///         // The refresh is unrecorded, but it is not necessarily inert: a
-///         // commit that raced a concurrent write can have landed without a
-///         // watermark. Refresh from scratch rather than assuming either.
-///         eprintln!("refresh in units unrecorded ({err}); refreshing in one pass");
-///         in_one_pass(view, incarnation.as_deref()).await?;
-///     }
-/// }
-/// # Ok(())
-/// # }
-/// ```
-pub use grouped_units::{
-    GroupedRefreshPlan, WrittenUnit, commit_grouped_refresh, plan_grouped_refresh,
-    write_grouped_unit,
-};
-pub use partitioned::{
-    PartitionedRefreshPlan, WrittenPartition, commit_partitioned_refresh, plan_partitioned_refresh,
-    write_refresh_partition, write_refresh_partition_with_inputs,
-};
 mod query;
 pub mod refresh;
 
-#[cfg(test)]
-mod differential;
-
 use std::collections::HashMap;
 use std::sync::Arc;
 
 use arrow_schema::{DataType, Field as ArrowField, FieldRef, Schema as ArrowSchema, SchemaRef};
 use datafusion_common::ScalarValue;
-use lance_core::ROW_ID;
 use lance_datafusion::planner::Planner;
 use serde::{Deserialize, Serialize};
 
 use crate::connection::Connection;
-use crate::database::listing::OPT_NEW_TABLE_ENABLE_STABLE_ROW_IDS;
 use crate::database::{CreateTableRequest, Database, OpenTableRequest};
 use crate::embeddings::EmbeddingDefinition;
 use crate::function::FunctionBinding;
 use crate::job::Job;
+use crate::listing::{Listing, ListingOptions};
 use crate::table::Table;
 use crate::table::computed_columns::{
     FUNCTION_BINDINGS_META_KEY, computed_column_from_field, computed_columns,
@@ -116,26 +42,6 @@ pub use refresh::{RefreshMaterializedViewResult, RefreshMode};
 /// Schema metadata key holding the view definition; see [`DEFINITION_FORMAT`].
 pub const DEFINITION_META_KEY: &str = "mv.definition";
 
-/// Schema metadata key holding the view's incarnation: a token minted at each
-/// physical creation of a view table, so a view dropped and recreated under
-/// the same name and definition is still told apart from the one a caller
-/// captured. A view whose metadata was replaced wholesale, or one declared
-/// before tokens existed, carries none until its next refresh mints one.
-pub const INCARNATION_META_KEY: &str = "mv.incarnation";
-
-/// Schema metadata key holding the source table version the view was last
-/// refreshed to. Absent until the first refresh.
-pub const SOURCE_VERSION_META_KEY: &str = "mv.source_version";
-
-/// Schema metadata key holding the wall-clock time of the last refresh,
-/// in milliseconds since the epoch.
-pub const REFRESHED_AT_MS_META_KEY: &str = "mv.refreshed_at_ms";
-
-/// Column recording which source row produced each view row: the source's
-/// stable `_rowid` at refresh time, which is why sources must keep stable
-/// row ids.
-pub const SOURCE_ROW_ID_COLUMN: &str = "__source_row_id";
-
 /// Field metadata namespace for declarations about schema structure, such as
 /// an unenforced primary key.
 const SCHEMA_DECLARATION_META_PREFIX: &str = "lance-schema:";
@@ -154,26 +60,16 @@ const COLUMN_DEFINITIONS_META_KEY: &str = "lancedb::column_definitions";
 
 /// The newest layout this version reads under [`DEFINITION_META_KEY`]:
 /// `{"format": N, "query": "<SQL>"}`, the query as
-/// [`MaterializedViewDefinition::to_sql`] renders it. Format 3 identifies a
-/// native duplicate-pair source, format 4 a dedup result source, format 2 a query with `GROUP BY`, and
-/// format 1 an ordinary query. A reader refuses a newer format rather than
+/// [`MaterializedViewDefinition::to_sql`] renders it. Format 2 identifies a
+/// query with `GROUP BY`, and format 1 an ordinary query. A reader refuses a newer format rather than
 /// guess at it. The layout also carries `"kind": "query"`, which readers
 /// older than the format number report as an unrefreshable view instead of
 /// failing to read the metadata.
-pub const DEFINITION_FORMAT: u64 = 4;
+pub const DEFINITION_FORMAT: u64 = 2;
 
 /// The format `definition` is written in; see [`DEFINITION_FORMAT`].
 fn format_of(definition: &MaterializedViewDefinition) -> u64 {
-    if let Some(source) = &definition.vector_source {
-        match source.kind {
-            VectorSourceKind::Pairs => 3,
-            VectorSourceKind::Dedup => 4,
-        }
-    } else if definition.is_grouped() {
-        2
-    } else {
-        1
-    }
+    if definition.is_grouped() { 2 } else { 1 }
 }
 
 /// Legacy `kind` tag of the structured layout written before
@@ -236,9 +132,8 @@ pub enum LateralSource {
         /// The list column.
         column: String,
     },
-    /// `name(args)`: a Function in `FROM` position, returning rows. The
-    /// server stages its output in a hidden table, recorded under
-    /// [`STAGING_META_KEY`]; a local database cannot refresh this form.
+    /// `name(args)`: a Function in `FROM` position, returning rows. Local
+    /// databases do not execute table functions during refresh.
     Function {
         /// The Function's name.
         name: String,
@@ -254,37 +149,17 @@ pub(crate) struct ViewUnnest {
     pub alias: String,
 }
 
-/// Schema metadata key holding a [`StagingBinding`], present only on a view
-/// whose query calls a Function in `FROM` position.
-pub const STAGING_META_KEY: &str = "mv.staging";
-
-/// Where a Function in `FROM` position has its output staged: a hidden
-/// table carrying every source column plus `column`, the Function's list
-/// output. Refresh scans this table in place of the query's source.
-#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
-pub struct StagingBinding {
-    /// The staging table's name.
-    pub table: String,
-    /// Its namespace path; empty is the root namespace.
-    #[serde(default, skip_serializing_if = "Vec::is_empty")]
-    pub namespace: Vec<String>,
-    /// The list column holding the Function's output.
-    pub column: String,
-}
-
-/// The list column refresh unnests for `definition`, given the staging its
-/// Function output lives in. `None` when the query has no lateral item.
+/// The list column refresh unnests for `definition`. `None` when the query
+/// has no lateral item.
 pub(crate) fn physical_unnest(
     definition: &MaterializedViewDefinition,
-    staging: Option<&StagingBinding>,
 ) -> Result<Option<ViewUnnest>> {
     let Some(lateral) = &definition.lateral else {
         return Ok(None);
     };
-    let column = match (&lateral.source, staging) {
-        (LateralSource::Unnest { column }, _) => column.clone(),
-        (LateralSource::Function { .. }, Some(staging)) => staging.column.clone(),
-        (LateralSource::Function { name, .. }, None) => {
+    let column = match &lateral.source {
+        LateralSource::Unnest { column } => column.clone(),
+        LateralSource::Function { name, .. } => {
             return Err(Error::NotSupported {
                 message: format!(
                     "'{name}' in FROM position is a Function; views over Function rows are \
@@ -299,25 +174,11 @@ pub(crate) fn physical_unnest(
     }))
 }
 
-/// Read the staging binding off a view's schema metadata, if it has one.
-pub fn read_staging(metadata: &HashMap<String, String>) -> Result<Option<StagingBinding>> {
-    metadata
-        .get(STAGING_META_KEY)
-        .map(|raw| {
-            serde_json::from_str(raw).map_err(|e| Error::Runtime {
-                message: format!("unreadable materialized view staging binding: {e}"),
-            })
-        })
-        .transpose()
-}
-
 /// The query that defines a materialized view, in the relational shape
 /// refresh maintains. Stored as SQL; see [`MaterializedViewDefinition::from_sql`]
 /// for the shape.
 #[derive(Debug, Clone, PartialEq, Eq)]
 pub struct MaterializedViewDefinition {
-    /// A table-bound native vector source. It is never evaluated per source row.
-    pub vector_source: Option<Box<VectorSource>>,
     /// Name of the source table, in the same database as the view.
     pub source_table: String,
     /// Namespace path holding the source table; empty is the root namespace.
@@ -408,10 +269,10 @@ pub enum StoredDefinition {
 #[doc(hidden)]
 #[derive(Debug, Clone, PartialEq, Eq)]
 pub struct MaterializedViewInfo {
-    /// The parsed view definition.
-    pub definition: MaterializedViewDefinition,
-    /// The current physical incarnation, when one has been minted.
-    pub incarnation: Option<String>,
+    /// The defining SQL as stored.
+    pub definition_sql: String,
+    /// The definition parsed into the local refresh grammar, when supported.
+    pub parsed_definition: Option<MaterializedViewDefinition>,
 }
 
 /// The backend-independent request used to create a remote materialized view.
@@ -456,12 +317,78 @@ struct LegacyDefinition {
 /// Serialize `definition` into the layout stored under
 /// [`DEFINITION_META_KEY`].
 pub(crate) fn definition_to_metadata(definition: &MaterializedViewDefinition) -> Result<String> {
-    Ok(serde_json::json!({
+    Ok(definition_metadata_from_sql_with_format(
+        &definition.to_sql(),
+        format_of(definition),
+    ))
+}
+
+/// Serialize an arbitrary SQL definition into the materialized-view metadata
+/// envelope. Sophon uses this for SQL queries that are broader than the local
+/// refresh engine's supported shape.
+pub fn definition_metadata_from_sql(sql: &str) -> String {
+    definition_metadata_from_sql_with_format(sql, 1)
+}
+
+/// Serialize an arbitrary SQL definition together with the resolution
+/// defaults needed to plan unqualified identifiers on a later refresh.
+pub fn definition_metadata_from_sql_with_defaults(
+    sql: &str,
+    default_database: &str,
+    default_namespace_path: &[String],
+) -> String {
+    serde_json::json!({
         "kind": "query",
-        "format": format_of(definition),
-        "query": definition.to_sql(),
+        "format": 1,
+        "query": sql,
+        "default_database": default_database,
+        "default_namespace_path": default_namespace_path,
     })
-    .to_string())
+    .to_string()
+}
+
+fn definition_metadata_from_sql_with_format(sql: &str, format: u64) -> String {
+    serde_json::json!({
+        "kind": "query",
+        "format": format,
+        "query": sql,
+    })
+    .to_string()
+}
+
+/// Return the defining SQL stored in [`DEFINITION_META_KEY`], without
+/// requiring the local refresh parser to understand it.
+pub fn read_definition_sql(metadata: &HashMap<String, String>) -> Result<Option<String>> {
+    Ok(read_definition_sql_with_defaults(metadata)?.map(|definition| definition.query))
+}
+
+/// A stored SQL definition and the optional name-resolution defaults recorded
+/// by a remote SQL engine.
+#[derive(Debug, Clone, PartialEq, Eq, Deserialize)]
+pub struct MaterializedViewSqlDefinition {
+    /// The SQL query that defines the materialized view.
+    pub query: String,
+    /// The database used to resolve unqualified references when the view was created.
+    #[serde(default)]
+    pub default_database: Option<String>,
+    /// The namespace used to resolve unqualified references when the view was created.
+    #[serde(default)]
+    pub default_namespace_path: Option<Vec<String>>,
+}
+
+/// Read the SQL definition and its optional resolution defaults from
+/// [`DEFINITION_META_KEY`].
+pub fn read_definition_sql_with_defaults(
+    metadata: &HashMap<String, String>,
+) -> Result<Option<MaterializedViewSqlDefinition>> {
+    let Some(raw) = metadata.get(DEFINITION_META_KEY) else {
+        return Ok(None);
+    };
+    serde_json::from_str(raw)
+        .map(Some)
+        .map_err(|e| Error::Runtime {
+            message: format!("unreadable materialized view definition: {e}"),
+        })
 }
 
 /// Read a view definition off a schema metadata map, if it carries one.
@@ -516,7 +443,6 @@ pub fn read_definition(metadata: &HashMap<String, String>) -> Result<Option<Stor
         )));
     }
     Ok(Some(StoredDefinition::Query(MaterializedViewDefinition {
-        vector_source: None,
         source_table: legacy.source_table,
         source_namespace: legacy.source_namespace,
         lateral: None,
@@ -527,15 +453,32 @@ pub fn read_definition(metadata: &HashMap<String, String>) -> Result<Option<Stor
     })))
 }
 
+/// Refuse a user write to `name` when it is a materialized view, whose rows
+/// only refresh writes. Any stored definition counts, even one this version
+/// cannot read.
+pub(crate) fn ensure_not_a_view(
+    name: &str,
+    metadata: &HashMap<String, String>,
+    operation: &str,
+) -> Result<()> {
+    if metadata.contains_key(DEFINITION_META_KEY) {
+        return Err(Error::NotSupported {
+            message: format!(
+                "cannot {operation} '{name}': it is a materialized view, which only refresh writes"
+            ),
+        });
+    }
+    Ok(())
+}
+
 pub(crate) fn materialized_view_info_from_metadata(
     name: &str,
     metadata: &HashMap<String, String>,
 ) -> Result<MaterializedViewInfo> {
-    let incarnation = metadata.get(INCARNATION_META_KEY).cloned();
     match read_definition(metadata)? {
         Some(StoredDefinition::Query(definition)) => Ok(MaterializedViewInfo {
-            definition,
-            incarnation,
+            definition_sql: definition.to_sql(),
+            parsed_definition: Some(definition),
         }),
         Some(StoredDefinition::Newer { format }) => Err(Error::NotSupported {
             message: format!(
@@ -563,19 +506,14 @@ pub(crate) struct Planned {
     pub fields: Vec<ArrowField>,
     pub lineage: Lineage,
     /// Source columns the query reads; a read through the unnest alias is
-    /// recorded as the list column, which is what the source has and what
-    /// incremental refresh watches.
+    /// recorded as the list column present in the source schema.
     pub inputs: Vec<String>,
 }
 
 pub(crate) fn plan(
     source_schema: SchemaRef,
     definition: &MaterializedViewDefinition,
-    staging: Option<&StagingBinding>,
 ) -> Result<Planned> {
-    if definition.vector_source.is_some() {
-        return duplicate_pairs::plan(source_schema, definition);
-    }
     let filter = definition
         .filter
         .as_deref()
@@ -593,7 +531,7 @@ pub(crate) fn plan(
     // Projections are typed against the source, or for an unnested view
     // against the source with the list column replaced by its element
     // under the alias, where `c.chunk` is an ordinary nested path.
-    let unnest = physical_unnest(definition, staging)?;
+    let unnest = physical_unnest(definition)?;
     let source_schema = match &unnest {
         None => source_schema,
         Some(unnest) => {
@@ -607,12 +545,9 @@ pub(crate) fn plan(
         }
     };
     let projections: Vec<(String, String)> = if definition.selects_star() {
-        // `SELECT *`. A source that is itself a view carries its own
-        // provenance column; the new view records its own, not a copy.
         source_schema
             .fields()
             .iter()
-            .filter(|f| f.name() != SOURCE_ROW_ID_COLUMN)
             .map(|f| (f.name().clone(), query::ident_sql(f.name())))
             .collect()
     } else {
@@ -653,25 +588,12 @@ pub(crate) fn plan(
                 name: output.clone(),
             });
         }
-        if output == SOURCE_ROW_ID_COLUMN || output == ROW_ID {
-            return Err(Error::InvalidInput {
-                message: format!("view column name '{output}' is reserved"),
-            });
-        }
-
         let parsed = planner
             .parse_expr(expression)
             .map_err(|e| Error::InvalidExpression {
                 column: output.clone(),
                 message: e.to_string(),
             })?;
-        // Before optimization: the simplifier folds a stable-but-not-immutable
-        // call like now() into a literal, hiding it from the check while the
-        // stored definition keeps the call.
-        ensure_immutable(&parsed, |message| Error::InvalidExpression {
-            column: output.clone(),
-            message,
-        })?;
         let expr = planner
             .optimize_expr(parsed)
             .map_err(|e| Error::InvalidExpression {
@@ -734,7 +656,6 @@ pub(crate) fn plan(
     }
 
     let definition = MaterializedViewDefinition {
-        vector_source: None,
         source_table: definition.source_table.clone(),
         source_namespace: definition.source_namespace.clone(),
         lateral: definition.lateral.clone(),
@@ -760,17 +681,14 @@ pub(crate) fn plan(
     })
 }
 
-/// Check that `filter` is an immutable boolean predicate over `source_schema`,
-/// returning the columns it reads.
+/// Check that `filter` is a boolean predicate over `source_schema`, returning
+/// the columns it reads.
 pub(crate) fn plan_filter(source_schema: &SchemaRef, filter: &str) -> Result<Vec<String>> {
     let expr = Planner::new(source_schema.clone())
         .parse_filter(filter)
         .map_err(|e| Error::InvalidInput {
             message: format!("invalid view filter: {e}"),
         })?;
-    ensure_immutable(&expr, |message| Error::InvalidInput {
-        message: format!("invalid view filter: {message}"),
-    })?;
     let filter_inputs = resolve_inputs(source_schema, &expr, |message| Error::InvalidInput {
         message: format!("invalid view filter: {message}"),
     })?;
@@ -839,41 +757,6 @@ pub(crate) fn flattened_schema(
     Ok(Arc::new(ArrowSchema::new(fields)))
 }
 
-/// Reject any function that is not immutable: a view definition has to
-/// evaluate identically across refreshes, or incremental maintenance would
-/// mix rows from different evaluations of the same definition.
-fn ensure_immutable(expr: &datafusion_expr::Expr, error: impl Fn(String) -> Error) -> Result<()> {
-    use datafusion_common::tree_node::{TreeNode, TreeNodeRecursion};
-    use datafusion_expr::Volatility;
-
-    // Labeled immutable but not determined by row values alone: version()
-    // depends on the build, the arrow_* introspectors on schema state.
-    const NOT_VALUE_DETERMINED: &[&str] =
-        &["version", "arrow_typeof", "arrow_field", "arrow_metadata"];
-
-    let mut offending: Option<String> = None;
-    expr.apply(|node| {
-        if let datafusion_expr::Expr::ScalarFunction(function) = node {
-            let name = function.func.name();
-            if function.func.signature().volatility != Volatility::Immutable
-                || NOT_VALUE_DETERMINED.contains(&name)
-            {
-                offending = Some(name.to_string());
-                return Ok(TreeNodeRecursion::Stop);
-            }
-        }
-        Ok(TreeNodeRecursion::Continue)
-    })
-    .map_err(|e| error(e.to_string()))?;
-    match offending {
-        Some(name) => Err(error(format!(
-            "function '{name}' is not immutable and would evaluate differently \
-             across refreshes"
-        ))),
-        None => Ok(()),
-    }
-}
-
 /// The root of a possibly-dotted column path: `metadata.age` -> `metadata`.
 fn root(path: &str) -> &str {
     path.split('.').next().unwrap_or(path)
@@ -1125,7 +1008,7 @@ fn project_schema(schema: &ArrowSchema, columns: &[String]) -> SchemaRef {
 }
 
 /// A validated view declaration, ready to become a table: the projected
-/// fields plus [`SOURCE_ROW_ID_COLUMN`], definition stamped in metadata.
+/// fields with the definition stamped in metadata.
 /// Produced only by [`prepare_declaration`].
 #[derive(Clone)]
 pub struct PreparedDeclaration {
@@ -1158,8 +1041,7 @@ impl PreparedDeclaration {
     }
 
     /// The schema the view will have: the declared columns in order, any
-    /// internal projections added by [`PreparedDeclaration::input_column`],
-    /// then [`SOURCE_ROW_ID_COLUMN`].
+    /// internal projections added by [`PreparedDeclaration::input_column`].
     pub fn schema(&self) -> &SchemaRef {
         &self.schema
     }
@@ -1168,11 +1050,6 @@ impl PreparedDeclaration {
     /// read: the column the view projects it to, if any, otherwise an
     /// internal projection added here, named by [`input_column_name`].
     pub fn input_column(&mut self, source_column: &str) -> Result<String> {
-        if self.definition.vector_source.is_some() {
-            return Err(Error::InvalidInput {
-                message: "native vector views have no per-source-row inputs".into(),
-            });
-        }
         // A grouped view's rows are groups; no source row carries a value into one.
         if self.definition.is_grouped() {
             return Err(Error::InvalidInput {
@@ -1194,14 +1071,13 @@ impl PreparedDeclaration {
         if self.schema.field_with_name(&name).is_ok() {
             return Err(Error::ColumnAlreadyExists { name });
         }
-        let row_id = self.row_id_index()?;
         let mut fields: Vec<ArrowField> = self
             .schema
             .fields()
             .iter()
             .map(|f| f.as_ref().clone())
             .collect();
-        fields.insert(row_id, without_declarations(&field.with_name(name.clone())));
+        fields.push(without_declarations(&field.with_name(name.clone())));
         self.definition.projections.push(ViewProjection {
             output: name.clone(),
             expression: source_column
@@ -1273,11 +1149,6 @@ impl PreparedDeclaration {
         columns: Vec<(usize, ArrowField)>,
         bindings: &[FunctionBinding],
     ) -> Result<Self> {
-        if self.definition.vector_source.is_some() {
-            return Err(Error::InvalidInput {
-                message: "computed columns on native vector views are not supported".into(),
-            });
-        }
         let invalid = |message: String| Error::InvalidInput { message };
         if columns.is_empty() {
             return Err(invalid("at least one computed column is needed".into()));
@@ -1303,10 +1174,7 @@ impl PreparedDeclaration {
         columns.sort_by_key(|(position, _)| *position);
         for (inserted, (position, field)) in columns.iter().enumerate() {
             let name = field.name().as_str();
-            if name == SOURCE_ROW_ID_COLUMN
-                || name == ROW_ID
-                || name.starts_with(INPUT_COLUMN_PREFIX)
-            {
+            if name.starts_with(INPUT_COLUMN_PREFIX) {
                 return Err(invalid(format!("view column name '{name}' is reserved")));
             }
             if fields.iter().any(|f| f.name() == name) {
@@ -1348,16 +1216,8 @@ impl PreparedDeclaration {
         Ok(self)
     }
 
-    fn row_id_index(&self) -> Result<usize> {
-        self.schema
-            .index_of(SOURCE_ROW_ID_COLUMN)
-            .map_err(|e| Error::Runtime {
-                message: e.to_string(),
-            })
-    }
-
     /// Columns the declaration lists: everything before the internal
-    /// projections and the provenance column.
+    /// projections.
     fn visible_count(&self) -> usize {
         self.definition.projections.len() - self.internal_inputs
             + computed_columns(&self.schema).len()
@@ -1366,9 +1226,7 @@ impl PreparedDeclaration {
     /// Create the view table and verify it, consuming the declaration.
     ///
     /// The view goes at the root of the source's own database, where refresh
-    /// resolves the recorded source coordinate. Stable row ids are requested
-    /// at both levels and verified rather than trusted; nothing is rolled
-    /// back on failure.
+    /// resolves the recorded source coordinate.
     pub async fn create(self, name: &str) -> Result<MaterializedView> {
         self.create_in(&[], name).await
     }
@@ -1382,53 +1240,16 @@ impl PreparedDeclaration {
     ) -> Result<MaterializedView> {
         let empty: Vec<std::result::Result<arrow_array::RecordBatch, arrow_schema::ArrowError>> =
             vec![];
-        // Minted here, not at preparation: a declaration can be cloned and
-        // create more than one physical table, and each needs its own token.
-        let incarnation = uuid::Uuid::new_v4().to_string();
-        let mut metadata = self.schema.metadata().clone();
-        metadata.insert(INCARNATION_META_KEY.to_string(), incarnation.clone());
-        let schema = Arc::new(ArrowSchema::new_with_metadata(
-            self.schema.fields().clone(),
-            metadata,
-        ));
         let reader: Box<dyn arrow_array::RecordBatchReader + Send> =
-            Box::new(arrow_array::RecordBatchIterator::new(empty, schema));
+            Box::new(arrow_array::RecordBatchIterator::new(empty, self.schema));
         let mut request = CreateTableRequest::new(name.to_string(), Box::new(reader));
         request.namespace_path = namespace_path.to_vec();
-        let write_params = request
-            .write_options
-            .lance_write_params
-            .get_or_insert_with(Default::default);
-        write_params.enable_stable_row_ids = true;
-        let store_params = write_params
-            .store_params
-            .get_or_insert_with(Default::default);
-        crate::connection::merge_storage_options(
-            store_params,
-            [(
-                OPT_NEW_TABLE_ENABLE_STABLE_ROW_IDS.to_string(),
-                "true".to_string(),
-            )],
-        );
         let table = self.database.clone().create_table(request).await?;
         let table = Table::new(table, self.database);
-        let stable = match table.as_native() {
-            Some(native) => native.dataset.get().await?.manifest.uses_stable_row_ids(),
-            None => false,
-        };
-        if !stable {
-            return Err(Error::Runtime {
-                message: format!(
-                    "view '{name}' was created without stable row ids: the database \
-                     ignored the creation option; the table remains and is not \
-                     usable as a materialized view"
-                ),
-            });
-        }
         Ok(MaterializedView {
             table,
-            definition: self.definition,
-            incarnation: Some(incarnation),
+            definition_sql: self.definition.to_sql(),
+            parsed_definition: Some(self.definition),
         })
     }
 }
@@ -1484,7 +1305,6 @@ pub async fn prepare_declaration(
     limit: Option<u64>,
 ) -> Result<PreparedDeclaration> {
     let definition = MaterializedViewDefinition {
-        vector_source: None,
         source_table: source.name().to_string(),
         source_namespace: source.namespace().to_vec(),
         lateral: None,
@@ -1508,7 +1328,7 @@ pub async fn prepare_declaration(
 /// Validate `definition` over `source`, the table it names. The declaration
 /// is planned against the source as refresh will reach it, and the result
 /// creates the view with [`PreparedDeclaration::create`]. A query calling a
-/// Function in `FROM` position needs [`prepare_staged_definition`].
+/// Function in `FROM` position is not supported by local refresh.
 ///
 /// ```
 /// # #![recursion_limit = "256"]
@@ -1546,27 +1366,6 @@ pub async fn prepare_declaration(
 /// # Ok(())
 /// # }
 /// ```
-///
-/// An indexed, pinned source can materialize retained original rows using the
-/// default greedy direct-representative policy. A row is removed only when it
-/// directly matches a retained row; an A-B-C chain keeps A and C. The source is
-/// unchanged. Hosts distribute this through [`plan_partitioned_refresh`] and
-/// [`write_refresh_partition_with_inputs`], with a barrier between selection
-/// tasks and source-fragment materialization tasks.
-///
-/// ```
-/// # #![recursion_limit = "256"]
-/// use lancedb::materialized_view::{MaterializedViewDefinition, prepare_definition};
-/// # async fn dedup(images: &lancedb::Table) -> Result<(), Box<dyn std::error::Error>> {
-/// let version = images.version().await?;
-/// let definition = MaterializedViewDefinition::from_sql(&format!(
-///     "SELECT * FROM vector_dedup('images', {version}, 'phash', 4)"
-/// ))?;
-/// let clean = prepare_definition(images, definition).await?.create("images_clean").await?;
-/// clean.refresh().execute().await?;
-/// # Ok(())
-/// # }
-/// ```
 pub async fn prepare_definition(
     source: &Table,
     definition: MaterializedViewDefinition,
@@ -1583,59 +1382,12 @@ pub async fn prepare_definition(
             ),
         });
     }
-    prepare_with(source, definition, None).await
-}
-
-/// Validate a `definition` whose query calls a Function in `FROM` position,
-/// planned over `staging`: a table carrying every column of the query's
-/// source plus `column`, the Function's list output for that row. The view
-/// records the query as written and the staging under [`STAGING_META_KEY`];
-/// refresh scans the staging table and unnests `column`.
-///
-/// ```
-/// # #![recursion_limit = "256"]
-/// use lancedb::materialized_view::{MaterializedViewDefinition, prepare_staged_definition};
-///
-/// // `staging` holds every column of `docs` plus `chunks`, the list
-/// // `chunk(body)` returned for each row.
-/// # async fn declare(staging: &lancedb::Table) -> Result<(), Box<dyn std::error::Error>> {
-/// let definition = MaterializedViewDefinition::from_sql(
-///     "SELECT id, c.text, c.ordinal FROM docs, chunk(body) AS c",
-/// )?;
-/// let view = prepare_staged_definition(staging, definition, "chunks")
-///     .await?
-///     .create("chunks")
-///     .await?;
-/// view.refresh().execute().await?;
-/// # Ok(())
-/// # }
-/// ```
-pub async fn prepare_staged_definition(
-    staging: &Table,
-    definition: MaterializedViewDefinition,
-    column: impl Into<String>,
-) -> Result<PreparedDeclaration> {
-    if !matches!(
-        definition.lateral.as_ref().map(|l| &l.source),
-        Some(LateralSource::Function { .. })
-    ) {
-        return Err(Error::InvalidInput {
-            message: "only a query calling a Function in FROM position takes a staging table"
-                .into(),
-        });
-    }
-    let binding = StagingBinding {
-        table: staging.name().to_string(),
-        namespace: staging.namespace().to_vec(),
-        column: column.into(),
-    };
-    prepare_with(staging, definition, Some(binding)).await
+    prepare_with(source, definition).await
 }
 
 async fn prepare_with(
     source: &Table,
     definition: MaterializedViewDefinition,
-    staging: Option<StagingBinding>,
 ) -> Result<PreparedDeclaration> {
     let Some(caller_native) = source.as_native() else {
         return Err(Error::NotSupported {
@@ -1682,17 +1434,6 @@ async fn prepare_with(
             ),
         });
     }
-    if definition.vector_source.is_none()
-        && !native.dataset.get().await?.manifest.uses_stable_row_ids()
-    {
-        return Err(Error::InvalidInput {
-            message: format!(
-                "materialized views require stable row ids on the source table; \
-                 create '{}' with storage option new_table_enable_stable_row_ids=true",
-                source.name()
-            ),
-        });
-    }
     refresh::ensure_no_mem_wal(
         native.dataset.get().await?.as_ref(),
         "source table",
@@ -1710,45 +1451,23 @@ async fn prepare_with(
             message: format!("view column name '{}' is reserved", reserved.output),
         });
     }
-    let source_schema = if let Some(pairs) = &definition.vector_source {
-        let ds = native
-            .dataset
-            .get()
-            .await?
-            .checkout_version(pairs.dataset_version)
-            .await?;
-        crate::table::datafusion::udtf::duplicate_pairs::plan_duplicate_pairs(
-            Arc::new(ds.clone()),
-            &pairs.config()?,
-        )
-        .await?;
-        Arc::new(ArrowSchema::from(ds.schema()))
-    } else {
-        resolved.schema().await?
-    };
+    let source_schema = resolved.schema().await?;
     let source_metadata = source_schema.metadata().clone();
     let Planned {
         definition,
-        mut fields,
+        fields,
         lineage,
         ..
-    } = plan(source_schema.clone(), &definition, staging.as_ref())?;
+    } = plan(source_schema.clone(), &definition)?;
     if definition.is_grouped() {
         grouped::check(native.dataset.get().await?.as_ref(), &definition).await?;
     }
     // What later projections (`input_column`) are planned against: for an
     // unnested view the flattened schema, where the alias is a column.
-    let planning_schema = match physical_unnest(&definition, staging.as_ref())? {
+    let planning_schema = match physical_unnest(&definition)? {
         None => source_schema.clone(),
         Some(unnest) => flattened_schema(&source_schema, &unnest)?,
     };
-    if definition.vector_source.is_none() {
-        fields.push(ArrowField::new(
-            SOURCE_ROW_ID_COLUMN,
-            DataType::UInt64,
-            false,
-        ));
-    }
     // Only column-describing metadata comes along: structural declarations
     // describe how a table is written, and a view is written by refresh alone.
     let mut metadata: HashMap<String, String> = HashMap::new();
@@ -1766,14 +1485,6 @@ async fn prepare_with(
         DEFINITION_META_KEY.to_string(),
         definition_to_metadata(&definition)?,
     );
-    if let Some(staging) = &staging {
-        metadata.insert(
-            STAGING_META_KEY.to_string(),
-            serde_json::to_string(staging).map_err(|e| Error::Runtime {
-                message: format!("failed to serialize the staging binding: {e}"),
-            })?,
-        );
-    }
     Ok(PreparedDeclaration {
         schema: Arc::new(ArrowSchema::new_with_metadata(fields, metadata)),
         definition,
@@ -1792,13 +1503,31 @@ pub struct CreateMaterializedViewBuilder {
     namespace: Vec<String>,
     source: String,
     source_namespace: Vec<String>,
-    vector_source: Option<VectorSource>,
     projections: Vec<(String, String)>,
     filter: Option<String>,
     limit: Option<u64>,
     with_no_data: bool,
 }
 
+fn source_sql_identifier(
+    namespace_path: &[String],
+    source: &str,
+    namespace_in_schema_position: bool,
+) -> String {
+    fn quote(name: &str) -> String {
+        format!("\"{}\"", name.replace('"', "\"\""))
+    }
+
+    let namespace = if namespace_in_schema_position && !namespace_path.is_empty() {
+        vec![namespace_path.join("$")]
+    } else {
+        namespace_path.to_vec()
+    };
+    let mut parts = namespace.iter().map(|part| quote(part)).collect::<Vec<_>>();
+    parts.push(quote(source));
+    parts.join(".")
+}
+
 impl CreateMaterializedViewBuilder {
     pub(crate) fn new(connection: Connection, name: String, source: String) -> Self {
         Self {
@@ -1807,7 +1536,6 @@ impl CreateMaterializedViewBuilder {
             namespace: Vec::new(),
             source,
             source_namespace: Vec::new(),
-            vector_source: None,
             projections: Vec::new(),
             filter: None,
             limit: None,
@@ -1828,38 +1556,6 @@ impl CreateMaterializedViewBuilder {
         self
     }
 
-    /// Read native pairs or retained original rows from a fixed indexed snapshot.
-    /// This source selects its full output schema and cannot be combined with
-    /// projections, filters or limits. Remote creation uses the ordinary MV
-    /// endpoint and its registry job; it does not execute a SQL query client-side.
-    pub fn vector_source(mut self, source: VectorSource) -> Self {
-        self.vector_source = Some(source);
-        self
-    }
-
-    fn native_definition(&self) -> Result<Option<MaterializedViewDefinition>> {
-        let Some(source) = &self.vector_source else {
-            return Ok(None);
-        };
-        source.config()?;
-        if !self.projections.is_empty() || self.filter.is_some() || self.limit.is_some() {
-            return Err(Error::InvalidInput {
-                message: "native vector sources cannot be combined with select, where or limit"
-                    .into(),
-            });
-        }
-        Ok(Some(MaterializedViewDefinition {
-            vector_source: Some(Box::new(source.clone())),
-            source_table: self.source.clone(),
-            source_namespace: self.source_namespace.clone(),
-            projections: vec![ViewProjection::star()],
-            filter: None,
-            lateral: None,
-            group_by: Vec::new(),
-            limit: None,
-        }))
-    }
-
     /// The view's columns, as `(name, SQL expression)` pairs. Not calling
     /// this selects every source column, expanded at creation time.
     pub fn select(
@@ -1893,9 +1589,6 @@ impl CreateMaterializedViewBuilder {
     }
 
     fn query(&self) -> Result<String> {
-        if let Some(definition) = self.native_definition()? {
-            return Ok(definition.to_sql());
-        }
         fn quote(name: &str) -> String {
             format!("\"{}\"", name.replace('"', "\"\""))
         }
@@ -1909,13 +1602,11 @@ impl CreateMaterializedViewBuilder {
                 .collect::<Vec<_>>()
                 .join(", ")
         };
-        let source = self
-            .source_namespace
-            .iter()
-            .chain(std::iter::once(&self.source))
-            .map(|part| quote(part))
-            .collect::<Vec<_>>()
-            .join(".");
+        let source = source_sql_identifier(
+            &self.source_namespace,
+            &self.source,
+            self.connection.uri().starts_with("db://"),
+        );
         let mut query = format!("SELECT {projection} FROM {source}");
         if let Some(filter) = &self.filter {
             query.push_str(" WHERE ");
@@ -1928,9 +1619,7 @@ impl CreateMaterializedViewBuilder {
     }
 
     /// Submit creation and initial population, returning a [`Job`] that
-    /// settles when the view is ready. Ordinary sources must keep stable row
-    /// ids for provenance across compaction. Native vector sources also support
-    /// physical row IDs because their definitions pin the dataset version.
+    /// settles when the view is ready.
     pub async fn execute_async(self) -> Result<Job> {
         if self.connection.uri().starts_with("db://") {
             return self
@@ -1967,24 +1656,19 @@ impl CreateMaterializedViewBuilder {
     }
 
     async fn execute_native(self) -> Result<MaterializedView> {
-        let native_definition = self.native_definition()?;
         let source = self
             .connection
             .open_table(&self.source)
             .namespace(self.source_namespace.clone())
             .execute()
             .await?;
-        let prepared = if let Some(definition) = native_definition {
-            prepare_definition(&source, definition).await?
-        } else {
-            prepare_declaration(
-                &source,
-                (!self.projections.is_empty()).then_some(self.projections.as_slice()),
-                self.filter.as_deref(),
-                self.limit,
-            )
-            .await?
-        };
+        let prepared = prepare_declaration(
+            &source,
+            (!self.projections.is_empty()).then_some(self.projections.as_slice()),
+            self.filter.as_deref(),
+            self.limit,
+        )
+        .await?;
         let view = prepared.create_in(&self.namespace, &self.name).await?;
         if !self.with_no_data {
             view.refresh().execute().await?;
@@ -1993,28 +1677,29 @@ impl CreateMaterializedViewBuilder {
     }
 }
 
-/// A handle on a materialized view: the view table plus its parsed definition.
+/// A handle on a materialized view: the view table plus its defining SQL.
 #[derive(Debug, Clone)]
 pub struct MaterializedView {
     table: Table,
-    definition: MaterializedViewDefinition,
-    incarnation: Option<String>,
+    definition_sql: String,
+    parsed_definition: Option<MaterializedViewDefinition>,
 }
 
 impl MaterializedView {
     /// Interpret `table` as a materialized view: [`Error::NotAMaterializedView`]
-    /// for a plain table, [`Error::NotSupported`] for a query this version
-    /// cannot refresh.
+    /// for a plain table. A remote definition broader than the local refresh
+    /// grammar remains available through [`Self::definition_sql`].
     pub async fn from_table(table: Table) -> Result<Self> {
         let info = table.base_table().materialized_view_info().await?;
         Ok(Self {
             table,
-            definition: info.definition,
-            incarnation: info.incarnation,
+            definition_sql: info.definition_sql,
+            parsed_definition: info.parsed_definition,
         })
     }
 
-    /// The view, as the table it is. Queries, indexes and search all apply.
+    /// The view, as the table it is. Queries, indexes and search all apply;
+    /// writes such as `add`, `update`, `delete` and `merge_insert` do not.
     pub fn table(&self) -> &Table {
         &self.table
     }
@@ -2024,23 +1709,28 @@ impl MaterializedView {
         self.table.name()
     }
 
-    /// The query that defines the view.
-    pub fn definition(&self) -> &MaterializedViewDefinition {
-        &self.definition
+    /// The query parsed into the local refresh grammar.
+    ///
+    /// Sophon may store SQL broader than the local grammar; use
+    /// [`Self::definition_sql`] when the original SQL is sufficient.
+    pub fn definition(&self) -> Result<&MaterializedViewDefinition> {
+        self.parsed_definition
+            .as_ref()
+            .ok_or_else(|| Error::NotSupported {
+                message:
+                    "this materialized-view definition is supported only by the remote SQL engine"
+                        .to_string(),
+            })
     }
 
-    /// The view's incarnation token as of when this handle was opened; see
-    /// [`RefreshMaterializedViewBuilder::expect_incarnation`]. `None` for a
-    /// view that has none yet (see [`INCARNATION_META_KEY`]).
-    pub fn incarnation(&self) -> Option<&str> {
-        self.incarnation.as_deref()
+    /// The complete SQL query that defines the view.
+    pub fn definition_sql(&self) -> &str {
+        &self.definition_sql
     }
 
     /// Recompute the view from its source.
     ///
-    /// By default the refresh is incremental when the source's changes can be
-    /// reconciled into the view, and otherwise rebuilds; see
-    /// [`RefreshMaterializedViewBuilder`].
+    /// Every refresh rebuilds the view from the selected source version.
     ///
     /// ```no_run
     /// # #![recursion_limit = "256"]
@@ -2054,9 +1744,7 @@ impl MaterializedView {
     pub fn refresh(&self) -> RefreshMaterializedViewBuilder {
         RefreshMaterializedViewBuilder {
             view: self.clone(),
-            full: false,
             source_version: None,
-            expected_incarnation: None,
         }
     }
 }
@@ -2064,38 +1752,17 @@ impl MaterializedView {
 /// Builds a refresh. Created by [`MaterializedView::refresh`].
 pub struct RefreshMaterializedViewBuilder {
     view: MaterializedView,
-    full: bool,
     source_version: Option<u64>,
-    expected_incarnation: Option<String>,
 }
 
 impl RefreshMaterializedViewBuilder {
-    /// Rebuild the view even where an incremental refresh would do.
-    pub fn full(mut self, full: bool) -> Self {
-        self.full = full;
-        self
-    }
-
-    /// Refresh to this source table version instead of the latest.
+    /// Refresh a local view to this source table version instead of the latest.
+    /// Remote SQL refreshes do not support source-version pinning.
     pub fn source_version(mut self, version: u64) -> Self {
         self.source_version = Some(version);
         self
     }
 
-    /// Refresh only if the view is still the incarnation that minted `token`
-    /// (see [`MaterializedView::incarnation`]): a refresh requested against
-    /// one declaration must not land in a view dropped and recreated since,
-    /// even under the same name and definition.
-    ///
-    /// Best effort. The token is read from the latest stored manifest before
-    /// planning and again immediately before every commit, but it is not part
-    /// of the commit's own condition, so a recreation that lands between that
-    /// final read and the commit is not caught.
-    pub fn expect_incarnation(mut self, token: impl Into<String>) -> Self {
-        self.expected_incarnation = Some(token.into());
-        self
-    }
-
     /// Submit the refresh and return a job that settles with its result.
     pub async fn execute_async(self) -> Result<Job<RefreshMaterializedViewResult>> {
         if self.view.table.as_native().is_none() {
@@ -2103,34 +1770,18 @@ impl RefreshMaterializedViewBuilder {
                 .view
                 .table
                 .base_table()
-                .refresh_materialized_view_async(
-                    self.full,
-                    self.source_version,
-                    self.expected_incarnation.as_deref(),
-                )
+                .refresh_materialized_view_async(self.source_version)
                 .await;
         }
         Ok(Job::spawned(tokio::spawn(async move {
-            refresh::execute_refresh(
-                &self.view.table,
-                self.full,
-                self.source_version,
-                self.expected_incarnation.as_deref(),
-            )
-            .await
+            refresh::execute_refresh(&self.view.table, self.source_version).await
         })))
     }
 
     /// Refresh the view, waiting for the job to finish.
     pub async fn execute(self) -> Result<RefreshMaterializedViewResult> {
         if self.view.table.as_native().is_some() {
-            return refresh::execute_refresh(
-                &self.view.table,
-                self.full,
-                self.source_version,
-                self.expected_incarnation.as_deref(),
-            )
-            .await;
+            return refresh::execute_refresh(&self.view.table, self.source_version).await;
         }
         self.execute_async().await?.wait().await
     }
@@ -2175,8 +1826,26 @@ impl Connection {
     }
 
     /// The names of materialized views in the root namespace.
-    pub async fn list_materialized_views(&self) -> Result<Vec<String>> {
-        self.database().list_materialized_views(&[]).await
+    ///
+    /// Results are fetched lazily, one page at a time. See [`Listing`]
+    /// for cached-result and continuation-token semantics. Errors terminate iteration.
+    ///
+    /// ```
+    /// # async fn example(connection: &lancedb::Connection) -> lancedb::Result<()> {
+    /// use futures::TryStreamExt;
+    /// let mut items = connection.list_materialized_views(Default::default());
+    /// while let Some(item) = items.try_next().await? {
+    ///     println!("{item:?}");
+    /// }
+    /// # Ok(())
+    /// # }
+    /// ```
+    pub fn list_materialized_views(&self, options: ListingOptions) -> Listing<String> {
+        let database = self.database().clone();
+        Listing::new(options, move |options| {
+            let database = database.clone();
+            async move { database.list_materialized_views(&[], options).await }
+        })
     }
 
     /// Drop a materialized view.
@@ -2245,2037 +1914,332 @@ impl Connection {
 
 #[cfg(test)]
 mod tests {
-    use arrow_array::record_batch;
+    use arrow_array::{RecordBatch, record_batch};
 
     use super::*;
     use crate::connect;
-    use crate::table::WriteOptions;
 
-    async fn people_db() -> Connection {
+    #[test]
+    fn remote_source_namespace_uses_one_sql_schema_position() {
+        let namespace = vec!["analytics".to_string(), "daily".to_string()];
+        assert_eq!(
+            source_sql_identifier(&namespace, "source", true),
+            "\"analytics$daily\".\"source\""
+        );
+        assert_eq!(
+            source_sql_identifier(&namespace, "source", false),
+            "\"analytics\".\"daily\".\"source\""
+        );
+    }
+
+    #[test]
+    fn sql_definition_defaults_share_one_metadata_value() {
+        let metadata = HashMap::from([(
+            DEFINITION_META_KEY.to_string(),
+            definition_metadata_from_sql_with_defaults(
+                "SELECT * FROM source",
+                "db",
+                &["analytics".to_string()],
+            ),
+        )]);
+
+        let definition = read_definition_sql_with_defaults(&metadata)
+            .unwrap()
+            .unwrap();
+        assert_eq!(definition.query, "SELECT * FROM source");
+        assert_eq!(definition.default_database.as_deref(), Some("db"));
+        assert_eq!(
+            definition.default_namespace_path,
+            Some(vec!["analytics".to_string()])
+        );
+    }
+
+    #[tokio::test]
+    async fn refresh_always_rebuilds_without_source_row_ids() {
         let conn = connect("memory://").execute().await.unwrap();
-        let batch = record_batch!(
-            ("name", Utf8, ["ada", "grace", "alan"]),
-            ("age", Int32, [36, 85, 41])
-        )
-        .unwrap();
-        conn.create_table("people", batch)
-            .write_options(stable_row_ids())
+        let source = conn
+            .create_table("src", record_batch!(("x", Int32, [1, 2])).unwrap())
             .execute()
             .await
             .unwrap();
-        conn
-    }
-
-    /// Sources must keep stable row ids; see the create-time gate.
-    pub(super) fn stable_row_ids() -> WriteOptions {
-        WriteOptions {
-            lance_write_params: Some(lance::dataset::WriteParams {
-                enable_stable_row_ids: true,
-                ..Default::default()
-            }),
-        }
-    }
-
-    /// The error a doomed declaration against `people` produces.
-    async fn declare_err(
-        cfg: impl FnOnce(CreateMaterializedViewBuilder) -> CreateMaterializedViewBuilder,
-    ) -> Error {
-        let conn = people_db().await;
-        cfg(conn.create_materialized_view("bad", "people"))
-            .execute()
-            .await
-            .unwrap_err()
-    }
-
-    #[tokio::test]
-    async fn test_create_records_the_definition() {
-        let conn = people_db().await;
         let view = conn
-            .create_materialized_view("adults", "people")
-            .select([("name", "name"), ("shout", "upper(name)")])
-            .only_if("age >= 18")
-            .limit(10)
+            .create_materialized_view("copy", "src")
             .execute()
             .await
             .unwrap();
 
-        assert_eq!(view.name(), "adults");
-        assert_eq!(
-            view.definition(),
-            &MaterializedViewDefinition {
-                vector_source: None,
-                source_table: "people".into(),
-                source_namespace: Vec::new(),
-                projections: vec![
-                    ViewProjection {
-                        output: "name".into(),
-                        expression: "name".into()
-                    },
-                    ViewProjection {
-                        output: "shout".into(),
-                        expression: "upper(name)".into()
-                    },
-                ],
-                filter: Some("age >= 18".into()),
-                group_by: Vec::new(),
-                limit: Some(10),
-                lateral: None,
-            }
-        );
-
-        // The definition round-trips off the stored schema, not the handle.
-        let reopened = conn.open_materialized_view("adults").await.unwrap();
-        assert_eq!(reopened.definition(), view.definition());
-    }
-
-    #[tokio::test]
-    async fn test_view_schema_is_derived_from_the_query() {
-        let conn = people_db().await;
-        let view = conn
-            .create_materialized_view("shapes", "people")
-            .select([("shout", "upper(name)"), ("next_age", "age + 1")])
-            .execute()
-            .await
-            .unwrap();
-
-        let schema = view.table().schema().await.unwrap();
-        assert_eq!(
-            schema.field_with_name("shout").unwrap().data_type(),
-            &DataType::Utf8
-        );
-        assert_eq!(
-            schema.field_with_name("next_age").unwrap().data_type(),
-            &DataType::Int32
-        );
-        assert_eq!(
-            schema
-                .field_with_name(SOURCE_ROW_ID_COLUMN)
-                .unwrap()
-                .data_type(),
-            &DataType::UInt64
-        );
-        assert_eq!(view.table().count_rows(None).await.unwrap(), 3);
-    }
-
-    #[tokio::test]
-    async fn test_with_no_data_skips_initial_refresh() {
-        let conn = people_db().await;
-        let view = conn
-            .create_materialized_view("empty", "people")
-            .with_no_data(true)
-            .execute()
-            .await
-            .unwrap();
-        assert_eq!(view.table().count_rows(None).await.unwrap(), 0);
-    }
-
-    #[tokio::test]
-    async fn test_create_and_refresh_async_jobs() {
-        let conn = people_db().await;
-        let create_job = conn
-            .create_materialized_view("async_view", "people")
-            .with_no_data(true)
-            .execute_async()
-            .await
-            .unwrap();
-        assert!(create_job.id().is_none());
-        create_job.wait().await.unwrap();
-
-        let view = conn.open_materialized_view("async_view").await.unwrap();
-        assert_eq!(view.table().count_rows(None).await.unwrap(), 0);
-
-        let refresh_job = view.refresh().execute_async().await.unwrap();
-        assert!(refresh_job.id().is_none());
-        let result = refresh_job.wait().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Rebuild);
-        assert_eq!(result.rows_written, 3);
-        assert_eq!(view.table().count_rows(None).await.unwrap(), 3);
-
-        let drop_job = conn
-            .drop_materialized_view_async("async_view", &[])
-            .await
-            .unwrap();
-        assert!(drop_job.id().is_none());
-        drop_job.wait().await.unwrap();
-        assert!(conn.open_table("async_view").execute().await.is_err());
-    }
-
-    #[tokio::test]
-    async fn test_drop_materialized_view_rejects_plain_tables() {
-        let conn = people_db().await;
-        let error = conn
-            .drop_materialized_view("people", &[])
-            .await
-            .unwrap_err();
-        assert!(matches!(error, Error::NotAMaterializedView { .. }));
-
-        conn.create_materialized_view("drop_me", "people")
-            .with_no_data(true)
-            .execute()
-            .await
-            .unwrap();
-        conn.drop_materialized_view("drop_me", &[]).await.unwrap();
-        assert!(conn.open_table("drop_me").execute().await.is_err());
-    }
-
-    #[tokio::test]
-    async fn test_remote_query_quotes_resource_identifiers() {
-        let conn = people_db().await;
-        let query = conn
-            .create_materialized_view("unused", "odd\"source")
-            .source_namespace(vec!["raw data".into()])
-            .select([("double\"age", "age * 2")])
-            .only_if("age >= 18")
-            .limit(10)
-            .query()
-            .unwrap();
-        assert_eq!(
-            query,
-            "SELECT age * 2 AS \"double\"\"age\" FROM \"raw data\".\"odd\"\"source\" WHERE age >= 18 LIMIT 10"
-        );
-    }
-
-    #[tokio::test]
-    async fn native_vector_builder_preserves_source_and_rejects_extra_clauses() {
-        let conn = people_db().await;
-        let source = VectorSource {
-            kind: VectorSourceKind::Dedup,
-            dataset_version: 7,
-            column: "vector'field".into(),
-            distance_threshold: "4.0".into(),
-        };
-        let builder = || {
-            conn.create_materialized_view("clean", "odd.source'name")
-                .source_namespace(vec!["raw space".into()])
-                .vector_source(source.clone())
-        };
-        let query = builder().query().unwrap();
-        let definition = MaterializedViewDefinition::from_sql(&query).unwrap();
-        assert_eq!(definition.source_table, "odd.source'name");
-        assert_eq!(definition.source_namespace, ["raw space"]);
-        assert_eq!(definition.vector_source.as_deref(), Some(&source));
-        assert!(definition.selects_star());
-        assert!(builder().select([("id", "id")]).query().is_err());
-        assert!(builder().only_if("true").query().is_err());
-        assert!(builder().limit(1).query().is_err());
-        let invalid_source = VectorSource {
-            dataset_version: 0,
-            ..source
-        };
+        assert_eq!(view.table().count_rows(None).await.unwrap(), 2);
         assert!(
-            conn.create_materialized_view("bad", "people")
-                .vector_source(invalid_source)
-                .query()
+            view.table()
+                .schema()
+                .await
+                .unwrap()
+                .field_with_name("__source_row_id")
                 .is_err()
         );
-    }
 
-    /// No projection selects every source column, expanded now: the schema
-    /// captured at creation is the definition.
-    #[tokio::test]
-    async fn test_default_projection_captures_the_source_schema() {
-        let conn = people_db().await;
-        let view = conn
-            .create_materialized_view("copy", "people")
+        source
+            .add(record_batch!(("x", Int32, [3])).unwrap())
             .execute()
             .await
             .unwrap();
-        assert_eq!(
-            view.definition()
-                .projections
-                .iter()
-                .map(|p| p.output.as_str())
-                .collect::<Vec<_>>(),
-            vec!["name", "age"]
-        );
+        let refreshed = view.refresh().execute().await.unwrap();
+        assert_eq!(refreshed.mode, RefreshMode::Rebuild);
+        assert_eq!(refreshed.rows_written, 3);
+        assert_eq!(view.table().count_rows(None).await.unwrap(), 3);
+
+        let unchanged = view.refresh().execute().await.unwrap();
+        assert_eq!(unchanged.mode, RefreshMode::Rebuild);
+        assert_eq!(unchanged.rows_written, 3);
+
+        source.delete("true").await.unwrap();
+        let emptied = view.refresh().execute().await.unwrap();
+        assert_eq!(emptied.mode, RefreshMode::Rebuild);
+        assert_eq!(emptied.rows_written, 0);
+        assert_eq!(view.table().count_rows(None).await.unwrap(), 0);
     }
 
-    #[tokio::test]
-    async fn test_unknown_column_fails_at_create_time() {
-        let conn = people_db().await;
-        let err = conn
-            .create_materialized_view("bad", "people")
-            .select([("x", "missing + 1")])
-            .execute()
-            .await
-            .unwrap_err();
-        assert!(matches!(err, Error::InvalidExpression { column, .. } if column == "x"));
-        let names = conn.table_names().execute().await.unwrap();
-        assert!(!names.contains(&"bad".to_string()));
+    fn reader(batch: RecordBatch) -> Box<dyn arrow_array::RecordBatchReader + Send> {
+        let schema = batch.schema();
+        Box::new(arrow_array::RecordBatchIterator::new(
+            vec![Ok(batch)],
+            schema,
+        ))
     }
 
-    #[tokio::test]
-    async fn test_unknown_filter_column_fails_at_create_time() {
-        let err = declare_err(|b| b.only_if("missing > 1")).await;
-        assert!(matches!(err, Error::InvalidInput { message } if message.contains("missing")));
-    }
-
-    #[tokio::test]
-    async fn test_duplicate_output_is_rejected() {
-        let err = declare_err(|b| b.select([("dup", "age"), ("dup", "age + 1")])).await;
-        assert!(matches!(err, Error::ColumnAlreadyExists { name } if name == "dup"));
-    }
-
-    #[tokio::test]
-    async fn test_reserved_output_name_is_rejected() {
-        let err = declare_err(|b| b.select([(SOURCE_ROW_ID_COLUMN, "age")])).await;
-        assert!(matches!(err, Error::InvalidInput { message } if message.contains("reserved")));
-    }
-
-    #[tokio::test]
-    async fn test_missing_source_fails() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let err = conn
-            .create_materialized_view("v", "nope")
-            .execute()
-            .await
-            .unwrap_err();
-        assert!(matches!(err, Error::TableNotFound { .. }));
-    }
-
-    /// Provenance has to survive source compactions and updates, and stable
-    /// row ids cannot be enabled after a table exists -- so the requirement
-    /// is checked at the last moment the caller can still act on it.
-    #[tokio::test]
-    async fn test_source_without_stable_row_ids_is_refused() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let batch = record_batch!(("x", Int32, [1, 2])).unwrap();
-        conn.create_table("plain", batch).execute().await.unwrap();
-
-        let err = conn
-            .create_materialized_view("v", "plain")
-            .execute()
-            .await
-            .unwrap_err();
-        assert!(
-            matches!(err, Error::InvalidInput { message } if message.contains("stable row ids"))
-        );
-        assert!(
-            !conn
-                .table_names()
-                .execute()
-                .await
-                .unwrap()
-                .contains(&"v".to_string())
-        );
-    }
-
-    #[tokio::test]
-    async fn test_name_collision_fails() {
-        let conn = people_db().await;
-        let err = conn
-            .create_materialized_view("people", "people")
-            .execute()
-            .await
-            .unwrap_err();
-        assert!(matches!(err, Error::TableAlreadyExists { .. }));
-    }
-
-    #[tokio::test]
-    async fn test_a_plain_table_is_not_a_view() {
-        let conn = people_db().await;
-        let table = conn.open_table("people").execute().await.unwrap();
-        let err = MaterializedView::from_table(table).await.unwrap_err();
-        assert!(matches!(err, Error::NotAMaterializedView { name } if name == "people"));
-
-        let err = conn.open_materialized_view("people").await.unwrap_err();
-        assert!(matches!(err, Error::NotAMaterializedView { .. }));
-    }
-
-    /// The reason the kind is tagged: a definition written by a newer version
-    /// reads back as a view this one cannot refresh, not as a plain table.
-    #[tokio::test]
-    async fn test_unrecognized_kind_is_refused_by_name() {
-        let conn = people_db().await;
-        conn.create_materialized_view("v", "people")
-            .execute()
-            .await
-            .unwrap();
-        let table = conn.open_table("v").execute().await.unwrap();
-        table
-            .as_native()
-            .unwrap()
-            .replace_schema_metadata(HashMap::from([(
-                DEFINITION_META_KEY.to_string(),
-                r#"{"kind": "join"}"#.to_string(),
-            )]))
-            .await
-            .unwrap();
-
-        let err = conn.open_materialized_view("v").await.unwrap_err();
-        assert!(matches!(err, Error::NotSupported { message } if message.contains("join")));
-    }
-
-    #[tokio::test]
-    async fn test_list_reports_views_and_only_views() {
-        let conn = people_db().await;
-        conn.create_materialized_view("adults", "people")
-            .only_if("age >= 18")
-            .execute()
-            .await
-            .unwrap();
-
-        let views = conn.list_materialized_views().await.unwrap();
-        assert_eq!(views, vec!["adults"]);
-        let view = conn.open_materialized_view("adults").await.unwrap();
-        assert_eq!(view.definition().filter.as_deref(), Some("age >= 18"));
-    }
-
-    /// The creation option outranks a connection configured to create
-    /// unstable tables: the view still gets stable row ids, on the same
-    /// store (no fork -- the table must be reachable through the
-    /// connection afterwards).
-    #[tokio::test]
-    async fn test_view_is_stable_despite_connection_override() {
-        let conn = connect("memory://")
-            .storage_options([("new_table_enable_stable_row_ids", "false")])
-            .execute()
-            .await
-            .unwrap();
-        let batch = record_batch!(("x", Int32, [1])).unwrap();
-        conn.create_table("src", batch)
-            .storage_option("new_table_enable_stable_row_ids", "true")
-            .execute()
-            .await
-            .unwrap();
-
-        let view = conn
-            .create_materialized_view("v", "src")
-            .execute()
-            .await
-            .unwrap();
-        let stable = view
-            .table()
-            .as_native()
-            .unwrap()
-            .dataset
-            .get()
-            .await
-            .unwrap()
-            .manifest
-            .uses_stable_row_ids();
-        assert!(stable);
-        conn.open_materialized_view("v").await.unwrap();
-    }
-
-    /// A committed filter has to be usable as a predicate.
-    #[tokio::test]
-    async fn test_non_boolean_filter_is_rejected() {
-        let err = declare_err(|b| b.only_if("age + 1")).await;
-        assert!(matches!(err, Error::InvalidInput { message } if message.contains("boolean")));
-    }
-
-    /// Nested references stay dotted paths; resolution is by root field.
-    #[tokio::test]
-    async fn test_struct_columns_can_be_declared() {
-        use arrow_array::{ArrayRef, Int32Array, StructArray};
-
-        let conn = connect("memory://").execute().await.unwrap();
-        let ages = StructArray::from(vec![(
-            Arc::new(ArrowField::new("age", DataType::Int32, false)),
-            Arc::new(Int32Array::from(vec![36, 17])) as ArrayRef,
-        )]);
-        let batch =
-            arrow_array::RecordBatch::try_from_iter(vec![("metadata", Arc::new(ages) as ArrayRef)])
-                .unwrap();
-        conn.create_table("people", batch)
-            .write_options(stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-
-        let view = conn
-            .create_materialized_view("ages", "people")
-            .select([("age", "metadata.age")])
-            .only_if("metadata.age >= 18")
-            .execute()
-            .await
-            .unwrap();
-        let schema = view.table().schema().await.unwrap();
-        assert_eq!(
-            schema.field_with_name("age").unwrap().data_type(),
-            &DataType::Int32
-        );
-    }
-
-    /// A newer-kind view must not disappear from the listing.
-    #[tokio::test]
-    async fn test_unrecognized_kind_is_listed_by_name() {
-        let conn = people_db().await;
-        conn.create_materialized_view("v", "people")
-            .execute()
-            .await
-            .unwrap();
-        let table = conn.open_table("v").execute().await.unwrap();
-        table
-            .as_native()
-            .unwrap()
-            .replace_schema_metadata(HashMap::from([(
-                DEFINITION_META_KEY.to_string(),
-                r#"{"kind": "join"}"#.to_string(),
-            )]))
-            .await
-            .unwrap();
-
-        let views = conn.list_materialized_views().await.unwrap();
-        assert_eq!(views, vec!["v"]);
-    }
-
-    /// A definition must evaluate identically across refreshes; anything
-    /// less makes incremental maintenance a mix of evaluations.
-    #[tokio::test]
-    async fn test_volatile_and_unstable_expressions_are_rejected() {
-        let conn = people_db().await;
-        for expression in [
-            "random()",
-            "now()",
-            "version()",
-            "arrow_typeof(age)",
-            "arrow_metadata(age, 'k')",
-        ] {
-            let err = conn
-                .create_materialized_view("bad", "people")
-                .select([("x", expression)])
-                .execute()
-                .await
-                .unwrap_err();
-            assert!(
-                matches!(err, Error::InvalidExpression { message, .. }
-                    if message.contains("not immutable")),
-                "{expression} was not rejected"
-            );
-        }
-        for filter in ["age > random() * 100", "age >= 0 and now() is not null"] {
-            let err = conn
-                .create_materialized_view("bad", "people")
-                .only_if(filter)
-                .execute()
-                .await
-                .unwrap_err();
-            assert!(
-                matches!(err, Error::InvalidInput { message } if message.contains("not immutable")),
-                "{filter} was not rejected"
-            );
-        }
-    }
-
-    /// A column projected as itself stays the column it was: blob discovery
-    /// and the blob APIs key off field metadata, which a bare rebuild of the
-    /// field would drop.
-    #[tokio::test]
-    async fn test_identity_projection_keeps_field_metadata() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let schema = Arc::new(ArrowSchema::new_with_metadata(
-            vec![
-                ArrowField::new("id", DataType::Int32, true),
-                crate::blob("payload", true),
-            ],
-            HashMap::new(),
-        ));
-        conn.create_empty_table("src", schema)
-            .write_options(stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-
-        let view = conn
-            .create_materialized_view("v", "src")
-            .execute()
-            .await
-            .unwrap();
-        let view_schema = view.table().schema().await.unwrap();
-
-        let payload = view_schema.field_with_name("payload").unwrap();
-        assert!(
-            crate::blob::is_blob(payload),
-            "default projection dropped the blob marker: {:?}",
-            payload.metadata()
-        );
-        assert_eq!(
-            view.table().blob_columns().await.unwrap(),
-            vec!["payload".to_string()],
-            "blob discovery no longer finds the projected column"
-        );
-        assert!(view_schema.metadata().contains_key(DEFINITION_META_KEY));
-        // Structural declarations describe how a table is written; a view is
-        // written by refresh, and its fields are always nullable.
-        assert!(!view_schema.metadata().contains_key("lance:primary_key"));
-
-        // A computed column is a new value and carries no source metadata.
-        let computed = conn
-            .create_materialized_view("c", "src")
-            .select([("payload", "payload"), ("n", "id + 1")])
-            .execute()
-            .await
-            .unwrap();
-        let computed_schema = computed.table().schema().await.unwrap();
-        assert!(crate::blob::is_blob(
-            computed_schema.field_with_name("payload").unwrap()
-        ));
-        assert!(
-            computed_schema
-                .field_with_name("n")
-                .unwrap()
-                .metadata()
-                .is_empty()
-        );
-    }
-
-    /// A nested column projected straight through is still that column, and a
-    /// declaration buried in a struct child binds as hard as one on top.
-    #[tokio::test]
-    async fn test_nested_projection_metadata_and_declarations() {
-        // The schema below carries the legacy v1 blob marker, which Lance only
-        // allows writing at file version <= 2.1.
-        let conn = connect("memory://")
-            .storage_options([(
-                crate::database::listing::OPT_NEW_TABLE_STORAGE_VERSION,
-                "2.1",
-            )])
-            .execute()
-            .await
-            .unwrap();
-        let payload = crate::blob("payload", true).with_metadata(HashMap::from([
-            ("lance-encoding:blob".to_string(), "true".to_string()),
-            (
-                "lance-schema:unenforced-primary-key".to_string(),
-                "0".to_string(),
+    fn assert_refused<T: std::fmt::Debug>(result: Result<T>, operation: &str) {
+        match result {
+            Err(Error::NotSupported { message }) => assert!(
+                message.contains("is a materialized view, which only refresh writes"),
+                "{operation}: {message}"
             ),
-        ]));
-        let schema = Arc::new(ArrowSchema::new(vec![
-            ArrowField::new("id", DataType::Int32, true),
-            ArrowField::new("meta", DataType::Struct(vec![payload].into()), true),
-        ]));
-        conn.create_empty_table("src", schema)
-            .write_options(stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-
-        // A nested path is a direct projection: the leaf's metadata comes with
-        // it, so the blob stays a blob rather than a plain struct.
-        let lifted = conn
-            .create_materialized_view("lifted", "src")
-            .select([("payload", "meta.payload")])
-            .execute()
-            .await
-            .unwrap();
-        let field = lifted.table().schema().await.unwrap();
-        let field = field.field_with_name("payload").unwrap().clone();
-        assert_eq!(
-            field.metadata().get("lance-encoding:blob"),
-            Some(&"true".to_string()),
-            "nested projection lost the leaf's metadata"
-        );
-        assert!(
-            !field
-                .metadata()
-                .contains_key("lance-schema:unenforced-primary-key"),
-            "a structural declaration rode along"
-        );
-
-        // Projecting the struct whole must not carry the child's declaration
-        // out to a view whose fields are nullable.
-        let whole = conn
-            .create_materialized_view("whole", "src")
-            .select([("meta", "meta")])
-            .execute()
-            .await
-            .unwrap();
-        let schema = whole.table().schema().await.unwrap();
-        let DataType::Struct(children) = schema.field_with_name("meta").unwrap().data_type() else {
-            panic!("meta is not a struct");
-        };
-        let child = children.iter().find(|c| c.name() == "payload").unwrap();
-        assert!(
-            !child
-                .metadata()
-                .contains_key("lance-schema:unenforced-primary-key"),
-            "a nested declaration survived: {:?}",
-            child.metadata()
-        );
-        assert_eq!(
-            child.metadata().get("lance-encoding:blob"),
-            Some(&"true".to_string())
-        );
+            other => panic!("{operation} on a view was not refused: {other:?}"),
+        }
     }
 
-    /// A map's entries are fields like any other, and a declaration on one
-    /// binds the view's writes just as hard as one on top.
     #[tokio::test]
-    async fn test_map_declarations_are_stripped() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let value =
-            ArrowField::new("value", DataType::Utf8, false).with_metadata(HashMap::from([(
-                "lance-schema:unenforced-clustering-key:position".to_string(),
-                "1".to_string(),
-            )]));
-        let entries = ArrowField::new(
-            "entries",
-            DataType::Struct(vec![ArrowField::new("key", DataType::Utf8, false), value].into()),
-            false,
-        );
-        let schema = Arc::new(ArrowSchema::new(vec![ArrowField::new(
-            "props",
-            DataType::Map(Arc::new(entries), false),
-            true,
-        )]));
-        conn.create_empty_table("src", schema)
-            .write_options(stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
+    async fn direct_writes_to_a_view_are_refused() {
+        use arrow_schema::Schema;
+        use datafusion::prelude::SessionContext;
+        use lance::dataset::{ColumnAlteration, NewColumnTransform};
 
-        let view = conn
-            .create_materialized_view("view", "src")
-            .execute()
-            .await
-            .unwrap();
-        let schema = view.table().schema().await.unwrap();
-        let DataType::Map(entries, _) = schema.field_with_name("props").unwrap().data_type() else {
-            panic!("props is not a map");
-        };
-        let DataType::Struct(children) = entries.data_type() else {
-            panic!("map entries are not a struct");
-        };
-        let value = children.iter().find(|c| c.name() == "value").unwrap();
-        assert!(
-            !value
-                .metadata()
-                .contains_key("lance-schema:unenforced-clustering-key:position"),
-            "a declaration survived inside a map: {:?}",
-            value.metadata()
-        );
-    }
+        use crate::table::AddDataMode;
+        use crate::table::datafusion::BaseTableAdapter;
 
-    /// A computed column is declared by field metadata. Projecting one --
-    /// as itself or under an alias -- must carry its description without its
-    /// declaration, which the target table would reject as foreign.
-    #[tokio::test]
-    async fn test_view_over_a_computed_column() {
         let conn = connect("memory://").execute().await.unwrap();
-        let batch = record_batch!(("id", Int32, [1, 2])).unwrap();
         let source = conn
-            .create_table("src", batch)
-            .write_options(stable_row_ids())
+            .create_table("src", record_batch!(("x", Int32, [1, 2])).unwrap())
             .execute()
             .await
             .unwrap();
+        let view = conn
+            .create_materialized_view("m", "src")
+            .execute()
+            .await
+            .unwrap();
+        let downstream = conn
+            .create_materialized_view("m2", "m")
+            .execute()
+            .await
+            .unwrap();
+        let table = view.table();
+        let row = || record_batch!(("x", Int32, [99])).unwrap();
+
+        assert_refused(table.add(row()).execute().await, "add");
+        assert_refused(
+            table
+                .add(row())
+                .mode(AddDataMode::Overwrite)
+                .execute()
+                .await,
+            "overwrite",
+        );
+        assert_refused(
+            table.update().column("x", "x + 1").execute().await,
+            "update",
+        );
+        assert_refused(table.delete("x = 1").await, "delete");
+        let mut merge = table.merge_insert(&["x"]);
+        merge.when_not_matched_insert_all();
+        assert_refused(merge.execute(reader(row())).await, "merge_insert");
+        assert_refused(
+            table
+                .add_columns()
+                .transform(NewColumnTransform::SqlExpressions(vec![(
+                    "y".into(),
+                    "x * 2".into(),
+                )]))
+                .execute()
+                .await,
+            "add_columns",
+        );
+        assert_refused(
+            table
+                .add_columns()
+                .transform(NewColumnTransform::AllNulls(Arc::new(Schema::new(vec![
+                    ArrowField::new("y", DataType::Int32, true),
+                ]))))
+                .execute()
+                .await,
+            "add_columns (nulls)",
+        );
+        assert_refused(
+            table
+                .alter_columns(&[ColumnAlteration::new("x".into()).rename("z".into())])
+                .await,
+            "alter_columns",
+        );
+        assert_refused(table.drop_columns(&["x"]).await, "drop_columns");
+        assert_refused(
+            downstream.table().add(row()).execute().await,
+            "add to a chained view",
+        );
+
+        let ctx = SessionContext::new();
+        let adapter = BaseTableAdapter::try_new(table.base_table().clone())
+            .await
+            .unwrap();
+        ctx.register_table("m", Arc::new(adapter)).unwrap();
+        let insert = match ctx.sql("INSERT INTO m VALUES (99)").await {
+            Ok(frame) => frame.collect().await.map(|_| ()),
+            Err(error) => Err(error),
+        };
+        let message = insert.unwrap_err().to_string();
+        assert!(message.contains("is a materialized view"), "{message}");
+
+        assert_eq!(table.count_rows(None).await.unwrap(), 2);
+        assert_eq!(table.schema().await.unwrap().fields().len(), 1);
+
+        // Refresh, chained, is the writer.
+        source.add(row()).execute().await.unwrap();
+        assert_eq!(view.refresh().execute().await.unwrap().rows_written, 3);
+        assert_eq!(
+            downstream.refresh().execute().await.unwrap().rows_written,
+            3
+        );
+        assert_eq!(
+            downstream
+                .table()
+                .count_rows(Some("x = 99".into()))
+                .await
+                .unwrap(),
+            1
+        );
+
+        // The source is a plain table and still takes every write.
+        source
+            .update()
+            .column("x", "x + 1")
+            .execute()
+            .await
+            .unwrap();
+        source.delete("x = 2").await.unwrap();
+        let mut merge = source.merge_insert(&["x"]);
+        merge.when_not_matched_insert_all();
+        merge
+            .execute(reader(record_batch!(("x", Int32, [7])).unwrap()))
+            .await
+            .unwrap();
         source
             .add_columns()
-            .computed("doubled", "id * 2")
-            .execute()
-            .await
-            .unwrap();
-
-        // Default projection reaches the computed column too.
-        let whole = conn
-            .create_materialized_view("whole", "src")
-            .execute()
-            .await
-            .unwrap();
-        let schema = whole.table().schema().await.unwrap();
-        let field = schema.field_with_name("doubled").unwrap();
-        assert!(
-            !field
-                .metadata()
-                .keys()
-                .any(|k| k.starts_with("computed_column")),
-            "a computed-column declaration rode along: {:?}",
-            field.metadata()
-        );
-
-        // And under an alias.
-        conn.create_materialized_view("aliased", "src")
-            .select([("twice", "doubled")])
+            .transform(NewColumnTransform::SqlExpressions(vec![(
+                "y".into(),
+                "x * 2".into(),
+            )]))
             .execute()
             .await
             .unwrap();
+        source.drop_columns(&["y"]).await.unwrap();
+        assert_eq!(view.refresh().execute().await.unwrap().rows_written, 3);
     }
 
-    /// Embedding configuration names columns. It comes along only for the
-    /// columns a view actually projects, under the names the view gives them.
     #[tokio::test]
-    async fn test_embedding_config_follows_the_projection() {
-        let config = r#"[{"name":"f","model":{},"source_column":"text","vector_column":"vec"}]"#;
+    async fn indexes_and_optimize_still_apply_to_a_view() {
+        use crate::index::Index;
+        use crate::table::{CompactionOptions, OptimizeAction};
+
         let conn = connect("memory://").execute().await.unwrap();
-        let schema = Arc::new(ArrowSchema::new_with_metadata(
-            vec![
-                ArrowField::new("text", DataType::Utf8, true),
-                ArrowField::new("vec", DataType::Float32, true),
-            ],
-            HashMap::from([("embedding_functions".to_string(), config.to_string())]),
-        ));
-        conn.create_empty_table("src", schema)
-            .write_options(stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-
-        let carried = |view: &MaterializedView| {
-            let view = view.table().clone();
-            async move {
-                view.schema()
-                    .await
-                    .unwrap()
-                    .metadata()
-                    .get("embedding_functions")
-                    .cloned()
-            }
-        };
-
-        // Both columns projected as themselves: kept as it stands.
-        let whole = conn
-            .create_materialized_view("whole", "src")
-            .execute()
-            .await
-            .unwrap();
-        let kept = carried(&whole).await.expect("config dropped");
-        assert!(kept.contains(r#""source_column":"text""#), "{kept}");
-        assert!(kept.contains(r#""vector_column":"vec""#), "{kept}");
-
-        // Only the source column: the configuration names a vector column the
-        // view does not have, so it describes nothing and goes.
-        let partial = conn
-            .create_materialized_view("partial", "src")
-            .select([("text", "text")])
-            .execute()
-            .await
-            .unwrap();
-        assert_eq!(carried(&partial).await, None);
-
-        // Renamed: the configuration follows the names the view uses.
-        let renamed = conn
-            .create_materialized_view("renamed", "src")
-            .select([("body", "text"), ("embedding", "vec")])
-            .execute()
-            .await
-            .unwrap();
-        let remapped = carried(&renamed).await.expect("config dropped");
-        assert!(remapped.contains(r#""source_column":"body""#), "{remapped}");
-        assert!(
-            remapped.contains(r#""vector_column":"embedding""#),
-            "{remapped}"
-        );
-
-        // The Node bindings spell the same configuration in camelCase, and
-        // the Rust definition names the destination `dest_column`.
-        for (config, source_key, dest_key) in [
-            (
-                r#"[{"name":"f","model":{},"sourceColumn":"text","vectorColumn":"vec"}]"#,
-                "sourceColumn",
-                "vectorColumn",
-            ),
-            (
-                r#"[{"name":"f","model":{},"source_column":"text","dest_column":"vec"}]"#,
-                "source_column",
-                "dest_column",
-            ),
-        ] {
-            let schema = Arc::new(ArrowSchema::new_with_metadata(
-                vec![
-                    ArrowField::new("text", DataType::Utf8, true),
-                    ArrowField::new("vec", DataType::Float32, true),
-                ],
-                HashMap::from([("embedding_functions".to_string(), config.to_string())]),
-            ));
-            let name = format!("src_{source_key}");
-            conn.create_empty_table(&name, schema)
-                .write_options(stable_row_ids())
-                .execute()
-                .await
-                .unwrap();
-            let view = conn
-                .create_materialized_view(format!("v_{source_key}"), &name)
-                .select([("body", "text"), ("embedding", "vec")])
-                .execute()
-                .await
-                .unwrap();
-            let carried = carried(&view).await.expect("config dropped");
-            assert!(
-                carried.contains(&format!(r#""{source_key}":"body""#)),
-                "{carried}"
-            );
-            assert!(
-                carried.contains(&format!(r#""{dest_key}":"embedding""#)),
-                "{carried}"
-            );
-        }
-
-        // A computed column is not the source column under another name.
-        let computed = conn
-            .create_materialized_view("computed", "src")
-            .select([("body", "upper(text)"), ("embedding", "vec")])
-            .execute()
-            .await
-            .unwrap();
-        assert_eq!(carried(&computed).await, None);
-
-        // One column projected twice is two columns in the view, and the
-        // configuration has to describe both rather than whichever came last.
-        let twice = conn
-            .create_materialized_view("twice", "src")
-            .select([("body", "text"), ("a", "vec"), ("b", "vec")])
-            .execute()
-            .await
-            .unwrap();
-        let carried = carried(&twice).await.expect("config dropped");
-        let entries: Vec<serde_json::Value> = serde_json::from_str(&carried).unwrap();
-        let mut vectors: Vec<&str> = entries
-            .iter()
-            .filter_map(|e| e["vector_column"].as_str())
-            .collect();
-        vectors.sort_unstable();
-        assert_eq!(vectors, ["a", "b"], "{carried}");
-    }
-
-    /// The native Rust producer records embeddings as column definitions
-    /// rather than as `embedding_functions`, and a query embeds its own text
-    /// through them. They are positional, so the view's list covers every one
-    /// of its fields.
-    #[tokio::test]
-    async fn test_native_column_definitions_follow_the_projection() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let rich = crate::table::TableDefinition::new(
-            Arc::new(ArrowSchema::new(vec![
-                ArrowField::new("text", DataType::Utf8, true),
-                ArrowField::new("vector", DataType::Float32, true),
-            ])),
-            vec![
-                ColumnDefinition {
-                    kind: ColumnKind::Physical,
-                },
-                ColumnDefinition {
-                    kind: ColumnKind::Embedding(EmbeddingDefinition::new(
-                        "text",
-                        "model",
-                        Some("vector"),
-                    )),
-                },
-            ],
-        )
-        .into_rich_schema();
-        conn.create_empty_table("src", rich)
-            .write_options(stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-
-        let view = conn
-            .create_materialized_view("view", "src")
-            .select([("body", "text"), ("embedding", "vector")])
-            .execute()
-            .await
-            .unwrap();
-        let schema = view.table().schema().await.unwrap();
-        let raw = schema
-            .metadata()
-            .get(COLUMN_DEFINITIONS_META_KEY)
-            .expect("the view dropped the native column definitions");
-        let definitions: Vec<ColumnDefinition> = serde_json::from_str(raw).unwrap();
-        assert_eq!(
-            definitions.len(),
-            schema.fields().len(),
-            "column definitions are positional"
-        );
-        let ColumnKind::Embedding(embedding) = &definitions[1].kind else {
-            panic!("the embedding column came back physical: {raw}");
-        };
-        assert_eq!(embedding.source_column, "body");
-        assert_eq!(embedding.dest_column.as_deref(), Some("embedding"));
-        assert_eq!(embedding.embedding_name, "model");
-        assert!(matches!(definitions[0].kind, ColumnKind::Physical));
-        assert!(matches!(definitions[2].kind, ColumnKind::Physical));
-
-        // Without the column the function reads, the view cannot recompute
-        // the embedding, so it carries no definition for it.
-        let partial = conn
-            .create_materialized_view("partial", "src")
-            .select([("embedding", "vector")])
-            .execute()
-            .await
-            .unwrap();
-        assert_eq!(
-            partial
-                .table()
-                .schema()
-                .await
-                .unwrap()
-                .metadata()
-                .get(COLUMN_DEFINITIONS_META_KEY),
-            None
-        );
-    }
-
-    /// A scan takes the cap as i64, so a larger one is refused where it is
-    /// declared rather than at the refresh that cannot run it. What a cap of
-    /// zero means is a refresh question, tested there.
-    #[tokio::test]
-    async fn test_limit_above_i64_max_is_refused_at_creation() {
-        let conn = people_db().await;
-        let err = conn
-            .create_materialized_view("too_big", "people")
-            .limit(i64::MAX as u64 + 1)
-            .execute()
-            .await
-            .unwrap_err();
-        assert!(
-            matches!(&err, Error::InvalidInput { message } if message.contains("exceeds the maximum")),
-            "got {err:?}"
-        );
-
-        // The boundary itself is accepted.
-        conn.create_materialized_view("at_max", "people")
-            .limit(i64::MAX as u64)
-            .execute()
-            .await
-            .unwrap();
-    }
-
-    #[tokio::test]
-    async fn test_drop_is_drop_table() {
-        let conn = people_db().await;
-        conn.create_materialized_view("v", "people")
-            .execute()
-            .await
-            .unwrap();
-        conn.drop_table("v", &[]).await.unwrap();
-        assert!(conn.list_materialized_views().await.unwrap().is_empty());
-    }
-
-    /// The public declaration contract: prepare validates the source and
-    /// create consumes the declaration into a verified view table; a
-    /// source that cannot anchor refresh and a target outside the
-    /// source's database are both refused.
-    #[tokio::test]
-    async fn prepare_and_create_bind_the_declaration_lifecycle() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let batch = record_batch!(("id", Int32, [1, 2]), ("value", Int32, [3, 4])).unwrap();
         let source = conn
-            .create_table("src", batch.clone())
-            .write_options(stable_row_ids())
+            .create_table("src", record_batch!(("x", Int32, [1, 2, 3])).unwrap())
             .execute()
             .await
             .unwrap();
-
-        let projections = [
-            ("id".to_string(), "id".to_string()),
-            ("double".to_string(), "value * 2".to_string()),
-        ];
-        let prepared = prepare_declaration(&source, Some(&projections), Some("value > 0"), None)
-            .await
-            .unwrap();
-        assert_eq!(prepared.definition().source_table, "src");
-
-        let view = prepared.create("v").await.unwrap();
-        let schema = view.table().schema().await.unwrap();
-        let names: Vec<&str> = schema.fields().iter().map(|f| f.name().as_str()).collect();
-        assert_eq!(names, ["id", "double", SOURCE_ROW_ID_COLUMN]);
-        assert!(schema.metadata().contains_key(DEFINITION_META_KEY));
-
-        // The same call rejects a source without stable row ids, so an
-        // external creation path cannot skip the check.
-        conn.create_table("plain", batch).execute().await.unwrap();
-        let plain = conn.open_table("plain").execute().await.unwrap();
-        let err = prepare_declaration(&plain, None, None, None)
-            .await
-            .unwrap_err();
-        assert!(err.to_string().contains("stable row ids"), "{err}");
-
-        // A handle whose location does not resolve back through its name is
-        // refused: the definition would record a name reaching other data.
-        let plain_uri = plain
-            .as_native()
-            .unwrap()
-            .dataset
-            .get()
-            .await
-            .unwrap()
-            .uri()
-            .to_string();
-        let masquerade = conn
-            .open_table("src")
-            .location(plain_uri)
-            .execute()
-            .await
-            .unwrap();
-        let err = prepare_declaration(&masquerade, None, None, None)
-            .await
-            .unwrap_err();
-        assert!(
-            err.to_string().contains("does not resolve to itself"),
-            "{err}"
-        );
-
-        // A table created at a custom location is refused outright: its
-        // recorded name reaches nothing at the database root, so the
-        // canonical reopen fails before any URI comparison.
-        let custom = conn
-            .create_table(
-                "custom_loc",
-                record_batch!(("id", Int32, [1, 2]), ("value", Int32, [3, 4])).unwrap(),
-            )
-            .location("memory://elsewhere/custom_loc")
-            .write_options(stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-        let err = prepare_declaration(&custom, None, None, None)
-            .await
-            .unwrap_err();
-        assert!(err.to_string().contains("custom_loc"), "{err}");
-    }
-
-    /// A view declared over a namespaced source records that namespace, and
-    /// refresh resolves the source through it -- the coordinate round-trips.
-    #[tokio::test]
-    async fn a_namespaced_source_round_trips_through_refresh() {
-        use lance_namespace::models::CreateNamespaceRequest;
-
-        let tmp = tempfile::tempdir().unwrap();
-        let mut properties = std::collections::HashMap::new();
-        properties.insert("root".to_string(), tmp.path().to_str().unwrap().to_string());
-        let conn = crate::connect_namespace("dir", properties)
-            .execute()
-            .await
-            .unwrap();
-        conn.create_namespace(CreateNamespaceRequest {
-            id: Some(vec!["ns".into()]),
-            ..Default::default()
-        })
-        .await
-        .unwrap();
-
-        let batch = record_batch!(
-            ("name", Utf8, ["ada", "grace", "alan"]),
-            ("age", Int32, [36, 85, 41])
-        )
-        .unwrap();
-        conn.create_table("people", batch)
-            .namespace(vec!["ns".to_string()])
-            .write_options(stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-
-        // A decoy of the same name at the root: resolving the source at the
-        // wrong namespace materializes one row here instead of three.
-        let decoy = record_batch!(("name", Utf8, ["mallory"]), ("age", Int32, [42])).unwrap();
-        conn.create_table("people", decoy)
-            .write_options(stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-
         let view = conn
-            .create_materialized_view("adults", "people")
-            .with_no_data(true)
-            .namespace(vec!["ns".to_string()])
-            .source_namespace(vec!["ns".to_string()])
-            .select([("name", "name")])
-            .only_if("age >= 18")
+            .create_materialized_view("m", "src")
             .execute()
             .await
             .unwrap();
-
-        assert_eq!(view.definition().source_table, "people");
-        assert_eq!(view.definition().source_namespace, vec!["ns".to_string()]);
-        assert_eq!(view.table().namespace(), &["ns"]);
-
-        // Refresh resolves the source at the recorded namespace, not at root.
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.rows_written, 3);
-    }
-
-    fn definition(source_namespace: Vec<String>) -> MaterializedViewDefinition {
-        MaterializedViewDefinition {
-            vector_source: None,
-            source_table: "people".to_string(),
-            source_namespace,
-            lateral: None,
-            projections: vec![ViewProjection {
-                output: "name".to_string(),
-                expression: "name".to_string(),
-            }],
-            filter: None,
-            group_by: Vec::new(),
-            limit: None,
-        }
-    }
-
-    fn read(stored: impl Into<String>) -> Result<Option<StoredDefinition>> {
-        read_definition(&HashMap::from([(
-            DEFINITION_META_KEY.to_string(),
-            stored.into(),
-        )]))
-    }
-
-    /// What is stored is the query, under a format number; the same query
-    /// reads back whatever namespace the source sits in.
-    #[test]
-    fn the_stored_layout_is_the_canonical_query() {
-        for (namespace, query) in [
-            (Vec::new(), "SELECT name FROM people"),
-            (vec!["ns".to_string()], "SELECT name FROM ns.people"),
-        ] {
-            let stored = definition_to_metadata(&definition(namespace.clone())).unwrap();
-            let value: serde_json::Value = serde_json::from_str(&stored).unwrap();
-            assert_eq!(
-                value,
-                serde_json::json!({"kind": "query", "format": 1, "query": query})
-            );
-            assert_eq!(
-                read(stored).unwrap(),
-                Some(StoredDefinition::Query(definition(namespace)))
-            );
-        }
-    }
-
-    /// The structured layout written before the format number still reads,
-    /// under both of its kind tags, and a tag that disagrees with its
-    /// namespace is an error: under `select` old readers resolved it at root.
-    #[test]
-    fn legacy_layouts_read_back() {
-        let legacy = |kind: &str, namespace: Vec<&str>| {
-            serde_json::json!({
-                "kind": kind,
-                "source_table": "people",
-                "source_namespace": namespace,
-                "projections": [{"output": "name", "expression": "name"}],
-                "inputs": ["name"],
-            })
-            .to_string()
-        };
-        assert_eq!(
-            read(legacy(SELECT_KIND, vec![])).unwrap(),
-            Some(StoredDefinition::Query(definition(Vec::new())))
-        );
-        assert_eq!(
-            read(legacy(NAMESPACED_SELECT_KIND, vec!["ns"])).unwrap(),
-            Some(StoredDefinition::Query(definition(vec!["ns".into()])))
-        );
-        assert!(
-            read(r#"{"kind":"select","source_table":"people","projections":[]}"#)
-                .unwrap()
-                .is_some(),
-            "a pre-namespace definition carries no namespace key"
-        );
-        for (kind, namespace) in [(SELECT_KIND, vec!["ns"]), (NAMESPACED_SELECT_KIND, vec![])] {
-            let err = read(legacy(kind, namespace)).unwrap_err();
-            assert!(
-                err.to_string()
-                    .contains("does not match its source namespace"),
-                "kind '{kind}': {err}"
-            );
-        }
-    }
-
-    /// A newer writer's definition is reported as such, never guessed at,
-    /// and a definition that is not a definition at all is an error rather
-    /// than a plain table.
-    #[test]
-    fn a_newer_format_is_reported_not_guessed() {
-        assert_eq!(
-            read(r#"{"format":5,"query":"SELECT name FROM people"}"#).unwrap(),
-            Some(StoredDefinition::Newer { format: "5".into() })
-        );
-        assert_eq!(
-            read(r#"{"kind":"join"}"#).unwrap(),
-            Some(StoredDefinition::Newer {
-                format: "kind 'join'".into()
-            })
-        );
-        for stored in [
-            "{}",
-            r#"{"format":"one"}"#,
-            r#"{"format":1}"#,
-            r#"{"format":1,"query":"SELECT name FROM people GROUP BY name"}"#,
-            r#"{"format":2,"query":"SELECT name FROM people ORDER BY name"}"#,
-        ] {
-            assert!(read(stored).is_err(), "{stored}");
-        }
-    }
-
-    /// Planning records what the query reads of the source: a nested path
-    /// as itself, a read through an unnest alias as the list column.
-    #[test]
-    fn planning_records_the_source_columns_read() {
-        let element = DataType::Struct(
-            vec![
-                ArrowField::new("chunk", DataType::Utf8, true),
-                ArrowField::new("ordinal", DataType::Int32, true),
-            ]
-            .into(),
-        );
-        let schema = Arc::new(ArrowSchema::new(vec![
-            ArrowField::new("id", DataType::Int64, false),
-            ArrowField::new(
-                "meta",
-                DataType::Struct(vec![ArrowField::new("title", DataType::Utf8, true)].into()),
-                true,
-            ),
-            ArrowField::new(
-                "chunks",
-                DataType::List(Arc::new(ArrowField::new("item", element, true))),
-                true,
-            ),
-        ]));
-        let planned = plan(
-            schema.clone(),
-            &MaterializedViewDefinition::from_sql(
-                "SELECT id, meta.title, c.chunk FROM docs, UNNEST(chunks) AS c WHERE c.ordinal < 3",
-            )
-            .unwrap(),
-            None,
-        )
-        .unwrap();
-        assert_eq!(planned.inputs, ["chunks", "id", "meta.title"]);
-        assert_eq!(
-            planned
-                .fields
-                .iter()
-                .map(|f| f.name().as_str())
-                .collect::<Vec<_>>(),
-            ["id", "title", "chunk"]
-        );
-        assert_eq!(
-            planned.fields[2].data_type(),
-            &DataType::Utf8,
-            "the element's field is read through the alias"
-        );
-
-        let star = plan(
-            schema,
-            &MaterializedViewDefinition::from_sql("SELECT * FROM docs, UNNEST(chunks) AS c")
-                .unwrap(),
-            None,
-        )
-        .unwrap();
-        assert_eq!(
-            star.definition.to_sql(),
-            "SELECT id, meta, c FROM docs, UNNEST(chunks) AS c"
-        );
-        let err = plan(
-            Arc::new(ArrowSchema::new(vec![ArrowField::new(
-                "id",
-                DataType::Int64,
-                false,
-            )])),
-            &MaterializedViewDefinition::from_sql("SELECT id FROM docs, UNNEST(id) AS c").unwrap(),
-            None,
-        )
-        .unwrap_err();
-        assert!(err.to_string().contains("not a list"), "{err}");
-    }
-
-    /// A binding as the server records it: one Utf8 input over `input`
-    /// bound to a nullable parameter, one Int32 output named `output`, with
-    /// the exact schemas the durable contract requires.
-    pub fn test_binding(binding_id: &str, input: &str, output: &str) -> FunctionBinding {
-        let input_schema = lance_namespace::schema::arrow_schema_to_json(&ArrowSchema::new(vec![
-            ArrowField::new("text", DataType::Utf8, true),
-        ]))
-        .unwrap();
-        let output_schema = lance_namespace::schema::arrow_schema_to_json(&ArrowSchema::new(vec![
-            ArrowField::new(output, DataType::Int32, true),
-        ]))
-        .unwrap();
-        let input_type = input_schema.fields[0].r#type.r#type.clone();
-        let output_type = output_schema.fields[0].r#type.r#type.clone();
-        FunctionBinding::from_json(
-            &serde_json::json!({
-                "binding_id": binding_id,
-                "function": {"name": "embed", "version": "1", "object_id": "fixture", "location": "memory:///fixture",
-                    "manifest_digest": "sha256:7e22f815b6648e14f093a3979a8e5a2082fa773ebe1ec84b135cae7e84d6f8e6"},
-                "inputs": [{
-                    "parameter": "text", "field_id": -1, "field_path": input,
-                    "arrow_type": input_type, "nullable": true,
-                }],
-                "outputs": [{
-                    "result_field": "$value", "output_name": output, "output_field_id": -1,
-                    "output_ordinal": 0, "arrow_type": output_type, "nullable": false,
-                }],
-                "input_schema": serde_json::to_value(input_schema).unwrap(),
-                "output_schema": serde_json::to_value(output_schema).unwrap(),
-            })
-            .to_string(),
-        )
-        .unwrap()
-    }
-
-    /// A computed column as the server declares it on a table: bound to a
-    /// registered Function.
-    pub fn computed_field(name: &str, binding_id: &str, input: &str) -> ArrowField {
-        ArrowField::new(name, DataType::Int32, true).with_metadata(
-            crate::table::computed_columns::function_computed_column_metadata(
-                binding_id,
-                0,
-                &[input.to_string()],
-            ),
-        )
-    }
-
-    pub async fn people(conn: &Connection) -> Table {
-        let batch =
-            record_batch!(("id", Int32, [1, 2, 3]), ("name", Utf8, ["a", "b", "c"])).unwrap();
-        conn.create_table("people", batch)
-            .write_options(stable_row_ids())
+        view.table()
+            .create_index(&["x"], Index::Auto)
             .execute()
             .await
-            .unwrap()
-    }
+            .unwrap();
+        assert_eq!(view.table().list_indices().await.unwrap().len(), 1);
+        view.table()
+            .optimize(OptimizeAction::Compact {
+                options: CompactionOptions::default(),
+                remap_options: None,
+            })
+            .await
+            .unwrap();
+        view.table().optimize(OptimizeAction::All).await.unwrap();
 
-    /// `people` with both columns non-nullable, for nullability cases.
-    pub async fn strict_people(conn: &Connection) -> Table {
-        let schema = Arc::new(ArrowSchema::new(vec![
-            ArrowField::new("id", DataType::Int32, false),
-            ArrowField::new("name", DataType::Utf8, false),
-        ]));
-        let batch = arrow_array::RecordBatch::try_new(
-            schema,
-            vec![
-                Arc::new(arrow_array::Int32Array::from(vec![1, 2, 3])),
-                Arc::new(arrow_array::StringArray::from(vec!["a", "b", "c"])),
-            ],
-        )
-        .unwrap();
-        conn.create_table("people", batch)
-            .write_options(stable_row_ids())
+        source
+            .add(record_batch!(("x", Int32, [4])).unwrap())
             .execute()
             .await
-            .unwrap()
-    }
-
-    async fn prepared_people(conn: &Connection) -> PreparedDeclaration {
-        let source = people(conn).await;
-        prepare_declaration(
-            &source,
-            Some(&[
-                ("id".to_string(), "id".to_string()),
-                ("name".to_string(), "name".to_string()),
-            ]),
-            None,
-            None,
-        )
-        .await
-        .unwrap()
-    }
-
-    #[tokio::test]
-    async fn a_computed_column_is_declared_null_with_its_binding() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let view = prepared_people(&conn)
-            .await
-            .with_computed_columns(
-                vec![(2, computed_field("emb", "fb_1", "name"))],
-                &[test_binding("fb_1", "name", "emb")],
-            )
-            .unwrap()
-            .create("v")
-            .await
             .unwrap();
-
-        let schema = view.table().schema().await.unwrap();
-        let names: Vec<&str> = schema.fields().iter().map(|f| f.name().as_str()).collect();
-        assert_eq!(names, ["id", "name", "emb", SOURCE_ROW_ID_COLUMN]);
-        let declared: Vec<String> = computed_columns(&schema)
-            .into_iter()
-            .map(|c| c.name)
-            .collect();
-        assert_eq!(declared, ["emb"]);
-        let bindings = crate::table::computed_columns::function_bindings(&schema).unwrap();
-        assert_eq!(bindings.len(), 1);
-        assert_eq!(bindings[0].binding_id(), "fb_1");
-        // The stored definition is the query alone; bindings live beside it.
-        let stored: serde_json::Value =
-            serde_json::from_str(&schema.metadata()[DEFINITION_META_KEY]).unwrap();
-        assert_eq!(stored["format"], 1);
-        assert_eq!(view.definition().projections.len(), 2);
-        assert_eq!(view.table().count_rows(None).await.unwrap(), 0);
-        assert_eq!(conn.open_materialized_view("v").await.unwrap().name(), "v");
-    }
-
-    #[tokio::test]
-    async fn computed_column_declarations_are_validated() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let prepared = prepared_people(&conn).await;
-        let binding = test_binding("fb_1", "name", "emb");
-        let fails = |prepared: PreparedDeclaration,
-                     columns: Vec<(usize, ArrowField)>,
-                     bindings: &[FunctionBinding]| {
-            prepared
-                .with_computed_columns(columns, bindings)
-                .err()
-                .map(|e| e.to_string())
-                .expect("the declaration should be refused")
-        };
-        let emb = |binding_id: &str| computed_field("emb", binding_id, "name");
-
-        let err = fails(
-            prepared.clone(),
-            vec![(2, emb("fb_1").with_nullable(false))],
-            std::slice::from_ref(&binding),
-        );
-        assert!(err.contains("must be nullable"), "{err}");
-
-        let plain = ArrowField::new("emb", DataType::Int32, true);
-        let err = fails(
-            prepared.clone(),
-            vec![(2, plain)],
-            std::slice::from_ref(&binding),
-        );
-        assert!(
-            err.contains("does not carry a computed-column declaration"),
-            "{err}"
-        );
-
-        // The rest is the computed-column contract: a binding the field does
-        // not name, an output the binding does not map to this field, an
-        // input the view does not hold.
-        let err = fails(
-            prepared.clone(),
-            vec![(2, emb("fb_other"))],
-            std::slice::from_ref(&binding),
-        );
-        assert!(err.contains("does not match binding 'fb_1'"), "{err}");
-        let err = fails(
-            prepared.clone(),
-            vec![(2, emb("fb_1"))],
-            &[test_binding("fb_1", "name", "different_output")],
-        );
-        assert!(err.contains("different_output"), "{err}");
-        let err = fails(
-            prepared.clone(),
-            vec![(2, emb("fb_1"))],
-            &[test_binding("fb_1", "bio", "emb")],
-        );
-        assert!(err.contains("'bio'"), "{err}");
-
-        let err = fails(
-            prepared.clone(),
-            vec![(2, computed_field("name", "fb_1", "name"))],
-            &[test_binding("fb_1", "name", "name")],
-        );
-        assert!(err.contains("already exists"), "{err}");
-        let err = fails(
-            prepared.clone(),
-            vec![(2, computed_field(SOURCE_ROW_ID_COLUMN, "fb_1", "name"))],
-            &[test_binding("fb_1", "name", SOURCE_ROW_ID_COLUMN)],
-        );
-        assert!(err.contains("reserved"), "{err}");
-        let err = fails(
-            prepared.clone(),
-            vec![(7, emb("fb_1"))],
-            std::slice::from_ref(&binding),
-        );
-        assert!(
-            err.contains("placed at 7, past the view's 2 columns"),
-            "{err}"
-        );
-        let err = fails(prepared, Vec::new(), std::slice::from_ref(&binding));
-        assert!(err.contains("at least one computed column"), "{err}");
-    }
-
-    /// A source column a computed column reads without the view projecting
-    /// it becomes an internal projection before the provenance column, with
-    /// the source's nullability; a projected column is read from its
-    /// projection.
-    /// `Some(&[])` is a declaration that projects nothing yet: a view of
-    /// computed columns alone, whose inputs `input_column` places. `None`
-    /// is `SELECT *`. The two must not collapse into each other.
-    #[tokio::test]
-    async fn an_empty_projection_list_is_not_a_star() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = strict_people(&conn).await;
-
-        let mut prepared = prepare_declaration(&source, Some(&[]), None, None)
-            .await
-            .unwrap();
-        assert!(prepared.definition().projections.is_empty());
-        assert_eq!(prepared.input_column("name").unwrap(), "__input_name");
-        let view = prepared
-            .with_computed_columns(
-                vec![(0, computed_field("emb", "fb_1", "__input_name"))],
-                &[test_binding("fb_1", "__input_name", "emb")],
-            )
-            .unwrap()
-            .create("only")
-            .await
-            .unwrap();
-        let schema = view.table().schema().await.unwrap();
-        let names: Vec<&str> = schema.fields().iter().map(|f| f.name().as_str()).collect();
-        assert_eq!(names, ["emb", "__input_name", SOURCE_ROW_ID_COLUMN]);
+        assert_eq!(view.refresh().execute().await.unwrap().rows_written, 4);
+        view.table().optimize(OptimizeAction::All).await.unwrap();
         assert_eq!(
-            view.definition().to_sql(),
-            "SELECT name AS __input_name FROM people"
+            view.table().count_rows(Some("x > 2".into())).await.unwrap(),
+            2
         );
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.rows_written, 3);
-        let reopened = conn.open_materialized_view("only").await.unwrap();
-        assert_eq!(reopened.definition(), view.definition());
-
-        let all = prepare_declaration(&source, None, None, None)
-            .await
-            .unwrap();
-        assert!(
-            !all.definition().selects_star(),
-            "planning expands the star"
-        );
-        let outputs: Vec<&str> = all
-            .definition()
-            .projections
-            .iter()
-            .map(|p| p.output.as_str())
-            .collect();
-        assert_eq!(outputs, ["id", "name"]);
     }
 
     #[tokio::test]
-    async fn an_unprojected_input_becomes_an_internal_projection() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = strict_people(&conn).await;
-        let mut prepared = prepare_declaration(
-            &source,
-            Some(&[("key".to_string(), "id".to_string())]),
-            None,
-            None,
-        )
-        .await
-        .unwrap();
-        assert_eq!(prepared.input_column("id").unwrap(), "key");
-        assert_eq!(prepared.input_column("name").unwrap(), "__input_name");
-        assert_eq!(prepared.input_column("name").unwrap(), "__input_name");
-        let err = prepared.input_column("missing").unwrap_err().to_string();
-        assert!(err.contains("no column 'missing'"), "{err}");
-
-        let view = prepared
-            .with_computed_columns(
-                vec![(1, computed_field("emb", "fb_1", "__input_name"))],
-                &[test_binding("fb_1", "__input_name", "emb")],
-            )
-            .unwrap()
-            .create("v")
-            .await
-            .unwrap();
-        let schema = view.table().schema().await.unwrap();
-        let names: Vec<&str> = schema.fields().iter().map(|f| f.name().as_str()).collect();
-        assert_eq!(names, ["key", "emb", "__input_name", SOURCE_ROW_ID_COLUMN]);
-        let input = schema.field_with_name("__input_name").unwrap();
-        assert_eq!(input.data_type(), &DataType::Utf8);
-        assert!(
-            !input.is_nullable(),
-            "the copy keeps the source's nullability"
-        );
-        assert!(!schema.field_with_name("key").unwrap().is_nullable());
-        let projections: Vec<(&str, &str)> = view
-            .definition()
-            .projections
-            .iter()
-            .map(|p| (p.output.as_str(), p.expression.as_str()))
-            .collect();
-        assert_eq!(projections, [("key", "id"), ("__input_name", "name")]);
-    }
-
-    /// Two outputs of one binding land at consecutive positions: each
-    /// insertion widens the range the next may take.
-    #[tokio::test]
-    async fn sibling_computed_columns_take_consecutive_positions() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = people(&conn).await;
-        let prepared = prepare_declaration(
-            &source,
-            Some(&[("id".to_string(), "id".to_string())]),
-            None,
-            None,
-        )
-        .await
-        .unwrap();
-        let metadata = |ordinal: u32| {
-            crate::table::computed_columns::function_computed_column_metadata(
-                "fb_pair",
-                ordinal,
-                &["id".to_string()],
-            )
-        };
-        let left = ArrowField::new("left", DataType::Int32, true).with_metadata(metadata(0));
-        let right = ArrowField::new("right", DataType::Int32, true).with_metadata(metadata(1));
-        let input_schema = lance_namespace::schema::arrow_schema_to_json(&ArrowSchema::new(vec![
-            ArrowField::new("value", DataType::Int32, true),
-        ]))
-        .unwrap();
-        let output_schema = lance_namespace::schema::arrow_schema_to_json(&ArrowSchema::new(vec![
-            ArrowField::new("left", DataType::Int32, true),
-            ArrowField::new("right", DataType::Int32, true),
-        ]))
-        .unwrap();
-        let int = input_schema.fields[0].r#type.r#type.clone();
-        let binding = FunctionBinding::from_json(
-            &serde_json::json!({
-                "binding_id": "fb_pair",
-                "function": {"name": "pair", "version": "1", "object_id": "fixture", "location": "memory:///fixture",
-                    "manifest_digest": "sha256:7e22f815b6648e14f093a3979a8e5a2082fa773ebe1ec84b135cae7e84d6f8e6"},
-                "inputs": [{"parameter": "value", "field_id": -1, "field_path": "id",
-                            "arrow_type": int, "nullable": true}],
-                "outputs": [
-                    {"result_field": "left", "output_name": "left", "output_field_id": -1,
-                     "output_ordinal": 0, "arrow_type": int, "nullable": false},
-                    {"result_field": "right", "output_name": "right", "output_field_id": -1,
-                     "output_ordinal": 1, "arrow_type": int, "nullable": false},
-                ],
-                "input_schema": serde_json::to_value(input_schema).unwrap(),
-                "output_schema": serde_json::to_value(output_schema).unwrap(),
-            })
-            .to_string(),
-        )
-        .unwrap();
-        let view = prepared
-            .with_computed_columns(vec![(1, left), (2, right)], &[binding])
-            .unwrap()
-            .create("v")
-            .await
-            .unwrap();
-        let names: Vec<String> = view
-            .table()
-            .schema()
-            .await
-            .unwrap()
-            .fields()
-            .iter()
-            .map(|f| f.name().clone())
-            .collect();
-        assert_eq!(names, ["id", "left", "right", SOURCE_ROW_ID_COLUMN]);
-    }
-
-    /// A SQL declaration as `add_columns().computed()` records it.
-    pub fn sql_field(
-        name: &str,
-        data_type: DataType,
-        expression: &str,
-        inputs: &str,
-    ) -> ArrowField {
+    async fn computed_columns_of_a_view_are_still_filled_and_dropped() {
         use crate::table::computed_columns::{
             COMPUTED_COLUMN_META_KEY, EXPRESSION_META_KEY, INPUTS_META_KEY, KIND_META_KEY, SQL_KIND,
         };
-        ArrowField::new(name, data_type, true).with_metadata(HashMap::from([
-            (COMPUTED_COLUMN_META_KEY.to_string(), "true".to_string()),
-            (KIND_META_KEY.to_string(), SQL_KIND.to_string()),
-            (EXPRESSION_META_KEY.to_string(), expression.to_string()),
-            (INPUTS_META_KEY.to_string(), inputs.to_string()),
-        ]))
-    }
 
-    /// A SQL declaration is re-planned at admission: it must parse against
-    /// the view, yield the declared type, and read the inputs it declares.
-    #[tokio::test]
-    async fn a_sql_declaration_is_planned_at_admission() {
         let conn = connect("memory://").execute().await.unwrap();
-        let fails = |prepared: PreparedDeclaration, field: ArrowField| {
-            prepared
-                .with_computed_columns(vec![(1, field)], &[])
-                .err()
-                .map(|e| e.to_string())
-                .expect("the declaration should be refused")
-        };
-        let prepared = prepared_people(&conn).await;
-        let err = fails(
-            prepared.clone(),
-            sql_field("bad", DataType::Int32, "missing + 1", r#"["missing"]"#),
-        );
-        assert!(err.contains("missing"), "{err}");
-        let err = fails(
-            prepared.clone(),
-            sql_field("wide", DataType::Int64, "id + 1", r#"["id"]"#),
-        );
-        assert!(
-            err.contains("declared as Int64 but its expression yields Int32"),
-            "{err}"
-        );
-        let err = fails(
-            prepared.clone(),
-            sql_field("lying", DataType::Int32, "id + 1", r#"["name"]"#),
-        );
-        assert!(err.contains("declares inputs"), "{err}");
-
+        let source = conn
+            .create_table("src", record_batch!(("x", Int32, [1, 2])).unwrap())
+            .execute()
+            .await
+            .unwrap();
+        let prepared = prepare_declaration(&source, Some(&[("x".into(), "x".into())]), None, None)
+            .await
+            .unwrap();
+        let doubled =
+            ArrowField::new("doubled", DataType::Int32, true).with_metadata(HashMap::from([
+                (COMPUTED_COLUMN_META_KEY.into(), "true".into()),
+                (KIND_META_KEY.into(), SQL_KIND.into()),
+                (EXPRESSION_META_KEY.into(), "x * 2".into()),
+                (INPUTS_META_KEY.into(), "[\"x\"]".into()),
+            ]));
         let view = prepared
-            .with_computed_columns(
-                vec![(1, sql_field("next", DataType::Int32, "id + 1", r#"["id"]"#))],
-                &[],
-            )
+            .with_computed_columns(vec![(1, doubled)], &[])
             .unwrap()
-            .create("v")
+            .create("m")
             .await
             .unwrap();
-        let names: Vec<String> = view
-            .table()
-            .schema()
-            .await
-            .unwrap()
-            .fields()
-            .iter()
-            .map(|f| f.name().clone())
-            .collect();
-        assert_eq!(names, ["id", "next", "name", SOURCE_ROW_ID_COLUMN]);
-    }
-
-    /// Creation persists a declaration only when it re-plans and the data
-    /// carries no values for it, whichever door created the table.
-    #[tokio::test]
-    async fn a_created_table_cannot_carry_computed_values() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let schema = Arc::new(ArrowSchema::new(vec![
-            ArrowField::new("x", DataType::Int32, false),
-            sql_field("forged", DataType::Int32, "x + 1", r#"["x"]"#),
-        ]));
-        let filled = arrow_array::RecordBatch::try_new(
-            schema.clone(),
-            vec![
-                Arc::new(arrow_array::Int32Array::from(vec![1])),
-                Arc::new(arrow_array::Int32Array::from(vec![999])),
-            ],
-        )
-        .unwrap();
-        let err = conn
-            .create_table("forged", filled)
-            .execute()
-            .await
-            .unwrap_err()
-            .to_string();
-        assert!(err.contains("cannot be written directly"), "{err}");
-        assert!(
-            !conn
-                .table_names()
-                .execute()
+        view.refresh().execute().await.unwrap();
+        view.table().refresh_column("doubled").await.unwrap();
+        assert_eq!(
+            view.table()
+                .count_rows(Some("doubled = x * 2".into()))
                 .await
-                .unwrap()
-                .contains(&"forged".to_string())
+                .unwrap(),
+            2
         );
-
-        let unfilled = arrow_array::RecordBatch::try_new(
-            schema,
-            vec![
-                Arc::new(arrow_array::Int32Array::from(vec![1])),
-                Arc::new(arrow_array::Int32Array::new_null(1)),
-            ],
-        )
-        .unwrap();
-        let table = conn
-            .create_table("declared", unfilled)
-            .execute()
-            .await
-            .unwrap();
-        assert_eq!(table.refresh_column("forged").await.unwrap().rows_filled, 1);
-
-        // A declaration with only its marker is broken, not absent.
-        let half = ArrowField::new("half", DataType::Int32, true).with_metadata(HashMap::from([(
-            crate::table::computed_columns::COMPUTED_COLUMN_META_KEY.to_string(),
-            "true".to_string(),
-        )]));
-        let batch = arrow_array::RecordBatch::try_new(
-            Arc::new(ArrowSchema::new(vec![
-                ArrowField::new("x", DataType::Int32, false),
-                half,
-            ])),
-            vec![
-                Arc::new(arrow_array::Int32Array::from(vec![1])),
-                Arc::new(arrow_array::Int32Array::from(vec![999])),
-            ],
-        )
-        .unwrap();
-        let err = conn
-            .create_table("half", batch)
-            .execute()
-            .await
-            .unwrap_err()
-            .to_string();
-        assert!(
-            err.contains("incomplete computed-column declaration"),
-            "{err}"
+        assert_refused(
+            view.table().drop_columns(&["x", "doubled"]).await,
+            "drop_columns of a stored and a computed column",
         );
-
-        let bogus = Arc::new(ArrowSchema::new(vec![
-            ArrowField::new("x", DataType::Int32, false),
-            sql_field("bad", DataType::Int32, "missing + 1", r#"["missing"]"#),
-        ]));
-        let batch = arrow_array::RecordBatch::new_empty(bogus);
-        let err = conn
-            .create_table("bogus", batch)
-            .execute()
-            .await
-            .unwrap_err()
-            .to_string();
-        assert!(err.contains("missing"), "{err}");
-    }
-
-    /// The internal-input prefix is reserved for the declaration, like the
-    /// provenance column, so an alias cannot masquerade as an internal input.
-    #[tokio::test]
-    async fn the_internal_input_prefix_is_reserved() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = people(&conn).await;
-        let err = prepare_declaration(
-            &source,
-            Some(&[("__input_x".to_string(), "name".to_string())]),
-            None,
-            None,
-        )
-        .await
-        .unwrap_err()
-        .to_string();
-        assert!(err.contains("'__input_x' is reserved"), "{err}");
-    }
-
-    /// A computed column may not read another, through any path: a
-    /// Function bound to a child of a computed struct is refused like a SQL
-    /// declaration over it.
-    #[tokio::test]
-    async fn a_computed_column_cannot_read_a_computed_root() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let prepared = prepared_people(&conn).await;
-        let payload = sql_field(
-            "payload",
-            DataType::Struct(vec![ArrowField::new("value", DataType::Utf8, true)].into()),
-            "named_struct('value', name)",
-            r#"["name"]"#,
-        );
-        let err = prepared
-            .with_computed_columns(
-                vec![
-                    (2, payload),
-                    (3, computed_field("emb", "fb_dependent", "payload.value")),
-                ],
-                &[test_binding("fb_dependent", "payload.value", "emb")],
-            )
-            .err()
-            .map(|e| e.to_string())
-            .expect("a computed root as a Function input should be refused");
-        assert!(err.contains("reads computed column 'payload'"), "{err}");
-    }
-
-    /// The root check uses the canonical path parser: a quoted top-level
-    /// name containing a dot is one root, not two segments.
-    #[tokio::test]
-    async fn a_quoted_computed_root_is_still_refused() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let prepared = prepared_people(&conn).await;
-        let payload = sql_field(
-            "payload.dot",
-            DataType::Struct(vec![ArrowField::new("value", DataType::Utf8, true)].into()),
-            "named_struct('value', name)",
-            r#"["name"]"#,
-        );
-        let input = "`payload.dot`.value";
-        let err = prepared
-            .with_computed_columns(
-                vec![
-                    (2, payload),
-                    (3, computed_field("emb", "fb_dependent", input)),
-                ],
-                &[test_binding("fb_dependent", input, "emb")],
-            )
-            .err()
-            .map(|e| e.to_string())
-            .expect("a quoted computed root should be refused");
-        assert!(err.contains("reads computed column 'payload.dot'"), "{err}");
-    }
-
-    /// Namespace-backed creation admits declarations by the same rule, and
-    /// refuses before the namespace records the table.
-    #[tokio::test]
-    async fn a_namespace_created_table_cannot_carry_computed_values() {
-        let tmp = tempfile::tempdir().unwrap();
-        let mut properties = std::collections::HashMap::new();
-        properties.insert("root".to_string(), tmp.path().to_str().unwrap().to_string());
-        let conn = crate::connect_namespace("dir", properties)
-            .execute()
-            .await
-            .unwrap();
-        let half = ArrowField::new("half", DataType::Int32, true).with_metadata(HashMap::from([(
-            crate::table::computed_columns::COMPUTED_COLUMN_META_KEY.to_string(),
-            "true".to_string(),
-        )]));
-        let batch = arrow_array::RecordBatch::try_new(
-            Arc::new(ArrowSchema::new(vec![
-                ArrowField::new("id", DataType::Int32, false),
-                half,
-            ])),
-            vec![
-                Arc::new(arrow_array::Int32Array::from(vec![1])),
-                Arc::new(arrow_array::Int32Array::from(vec![999])),
-            ],
-        )
-        .unwrap();
-        let err = conn
-            .create_table("malformed", batch)
-            .execute()
-            .await
-            .unwrap_err()
-            .to_string();
-        assert!(
-            err.contains("incomplete computed-column declaration"),
-            "{err}"
-        );
-        assert!(conn.table_names().execute().await.unwrap().is_empty());
-
-        let schema = Arc::new(ArrowSchema::new(vec![
-            ArrowField::new("id", DataType::Int32, false),
-            sql_field("next", DataType::Int32, "id + 1", r#"["id"]"#),
-        ]));
-        let filled = arrow_array::RecordBatch::try_new(
-            schema.clone(),
-            vec![
-                Arc::new(arrow_array::Int32Array::from(vec![1])),
-                Arc::new(arrow_array::Int32Array::from(vec![999])),
-            ],
-        )
-        .unwrap();
-        let err = conn
-            .create_table("forged", filled)
-            .execute()
-            .await
-            .unwrap_err()
-            .to_string();
-        assert!(err.contains("cannot be written directly"), "{err}");
-
-        let unfilled = arrow_array::RecordBatch::try_new(
-            schema,
-            vec![
-                Arc::new(arrow_array::Int32Array::from(vec![1])),
-                Arc::new(arrow_array::Int32Array::new_null(1)),
-            ],
-        )
-        .unwrap();
-        let table = conn
-            .create_table("declared", unfilled)
-            .execute()
-            .await
-            .unwrap();
-        assert_eq!(table.refresh_column("next").await.unwrap().rows_filled, 1);
-    }
-
-    /// A projected column keeps its nullability; a computed value is
-    /// nullable.
-    #[tokio::test]
-    async fn an_identity_projection_keeps_source_nullability() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = strict_people(&conn).await;
-        let prepared = prepare_declaration(
-            &source,
-            Some(&[
-                ("id".to_string(), "id".to_string()),
-                ("n".to_string(), "name".to_string()),
-                ("next".to_string(), "id + 1".to_string()),
-            ]),
-            None,
-            None,
-        )
-        .await
-        .unwrap();
-        let nullable: Vec<bool> = prepared
-            .schema()
-            .fields()
-            .iter()
-            .map(|f| f.is_nullable())
-            .collect();
-        assert_eq!(nullable, [false, false, true, false]);
+        view.table().drop_columns(&["doubled"]).await.unwrap();
+        view.refresh().execute().await.unwrap();
+        assert_eq!(view.table().count_rows(None).await.unwrap(), 2);
     }
 }
diff --git a/rust/lancedb/src/materialized_view/differential.rs b/rust/lancedb/src/materialized_view/differential.rs
deleted file mode 100644
index 396b4e65b..000000000
--- a/rust/lancedb/src/materialized_view/differential.rs
+++ /dev/null
@@ -1,730 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-//! Differential refresh testing.
-//!
-//! The refresh contract is a property: after any sequence of source
-//! mutations, a view maintained by default (incremental-where-possible)
-//! refreshes equals the definition evaluated against the source directly,
-//! and so does a forced rebuild. The oracle is an independent read of the
-//! source -- plain column scan, filter applied in Rust -- so it shares
-//! nothing with the refresh path it checks.
-//!
-//! The oracle runs after every step, not just at the end: a later mutation
-//! that forces a rebuild would silently heal an incremental error, and those
-//! transient errors are exactly the bugs this exists to catch.
-
-use arrow_array::{Float32Array, Int32Array, RecordBatch};
-use arrow_schema::{DataType, Field as ArrowField, Schema as ArrowSchema};
-use futures::{StreamExt, TryStreamExt};
-use lance::dataset::NewColumnTransform;
-use std::sync::Arc;
-
-use super::MaterializedView;
-use super::refresh::RefreshMode;
-use crate::connect;
-use crate::connection::Connection;
-use crate::query::{ExecutableQuery, QueryBase, Select};
-use crate::table::{CompactionOptions, OptimizeAction, Table};
-
-/// One source mutation, one per correctness-relevant class.
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
-enum SrcOp {
-    /// Fresh non-colliding ids of both parities, so every other op has
-    /// view-resident rows to act on: the only op that should refresh
-    /// incrementally.
-    AppendNew,
-    /// Deletion in surviving fragments must break the pure-append check.
-    DeleteEven,
-    /// An in-place update; on the filtered shape it crosses the predicate,
-    /// so rows must leave the view.
-    UpdateOddScore,
-    /// Fragment rewrite/renumber must break the pure-append check.
-    Compact,
-    /// A column the view does not read must NOT force a rebuild.
-    AddColumn,
-    /// merge_insert commits an Update whose by-source arm deletes rows, so a
-    /// classifier that reads Update as "changed only" loses those deletions.
-    MergeDropLargest,
-    /// merge_insert that both changes existing rows and inserts new ones in
-    /// one transaction.
-    MergeUpsert,
-}
-
-const ALL_OPS: [SrcOp; 7] = [
-    SrcOp::AppendNew,
-    SrcOp::DeleteEven,
-    SrcOp::UpdateOddScore,
-    SrcOp::Compact,
-    SrcOp::AddColumn,
-    SrcOp::MergeDropLargest,
-    SrcOp::MergeUpsert,
-];
-
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
-enum Shape {
-    /// SELECT id, score.
-    Identity,
-    /// SELECT id, score WHERE score > 50: additionally sensitive to rows
-    /// crossing the predicate.
-    Filtered,
-    /// SELECT id, score LIMIT 4. Which rows are held depends on the order
-    /// they were first materialized, so the oracle checks containment and
-    /// the cap rather than equality.
-    Limited,
-}
-
-impl Shape {
-    fn filter(&self) -> Option<&'static str> {
-        match self {
-            Self::Identity | Self::Limited => None,
-            Self::Filtered => Some("score > 50"),
-        }
-    }
-
-    fn matches(&self, score: f32) -> bool {
-        match self {
-            Self::Identity | Self::Limited => true,
-            Self::Filtered => score > 50.0,
-        }
-    }
-
-    fn limit(&self) -> Option<usize> {
-        match self {
-            Self::Limited => Some(4),
-            _ => None,
-        }
-    }
-}
-
-struct Case {
-    conn: Connection,
-    source: Table,
-    view: MaterializedView,
-    shape: Shape,
-    next_id: i32,
-    added_columns: u32,
-}
-
-fn rows_batch(ids: &[i32]) -> RecordBatch {
-    let scores: Vec<f32> = ids.iter().map(|id| (*id * 10) as f32).collect();
-    RecordBatch::try_new(
-        Arc::new(ArrowSchema::new(vec![
-            ArrowField::new("id", DataType::Int32, true),
-            ArrowField::new("score", DataType::Float32, true),
-        ])),
-        vec![
-            Arc::new(Int32Array::from(ids.to_vec())),
-            Arc::new(Float32Array::from(scores)),
-        ],
-    )
-    .unwrap()
-}
-
-fn merge_batch(ids: &[i32]) -> RecordBatch {
-    let scores: Vec<f32> = ids.iter().map(|id| (*id * 10 + 5) as f32).collect();
-    RecordBatch::try_new(
-        Arc::new(ArrowSchema::new(vec![
-            ArrowField::new("id", DataType::Int32, true),
-            ArrowField::new("score", DataType::Float32, true),
-        ])),
-        vec![
-            Arc::new(Int32Array::from(ids.to_vec())),
-            Arc::new(Float32Array::from(scores)),
-        ],
-    )
-    .unwrap()
-}
-
-impl Case {
-    async fn new(shape: Shape) -> Self {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = conn
-            .create_table("src", rows_batch(&[1, 2, 3, 4]))
-            .write_options(crate::materialized_view::tests::stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-        let mut builder = conn
-            .create_materialized_view("view", "src")
-            .select([("id", "id"), ("score", "score")]);
-        if let Some(filter) = shape.filter() {
-            builder = builder.only_if(filter);
-        }
-        if let Some(limit) = shape.limit() {
-            builder = builder.limit(limit as u64);
-        }
-        let view = builder.execute().await.unwrap();
-        Self {
-            conn,
-            source,
-            view,
-            shape,
-            next_id: 100,
-            added_columns: 0,
-        }
-    }
-
-    async fn apply(&mut self, op: SrcOp) {
-        match op {
-            SrcOp::AppendNew => {
-                // Mixed parity: the middle id is odd, so UpdateOddScore always
-                // has a filter-matching appended row to evict.
-                let ids = vec![self.next_id, self.next_id + 101, self.next_id + 202];
-                self.next_id += 303;
-                self.source.add(rows_batch(&ids)).execute().await.unwrap();
-            }
-            SrcOp::DeleteEven => {
-                self.source.delete("id % 2 = 0").await.unwrap();
-            }
-            SrcOp::UpdateOddScore => {
-                self.source
-                    .update()
-                    .column("score", "-1.0")
-                    .only_if("id % 2 = 1")
-                    .execute()
-                    .await
-                    .unwrap();
-            }
-            SrcOp::Compact => {
-                self.source
-                    .optimize(OptimizeAction::Compact {
-                        options: CompactionOptions::default(),
-                        remap_options: None,
-                    })
-                    .await
-                    .unwrap();
-            }
-            SrcOp::MergeDropLargest => {
-                let mut ids = self.source_ids().await;
-                ids.sort_unstable();
-                ids.pop();
-                if ids.is_empty() {
-                    return;
-                }
-                let batch = rows_batch(&ids);
-                let reader =
-                    arrow_array::RecordBatchIterator::new(vec![Ok(batch.clone())], batch.schema());
-                let mut merge = self.source.merge_insert(&["id"]);
-                merge.when_not_matched_by_source_delete(None);
-                merge.execute(Box::new(reader)).await.unwrap();
-            }
-            SrcOp::MergeUpsert => {
-                let mut ids = self.source_ids().await;
-                ids.sort_unstable();
-                // One row that exists (updated in place) and one that does not.
-                let existing = ids.first().copied().unwrap_or(self.next_id);
-                let fresh = self.next_id;
-                self.next_id += 1;
-                let batch = merge_batch(&[existing, fresh]);
-                let reader =
-                    arrow_array::RecordBatchIterator::new(vec![Ok(batch.clone())], batch.schema());
-                let mut merge = self.source.merge_insert(&["id"]);
-                merge
-                    .when_matched_update_all(None)
-                    .when_not_matched_insert_all();
-                merge.execute(Box::new(reader)).await.unwrap();
-            }
-            SrcOp::AddColumn => {
-                self.added_columns += 1;
-                let field = ArrowField::new(
-                    format!("extra_{}", self.added_columns),
-                    DataType::Int32,
-                    true,
-                );
-                self.source
-                    .add_columns()
-                    .transform(NewColumnTransform::AllNulls(Arc::new(ArrowSchema::new(
-                        vec![field],
-                    ))))
-                    .execute()
-                    .await
-                    .unwrap();
-            }
-        }
-    }
-
-    async fn source_ids(&self) -> Vec<i32> {
-        read_rows(
-            self.source
-                .query()
-                .select(Select::columns(&["id", "score"])),
-        )
-        .await
-        .into_iter()
-        .map(|(id, _)| id)
-        .collect()
-    }
-
-    /// The definition's result, read independently of the refresh path:
-    /// plain column scan, filter applied here, sorted.
-    async fn oracle(&self) -> Vec<(i32, i32)> {
-        let mut rows = read_rows(
-            self.source
-                .query()
-                .select(Select::columns(&["id", "score"])),
-        )
-        .await
-        .into_iter()
-        .filter(|(_, score)| self.shape.matches(*score as f32))
-        .collect::<Vec<_>>();
-        rows.sort_unstable();
-        rows
-    }
-
-    async fn view_rows(&self) -> Vec<(i32, i32)> {
-        let mut rows = read_rows(
-            self.view
-                .table()
-                .query()
-                .select(Select::columns(&["id", "score"])),
-        )
-        .await;
-        rows.sort_unstable();
-        rows
-    }
-
-    async fn check(&self, label: &str) -> Result<(), String> {
-        let expected = self.oracle().await;
-        let actual = self.view_rows().await;
-        let Some(cap) = self.shape.limit() else {
-            if expected != actual {
-                return Err(format!(
-                    "{label}: view diverged from oracle\n  expected: {expected:?}\n  actual:   {actual:?}"
-                ));
-            }
-            return Ok(());
-        };
-        // A capped view holds some subset of the definition's result, never
-        // more than the cap, and never the same row twice.
-        if actual.len() > cap {
-            return Err(format!(
-                "{label}: view holds {} rows, over its cap of {cap}: {actual:?}",
-                actual.len()
-            ));
-        }
-        let mut unique = actual.clone();
-        unique.dedup();
-        if unique.len() != actual.len() {
-            return Err(format!("{label}: view holds a row twice: {actual:?}"));
-        }
-        if let Some(stray) = actual.iter().find(|row| !expected.contains(row)) {
-            return Err(format!(
-                "{label}: view holds {stray:?}, which the definition does not select: {expected:?}"
-            ));
-        }
-        // Below the cap the view must be complete, or a row was lost.
-        if actual.len() < cap.min(expected.len()) {
-            return Err(format!(
-                "{label}: view holds {} of {} selectable rows under a cap of {cap}: {actual:?}",
-                actual.len(),
-                expected.len()
-            ));
-        }
-        Ok(())
-    }
-}
-
-async fn read_rows(query: impl ExecutableQuery) -> Vec<(i32, i32)> {
-    let batches = query
-        .execute()
-        .await
-        .unwrap()
-        .try_collect::<Vec<_>>()
-        .await
-        .unwrap();
-    batches
-        .iter()
-        .flat_map(|batch| {
-            let ids = batch["id"].as_any().downcast_ref::<Int32Array>().unwrap();
-            let scores = batch["score"]
-                .as_any()
-                .downcast_ref::<Float32Array>()
-                .unwrap();
-            // Scores are integer-valued by construction; compare exactly.
-            (0..batch.num_rows())
-                .map(|i| (ids.value(i), scores.value(i) as i32))
-                .collect::<Vec<_>>()
-        })
-        .collect()
-}
-
-/// Drive one mutation sequence: refresh + oracle-check after every step,
-/// then a forced rebuild checked against the same oracle.
-async fn run_sequence(ops: &[SrcOp], shape: Shape) -> Result<(), String> {
-    let label = format!("{shape:?} {ops:?}");
-    let mut case = Case::new(shape).await;
-    case.view
-        .refresh()
-        .execute()
-        .await
-        .map_err(|e| format!("{label}: initial refresh failed: {e}"))?;
-    case.check(&format!("{label} (initial)")).await?;
-
-    for (step, op) in ops.iter().enumerate() {
-        case.apply(*op).await;
-        case.view
-            .refresh()
-            .execute()
-            .await
-            .map_err(|e| format!("{label}: refresh at step {step} failed: {e}"))?;
-        case.check(&format!("{label} (step {step}, {op:?})"))
-            .await?;
-    }
-
-    case.view
-        .refresh()
-        .full(true)
-        .execute()
-        .await
-        .map_err(|e| format!("{label}: final full refresh failed: {e}"))?;
-    case.check(&format!("{label} (final rebuild)")).await?;
-    // Silence the unused-connection lint without dropping it mid-case.
-    let _ = &case.conn;
-    Ok(())
-}
-
-/// Every op sequence up to `max_len`.
-fn all_sequences(max_len: u32) -> Vec<Vec<SrcOp>> {
-    let mut sequences = Vec::new();
-    for len in 1..=max_len {
-        for mut index in 0..ALL_OPS.len().pow(len) {
-            let mut ops = Vec::with_capacity(len as usize);
-            for _ in 0..len {
-                ops.push(ALL_OPS[index % ALL_OPS.len()]);
-                index /= ALL_OPS.len();
-            }
-            sequences.push(ops);
-        }
-    }
-    sequences
-}
-
-async fn run_exhaustive(max_len: u32) {
-    let mut cases = Vec::new();
-    for shape in [Shape::Identity, Shape::Filtered, Shape::Limited] {
-        for ops in all_sequences(max_len) {
-            cases.push((ops, shape));
-        }
-    }
-    let failures: Vec<String> = futures::stream::iter(cases)
-        .map(|(ops, shape)| async move { run_sequence(&ops, shape).await.err() })
-        .buffer_unordered(8)
-        .filter_map(|failure| async move { failure })
-        .collect()
-        .await;
-    assert!(
-        failures.is_empty(),
-        "{} sequences diverged; first: {}",
-        failures.len(),
-        failures[0]
-    );
-}
-
-#[tokio::test(flavor = "multi_thread")]
-async fn differential_exhaustive() {
-    run_exhaustive(3).await;
-}
-
-#[tokio::test(flavor = "multi_thread")]
-#[ignore = "longer sweep; run manually"]
-async fn differential_exhaustive_deep() {
-    run_exhaustive(4).await;
-}
-
-/// Named interleavings that double as repro handles. The mode assertions pin
-/// the classifier, which value comparison alone cannot: a wrongly rebuilt
-/// view still matches the oracle.
-#[tokio::test(flavor = "multi_thread")]
-async fn differential_named_regressions() {
-    // An append is the one op that must stay incremental.
-    let mut case = Case::new(Shape::Identity).await;
-    case.view.refresh().execute().await.unwrap();
-    case.apply(SrcOp::AppendNew).await;
-    let result = case.view.refresh().execute().await.unwrap();
-    assert_eq!(result.mode, RefreshMode::Incremental);
-    case.check("append stays incremental").await.unwrap();
-
-    // A column the view does not read must not force a rebuild.
-    let mut case = Case::new(Shape::Identity).await;
-    case.view.refresh().execute().await.unwrap();
-    case.apply(SrcOp::AddColumn).await;
-    let result = case.view.refresh().execute().await.unwrap();
-    assert_eq!(result.mode, RefreshMode::Incremental);
-    assert_eq!(result.rows_written, 0);
-
-    // Compaction rearranges rows without changing them: the watermark
-    // advances and nothing rebuilds.
-    let mut case = Case::new(Shape::Identity).await;
-    case.view.refresh().execute().await.unwrap();
-    case.apply(SrcOp::AppendNew).await;
-    case.view.refresh().execute().await.unwrap();
-    case.apply(SrcOp::Compact).await;
-    let result = case.view.refresh().execute().await.unwrap();
-    assert_eq!(result.mode, RefreshMode::Incremental);
-    assert_eq!(result.rows_written, 0);
-    case.check("compaction alone").await.unwrap();
-
-    // Fragment bookkeeping stays coherent across the compaction: the next
-    // append is separable and computed alone.
-    case.apply(SrcOp::AppendNew).await;
-    let result = case.view.refresh().execute().await.unwrap();
-    assert_eq!(result.mode, RefreshMode::Incremental);
-    assert_eq!(result.rows_written, 3);
-    case.check("compact then append").await.unwrap();
-
-    // A row updated to no longer match the filter must leave the view --
-    // and the fixture must prove the eviction happened, not merely that the
-    // end state matches: an update that never touched a view-resident row
-    // would also "match".
-    let mut case = Case::new(Shape::Filtered).await;
-    case.apply(SrcOp::AppendNew).await;
-    case.view.refresh().execute().await.unwrap();
-    let before = case.view_rows().await.len();
-    case.apply(SrcOp::UpdateOddScore).await;
-    case.view.refresh().execute().await.unwrap();
-    let after = case.view_rows().await.len();
-    assert!(
-        after < before,
-        "no view-resident row was evicted ({before} -> {after}); the fixture \
-         no longer exercises the filtered-update transition"
-    );
-    case.check("update crosses the filter").await.unwrap();
-}
-
-// ---------------------------------------------------------------------------
-// Concurrency
-// ---------------------------------------------------------------------------
-//
-// The sequential cases above cannot observe a cross-process race: the
-// per-view refresh lock is process-local, so a second refresh in this
-// process queues behind the first. What is missing is not more op
-// sequences but a second process. These cases add one, and assert the same
-// property the harness always asserts -- the view holds each row once.
-
-/// Rows the definition selects from the source: every id but the first,
-/// read straight from the source, sharing nothing with the refresh path.
-async fn concurrency_oracle(conn: &Connection) -> Vec<i32> {
-    let batches: Vec<RecordBatch> = conn
-        .open_table("src")
-        .execute()
-        .await
-        .unwrap()
-        .query()
-        .select(Select::columns(&["id"]))
-        .execute()
-        .await
-        .unwrap()
-        .try_collect()
-        .await
-        .unwrap();
-    let mut ids = Vec::new();
-    for batch in &batches {
-        let column = batch
-            .column(0)
-            .as_any()
-            .downcast_ref::<Int32Array>()
-            .unwrap();
-        for i in 0..batch.num_rows() {
-            if column.value(i) > 1 {
-                ids.push(column.value(i));
-            }
-        }
-    }
-    ids.sort_unstable();
-    ids
-}
-
-/// The view's ids, sorted.
-async fn concurrency_view_ids(conn: &Connection) -> Vec<i32> {
-    let batches: Vec<RecordBatch> = conn
-        .open_table("mv")
-        .execute()
-        .await
-        .unwrap()
-        .query()
-        .select(Select::columns(&["id"]))
-        .execute()
-        .await
-        .unwrap()
-        .try_collect()
-        .await
-        .unwrap();
-    let mut ids = Vec::new();
-    for batch in &batches {
-        let column = batch
-            .column(0)
-            .as_any()
-            .downcast_ref::<Int32Array>()
-            .unwrap();
-        for i in 0..batch.num_rows() {
-            ids.push(column.value(i));
-        }
-    }
-    ids.sort_unstable();
-    ids
-}
-
-/// One refresh of the view at `MV_RACE_DIR`, in its own process.
-///
-/// Setup happens before the start barrier so warm-up does not stagger the
-/// two processes. What makes the race certain rather than likely is the
-/// second barrier inside `refresh()` itself, which holds every participant
-/// between staging and commit.
-#[tokio::test]
-#[ignore = "spawned as a child process by the concurrency cases"]
-async fn cross_process_refresh_child() {
-    let Ok(dir) = std::env::var("MV_RACE_DIR") else {
-        return;
-    };
-    let dir = std::path::PathBuf::from(dir);
-    let tag = std::env::var("MV_RACE_TAG").unwrap();
-
-    let conn = connect(dir.to_str().unwrap()).execute().await.unwrap();
-    let table = conn.open_table("mv").execute().await.unwrap();
-    let _ = table.schema().await.unwrap();
-    let _ = table.count_rows(None).await.unwrap();
-    let source = conn.open_table("src").execute().await.unwrap();
-    let _ = source.count_rows(None).await.unwrap();
-    let view = MaterializedView::from_table(table).await.unwrap();
-
-    std::fs::write(dir.join(format!("ready-{tag}")), b"1").unwrap();
-    while !dir.join("START").exists() {
-        std::thread::sleep(std::time::Duration::from_millis(2));
-    }
-
-    let outcome = match view.refresh().execute().await {
-        Ok(result) => format!("committed rows={}", result.rows_written),
-        Err(err) if is_commit_conflict(&err) => "conflicted".to_string(),
-        Err(err) => format!("failed {err}"),
-    };
-    std::fs::write(dir.join(format!("outcome-{tag}")), outcome).unwrap();
-}
-
-/// Whether a refresh lost its commit to a concurrent one, as opposed to
-/// failing for any other reason.
-fn is_commit_conflict(err: &crate::Error) -> bool {
-    let text = err.to_string();
-    text.contains("Retryable commit conflict") || text.contains("preempted by concurrent")
-}
-
-/// Two processes refreshing one view concurrently must leave the view
-/// equal to the oracle: each selected row present exactly once.
-///
-/// Both plan the same incremental delta from one watermark. A refresh is
-/// meant to land on the generation it planned or leave nothing behind, so
-/// at most one of them may write.
-#[tokio::test]
-async fn concurrent_refreshes_hold_each_row_once() {
-    let dir = tempfile::tempdir().unwrap();
-    let path = dir.path().to_str().unwrap().to_string();
-    let conn = connect(&path).execute().await.unwrap();
-    conn.create_table("src", rows_batch(&[1, 2, 3, 4]))
-        .write_options(crate::materialized_view::tests::stable_row_ids())
-        .execute()
-        .await
-        .unwrap();
-    let view = conn
-        .create_materialized_view("mv", "src")
-        .select([("id", "id"), ("score", "score")])
-        .only_if("id > 1")
-        .execute()
-        .await
-        .unwrap();
-    // Seed the watermark so the racing refreshes are both incremental.
-    view.refresh().execute().await.unwrap();
-
-    // Large enough that a refresh is real work rather than a formality.
-    let ids: Vec<i32> = (100..200_100).collect();
-    conn.open_table("src")
-        .execute()
-        .await
-        .unwrap()
-        .add(rows_batch(&ids))
-        .execute()
-        .await
-        .unwrap();
-
-    let tags = ["a", "b"];
-    let exe = std::env::current_exe().unwrap();
-    let children: Vec<std::process::Child> = tags
-        .iter()
-        .map(|tag| {
-            std::process::Command::new(&exe)
-                .args([
-                    "--exact",
-                    "materialized_view::differential::cross_process_refresh_child",
-                    "--ignored",
-                    "--nocapture",
-                ])
-                .env("MV_RACE_DIR", dir.path())
-                .env("MV_RACE_SYNC", dir.path())
-                .env("MV_RACE_PEERS", "2")
-                .env("MV_RACE_TAG", tag)
-                .stdout(std::process::Stdio::null())
-                .stderr(std::process::Stdio::null())
-                .spawn()
-                .unwrap()
-        })
-        .collect();
-
-    let deadline = std::time::Instant::now() + std::time::Duration::from_secs(180);
-    while tags
-        .iter()
-        .any(|tag| !dir.path().join(format!("ready-{tag}")).exists())
-    {
-        assert!(
-            std::time::Instant::now() < deadline,
-            "children never became ready"
-        );
-        std::thread::sleep(std::time::Duration::from_millis(10));
-    }
-    std::fs::write(dir.path().join("START"), b"1").unwrap();
-    for (tag, mut child) in tags.iter().zip(children) {
-        let status = loop {
-            match child.try_wait().unwrap() {
-                Some(status) => break status,
-                None if std::time::Instant::now() >= deadline => {
-                    child.kill().unwrap();
-                    panic!("child {tag} never finished");
-                }
-                None => std::thread::sleep(std::time::Duration::from_millis(10)),
-            }
-        };
-        assert!(status.success(), "child {tag} exited {status}");
-    }
-
-    // Both refreshes reached the commit boundary before either committed --
-    // the in-refresh barrier guarantees it -- so exactly one may win.
-    let outcomes: Vec<String> = tags
-        .iter()
-        .map(|tag| {
-            std::fs::read_to_string(dir.path().join(format!("outcome-{tag}")))
-                .unwrap_or_else(|_| panic!("child {tag} recorded no outcome"))
-        })
-        .collect();
-    for tag in tags {
-        assert!(
-            dir.path().join(format!("planned-{tag}")).exists(),
-            "child {tag} never reached the commit boundary, so nothing was synchronized"
-        );
-    }
-    let committed = outcomes.iter().filter(|o| o.contains("committed")).count();
-    let conflicted = outcomes.iter().filter(|o| o.contains("conflicted")).count();
-    assert_eq!(
-        (committed, conflicted),
-        (1, 1),
-        "exactly one refresh may win the generation both planned: {outcomes:?}"
-    );
-
-    let expected = concurrency_oracle(&conn).await;
-    let actual = concurrency_view_ids(&conn).await;
-    assert_eq!(
-        actual.len(),
-        expected.len(),
-        "the view holds {} rows, the oracle {}: a losing refresh left rows behind",
-        actual.len(),
-        expected.len()
-    );
-    assert_eq!(actual, expected, "the view does not match the oracle");
-}
diff --git a/rust/lancedb/src/materialized_view/duplicate_pairs.rs b/rust/lancedb/src/materialized_view/duplicate_pairs.rs
deleted file mode 100644
index d0dc219ad..000000000
--- a/rust/lancedb/src/materialized_view/duplicate_pairs.rs
+++ /dev/null
@@ -1,478 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-//! Native pair generation as partitioned materialized-view refresh work.
-
-use std::sync::Arc;
-
-use arrow_schema::{Schema, SchemaRef};
-use datafusion_execution::{SendableRecordBatchStream, TaskContext};
-use datafusion_physical_plan::ExecutionPlan;
-use datafusion_sql::sqlparser::ast::{
-    Expr, FunctionArg, FunctionArgExpr, TableFunctionArgs, Value,
-};
-use futures::TryStreamExt;
-use lance::Dataset;
-use lance::dataset::transaction::Operation;
-use lance::dataset::{InsertBuilder, WriteDestination, WriteMode, WriteParams};
-use lance_table::format::Fragment;
-use serde::{Deserialize, Serialize};
-
-use super::grouped_units::GroupedRefreshPlan;
-use super::refresh::{
-    RefreshMaterializedViewResult, ensure_incarnation, ensure_no_mem_wal, open_source,
-};
-use super::{MaterializedViewDefinition, Planned, ViewProjection};
-use crate::table::Table;
-use crate::table::datafusion::udtf::duplicate_pairs::{
-    DUPLICATE_PAIRS_OPERATOR_VERSION, DuplicatePairTask, DuplicatePairsConfig, DuplicatePairsExec,
-    duplicate_pair_schema, plan_duplicate_pairs,
-};
-use crate::{Error, Result};
-
-pub(super) const FUNCTION_NAME: &str = "vector_duplicate_pairs";
-pub(super) const DEDUP_FUNCTION_NAME: &str = "vector_dedup";
-
-/// The result of a pinned native vector source.
-#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
-pub enum VectorSourceKind {
-    /// The qualifying pair relation.
-    Pairs,
-    /// Original source rows retained by greedy direct representatives.
-    Dedup,
-}
-
-/// A native table-function source, pinned for the lifetime of the view.
-#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
-#[serde(deny_unknown_fields)]
-pub struct VectorSource {
-    pub kind: VectorSourceKind,
-    pub dataset_version: u64,
-    pub column: String,
-    /// Validated numeric SQL spelling; execution and task identity use f32 bits.
-    pub distance_threshold: String,
-}
-
-impl VectorSource {
-    pub(super) fn function_name(&self) -> &'static str {
-        match self.kind {
-            VectorSourceKind::Pairs => FUNCTION_NAME,
-            VectorSourceKind::Dedup => DEDUP_FUNCTION_NAME,
-        }
-    }
-    pub(crate) fn config(&self) -> Result<DuplicatePairsConfig> {
-        let threshold: f32 = self
-            .distance_threshold
-            .parse()
-            .map_err(|_| invalid("invalid pair threshold"))?;
-        if self.dataset_version == 0 || self.column.is_empty() || !threshold.is_finite() {
-            return Err(invalid(
-                "duplicate pairs require a positive snapshot, a column and a finite threshold",
-            ));
-        }
-        Ok(DuplicatePairsConfig {
-            dataset_version: self.dataset_version,
-            column: self.column.clone(),
-            distance_threshold: threshold,
-        })
-    }
-}
-
-fn invalid(message: impl Into<String>) -> Error {
-    Error::InvalidInput {
-        message: message.into(),
-    }
-}
-
-pub(super) fn source(
-    args: &TableFunctionArgs,
-    kind: VectorSourceKind,
-) -> Result<(Vec<String>, String, VectorSource)> {
-    if args.settings.is_some() {
-        return Err(invalid("duplicate pairs do not accept function settings"));
-    }
-    let exprs = args
-        .args
-        .iter()
-        .map(|arg| match arg {
-            FunctionArg::Unnamed(FunctionArgExpr::Expr(expr)) => Ok(expr),
-            _ => Err(invalid(
-                "duplicate pairs require four positional literal arguments",
-            )),
-        })
-        .collect::<Result<Vec<_>>>()?;
-    let [table, version, column, threshold] = exprs.as_slice() else {
-        return Err(invalid(
-            "native vector sources require (table, dataset_version, column, distance_threshold)",
-        ));
-    };
-    let string = |expr: &Expr| match expr {
-        Expr::Value(value) => match &value.value {
-            Value::SingleQuotedString(value) => Ok(value.clone()),
-            _ => Err(invalid("table and column must be string literals")),
-        },
-        _ => Err(invalid("table and column must be string literals")),
-    };
-    let table = string(table)?;
-    let plain = MaterializedViewDefinition::from_sql(&format!("SELECT * FROM {table}"))?;
-    if plain.vector_source.is_some()
-        || plain.lateral.is_some()
-        || plain.filter.is_some()
-        || plain.is_grouped()
-        || plain.limit.is_some()
-    {
-        return Err(invalid("duplicate pairs must name one source table"));
-    }
-    let dataset_version = match version {
-        Expr::Value(value) => match &value.value {
-            Value::Number(number, _) => number
-                .parse()
-                .map_err(|_| invalid("invalid source version"))?,
-            _ => return Err(invalid("source version must be an integer literal")),
-        },
-        _ => return Err(invalid("source version must be an integer literal")),
-    };
-    let config = VectorSource {
-        kind,
-        dataset_version,
-        column: string(column)?,
-        distance_threshold: threshold.to_string(),
-    };
-    config.config()?;
-    Ok((plain.source_namespace, plain.source_table, config))
-}
-
-pub(super) fn check_shape(definition: &MaterializedViewDefinition) -> Result<()> {
-    if definition
-        .vector_source
-        .as_ref()
-        .is_some_and(|s| s.kind == VectorSourceKind::Dedup)
-    {
-        if !definition.selects_star()
-            || definition.filter.is_some()
-            || definition.limit.is_some()
-            || definition.lateral.is_some()
-            || definition.is_grouped()
-        {
-            return Err(invalid(
-                "a vector_dedup view must select * without other clauses",
-            ));
-        }
-        return Ok(());
-    }
-    let schema = duplicate_pair_schema();
-    let projections = schema
-        .fields()
-        .iter()
-        .map(|field| ViewProjection {
-            output: field.name().clone(),
-            expression: field.name().clone(),
-        })
-        .collect::<Vec<_>>();
-    if (!definition.selects_star() && definition.projections != projections)
-        || definition.filter.is_some()
-        || definition.limit.is_some()
-        || definition.lateral.is_some()
-        || definition.is_grouped()
-    {
-        return Err(invalid(
-            "a native pair view selects *, or row_id_a, row_id_b, distance, without other clauses",
-        ));
-    }
-    Ok(())
-}
-
-pub(super) fn plan(
-    source_schema: SchemaRef,
-    definition: &MaterializedViewDefinition,
-) -> Result<Planned> {
-    check_shape(definition)?;
-    let config = definition.vector_source.as_ref().expect("native source");
-    config.config()?;
-    source_schema.field_with_name(&config.column)?;
-    if config.kind == VectorSourceKind::Dedup {
-        return Ok(Planned {
-            definition: definition.clone(),
-            fields: source_schema
-                .fields()
-                .iter()
-                .map(|f| super::without_declarations(f))
-                .collect(),
-            lineage: source_schema
-                .fields()
-                .iter()
-                .map(|f| (f.name().clone(), vec![f.name().clone()]))
-                .collect(),
-            inputs: source_schema
-                .fields()
-                .iter()
-                .map(|f| f.name().clone())
-                .collect(),
-        });
-    }
-    let schema = duplicate_pair_schema();
-    let projections = schema
-        .fields()
-        .iter()
-        .map(|field| ViewProjection {
-            output: field.name().clone(),
-            expression: field.name().clone(),
-        })
-        .collect();
-    let mut definition = definition.clone();
-    let inputs = vec![config.column.clone()];
-    definition.projections = projections;
-    Ok(Planned {
-        definition,
-        fields: schema.fields().iter().map(|f| f.as_ref().clone()).collect(),
-        lineage: Default::default(),
-        inputs,
-    })
-}
-
-/// A saved native refresh plan, including every physical partition identity.
-#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
-#[serde(deny_unknown_fields)]
-pub struct DuplicatePairsRefreshPlan {
-    pub snapshot: GroupedRefreshPlan,
-    pub source_uri: String,
-    pub definition_sql: String,
-    pub operator_version: u32,
-    pub threshold_bits: u32,
-    pub tasks: Vec<DuplicatePairTask>,
-}
-
-#[derive(Debug, Clone, Serialize, Deserialize)]
-#[serde(deny_unknown_fields)]
-pub struct WrittenPairsPartition {
-    pub(super) unit: u32,
-    pub(super) plan_id: String,
-    pub(super) task: DuplicatePairTask,
-    pub(super) fragments: Vec<Fragment>,
-    pub(super) rows: u64,
-}
-
-impl DuplicatePairsRefreshPlan {
-    pub(super) fn identity(&self) -> Result<String> {
-        use sha2::{Digest, Sha256};
-        let encoded = serde_json::to_vec(&(
-            &self.snapshot,
-            &self.source_uri,
-            &self.definition_sql,
-            self.operator_version,
-            self.threshold_bits,
-        ))
-        .map_err(|e| invalid(e.to_string()))?;
-        Ok(format!("{:x}", Sha256::digest(encoded)))
-    }
-}
-
-async fn open(view: &Table) -> Result<(Dataset, MaterializedViewDefinition, Arc<Dataset>)> {
-    let native = view
-        .as_native()
-        .ok_or_else(|| invalid("native pair refresh requires a local view"))?;
-    native.dataset.ensure_mutable()?;
-    native.dataset.reload().await?;
-    let view_ds = native.dataset.get().await?.as_ref().clone();
-    let Some(super::StoredDefinition::Query(definition)) =
-        super::read_definition(&view_ds.schema().metadata)?
-    else {
-        return Err(invalid("view does not carry a supported definition"));
-    };
-    let config = definition
-        .vector_source
-        .as_ref()
-        .ok_or_else(|| invalid("not a native pair view"))?
-        .config()?;
-    let source = Arc::new(
-        open_source(view, &definition, None)
-            .await?
-            .checkout_version(config.dataset_version)
-            .await?,
-    );
-    ensure_no_mem_wal(&source, "source table", &definition.source_table).await?;
-    let planned = plan(Arc::new(Schema::from(source.schema())), &definition)?;
-    let expected = Schema::new(planned.fields);
-    let physical = Schema::from(view_ds.schema());
-    if physical.fields().len() != expected.fields().len()
-        || physical
-            .fields()
-            .iter()
-            .zip(expected.fields())
-            .any(|(a, b)| {
-                a.name() != b.name()
-                    || a.data_type() != b.data_type()
-                    || a.is_nullable() != b.is_nullable()
-            })
-    {
-        return Err(invalid("native pair view schema changed since creation"));
-    }
-    Ok((view_ds, planned.definition, source))
-}
-
-pub(super) async fn plan_refresh(
-    view: &Table,
-    pinned: Option<u64>,
-) -> Result<DuplicatePairsRefreshPlan> {
-    let (view_ds, definition, source) = open(view).await?;
-    let config = definition
-        .vector_source
-        .as_ref()
-        .expect("native")
-        .config()?;
-    if pinned.is_some_and(|v| v != config.dataset_version) {
-        return Err(invalid(
-            "refresh source version differs from the native pair view's fixed snapshot",
-        ));
-    }
-    let tasks = plan_duplicate_pairs(source.clone(), &config).await?;
-    let units = u32::try_from(tasks.len()).map_err(|_| invalid("too many index partitions"))?;
-    let incarnation = view_ds
-        .schema()
-        .metadata
-        .get(super::INCARNATION_META_KEY)
-        .cloned()
-        .ok_or_else(|| invalid("native pair view has no incarnation"))?;
-    Ok(DuplicatePairsRefreshPlan {
-        snapshot: GroupedRefreshPlan {
-            units,
-            source_version: config.dataset_version,
-            view_version: view_ds.version().version,
-            incarnation,
-        },
-        source_uri: source.uri().to_string(),
-        definition_sql: definition.to_sql(),
-        operator_version: DUPLICATE_PAIRS_OPERATOR_VERSION,
-        threshold_bits: config.distance_threshold.to_bits(),
-        tasks,
-    })
-}
-
-pub(super) async fn open_planned(
-    view: &Table,
-    plan: &DuplicatePairsRefreshPlan,
-) -> Result<(Dataset, MaterializedViewDefinition, Arc<Dataset>)> {
-    let (view_ds, definition, source) = open(view).await?;
-    ensure_incarnation(&view_ds, Some(&plan.snapshot.incarnation), view.name()).await?;
-    let config = definition
-        .vector_source
-        .as_ref()
-        .expect("native")
-        .config()?;
-    if source.uri() != plan.source_uri
-        || source.version().version != plan.snapshot.source_version
-        || definition.to_sql() != plan.definition_sql
-        || config.distance_threshold.to_bits() != plan.threshold_bits
-        || plan.operator_version != DUPLICATE_PAIRS_OPERATOR_VERSION
-        || plan.tasks.len() != plan.snapshot.units as usize
-    {
-        return Err(invalid(
-            "native pair task belongs to a different snapshot, configuration or operator",
-        ));
-    }
-    Ok((view_ds, definition, source))
-}
-
-pub(super) async fn write_unit(
-    view: &Table,
-    unit: u32,
-    plan: &DuplicatePairsRefreshPlan,
-) -> Result<WrittenPairsPartition> {
-    let (view_ds, definition, source) = open_planned(view, plan).await?;
-    let task = plan
-        .tasks
-        .get(unit as usize)
-        .ok_or_else(|| invalid("pair partition is outside the plan"))?
-        .clone();
-    let config = definition
-        .vector_source
-        .as_ref()
-        .expect("native")
-        .config()?;
-    let exec = DuplicatePairsExec::try_new(source, config, vec![task.clone()])?;
-    let stream = exec.execute(0, Arc::new(TaskContext::default()))?;
-    let rows = Arc::new(std::sync::atomic::AtomicU64::new(0));
-    let count = rows.clone();
-    let stream = stream.inspect_ok(move |batch| {
-        count.fetch_add(
-            batch.num_rows() as u64,
-            std::sync::atomic::Ordering::Relaxed,
-        );
-    });
-    let stream: SendableRecordBatchStream = Box::pin(
-        datafusion_physical_plan::stream::RecordBatchStreamAdapter::new(
-            duplicate_pair_schema(),
-            stream,
-        ),
-    );
-    let txn = InsertBuilder::new(WriteDestination::Dataset(Arc::new(view_ds)))
-        .with_params(&WriteParams {
-            mode: WriteMode::Append,
-            ..Default::default()
-        })
-        .execute_uncommitted_stream(stream)
-        .await?;
-    let Operation::Append { fragments } = txn.operation else {
-        return Err(invalid(
-            "native pair partition did not stage append fragments",
-        ));
-    };
-    Ok(WrittenPairsPartition {
-        unit,
-        plan_id: plan.identity()?,
-        task,
-        fragments,
-        rows: rows.load(std::sync::atomic::Ordering::Relaxed),
-    })
-}
-
-pub(super) async fn commit(
-    view: &Table,
-    plan: &DuplicatePairsRefreshPlan,
-    units: Vec<WrittenPairsPartition>,
-    expected: Option<&str>,
-) -> Result<RefreshMaterializedViewResult> {
-    let (_, definition, source) = open_planned(view, plan).await?;
-    let config = definition
-        .vector_source
-        .as_ref()
-        .expect("native")
-        .config()?;
-    if plan_duplicate_pairs(source.clone(), &config).await? != plan.tasks {
-        return Err(invalid(
-            "pair plan does not cover the pinned index partitions exactly",
-        ));
-    }
-    let identity = plan.identity()?;
-    let mut ordered = vec![None; plan.snapshot.units as usize];
-    for written in units {
-        if written.plan_id != identity
-            || plan.tasks.get(written.unit as usize) != Some(&written.task)
-        {
-            return Err(invalid("pair partition receipt belongs to another plan"));
-        }
-        let slot = ordered
-            .get_mut(written.unit as usize)
-            .ok_or_else(|| invalid("unknown pair partition receipt"))?;
-        if slot.is_some() {
-            return Err(invalid("duplicate pair partition receipt"));
-        }
-        *slot = Some(written);
-    }
-    let units = ordered
-        .into_iter()
-        .collect::<Option<Vec<_>>>()
-        .ok_or_else(|| invalid("missing pair partition receipt"))?;
-    let rows = units.iter().map(|u| u.rows).sum();
-    let fragments = units.into_iter().flat_map(|u| u.fragments).collect();
-    super::grouped_units::commit_fragments(
-        view,
-        &plan.snapshot,
-        &definition,
-        source.manifest.timestamp_nanos,
-        fragments,
-        rows,
-        expected,
-    )
-    .await
-}
diff --git a/rust/lancedb/src/materialized_view/grouped.rs b/rust/lancedb/src/materialized_view/grouped.rs
index 095f20f11..3120e2b43 100644
--- a/rust/lancedb/src/materialized_view/grouped.rs
+++ b/rust/lancedb/src/materialized_view/grouped.rs
@@ -12,7 +12,7 @@ use std::sync::atomic::{AtomicU64, Ordering};
 use arrow_array::cast::AsArray;
 use arrow_array::{Array, FixedSizeListArray, UInt32Array};
 use arrow_buffer::NullBuffer;
-use arrow_schema::{DataType, Field as ArrowField, Schema as ArrowSchema, SchemaRef};
+use arrow_schema::{DataType, Schema as ArrowSchema, SchemaRef};
 use datafusion::catalog::default_table_source::provider_as_source;
 use datafusion::datasource::empty::EmptyTable;
 use datafusion::execution::FunctionRegistry;
@@ -36,20 +36,16 @@ use datafusion_sql::sqlparser::parser::Parser;
 use futures::StreamExt;
 use lance::Dataset;
 use lance::index::{DatasetIndexExt, DatasetIndexInternalExt};
-use lance_core::ROW_ID;
 use lance_datafusion::exec::SessionContextExt;
 use lance_index::metrics::NoOpMetricsCollector;
-use lance_index::vector::VectorIndex;
 use lance_index::vector::ivf::{IvfTransformer, new_ivf_transformer};
 use lance_linalg::distance::DistanceType;
 use lance_linalg::kernels::normalize_fsl;
-use roaring::RoaringBitmap;
 use uuid::Uuid;
 
 use super::refresh::to_view_batch;
 use super::{
-    MaterializedViewDefinition, Planned, SOURCE_ROW_ID_COLUMN, ViewProjection, ensure_immutable,
-    plan_filter, query, without_declarations,
+    MaterializedViewDefinition, Planned, ViewProjection, plan_filter, query, without_declarations,
 };
 use crate::{Error, Result};
 
@@ -68,50 +64,6 @@ pub(super) struct IvfBinding {
     pub(super) transformer: Arc<IvfTransformer>,
     /// A cosine index assigns L2 over unit vectors, as lance's index path does.
     pub(super) normalize: bool,
-    /// Partitions of the model; the ids `ivf_partition` returns are below it.
-    pub(super) partitions: u32,
-}
-
-/// The segments of the one IVF index on a column, opened, and the fragments
-/// they cover between them; every other fragment's rows are unindexed.
-/// `covered` is `None` when a segment does not say which fragments it holds
-/// (`fragment_bitmap: None`, which lance defines as unknown rather than
-/// empty): its rows would be read once by the partition reader and again as
-/// unindexed, so no caller may split this index.
-pub(super) struct IvfSegments {
-    pub(super) segments: Vec<IvfSegment>,
-    pub(super) covered: Option<RoaringBitmap>,
-}
-
-/// One opened segment and the fragments it holds rows for. Its postings
-/// still name rows of fragments it has since lost -- a column rewrite
-/// attaches a new file and takes the fragment out of this bitmap -- so a
-/// reader of its partitions keeps only the rows it still owns.
-pub(super) struct IvfSegment {
-    pub(super) index: Arc<dyn VectorIndex>,
-    pub(super) fragments: Option<RoaringBitmap>,
-}
-
-impl IvfSegments {
-    pub(super) fn is_unindexed(&self, fragment_id: u64) -> Option<bool> {
-        Some(!self.covered.as_ref()?.contains(fragment_id as u32))
-    }
-}
-
-/// Coverage after folding in one more segment. Lance defines a segment
-/// without a fragment bitmap as unknown coverage, not empty, and one such
-/// segment is enough to lose the coverage of the whole index.
-fn coverage(
-    covered: Option<RoaringBitmap>,
-    bitmap: Option<&RoaringBitmap>,
-) -> Option<RoaringBitmap> {
-    match (covered, bitmap) {
-        (Some(mut covered), Some(bitmap)) => {
-            covered |= bitmap;
-            Some(covered)
-        }
-        _ => None,
-    }
 }
 
 /// `bindings` maps a source column to its index; planning runs unbound.
@@ -278,16 +230,6 @@ pub(super) async fn bind(
 }
 
 async fn bind_column(source: &Dataset, column: &str) -> Result<IvfBinding> {
-    bind_column_with_segments(source, column)
-        .await
-        .map(|(binding, _)| binding)
-}
-
-/// The binding plus the opened segments it was read from.
-pub(super) async fn bind_column_with_segments(
-    source: &Dataset,
-    column: &str,
-) -> Result<(IvfBinding, IvfSegments)> {
     let field = source
         .schema()
         .field(column)
@@ -298,8 +240,6 @@ pub(super) async fn bind_column_with_segments(
     // on its own fragments carries its own centroids, and grouping every row
     // by one segment's model would bucket the other segments' rows wrongly.
     let mut found: Option<(String, Uuid, DistanceType, FixedSizeListArray)> = None;
-    let mut segments = Vec::new();
-    let mut covered = Some(RoaringBitmap::new());
     for index in source.load_indices().await?.iter() {
         if index.fields != [field.id] {
             continue;
@@ -314,12 +254,6 @@ pub(super) async fn bind_column_with_segments(
             continue;
         };
         let metric = vector.metric_type();
-        covered = coverage(covered, index.fragment_bitmap.as_ref());
-        let fragments = index.fragment_bitmap.clone();
-        segments.push(IvfSegment {
-            index: vector.clone(),
-            fragments,
-        });
         match &found {
             None => found = Some((index.name.clone(), index.uuid, metric, centroids)),
             Some((name, _, seen_metric, seen)) if *name == index.name => {
@@ -369,32 +303,20 @@ pub(super) async fn bind_column_with_segments(
     // Lance's index path assigns cosine by L2 over unit vectors.
     let normalize = metric == DistanceType::Cosine;
     let distance = if normalize { DistanceType::L2 } else { metric };
-    let partitions = u32::try_from(centroids.len()).map_err(|_| Error::InvalidInput {
-        message: format!("{IVF_PARTITION}({column}): too many partitions"),
-    })?;
-    Ok((
-        IvfBinding {
-            index: uuid,
-            transformer: Arc::new(new_ivf_transformer(centroids, distance, vec![])),
-            normalize,
-            partitions,
-        },
-        IvfSegments { segments, covered },
-    ))
+    Ok(IvfBinding {
+        index: uuid,
+        transformer: Arc::new(new_ivf_transformer(centroids, distance, vec![])),
+        normalize,
+    })
 }
 
 pub(super) fn empty_source(source_schema: &SchemaRef) -> Arc<dyn TableSource> {
-    let mut fields = source_schema.fields().to_vec();
-    fields.push(Arc::new(ArrowField::new(ROW_ID, DataType::UInt64, false)));
-    provider_as_source(Arc::new(EmptyTable::new(Arc::new(ArrowSchema::new(
-        fields,
-    )))))
+    provider_as_source(Arc::new(EmptyTable::new(source_schema.clone())))
 }
 
-/// The query DataFusion runs: the view's projections plus the group's
-/// smallest source row id, which stands as the row's provenance. `filtered`
-/// puts the view's predicate in the query, for a caller whose rows did not
-/// come from a lance scan that already applied it.
+/// The query DataFusion runs. `filtered` puts the view's predicate in the
+/// query for a caller whose rows did not come from a Lance scan that already
+/// applied it.
 fn sql(definition: &MaterializedViewDefinition, filtered: bool) -> String {
     let items: Vec<String> = definition
         .projections
@@ -406,7 +328,7 @@ fn sql(definition: &MaterializedViewDefinition, filtered: bool) -> String {
         _ => String::new(),
     };
     format!(
-        "SELECT {}, min({ROW_ID}) AS {ROW_ID} FROM {SOURCE}{predicate} GROUP BY {}",
+        "SELECT {} FROM {SOURCE}{predicate} GROUP BY {}",
         items.join(", "),
         definition.group_by.join(", ")
     )
@@ -499,11 +421,6 @@ pub(super) fn plan(
     };
     let mut projections: Vec<ViewProjection> = Vec::with_capacity(definition.projections.len());
     for p in &definition.projections {
-        if p.output == SOURCE_ROW_ID_COLUMN || p.output == ROW_ID {
-            return Err(Error::InvalidInput {
-                message: format!("view column name '{}' is reserved", p.output),
-            });
-        }
         if projections.iter().any(|seen| seen.output == p.output) {
             return Err(Error::ColumnAlreadyExists {
                 name: p.output.clone(),
@@ -549,14 +466,11 @@ pub(super) fn plan(
             message: format!("invalid grouped view: {e}"),
         })?;
     for expr in &exprs {
-        ensure_immutable(expr, |message| Error::InvalidInput {
-            message: format!("invalid grouped view: {message}"),
-        })?;
         inputs.extend(
             expr.column_refs()
                 .into_iter()
                 .map(|c| c.name.clone())
-                .filter(|name| name != ROW_ID && source_schema.field_with_name(name).is_ok()),
+                .filter(|name| source_schema.field_with_name(name).is_ok()),
         );
     }
     inputs.sort();
@@ -566,7 +480,6 @@ pub(super) fn plan(
         .schema()
         .fields()
         .iter()
-        .filter(|f| f.name() != ROW_ID)
         .map(|f| without_declarations(f))
         .collect();
     Ok(Planned {
@@ -586,7 +499,6 @@ pub(super) async fn stream(
     rows_written: Arc<AtomicU64>,
 ) -> Result<SendableRecordBatchStream> {
     let mut scanner = source.scan();
-    scanner.with_row_id();
     if let Some(filter) = &definition.filter {
         scanner.filter(filter)?;
     }
@@ -611,24 +523,3 @@ pub(super) async fn stream(
     });
     Ok(Box::pin(RecordBatchStreamAdapter::new(schema, mapped)))
 }
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    /// A segment that does not name the fragments it holds leaves the whole
-    /// index's coverage unknown: reading "everything it does not cover" would
-    /// read its rows a second time.
-    #[test]
-    fn one_segment_without_a_bitmap_loses_the_coverage() {
-        let first = RoaringBitmap::from_iter([0u32, 1]);
-        let second = RoaringBitmap::from_iter([2u32]);
-        let both = coverage(
-            coverage(Some(RoaringBitmap::new()), Some(&first)),
-            Some(&second),
-        );
-        assert_eq!(both, Some(RoaringBitmap::from_iter([0u32, 1, 2])));
-        assert_eq!(coverage(both, None), None);
-        assert_eq!(coverage(None, Some(&first)), None);
-    }
-}
diff --git a/rust/lancedb/src/materialized_view/grouped_units.rs b/rust/lancedb/src/materialized_view/grouped_units.rs
deleted file mode 100644
index 7f408be4d..000000000
--- a/rust/lancedb/src/materialized_view/grouped_units.rs
+++ /dev/null
@@ -1,1368 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-//! A grouped view keyed by one `ivf_partition(col)` refreshed in units, one
-//! per index partition: the index already holds each partition's row ids, so
-//! a unit reads only its rows, computes only its groups and writes its own
-//! fragments, and a single commit replaces the view. Nothing is shuffled and
-//! no worker holds more than one partition's groups.
-
-use std::collections::HashMap;
-use std::sync::Arc;
-use std::sync::atomic::{AtomicU64, Ordering};
-
-use arrow_array::Array;
-use arrow_array::RecordBatch;
-use arrow_array::cast::AsArray;
-use arrow_array::types::UInt64Type;
-use arrow_schema::{Schema as ArrowSchema, SchemaRef};
-use datafusion::catalog::default_table_source::provider_as_source;
-use datafusion::physical_plan::SendableRecordBatchStream;
-use datafusion::physical_plan::stream::RecordBatchStreamAdapter;
-use datafusion::prelude::SessionContext;
-use futures::{StreamExt, TryStreamExt};
-use lance::Dataset;
-use lance::dataset::transaction::{Operation, Transaction};
-use lance::dataset::{
-    CommitBuilder, InsertBuilder, ProjectionRequest, WriteDestination, WriteMode, WriteParams,
-};
-use lance_core::ROW_ID;
-use lance_core::utils::address::RowAddress;
-use lance_datafusion::exec::SessionContextExt;
-use lance_index::metrics::NoOpMetricsCollector;
-use lance_table::format::Fragment;
-use serde::{Deserialize, Serialize};
-
-use super::grouped::{self, IVF_PARTITION};
-use super::refresh::{
-    RefreshMaterializedViewResult, RefreshMode, ensure_incarnation, ensure_no_mem_wal, open_source,
-    refresh_lock, stamp_watermark, to_view_batch,
-};
-use super::{MaterializedViewDefinition, StoredDefinition};
-use crate::table::Table;
-use crate::{Error, Result};
-
-/// Row ids per `take`: bounds one unit's memory while it reads a partition.
-const TAKE_CHUNK: usize = 8192;
-
-/// Rows per partition, and in total, the plan reads to see whether the index
-/// assigns by its own centroids. A sample: [`write_grouped_unit`] proves the
-/// rows it takes, and this only keeps a doomed refresh off the fleet.
-const PROBE_PER_UNIT: usize = 8;
-const PROBE_ROWS: usize = 256;
-
-/// How a grouped view can be refreshed in units.
-#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
-#[serde(deny_unknown_fields)]
-pub struct GroupedRefreshPlan {
-    /// Units `0..units`: unit `p` before the last holds index partition
-    /// `p`'s groups; the last holds the rows the index cannot place (null or
-    /// unplaceable vectors), grouped under a NULL key. One per index
-    /// partition, plus that one.
-    pub units: u32,
-    /// The source version every unit reads and the commit records.
-    pub source_version: u64,
-    /// The view version the plan was made against; the commit lands on it
-    /// or not at all.
-    pub view_version: u64,
-    /// The view this plan belongs to: its incarnation, which a drop and
-    /// recreate replaces. Two views of the same shape reach the same
-    /// counters, so without it a result written for one could be published
-    /// into the other.
-    pub incarnation: String,
-}
-
-/// The column a grouped view's single `ivf_partition(column)` key names, if
-/// that is its whole `GROUP BY`.
-fn ivf_key_column(definition: &MaterializedViewDefinition) -> Option<String> {
-    let [key] = definition.group_by.as_slice() else {
-        return None;
-    };
-    let inner = key
-        .strip_prefix(&format!("{IVF_PARTITION}("))?
-        .strip_suffix(')')?;
-    let column = inner.trim().trim_matches('`');
-    (!column.is_empty() && !column.contains(',')).then(|| column.to_string())
-}
-
-/// What a grouped refresh of `view` reads: its definition, source and binding.
-struct Grouped {
-    view_ds: Dataset,
-    definition: MaterializedViewDefinition,
-    source: Dataset,
-    column: String,
-    inputs: Vec<String>,
-    binding: grouped::IvfBinding,
-    segments: grouped::IvfSegments,
-}
-
-async fn open_grouped(view: &Table, pinned: Option<u64>) -> Result<Option<Grouped>> {
-    let view_native = view.as_native().ok_or_else(|| Error::NotSupported {
-        message: "materialized views are supported only on local tables".into(),
-    })?;
-    view_native.dataset.reload().await?;
-    let view_ds = view_native.dataset.get().await?.as_ref().clone();
-    let Some(StoredDefinition::Query(definition)) =
-        super::read_definition(&view_ds.schema().metadata)?
-    else {
-        return Ok(None);
-    };
-    let Some(column) = ivf_key_column(&definition) else {
-        return Ok(None);
-    };
-    let staging = super::read_staging(&view_ds.schema().metadata)?;
-    let source = open_source(view, &definition, staging.as_ref()).await?;
-    let source = match pinned {
-        Some(version) => source.checkout_version(version).await?,
-        None => source,
-    };
-    ensure_no_mem_wal(&source, "source table", &definition.source_table).await?;
-    let source_schema = Arc::new(ArrowSchema::from(source.schema()));
-    let planned = super::plan(source_schema, &definition, staging.as_ref())?;
-    let (binding, segments) = grouped::bind_column_with_segments(&source, &column).await?;
-    Ok(Some(Grouped {
-        view_ds,
-        definition: planned.definition,
-        source,
-        column,
-        inputs: planned.inputs,
-        binding,
-        segments,
-    }))
-}
-
-/// The units `view` can be refreshed in against `source_version` (the
-/// latest when `None`), or `None` when it is not a grouped view keyed by one
-/// `ivf_partition(column)`.
-pub async fn plan_grouped_refresh(
-    view: &Table,
-    source_version: Option<u64>,
-) -> Result<Option<GroupedRefreshPlan>> {
-    let Some(grouped) = open_grouped(view, source_version).await? else {
-        return Ok(None);
-    };
-    // The split reads indexed rows through the index and the rest by
-    // scanning what it does not cover, so an index that does not say what it
-    // covers cannot be split at all. The caller refreshes single-pass.
-    if grouped.segments.covered.is_none() {
-        return Ok(None);
-    }
-    // Without one there is nothing to bind a unit's result to, so the view
-    // is refreshed in one pass, where the rows never leave the process.
-    let Some(incarnation) = grouped
-        .view_ds
-        .schema()
-        .metadata
-        .get(super::INCARNATION_META_KEY)
-        .cloned()
-    else {
-        return Ok(None);
-    };
-    // The split is the index's posting lists and the key is recomputed, so a
-    // view whose index assigns by something else has no units at all.
-    if !assigns_by_its_centroids(&grouped).await? {
-        return Ok(None);
-    }
-    Ok(Some(GroupedRefreshPlan {
-        units: grouped.binding.partitions + 1,
-        source_version: grouped.source.version().version,
-        view_version: grouped.view_ds.version().version,
-        incarnation,
-    }))
-}
-
-/// Fragments one unit wrote, uncommitted, for [`commit_grouped_refresh`].
-/// Only [`write_grouped_unit`] makes one: it names the unit and the plan,
-/// and the commit publishes a set of them as the view's whole contents, so
-/// a result belonging to another plan, another view, or to a unit already
-/// accounted for has to be refusable. Travels to whoever commits, so it
-/// serializes, but its fields are not a caller's to write.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-#[serde(deny_unknown_fields)]
-pub struct WrittenUnit {
-    unit: u32,
-    plan: GroupedRefreshPlan,
-    fragments: Vec<Fragment>,
-    rows: u64,
-}
-
-impl WrittenUnit {
-    /// The unit of the plan this result holds.
-    pub fn unit(&self) -> u32 {
-        self.unit
-    }
-
-    /// The plan it was computed for.
-    pub fn plan(&self) -> &GroupedRefreshPlan {
-        &self.plan
-    }
-
-    /// View rows this unit wrote, its groups.
-    pub fn rows(&self) -> u64 {
-        self.rows
-    }
-
-    /// The fragments it staged, uncommitted until the whole set is published.
-    pub fn fragments(&self) -> &[Fragment] {
-        &self.fragments
-    }
-}
-
-/// The grouped view `plan` was made for, opened at the source version it
-/// pins. Every holder of a plan asks the same two questions: is this a
-/// grouped view, and is it the view the plan names.
-///
-/// The pinned version is also what holds every unit to one index: indices
-/// are part of the source manifest, so a rebuild lands in a later version
-/// that a unit reading this one never sees, and units written on either
-/// side of it still group by the same centroids.
-async fn open_planned(view: &Table, plan: &GroupedRefreshPlan) -> Result<Grouped> {
-    let grouped = open_grouped(view, Some(plan.source_version))
-        .await?
-        .ok_or_else(|| Error::InvalidInput {
-            message: format!(
-                "'{}' is not a grouped view keyed by {IVF_PARTITION}",
-                view.name()
-            ),
-        })?;
-    ensure_incarnation(&grouped.view_ds, Some(&plan.incarnation), view.name()).await?;
-    Ok(grouped)
-}
-
-/// Compute unit `unit` of `plan` against source version `source_version`
-/// and write its groups as uncommitted fragments of the view.
-pub async fn write_grouped_unit(
-    view: &Table,
-    unit: u32,
-    plan: &GroupedRefreshPlan,
-) -> Result<WrittenUnit> {
-    let grouped = open_planned(view, plan).await?;
-    if unit >= plan.units {
-        return Err(Error::InvalidInput {
-            message: format!(
-                "unit {unit} is not in the plan of '{}' ({} units)",
-                view.name(),
-                plan.units
-            ),
-        });
-    }
-    let schema: SchemaRef = Arc::new(ArrowSchema::from(grouped.view_ds.schema()));
-    let rows_written = Arc::new(AtomicU64::new(0));
-    let rows = unit_rows(&grouped, unit).await?;
-    let stream = group_rows(&grouped, rows, schema.clone(), rows_written.clone()).await?;
-    let write_txn = InsertBuilder::new(WriteDestination::Dataset(Arc::new(grouped.view_ds)))
-        .with_params(&WriteParams {
-            mode: WriteMode::Append,
-            ..Default::default()
-        })
-        .execute_uncommitted_stream(stream)
-        .await?;
-    let Operation::Append { fragments } = write_txn.operation else {
-        return Err(Error::Runtime {
-            message: "expected an append when writing a grouped unit".into(),
-        });
-    };
-    Ok(WrittenUnit {
-        unit,
-        plan: plan.clone(),
-        fragments,
-        rows: rows_written.load(Ordering::Relaxed),
-    })
-}
-
-/// The row ids the index places in partition `unit`, at most `limit` of them.
-/// A segment's postings outlive its ownership -- a column rewrite attaches a
-/// new file and takes the fragment out of its bitmap while leaving the rows
-/// listed -- so each segment's rows are kept only while it still holds their
-/// fragment. The fragment [`unit_rows`] scans them from instead is the one
-/// the rewrite left them in.
-async fn indexed_row_ids(grouped: &Grouped, unit: u32, limit: usize) -> Result<Vec<u64>> {
-    let addresses = lance::dataset::rowids::get_row_id_index(&grouped.source).await?;
-    let fragment_of = |row_id: u64| -> Result<Option<u32>> {
-        Ok(match &addresses {
-            Some(index) => index.get(row_id)?.map(|address| address.fragment_id()),
-            None => Some(RowAddress::new_from_u64(row_id).fragment_id()),
-        })
-    };
-    let mut row_ids = Vec::new();
-    for segment in &grouped.segments.segments {
-        let owned = segment.fragments.as_ref().ok_or_else(|| Error::Runtime {
-            message: "the index stopped naming the fragments it covers mid-refresh".into(),
-        })?;
-        let mut stream = segment
-            .index
-            .partition_reader(unit as usize, false, &NoOpMetricsCollector)
-            .await?;
-        while let Some(batch) = stream.try_next().await? {
-            for row_id in batch[ROW_ID].as_primitive::<UInt64Type>().values() {
-                if row_ids.len() >= limit {
-                    return Ok(row_ids);
-                }
-                if fragment_of(*row_id)?.is_some_and(|fragment| owned.contains(fragment)) {
-                    row_ids.push(*row_id);
-                }
-            }
-        }
-    }
-    Ok(row_ids)
-}
-
-/// The first row of `batch` that `unit` holds but `ivf_partition` does not
-/// put there, if there is one. `batch` carries the key column.
-fn assigned_elsewhere(grouped: &Grouped, batch: &RecordBatch, unit: u32) -> Result<Option<usize>> {
-    let vectors = batch[grouped.column.as_str()].as_fixed_size_list();
-    let assigned = grouped.binding.assign(vectors)?;
-    Ok((0..batch.num_rows()).find(|&row| !(assigned.is_valid(row) && assigned.value(row) == unit)))
-}
-
-/// Whether the index's posting lists are `ivf_partition`'s buckets, over a
-/// sample of each partition. Lance also builds an index from precomputed
-/// assignments, and records nowhere that it did, so the only way to ask is
-/// to recompute some of them.
-async fn assigns_by_its_centroids(grouped: &Grouped) -> Result<bool> {
-    let mut expected = HashMap::new();
-    for unit in 0..grouped.binding.partitions {
-        if expected.len() >= PROBE_ROWS {
-            break;
-        }
-        for row_id in indexed_row_ids(grouped, unit, PROBE_PER_UNIT).await? {
-            expected.insert(row_id, unit);
-        }
-    }
-    if expected.is_empty() {
-        return Ok(true);
-    }
-    let sample: Vec<u64> = expected.keys().copied().collect();
-    let columns = [ROW_ID, grouped.column.as_str()];
-    let projection = ProjectionRequest::from_columns(columns, grouped.source.schema());
-    let batch = grouped.source.take_rows(&sample, projection).await?;
-    let row_ids = batch[ROW_ID].as_primitive::<UInt64Type>().clone();
-    let assigned = grouped
-        .binding
-        .assign(batch[grouped.column.as_str()].as_fixed_size_list())?;
-    Ok((0..batch.num_rows()).all(|row| {
-        assigned.is_valid(row) && Some(&assigned.value(row)) == expected.get(&row_ids.value(row))
-    }))
-}
-
-/// The source rows of one unit: the index's rows for partition `unit` plus
-/// the unindexed rows that assign to it, or for the last unit every row the
-/// assigner cannot place. Each batch carries the inputs and `_rowid`.
-async fn unit_rows(grouped: &Grouped, unit: u32) -> Result<Vec<RecordBatch>> {
-    let mut columns: Vec<&str> = vec![ROW_ID];
-    columns.extend(grouped.inputs.iter().map(String::as_str));
-    if !grouped.inputs.contains(&grouped.column) {
-        columns.push(&grouped.column);
-    }
-    let source = Arc::new(grouped.source.clone());
-    let mut batches = Vec::new();
-    let unassigned = unit == grouped.binding.partitions;
-
-    if !unassigned {
-        // Rows the index placed in this partition; a deleted row is dropped
-        // by the take.
-        let row_ids = indexed_row_ids(grouped, unit, usize::MAX).await?;
-        let projection = ProjectionRequest::from_columns(&columns, source.schema());
-        for chunk in row_ids.chunks(TAKE_CHUNK) {
-            let batch = source.take_rows(chunk, projection.clone()).await?;
-            if batch.num_rows() > 0 {
-                // What the plan sampled, this unit proves: a row grouped
-                // here that belongs to another key would be aggregated in
-                // both units and published as two rows of one group.
-                if let Some(row) = assigned_elsewhere(grouped, &batch, unit)? {
-                    let row_id = batch[ROW_ID].as_primitive::<UInt64Type>().value(row);
-                    return Err(Error::NotSupported {
-                        message: format!(
-                            "{IVF_PARTITION}({}): the index lists row {row_id} under \
-                             partition {unit}, which it does not assign to; this view \
-                             is refreshed in one pass",
-                            grouped.column
-                        ),
-                    });
-                }
-                batches.push(in_column_order(&batch, &columns)?);
-            }
-        }
-    }
-
-    // Rows the index has not seen assign here; the last unit also takes the
-    // rows no assignment places, from every fragment.
-    let mut fragments = Vec::new();
-    for fragment in source.get_fragments() {
-        let unindexed = grouped
-            .segments
-            .is_unindexed(fragment.id() as u64)
-            .ok_or_else(|| Error::Runtime {
-                message: "the index stopped naming the fragments it covers mid-refresh".into(),
-            })?;
-        if unassigned || unindexed {
-            fragments.push(fragment.metadata().clone());
-        }
-    }
-    if !fragments.is_empty() {
-        let mut scanner = source.scan();
-        scanner.with_fragments(fragments);
-        scanner.with_row_id();
-        scanner.project(&columns)?;
-        let mut stream = scanner.try_into_stream().await?;
-        while let Some(batch) = stream.try_next().await? {
-            let vectors = batch[grouped.column.as_str()].as_fixed_size_list();
-            let assigned = grouped.binding.assign(vectors)?;
-            let keep: arrow_array::BooleanArray = (0..batch.num_rows())
-                .map(|row| {
-                    Some(if unassigned {
-                        assigned.is_null(row)
-                    } else {
-                        assigned.is_valid(row) && assigned.value(row) == unit
-                    })
-                })
-                .collect();
-            let kept = arrow_select::filter::filter_record_batch(&batch, &keep)?;
-            if kept.num_rows() > 0 {
-                batches.push(in_column_order(&kept, &columns)?);
-            }
-        }
-    }
-    Ok(batches)
-}
-
-/// `batch` with its columns in `columns` order: the take and the scan place
-/// `_rowid` differently, and the query stream coalesces by position.
-fn in_column_order(batch: &RecordBatch, columns: &[&str]) -> Result<RecordBatch> {
-    let indices = columns
-        .iter()
-        .map(|column| batch.schema().index_of(column))
-        .collect::<std::result::Result<Vec<_>, _>>()?;
-    Ok(batch.project(&indices)?)
-}
-
-/// The grouped query over `rows`, in the view's schema.
-async fn group_rows(
-    grouped: &Grouped,
-    rows: Vec<RecordBatch>,
-    schema: SchemaRef,
-    rows_written: Arc<AtomicU64>,
-) -> Result<SendableRecordBatchStream> {
-    let input_schema = match rows.first() {
-        Some(batch) => batch.schema(),
-        None => {
-            return Ok(Box::pin(RecordBatchStreamAdapter::new(
-                schema,
-                futures::stream::empty(),
-            )));
-        }
-    };
-    let rows: SendableRecordBatchStream = Box::pin(RecordBatchStreamAdapter::new(
-        input_schema,
-        futures::stream::iter(rows.into_iter().map(Ok)),
-    ));
-    let state = grouped::session(grouped::bind(&grouped.source, &grouped.definition).await?);
-    let ctx = SessionContext::new_with_state(state.clone());
-    let source = ctx.read_one_shot(rows)?.into_view();
-    let planned = grouped::logical_plan(
-        &state,
-        provider_as_source(source),
-        &grouped.definition,
-        // Neither the indexed take nor the fragment scan filters: the rows
-        // reach this query raw, so the predicate belongs in it.
-        true,
-    )?;
-    let groups = ctx
-        .execute_logical_plan(planned)
-        .await?
-        .execute_stream()
-        .await?;
-    let out_schema = schema.clone();
-    let mapped = groups.map(move |batch| {
-        let batch = to_view_batch(&batch?, &out_schema)?;
-        rows_written.fetch_add(batch.num_rows() as u64, Ordering::Relaxed);
-        Ok(batch)
-    });
-    Ok(Box::pin(RecordBatchStreamAdapter::new(schema, mapped)))
-}
-
-/// Replace the view's rows with the fragments every unit wrote, in one
-/// commit, and record `plan.source_version` as what the view reflects.
-///
-/// The commit is refused if the view moved since the plan: a refresh that
-/// landed meanwhile replaced rows the units never saw. Concurrent refreshes
-/// of one view are serialized by the caller, as for a single-pass rebuild;
-/// this check catches the ones that slipped through.
-pub async fn commit_grouped_refresh(
-    view: &Table,
-    plan: &GroupedRefreshPlan,
-    units: Vec<WrittenUnit>,
-    expected_incarnation: Option<&str>,
-) -> Result<RefreshMaterializedViewResult> {
-    let grouped = open_planned(view, plan).await?;
-    let units = complete_set(view.name(), plan, units)?;
-    let rows_written = units.iter().map(|unit| unit.rows).sum();
-    let new_fragments = units.into_iter().flat_map(|unit| unit.fragments).collect();
-    commit_fragments(
-        view,
-        plan,
-        &grouped.definition,
-        grouped.source.manifest.timestamp_nanos,
-        new_fragments,
-        rows_written,
-        expected_incarnation,
-    )
-    .await
-}
-
-/// The common all-or-nothing unit sink, shared by grouped and native views.
-#[allow(clippy::too_many_arguments)]
-pub(super) async fn commit_fragments(
-    view: &Table,
-    plan: &GroupedRefreshPlan,
-    definition: &MaterializedViewDefinition,
-    source_ts: u128,
-    new_fragments: Vec<Fragment>,
-    rows_written: u64,
-    expected_incarnation: Option<&str>,
-) -> Result<RefreshMaterializedViewResult> {
-    let view_native = view.as_native().ok_or_else(|| Error::NotSupported {
-        message: "materialized views are supported only on local tables".into(),
-    })?;
-    view_native.dataset.ensure_mutable()?;
-    let lock = refresh_lock(view_native.dataset.get().await?.uri());
-    let _guard = lock.lock().await;
-    if let Some(expected) = expected_incarnation
-        && expected != plan.incarnation
-    {
-        return Err(Error::Runtime {
-            message: format!(
-                "the refresh of '{}' was requested for another incarnation than the one \
-                 its units were planned against",
-                view.name()
-            ),
-        });
-    }
-    view_native.dataset.reload().await?;
-    let ds = view_native.dataset.get().await?.as_ref().clone();
-    ensure_incarnation(&ds, Some(&plan.incarnation), view.name()).await?;
-    if super::read_definition(&ds.schema().metadata)?
-        != Some(super::StoredDefinition::Query(definition.clone()))
-    {
-        return Err(Error::InvalidInput {
-            message: "view definition changed while its units were written".into(),
-        });
-    }
-    let ds = Arc::new(ds);
-    if ds.version().version != plan.view_version {
-        return Err(Error::Runtime {
-            message: format!(
-                "'{}' moved from version {} to {} while its units were written; the \
-                 refresh is unrecorded and must be replanned",
-                view.name(),
-                plan.view_version,
-                ds.version().version
-            ),
-        });
-    }
-    let removed_fragment_ids: Vec<u64> = ds.get_fragments().iter().map(|f| f.id() as u64).collect();
-    // Fragment ids are assigned at commit; the units' fragments all carry 0.
-    let transaction = Transaction::new(
-        plan.view_version,
-        Operation::Update {
-            removed_fragment_ids,
-            updated_fragments: Vec::new(),
-            new_fragments,
-            fields_modified: Vec::new(),
-            compacted_sstables: Vec::new(),
-            fields_for_preserving_frag_bitmap: Vec::new(),
-            update_mode: None,
-            inserted_rows_filter: None,
-            updated_fragment_offsets: None,
-        },
-        None,
-    );
-    #[cfg(test)]
-    super::refresh::tests::hold_before_publish(ds.uri()).await;
-    let committed = CommitBuilder::new(WriteDestination::Dataset(ds))
-        .execute(transaction)
-        .await?;
-    // The version check above is not the fence: lance rebases this `Update`
-    // over a concurrent append rather than refusing it, which would leave
-    // the appended rows in a view certified as the units' whole output.
-    if committed.version().version != plan.view_version + 1 {
-        return Err(Error::Runtime {
-            message: format!(
-                "a concurrent commit raced the refresh of '{}' (view version {}); the \
-                 refresh is unrecorded and must be replanned",
-                view.name(),
-                committed.version().version
-            ),
-        });
-    }
-    let version = stamp_watermark(
-        view_native,
-        committed,
-        plan.source_version,
-        source_ts,
-        Some(definition),
-        expected_incarnation,
-    )
-    .await?;
-    Ok(RefreshMaterializedViewResult {
-        mode: RefreshMode::Rebuild,
-        rows_written,
-        source_version: plan.source_version,
-        version,
-    })
-}
-
-/// The units in plan order, exactly one per unit of `plan`. The commit
-/// publishes them as the view's whole contents, so a missing, repeated or
-/// foreign result would certify a view nobody computed.
-fn complete_set(
-    view: &str,
-    plan: &GroupedRefreshPlan,
-    units: Vec<WrittenUnit>,
-) -> Result<Vec<WrittenUnit>> {
-    let mut ordered: Vec<Option<WrittenUnit>> = vec![None; plan.units as usize];
-    for written in units {
-        if &written.plan != plan {
-            return Err(Error::InvalidInput {
-                message: format!(
-                    "a unit of another plan of '{view}' was offered to this refresh; the \
-                     refresh is unrecorded and must be replanned"
-                ),
-            });
-        }
-        let slot = ordered
-            .get_mut(written.unit as usize)
-            .ok_or_else(|| Error::InvalidInput {
-                message: format!(
-                    "unit {} is not in the plan of '{view}' ({} units)",
-                    written.unit, plan.units
-                ),
-            })?;
-        if slot.is_some() {
-            return Err(Error::InvalidInput {
-                message: format!("unit {} of '{view}' was written twice", written.unit),
-            });
-        }
-        // A unit holds one key, so it writes that key's one group.
-        if written.rows > 1 {
-            return Err(Error::InvalidInput {
-                message: format!(
-                    "unit {} of '{view}' wrote {} groups; the split did not match the \
-                     grouping and the refresh must be replanned",
-                    written.unit, written.rows
-                ),
-            });
-        }
-        *slot = Some(written);
-    }
-    ordered
-        .into_iter()
-        .enumerate()
-        .map(|(unit, written)| {
-            written.ok_or_else(|| Error::InvalidInput {
-                message: format!(
-                    "unit {unit} of '{view}' is missing; the refresh holds {} units and \
-                     publishes all of them or none",
-                    plan.units
-                ),
-            })
-        })
-        .collect()
-}
-
-#[cfg(test)]
-mod tests {
-    use super::super::refresh::tests::{clustered_source, declare, rows};
-    use super::*;
-    use crate::connect;
-    use crate::index::Index;
-    use crate::index::vector::IvfFlatIndexBuilder;
-    use arrow_array::types::Float32Type;
-    use arrow_array::{ArrayRef, FixedSizeListArray, Int32Array, RecordBatchIterator};
-    use lance::index::DatasetIndexExt;
-    use lance::index::vector::VectorIndexParams;
-    use lance_index::IndexType;
-    use lance_index::vector::ivf::IvfBuildParams;
-
-    const BUCKETS: &str = "SELECT ivf_partition(vec) AS bucket, count(*) AS n, min(id) AS lo, \
-         max(id) AS hi FROM images GROUP BY ivf_partition(vec)";
-
-    /// The clustered source under a two-partition cosine index, then edited
-    /// behind the index: a row deleted from each cluster, a row appended to
-    /// each, and one zero vector cosine cannot place.
-    async fn edited_source(conn: &crate::Connection) -> Table {
-        let source = clustered_source(conn).await;
-        source
-            .create_index(
-                &["vec"],
-                Index::IvfFlat(
-                    IvfFlatIndexBuilder::default()
-                        .num_partitions(2)
-                        .distance_type(crate::DistanceType::Cosine),
-                ),
-            )
-            .execute()
-            .await
-            .unwrap();
-        source.delete("id IN (3, 13)").await.unwrap();
-        let appended = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
-            [
-                Some(vec![Some(1.5), Some(0.0)]),
-                Some(vec![Some(101.5), Some(100.0)]),
-                Some(vec![Some(0.0), Some(0.0)]),
-            ],
-            2,
-        );
-        source
-            .add(
-                RecordBatch::try_from_iter(vec![
-                    (
-                        "id",
-                        Arc::new(Int32Array::from(vec![20, 21, 22])) as ArrayRef,
-                    ),
-                    ("vec", Arc::new(appended) as ArrayRef),
-                ])
-                .unwrap(),
-            )
-            .execute()
-            .await
-            .unwrap();
-        source
-    }
-
-    async fn refresh_in_units(
-        view: &Table,
-        expected_incarnation: Option<&str>,
-    ) -> Result<(
-        GroupedRefreshPlan,
-        Vec<WrittenUnit>,
-        RefreshMaterializedViewResult,
-    )> {
-        let plan = plan_grouped_refresh(view, None)
-            .await?
-            .expect("an ivf-keyed view");
-        let mut units = Vec::new();
-        for unit in 0..plan.units {
-            units.push(write_grouped_unit(view, unit, &plan).await?);
-        }
-        let result =
-            commit_grouped_refresh(view, &plan, units.clone(), expected_incarnation).await?;
-        Ok((plan, units, result))
-    }
-
-    /// Every partition's unit reads its indexed rows plus the appended rows
-    /// that assign to it, the deleted rows are gone, and the last unit holds
-    /// the unplaceable row: together they are the single-pass result.
-    #[tokio::test]
-    async fn units_compose_to_the_single_pass_result() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = edited_source(&conn).await;
-        let single = declare(&source, "single", BUCKETS).await.unwrap();
-        single.refresh().execute().await.unwrap();
-        let expected = rows(single.table(), &["bucket", "n", "lo", "hi"]).await;
-        assert_eq!(expected.len(), 3, "{expected:?}");
-        assert!(expected.iter().any(|row| row == " 1 22 22"), "{expected:?}");
-
-        let view = declare(&source, "buckets", BUCKETS).await.unwrap();
-        let (plan, units, result) = refresh_in_units(view.table(), None).await.unwrap();
-        assert_eq!(plan.units, 3, "two partitions plus the unplaceable rows");
-        assert_eq!(
-            rows(view.table(), &["bucket", "n", "lo", "hi"]).await,
-            expected
-        );
-        assert_eq!(units.iter().map(|u| u.rows).sum::<u64>(), 3);
-        assert_eq!(units.iter().map(|u| u.rows).collect::<Vec<_>>(), [1, 1, 1]);
-        assert_eq!(result.rows_written, 3);
-        assert_eq!(result.source_version, plan.source_version);
-        assert_eq!(view.table().version().await.unwrap(), result.version);
-
-        // The commit is a refresh: the view reflects the source version it
-        // read and a second pass finds nothing to do.
-        let again = view.refresh().execute().await.unwrap();
-        assert_eq!(again.rows_written, 0, "{again:?}");
-        assert_eq!(
-            rows(view.table(), &["bucket", "n", "lo", "hi"]).await,
-            expected
-        );
-    }
-
-    /// A second refresh in units replaces the first one's rows rather than
-    /// appending to them.
-    #[tokio::test]
-    async fn a_refresh_in_units_replaces_the_previous_rows() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = edited_source(&conn).await;
-        let view = declare(&source, "buckets", BUCKETS).await.unwrap();
-        refresh_in_units(view.table(), None).await.unwrap();
-        source.delete("id = 22").await.unwrap();
-        refresh_in_units(view.table(), None).await.unwrap();
-        let mut groups = rows(view.table(), &["n", "lo", "hi"]).await;
-        groups.sort();
-        assert_eq!(groups, ["10 0 20", "10 10 21"]);
-    }
-
-    #[tokio::test]
-    async fn a_view_not_keyed_by_ivf_partition_has_no_units() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = clustered_source(&conn).await;
-        let view = declare(
-            &source,
-            "by_id",
-            "SELECT id % 2 AS k, count(*) AS n FROM images GROUP BY id % 2",
-        )
-        .await
-        .unwrap();
-        assert_eq!(
-            plan_grouped_refresh(view.table(), None).await.unwrap(),
-            None
-        );
-    }
-
-    #[tokio::test]
-    async fn a_unit_outside_the_plan_is_refused() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = edited_source(&conn).await;
-        let view = declare(&source, "buckets", BUCKETS).await.unwrap();
-        let plan = plan_grouped_refresh(view.table(), None)
-            .await
-            .unwrap()
-            .unwrap();
-        let err = write_grouped_unit(view.table(), plan.units, &plan)
-            .await
-            .unwrap_err();
-        assert!(err.to_string().contains("not in the plan"), "{err}");
-    }
-
-    /// A commit that lands on a view someone else refreshed meanwhile is
-    /// refused rather than recorded over their rows.
-    #[tokio::test]
-    async fn a_raced_commit_is_refused() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = edited_source(&conn).await;
-        let view = declare(&source, "buckets", BUCKETS).await.unwrap();
-        let plan = plan_grouped_refresh(view.table(), None)
-            .await
-            .unwrap()
-            .unwrap();
-        let units = vec![write_grouped_unit(view.table(), 0, &plan).await.unwrap()];
-        view.refresh().execute().await.unwrap();
-        let before = rows(view.table(), &["bucket", "n", "lo", "hi"]).await;
-        let raced = commit_grouped_refresh(view.table(), &plan, units, None).await;
-        assert!(raced.is_err(), "{raced:?}");
-        assert_eq!(
-            rows(view.table(), &["bucket", "n", "lo", "hi"]).await,
-            before
-        );
-    }
-
-    /// A view with a predicate holds the rows it selects however it is
-    /// refreshed: the units read their rows through the index and by
-    /// scanning fragments, neither of which filters, so the predicate has
-    /// to be in the grouped query itself.
-    #[tokio::test]
-    async fn a_filtered_view_refreshes_in_units_like_a_single_pass() {
-        const FILTERED: &str = "SELECT ivf_partition(vec) AS bucket, count(*) AS n, \
-             min(id) AS lo, max(id) AS hi FROM images WHERE id < 5 \
-             GROUP BY ivf_partition(vec)";
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = edited_source(&conn).await;
-        let single = declare(&source, "single", FILTERED).await.unwrap();
-        single.refresh().execute().await.unwrap();
-        let expected = rows(single.table(), &["bucket", "n", "lo", "hi"]).await;
-
-        let view = declare(&source, "buckets", FILTERED).await.unwrap();
-        refresh_in_units(view.table(), None).await.unwrap();
-        assert_eq!(
-            rows(view.table(), &["bucket", "n", "lo", "hi"]).await,
-            expected
-        );
-    }
-
-    /// The commit publishes its units as the view's whole contents, so it
-    /// takes the plan's units exactly once each and nothing else.
-    #[tokio::test]
-    async fn only_a_complete_set_of_this_plan_s_units_is_published() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = edited_source(&conn).await;
-        let view = declare(&source, "buckets", BUCKETS).await.unwrap();
-        let plan = plan_grouped_refresh(view.table(), None)
-            .await
-            .unwrap()
-            .unwrap();
-        let mut units = Vec::new();
-        for unit in 0..plan.units {
-            units.push(write_grouped_unit(view.table(), unit, &plan).await.unwrap());
-        }
-
-        let missing = commit_grouped_refresh(view.table(), &plan, units[..2].to_vec(), None).await;
-        assert!(
-            missing.unwrap_err().to_string().contains("is missing"),
-            "a partial set was published"
-        );
-        let mut twice = units.clone();
-        twice[1] = units[0].clone();
-        let repeated = commit_grouped_refresh(view.table(), &plan, twice, None).await;
-        assert!(
-            repeated.unwrap_err().to_string().contains("written twice"),
-            "a repeated unit was published"
-        );
-        let mut foreign = units.clone();
-        foreign[0].plan.source_version += 1;
-        let stale = commit_grouped_refresh(view.table(), &plan, foreign, None).await;
-        assert!(
-            stale.unwrap_err().to_string().contains("another plan"),
-            "a unit of another plan was published"
-        );
-        assert_eq!(rows(view.table(), &["bucket"]).await, Vec::<String>::new());
-
-        // The complete set still lands.
-        commit_grouped_refresh(view.table(), &plan, units, None)
-            .await
-            .unwrap();
-        assert_eq!(rows(view.table(), &["bucket"]).await.len(), 3);
-    }
-
-    /// A column rewrite attaches a new file to the row's fragment and takes
-    /// that fragment out of the index segment's bitmap, but the segment's
-    /// postings still name its rows. Taking them anyway, while the fragment
-    /// is also scanned as unindexed, counts those rows twice and buckets the
-    /// stale copy by the value the rewrite replaced.
-    #[tokio::test]
-    async fn a_rewritten_fragment_is_read_once() {
-        use lance::dataset::{
-            MergeInsertBuilder, MergeInsertWriteMode, WhenMatched, WhenNotMatched,
-        };
-
-        let conn = connect("memory://").execute().await.unwrap();
-        // A column the rewrite leaves alone: RewriteColumns is only for a
-        // source that covers some of the row, not all of it.
-        let vectors = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
-            (0..20).map(|i| {
-                let base = if i < 10 { 0.0 } else { 100.0 };
-                Some(vec![Some(base + 1.0 + i as f32 * 0.1), Some(base)])
-            }),
-            2,
-        );
-        let source = conn
-            .create_table(
-                "images",
-                RecordBatch::try_from_iter(vec![
-                    (
-                        "id",
-                        Arc::new(Int32Array::from_iter_values(0..20)) as ArrayRef,
-                    ),
-                    ("vec", Arc::new(vectors) as ArrayRef),
-                    (
-                        "tag",
-                        Arc::new(Int32Array::from_iter_values((0..20).map(|i| i * 10))) as ArrayRef,
-                    ),
-                ])
-                .unwrap(),
-            )
-            .write_options(super::super::tests::stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-        source
-            .create_index(
-                &["vec"],
-                Index::IvfFlat(IvfFlatIndexBuilder::default().num_partitions(2)),
-            )
-            .execute()
-            .await
-            .unwrap();
-
-        // Row 0 keeps its id and its fragment; its vector is replaced in a
-        // file the index segment does not cover.
-        let batch = RecordBatch::try_from_iter(vec![
-            ("id", Arc::new(Int32Array::from(vec![0])) as ArrayRef),
-            (
-                "vec",
-                Arc::new(
-                    FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
-                        [Some(vec![Some(101.0), Some(100.0)])],
-                        2,
-                    ),
-                ) as ArrayRef,
-            ),
-        ])
-        .unwrap();
-        let schema = batch.schema();
-        let dataset = source.as_native().unwrap().dataset.get().await.unwrap();
-        let mut builder =
-            MergeInsertBuilder::try_new(Arc::new(dataset.as_ref().clone()), vec!["id".to_string()])
-                .unwrap();
-        builder
-            .when_matched(WhenMatched::UpdateAll)
-            .when_not_matched(WhenNotMatched::DoNothing)
-            .write_mode(MergeInsertWriteMode::RewriteColumns);
-        builder
-            .try_build()
-            .unwrap()
-            .execute_reader(arrow_array::RecordBatchIterator::new([Ok(batch)], schema))
-            .await
-            .unwrap();
-
-        let single = declare(&source, "single", BUCKETS).await.unwrap();
-        single.refresh().execute().await.unwrap();
-        let expected = rows(single.table(), &["bucket", "n", "lo", "hi"]).await;
-
-        let view = declare(&source, "buckets", BUCKETS).await.unwrap();
-        refresh_in_units(view.table(), None).await.unwrap();
-        assert_eq!(
-            rows(view.table(), &["bucket", "n", "lo", "hi"]).await,
-            expected,
-            "the rewritten row was counted twice or bucketed by its old vector"
-        );
-    }
-
-    /// Units written on either side of an index rebuild still group by one
-    /// model: the plan pins a source version, indices live in the source
-    /// manifest, and a rebuild lands in a later version the units never
-    /// read. Without that, two units of one plan could assign their rows by
-    /// different centroids.
-    #[tokio::test]
-    async fn a_rebuilt_index_does_not_reach_the_units_of_a_plan() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = edited_source(&conn).await;
-        let view = declare(&source, "buckets", BUCKETS).await.unwrap();
-        let plan = plan_grouped_refresh(view.table(), None)
-            .await
-            .unwrap()
-            .unwrap();
-        let first = write_grouped_unit(view.table(), 0, &plan).await.unwrap();
-
-        source
-            .create_index(
-                &["vec"],
-                Index::IvfFlat(
-                    IvfFlatIndexBuilder::default()
-                        .num_partitions(2)
-                        .distance_type(crate::DistanceType::Cosine),
-                ),
-            )
-            .replace(true)
-            .execute()
-            .await
-            .unwrap();
-        let rebuilt = plan_grouped_refresh(view.table(), None)
-            .await
-            .unwrap()
-            .unwrap();
-        assert_eq!(rebuilt.units, plan.units, "same shape");
-        assert!(
-            rebuilt.source_version > plan.source_version,
-            "the rebuild is a later source version"
-        );
-
-        // The remaining units read the version the plan pinned, so they are
-        // grouped by the index that version holds, not the new one.
-        let mut units = vec![first];
-        for unit in 1..plan.units {
-            units.push(write_grouped_unit(view.table(), unit, &plan).await.unwrap());
-        }
-        commit_grouped_refresh(view.table(), &plan, units, None)
-            .await
-            .unwrap();
-        assert_eq!(
-            rows(view.table(), &["bucket", "n", "lo", "hi"]).await.len(),
-            3
-        );
-    }
-
-    /// Two views of the same shape over the same source reach the same plan
-    /// counters, so the results of one must not satisfy the other: they are
-    /// fragments written into a different dataset.
-    #[tokio::test]
-    async fn units_written_for_another_view_are_refused() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = edited_source(&conn).await;
-        let mine = declare(&source, "buckets", BUCKETS).await.unwrap();
-        let theirs = declare(&source, "twin", BUCKETS).await.unwrap();
-        let my_plan = plan_grouped_refresh(mine.table(), None)
-            .await
-            .unwrap()
-            .unwrap();
-        let their_plan = plan_grouped_refresh(theirs.table(), None)
-            .await
-            .unwrap()
-            .unwrap();
-        assert_eq!(
-            (my_plan.units, my_plan.view_version, my_plan.source_version),
-            (
-                their_plan.units,
-                their_plan.view_version,
-                their_plan.source_version
-            ),
-            "the counters alone do not tell these views apart"
-        );
-
-        let mut mine_units = Vec::new();
-        for unit in 0..my_plan.units {
-            mine_units.push(
-                write_grouped_unit(mine.table(), unit, &my_plan)
-                    .await
-                    .unwrap(),
-            );
-        }
-        let misrouted =
-            commit_grouped_refresh(theirs.table(), &their_plan, mine_units.clone(), None).await;
-        assert!(
-            misrouted.unwrap_err().to_string().contains("another plan"),
-            "another view's units were published"
-        );
-        assert_eq!(
-            rows(theirs.table(), &["bucket"]).await,
-            Vec::<String>::new()
-        );
-
-        // And a unit cannot be written for a view the plan does not name.
-        let foreign = write_grouped_unit(theirs.table(), 0, &my_plan).await;
-        assert!(
-            foreign
-                .unwrap_err()
-                .to_string()
-                .contains("not the incarnation"),
-            "a unit was written under another view's plan"
-        );
-    }
-
-    /// A write that lands on the view between the plan check and the commit
-    /// is not a conflict lance refuses -- the update rebases over it -- so
-    /// the publication checks the generation it actually landed on.
-    #[tokio::test(flavor = "multi_thread")]
-    async fn a_write_racing_the_publication_is_refused() {
-        use super::super::refresh::tests::{
-            DRIFT_LOCK, DRIFT_PLANNED, DRIFT_RELEASED, DRIFT_TARGET,
-        };
-        let _serial = DRIFT_LOCK.lock().await;
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = edited_source(&conn).await;
-        // Its own name: memory:// uris repeat across tests, so a view called
-        // like its siblings would arm the hook for one of their commits.
-        let view = declare(&source, "racing_buckets", BUCKETS).await.unwrap();
-        let plan = plan_grouped_refresh(view.table(), None)
-            .await
-            .unwrap()
-            .unwrap();
-        let mut units = Vec::new();
-        for unit in 0..plan.units {
-            units.push(write_grouped_unit(view.table(), unit, &plan).await.unwrap());
-        }
-        let uri = view
-            .table()
-            .as_native()
-            .unwrap()
-            .dataset
-            .get()
-            .await
-            .unwrap()
-            .uri()
-            .to_string();
-        *DRIFT_TARGET.lock().unwrap() = Some(uri);
-
-        let publishing = {
-            let table = view.table().clone();
-            let plan = plan.clone();
-            tokio::spawn(async move { commit_grouped_refresh(&table, &plan, units, None).await })
-        };
-        tokio::time::timeout(std::time::Duration::from_secs(30), DRIFT_PLANNED.notified())
-            .await
-            .expect("the commit never reached the publication boundary");
-        view.table()
-            .add(
-                RecordBatch::try_from_iter(vec![
-                    ("bucket", Arc::new(Int32Array::from(vec![7])) as ArrayRef),
-                    ("n", Arc::new(Int32Array::from(vec![1])) as ArrayRef),
-                    ("lo", Arc::new(Int32Array::from(vec![0])) as ArrayRef),
-                    ("hi", Arc::new(Int32Array::from(vec![0])) as ArrayRef),
-                    (
-                        super::super::SOURCE_ROW_ID_COLUMN,
-                        Arc::new(arrow_array::UInt64Array::from(vec![0u64])) as ArrayRef,
-                    ),
-                ])
-                .unwrap(),
-            )
-            .execute()
-            .await
-            .unwrap();
-        DRIFT_RELEASED.notify_one();
-
-        let err = publishing.await.unwrap().unwrap_err();
-        assert!(err.to_string().contains("raced the refresh"), "{err}");
-    }
-
-    #[test]
-    fn the_key_column_is_read_off_a_single_ivf_key() {
-        let mut definition = MaterializedViewDefinition::from_sql(
-            "SELECT ivf_partition(vec) AS b, count(*) AS n FROM t GROUP BY ivf_partition(vec)",
-        )
-        .unwrap();
-        assert_eq!(ivf_key_column(&definition).as_deref(), Some("vec"));
-        definition.group_by = vec!["k".to_string()];
-        assert_eq!(ivf_key_column(&definition), None);
-        definition.group_by = vec!["ivf_partition(vec)".to_string(), "k".to_string()];
-        assert_eq!(ivf_key_column(&definition), None);
-    }
-
-    /// A source whose index does not assign by its own centroids: lance
-    /// takes precomputed partitions, so row 1 is stored under partition 1
-    /// and `ivf_partition` puts it in 0.
-    async fn precomputed_source(conn: &crate::Connection, dir: &std::path::Path) -> Table {
-        let vectors = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
-            [
-                Some(vec![Some(1.0), Some(0.0)]),
-                Some(vec![Some(2.0), Some(0.0)]),
-                Some(vec![Some(100.0), Some(100.0)]),
-            ],
-            2,
-        );
-        let source = conn
-            .create_table(
-                "images",
-                RecordBatch::try_from_iter(vec![
-                    ("id", Arc::new(Int32Array::from(vec![0, 1, 2])) as ArrayRef),
-                    ("vec", Arc::new(vectors) as ArrayRef),
-                ])
-                .unwrap(),
-            )
-            .write_options(crate::materialized_view::tests::stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-
-        let assignments = arrow_array::record_batch!(
-            ("row_id", UInt64, [0, 1, 2]),
-            ("partition", UInt32, [0, 1, 1])
-        )
-        .unwrap();
-        let file = dir.join("partitions.lance");
-        let file = file.to_str().unwrap();
-        lance::Dataset::write(
-            RecordBatchIterator::new(vec![Ok(assignments.clone())], assignments.schema()),
-            file,
-            None,
-        )
-        .await
-        .unwrap();
-
-        let centroids = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
-            [
-                Some(vec![Some(0.0), Some(0.0)]),
-                Some(vec![Some(100.0), Some(100.0)]),
-            ],
-            2,
-        );
-        let mut ivf = IvfBuildParams::try_with_centroids(2, Arc::new(centroids)).unwrap();
-        ivf.precomputed_partitions_file = Some(file.to_string());
-        let mut dataset = source
-            .as_native()
-            .unwrap()
-            .dataset
-            .get()
-            .await
-            .unwrap()
-            .as_ref()
-            .clone();
-        dataset
-            .create_index(
-                &["vec"],
-                IndexType::Vector,
-                None,
-                &VectorIndexParams::with_ivf_flat_params(
-                    lance_linalg::distance::DistanceType::L2,
-                    ivf,
-                ),
-                true,
-            )
-            .await
-            .unwrap();
-        conn.open_table("images").execute().await.unwrap()
-    }
-
-    async fn incarnation(view: &Table) -> String {
-        view.as_native()
-            .unwrap()
-            .dataset
-            .get()
-            .await
-            .unwrap()
-            .schema()
-            .metadata
-            .get(crate::materialized_view::INCARNATION_META_KEY)
-            .cloned()
-            .unwrap()
-    }
-
-    /// The split is the index's posting lists and the key is
-    /// `ivf_partition`. Where they disagree one group is aggregated in two
-    /// units and published twice, so such a view has no units at all.
-    #[tokio::test]
-    async fn an_index_that_does_not_assign_by_its_centroids_has_no_units() {
-        let dir = tempfile::tempdir().unwrap();
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = precomputed_source(&conn, dir.path()).await;
-        let view = declare(&source, "buckets", BUCKETS).await.unwrap();
-        assert_eq!(
-            plan_grouped_refresh(view.table(), None).await.unwrap(),
-            None
-        );
-        view.refresh().execute().await.unwrap();
-        assert_eq!(
-            rows(view.table(), &["bucket", "n", "lo", "hi"]).await,
-            ["0 2 0 1", "1 1 2 2"]
-        );
-    }
-
-    /// The publication is the last place the split can be caught: a unit
-    /// that holds one key writes one group, and a set that says otherwise is
-    /// not published however it was produced.
-    #[tokio::test]
-    async fn a_unit_that_wrote_more_than_one_group_is_not_published() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = edited_source(&conn).await;
-        let view = declare(&source, "buckets", BUCKETS).await.unwrap();
-        let plan = plan_grouped_refresh(view.table(), None)
-            .await
-            .unwrap()
-            .unwrap();
-        let mut units = Vec::new();
-        for unit in 0..plan.units {
-            units.push(write_grouped_unit(view.table(), unit, &plan).await.unwrap());
-        }
-        units[1].rows = 2;
-        let err = commit_grouped_refresh(view.table(), &plan, units, None)
-            .await
-            .unwrap_err();
-        assert!(err.to_string().contains("wrote 2 groups"), "{err}");
-        assert_eq!(rows(view.table(), &["bucket"]).await, Vec::<String>::new());
-    }
-
-    /// The plan reads a sample, so the unit proves the rest: it holds only
-    /// rows that assign to it, or it refuses rather than write a group
-    /// another unit holds too.
-    #[tokio::test]
-    async fn a_unit_holding_a_row_that_assigns_elsewhere_is_refused() {
-        let dir = tempfile::tempdir().unwrap();
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = precomputed_source(&conn, dir.path()).await;
-        let view = declare(&source, "buckets", BUCKETS).await.unwrap();
-        let plan = GroupedRefreshPlan {
-            units: 3,
-            source_version: source.version().await.unwrap(),
-            view_version: view.table().version().await.unwrap(),
-            incarnation: incarnation(view.table()).await,
-        };
-        assert_eq!(
-            write_grouped_unit(view.table(), 0, &plan)
-                .await
-                .unwrap()
-                .rows,
-            1
-        );
-        let err = write_grouped_unit(view.table(), 1, &plan)
-            .await
-            .unwrap_err();
-        assert!(err.to_string().contains("does not assign to"), "{err}");
-    }
-}
diff --git a/rust/lancedb/src/materialized_view/partitioned.rs b/rust/lancedb/src/materialized_view/partitioned.rs
deleted file mode 100644
index 58192089b..000000000
--- a/rust/lancedb/src/materialized_view/partitioned.rs
+++ /dev/null
@@ -1,192 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-//! Shared plan/write/commit surface for materialized views with independent units.
-
-use serde::{Deserialize, Serialize};
-
-use super::duplicate_pairs::{
-    self, DuplicatePairsRefreshPlan, VectorSourceKind, WrittenPairsPartition,
-};
-use super::vector_dedup::{self, VectorDedupPlan, WrittenDedupUnit};
-use super::{GroupedRefreshPlan, RefreshMaterializedViewResult, StoredDefinition, WrittenUnit};
-use crate::table::Table;
-use crate::{Error, Result};
-
-/// Serializable work description consumed by a host's existing job workers.
-/// The grouped variant preserves the previous saved-plan representation.
-#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
-#[serde(untagged)]
-pub enum PartitionedRefreshPlan {
-    VectorDedup(VectorDedupPlan),
-    DuplicatePairs(DuplicatePairsRefreshPlan),
-    Grouped(GroupedRefreshPlan),
-}
-
-impl PartitionedRefreshPlan {
-    /// The shared source snapshot and destination generation fence.
-    pub fn snapshot(&self) -> &GroupedRefreshPlan {
-        match self {
-            Self::VectorDedup(plan) => &plan.snapshot,
-            Self::DuplicatePairs(plan) => &plan.snapshot,
-            Self::Grouped(plan) => plan,
-        }
-    }
-    /// Selection tasks that must complete before remaining tasks can start.
-    pub fn dependency_units(&self) -> u32 {
-        match self {
-            Self::VectorDedup(plan) => plan.dependencies(),
-            _ => 0,
-        }
-    }
-    /// Number of independent units in this immutable plan.
-    pub fn units(&self) -> u32 {
-        self.snapshot().units
-    }
-}
-
-/// One completed unit's uncommitted fragments. Failed attempts produce no receipt.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-#[serde(untagged)]
-pub enum WrittenPartition {
-    VectorDedup(WrittenDedupUnit),
-    DuplicatePairs(WrittenPairsPartition),
-    Grouped(WrittenUnit),
-}
-
-impl WrittenPartition {
-    /// Number of pair or grouped rows staged by this unit.
-    pub fn rows(&self) -> u64 {
-        match self {
-            Self::VectorDedup(unit) => unit.rows,
-            Self::DuplicatePairs(unit) => unit.rows,
-            Self::Grouped(unit) => unit.rows(),
-        }
-    }
-    /// Uncommitted output files, retained only after task completion.
-    pub fn fragments(&self) -> &[lance_table::format::Fragment] {
-        match self {
-            Self::VectorDedup(unit) => &unit.fragments,
-            Self::DuplicatePairs(unit) => &unit.fragments,
-            Self::Grouped(unit) => unit.fragments(),
-        }
-    }
-}
-
-/// Plan native pair or indexed grouped work; return `None` for ordinary views.
-/// `pinned`, when supplied, must match a native view's declared source version.
-pub async fn plan_partitioned_refresh(
-    view: &Table,
-    pinned: Option<u64>,
-) -> Result<Option<PartitionedRefreshPlan>> {
-    if let Some(StoredDefinition::Query(definition)) =
-        super::read_definition(view.schema().await?.metadata())?
-        && definition.vector_source.is_some()
-    {
-        if definition
-            .vector_source
-            .as_ref()
-            .is_some_and(|s| s.kind == VectorSourceKind::Dedup)
-        {
-            return vector_dedup::plan_refresh(view, pinned)
-                .await
-                .map(|p| Some(PartitionedRefreshPlan::VectorDedup(p)));
-        }
-        return duplicate_pairs::plan_refresh(view, pinned)
-            .await
-            .map(|p| Some(PartitionedRefreshPlan::DuplicatePairs(p)));
-    }
-    super::plan_grouped_refresh(view, pinned)
-        .await
-        .map(|plan| plan.map(PartitionedRefreshPlan::Grouped))
-}
-
-/// Stream one unit into uncommitted fragments. Persist the returned receipt only
-/// on success. A failed or interrupted call must be retried from the unit's start.
-pub async fn write_refresh_partition(
-    view: &Table,
-    unit: u32,
-    plan: &PartitionedRefreshPlan,
-) -> Result<WrittenPartition> {
-    write_refresh_partition_with_inputs(view, unit, plan, &[]).await
-}
-
-/// Write a unit after its durable selection dependencies have completed.
-/// Supply exactly the first `plan.dependency_units()` receipts for a dependent
-/// unit. A missing, duplicate or foreign receipt fails before output is staged.
-pub async fn write_refresh_partition_with_inputs(
-    view: &Table,
-    unit: u32,
-    plan: &PartitionedRefreshPlan,
-    dependencies: &[WrittenPartition],
-) -> Result<WrittenPartition> {
-    match plan {
-        PartitionedRefreshPlan::VectorDedup(plan) => {
-            let dependencies = dependencies
-                .iter()
-                .map(|unit| match unit {
-                    WrittenPartition::VectorDedup(unit) => Ok(unit.clone()),
-                    _ => Err(Error::InvalidInput {
-                        message: "foreign dedup dependency".into(),
-                    }),
-                })
-                .collect::<Result<Vec<_>>>()?;
-            vector_dedup::write_unit(view, unit, plan, &dependencies)
-                .await
-                .map(WrittenPartition::VectorDedup)
-        }
-        PartitionedRefreshPlan::DuplicatePairs(plan) => {
-            duplicate_pairs::write_unit(view, unit, plan)
-                .await
-                .map(WrittenPartition::DuplicatePairs)
-        }
-        PartitionedRefreshPlan::Grouped(plan) => super::write_grouped_unit(view, unit, plan)
-            .await
-            .map(WrittenPartition::Grouped),
-    }
-}
-
-/// Publish a complete set of unit receipts through the common MV commit sink.
-/// `expected` fences the destination incarnation. No partial set is publishable.
-pub async fn commit_partitioned_refresh(
-    view: &Table,
-    plan: &PartitionedRefreshPlan,
-    units: Vec<WrittenPartition>,
-    expected: Option<&str>,
-) -> Result<RefreshMaterializedViewResult> {
-    let foreign = || Error::InvalidInput {
-        message: "refresh receipt does not match the plan kind".into(),
-    };
-    match plan {
-        PartitionedRefreshPlan::VectorDedup(plan) => {
-            let units = units
-                .into_iter()
-                .map(|unit| match unit {
-                    WrittenPartition::VectorDedup(unit) => Ok(unit),
-                    _ => Err(foreign()),
-                })
-                .collect::<Result<Vec<_>>>()?;
-            vector_dedup::commit(view, plan, units, expected).await
-        }
-        PartitionedRefreshPlan::DuplicatePairs(plan) => {
-            let units = units
-                .into_iter()
-                .map(|unit| match unit {
-                    WrittenPartition::DuplicatePairs(unit) => Ok(unit),
-                    _ => Err(foreign()),
-                })
-                .collect::<Result<Vec<_>>>()?;
-            duplicate_pairs::commit(view, plan, units, expected).await
-        }
-        PartitionedRefreshPlan::Grouped(plan) => {
-            let units = units
-                .into_iter()
-                .map(|unit| match unit {
-                    WrittenPartition::Grouped(unit) => Ok(unit),
-                    _ => Err(foreign()),
-                })
-                .collect::<Result<Vec<_>>>()?;
-            super::commit_grouped_refresh(view, plan, units, expected).await
-        }
-    }
-}
diff --git a/rust/lancedb/src/materialized_view/query.rs b/rust/lancedb/src/materialized_view/query.rs
index 4bfd5542e..c1a987b2c 100644
--- a/rust/lancedb/src/materialized_view/query.rs
+++ b/rust/lancedb/src/materialized_view/query.rs
@@ -264,36 +264,7 @@ fn extract(query: &Query) -> Result<MaterializedViewDefinition> {
     };
 
     let mut from = select.from.iter();
-    let mut vector_source = None;
     let (source_namespace, source_table) = match from.next().map(|f| &f.relation) {
-        Some(TableFactor::Table {
-            name,
-            args: Some(args),
-            alias: None,
-            with_hints,
-            version: None,
-            with_ordinality: false,
-            partitions,
-            json_path: None,
-            sample: None,
-            index_hints,
-            ..
-        }) if matches!(
-            name.to_string().as_str(),
-            super::duplicate_pairs::FUNCTION_NAME | super::duplicate_pairs::DEDUP_FUNCTION_NAME
-        ) && with_hints.is_empty()
-            && partitions.is_empty()
-            && index_hints.is_empty() =>
-        {
-            let kind = if name.to_string() == super::duplicate_pairs::FUNCTION_NAME {
-                super::VectorSourceKind::Pairs
-            } else {
-                super::VectorSourceKind::Dedup
-            };
-            let (namespace, table, config) = super::duplicate_pairs::source(args, kind)?;
-            vector_source = Some(Box::new(config));
-            (namespace, table)
-        }
         Some(TableFactor::Table { args: Some(_), .. }) => {
             return Err(invalid(
                 "a view reads a table; a Function in FROM position follows it: \
@@ -445,7 +416,6 @@ fn extract(query: &Query) -> Result<MaterializedViewDefinition> {
     };
 
     let definition = MaterializedViewDefinition {
-        vector_source,
         source_table,
         source_namespace,
         lateral,
@@ -454,9 +424,6 @@ fn extract(query: &Query) -> Result<MaterializedViewDefinition> {
         group_by,
         limit,
     };
-    if definition.vector_source.is_some() {
-        super::duplicate_pairs::check_shape(&definition)?;
-    }
     check_grouping(&definition)?;
     Ok(definition)
 }
@@ -516,18 +483,7 @@ pub fn render(definition: &MaterializedViewDefinition) -> String {
         .chain(std::iter::once(&definition.source_table))
         .map(|part| ident_sql(part))
         .collect();
-    if let Some(native) = &definition.vector_source {
-        sql.push_str(&format!(
-            "{}('{}', {}, '{}', {})",
-            native.function_name(),
-            table.join(".").replace('\'', "''"),
-            native.dataset_version,
-            native.column.replace('\'', "''"),
-            native.distance_threshold
-        ));
-    } else {
-        sql.push_str(&table.join("."));
-    }
+    sql.push_str(&table.join("."));
     if let Some(lateral) = &definition.lateral {
         match &lateral.source {
             LateralSource::Unnest { column } => {
@@ -555,45 +511,6 @@ pub fn render(definition: &MaterializedViewDefinition) -> String {
 mod tests {
     use super::*;
 
-    #[test]
-    fn native_pairs_are_a_table_source_with_literal_snapshot_arguments() {
-        for threshold in ["4", "4.0", "-1", "0.05"] {
-            let sql =
-                format!("SELECT * FROM vector_duplicate_pairs('images', 3, 'phash', {threshold})");
-            let definition = parse(&sql).unwrap();
-            assert_eq!(definition.source_table, "images");
-            assert!(definition.lateral.is_none());
-            assert_eq!(
-                definition.vector_source.as_ref().unwrap().dataset_version,
-                3
-            );
-            assert_eq!(parse(&render(&definition)).unwrap(), definition);
-            let metadata = std::collections::HashMap::from([(
-                super::super::DEFINITION_META_KEY.to_string(),
-                definition.to_json().unwrap(),
-            )]);
-            assert_eq!(
-                super::super::read_definition(&metadata).unwrap(),
-                Some(super::super::StoredDefinition::Query(definition))
-            );
-        }
-        for sql in [
-            "SELECT row_id_a FROM vector_duplicate_pairs('images', 3, 'phash', 4)",
-            "SELECT * FROM vector_duplicate_pairs('images', 0, 'phash', 4)",
-            "SELECT * FROM vector_duplicate_pairs('images', 3, 'phash', 1e100)",
-            "SELECT * FROM vector_duplicate_pairs('images', 3, 'phash', cutoff)",
-            "SELECT * FROM vector_duplicate_pairs('images', 3, 'phash', 4) LIMIT 1",
-            "SELECT * FROM vector_duplicate_pairs('images', 3, 'phash', 4) WHERE distance < 2",
-            "SELECT * FROM vector_duplicate_pairs('images', 3, 'phash', 4) AS p",
-            "SELECT * FROM vector_duplicate_pairs('images', 3, 'phash', 4), f(phash) AS p",
-        ] {
-            assert!(
-                parse(sql).is_err(),
-                "accepted unsupported native view: {sql}"
-            );
-        }
-    }
-
     #[test]
     fn a_query_round_trips_through_its_canonical_form() {
         for (sql, canonical) in [
diff --git a/rust/lancedb/src/materialized_view/refresh.rs b/rust/lancedb/src/materialized_view/refresh.rs
index 9f0ea2646..b4ff22397 100644
--- a/rust/lancedb/src/materialized_view/refresh.rs
+++ b/rust/lancedb/src/materialized_view/refresh.rs
@@ -1,63 +1,37 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-//! Refreshing materialized views.
+//! Full refresh for materialized views.
 //!
-//! A refresh pins one source version and brings the view to exactly the
-//! definition's result at that version: added rows are computed and appended,
-//! removed or changed rows are evicted by provenance id and recomputed in the
-//! same pass. Compaction outputs cost nothing, which is only sound while
-//! [`SOURCE_ROW_ID_COLUMN`] stays valid across the rewrite. Anything the
-//! classifier cannot prove intact rebuilds; an indexed rebuild swaps all
-//! fragments in one commit that retains index definitions.
-//!
-//! The watermark ([`SOURCE_VERSION_META_KEY`]) lands in a follow-up commit; a
-//! crash or race between the two leaves the view visibly unstamped and the
-//! next refresh rebuilds. In-process refreshes serialize on a per-view lock;
-//! across processes the commit's inserted-rows filter carries a shared token,
-//! so two refreshes of one view conflict and only one lands.
+//! A refresh evaluates the definition at one source version, stages the
+//! complete result, and atomically replaces every fragment of the view.
 
-use std::collections::{HashMap, HashSet};
+use std::collections::HashMap;
 use std::sync::atomic::{AtomicU64, Ordering};
 use std::sync::{Arc, Mutex as StdMutex, OnceLock};
-use std::time::{SystemTime, UNIX_EPOCH};
 
 use arrow_array::cast::AsArray;
-use arrow_array::types::UInt64Type;
-use arrow_array::{RecordBatch, UInt64Array, new_null_array};
-use arrow_schema::{DataType, Field as ArrowField, FieldRef, Schema as ArrowSchema, SchemaRef};
-use datafusion::common::ScalarValue;
+use arrow_array::{RecordBatch, new_null_array};
+use arrow_schema::{Schema as ArrowSchema, SchemaRef};
 use datafusion::error::DataFusionError;
 use datafusion::physical_expr::PhysicalExpr;
 use datafusion::physical_plan::SendableRecordBatchStream;
 use datafusion::physical_plan::stream::RecordBatchStreamAdapter;
-use datafusion::prelude::{col, lit};
-use futures::{StreamExt, TryStreamExt};
+use futures::StreamExt;
 use lance::Dataset;
 use lance::dataset::mem_wal::DatasetMemWalExt;
-use lance::dataset::transaction::{Operation, Transaction, UpdateMode};
-use lance::dataset::write::delete::DeleteBuilder;
+use lance::dataset::transaction::{Operation, Transaction};
 use lance::dataset::write::merge_insert::inserted_rows::{
     KeyExistenceFilter, KeyExistenceFilterBuilder, KeyValue,
 };
 use lance::dataset::{CommitBuilder, InsertBuilder, WriteDestination, WriteMode, WriteParams};
-use lance_core::{ROW_CREATED_AT_VERSION, ROW_ID, ROW_LAST_UPDATED_AT_VERSION};
-
 use lance_datafusion::planner::Planner;
-use lance_file::version::ConcreteFileVersion;
-use lance_table::format::Fragment;
 use serde::{Deserialize, Serialize};
 
-use super::{
-    DEFINITION_META_KEY, INCARNATION_META_KEY, MaterializedViewDefinition,
-    REFRESHED_AT_MS_META_KEY, SOURCE_ROW_ID_COLUMN, SOURCE_VERSION_META_KEY,
-    definition_to_metadata,
-};
+use super::{MaterializedViewDefinition, StoredDefinition};
 use crate::database::OpenTableRequest;
-use crate::table::computed_columns::{
-    computed_column_from_field, computed_columns, ensure_declarations_are_planned,
-};
-use crate::table::{NativeTable, NativeTableExt, Table};
+use crate::table::computed_columns::{computed_column_from_field, ensure_declarations_are_planned};
+use crate::table::{NativeTableExt, Table};
 use crate::{Error, Result};
 
 /// How a refresh brought the view up to date.
@@ -66,10 +40,6 @@ use crate::{Error, Result};
 pub enum RefreshMode {
     /// The view was recomputed from scratch.
     Rebuild,
-    /// Rows from source fragments added since the last refresh were appended.
-    Incremental,
-    /// The view was already at the requested source version.
-    NoOp,
 }
 
 /// The result of refreshing a materialized view.
@@ -77,28 +47,15 @@ pub enum RefreshMode {
 pub struct RefreshMaterializedViewResult {
     /// How the view was brought up to date.
     pub mode: RefreshMode,
-    /// Rows written to the view: everything on a rebuild, and on an
-    /// incremental refresh both the rows added and the rows recomputed in
-    /// place of ones the source changed.
+    /// Rows written to the replacement view.
     pub rows_written: u64,
-    /// The source table version the view now reflects.
+    /// The source table version the view reflects.
     pub source_version: u64,
     /// The view table version after the refresh.
     pub version: u64,
 }
 
-/// Schema metadata key holding the view table version a successful refresh
-/// left behind. Any other commit on the view is drift, and refresh rebuilds.
-pub const VIEW_VERSION_META_KEY: &str = "mv.view_version";
-
-/// Schema metadata key holding the commit timestamp of the watermark's source
-/// manifest. A dropped and recreated source reuses version numbers but never
-/// their timestamps, so a mismatch means the watermark describes a different
-/// incarnation and refresh rebuilds.
-pub const SOURCE_VERSION_TS_META_KEY: &str = "mv.source_version_ts";
-
-/// One refresh per view at a time within this process.
-pub(super) fn refresh_lock(uri: &str) -> Arc<tokio::sync::Mutex<()>> {
+fn refresh_lock(uri: &str) -> Arc<tokio::sync::Mutex<()>> {
     static LOCKS: OnceLock<StdMutex<HashMap<String, Arc<tokio::sync::Mutex<()>>>>> =
         OnceLock::new();
     LOCKS
@@ -110,57 +67,27 @@ pub(super) fn refresh_lock(uri: &str) -> Arc<tokio::sync::Mutex<()>> {
         .clone()
 }
 
-/// Internal implementation of the refresh logic.
+/// Recompute a local materialized view from one source version.
 pub(crate) async fn execute_refresh(
     view: &Table,
-    full: bool,
     pinned: Option<u64>,
-    expected_incarnation: Option<&str>,
 ) -> Result<RefreshMaterializedViewResult> {
-    // Native table functions use the same partition writer and commit sink in
-    // local execution; Sophon drives these units through its job registry.
-    if let Some(super::StoredDefinition::Query(definition)) =
-        super::read_definition(view.schema().await?.metadata())?
-        && definition.vector_source.is_some()
-    {
-        let plan = super::plan_partitioned_refresh(view, pinned)
-            .await?
-            .ok_or_else(|| Error::Runtime {
-                message: "native pair view has no partition plan".into(),
-            })?;
-        let mut units = Vec::with_capacity(plan.units() as usize);
-        for unit in 0..plan.units() {
-            let dependencies = &units[..units.len().min(plan.dependency_units() as usize)];
-            units.push(
-                super::write_refresh_partition_with_inputs(view, unit, &plan, dependencies).await?,
-            );
-        }
-        return super::commit_partitioned_refresh(view, &plan, units, expected_incarnation).await;
-    }
-    let view_native = view.as_native().ok_or_else(|| Error::NotSupported {
+    let native = view.as_native().ok_or_else(|| Error::NotSupported {
         message: "materialized views are supported only on local tables".into(),
     })?;
-    view_native.dataset.ensure_mutable()?;
-    let lock = refresh_lock(view_native.dataset.get().await?.uri());
+    native.dataset.ensure_mutable()?;
+    let lock = refresh_lock(native.dataset.get().await?.uri());
     let _guard = lock.lock().await;
-    // Force-load the latest view state under the lock: each handle caches
-    // lazily, and a second handle would otherwise plan from a snapshot taken
-    // before another handle's commit -- appending the same rows again or
-    // reporting NoOp over a mutated view.
-    view_native.dataset.reload().await?;
-    let view_ds = view_native.dataset.get().await?.as_ref().clone();
+    native.dataset.reload().await?;
+    let view_ds = native.dataset.get().await?.as_ref().clone();
+    ensure_no_mem_wal(&view_ds, "materialized view", view.name()).await?;
 
-    ensure_incarnation(&view_ds, expected_incarnation, view.name()).await?;
-
-    // The definition a handle cached at open may since have been replaced;
-    // what refresh executes and what it stamps must be one generation.
     let definition = match super::read_definition(&view_ds.schema().metadata)? {
-        Some(super::StoredDefinition::Query(definition)) => definition,
-        Some(super::StoredDefinition::Newer { format }) => {
+        Some(StoredDefinition::Query(definition)) => definition,
+        Some(StoredDefinition::Newer { format }) => {
             return Err(Error::NotSupported {
                 message: format!(
-                    "materialized view '{}' is stored in format {format}, which this \
-                     version of lancedb cannot refresh",
+                    "materialized view '{}' is stored in format {format}, which this version of lancedb cannot refresh",
                     view.name()
                 ),
             });
@@ -171,572 +98,218 @@ pub(crate) async fn execute_refresh(
             });
         }
     };
-    let definition = &definition;
-    let staging = super::read_staging(&view_ds.schema().metadata)?;
-    ensure_no_mem_wal(&view_ds, "materialized view", view.name()).await?;
-
-    let source_ds = open_source(view, definition, staging.as_ref()).await?;
-    let source_ds = match pinned {
-        Some(version) => source_ds.checkout_version(version).await?,
-        None => source_ds,
+    let source = open_source(view, &definition).await?;
+    let source = match pinned {
+        Some(version) => source.checkout_version(version).await?,
+        None => source,
     };
-    ensure_no_mem_wal(&source_ds, "source table", &definition.source_table).await?;
-    let source_version = source_ds.version().version;
-    let source_ts = source_ds.manifest.timestamp_nanos;
+    ensure_no_mem_wal(&source, "source table", &definition.source_table).await?;
+    let source_version = source.version().version;
 
-    // Re-plan the persisted definition against the current source schema and
-    // require its planned output to be exactly the view's physical schema: a
-    // definition the stored table cannot represent must not be certified.
-    let source_schema = Arc::new(ArrowSchema::from(source_ds.schema()));
+    let source_schema = Arc::new(ArrowSchema::from(source.schema()));
     let super::Planned {
-        definition: replanned,
+        definition,
         fields: planned_fields,
         inputs,
         ..
-    } = super::plan(source_schema.clone(), definition, staging.as_ref()).map_err(|e| match e {
-        // The stored query planned when the view was declared; what changed
-        // since is the source.
+    } = super::plan(source_schema, &definition).map_err(|error| match error {
         Error::InvalidExpression { column, message } => Error::Schema {
             message: format!(
-                "view column '{column}' no longer plans against '{}' (a source column \
-                 was dropped or renamed): {message}",
+                "view column '{column}' no longer plans against '{}' (a source column was dropped or renamed): {message}",
                 definition.source_table
             ),
         },
         Error::InvalidInput { message } => Error::Schema {
             message: format!(
-                "the stored query no longer plans against '{}' (a source column was \
-                 dropped or renamed): {message}",
+                "the stored query no longer plans against '{}' (a source column was dropped or renamed): {message}",
                 definition.source_table
             ),
         },
-        e => e,
+        error => error,
     })?;
-    let mut planned_fields = planned_fields;
-    planned_fields.push(arrow_schema::Field::new(
-        SOURCE_ROW_ID_COLUMN,
-        arrow_schema::DataType::UInt64,
-        false,
-    ));
-    // A computed column is not planned from the source: refresh writes it
-    // NULL and its declaration's owner fills it. Its declaration must still
-    // be complete, and it must be able to hold NULL.
-    let physical = ArrowSchema::from(view_ds.schema());
-    let mut computed = computed_columns(&physical).into_iter().map(|c| c.name);
-    if let Some(name) = computed.by_ref().find(|name| {
-        physical
-            .field_with_name(name)
-            .is_ok_and(|f| !f.is_nullable())
-    }) {
+
+    let physical = Arc::new(ArrowSchema::from(view_ds.schema()));
+    ensure_declarations_are_planned(&physical)?;
+    if let Some(field) = physical
+        .fields()
+        .iter()
+        .find(|field| computed_column_from_field(field).is_some() && !field.is_nullable())
+    {
         return Err(Error::Schema {
             message: format!(
-                "computed column '{name}' of view '{}' cannot hold NULL; recreate the view",
+                "computed column '{}' of view '{}' cannot hold NULL; recreate the view",
+                field.name(),
                 view.name()
             ),
         });
     }
-    ensure_declarations_are_planned(&physical)?;
-    let physical_planned: Vec<&FieldRef> = physical
+    let stored_fields: Vec<_> = physical
         .fields()
         .iter()
-        .filter(|f| computed_column_from_field(f).is_none())
+        .filter(|field| computed_column_from_field(field).is_none())
         .collect();
-    // A projected column that became nullable at the source still fits the
-    // view's nullable field; the reverse would not.
-    let matches = planned_fields.len() == physical_planned.len()
-        && planned_fields.iter().zip(&physical_planned).all(|(e, p)| {
-            e.name() == p.name()
-                && e.data_type() == p.data_type()
-                && (p.is_nullable() || !e.is_nullable())
-        });
+    let matches = planned_fields.len() == stored_fields.len()
+        && planned_fields
+            .iter()
+            .zip(stored_fields)
+            .all(|(planned, stored)| {
+                planned.name() == stored.name()
+                    && planned.data_type() == stored.data_type()
+                    && (stored.is_nullable() || !planned.is_nullable())
+            });
     if !matches {
         return Err(Error::Schema {
             message: format!(
-                "the stored definition of view '{}' does not produce this \
-                 view's schema; recreate the view",
+                "the stored definition of view '{}' does not produce this view's schema; recreate the view",
                 view.name()
             ),
         });
     }
-    // The stored query is rewritten whenever its stored form differs from
-    // the current one: a legacy layout, or a spelling the canonicalizer no
-    // longer produces. Whether the rows change is a separate question: a
-    // legacy raw filter like `"Party" = 'D'` read the double quotes as a
-    // string literal, so its watermark certifies different rows than the
-    // canonical predicate, and only a rebuild can replace them.
-    let current = definition_to_metadata(&replanned)?;
-    let persist = view_ds.schema().metadata.get(DEFINITION_META_KEY) != Some(&current);
-    let unnest = super::physical_unnest(&replanned, staging.as_ref())?;
-    let definition_changed = !same_meaning(&source_schema, definition, &replanned, unnest.as_ref());
-    let definition = &replanned;
-    let persist = persist.then_some(definition);
 
-    if definition_changed {
-        return rebuild(
-            view_native,
-            &view_ds,
-            &source_ds,
-            source_version,
-            source_ts,
-            definition,
-            &inputs,
-            unnest.as_ref(),
-            true,
-            expected_incarnation,
-        )
-        .await;
-    }
-
-    let metadata = &view_ds.schema().metadata;
-    let watermark: Option<u64> = metadata
-        .get(SOURCE_VERSION_META_KEY)
-        .and_then(|raw| raw.parse().ok());
-    let recorded_ts: Option<u128> = metadata
-        .get(SOURCE_VERSION_TS_META_KEY)
-        .and_then(|raw| raw.parse().ok());
-    // The watermark speaks only for the view state its refresh left behind;
-    // any other commit on the view since then is drift, except a fill of its
-    // computed columns, which rewrites nothing refresh certifies.
-    let recorded_view_version = metadata
-        .get(VIEW_VERSION_META_KEY)
-        .and_then(|raw| raw.parse::<u64>().ok());
-    let view_intact = match recorded_view_version {
-        Some(recorded) if recorded == view_ds.version().version => true,
-        Some(recorded) if recorded < view_ds.version().version => {
-            only_computed_rewrites_since(&view_ds, recorded).await?
-        }
-        _ => false,
-    };
-
-    if !full && watermark == Some(source_version) && view_intact && recorded_ts == Some(source_ts) {
-        return Ok(RefreshMaterializedViewResult {
-            mode: RefreshMode::NoOp,
-            rows_written: 0,
-            source_version,
-            version: view_ds.version().version,
-        });
-    }
-
-    // A group spans fragments: there is no per-fragment increment.
-    if definition.is_grouped() {
-        return rebuild(
-            view_native,
-            &view_ds,
-            &source_ds,
-            source_version,
-            source_ts,
-            definition,
-            &inputs,
-            None,
-            persist.is_some(),
-            expected_incarnation,
-        )
-        .await;
-    }
-    let watermark = watermark.filter(|_| view_intact);
-    match plan_increment(
-        &source_ds,
-        source_version,
-        watermark,
-        recorded_ts,
-        full,
-        definition,
+    let rows_written = Arc::new(AtomicU64::new(0));
+    let unnest = super::physical_unnest(&definition)?;
+    let stream = compute_stream(
+        &source,
+        &definition,
         &inputs,
+        unnest.as_ref(),
+        physical,
+        rows_written.clone(),
     )
-    .await
-    {
-        Some(increment) => {
-            let reconciled = incremental(
-                view_native,
-                &view_ds,
-                &source_ds,
-                source_version,
-                source_ts,
-                increment,
-                definition,
-                &inputs,
-                unnest.as_ref(),
-                persist,
-                watermark,
-                expected_incarnation,
-            )
-            .await?;
-            match reconciled {
-                Some(result) => Ok(result),
-                // The delta was too large to reconcile in bounded memory.
-                None => {
-                    rebuild(
-                        view_native,
-                        &view_ds,
-                        &source_ds,
-                        source_version,
-                        source_ts,
-                        definition,
-                        &inputs,
-                        unnest.as_ref(),
-                        persist.is_some(),
-                        expected_incarnation,
-                    )
-                    .await
-                }
-            }
-        }
-        None => {
-            rebuild(
-                view_native,
-                &view_ds,
-                &source_ds,
-                source_version,
-                source_ts,
-                definition,
-                &inputs,
-                unnest.as_ref(),
-                persist.is_some(),
-                expected_incarnation,
-            )
-            .await
-        }
-    }
-}
-
-/// The source fragments whose rows are new since the watermark, or `None`
-/// where the view has to rebuild. Two tiers: the transaction walk is exact
-/// where it applies; the fragment-signature check is the fallback for deltas
-/// the walk cannot read, and under it any fragment churn rebuilds.
-async fn plan_increment(
-    source_ds: &Dataset,
-    source_version: u64,
-    watermark: Option<u64>,
-    recorded_ts: Option<u128>,
-    full: bool,
-    definition: &MaterializedViewDefinition,
-    inputs: &[String],
-) -> Option<Increment> {
-    if full {
-        return None;
-    }
-    let watermark = watermark?;
-    if watermark > source_version {
-        return None;
-    }
-    let old = source_ds.checkout_version(watermark).await.ok()?;
-    // A recreated source reuses version numbers, never their timestamps: a
-    // mismatch means the watermark describes a different incarnation.
-    if recorded_ts != Some(old.manifest.timestamp_nanos) {
-        return None;
-    }
-    let old_ids: HashSet<u64> = old.get_fragments().iter().map(|f| f.id() as u64).collect();
-    let live: Vec<Fragment> = source_ds
-        .get_fragments()
-        .iter()
-        .map(|f| f.metadata().clone())
-        .collect();
-
-    if let Some(delta) = appends_and_rewrites(source_ds, watermark, source_version).await {
-        // A rewrite that consumed a fragment neither present at the watermark
-        // nor produced by an earlier rewrite swallowed a mid-delta append;
-        // its rows cannot be told apart from already-materialized ones.
-        let folded = delta
-            .rewritten
-            .iter()
-            .any(|id| !old_ids.contains(id) && !delta.produced.contains(id));
-        if folded {
-            return None;
-        }
-        // An update rewrites a whole fragment. If it touched one the watermark
-        // never saw, that fragment holds rows appended since -- new rows the
-        // update did not change, which the recompute does not cover and which
-        // this fragment's exclusion from the append set would drop.
-        if delta
-            .updated_in_place
-            .iter()
-            .any(|id| !old_ids.contains(id))
-        {
-            return None;
-        }
-        // Rows past the cap left every delta when the watermark advanced, so
-        // a capped view cannot reconcile a removal incrementally. The rebuild
-        // is cheap for the same reason it is capped: the scan stops there.
-        if definition.limit.is_some() && (delta.deleted_rows || delta.updated_rows) {
-            return None;
-        }
-        // Legacy storage cannot serve the row-version columns update
-        // discovery scans; deletes and appends need none of them.
-        if delta.updated_rows
-            && source_ds.manifest.data_storage_format.lance_file_format() == ConcreteFileVersion::V1
-        {
-            return None;
-        }
-        // Every other fragment new at head is an append: appends and rewrites
-        // are the only operations in the delta that add fragments, and the
-        // rewrite outputs are already-materialized rows rearranged.
-        return Some(Increment {
-            appended: live
-                .into_iter()
-                .filter(|f| !old_ids.contains(&f.id) && !delta.produced.contains(&f.id))
-                .collect(),
-            evict_deleted: delta.deleted_rows,
-            replace_updated: delta.updated_rows,
-        });
-    }
-
-    is_pure_append(&old, source_ds, &relevant_field_ids(source_ds, inputs)).then(|| Increment {
-        appended: live
-            .into_iter()
-            .filter(|f| !old_ids.contains(&f.id))
-            .collect(),
-        evict_deleted: false,
-        replace_updated: false,
+    .await?;
+    let committed = replace_fragments(view_ds, stream).await?;
+    let version = committed.version().version;
+    native.dataset.update(committed);
+    Ok(RefreshMaterializedViewResult {
+        mode: RefreshMode::Rebuild,
+        rows_written: rows_written.load(Ordering::Relaxed),
+        source_version,
+        version,
     })
 }
 
-/// Version gap beyond which per-version transaction reads stop being cheaper
-/// than one fragment scan.
-const MAX_TRANSACTION_WALK: u64 = 512;
-
-/// Fragment ids moved by the `Rewrite` operations of the delta. Only rewrite
-/// ids are real in transaction files (an Append's are placeholders assigned
-/// at commit), so appends are derived as new-at-head minus rewrite outputs.
-/// What an incremental refresh must do to bring the view up to date.
-struct Increment {
-    /// Source fragments whose rows are not in the view yet.
-    appended: Vec<Fragment>,
-    /// Source rows left the range, so the view holds rows to evict.
-    evict_deleted: bool,
-    /// Source rows changed in the range, so the view holds rows to replace.
-    replace_updated: bool,
-}
-
-struct TxnDelta {
-    /// Fragments consumed by `Rewrite` operations.
-    rewritten: HashSet<u64>,
-    /// Fragments produced by `Rewrite` operations.
-    produced: HashSet<u64>,
-    /// The delta removed source rows, so the view holds rows to evict.
-    deleted_rows: bool,
-    /// The delta changed source rows in place, so the view holds rows to
-    /// recompute.
-    updated_rows: bool,
-    /// Fragments an update modified in place, as opposed to produced.
-    updated_in_place: HashSet<u64>,
-}
-
-/// Read the delta from the transaction log, `None` where it holds anything
-/// but appends and rewrites or cannot be read; `None` only sends the caller
-/// to a slower check. `ReserveFragments` moves no rows and rides along.
-async fn appends_and_rewrites(cur: &Dataset, from: u64, to: u64) -> Option<TxnDelta> {
-    if to <= from || to - from > MAX_TRANSACTION_WALK {
-        return None;
+/// Atomically replace all fragments of a materialized view with a complete
+/// query result. This is shared with Sophon's SQL refresh job.
+pub async fn replace_materialized_view_fragments(
+    view: &Table,
+    stream: SendableRecordBatchStream,
+) -> Result<u64> {
+    let native = view.as_native().ok_or_else(|| Error::NotSupported {
+        message: "materialized views are supported only on local tables".into(),
+    })?;
+    native.dataset.ensure_mutable()?;
+    let lock = refresh_lock(native.dataset.get().await?.uri());
+    let _guard = lock.lock().await;
+    native.dataset.reload().await?;
+    let dataset = native.dataset.get().await?.as_ref().clone();
+    if super::read_definition_sql(&dataset.schema().metadata)?.is_none() {
+        return Err(Error::NotAMaterializedView {
+            name: view.name().to_string(),
+        });
     }
-    let mut delta = TxnDelta {
-        rewritten: HashSet::new(),
-        produced: HashSet::new(),
-        deleted_rows: false,
-        updated_rows: false,
-        updated_in_place: HashSet::new(),
+    let committed = replace_fragments(dataset, stream).await?;
+    let version = committed.version().version;
+    native.dataset.update(committed);
+    Ok(version)
+}
+
+async fn replace_fragments(dataset: Dataset, stream: SendableRecordBatchStream) -> Result<Dataset> {
+    let dataset = Arc::new(dataset);
+    let read_version = dataset.version().version;
+    let removed_fragment_ids = dataset
+        .get_fragments()
+        .iter()
+        .map(|fragment| fragment.id() as u64)
+        .collect();
+    let write = InsertBuilder::new(WriteDestination::Dataset(dataset.clone()))
+        .with_params(&WriteParams {
+            mode: WriteMode::Append,
+            ..Default::default()
+        })
+        .execute_uncommitted_stream(stream)
+        .await?;
+    let Operation::Append {
+        fragments: new_fragments,
+    } = write.operation
+    else {
+        return Err(Error::Runtime {
+            message: "expected an append while staging replacement rows".into(),
+        });
     };
-    for version in (from + 1)..=to {
-        let Ok(Some(txn)) = cur.read_transaction_by_version(version).await else {
-            return None;
-        };
-        match txn.operation {
-            Operation::Append { .. } | Operation::ReserveFragments { .. } => {}
-            // A delete removes source rows without changing the ones that
-            // remain, so the view's other rows stay valid: the refresh
-            // evicts exactly the ids that left.
-            Operation::Delete { .. } => delta.deleted_rows = true,
-            // Update outputs carry no new rows, so they are excluded from
-            // the append set like rewrite outputs; the changed rows are
-            // replaced individually below.
+    let inserted_rows_filter = refresh_conflict_filter(&dataset)?;
+    let committed = CommitBuilder::new(WriteDestination::Dataset(dataset))
+        .execute(Transaction::new(
+            read_version,
             Operation::Update {
                 removed_fragment_ids,
+                updated_fragments: Vec::new(),
                 new_fragments,
-                updated_fragments,
-                ..
-            } => {
-                delta.updated_rows = true;
-                // merge_insert reaches here too, and its by-source arm deletes
-                // rows rather than changing them.
-                delta.deleted_rows = true;
-                delta.rewritten.extend(removed_fragment_ids.iter().copied());
-                // Only pre-existing fragment ids are real here; created ones
-                // are placeholders. Rewritten rows are excluded by creation
-                // version below, not by fragment identity.
-                delta
-                    .produced
-                    .extend(updated_fragments.iter().map(|f| f.id));
-                delta
-                    .updated_in_place
-                    .extend(updated_fragments.iter().map(|f| f.id));
-                let _ = new_fragments;
-            }
-            Operation::Rewrite { groups, .. } => {
-                for group in groups {
-                    delta
-                        .rewritten
-                        .extend(group.old_fragments.iter().map(|f| f.id));
-                    delta
-                        .produced
-                        .extend(group.new_fragments.iter().map(|f| f.id));
-                }
-            }
-            _ => return None,
-        }
+                fields_modified: Vec::new(),
+                compacted_sstables: Vec::new(),
+                fields_for_preserving_frag_bitmap: Vec::new(),
+                update_mode: None,
+                inserted_rows_filter: Some(inserted_rows_filter),
+                updated_fragment_offsets: None,
+            },
+            None,
+        ))
+        .await?;
+    if committed.version().version != read_version + 1 {
+        return Err(Error::Runtime {
+            message: format!(
+                "a concurrent commit raced this refresh (view version {}, expected {})",
+                committed.version().version,
+                read_version + 1
+            ),
+        });
     }
-    Some(delta)
+    Ok(committed)
 }
 
-/// Fallback pure-append check: every old fragment still present with an
-/// identical signature over the columns the view reads. Compaction, deletes
-/// and updates each break it and force a rebuild; a change to a column the
-/// view does not read leaves it alone, which is what lets this tier pass
-/// deltas the transaction walk cannot.
-fn is_pure_append(old: &Dataset, cur: &Dataset, relevant: &HashSet<i32>) -> bool {
-    let signature = |fragment: &lance::dataset::fragment::FileFragment| {
-        fragment_signature(fragment.metadata(), relevant)
-    };
-    let current: HashSet<(u64, String)> = cur.get_fragments().iter().map(signature).collect();
-    old.get_fragments()
-        .iter()
-        .all(|fragment| current.contains(&signature(fragment)))
+const REFRESH_TOKEN_ID: u64 = u64::MAX;
+
+fn refresh_conflict_filter(dataset: &Dataset) -> Result<KeyExistenceFilter> {
+    let field_id = dataset
+        .schema()
+        .fields
+        .first()
+        .map(|field| field.id)
+        .ok_or_else(|| Error::Runtime {
+            message: "a materialized view must have at least one field".into(),
+        })?;
+    let mut filter = KeyExistenceFilterBuilder::new(vec![field_id]);
+    filter
+        .insert(KeyValue::UInt64(REFRESH_TOKEN_ID))
+        .map_err(|error| Error::Runtime {
+            message: format!(
+                "failed to build the materialized-view refresh conflict filter: {error}"
+            ),
+        })?;
+    Ok(filter.build())
 }
 
-/// A fragment's identity as the view observes it: data files and overlays
-/// touching the columns it reads, plus the deletion file. Overlays change no
-/// file path, so they must be part of the signature.
-fn fragment_signature(metadata: &Fragment, relevant: &HashSet<i32>) -> (u64, String) {
-    let touches_relevant =
-        |fields: &[i32]| relevant.is_empty() || fields.iter().any(|id| relevant.contains(id));
-    let mut files: Vec<&str> = metadata
-        .files
-        .iter()
-        .filter(|file| touches_relevant(&file.fields))
-        .map(|file| file.path.as_str())
-        .collect();
-    files.sort_unstable();
-    let mut overlays: Vec<String> = metadata
-        .overlays
-        .iter()
-        .filter(|overlay| touches_relevant(&overlay.data_file.fields))
-        .map(|overlay| format!("{}@{}", overlay.data_file.path, overlay.committed_version))
-        .collect();
-    overlays.sort_unstable();
-    (
-        metadata.id,
-        format!(
-            "{}|{}|{:?}",
-            files.join(","),
-            overlays.join(","),
-            metadata.deletion_file
-        ),
-    )
-}
-
-/// Field ids (with struct descendants) of the source columns the view reads.
-fn relevant_field_ids(source: &Dataset, inputs: &[String]) -> HashSet<i32> {
-    fn collect(field: &lance_core::datatypes::Field, ids: &mut HashSet<i32>) {
-        ids.insert(field.id);
-        for child in &field.children {
-            collect(child, ids);
-        }
-    }
-    let mut ids = HashSet::new();
-    for input in inputs {
-        if let Some(field) = source.schema().field(input) {
-            collect(field, &mut ids);
-        }
-    }
-    ids
-}
-
-/// Whether two plannings of a view compute the same rows and columns: the
-/// same source, unnest and limit, and expressions the planner reads as the
-/// same logical expression, whatever their spelling. A definition that does
-/// not plan compares as different.
-fn same_meaning(
-    source_schema: &SchemaRef,
-    stored: &MaterializedViewDefinition,
-    replanned: &MaterializedViewDefinition,
-    unnest: Option<&super::ViewUnnest>,
-) -> bool {
-    if stored.source_table != replanned.source_table
-        || stored.source_namespace != replanned.source_namespace
-        || stored.lateral != replanned.lateral
-        || stored.limit != replanned.limit
-        || stored.projections.len() != replanned.projections.len()
-        || stored.filter.is_some() != replanned.filter.is_some()
-        || stored.group_by.len() != replanned.group_by.len()
-    {
-        return false;
-    }
-    // Lance's planner does not read aggregates; a grouped definition is
-    // compared in its canonical spelling, which planning produced.
-    if stored.is_grouped() {
-        return stored.projections == replanned.projections
-            && stored.group_by == replanned.group_by
-            && stored.filter == replanned.filter;
-    }
-    let schema = match unnest {
-        None => source_schema.clone(),
-        Some(unnest) => match super::flattened_schema(source_schema, unnest) {
-            Ok(schema) => schema,
-            Err(_) => return false,
-        },
-    };
-    let planner = Planner::new(schema);
-    let same_expr = |a: &str, b: &str| match (planner.parse_expr(a), planner.parse_expr(b)) {
-        (Ok(a), Ok(b)) => a == b,
-        _ => false,
-    };
-    stored
-        .projections
-        .iter()
-        .zip(&replanned.projections)
-        .all(|(a, b)| a.output == b.output && same_expr(&a.expression, &b.expression))
-        && match (&stored.filter, &replanned.filter) {
-            (Some(a), Some(b)) => same_expr(a, b),
-            _ => true,
-        }
-}
-
-/// Reject MemWAL/LSM state on a refresh participant: un-compacted tiers are
-/// invisible to the fragment-planned refresh scan. An active write spec and
-/// retained rows both disqualify; shard directories on storage are the
-/// durable evidence of the latter.
+/// Reject MemWAL/MemWAL state because an ordinary dataset scan cannot see its
+/// un-compacted tiers.
 pub(crate) async fn ensure_no_mem_wal(dataset: &Dataset, role: &str, name: &str) -> Result<()> {
     let retained = !dataset.list_mem_wal_latest_shard_ids().await?.is_empty();
     if retained || dataset.mem_wal_index_details().await?.is_some() {
         return Err(Error::NotSupported {
             message: format!(
-                "{role} '{name}' has an LSM write spec or retained un-compacted \
-                 rows: rows in un-compacted tiers are invisible to refresh"
+                "{role} '{name}' has a MemWAL write spec or retained un-compacted rows"
             ),
         });
     }
     Ok(())
 }
 
-/// The table refresh scans: the staging table when the query calls a
-/// Function in FROM position, otherwise the query's source.
-pub(super) async fn open_source(
-    view: &Table,
-    definition: &MaterializedViewDefinition,
-    staging: Option<&super::StagingBinding>,
-) -> Result<Dataset> {
+async fn open_source(view: &Table, definition: &MaterializedViewDefinition) -> Result<Dataset> {
     let database = view.database_opt().ok_or_else(|| Error::InvalidInput {
         message: "the view was not opened through a database connection".into(),
     })?;
-    let (name, namespace_path) = match staging {
-        Some(staging) => (staging.table.clone(), staging.namespace.clone()),
-        None => (
-            definition.source_table.clone(),
-            definition.source_namespace.clone(),
-        ),
-    };
     let source = database
         .open_table(OpenTableRequest {
-            name,
-            namespace_path,
+            name: definition.source_table.clone(),
+            namespace_path: definition.source_namespace.clone(),
             index_cache_size: None,
             lance_read_params: None,
             location: None,
@@ -747,584 +320,7 @@ pub(super) async fn open_source(
     let native = source.as_native().ok_or_else(|| Error::NotSupported {
         message: "materialized views are supported only on local tables".into(),
     })?;
-    let dataset = native.dataset.get().await?.as_ref().clone();
-    if definition.vector_source.is_none() && !dataset.manifest.uses_stable_row_ids() {
-        return Err(Error::InvalidInput {
-            message: format!(
-                "source table '{}' does not have stable row ids; it is not the \
-                 table this view was declared over",
-                definition.source_table
-            ),
-        });
-    }
-    Ok(dataset)
-}
-
-#[allow(clippy::too_many_arguments)]
-async fn incremental(
-    view_native: &NativeTable,
-    view_ds: &Dataset,
-    source_ds: &Dataset,
-    source_version: u64,
-    source_ts: u128,
-    increment: Increment,
-    definition: &MaterializedViewDefinition,
-    inputs: &[String],
-    unnest: Option<&super::ViewUnnest>,
-    persist: Option<&MaterializedViewDefinition>,
-    watermark: Option<u64>,
-    expected_incarnation: Option<&str>,
-) -> Result<Option<RefreshMaterializedViewResult>> {
-    let new_fragments = increment.appended;
-    let watermark_version = watermark.unwrap_or(0);
-    // Provenance ids this refresh removes: dropped rows, plus changed rows
-    // recomputed in the same commit. One view row per source row makes the
-    // eviction exact. Staged, not committed: removals ride with the rows
-    // that replace them, so a reader never sees the view without either.
-    let mut eviction = Eviction::new(view_ds, EVICTION_CHUNK);
-    let mut updated_rows = false;
-    if (increment.evict_deleted || increment.replace_updated)
-        && let Some(watermark) = watermark
-    {
-        let delta = source_ds
-            .delta()
-            .with_begin_version(watermark)
-            .with_end_version(source_version)
-            .build()?;
-        // Reconciling holds the delta's provenance ids in staged deletion
-        // vectors; past the cap, the streamed rebuild is the bounded path.
-        let cap = eviction_rebuild_cap();
-        let mut evicted = 0usize;
-        if increment.evict_deleted {
-            let mut stream = delta.get_deleted_row_ids().await?;
-            while let Some(batch) = stream.try_next().await? {
-                let ids = row_ids_of(&batch)?;
-                evicted += ids.len();
-                if evicted > cap {
-                    return Ok(None);
-                }
-                eviction.push(ids).await?;
-            }
-        }
-        if increment.replace_updated {
-            // Ids only: `get_updated_rows` carries every column of every
-            // updated row, and discovery needs none of them.
-            let mut scanner = source_ds.scan();
-            scanner.with_row_id().project(&[ROW_CREATED_AT_VERSION])?;
-            // A fixed bound: the configured default could make one discovery
-            // batch arbitrarily large before the fallback cap is consulted.
-            scanner.batch_size(8192);
-            scanner.filter(&format!(
-                "{ROW_CREATED_AT_VERSION} <= {watermark} 
-                 AND {ROW_LAST_UPDATED_AT_VERSION} > {watermark} 
-                 AND {ROW_LAST_UPDATED_AT_VERSION} <= {source_version}"
-            ))?;
-            let mut stream = scanner.try_into_stream().await?;
-            while let Some(batch) = stream.try_next().await? {
-                let ids = row_ids_of(&batch)?;
-                evicted += ids.len();
-                if evicted > cap {
-                    return Ok(None);
-                }
-                updated_rows |= !ids.is_empty();
-                eviction.push(ids).await?;
-            }
-        }
-    }
-    let eviction = eviction.finish().await?;
-
-    // The cap counts rows already materialized, in first-materialized order.
-    let remaining = match definition.limit {
-        Some(limit) => {
-            let held = view_ds.count_rows(None).await? as u64;
-            Some(limit.saturating_sub(held))
-        }
-        None => None,
-    };
-
-    let mut result = RefreshMaterializedViewResult {
-        mode: RefreshMode::Incremental,
-        rows_written: 0,
-        source_version,
-        version: view_ds.version().version,
-    };
-    let nothing_to_add = (new_fragments.is_empty() && !updated_rows) || remaining == Some(0);
-    if nothing_to_add && eviction.is_none() {
-        result.version = stamp_watermark(
-            view_native,
-            view_ds.clone(),
-            source_version,
-            source_ts,
-            persist,
-            expected_incarnation,
-        )
-        .await?;
-        return Ok(Some(result));
-    }
-    // Rows left but none arrive: the removals still have to be published.
-    if nothing_to_add {
-        let filter = refresh_filter(&empty_keys(view_ds)?)?;
-        let published = publish(
-            view_ds,
-            eviction,
-            Vec::new(),
-            Some(filter),
-            expected_incarnation,
-        )
-        .await?;
-        result.version = stamp_watermark(
-            view_native,
-            published,
-            source_version,
-            source_ts,
-            persist,
-            expected_incarnation,
-        )
-        .await?;
-        return Ok(Some(result));
-    }
-
-    // Appends carry the view's schema as it stands; the watermark moves in a
-    // follow-up commit (see the module docs for the crash window).
-    let schema = Arc::new(ArrowSchema::from(view_ds.schema()));
-    let rows_written = Arc::new(AtomicU64::new(0));
-    // compute_stream counts what it produces; the truncation below can drop
-    // some of that, so the written count comes from the tee instead.
-    let computed = Arc::new(AtomicU64::new(0));
-    let mut stream = compute_stream(
-        source_ds,
-        definition,
-        inputs,
-        unnest,
-        RowScope {
-            fragments: Some(new_fragments),
-            // An update rewrites whole fragments, so a fragment new at head
-            // can hold rows the view already has. Their creation version
-            // does not change, so it -- not fragment identity -- says which
-            // rows are new.
-            created_after: increment.replace_updated.then_some(watermark_version),
-            limit: remaining,
-            ..Default::default()
-        },
-        schema.clone(),
-        computed.clone(),
-    )
-    .await?;
-
-    // The updated rows' current values, computed the same way and appended
-    // in the same commit as the new fragments' rows.
-    if updated_rows {
-        let recomputed = compute_stream(
-            source_ds,
-            definition,
-            inputs,
-            unnest,
-            RowScope {
-                updated_between: Some((watermark_version, source_version)),
-                ..Default::default()
-            },
-            schema.clone(),
-            computed.clone(),
-        )
-        .await?;
-        stream = Box::pin(RecordBatchStreamAdapter::new(
-            schema.clone(),
-            recomputed.chain(stream),
-        ));
-    }
-
-    // Nothing survived the filter: the watermark still has to advance or the
-    // same fragments would be rescanned forever, but any removals still do.
-    let Some(first) = stream.try_next().await? else {
-        let published = if eviction.is_some() {
-            publish(
-                view_ds,
-                eviction,
-                Vec::new(),
-                Some(refresh_filter(&empty_keys(view_ds)?)?),
-                expected_incarnation,
-            )
-            .await?
-        } else {
-            view_ds.clone()
-        };
-        result.version = stamp_watermark(
-            view_native,
-            published,
-            source_version,
-            source_ts,
-            persist,
-            expected_incarnation,
-        )
-        .await?;
-        return Ok(Some(result));
-    };
-    let stream: SendableRecordBatchStream = Box::pin(RecordBatchStreamAdapter::new(
-        schema,
-        futures::stream::iter([Ok(first)]).chain(stream),
-    ));
-
-    // Any two refreshes of one view must not both commit: the filter's
-    // shared token makes lance reject the loser on key overlap however
-    // their planned rows relate.
-    let keys = Arc::new(StdMutex::new(KeyExistenceFilterBuilder::new(vec![
-        source_row_id_field_id(view_ds)?,
-    ])));
-    let stream = collect_source_row_ids(stream, keys.clone(), rows_written.clone());
-
-    let ds = Arc::new(view_ds.clone());
-    let write_txn = InsertBuilder::new(WriteDestination::Dataset(ds.clone()))
-        .with_params(&WriteParams {
-            mode: WriteMode::Append,
-            ..Default::default()
-        })
-        .execute_uncommitted_stream(stream)
-        .await?;
-    let Operation::Append {
-        fragments: new_fragments,
-    } = write_txn.operation
-    else {
-        return Err(Error::Runtime {
-            message: "expected an append when staging the view's new rows".into(),
-        });
-    };
-    let filter = refresh_filter(&keys)?;
-    let appended = publish(
-        view_ds,
-        eviction,
-        new_fragments,
-        Some(filter),
-        expected_incarnation,
-    )
-    .await?;
-    result.rows_written = rows_written.load(Ordering::Relaxed);
-    result.version = stamp_watermark(
-        view_native,
-        appended,
-        source_version,
-        source_ts,
-        persist,
-        expected_incarnation,
-    )
-    .await?;
-    Ok(Some(result))
-}
-
-#[allow(clippy::too_many_arguments)]
-async fn rebuild(
-    view_native: &NativeTable,
-    view_ds: &Dataset,
-    source_ds: &Dataset,
-    source_version: u64,
-    source_ts: u128,
-    definition: &MaterializedViewDefinition,
-    inputs: &[String],
-    unnest: Option<&super::ViewUnnest>,
-    persist_definition: bool,
-    expected_incarnation: Option<&str>,
-) -> Result<RefreshMaterializedViewResult> {
-    let rows_written = Arc::new(AtomicU64::new(0));
-    let schema = Arc::new(ArrowSchema::from(view_ds.schema()));
-    let stream = compute_stream(
-        source_ds,
-        definition,
-        inputs,
-        unnest,
-        RowScope {
-            limit: definition.limit,
-            ..Default::default()
-        },
-        schema,
-        rows_written.clone(),
-    )
-    .await?;
-    let keys = Arc::new(StdMutex::new(KeyExistenceFilterBuilder::new(vec![
-        source_row_id_field_id(view_ds)?,
-    ])));
-    let stream = collect_source_row_ids(stream, keys.clone(), Arc::new(AtomicU64::new(0)));
-    // Every rebuild is one fragment swap, indexed or not: an Update commit
-    // carries no schema metadata, so it cannot erase a definition update
-    // that raced in the way an overwrite (which adopts its stream's schema)
-    // durably would -- and it must land on the planned generation or abort.
-    let replaced =
-        replace_retaining_indices(view_ds.clone(), stream, keys, expected_incarnation).await?;
-    let version = stamp_watermark(
-        view_native,
-        replaced,
-        source_version,
-        source_ts,
-        persist_definition.then_some(definition),
-        expected_incarnation,
-    )
-    .await?;
-    Ok(RefreshMaterializedViewResult {
-        mode: RefreshMode::Rebuild,
-        rows_written: rows_written.load(Ordering::Relaxed),
-        source_version,
-        version,
-    })
-}
-
-/// Replace all of the view's data in one commit that retains its index
-/// definitions: new fragments staged uncommitted, one `Update` removing every
-/// old fragment. `Update` prunes index bitmaps only for modified fields and
-/// none are modified here, so readers never see the view unindexed or empty.
-async fn replace_retaining_indices(
-    view_ds: Dataset,
-    stream: SendableRecordBatchStream,
-    keys: Arc<StdMutex<KeyExistenceFilterBuilder>>,
-    expected_incarnation: Option<&str>,
-) -> Result<Dataset> {
-    let ds = Arc::new(view_ds);
-    let read_version = ds.version().version;
-    #[cfg(test)]
-    tests::hold_before_publish(ds.uri()).await;
-    ensure_incarnation(&ds, expected_incarnation, ds.uri()).await?;
-    let removed_fragment_ids: Vec<u64> = ds.get_fragments().iter().map(|f| f.id() as u64).collect();
-
-    let write_txn = InsertBuilder::new(WriteDestination::Dataset(ds.clone()))
-        .with_params(&WriteParams {
-            mode: WriteMode::Append,
-            ..Default::default()
-        })
-        .execute_uncommitted_stream(stream)
-        .await?;
-    let Operation::Append {
-        fragments: new_fragments,
-    } = write_txn.operation
-    else {
-        return Err(Error::Runtime {
-            message: "expected an append when staging the view's replacement rows".into(),
-        });
-    };
-
-    // Built only now: the tee fills as the staging drains the stream.
-    let filter = refresh_filter(&keys)?;
-    let transaction = Transaction::new(
-        read_version,
-        Operation::Update {
-            removed_fragment_ids,
-            updated_fragments: Vec::new(),
-            new_fragments,
-            fields_modified: Vec::new(),
-            compacted_sstables: Vec::new(),
-            fields_for_preserving_frag_bitmap: Vec::new(),
-            update_mode: None,
-            // Two refreshes that materialized the same source rows must not
-            // both land -- a raced first rebuild would double the view.
-            inserted_rows_filter: Some(filter),
-            updated_fragment_offsets: None,
-        },
-        None,
-    );
-    let committed = CommitBuilder::new(WriteDestination::Dataset(ds))
-        .execute(transaction)
-        .await?;
-    if committed.version().version != read_version + 1 {
-        return Err(Error::Runtime {
-            message: format!(
-                "a concurrent commit raced this refresh (view version {}); the 
-                 refresh is unrecorded and the next one will rebuild",
-                committed.version().version
-            ),
-        });
-    }
-    Ok(committed)
-}
-
-/// Record that the view now reflects `source_version`, including the view
-/// Refuse to act on a view that is not `expected`'s incarnation, judged from
-/// the latest stored manifest. Not a commit condition; see
-/// `RefreshMaterializedViewBuilder::expect_incarnation`.
-pub(super) async fn ensure_incarnation(
-    view_ds: &Dataset,
-    expected: Option<&str>,
-    what: &str,
-) -> Result<()> {
-    let Some(expected) = expected else {
-        return Ok(());
-    };
-    let mut latest = view_ds.clone();
-    latest.checkout_latest().await?;
-    match latest.schema().metadata.get(INCARNATION_META_KEY) {
-        Some(actual) if actual == expected => Ok(()),
-        Some(_) => Err(Error::Runtime {
-            message: format!(
-                "materialized view '{what}' is not the incarnation this refresh was \
-                 requested for: it was dropped and recreated"
-            ),
-        }),
-        None => Err(Error::Runtime {
-            message: format!(
-                "materialized view '{what}' carries no incarnation token: its schema \
-                 metadata was replaced since the token was captured"
-            ),
-        }),
-    }
-}
-
-/// version this very commit produces. The version is predicted and then
-/// verified; on a mismatch another commit raced in between, and the stamp
-/// ABORTS rather than certify that commit as the refresh's own generation.
-/// The view is left visibly unstamped, so the next refresh rebuilds.
-pub(super) async fn stamp_watermark(
-    view_native: &NativeTable,
-    mut dataset: Dataset,
-    source_version: u64,
-    source_ts: u128,
-    definition: Option<&MaterializedViewDefinition>,
-    expected_incarnation: Option<&str>,
-) -> Result<u64> {
-    ensure_incarnation(&dataset, expected_incarnation, dataset.uri()).await?;
-    let predicted = dataset.version().version + 1;
-    // A view with no token (declared before tokens existed, or its metadata
-    // replaced wholesale) starts a new incarnation here.
-    let incarnation = dataset
-        .schema()
-        .metadata
-        .get(INCARNATION_META_KEY)
-        .cloned()
-        .unwrap_or_else(|| uuid::Uuid::new_v4().to_string());
-    let mut metadata = vec![(INCARNATION_META_KEY.to_string(), Some(incarnation))];
-    if let Some(definition) = definition {
-        metadata.push((
-            DEFINITION_META_KEY.to_string(),
-            Some(definition_to_metadata(definition)?),
-        ));
-    }
-    metadata.extend([
-        (
-            SOURCE_VERSION_META_KEY.to_string(),
-            Some(source_version.to_string()),
-        ),
-        (
-            SOURCE_VERSION_TS_META_KEY.to_string(),
-            Some(source_ts.to_string()),
-        ),
-        (
-            REFRESHED_AT_MS_META_KEY.to_string(),
-            Some(now_ms().to_string()),
-        ),
-        (
-            VIEW_VERSION_META_KEY.to_string(),
-            Some(predicted.to_string()),
-        ),
-    ]);
-    dataset.update_schema_metadata(metadata).await?;
-    let actual = dataset.version().version;
-    if actual != predicted {
-        return Err(Error::Runtime {
-            message: format!(
-                "a concurrent commit raced this refresh (view version {actual}, \
-                 expected {predicted}); the refresh is unrecorded and the next \
-                 one will rebuild"
-            ),
-        });
-    }
-    view_native.dataset.update(dataset);
-    Ok(predicted)
-}
-
-fn now_ms() -> u128 {
-    SystemTime::now()
-        .duration_since(UNIX_EPOCH)
-        .unwrap_or_default()
-        .as_millis()
-}
-
-/// Evaluate the definition over `source`, restricted to `fragments` when
-/// given, as batches in the view's schema. The filter is pushed into the
-/// scan; projections are `(name, expression)` pairs, never spliced into SQL;
-/// [`SOURCE_ROW_ID_COLUMN`] is filled by the scan's row id.
-/// Which source rows a compute pass reads.
-#[derive(Default)]
-struct RowScope {
-    /// Read only these fragments.
-    fragments: Option<Vec<Fragment>>,
-    /// Read only rows created after this version.
-    created_after: Option<u64>,
-    /// Read only rows changed in place after the first version and no later
-    /// than the second.
-    updated_between: Option<(u64, u64)>,
-    /// Stop after this many rows.
-    limit: Option<u64>,
-}
-
-/// Whether every commit on the view after `recorded` is a fill of its
-/// computed columns: a column rewrite or data replacement touching only
-/// those fields and neither adding nor removing rows, or the freshness
-/// stamp a fill leaves on them. A version whose transaction cannot be read
-/// is not proven, so it counts as drift.
-async fn only_computed_rewrites_since(view_ds: &Dataset, recorded: u64) -> Result<bool> {
-    // A fill may write any field under a computed column, so the whole
-    // subtree counts, not only the root.
-    let physical = ArrowSchema::from(view_ds.schema());
-    fn subtree(field: &lance_core::datatypes::Field, ids: &mut Vec<u32>) {
-        ids.push(field.id as u32);
-        for child in &field.children {
-            subtree(child, ids);
-        }
-    }
-    let mut computed_fields = Vec::new();
-    for column in computed_columns(&physical) {
-        if let Some(field) = view_ds.schema().field(&column.name) {
-            subtree(field, &mut computed_fields);
-        }
-    }
-    if computed_fields.is_empty() {
-        return Ok(false);
-    }
-    for version in recorded + 1..=view_ds.version().version {
-        let Some(transaction) = view_ds.read_transaction_by_version(version).await? else {
-            return Ok(false);
-        };
-        let fill = match &transaction.operation {
-            Operation::Update {
-                removed_fragment_ids,
-                new_fragments,
-                fields_modified,
-                update_mode: Some(UpdateMode::RewriteColumns),
-                ..
-            } => {
-                removed_fragment_ids.is_empty()
-                    && new_fragments.is_empty()
-                    && !fields_modified.is_empty()
-                    && fields_modified
-                        .iter()
-                        .all(|field| computed_fields.contains(field))
-            }
-            // What `refresh_column` commits for a SQL declaration.
-            Operation::DataReplacement { replacements } => {
-                !replacements.is_empty()
-                    && replacements.iter().all(|group| {
-                        !group.1.fields.is_empty()
-                            && group
-                                .1
-                                .fields
-                                .iter()
-                                .all(|field| computed_fields.contains(&(*field as u32)))
-                    })
-            }
-            // The stamp `refresh_column` writes after its fill (see
-            // `table::freshness`): field metadata on computed columns, no data.
-            Operation::UpdateConfig {
-                config_updates: None,
-                table_metadata_updates: None,
-                schema_metadata_updates: None,
-                field_metadata_updates,
-            } => {
-                !field_metadata_updates.is_empty()
-                    && field_metadata_updates
-                        .keys()
-                        .all(|field| computed_fields.contains(&(*field as u32)))
-            }
-            _ => false,
-        };
-        if !fill {
-            return Ok(false);
-        }
-    }
-    Ok(true)
+    Ok(native.dataset.get().await?.as_ref().clone())
 }
 
 async fn compute_stream(
@@ -1332,58 +328,23 @@ async fn compute_stream(
     definition: &MaterializedViewDefinition,
     inputs: &[String],
     unnest: Option<&super::ViewUnnest>,
-    scope: RowScope,
     schema: SchemaRef,
     rows_written: Arc<AtomicU64>,
 ) -> Result<SendableRecordBatchStream> {
     if definition.is_grouped() {
         return super::grouped::stream(source, definition, inputs, schema, rows_written).await;
     }
-    let RowScope {
-        fragments,
-        created_after,
-        updated_between,
-        limit,
-    } = scope;
     let mut scanner = source.scan();
-    if let Some(fragments) = fragments {
-        scanner.with_fragments(fragments);
+    if let Some(filter) = definition.filter.as_deref().filter(|_| unnest.is_none()) {
+        scanner.filter(filter)?;
     }
-    scanner.with_row_id();
-    // Narrowing keeps the definition's filter, so a row updated out of the
-    // view simply does not come back. Changed rows are named by the predicate
-    // `DatasetDelta::get_updated_rows` uses, not its streamed ids: an id list
-    // grows with the delta, this does not.
-    let updated_filter = updated_between.map(|(from, to)| {
-        format!(
-            "{ROW_CREATED_AT_VERSION} <= {from} \
-             AND {ROW_LAST_UPDATED_AT_VERSION} > {from} \
-             AND {ROW_LAST_UPDATED_AT_VERSION} <= {to}"
-        )
-    });
-    let created_filter =
-        created_after.map(|version| format!("{ROW_CREATED_AT_VERSION} > {version}"));
-    let clauses: Vec<String> = definition
-        .filter
-        .clone()
-        .filter(|_| unnest.is_none())
-        .map(|f| format!("({f})"))
-        .into_iter()
-        .chain(updated_filter)
-        .chain(created_filter)
-        .collect();
-    if !clauses.is_empty() {
-        scanner.filter(&clauses.join(" AND "))?;
-    }
-    // An expanded view cannot project or filter in the scan: both read the
-    // unnested element, which exists only after the per-batch expansion.
     let expanded = match unnest {
         Some(unnest) => Some(UnnestPlan::new(source, definition, inputs, unnest)?),
         None => {
             let transforms: Vec<(&str, &str)> = definition
                 .projections
                 .iter()
-                .map(|p| (p.output.as_str(), p.expression.as_str()))
+                .map(|projection| (projection.output.as_str(), projection.expression.as_str()))
                 .collect();
             scanner.project_with_transform(&transforms)?;
             None
@@ -1392,54 +353,43 @@ async fn compute_stream(
     if let Some(expanded) = &expanded {
         scanner.project(&expanded.raw_inputs)?;
     }
-    // A scan reads a limit of zero as no limit at all, so a view capped at
-    // nothing is answered without one.
-    if limit == Some(0) {
+    if definition.limit == Some(0) {
         return Ok(Box::pin(RecordBatchStreamAdapter::new(
-            schema.clone(),
+            schema,
             futures::stream::empty(),
         )));
     }
-    if let Some(limit) = limit {
+    if let Some(limit) = definition.limit {
         let limit = i64::try_from(limit).map_err(|_| Error::InvalidInput {
             message: format!("view limit {limit} exceeds the maximum of {}", i64::MAX),
         })?;
         scanner.limit(Some(limit), None)?;
     }
-
-    let out_schema = schema.clone();
+    let output_schema = schema.clone();
     let mapped = scanner.try_into_stream().await?.map(move |batch| {
-        let batch = batch.map_err(|e| DataFusionError::External(Box::new(e)))?;
+        let batch = batch.map_err(|error| DataFusionError::External(Box::new(error)))?;
         let batch = match &expanded {
-            None => batch,
             Some(expanded) => expanded.apply(&batch)?,
+            None => batch,
         };
-        let batch = to_view_batch(&batch, &out_schema)?;
+        let batch = to_view_batch(&batch, &output_schema)?;
         rows_written.fetch_add(batch.num_rows() as u64, Ordering::Relaxed);
         Ok(batch)
     });
     Ok(Box::pin(RecordBatchStreamAdapter::new(schema, mapped)))
 }
 
-/// `batch` in the view's physical schema: the row id becomes the
-/// provenance column and computed columns are written NULL for their owner
-/// to fill.
 pub(super) fn to_view_batch(
     batch: &RecordBatch,
-    out_schema: &SchemaRef,
+    output_schema: &SchemaRef,
 ) -> datafusion::common::Result<RecordBatch> {
-    let mut columns = Vec::with_capacity(out_schema.fields().len());
-    for field in out_schema.fields() {
+    let mut columns = Vec::with_capacity(output_schema.fields().len());
+    for field in output_schema.fields() {
         if computed_column_from_field(field).is_some() {
             columns.push(new_null_array(field.data_type(), batch.num_rows()));
             continue;
         }
-        let name = if field.name() == SOURCE_ROW_ID_COLUMN {
-            ROW_ID
-        } else {
-            field.name()
-        };
-        let column = batch.column_by_name(name).ok_or_else(|| {
+        let column = batch.column_by_name(field.name()).ok_or_else(|| {
             DataFusionError::Internal(format!(
                 "view column '{}' is not produced by the view's definition",
                 field.name()
@@ -1447,13 +397,9 @@ pub(super) fn to_view_batch(
         })?;
         columns.push(column.clone());
     }
-    Ok(RecordBatch::try_new(out_schema.clone(), columns)?)
+    Ok(RecordBatch::try_new(output_schema.clone(), columns)?)
 }
 
-/// The post-scan half of an unnested view's refresh: the scan reads
-/// `raw_inputs` plus the row id, and each batch is unnested on the list
-/// column, filtered, then projected by expressions typed against
-/// `read_schema`, where the element sits under the alias.
 struct UnnestPlan {
     column: String,
     raw_inputs: Vec<String>,
@@ -1469,7 +415,6 @@ impl UnnestPlan {
         inputs: &[String],
         unnest: &super::ViewUnnest,
     ) -> Result<Self> {
-        // Whole root columns: a nested input is projected by the expression.
         let mut raw_inputs: Vec<String> = inputs
             .iter()
             .map(|input| super::root(input).to_string())
@@ -1478,9 +423,7 @@ impl UnnestPlan {
         raw_inputs.sort();
         raw_inputs.dedup();
         let flattened = super::flattened_schema(&ArrowSchema::from(source.schema()), unnest)?;
-        // Physical expressions index columns by position, so the schema is
-        // exactly the scan's output: `raw_inputs` in order, then the row id.
-        let mut read_fields = Vec::with_capacity(raw_inputs.len() + 1);
+        let mut read_fields = Vec::with_capacity(raw_inputs.len());
         for name in &raw_inputs {
             let name = if *name == unnest.column {
                 &unnest.alias
@@ -1494,25 +437,27 @@ impl UnnestPlan {
                 })?;
             read_fields.push(field.clone());
         }
-        read_fields.push(ArrowField::new(ROW_ID, DataType::UInt64, false));
         let read_schema = Arc::new(ArrowSchema::new(read_fields));
         let planner = Planner::new(read_schema.clone());
         let physical = |what: &str, sql: &str| -> Result<Arc<dyn PhysicalExpr>> {
-            let err = |e: lance::Error| Error::Runtime {
-                message: format!("{what}: {e}"),
+            let error = |error: lance::Error| Error::Runtime {
+                message: format!("{what}: {error}"),
             };
-            let parsed = planner.parse_expr(sql).map_err(err)?;
-            let optimized = planner.optimize_expr(parsed).map_err(err)?;
-            planner.create_physical_expr(&optimized).map_err(err)
+            let parsed = planner.parse_expr(sql).map_err(error)?;
+            let optimized = planner.optimize_expr(parsed).map_err(error)?;
+            planner.create_physical_expr(&optimized).map_err(error)
         };
-        let mut projections = Vec::with_capacity(definition.projections.len());
-        for projection in &definition.projections {
-            let expr = physical(
-                &format!("view column '{}'", projection.output),
-                &projection.expression,
-            )?;
-            projections.push((projection.output.clone(), expr));
-        }
+        let projections = definition
+            .projections
+            .iter()
+            .map(|projection| {
+                physical(
+                    &format!("view column '{}'", projection.output),
+                    &projection.expression,
+                )
+                .map(|expression| (projection.output.clone(), expression))
+            })
+            .collect::<Result<_>>()?;
         let filter = definition
             .filter
             .as_deref()
@@ -1529,8 +474,7 @@ impl UnnestPlan {
 
     fn apply(&self, batch: &RecordBatch) -> datafusion::common::Result<RecordBatch> {
         let unnested = unnest_batch(batch, &self.column)
-            .map_err(|e| DataFusionError::External(Box::new(e)))?;
-        // Same columns, renamed: the list column is now the element under the alias.
+            .map_err(|error| DataFusionError::External(Box::new(error)))?;
         let unnested = RecordBatch::try_new(self.read_schema.clone(), unnested.columns().to_vec())?;
         let unnested = match &self.filter {
             None => unnested,
@@ -1544,25 +488,20 @@ impl UnnestPlan {
                 arrow_select::filter::filter_record_batch(&unnested, keep)?
             }
         };
-        let mut columns = Vec::with_capacity(self.projections.len() + 1);
-        for (output, expr) in &self.projections {
-            let value = expr.evaluate(&unnested)?.into_array(unnested.num_rows())?;
-            columns.push((output.clone(), value));
-        }
-        let row_id = unnested
-            .column_by_name(ROW_ID)
-            .expect("scan carries the row id")
-            .clone();
-        columns.push((ROW_ID.to_string(), row_id));
+        let columns = self
+            .projections
+            .iter()
+            .map(|(output, expression)| {
+                expression
+                    .evaluate(&unnested)?
+                    .into_array(unnested.num_rows())
+                    .map(|value| (output.clone(), value))
+            })
+            .collect::<datafusion::common::Result<Vec<_>>>()?;
         Ok(RecordBatch::try_from_iter(columns)?)
     }
 }
 
-/// Expand `list_column` one row per element, repeating every other column
-/// for each element; an empty or null list contributes no rows. The list
-/// column is replaced by its element type, so a projection reads the
-/// element's fields as `alias.field` after this. This is the row-cardinality
-/// step of an `expanded_select` view, applied per batch on the scan stream.
 fn unnest_batch(batch: &RecordBatch, list_column: &str) -> Result<RecordBatch> {
     use arrow_array::{Array, ListArray, UInt32Array};
     use arrow_select::take::take;
@@ -1583,30 +522,24 @@ fn unnest_batch(batch: &RecordBatch, list_column: &str) -> Result<RecordBatch> {
                 batch.column(list_index).data_type()
             ),
         })?;
-
-    // One take index per element, naming the source row it came from. A null
-    // list has no elements; its offsets are equal, so it repeats nothing.
     let offsets = list.value_offsets();
     let mut repeat = Vec::with_capacity(list.values().len());
     for row in 0..list.len() {
         if list.is_valid(row) {
-            let count = (offsets[row + 1] - offsets[row]) as usize;
-            repeat.extend(std::iter::repeat_n(row as u32, count));
+            repeat.extend(std::iter::repeat_n(
+                row as u32,
+                (offsets[row + 1] - offsets[row]) as usize,
+            ));
         }
     }
     let repeat = UInt32Array::from(repeat);
-    // The flattened elements, in the same order as `repeat`: only the ranges
-    // valid rows cover, so a null row's stale range (if any) is skipped.
-    let elements = {
-        let mut ranges = Vec::new();
-        for row in 0..list.len() {
-            if list.is_valid(row) {
-                ranges.extend((offsets[row] as u32)..(offsets[row + 1] as u32));
-            }
+    let mut ranges = Vec::new();
+    for row in 0..list.len() {
+        if list.is_valid(row) {
+            ranges.extend((offsets[row] as u32)..(offsets[row + 1] as u32));
         }
-        take(list.values().as_ref(), &UInt32Array::from(ranges), None)?
-    };
-
+    }
+    let elements = take(list.values().as_ref(), &UInt32Array::from(ranges), None)?;
     let mut fields = Vec::with_capacity(batch.num_columns());
     let mut columns = Vec::with_capacity(batch.num_columns());
     for (index, field) in batch.schema().fields().iter().enumerate() {
@@ -1636,3168 +569,38 @@ fn unnest_batch(batch: &RecordBatch, list_column: &str) -> Result<RecordBatch> {
     Ok(RecordBatch::try_new(schema, columns)?)
 }
 
-/// Commit the view's removals and additions as one change, on the exact
-/// generation the refresh planned from. Lance rejects an overlapping
-/// provenance key, but an unrelated write to the view is not a key conflict,
-/// so the generation is checked here too.
-async fn publish(
-    view_ds: &Dataset,
-    eviction: Option<(Vec<Fragment>, Vec<u64>)>,
-    new_fragments: Vec<Fragment>,
-    keys: Option<KeyExistenceFilter>,
-    expected_incarnation: Option<&str>,
-) -> Result<Dataset> {
-    let planned = view_ds.version().version;
-    #[cfg(test)]
-    tests::hold_before_publish(view_ds.uri()).await;
-    #[cfg(test)]
-    tests::hold_until_peers_planned();
-    ensure_incarnation(view_ds, expected_incarnation, view_ds.uri()).await?;
-    let (updated_fragments, removed_fragment_ids) = eviction.unwrap_or_default();
-    let committed = CommitBuilder::new(WriteDestination::Dataset(Arc::new(view_ds.clone())))
-        .execute(Transaction::new(
-            planned,
-            Operation::Update {
-                removed_fragment_ids,
-                updated_fragments,
-                new_fragments,
-                fields_modified: Vec::new(),
-                compacted_sstables: Vec::new(),
-                fields_for_preserving_frag_bitmap: Vec::new(),
-                update_mode: None,
-                inserted_rows_filter: keys,
-                updated_fragment_offsets: None,
-            },
-            None,
-        ))
-        .await?;
-    if committed.version().version != planned + 1 {
-        return Err(Error::Runtime {
-            message: format!(
-                "a concurrent commit raced this refresh (view version {}); 
-                 the refresh is unrecorded and the next one will rebuild",
-                committed.version().version
-            ),
-        });
-    }
-    Ok(committed)
-}
-
-/// A delta batch's row id column, borrowed: a delta batch is as large as one
-/// fragment's deletions, so copying it out would be the unbounded step the
-/// chunking exists to avoid.
-fn row_ids_of(batch: &RecordBatch) -> Result<&UInt64Array> {
-    let column = batch.column_by_name(ROW_ID).ok_or_else(|| Error::Runtime {
-        message: format!("'{ROW_ID}' is missing from a delta batch"),
-    })?;
-    column
-        .as_primitive_opt::<UInt64Type>()
-        .ok_or_else(|| Error::Runtime {
-            message: "row ids are not UInt64".into(),
-        })
-}
-
-/// A provenance id no source row can hold, carried in every refresh's
-/// inserted-rows filter: any two refreshes of one view overlap on it, so
-/// the loser of a race conflicts at commit whatever rows each planned.
-const REFRESH_TOKEN_ID: u64 = u64::MAX;
-
-/// A builder with no collected keys, for publishes that only remove rows.
-fn empty_keys(view_ds: &Dataset) -> Result<Arc<StdMutex<KeyExistenceFilterBuilder>>> {
-    Ok(Arc::new(StdMutex::new(KeyExistenceFilterBuilder::new(
-        vec![source_row_id_field_id(view_ds)?],
-    ))))
-}
-
-/// An inserted-rows filter holding the refresh token plus whatever the
-/// tee collected.
-fn refresh_filter(keys: &Arc<StdMutex<KeyExistenceFilterBuilder>>) -> Result<KeyExistenceFilter> {
-    let mut keys = keys.lock().map_err(|_| Error::Runtime {
-        message: "the provenance key filter was poisoned mid-refresh".into(),
-    })?;
-    keys.insert(KeyValue::UInt64(REFRESH_TOKEN_ID))
-        .map_err(|e| Error::Runtime {
-            message: format!("failed to mark the refresh's filter: {e}"),
-        })?;
-    Ok(keys.build())
-}
-
-/// Reconciled ids past this fall back to the streamed rebuild, whose memory
-/// does not grow with the delta.
-fn eviction_rebuild_cap() -> usize {
-    #[cfg(test)]
-    if let Some(cap) = tests::eviction_cap_override() {
-        return cap;
-    }
-    4 * 1024 * 1024
-}
-
-/// Provenance ids per staged eviction: the delete predicate carries one
-/// literal per id, so a whole delta at once is unbounded. Each chunk costs a
-/// pass over the view's provenance column, which is why it is not smaller.
-const EVICTION_CHUNK: usize = 64 * 1024;
-
-/// Accumulates the view's removals from bounded chunks of provenance ids into
-/// the one set of fragment changes the refresh publishes.
-struct Eviction {
-    /// The view as the chunks staged so far leave it. A chunk's delete has to
-    /// see the deletion vectors the earlier ones wrote, or it stages a
-    /// fragment that drops them.
-    snapshot: Dataset,
-    chunk: usize,
-    updated: HashMap<u64, Fragment>,
-    removed: Vec<u64>,
-    pending: Vec<u64>,
-    staged: bool,
-    /// Largest the buffer ever got, which is the bound under test.
-    #[cfg(test)]
-    peak: usize,
-}
-
-impl Eviction {
-    fn new(view_ds: &Dataset, chunk: usize) -> Self {
-        Self {
-            snapshot: view_ds.clone(),
-            chunk,
-            updated: HashMap::new(),
-            removed: Vec::new(),
-            pending: Vec::with_capacity(chunk),
-            staged: false,
-            #[cfg(test)]
-            peak: 0,
-        }
-    }
-
-    /// Fill a chunk at a time. Taking the batch whole and splitting it would
-    /// hold a fragment's worth of ids and recopy the tail per chunk.
-    async fn push(&mut self, ids: &UInt64Array) -> Result<()> {
-        for id in ids.values() {
-            self.pending.push(*id);
-            #[cfg(test)]
-            {
-                self.peak = self.peak.max(self.pending.len());
-            }
-            if self.pending.len() == self.chunk {
-                self.flush().await?;
-            }
-        }
-        Ok(())
-    }
-
-    /// Stage what is buffered, keeping the buffer's allocation.
-    async fn flush(&mut self) -> Result<()> {
-        let mut chunk = std::mem::take(&mut self.pending);
-        let staged = self.stage(&chunk).await;
-        chunk.clear();
-        self.pending = chunk;
-        staged
-    }
-
-    /// The staged fragment changes, or `None` where nothing was evicted.
-    async fn finish(mut self) -> Result<Option<(Vec<Fragment>, Vec<u64>)>> {
-        if !self.pending.is_empty() {
-            self.flush().await?;
-        }
-        if !self.staged {
-            return Ok(None);
-        }
-        let mut updated: Vec<Fragment> = self.updated.into_values().collect();
-        updated.sort_unstable_by_key(|f| f.id);
-        Ok(Some((updated, self.removed)))
-    }
-
-    async fn stage(&mut self, ids: &[u64]) -> Result<()> {
-        let (updated, removed) = stage_eviction(&self.snapshot, ids).await?;
-        self.snapshot = advance(&self.snapshot, &updated);
-        for fragment in updated {
-            self.updated.insert(fragment.id, fragment);
-        }
-        self.removed.extend(removed);
-        self.staged = true;
-        Ok(())
-    }
-}
-
-/// The view as staged removals leave it, without committing them. Fragments
-/// are replaced in place so the fragment bitmap stays in step; an emptied one
-/// is left alone, since the delta names each provenance id only once.
-fn advance(view_ds: &Dataset, updated: &[Fragment]) -> Dataset {
-    if updated.is_empty() {
-        return view_ds.clone();
-    }
-    let by_id: HashMap<u64, &Fragment> = updated.iter().map(|f| (f.id, f)).collect();
-    let fragments = view_ds
-        .manifest
-        .fragments
-        .iter()
-        .map(|f| by_id.get(&f.id).map_or_else(|| f.clone(), |u| (*u).clone()))
-        .collect();
-    let mut manifest = view_ds.manifest.as_ref().clone();
-    manifest.fragments = Arc::new(fragments);
-    let mut snapshot = view_ds.clone();
-    snapshot.manifest = Arc::new(manifest);
-    snapshot
-}
-
-/// The fragment changes that remove the view's rows for `ids`, staged rather
-/// than committed so they can ride in the refresh's single data commit.
-async fn stage_eviction(view_ds: &Dataset, ids: &[u64]) -> Result<(Vec<Fragment>, Vec<u64>)> {
-    // An expression rather than SQL text: the id list is a value here, not a
-    // predicate string that grows with the delta and has to be parsed.
-    let predicate = col(SOURCE_ROW_ID_COLUMN).in_list(
-        ids.iter()
-            .map(|id| lit(ScalarValue::UInt64(Some(*id))))
-            .collect(),
-        false,
-    );
-    let staged = DeleteBuilder::from_expr(Arc::new(view_ds.clone()), predicate)
-        .execute_uncommitted()
-        .await?;
-    let Operation::Delete {
-        updated_fragments,
-        deleted_fragment_ids,
-        ..
-    } = staged.transaction.operation
-    else {
-        return Err(Error::Runtime {
-            message: "expected a delete when staging the view's evictions".into(),
-        });
-    };
-    Ok((updated_fragments, deleted_fragment_ids))
-}
-
-fn source_row_id_field_id(view_ds: &Dataset) -> Result<i32> {
-    view_ds
-        .schema()
-        .field(SOURCE_ROW_ID_COLUMN)
-        .map(|f| f.id)
-        .ok_or_else(|| Error::Runtime {
-            message: format!("the view has no '{SOURCE_ROW_ID_COLUMN}' column"),
-        })
-}
-
-/// Tee the provenance ids of everything written into `keys`.
-fn collect_source_row_ids(
-    stream: SendableRecordBatchStream,
-    keys: Arc<StdMutex<KeyExistenceFilterBuilder>>,
-    written: Arc<AtomicU64>,
-) -> SendableRecordBatchStream {
-    let schema = stream.schema();
-    let mapped = stream.map(move |batch| {
-        let batch = batch?;
-        let column = batch
-            .column_by_name(SOURCE_ROW_ID_COLUMN)
-            .ok_or_else(|| {
-                DataFusionError::Internal(format!(
-                    "'{SOURCE_ROW_ID_COLUMN}' is missing from the rows being written"
-                ))
-            })?
-            .as_primitive_opt::<UInt64Type>()
-            .ok_or_else(|| {
-                DataFusionError::Internal(format!("'{SOURCE_ROW_ID_COLUMN}' is not a uint64"))
-            })?;
-        let mut keys = keys
-            .lock()
-            .map_err(|_| DataFusionError::Internal("provenance key filter poisoned".into()))?;
-        for id in column.values() {
-            keys.insert(KeyValue::UInt64(*id))
-                .map_err(|e| DataFusionError::Internal(e.to_string()))?;
-        }
-        written.fetch_add(batch.num_rows() as u64, Ordering::Relaxed);
-        Ok(batch)
-    });
-    Box::pin(RecordBatchStreamAdapter::new(schema, mapped))
-}
-
 #[cfg(test)]
-pub(crate) mod tests {
-
-    /// Park a refresh between planning and publication so a test can move
-    /// the view underneath it. Inert unless [`DRIFT_TARGET`] names this view.
-    pub(in crate::materialized_view) async fn hold_before_publish(uri: &str) {
-        {
-            let mut target = DRIFT_TARGET.lock().unwrap();
-            if target.as_deref() != Some(uri) {
-                return;
-            }
-            // Take it: memory:// uris are relative and repeat across tests, so
-            // leaving it armed would park an unrelated refresh forever.
-            *target = None;
-        }
-        DRIFT_PLANNED.notify_one();
-        DRIFT_RELEASED.notified().await;
-    }
-
-    /// The rendezvous below is one global pair, so the cases that use it run
-    /// one at a time rather than trading each other's signals.
-    pub(super) static EVICTION_CAP: StdMutex<Option<usize>> = StdMutex::new(None);
-    pub(super) fn eviction_cap_override() -> Option<usize> {
-        *EVICTION_CAP.lock().unwrap()
-    }
-
-    pub(in crate::materialized_view) static DRIFT_LOCK: tokio::sync::Mutex<()> =
-        tokio::sync::Mutex::const_new(());
-    pub(in crate::materialized_view) static DRIFT_TARGET: StdMutex<Option<String>> =
-        StdMutex::new(None);
-    pub(in crate::materialized_view) static DRIFT_PLANNED: tokio::sync::Notify =
-        tokio::sync::Notify::const_new();
-    pub(in crate::materialized_view) static DRIFT_RELEASED: tokio::sync::Notify =
-        tokio::sync::Notify::const_new();
-
-    /// Block until every participant in a cross-process race has planned and
-    /// staged its write, so the commits they then attempt genuinely contend
-    /// rather than depending on the scheduler to overlap them. Inert unless
-    /// `MV_RACE_SYNC` names a directory shared by the participants.
-    pub(super) fn hold_until_peers_planned() {
-        let (Ok(dir), Ok(tag), Ok(peers)) = (
-            std::env::var("MV_RACE_SYNC"),
-            std::env::var("MV_RACE_TAG"),
-            std::env::var("MV_RACE_PEERS"),
-        ) else {
-            return;
-        };
-        let dir = std::path::PathBuf::from(dir);
-        let peers: usize = peers.parse().unwrap();
-        std::fs::write(dir.join(format!("planned-{tag}")), b"1").unwrap();
-        let deadline = std::time::Instant::now() + std::time::Duration::from_secs(120);
-        while planned_count(&dir) < peers {
-            assert!(
-                std::time::Instant::now() < deadline,
-                "peers never reached the commit boundary"
-            );
-            std::thread::sleep(std::time::Duration::from_millis(2));
-        }
-    }
-
-    fn planned_count(dir: &std::path::Path) -> usize {
-        std::fs::read_dir(dir)
-            .map(|entries| {
-                entries
-                    .filter_map(|e| e.ok())
-                    .filter(|e| e.file_name().to_string_lossy().starts_with("planned-"))
-                    .count()
-            })
-            .unwrap_or(0)
-    }
-    use arrow_array::{Int32Array, record_batch};
-    use futures::TryStreamExt;
-    use lance::dataset::NewColumnTransform;
-    use lance_file::version::LanceFileVersion;
+mod tests {
+    use arrow_array::record_batch;
+    use datafusion::physical_plan::stream::RecordBatchStreamAdapter;
 
     use super::*;
     use crate::connect;
-    use crate::connection::Connection;
-    use crate::index::Index;
-    use crate::index::scalar::BTreeIndexBuilder;
-    use crate::materialized_view::MaterializedView;
-    use crate::query::{ExecutableQuery, QueryBase, Select};
-    use crate::table::{CompactionOptions, OptimizeAction};
-
-    async fn db_with_source(values: Vec<i32>) -> (Connection, Table) {
-        let conn = connect("memory://").execute().await.unwrap();
-        let batch = record_batch!(("x", Int32, values)).unwrap();
-        let table = conn
-            .create_table("src", batch)
-            .write_options(crate::materialized_view::tests::stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-        (conn, table)
-    }
-
-    async fn doubled_view(conn: &Connection) -> MaterializedView {
-        conn.create_materialized_view("doubled", "src")
-            .with_no_data(true)
-            .select([("x", "x"), ("twice", "x * 2")])
-            .execute()
-            .await
-            .unwrap()
-    }
-
-    /// A refreshed doubled view over a source holding `values`.
-    async fn refreshed_doubled(values: Vec<i32>) -> (Connection, Table, MaterializedView) {
-        let (conn, source) = db_with_source(values).await;
-        let view = doubled_view(&conn).await;
-        view.refresh().execute().await.unwrap();
-        (conn, source, view)
-    }
-
-    /// The per-batch expansion behind an `expanded_select` view: every
-    /// element of the list column becomes a row, the other columns repeat
-    /// for each, and an empty or null list contributes no rows at all --
-    /// which is exactly "zero rows out" for a table-valued function.
-    /// Four documents with a `list<struct<chunk, ordinal>>` column named
-    /// `c`: doc 1 has two chunks, doc 2 none, doc 3 a null list, doc 4 one.
-    fn chunked_batch() -> RecordBatch {
-        use arrow_array::builder::{Int32Builder, ListBuilder, StringBuilder, StructBuilder};
-        use arrow_array::{ArrayRef, Int64Array};
-        use arrow_schema::{DataType, Field, Fields};
-
-        let element_fields = Fields::from(vec![
-            Field::new("chunk", DataType::Utf8, true),
-            Field::new("ordinal", DataType::Int32, true),
-        ]);
-        let mut list = ListBuilder::new(StructBuilder::new(
-            element_fields,
-            vec![
-                Box::new(StringBuilder::new()),
-                Box::new(Int32Builder::new()),
-            ],
-        ));
-        for chunks in [Some(vec!["a", "b"]), Some(vec![]), None, Some(vec!["c"])] {
-            match chunks {
-                Some(chunks) => {
-                    for (i, c) in chunks.iter().enumerate() {
-                        let s = list.values();
-                        s.field_builder::<StringBuilder>(0).unwrap().append_value(c);
-                        s.field_builder::<Int32Builder>(1)
-                            .unwrap()
-                            .append_value(i as i32);
-                        s.append(true);
-                    }
-                    list.append(true);
-                }
-                None => list.append(false),
-            }
-        }
-        let meta = arrow_array::StructArray::from(vec![(
-            Arc::new(Field::new("title", DataType::Utf8, true)),
-            Arc::new(arrow_array::StringArray::from(vec!["t1", "t2", "t3", "t4"])) as ArrayRef,
-        )]);
-        RecordBatch::try_from_iter(vec![
-            (
-                "id",
-                Arc::new(Int64Array::from(vec![1, 2, 3, 4])) as ArrayRef,
-            ),
-            ("meta", Arc::new(meta) as ArrayRef),
-            ("c", Arc::new(list.finish()) as ArrayRef),
-        ])
-        .unwrap()
-    }
-
-    #[test]
-    fn unnest_repeats_siblings_per_element_and_drops_empty_lists() {
-        use arrow_array::{StringArray, StructArray};
-        use arrow_schema::{DataType, Field, Fields};
-
-        let batch = chunked_batch();
-        let element_fields = Fields::from(vec![
-            Field::new("chunk", DataType::Utf8, true),
-            Field::new("ordinal", DataType::Int32, true),
-        ]);
-        let out = unnest_batch(&batch, "c").unwrap();
-        assert_eq!(out.num_rows(), 3, "{out:?}");
-        let ids: Vec<i64> = out["id"]
-            .as_primitive::<arrow_array::types::Int64Type>()
-            .values()
-            .to_vec();
-        assert_eq!(ids, [1, 1, 4]);
-        let element = out["c"].as_any().downcast_ref::<StructArray>().unwrap();
-        let chunks: Vec<&str> = element
-            .column(0)
-            .as_any()
-            .downcast_ref::<StringArray>()
-            .unwrap()
-            .iter()
-            .flatten()
-            .collect();
-        assert_eq!(chunks, ["a", "b", "c"]);
-        let ordinals: Vec<i32> = element
-            .column(1)
-            .as_primitive::<arrow_array::types::Int32Type>()
-            .values()
-            .to_vec();
-        assert_eq!(ordinals, [0, 1, 0]);
-        // the element column is now the struct itself, not a list of it
-        assert_eq!(
-            out.schema().field_with_name("c").unwrap().data_type(),
-            &DataType::Struct(element_fields)
-        );
-    }
-
-    /// A Function in FROM position is refreshed from its staging table: the
-    /// query on the view stays the one the user wrote, the staging binding
-    /// says which table and list column refresh reads, and rows come out one
-    /// per element as with UNNEST. Without a staging, a local database
-    /// refuses the query rather than guess.
-    #[tokio::test]
-    async fn a_function_in_from_position_refreshes_from_its_staging() {
-        use arrow_array::StringArray;
-
-        let conn = connect("memory://").execute().await.unwrap();
-        let staging = conn
-            .create_table("docs__chunk", chunked_batch())
-            .write_options(crate::materialized_view::tests::stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-        let query =
-            "SELECT id AS doc, e.chunk AS text, e.ordinal FROM docs, chunk(meta.title, 2) AS e";
-        let definition = MaterializedViewDefinition::from_sql(query).unwrap();
-
-        let err = crate::materialized_view::prepare_definition(&staging, definition.clone())
-            .await
-            .unwrap_err();
-        assert!(
-            matches!(&err, Error::InvalidInput { message } if message.contains("reads 'docs'")),
-            "{err:?}"
-        );
-
-        let view = crate::materialized_view::prepare_staged_definition(&staging, definition, "c")
-            .await
-            .unwrap()
-            .create("chunks")
-            .await
-            .unwrap();
-        assert_eq!(view.definition().to_sql(), query);
-        let metadata = view.table().schema().await.unwrap().metadata().clone();
-        assert_eq!(
-            crate::materialized_view::read_staging(&metadata).unwrap(),
-            Some(crate::materialized_view::StagingBinding {
-                table: "docs__chunk".into(),
-                namespace: Vec::new(),
-                column: "c".into(),
-            })
-        );
-
-        view.refresh().execute().await.unwrap();
-        assert_eq!(read(view.table(), "ordinal").await, [0, 0, 1]);
-        let batches = view
-            .table()
-            .query()
-            .select(Select::columns(&["text"]))
-            .execute()
-            .await
-            .unwrap()
-            .try_collect::<Vec<_>>()
-            .await
-            .unwrap();
-        let out = arrow_select::concat::concat_batches(&batches[0].schema(), &batches).unwrap();
-        let texts: Vec<&str> = out["text"]
-            .as_any()
-            .downcast_ref::<StringArray>()
-            .unwrap()
-            .iter()
-            .flatten()
-            .collect();
-        assert_eq!(texts, ["a", "b", "c"]);
-
-        // The reopened view reads the same logical query and refreshes
-        // incrementally from the staging.
-        staging.add(chunked_batch()).execute().await.unwrap();
-        let reopened = conn.open_materialized_view("chunks").await.unwrap();
-        assert_eq!(reopened.definition().to_sql(), query);
-        let result = reopened.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(read(reopened.table(), "ordinal").await, [0, 0, 0, 0, 1, 1]);
-    }
-
-    /// An expanded view materializes one row per list element, with the
-    /// projections reading the element through the alias and the other
-    /// source columns repeated alongside; sources with no elements yield
-    /// no rows. The lineage is the list column, so a change to it is what
-    /// drives incremental refresh.
-    #[tokio::test]
-    async fn an_expanded_view_materializes_one_row_per_element() {
-        use arrow_array::{Int64Array, StringArray};
-
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = conn
-            .create_table("docs", chunked_batch())
-            .write_options(crate::materialized_view::tests::stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-        let mut view = crate::materialized_view::prepare_definition(
-            &source,
-            MaterializedViewDefinition::from_sql(
-                "SELECT id AS doc, meta.title AS title, e.ordinal + 1 AS nth \
-                 FROM docs, UNNEST(c) AS e WHERE e.ordinal < 5",
-            )
-            .unwrap(),
-        )
-        .await
-        .unwrap();
-        // A computed column's input read through the alias is an element
-        // field; the recorded source input is the list column.
-        let text = view.input_column("e.chunk").unwrap();
-        assert!(view.definition.lateral.is_some());
-        let view = view.create("chunks").await.unwrap();
-        view.refresh().execute().await.unwrap();
-
-        let batches = view
-            .table()
-            .query()
-            .select(Select::columns(&["doc", &text, "title"]))
-            .execute()
-            .await
-            .unwrap()
-            .try_collect::<Vec<_>>()
-            .await
-            .unwrap();
-        let out = arrow_select::concat::concat_batches(&batches[0].schema(), &batches).unwrap();
-        let docs: Vec<i64> = out["doc"]
-            .as_any()
-            .downcast_ref::<Int64Array>()
-            .unwrap()
-            .values()
-            .to_vec();
-        let strings = |column: &str| -> Vec<String> {
-            out[column]
-                .as_any()
-                .downcast_ref::<StringArray>()
-                .unwrap()
-                .iter()
-                .flatten()
-                .map(str::to_string)
-                .collect()
-        };
-        assert_eq!(docs, [1, 1, 4]);
-        assert_eq!(strings(&text), ["a", "b", "c"]);
-        assert_eq!(strings("title"), ["t1", "t1", "t4"]);
-        assert_eq!(read(view.table(), "nth").await, [1, 1, 2]);
-
-        // Appended documents expand incrementally; the existing rows stay.
-        source.add(chunked_batch()).execute().await.unwrap();
-        view.refresh().execute().await.unwrap();
-        assert_eq!(read(view.table(), "nth").await, [1, 1, 1, 1, 2, 2]);
-    }
-
-    async fn read(table: &Table, column: &str) -> Vec<i32> {
-        let batches = table
-            .query()
-            .select(Select::columns(&[column]))
-            .execute()
-            .await
-            .unwrap()
-            .try_collect::<Vec<_>>()
-            .await
-            .unwrap();
-        let mut values: Vec<i32> = batches
-            .iter()
-            .flat_map(|batch| {
-                batch[column]
-                    .as_any()
-                    .downcast_ref::<Int32Array>()
-                    .unwrap()
-                    .iter()
-                    .flatten()
-                    .collect::<Vec<_>>()
-            })
-            .collect();
-        values.sort();
-        values
-    }
-
-    async fn append(table: &Table, values: Vec<i32>) {
-        let batch = record_batch!(("x", Int32, values)).unwrap();
-        table.add(batch).execute().await.unwrap();
-    }
 
     #[tokio::test]
-    async fn test_first_refresh_materializes_the_view() {
-        let (conn, _) = db_with_source(vec![1, 2, 3]).await;
-        let view = doubled_view(&conn).await;
-
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Rebuild);
-        assert_eq!(result.rows_written, 3);
-        assert_eq!(read(view.table(), "twice").await, vec![2, 4, 6]);
-
-        // The watermark survives on the stored schema, not just the handle.
-        let reopened = conn.open_materialized_view("doubled").await.unwrap();
-        let again = reopened.refresh().execute().await.unwrap();
-        assert_eq!(again.mode, RefreshMode::NoOp);
-        assert_eq!(again.rows_written, 0);
-    }
-
-    #[tokio::test]
-    async fn test_filter_selects_the_source_rows() {
-        let (conn, _) = db_with_source(vec![1, 20, 3, 40]).await;
-        let view = conn
-            .create_materialized_view("big", "src")
-            .with_no_data(true)
-            .select([("x", "x")])
-            .only_if("x > 10")
+    async fn raced_first_full_refresh_lands_one_result() {
+        let directory = tempfile::tempdir().unwrap();
+        let connection = connect(directory.path().to_str().unwrap())
             .execute()
             .await
             .unwrap();
-
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.rows_written, 2);
-        assert_eq!(read(view.table(), "x").await, vec![20, 40]);
-    }
-
-    #[tokio::test]
-    async fn test_mixed_case_filter_is_canonicalized_for_lineage_and_refresh() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let batch = record_batch!(
-            ("id", Int32, [1, 2, 3]),
-            ("PartyAbbrev", Utf8, ["D", "R", "D"])
-        )
-        .unwrap();
-        conn.create_table("src", batch)
-            .write_options(crate::materialized_view::tests::stable_row_ids())
+        let source_batch = record_batch!(("x", Int32, [1, 2, 3])).unwrap();
+        connection
+            .create_table("src", source_batch.clone())
             .execute()
             .await
             .unwrap();
-        conn.create_materialized_view("democrats", "src")
-            .with_no_data(true)
-            .select([("id", "id")])
-            .only_if(r#""PartyAbbrev" = 'D'"#)
-            .execute()
-            .await
-            .unwrap();
-
-        // Reopen from schema metadata so these assertions cover the stored
-        // predicate and lineage, not only the declaration-time handle.
-        let view = conn.open_materialized_view("democrats").await.unwrap();
-        assert_eq!(
-            view.definition().filter.as_deref(),
-            Some("`PartyAbbrev` = 'D'")
-        );
-
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.rows_written, 2);
-        assert_eq!(read(view.table(), "id").await, vec![1, 3]);
-    }
-
-    /// A legacy layout whose query means what the canonical one means is
-    /// rewritten in the current layout on the next refresh, without a
-    /// rebuild: the rows it certified are the rows the query produces.
-    #[tokio::test]
-    async fn a_legacy_layout_with_the_same_meaning_is_rewritten_without_a_rebuild() {
-        let (conn, source, view) = refreshed_doubled(vec![1]).await;
-        let legacy = serde_json::json!({
-            "kind": "select",
-            "source_table": "src",
-            "projections": [
-                {"output": "x", "expression": "`x`"},
-                {"output": "twice", "expression": "x*2"},
-            ],
-            "inputs": ["x"],
-        })
-        .to_string();
-        let native = view.table().as_native().unwrap();
-        let mut dataset = native.dataset.get().await.unwrap().as_ref().clone();
-        let predicted = dataset.version().version + 1;
-        dataset
-            .update_schema_metadata([
-                (DEFINITION_META_KEY.to_string(), Some(legacy)),
-                (
-                    VIEW_VERSION_META_KEY.to_string(),
-                    Some(predicted.to_string()),
-                ),
-            ])
-            .await
-            .unwrap();
-        native.dataset.update(dataset);
-
-        append(&source, vec![2]).await;
-        let reopened = conn.open_materialized_view("doubled").await.unwrap();
-        let result = reopened.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(read(reopened.table(), "twice").await, vec![2, 4]);
-        let stored: serde_json::Value = serde_json::from_str(
-            &reopened.table().schema().await.unwrap().metadata()[DEFINITION_META_KEY],
-        )
-        .unwrap();
-        assert_eq!(
-            stored,
-            serde_json::json!({
-                "kind": "query",
-                "format": 1,
-                "query": "SELECT x, x * 2 AS twice FROM src",
-            })
-        );
-    }
-
-    #[tokio::test]
-    async fn test_legacy_raw_filter_rebuilds_and_persists_canonical_definition() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let batch = record_batch!(
-            ("id", Int32, [1, 2, 3]),
-            ("PartyAbbrev", Utf8, ["D", "R", "D"])
-        )
-        .unwrap();
-        conn.create_table("legacy_src", batch)
-            .write_options(crate::materialized_view::tests::stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-        let view = conn
-            .create_materialized_view("legacy_view", "legacy_src")
-            .with_no_data(true)
-            .select([("id", "id")])
-            .only_if(r#""PartyAbbrev" = 'X'"#)
-            .execute()
-            .await
-            .unwrap();
-        assert_eq!(view.refresh().execute().await.unwrap().rows_written, 0);
-
-        // Model a definition and up-to-date watermark written before filter
-        // canonicalization was applied to materialized views.
-        let legacy = serde_json::json!({
-            "kind": "select",
-            "source_table": "legacy_src",
-            "projections": [{"output": "id", "expression": "id"}],
-            "filter": r#""PartyAbbrev" = 'D'"#,
-            "inputs": ["id"],
-        })
-        .to_string();
-        let native = view.table().as_native().unwrap();
-        let mut dataset = native.dataset.get().await.unwrap().as_ref().clone();
-        let predicted = dataset.version().version + 1;
-        dataset
-            .update_schema_metadata([
-                (DEFINITION_META_KEY.to_string(), Some(legacy)),
-                (
-                    VIEW_VERSION_META_KEY.to_string(),
-                    Some(predicted.to_string()),
-                ),
-            ])
-            .await
-            .unwrap();
-        native.dataset.update(dataset);
-
-        let reopened = conn.open_materialized_view("legacy_view").await.unwrap();
-        let result = reopened.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Rebuild);
-        assert_eq!(result.rows_written, 2);
-        assert_eq!(read(reopened.table(), "id").await, vec![1, 3]);
-
-        // A fresh handle proves the migration was stored alongside the new
-        // watermark and therefore happens only once.
-        let migrated = conn.open_materialized_view("legacy_view").await.unwrap();
-        assert_eq!(
-            migrated.definition().filter.as_deref(),
-            Some("`PartyAbbrev` = 'D'")
-        );
-        assert_eq!(
-            migrated.refresh().execute().await.unwrap().mode,
-            RefreshMode::NoOp
-        );
-        assert_eq!(read(migrated.table(), "id").await, vec![1, 3]);
-    }
-
-    #[tokio::test]
-    async fn test_append_refreshes_incrementally() {
-        let (_conn, source, view) = refreshed_doubled(vec![1, 2]).await;
-
-        append(&source, vec![5]).await;
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(result.rows_written, 1);
-        assert_eq!(read(view.table(), "twice").await, vec![2, 4, 10]);
-    }
-
-    #[tokio::test]
-    async fn test_incremental_applies_the_filter() {
-        let (conn, source) = db_with_source(vec![1, 20]).await;
-        let view = conn
-            .create_materialized_view("big", "src")
-            .with_no_data(true)
-            .select([("x", "x")])
-            .only_if("x > 10")
-            .execute()
-            .await
-            .unwrap();
-        view.refresh().execute().await.unwrap();
-
-        append(&source, vec![3, 30]).await;
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(result.rows_written, 1);
-        assert_eq!(read(view.table(), "x").await, vec![20, 30]);
-    }
-
-    /// The watermark has to advance even when no appended row matches, or the
-    /// same fragments would be rescanned by every later refresh.
-    #[tokio::test]
-    async fn test_incremental_with_nothing_matching_advances_the_watermark() {
-        let (conn, source) = db_with_source(vec![20]).await;
-        let view = conn
-            .create_materialized_view("big", "src")
-            .with_no_data(true)
-            .select([("x", "x")])
-            .only_if("x > 10")
-            .execute()
-            .await
-            .unwrap();
-        view.refresh().execute().await.unwrap();
-
-        append(&source, vec![1, 2]).await;
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(result.rows_written, 0);
-
-        let again = view.refresh().execute().await.unwrap();
-        assert_eq!(again.mode, RefreshMode::NoOp);
-    }
-
-    /// Unlike a computed column, a view reflects source mutation: an update
-    /// rebuilds rather than going stale.
-    #[tokio::test]
-    async fn test_update_replaces_the_rows_it_changed() {
-        let (_conn, source, view) = refreshed_doubled(vec![1, 2, 3]).await;
-
-        // An update changes rows in place, so the view replaces exactly
-        // those rows and leaves the rest of what it holds alone.
-        source
-            .update()
-            .column("x", "20")
-            .only_if("x = 2")
-            .execute()
-            .await
-            .unwrap();
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(result.rows_written, 1, "only the changed row is recomputed");
-        assert_eq!(read(view.table(), "twice").await, vec![2, 6, 40]);
-    }
-
-    /// Legacy storage cannot serve the row-version columns update discovery
-    /// scans; appends stay incremental, updates rebuild rather than fail.
-    #[tokio::test]
-    async fn test_a_legacy_storage_source_is_reconciled() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let batch = record_batch!(("x", Int32, [1, 2, 3])).unwrap();
-        let source = conn
-            .create_table("legacy_src", batch)
-            .write_options(crate::table::WriteOptions {
-                lance_write_params: Some(lance::dataset::WriteParams {
-                    enable_stable_row_ids: true,
-                    data_storage_version: Some(LanceFileVersion::Legacy),
-                    ..Default::default()
-                }),
-            })
-            .execute()
-            .await
-            .unwrap();
-        let view = conn
-            .create_materialized_view("legacy_doubled", "legacy_src")
-            .with_no_data(true)
-            .select([("x", "x"), ("twice", "x * 2")])
-            .execute()
-            .await
-            .unwrap();
-        view.refresh().execute().await.unwrap();
-
-        source
-            .add(record_batch!(("x", Int32, [4])).unwrap())
-            .execute()
-            .await
-            .unwrap();
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(read(view.table(), "twice").await, vec![2, 4, 6, 8]);
-
-        source
-            .update()
-            .column("x", "20")
-            .only_if("x = 2")
-            .execute()
-            .await
-            .unwrap();
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Rebuild);
-        assert_eq!(read(view.table(), "twice").await, vec![2, 6, 8, 40]);
-    }
-
-    #[tokio::test]
-    async fn test_delete_evicts_the_view_rows_it_removed() {
-        let (_conn, source, view) = refreshed_doubled(vec![1, 2, 3]).await;
-
-        // A delete removes source rows without changing the ones that
-        // remain, so the view evicts exactly those rows and keeps the rest
-        // rather than recomputing every row it already held.
-        source.delete("x = 2").await.unwrap();
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(read(view.table(), "twice").await, vec![2, 6]);
-
-        // A delete and an append in one span: both are applied.
-        source.delete("x = 1").await.unwrap();
-        source
-            .add(record_batch!(("x", Int32, vec![4])).unwrap())
-            .execute()
-            .await
-            .unwrap();
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(read(view.table(), "twice").await, vec![6, 8]);
-    }
-
-    /// merge_insert commits an `Update`, and its by-source arm removes rows
-    /// rather than changing them, so the classifier must treat that
-    /// transaction form as a source of deletions.
-    #[tokio::test]
-    async fn test_merge_insert_by_source_delete_evicts_the_view_rows() {
-        let (_conn, source, view) = refreshed_doubled(vec![1, 2, 3]).await;
-
-        let batch = record_batch!(("x", Int32, vec![1, 3])).unwrap();
-        let reader = arrow_array::RecordBatchIterator::new(vec![Ok(batch.clone())], batch.schema());
-        let mut merge = source.merge_insert(&["x"]);
-        merge.when_not_matched_by_source_delete(None);
-        merge.execute(Box::new(reader)).await.unwrap();
-
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(read(view.table(), "twice").await, vec![2, 6]);
-    }
-
-    /// A refresh may only certify the generation it planned from. A write
-    /// that lands between planning and publication is drift the refresh did
-    /// not account for, so it aborts rather than stamp it as materialized.
-    #[tokio::test(flavor = "multi_thread")]
-    async fn test_refresh_aborts_rather_than_certify_view_drift() {
-        let _serial = DRIFT_LOCK.lock().await;
-        let (conn, source) = db_with_source(vec![1, 2, 3]).await;
-        let view = conn
-            .create_materialized_view("drifting_view", "src")
-            .with_no_data(true)
-            .select([("x", "x"), ("twice", "x * 2")])
-            .execute()
-            .await
-            .unwrap();
-        view.refresh().execute().await.unwrap();
-        let certified = source_version_of(view.table()).await;
-
-        // The source loses a row, so the next refresh plans an eviction.
-        source.delete("x = 2").await.unwrap();
-
-        let uri = view
-            .table()
-            .as_native()
-            .unwrap()
-            .dataset
-            .get()
-            .await
-            .unwrap()
-            .uri()
-            .to_string();
-        *DRIFT_TARGET.lock().unwrap() = Some(uri);
-        let refreshing = tokio::spawn(async move { view.refresh().execute().await });
-
-        // Move the view once the refresh has planned against it.
-        tokio::time::timeout(std::time::Duration::from_secs(30), DRIFT_PLANNED.notified())
-            .await
-            .expect("the refresh never reached the publication boundary");
-        let drifted = conn.open_table("drifting_view").execute().await.unwrap();
-        drifted.delete("twice = 6").await.unwrap();
-        DRIFT_RELEASED.notify_one();
-
-        // Publishing removals and additions as one change makes the drift a
-        // conflict lance itself rejects; a pure append, which touches no
-        // existing fragment, still relies on the generation check.
-        let err = refreshing.await.unwrap().unwrap_err();
-        let message = err.to_string();
-        assert!(
-            message.contains("raced this refresh") || message.contains("preempted by concurrent"),
-            "got {err:?}"
-        );
-        // The watermark still names the generation that was actually proven.
-        assert_eq!(source_version_of(&drifted).await, certified);
-    }
-
-    async fn source_version_of(table: &Table) -> Option<String> {
-        table
-            .schema()
-            .await
-            .unwrap()
-            .metadata()
-            .get(SOURCE_VERSION_META_KEY)
-            .cloned()
-    }
-
-    /// A transaction file carries placeholder ids for the fragments it
-    /// creates. Into an empty source those collide with the ids the commit
-    /// assigns, so treating them as already-materialized drops the very
-    /// first rows while the watermark still advances past them.
-    #[tokio::test]
-    async fn test_merge_into_an_empty_source_is_materialized() {
-        let (_conn, source, view) = refreshed_doubled(vec![]).await;
-        assert_eq!(read(view.table(), "twice").await, Vec::<i32>::new());
-
-        let batch = record_batch!(("x", Int32, vec![1, 2])).unwrap();
-        let reader = arrow_array::RecordBatchIterator::new(vec![Ok(batch.clone())], batch.schema());
-        let mut merge = source.merge_insert(&["x"]);
-        merge
-            .when_matched_update_all(None)
-            .when_not_matched_insert_all();
-        merge.execute(Box::new(reader)).await.unwrap();
-
-        view.refresh().execute().await.unwrap();
-        assert_eq!(read(view.table(), "twice").await, vec![2, 4]);
-        // A second refresh must not double them either.
-        view.refresh().execute().await.unwrap();
-        assert_eq!(read(view.table(), "twice").await, vec![2, 4]);
-    }
-
-    /// A refresh publishes what it removes and what it adds as one change,
-    /// so an update never exposes the view without the rows it is replacing.
-    #[tokio::test(flavor = "multi_thread")]
-    async fn test_an_update_is_never_visible_as_a_gap() {
-        let _serial = DRIFT_LOCK.lock().await;
-        let (conn, source) = db_with_source(vec![1, 2, 3]).await;
-        let view = conn
-            .create_materialized_view("atomic_view", "src")
-            .with_no_data(true)
-            .select([("x", "x"), ("twice", "x * 2")])
-            .execute()
-            .await
-            .unwrap();
-        view.refresh().execute().await.unwrap();
-
-        source
-            .update()
-            .column("x", "x + 10")
-            .execute()
-            .await
-            .unwrap();
-
-        let uri = view
-            .table()
-            .as_native()
-            .unwrap()
-            .dataset
-            .get()
-            .await
-            .unwrap()
-            .uri()
-            .to_string();
-        *DRIFT_TARGET.lock().unwrap() = Some(uri);
-        let refreshing = tokio::spawn(async move { view.refresh().execute().await });
-
-        // Read the view while the refresh is staged but not yet published.
-        tokio::time::timeout(std::time::Duration::from_secs(30), DRIFT_PLANNED.notified())
-            .await
-            .expect("the refresh never reached the publication boundary");
-        let midway = conn.open_table("atomic_view").execute().await.unwrap();
-        assert_eq!(
-            read(&midway, "twice").await,
-            vec![2, 4, 6],
-            "the pre-refresh rows must still be there in full"
-        );
-        DRIFT_RELEASED.notify_one();
-
-        refreshing.await.unwrap().unwrap();
-        let after = conn.open_table("atomic_view").execute().await.unwrap();
-        assert_eq!(read(&after, "twice").await, vec![22, 24, 26]);
-    }
-
-    async fn provenance_by_x(table: &Table) -> HashMap<i32, u64> {
-        let batches = table
-            .query()
-            .select(Select::columns(&["x", SOURCE_ROW_ID_COLUMN]))
-            .execute()
-            .await
-            .unwrap()
-            .try_collect::<Vec<_>>()
-            .await
-            .unwrap();
-        batches
-            .iter()
-            .flat_map(|batch| {
-                let xs = batch["x"].as_any().downcast_ref::<Int32Array>().unwrap();
-                let ids = batch[SOURCE_ROW_ID_COLUMN].as_primitive::<UInt64Type>();
-                (0..batch.num_rows())
-                    .map(|i| (xs.value(i), ids.value(i)))
-                    .collect::<Vec<_>>()
-            })
-            .collect()
-    }
-
-    /// One delta batch is as large as a fragment's deletions, so it arrives
-    /// well past a chunk: it has to be drained a chunk at a time without ever
-    /// holding the batch, and the passes' deletion vectors have to accumulate
-    /// rather than replace each other.
-    #[tokio::test]
-    async fn test_a_chunked_eviction_removes_every_row_it_names() {
-        let (_conn, _, view) = refreshed_doubled(vec![1, 2, 3, 4, 5, 6]).await;
-
-        let native = view.table().as_native().unwrap();
-        let view_ds = native.dataset.get().await.unwrap().as_ref().clone();
-        let provenance = provenance_by_x(view.table()).await;
-
-        let mut eviction = Eviction::new(&view_ds, 2);
-        let batch = UInt64Array::from_iter_values([1, 2, 3, 4].map(|x| provenance[&x]));
-        eviction.push(&batch).await.unwrap();
-        assert_eq!(
-            eviction.peak, 2,
-            "a batch past the chunk was buffered whole"
-        );
-        let staged = eviction.finish().await.unwrap();
-        assert!(staged.is_some(), "four ids over a chunk of two stage twice");
-        publish(&view_ds, staged, Vec::new(), None, None)
-            .await
-            .unwrap();
-        native.dataset.reload().await.unwrap();
-
-        assert_eq!(read(view.table(), "x").await, vec![5, 6]);
-    }
-
-    /// Two first rebuilds materialize the same source rows; the loser must
-    /// key-conflict at commit and land nothing, or the view doubles.
-    #[tokio::test]
-    async fn test_a_raced_first_rebuild_lands_nothing() {
-        let _guard = DRIFT_LOCK.lock().await;
-        let (conn, _) = db_with_source(vec![1, 2, 3]).await;
-        let view = conn
-            .create_materialized_view("raced_rebuild", "src")
-            .with_no_data(true)
-            .select([("x", "x"), ("twice", "x * 2")])
-            .execute()
-            .await
-            .unwrap();
-        let native = view.table().as_native().unwrap();
-        let view_ds = native.dataset.get().await.unwrap().as_ref().clone();
-        let uri = view_ds.uri().to_string();
-        *DRIFT_TARGET.lock().unwrap() = Some(uri);
-        let racing = tokio::spawn(async move { view.refresh().execute().await });
-        tokio::time::timeout(std::time::Duration::from_secs(30), DRIFT_PLANNED.notified())
-            .await
-            .expect("the rebuild never reached the publication boundary");
-
-        // The other process's first rebuild, reduced to its commit. Its
-        // source rows are DISJOINT from ours -- the sentinel alone must make
-        // the two rebuilds conflict.
-        let batch = record_batch!(
-            ("x", Int32, [8, 9]),
-            ("twice", Int32, [16, 18]),
-            ("__source_row_id", UInt64, [7u64, 8])
-        )
-        .unwrap();
-        let schema = Arc::new(ArrowSchema::from(view_ds.schema()));
-        let batch = RecordBatch::try_new(
-            schema.clone(),
-            schema
-                .fields()
-                .iter()
-                .map(|f| batch.column_by_name(f.name()).unwrap().clone())
-                .collect(),
-        )
-        .unwrap();
-        let stream: SendableRecordBatchStream = Box::pin(RecordBatchStreamAdapter::new(
-            schema.clone(),
-            futures::stream::iter([Ok(batch)]),
-        ));
-        let keys = Arc::new(StdMutex::new(KeyExistenceFilterBuilder::new(vec![
-            source_row_id_field_id(&view_ds).unwrap(),
-        ])));
-        let stream = collect_source_row_ids(stream, keys.clone(), Arc::new(AtomicU64::new(0)));
-        let write_txn = InsertBuilder::new(WriteDestination::Dataset(Arc::new(view_ds.clone())))
-            .with_params(&WriteParams {
-                mode: WriteMode::Append,
-                ..Default::default()
-            })
-            .execute_uncommitted_stream(stream)
-            .await
-            .unwrap();
-        let Operation::Append { fragments } = write_txn.operation else {
-            panic!("expected an append");
-        };
-        let filter = {
-            let mut keys = keys.lock().unwrap();
-            keys.insert(KeyValue::UInt64(super::REFRESH_TOKEN_ID))
-                .unwrap();
-            keys.build()
-        };
-        CommitBuilder::new(WriteDestination::Dataset(Arc::new(view_ds.clone())))
-            .execute(Transaction::new(
-                view_ds.version().version,
-                Operation::Update {
-                    removed_fragment_ids: Vec::new(),
-                    updated_fragments: Vec::new(),
-                    new_fragments: fragments,
-                    fields_modified: Vec::new(),
-                    compacted_sstables: Vec::new(),
-                    fields_for_preserving_frag_bitmap: Vec::new(),
-                    update_mode: None,
-                    inserted_rows_filter: Some(filter),
-                    updated_fragment_offsets: None,
-                },
-                None,
-            ))
-            .await
-            .unwrap();
-        DRIFT_RELEASED.notify_one();
-
-        let err = racing.await.unwrap().unwrap_err();
-        // The loser lands nothing: only the winner's rows remain.
-        let raced = conn.open_table("raced_rebuild").execute().await.unwrap();
-        assert_eq!(
-            read(&raced, "twice").await,
-            vec![16, 18],
-            "the losing rebuild must not union with the winner ({err})"
-        );
-    }
-
-    /// An incremental refresh racing any other refresh must land nothing,
-    /// even when their planned rows are disjoint: the shared token makes the
-    /// commits conflict.
-    #[tokio::test]
-    async fn test_a_raced_incremental_lands_nothing() {
-        let _guard = DRIFT_LOCK.lock().await;
-        let (conn, source) = db_with_source(vec![1, 2, 3]).await;
-        let view = conn
-            .create_materialized_view("raced_incremental", "src")
-            .with_no_data(true)
-            .select([("x", "x"), ("twice", "x * 2")])
-            .execute()
-            .await
-            .unwrap();
-        view.refresh().execute().await.unwrap();
-        append(&source, vec![4]).await;
-
-        let native = view.table().as_native().unwrap();
-        native.dataset.reload().await.unwrap();
-        let view_ds = native.dataset.get().await.unwrap().as_ref().clone();
-        *DRIFT_TARGET.lock().unwrap() = Some(view_ds.uri().to_string());
-        let racing = tokio::spawn(async move { view.refresh().execute().await });
-        tokio::time::timeout(std::time::Duration::from_secs(30), DRIFT_PLANNED.notified())
-            .await
-            .expect("the refresh never reached the publication boundary");
-
-        // The concurrent refresh, reduced to its commit: disjoint rows, the
-        // shared token alone must collide.
-        let batch = record_batch!(
-            ("x", Int32, [9]),
-            ("twice", Int32, [18]),
-            ("__source_row_id", UInt64, [8u64])
-        )
-        .unwrap();
-        let schema = Arc::new(ArrowSchema::from(view_ds.schema()));
-        let batch = RecordBatch::try_new(
-            schema.clone(),
-            schema
-                .fields()
-                .iter()
-                .map(|f| batch.column_by_name(f.name()).unwrap().clone())
-                .collect(),
-        )
-        .unwrap();
-        let stream: SendableRecordBatchStream = Box::pin(RecordBatchStreamAdapter::new(
-            schema.clone(),
-            futures::stream::iter([Ok(batch)]),
-        ));
-        let keys = empty_keys(&view_ds).unwrap();
-        let stream = collect_source_row_ids(stream, keys.clone(), Arc::new(AtomicU64::new(0)));
-        let write_txn = InsertBuilder::new(WriteDestination::Dataset(Arc::new(view_ds.clone())))
-            .with_params(&WriteParams {
-                mode: WriteMode::Append,
-                ..Default::default()
-            })
-            .execute_uncommitted_stream(stream)
-            .await
-            .unwrap();
-        let Operation::Append { fragments } = write_txn.operation else {
-            panic!("expected an append");
-        };
-        let filter = refresh_filter(&keys).unwrap();
-        CommitBuilder::new(WriteDestination::Dataset(Arc::new(view_ds.clone())))
-            .execute(Transaction::new(
-                view_ds.version().version,
-                Operation::Update {
-                    removed_fragment_ids: Vec::new(),
-                    updated_fragments: Vec::new(),
-                    new_fragments: fragments,
-                    fields_modified: Vec::new(),
-                    compacted_sstables: Vec::new(),
-                    fields_for_preserving_frag_bitmap: Vec::new(),
-                    update_mode: None,
-                    inserted_rows_filter: Some(filter),
-                    updated_fragment_offsets: None,
-                },
-                None,
-            ))
-            .await
-            .unwrap();
-        DRIFT_RELEASED.notify_one();
-
-        let err = racing.await.unwrap().unwrap_err();
-        let raced = conn
-            .open_table("raced_incremental")
-            .execute()
-            .await
-            .unwrap();
-        assert_eq!(
-            read(&raced, "twice").await,
-            vec![2, 4, 6, 18],
-            "the losing increment must not land its rows ({err})"
-        );
-    }
-
-    /// Past the eviction cap the refresh falls back to the streamed rebuild,
-    /// and the result is identical either way.
-    #[tokio::test]
-    async fn test_oversized_delta_falls_back_to_rebuild() {
-        let (conn, source) = db_with_source((1..=20).collect()).await;
-        let view = doubled_view(&conn).await;
-        view.refresh().execute().await.unwrap();
-
-        source.delete("x <= 10").await.unwrap();
-        *tests::EVICTION_CAP.lock().unwrap() = Some(4);
-        let result = view.refresh().execute().await;
-        *tests::EVICTION_CAP.lock().unwrap() = None;
-        let result = result.unwrap();
-        assert_eq!(
-            result.mode,
-            RefreshMode::Rebuild,
-            "ten evictions, cap of four"
-        );
-        assert_eq!(read(view.table(), "x").await, (11..=20).collect::<Vec<_>>());
-
-        // Under the cap the same shape stays incremental.
-        source.delete("x = 11").await.unwrap();
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(read(view.table(), "x").await, (12..=20).collect::<Vec<_>>());
-    }
-
-    /// A cap of zero is a view that holds nothing, not a view without a cap.
-    #[tokio::test]
-    async fn test_zero_limit_holds_no_rows() {
-        let (conn, source) = db_with_source(vec![1, 2, 3]).await;
-        let view = conn
-            .create_materialized_view("empty", "src")
-            .with_no_data(true)
-            .select([("x", "x")])
-            .limit(0)
-            .execute()
-            .await
-            .unwrap();
-
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.rows_written, 0);
-        assert_eq!(view.table().count_rows(None).await.unwrap(), 0);
-
-        // Still nothing after the source grows, and the watermark advances.
-        append(&source, vec![4]).await;
-        view.refresh().execute().await.unwrap();
-        assert_eq!(view.table().count_rows(None).await.unwrap(), 0);
-        assert_eq!(
-            view.refresh().execute().await.unwrap().mode,
-            RefreshMode::NoOp
-        );
-    }
-
-    /// An update rewrites a whole fragment. When it lands on one appended
-    /// since the watermark, that fragment also holds rows the update never
-    /// touched -- rows the recompute does not cover and the append set no
-    /// longer reaches.
-    #[tokio::test]
-    async fn test_update_touching_a_new_fragment_keeps_its_untouched_rows() {
-        let (_conn, source, view) = refreshed_doubled(vec![1, 2]).await;
-
-        // One fragment, appended after the watermark, holding both a row the
-        // update will change and a row it will not.
-        append(&source, vec![3, 40]).await;
-        source
-            .update()
-            .column("x", "99")
-            .only_if("x = 3")
-            .execute()
-            .await
-            .unwrap();
-
-        view.refresh().execute().await.unwrap();
-        assert_eq!(
-            read(view.table(), "twice").await,
-            vec![2, 4, 80, 198],
-            "a row appended into the updated fragment went missing"
-        );
-    }
-
-    async fn compact(source: &Table) {
-        source
-            .optimize(OptimizeAction::Compact {
-                options: CompactionOptions::default(),
-                remap_options: None,
-            })
-            .await
-            .unwrap();
-    }
-
-    /// A compaction rearranges rows without changing them, so it costs the
-    /// view nothing: the watermark advances and no row is recomputed.
-    #[tokio::test]
-    async fn test_compaction_alone_refreshes_incrementally() {
-        let (_conn, source, view) = refreshed_doubled(vec![1, 2]).await;
-        append(&source, vec![3]).await;
-        view.refresh().execute().await.unwrap();
-
-        compact(&source).await;
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(result.rows_written, 0);
-        assert_eq!(read(view.table(), "twice").await, vec![2, 4, 6]);
-        assert_eq!(
-            view.refresh().execute().await.unwrap().mode,
-            RefreshMode::NoOp
-        );
-    }
-
-    /// Rows appended after a compaction are separable through the transaction
-    /// log: only the appended fragments are computed.
-    #[tokio::test]
-    async fn test_append_after_compaction_stays_incremental() {
-        let (_conn, source, view) = refreshed_doubled(vec![1]).await;
-        append(&source, vec![2]).await;
-        view.refresh().execute().await.unwrap();
-
-        compact(&source).await;
-        append(&source, vec![3]).await;
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(result.rows_written, 1);
-        assert_eq!(read(view.table(), "twice").await, vec![2, 4, 6]);
-    }
-
-    /// An append swallowed by a later compaction cannot be told apart from
-    /// the rows the view already holds, so the refresh rebuilds -- once --
-    /// rather than duplicate or drop.
-    #[tokio::test]
-    async fn test_append_folded_into_compaction_rebuilds() {
-        let (_conn, source, view) = refreshed_doubled(vec![1]).await;
-
-        append(&source, vec![2]).await;
-        compact(&source).await;
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Rebuild);
-        assert_eq!(read(view.table(), "twice").await, vec![2, 4]);
-    }
-
-    /// The create-time gate holds across a drop-and-recreate of the source
-    /// under the same name.
-    #[tokio::test]
-    async fn test_refresh_refuses_a_recreated_source_without_stable_row_ids() {
-        let (conn, _, view) = refreshed_doubled(vec![1]).await;
-
-        conn.drop_table("src", &[]).await.unwrap();
-        let batch = record_batch!(("x", Int32, [9])).unwrap();
-        conn.create_table("src", batch).execute().await.unwrap();
-
-        let err = view.refresh().execute().await.unwrap_err();
-        assert!(
-            matches!(err, Error::InvalidInput { message } if message.contains("stable row ids"))
-        );
-    }
-
-    /// A change to a column the view does not read is not a reason to
-    /// rebuild: the exact signature check is scoped to the view's inputs.
-    #[tokio::test]
-    async fn test_unrelated_column_change_does_not_rebuild() {
-        let (_conn, source, view) = refreshed_doubled(vec![1, 2]).await;
-
-        source
-            .add_columns()
-            .transform(NewColumnTransform::AllNulls(Arc::new(ArrowSchema::new(
-                vec![arrow_schema::Field::new(
-                    "unrelated",
-                    arrow_schema::DataType::Int32,
-                    true,
-                )],
-            ))))
-            .execute()
-            .await
-            .unwrap();
-
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(result.rows_written, 0);
-        assert_eq!(read(view.table(), "twice").await, vec![2, 4]);
-    }
-
-    #[tokio::test]
-    async fn test_full_forces_a_rebuild() {
-        let (_conn, source, view) = refreshed_doubled(vec![1]).await;
-
-        append(&source, vec![2]).await;
-        let result = view.refresh().full(true).execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Rebuild);
-        assert_eq!(result.rows_written, 2);
-        assert_eq!(read(view.table(), "twice").await, vec![2, 4]);
-    }
-
-    /// A cap and incremental reconciliation do not compose: rows skipped at
-    /// the cap fall behind the watermark, so room freed later cannot be
-    /// refilled from any delta. A capped view rebuilds instead, which its
-    /// own cap keeps cheap.
-    #[tokio::test]
-    async fn test_limited_view_rebuilds_rather_than_reconcile() {
-        let (conn, source) = db_with_source(vec![1, 2]).await;
-        let view = conn
-            .create_materialized_view("capped", "src")
-            .with_no_data(true)
-            .select([("x", "x")])
-            .limit(2)
-            .execute()
-            .await
-            .unwrap();
-        view.refresh().execute().await.unwrap();
-        assert_eq!(read(view.table(), "x").await, vec![1, 2]);
-
-        append(&source, vec![3, 4]).await;
-        source
-            .update()
-            .column("x", "11")
-            .only_if("x = 1")
-            .execute()
-            .await
-            .unwrap();
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Rebuild);
-        let held = read(view.table(), "x").await;
-        assert_eq!(held.len(), 2, "the cap holds: {held:?}");
-        let selectable = read(&source, "x").await;
-        assert!(
-            held.iter().all(|x| selectable.contains(x)),
-            "{held:?} is not a subset of {selectable:?}"
-        );
-    }
-
-    #[tokio::test]
-    async fn test_limit_caps_the_view() {
-        let (conn, source) = db_with_source(vec![1, 2, 3]).await;
-        let view = conn
-            .create_materialized_view("capped", "src")
-            .with_no_data(true)
-            .select([("x", "x")])
-            .limit(4)
-            .execute()
-            .await
-            .unwrap();
-
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.rows_written, 3);
-
-        // The cap counts already-held rows, so only one appended row lands.
-        append(&source, vec![4, 5, 6]).await;
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(result.rows_written, 1);
-        assert_eq!(view.table().count_rows(None).await.unwrap(), 4);
-
-        // At the cap, later appends only move the watermark.
-        append(&source, vec![7]).await;
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.rows_written, 0);
-        assert_eq!(
-            view.refresh().execute().await.unwrap().mode,
-            RefreshMode::NoOp
-        );
-    }
-
-    /// The whole point of the fragment-swap commit: a rebuild must never
-    /// leave the view without its index definitions.
-    #[tokio::test]
-    async fn test_rebuild_retains_indexes() {
-        let (_conn, source, view) = refreshed_doubled(vec![1, 2, 3]).await;
-
-        view.table()
-            .create_index(&["twice"], Index::BTree(BTreeIndexBuilder::default()))
-            .execute()
-            .await
-            .unwrap();
-        assert_eq!(view.table().list_indices().await.unwrap().len(), 1);
-
-        source
-            .update()
-            .column("x", "x + 10")
-            .execute()
-            .await
-            .unwrap();
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Rebuild);
-        assert_eq!(view.table().list_indices().await.unwrap().len(), 1);
-        assert_eq!(read(view.table(), "twice").await, vec![22, 24, 26]);
-
-        // The swapped-in rows are reachable through an indexed query.
-        let batches = view
-            .table()
-            .query()
-            .only_if("twice = 24")
-            .execute()
-            .await
-            .unwrap()
-            .try_collect::<Vec<_>>()
-            .await
-            .unwrap();
-        assert_eq!(batches.iter().map(|b| b.num_rows()).sum::<usize>(), 1);
-    }
-
-    #[tokio::test]
-    async fn test_rebuild_of_an_empty_result_is_an_empty_view() {
-        let (conn, _) = db_with_source(vec![1, 2]).await;
-        let view = conn
-            .create_materialized_view("none", "src")
-            .with_no_data(true)
-            .select([("x", "x")])
-            .only_if("x > 100")
-            .execute()
-            .await
-            .unwrap();
-
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Rebuild);
-        assert_eq!(result.rows_written, 0);
-        assert_eq!(view.table().count_rows(None).await.unwrap(), 0);
-        assert_eq!(
-            view.refresh().execute().await.unwrap().mode,
-            RefreshMode::NoOp
-        );
-    }
-
-    /// Provenance: every view row records the source row that produced it.
-    #[tokio::test]
-    async fn test_source_row_ids_are_recorded() {
-        let (_conn, _, view) = refreshed_doubled(vec![1, 2, 3]).await;
-
-        let batches = view
-            .table()
-            .query()
-            .select(Select::columns(&[SOURCE_ROW_ID_COLUMN]))
-            .execute()
-            .await
-            .unwrap()
-            .try_collect::<Vec<_>>()
-            .await
-            .unwrap();
-        let total: usize = batches.iter().map(|b| b.num_rows()).sum();
-        assert_eq!(total, 3);
-        for batch in &batches {
-            assert_eq!(batch[SOURCE_ROW_ID_COLUMN].null_count(), 0);
-        }
-    }
-
-    #[tokio::test]
-    async fn test_dropping_a_source_input_fails_the_refresh() {
-        let (conn, source) = db_with_source(vec![1]).await;
-        let view = conn
-            .create_materialized_view("v", "src")
-            .with_no_data(true)
-            .select([("twice", "x * 2")])
-            .execute()
-            .await
-            .unwrap();
-        view.refresh().execute().await.unwrap();
-
-        append(&source, vec![2]).await;
-        source
-            .add_columns()
-            .transform(NewColumnTransform::AllNulls(Arc::new(ArrowSchema::new(
-                vec![arrow_schema::Field::new(
-                    "y",
-                    arrow_schema::DataType::Int32,
-                    true,
-                )],
-            ))))
-            .execute()
-            .await
-            .unwrap();
-        source.drop_columns(&["x"]).await.unwrap();
-
-        let err = view.refresh().execute().await.unwrap_err();
-        assert!(
-            matches!(&err, Error::Schema { message }
-                if message.contains("dropped or renamed") && message.contains("No field named x")),
-            "{err:?}"
-        );
-    }
-
-    /// A pinned refresh materializes the source as of `version`; catching up
-    /// to the appends beyond it stays incremental.
-    #[tokio::test]
-    async fn test_pinned_refresh_and_catch_up() {
-        let (conn, source) = db_with_source(vec![1]).await;
-        let view = doubled_view(&conn).await;
-        let pinned = source.version().await.unwrap();
-
-        append(&source, vec![2]).await;
-        let result = view
-            .refresh()
-            .source_version(pinned)
-            .execute()
-            .await
-            .unwrap();
-        assert_eq!(result.source_version, pinned);
-        assert_eq!(read(view.table(), "twice").await, vec![2]);
-
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(read(view.table(), "twice").await, vec![2, 4]);
-    }
-
-    /// Views chain: a view's stable row ids and provenance column make it a
-    /// source like any other, and the default projection takes its declared
-    /// columns without copying its provenance.
-    #[tokio::test]
-    async fn test_a_view_can_source_another_view() {
-        let (conn, source) = db_with_source(vec![1, 2, 30]).await;
-        let first = doubled_view(&conn).await;
-        first.refresh().execute().await.unwrap();
-
-        let second = conn
-            .create_materialized_view("second", "doubled")
-            .with_no_data(true)
-            .only_if("twice > 10")
-            .execute()
-            .await
-            .unwrap();
-        assert!(
-            second
-                .definition()
-                .projections
-                .iter()
-                .all(|p| p.output != SOURCE_ROW_ID_COLUMN)
-        );
-        let result = second.refresh().execute().await.unwrap();
-        assert_eq!(result.rows_written, 1);
-        assert_eq!(read(second.table(), "twice").await, vec![60]);
-
-        append(&source, vec![50]).await;
-        first.refresh().execute().await.unwrap();
-        let result = second.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(read(second.table(), "twice").await, vec![60, 100]);
-    }
-
-    /// The watermark speaks only for the state a refresh left behind: a
-    /// direct write to the view is drift, and the next refresh rebuilds
-    /// rather than preserving it as current.
-    #[tokio::test]
-    async fn test_direct_view_mutation_forces_a_rebuild() {
-        let (_conn, _, view) = refreshed_doubled(vec![1, 2]).await;
-
-        view.table().delete("x = 1").await.unwrap();
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Rebuild);
-        assert_eq!(read(view.table(), "twice").await, vec![2, 4]);
-        assert_eq!(
-            view.refresh().execute().await.unwrap().mode,
-            RefreshMode::NoOp
-        );
-    }
-
-    /// A dropped and recreated source reuses version numbers but never their
-    /// timestamps; the watermark must not vouch for the replacement's rows.
-    #[tokio::test]
-    async fn test_source_recreation_forces_a_rebuild() {
-        let (conn, _, view) = refreshed_doubled(vec![1]).await;
-
-        conn.drop_table("src", &[]).await.unwrap();
-        let batch = record_batch!(("x", Int32, [7])).unwrap();
-        conn.create_table("src", batch)
-            .write_options(crate::materialized_view::tests::stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Rebuild);
-        assert_eq!(read(view.table(), "twice").await, vec![14]);
-    }
-
-    /// A refresh bound to an incarnation refuses a view dropped and recreated
-    /// since, even under the same name and definition; the recreated view's
-    /// own token is accepted, and the token survives a refresh's stamp.
-    #[tokio::test]
-    async fn test_refresh_refuses_a_recreated_view_incarnation() {
-        let (conn, _, view) = refreshed_doubled(vec![1]).await;
-        let token = view.incarnation().unwrap().to_string();
-        view.refresh()
-            .expect_incarnation(&token)
-            .execute()
-            .await
-            .unwrap();
-        let reopened = conn.open_materialized_view("doubled").await.unwrap();
-        assert_eq!(reopened.incarnation(), Some(token.as_str()));
-
-        conn.drop_table("doubled", &[]).await.unwrap();
-        let recreated = doubled_view(&conn).await;
-        assert_ne!(recreated.incarnation(), Some(token.as_str()));
-
-        let err = recreated
-            .refresh()
-            .expect_incarnation(&token)
-            .execute()
-            .await
-            .unwrap_err();
-        assert!(err.to_string().contains("dropped and recreated"), "{err}");
-        assert_eq!(read(recreated.table(), "twice").await, Vec::<i32>::new());
-
-        recreated
-            .refresh()
-            .expect_incarnation(recreated.incarnation().unwrap())
-            .execute()
-            .await
-            .unwrap();
-        assert_eq!(read(recreated.table(), "twice").await, vec![2]);
-    }
-
-    /// A cloned declaration creates two physical tables; each gets its own
-    /// token.
-    #[tokio::test]
-    async fn test_cloned_declaration_mints_a_fresh_incarnation_per_create() {
-        let (conn, source) = db_with_source(vec![1]).await;
-        let prepared = crate::materialized_view::prepare_declaration(
-            &source,
-            Some(&[("x".into(), "x".into()), ("twice".into(), "x * 2".into())]),
-            None,
-            None,
-        )
-        .await
-        .unwrap();
-        let replacement = prepared.clone();
-        let first = prepared.create("cloned").await.unwrap();
-        let first_token = first.incarnation().unwrap().to_string();
-
-        conn.drop_table("cloned", &[]).await.unwrap();
-        let second = replacement.create("cloned").await.unwrap();
-        assert_ne!(second.incarnation(), Some(first_token.as_str()));
-    }
-
-    /// A recreation that lands after planning but before publication is
-    /// caught by the pre-commit read: the stale refresh fails and the
-    /// replacement stays empty under its own token.
-    #[tokio::test(flavor = "multi_thread")]
-    async fn test_bound_refresh_cannot_publish_into_a_raced_recreation() {
-        let _serial = DRIFT_LOCK.lock().await;
-        let (conn, _) = db_with_source(vec![1]).await;
-        let view = doubled_view(&conn).await;
-        let token = view.incarnation().unwrap().to_string();
-        let uri = view
-            .table()
-            .as_native()
-            .unwrap()
-            .dataset
-            .get()
-            .await
-            .unwrap()
-            .uri()
-            .to_string();
-
-        *DRIFT_TARGET.lock().unwrap() = Some(uri);
-        let refreshing =
-            tokio::spawn(async move { view.refresh().expect_incarnation(token).execute().await });
-        tokio::time::timeout(std::time::Duration::from_secs(30), DRIFT_PLANNED.notified())
-            .await
-            .expect("refresh never reached publication");
-
-        conn.drop_table("doubled", &[]).await.unwrap();
-        let replacement = doubled_view(&conn).await;
-        let replacement_token = replacement.incarnation().unwrap().to_string();
-        DRIFT_RELEASED.notify_one();
-
-        let result = refreshing.await.unwrap();
-        assert!(result.is_err(), "the stale refresh unexpectedly succeeded");
-        let reopened = conn.open_materialized_view("doubled").await.unwrap();
-        assert_eq!(reopened.incarnation(), Some(replacement_token.as_str()));
-        assert_eq!(read(reopened.table(), "twice").await, Vec::<i32>::new());
-    }
-
-    /// Replacing the schema metadata wholesale drops the token. A refresh
-    /// bound to the old token is refused for that reason, not as a
-    /// recreation; an unbound refresh mints the view a fresh one.
-    #[tokio::test]
-    async fn test_a_view_whose_metadata_was_replaced_starts_a_new_incarnation() {
-        let (conn, _, view) = refreshed_doubled(vec![1]).await;
-        let token = view.incarnation().unwrap().to_string();
-        let mut metadata = HashMap::new();
-        metadata.insert(
-            crate::materialized_view::DEFINITION_META_KEY.to_string(),
-            crate::materialized_view::definition_to_metadata(view.definition()).unwrap(),
-        );
-        view.table()
-            .as_native()
-            .unwrap()
-            .replace_schema_metadata(metadata)
-            .await
-            .unwrap();
-        assert_eq!(
-            conn.open_materialized_view("doubled")
-                .await
-                .unwrap()
-                .incarnation(),
-            None
-        );
-
-        let err = view
-            .refresh()
-            .expect_incarnation(&token)
-            .execute()
-            .await
-            .unwrap_err();
-        assert!(err.to_string().contains("no incarnation token"), "{err}");
-
-        view.refresh().execute().await.unwrap();
-        let reopened = conn.open_materialized_view("doubled").await.unwrap();
-        assert!(reopened.incarnation().is_some());
-        assert_ne!(reopened.incarnation(), Some(token.as_str()));
-    }
-
-    /// In-process refreshes of one view serialize: the loser of the race
-    /// observes the winner's watermark instead of appending the same rows.
-    #[tokio::test(flavor = "multi_thread")]
-    async fn test_concurrent_refreshes_do_not_duplicate() {
-        let (_conn, source, view) = refreshed_doubled(vec![1]).await;
-
-        append(&source, vec![2, 3]).await;
-        let (a, b) = tokio::join!(view.refresh().execute(), view.refresh().execute());
-        let (a, b) = (a.unwrap(), b.unwrap());
-        assert_eq!(read(view.table(), "twice").await, vec![2, 4, 6]);
-        let modes = [a.mode, b.mode];
-        assert!(modes.contains(&RefreshMode::Incremental));
-        assert!(modes.contains(&RefreshMode::NoOp));
-    }
-
-    /// A second handle's lazy cache must not defeat the lock: after another
-    /// handle's refresh commits, the stale handle plans from the reloaded
-    /// state and no-ops instead of appending the same fragments again.
-    #[tokio::test]
-    async fn test_a_second_handle_does_not_double_append() {
-        let (conn, source, view) = refreshed_doubled(vec![1, 2, 3]).await;
-        let stale = conn.open_materialized_view("doubled").await.unwrap();
-
-        append(&source, vec![4]).await;
-        view.refresh().execute().await.unwrap();
-
-        let result = stale.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::NoOp);
-        assert_eq!(read(view.table(), "twice").await, vec![2, 4, 6, 8]);
-    }
-
-    /// A commit racing between a refresh's data commit and its stamp must
-    /// not be certified as the refresh's generation: the stamp aborts, and
-    /// the next refresh rebuilds from the drifted state.
-    #[tokio::test]
-    async fn test_stamp_aborts_on_a_racing_commit() {
-        let (_conn, _, view) = refreshed_doubled(vec![1, 2]).await;
-
-        let view_native = view.table().as_native().unwrap();
-        let stale = view_native.dataset.get().await.unwrap().as_ref().clone();
-        view.table().delete("x = 1").await.unwrap();
-
-        let err = stamp_watermark(view_native, stale, 99, 99, None, None).await;
-        assert!(err.is_err());
-
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Rebuild);
-        assert_eq!(read(view.table(), "twice").await, vec![2, 4]);
-    }
-
-    /// What refresh executes and what it stamps must be one generation: a
-    /// replaced definition wins over whatever a stale handle cached.
-    #[tokio::test]
-    async fn test_refresh_uses_the_latest_persisted_definition() {
-        let (_conn, _, view) = refreshed_doubled(vec![1, 2]).await;
-
-        let replacement = crate::materialized_view::MaterializedViewDefinition {
-            vector_source: None,
-            source_table: "src".into(),
-            source_namespace: Vec::new(),
-            projections: vec![
-                crate::materialized_view::ViewProjection {
-                    output: "x".into(),
-                    expression: "x".into(),
-                },
-                crate::materialized_view::ViewProjection {
-                    output: "twice".into(),
-                    expression: "x * 3".into(),
-                },
-            ],
-            filter: None,
-            group_by: Vec::new(),
-            limit: None,
-            lateral: None,
-        };
-        let mut metadata = HashMap::new();
-        metadata.insert(
-            crate::materialized_view::DEFINITION_META_KEY.to_string(),
-            crate::materialized_view::definition_to_metadata(&replacement).unwrap(),
-        );
-        view.table()
-            .as_native()
-            .unwrap()
-            .replace_schema_metadata(metadata)
-            .await
-            .unwrap();
-
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Rebuild);
-        assert_eq!(read(view.table(), "twice").await, vec![3, 6]);
-    }
-
-    /// A persisted definition that does not produce this view's schema must
-    /// not refresh at all, let alone be certified.
-    #[tokio::test]
-    async fn test_definition_view_schema_mismatch_is_refused() {
-        let (_conn, _, view) = refreshed_doubled(vec![1, 2]).await;
-
-        let narrower = crate::materialized_view::MaterializedViewDefinition {
-            vector_source: None,
-            source_table: "src".into(),
-            source_namespace: Vec::new(),
-            projections: vec![crate::materialized_view::ViewProjection {
-                output: "x".into(),
-                expression: "x".into(),
-            }],
-            filter: None,
-            group_by: Vec::new(),
-            limit: None,
-            lateral: None,
-        };
-        let mut metadata = HashMap::new();
-        metadata.insert(
-            crate::materialized_view::DEFINITION_META_KEY.to_string(),
-            crate::materialized_view::definition_to_metadata(&narrower).unwrap(),
-        );
-        view.table()
-            .as_native()
-            .unwrap()
-            .replace_schema_metadata(metadata)
-            .await
-            .unwrap();
-
-        let err = view.refresh().execute().await.unwrap_err();
-        assert!(matches!(err, Error::Schema { message } if message.contains("does not produce")),);
-    }
-
-    /// An overlay replaces cell values without changing any file path; the
-    /// signature must see it, scoped to the columns the view reads like
-    /// data files are.
-    #[test]
-    fn test_fragment_signature_sees_overlays() {
-        use lance_file::version::ConcreteFileVersion;
-        use lance_table::format::DataFile;
-        use lance_table::format::overlay::{DataOverlayFile, OverlayCoverage};
-
-        let base = Fragment::new(7);
-        let mut file = DataFile::new_unstarted("f0.lance", ConcreteFileVersion::V2_1);
-        file.fields = vec![0, 1].into();
-        let mut with_file = base.clone();
-        with_file.files.push(file.clone());
-
-        let overlay = |field: i32| {
-            let mut data_file = DataFile::new_unstarted("o0.lance", ConcreteFileVersion::V2_1);
-            data_file.fields = vec![field].into();
-            DataOverlayFile {
-                data_file,
-                coverage: OverlayCoverage::PerField(Vec::new()),
-                committed_version: 9,
-            }
-        };
-        let relevant: HashSet<i32> = [0].into_iter().collect();
-
-        let mut overlaid_relevant = with_file.clone();
-        overlaid_relevant.overlays.push(overlay(0));
-        assert_ne!(
-            fragment_signature(&with_file, &relevant),
-            fragment_signature(&overlaid_relevant, &relevant),
-        );
-
-        let mut overlaid_unrelated = with_file.clone();
-        overlaid_unrelated.overlays.push(overlay(5));
-        assert_eq!(
-            fragment_signature(&with_file, &relevant),
-            fragment_signature(&overlaid_unrelated, &relevant),
-        );
-    }
-
-    /// An output whose name needs quoting flows through as a projection
-    /// alias, never spliced into SQL text.
-    #[tokio::test]
-    async fn test_output_names_needing_quotes() {
-        let (conn, _) = db_with_source(vec![1, 2]).await;
-        let view = conn
-            .create_materialized_view("v", "src")
-            .with_no_data(true)
-            .select([("double value", "x * 2")])
-            .execute()
-            .await
-            .unwrap();
-
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.rows_written, 2);
-        assert_eq!(read(view.table(), "double value").await, vec![2, 4]);
-    }
-
-    /// MemWAL tiers are visible to reads but not to the refresh scan, so LSM
-    /// state disqualifies every participant: the source at create, either
-    /// side at refresh, and the view can never accept a spec. Retained
-    /// un-compacted rows (the catch-up flag outlives unset) count as state.
-    #[tokio::test]
-    async fn lsm_state_disqualifies_source_and_view() {
-        use crate::table::LsmWriteSpec;
-        use arrow_array::RecordBatchIterator;
-
-        let tmp_dir = tempfile::tempdir().unwrap();
-        let conn = connect(tmp_dir.path().to_str().unwrap())
-            .execute()
-            .await
-            .unwrap();
-        // Hand-rolled: the LSM primary key must be non-nullable, which
-        // record_batch! cannot express.
-        let schema = Arc::new(ArrowSchema::new(vec![
-            arrow_schema::Field::new("id", arrow_schema::DataType::Int64, false),
-            arrow_schema::Field::new("x", arrow_schema::DataType::Int32, false),
-        ]));
-        let batch = RecordBatch::try_new(
-            schema.clone(),
-            vec![
-                Arc::new(arrow_array::Int64Array::from(vec![1, 2])) as _,
-                Arc::new(Int32Array::from(vec![1, 2])) as _,
-            ],
-        )
-        .unwrap();
-        let table = conn
-            .create_table("src", batch.clone())
-            .write_options(crate::materialized_view::tests::stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-        table.set_unenforced_primary_key(["id"]).await.unwrap();
-        table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
-            .await
-            .unwrap();
-
-        // An active-LSM source is refused at create.
-        let err = conn
-            .create_materialized_view("v", "src")
-            .with_no_data(true)
-            .execute()
-            .await
-            .unwrap_err();
-        assert!(err.to_string().contains("un-compacted"), "{err}");
-
-        // Unset with nothing written clears the state: the view creates,
-        // and a spec can never be installed over it.
-        table.unset_lsm_write_spec().await.unwrap();
-        let view = conn
-            .create_materialized_view("v", "src")
+        let view = connection
+            .create_materialized_view("copy", "src")
             .with_no_data(true)
             .execute()
             .await
             .unwrap();
-        let err = view
-            .table()
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
-            .await
-            .unwrap_err();
-        assert!(err.to_string().contains("materialized view"), "{err}");
-
-        // A source that acquires a spec after create fails refresh.
-        table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
-            .await
-            .unwrap();
-        let err = view.refresh().execute().await.unwrap_err();
-        assert!(err.to_string().contains("source table 'src'"), "{err}");
-
-        // Retained rows outlive unset: write through the WAL, unset, and
-        // refresh still refuses.
-        let mut merge = table.merge_insert(&["id"]);
-        merge
-            .when_matched_update_all(None)
-            .when_not_matched_insert_all()
-            .use_lsm(true);
-        merge
-            .execute(Box::new(RecordBatchIterator::new(
-                vec![Ok(batch.clone())],
-                batch.schema(),
-            )))
-            .await
-            .unwrap();
-        table.unset_lsm_write_spec().await.unwrap();
-        let err = view.refresh().execute().await.unwrap_err();
-        assert!(err.to_string().contains("source table 'src'"), "{err}");
-    }
-
-    /// A view with a computed column, declared over `people` and refreshed.
-    async fn refreshed_computed_view(conn: &Connection) -> MaterializedView {
-        use crate::materialized_view::tests::{computed_field, people, test_binding};
-        let source = people(conn).await;
-        let view = crate::materialized_view::prepare_declaration(
-            &source,
-            Some(&[
-                ("id".to_string(), "id".to_string()),
-                ("name".to_string(), "name".to_string()),
-            ]),
-            None,
-            None,
-        )
-        .await
-        .unwrap()
-        .with_computed_columns(
-            vec![(2, computed_field("emb", "fb_1", "name"))],
-            &[test_binding("fb_1", "name", "emb")],
-        )
-        .unwrap()
-        .create("v")
-        .await
-        .unwrap();
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Rebuild);
-        view
-    }
-
-    async fn unfilled(view: &MaterializedView) -> usize {
-        view.table()
-            .count_rows(Some("emb IS NULL".to_string()))
-            .await
-            .unwrap()
-    }
-
-    async fn append_people(conn: &Connection, ids: Vec<i32>, names: Vec<&str>) {
-        let batch = record_batch!(("id", Int32, ids), ("name", Utf8, names)).unwrap();
-        conn.open_table("people")
-            .execute()
-            .await
-            .unwrap()
-            .add(batch)
-            .execute()
-            .await
-            .unwrap();
-    }
-
-    /// Commit the fill job's shape on the view: a column rewrite of
-    /// `fields`, touching no rows. The data is left as it is; what matters
-    /// here is how the next refresh classifies the commit.
-    async fn commit_column_rewrite(view: &MaterializedView, fields: &[&str]) {
-        let native = view.table().as_native().unwrap();
-        native.dataset.reload().await.unwrap();
-        let dataset = native.dataset.get().await.unwrap().as_ref().clone();
-        let fields_modified = fields
-            .iter()
-            .map(|name| dataset.schema().field(name).unwrap().id as u32)
-            .collect();
-        let updated_fragments = dataset
-            .get_fragments()
-            .iter()
-            .map(|fragment| fragment.metadata().clone())
-            .collect();
-        let operation = Operation::Update {
-            removed_fragment_ids: Vec::new(),
-            updated_fragments,
-            new_fragments: Vec::new(),
-            fields_modified,
-            compacted_sstables: Vec::new(),
-            fields_for_preserving_frag_bitmap: Vec::new(),
-            update_mode: Some(UpdateMode::RewriteColumns),
-            inserted_rows_filter: None,
-            updated_fragment_offsets: None,
-        };
-        let read_version = dataset.version().version;
-        CommitBuilder::new(WriteDestination::Dataset(Arc::new(dataset)))
-            .execute(Transaction::new(read_version, operation, None))
-            .await
-            .unwrap();
-    }
-
-    /// Refresh never computes a computed column: every row it writes, on a
-    /// rebuild, an append and a rewrite, carries NULL there, and the
-    /// declaration survives all three.
-    #[tokio::test]
-    async fn test_computed_columns_are_written_null_and_kept() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let view = refreshed_computed_view(&conn).await;
-        assert_eq!(unfilled(&view).await, 3);
-
-        append_people(&conn, vec![4, 5], vec!["d", "e"]).await;
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(unfilled(&view).await, 5);
-
-        conn.open_table("people")
-            .execute()
-            .await
-            .unwrap()
-            .update()
-            .column("name", "'z'")
-            .only_if("id = 1")
-            .execute()
-            .await
-            .unwrap();
-        view.refresh().execute().await.unwrap();
-        assert_eq!(unfilled(&view).await, 5);
-        assert_eq!(read(view.table(), "id").await, vec![1, 2, 3, 4, 5]);
-
         let schema = view.table().schema().await.unwrap();
-        assert!(
-            crate::table::computed_columns::function_bindings(&schema)
-                .unwrap()
-                .iter()
-                .any(|b| b.binding_id() == "fb_1"),
-            "the binding envelope was lost"
-        );
-        assert!(
-            computed_column_from_field(schema.field_with_name("emb").unwrap()).is_some(),
-            "the declaration was lost"
-        );
-        assert_eq!(
-            view.refresh().execute().await.unwrap().mode,
-            RefreshMode::NoOp
-        );
-    }
-
-    /// Field metadata on `field` only, the commit shape of the freshness
-    /// stamp `refresh_column` leaves after its fill.
-    async fn commit_field_metadata(view: &MaterializedView, field: &str, key: &str) {
-        let native = view.table().as_native().unwrap();
-        native.dataset.reload().await.unwrap();
-        let mut dataset = native.dataset.get().await.unwrap().as_ref().clone();
-        dataset
-            .update_field_metadata()
-            .update(field, [(key.to_string(), "{}".to_string())])
-            .unwrap()
-            .await
-            .unwrap();
-    }
-
-    /// The stamp is metadata on the computed column and rewrites nothing
-    /// refresh certifies, so it is not drift; the same commit shape on a
-    /// projected column is, like any other write to it.
-    #[tokio::test]
-    async fn test_a_freshness_stamp_is_not_drift() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let view = refreshed_computed_view(&conn).await;
-
-        commit_field_metadata(
-            &view,
-            "emb",
-            crate::table::computed_columns::SOURCE_SIGNATURE_META_KEY,
-        )
-        .await;
-        assert_eq!(
-            view.refresh().execute().await.unwrap().mode,
-            RefreshMode::NoOp
-        );
-
-        commit_field_metadata(&view, "id", "probe").await;
-        assert_eq!(
-            view.refresh().execute().await.unwrap().mode,
-            RefreshMode::Rebuild
-        );
-    }
-
-    /// The fill job's commit rewrites only computed columns. It is the one
-    /// commit on a view that is not drift: the next refresh carries on from
-    /// its watermark instead of rebuilding, which would null what the fill
-    /// just wrote.
-    #[tokio::test]
-    async fn test_a_computed_column_fill_is_not_drift() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let view = refreshed_computed_view(&conn).await;
-
-        commit_column_rewrite(&view, &["emb"]).await;
-        assert_eq!(
-            view.refresh().execute().await.unwrap().mode,
-            RefreshMode::NoOp
-        );
-
-        commit_column_rewrite(&view, &["emb"]).await;
-        append_people(&conn, vec![4], vec!["d"]).await;
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Incremental);
-        assert_eq!(result.rows_written, 1);
-        assert_eq!(read(view.table(), "id").await, vec![1, 2, 3, 4]);
-    }
-
-    /// A column rewrite that reaches a projected column is drift like any
-    /// other write: refresh certifies those columns and must recompute them.
-    #[tokio::test]
-    async fn test_a_rewrite_of_a_projected_column_is_drift() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let view = refreshed_computed_view(&conn).await;
-
-        commit_column_rewrite(&view, &["emb", "name"]).await;
-        assert_eq!(
-            view.refresh().execute().await.unwrap().mode,
-            RefreshMode::Rebuild
-        );
-    }
-
-    /// The declaration contract is checked before any refresh mutation: a
-    /// missing binding envelope and a column that lost its declaration both
-    /// fail closed.
-    #[tokio::test]
-    async fn test_a_broken_declaration_is_refused_before_refresh() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let view = refreshed_computed_view(&conn).await;
-        let native = view.table().as_native().unwrap();
-        let mut dataset = native.dataset.get().await.unwrap().as_ref().clone();
-        dataset
-            .update_schema_metadata(vec![(
-                crate::table::computed_columns::FUNCTION_BINDINGS_META_KEY.to_string(),
-                None,
-            )])
-            .await
-            .unwrap();
-        let err = view.refresh().execute().await.unwrap_err().to_string();
-        assert!(err.contains("references missing binding 'fb_1'"), "{err}");
-
-        let conn = connect("memory://").execute().await.unwrap();
-        let view = refreshed_computed_view(&conn).await;
-        let native = view.table().as_native().unwrap();
-        let mut dataset = native.dataset.get().await.unwrap().as_ref().clone();
-        dataset
-            .replace_field_metadata(vec![(
-                dataset.schema().field("emb").unwrap().id as u32,
-                HashMap::new(),
-            )])
-            .await
-            .unwrap();
-        let err = view.refresh().execute().await.unwrap_err().to_string();
-        assert!(err.contains("does not match binding 'fb_1'"), "{err}");
-    }
-
-    /// An input the view does not project is materialized on every refresh
-    /// path, before the provenance column, with the source's values.
-    #[tokio::test]
-    async fn test_internal_inputs_are_materialized_and_refreshed() {
-        use crate::materialized_view::tests::{computed_field, strict_people, test_binding};
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = strict_people(&conn).await;
-        let mut prepared = crate::materialized_view::prepare_declaration(
-            &source,
-            Some(&[("id".to_string(), "id".to_string())]),
-            None,
-            None,
-        )
-        .await
-        .unwrap();
-        let input = prepared.input_column("name").unwrap();
-        let view = prepared
-            .with_computed_columns(
-                vec![(1, computed_field("emb", "fb_1", &input))],
-                &[test_binding("fb_1", &input, "emb")],
-            )
-            .unwrap()
-            .create("v")
-            .await
-            .unwrap();
-        let names: Vec<String> = view
+        let result_batch =
+            RecordBatch::try_new(schema.clone(), source_batch.columns().to_vec()).unwrap();
+        let planned = view
             .table()
-            .schema()
-            .await
-            .unwrap()
-            .fields()
-            .iter()
-            .map(|f| f.name().clone())
-            .collect();
-        assert_eq!(names, ["id", "emb", "__input_name", SOURCE_ROW_ID_COLUMN]);
-
-        let unfilled_inputs = || async {
-            view.table()
-                .count_rows(Some("__input_name IS NULL".to_string()))
-                .await
-                .unwrap()
-        };
-        assert_eq!(
-            view.refresh().execute().await.unwrap().mode,
-            RefreshMode::Rebuild
-        );
-        assert_eq!(view.table().count_rows(None).await.unwrap(), 3);
-        assert_eq!(unfilled_inputs().await, 0);
-
-        let more = arrow_array::RecordBatch::try_new(
-            source.schema().await.unwrap(),
-            vec![
-                Arc::new(Int32Array::from(vec![4])),
-                Arc::new(arrow_array::StringArray::from(vec!["d"])),
-            ],
-        )
-        .unwrap();
-        source.add(more).execute().await.unwrap();
-        assert_eq!(
-            view.refresh().execute().await.unwrap().mode,
-            RefreshMode::Incremental
-        );
-        assert_eq!(unfilled_inputs().await, 0);
-        assert_eq!(
-            view.table()
-                .count_rows(Some("__input_name = 'd'".to_string()))
-                .await
-                .unwrap(),
-            1
-        );
-
-        source
-            .update()
-            .column("name", "'z'")
-            .only_if("id = 1")
-            .execute()
-            .await
-            .unwrap();
-        view.refresh().execute().await.unwrap();
-        assert_eq!(
-            view.table()
-                .count_rows(Some("__input_name = 'z'".to_string()))
-                .await
-                .unwrap(),
-            1
-        );
-        assert_eq!(
-            unfilled(&view).await,
-            4,
-            "rewritten and new rows are unfilled"
-        );
-    }
-
-    /// A SQL declaration is filled by `refresh_column` on the view, which
-    /// commits a data replacement and then its freshness stamp; the next
-    /// refresh continues from its watermark and keeps what the fill wrote,
-    /// and only rows the view added since come back unfilled.
-    #[tokio::test]
-    async fn test_a_sql_fill_is_not_drift() {
-        use crate::materialized_view::tests::{people, sql_field};
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = people(&conn).await;
-        let view = crate::materialized_view::prepare_declaration(
-            &source,
-            Some(&[("id".to_string(), "id".to_string())]),
-            None,
-            None,
-        )
-        .await
-        .unwrap()
-        .with_computed_columns(
-            vec![(
-                1,
-                sql_field("next", arrow_schema::DataType::Int32, "id + 1", r#"["id"]"#),
-            )],
-            &[],
-        )
-        .unwrap()
-        .create("v")
-        .await
-        .unwrap();
-        let filled = || async {
-            view.table()
-                .count_rows(Some("next = id + 1".to_string()))
-                .await
-                .unwrap()
-        };
-        assert_eq!(
-            view.refresh().execute().await.unwrap().mode,
-            RefreshMode::Rebuild
-        );
-        assert_eq!(
-            view.table()
-                .refresh_column("next")
-                .await
-                .unwrap()
-                .rows_filled,
-            3
-        );
-        assert_eq!(filled().await, 3);
-        assert_eq!(
-            view.refresh().execute().await.unwrap().mode,
-            RefreshMode::NoOp
-        );
-        assert_eq!(filled().await, 3);
-
-        append_people(&conn, vec![4], vec!["d"]).await;
-        assert_eq!(
-            view.refresh().execute().await.unwrap().mode,
-            RefreshMode::Incremental
-        );
-        assert_eq!(filled().await, 3);
-        assert_eq!(
-            view.table()
-                .refresh_column("next")
-                .await
-                .unwrap()
-                .rows_filled,
-            1
-        );
-        assert_eq!(filled().await, 4);
-        assert_eq!(
-            view.refresh().execute().await.unwrap().mode,
-            RefreshMode::NoOp
-        );
-    }
-
-    /// A fill of a nested computed column writes its child fields; that is
-    /// still a fill, not drift.
-    #[tokio::test]
-    async fn test_a_nested_sql_fill_is_not_drift() {
-        use crate::materialized_view::tests::{people, sql_field};
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = people(&conn).await;
-        let payload = sql_field(
-            "payload",
-            arrow_schema::DataType::Struct(
-                vec![arrow_schema::Field::new(
-                    "value",
-                    arrow_schema::DataType::Utf8,
-                    true,
-                )]
-                .into(),
-            ),
-            "named_struct('value', name)",
-            r#"["name"]"#,
-        );
-        let view = crate::materialized_view::prepare_declaration(
-            &source,
-            Some(&[("name".to_string(), "name".to_string())]),
-            None,
-            None,
-        )
-        .await
-        .unwrap()
-        .with_computed_columns(vec![(1, payload)], &[])
-        .unwrap()
-        .create("v")
-        .await
-        .unwrap();
-        view.refresh().execute().await.unwrap();
-        assert_eq!(
-            view.table()
-                .refresh_column("payload")
-                .await
-                .unwrap()
-                .rows_filled,
-            3
-        );
-        assert_eq!(
-            view.refresh().execute().await.unwrap().mode,
-            RefreshMode::NoOp
-        );
-        assert_eq!(
-            view.table()
-                .count_rows(Some("payload.value = name".to_string()))
-                .await
-                .unwrap(),
-            3
-        );
-    }
-
-    async fn grouped_source(conn: &Connection) -> Table {
-        conn.create_table(
-            "src",
-            record_batch!(("k", Int32, [1, 1, 2, 3]), ("x", Int32, [10, 11, 20, 30])).unwrap(),
-        )
-        .write_options(crate::materialized_view::tests::stable_row_ids())
-        .execute()
-        .await
-        .unwrap()
-    }
-
-    pub(in crate::materialized_view) async fn declare(
-        source: &Table,
-        name: &str,
-        sql: &str,
-    ) -> Result<MaterializedView> {
-        crate::materialized_view::prepare_definition(
-            source,
-            MaterializedViewDefinition::from_sql(sql)?,
-        )
-        .await?
-        .create(name)
-        .await
-    }
-
-    /// Rows of `table` as `columns` rendered to text, sorted.
-    pub(in crate::materialized_view) async fn rows(table: &Table, columns: &[&str]) -> Vec<String> {
-        let batches = table
-            .query()
-            .select(Select::columns(columns))
-            .execute()
-            .await
-            .unwrap()
-            .try_collect::<Vec<_>>()
-            .await
-            .unwrap();
-        let mut rows = Vec::new();
-        for batch in &batches {
-            let formatters: Vec<_> = batch
-                .columns()
-                .iter()
-                .map(|c| {
-                    arrow_cast::display::ArrayFormatter::try_new(c.as_ref(), &Default::default())
-                        .unwrap()
-                })
-                .collect();
-            for row in 0..batch.num_rows() {
-                let cells: Vec<String> = formatters
-                    .iter()
-                    .map(|f| f.value(row).to_string())
-                    .collect();
-                rows.push(cells.join(" "));
-            }
-        }
-        rows.sort();
-        rows
-    }
-
-    #[tokio::test]
-    async fn a_grouped_view_holds_one_row_per_group() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = grouped_source(&conn).await;
-        let view = declare(
-            &source,
-            "groups",
-            "SELECT k, array_agg(x) AS xs, sum(x) AS total FROM src WHERE x < 30 GROUP BY k",
-        )
-        .await
-        .unwrap();
-        assert_eq!(
-            view.definition().to_sql(),
-            "SELECT k, array_agg(x) AS xs, sum(x) AS total FROM src WHERE x < 30 GROUP BY k"
-        );
-
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Rebuild);
-        assert_eq!(result.rows_written, 2);
-        assert_eq!(rows(view.table(), &["k", "total"]).await, ["1 21", "2 20"]);
-        let unchanged = view.refresh().execute().await.unwrap();
-        assert_eq!(unchanged.mode, RefreshMode::NoOp);
-
-        // Any source change recomputes every group.
-        source
-            .add(record_batch!(("k", Int32, [2]), ("x", Int32, [5])).unwrap())
-            .execute()
-            .await
-            .unwrap();
-        let result = view.refresh().execute().await.unwrap();
-        assert_eq!(result.mode, RefreshMode::Rebuild);
-        assert_eq!(rows(view.table(), &["k", "total"]).await, ["1 21", "2 25"]);
-
-        // A view over the groups expands them again.
-        let expanded = declare(
-            view.table(),
-            "elements",
-            "SELECT k, e AS x FROM groups, UNNEST(xs) AS e",
-        )
-        .await
-        .unwrap();
-        expanded.refresh().execute().await.unwrap();
-        assert_eq!(
-            rows(expanded.table(), &["k", "x"]).await,
-            ["1 10", "1 11", "2 20", "2 5"]
-        );
-    }
-
-    /// Provenance is one source row per group, so the view's row-id keys
-    /// stay unique.
-    #[tokio::test]
-    async fn a_grouped_view_records_one_source_row_per_group() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = grouped_source(&conn).await;
-        let view = declare(
-            &source,
-            "groups",
-            "SELECT k, count(*) AS n FROM src GROUP BY k",
-        )
-        .await
-        .unwrap();
-        view.refresh().execute().await.unwrap();
-        let ids = rows(view.table(), &[SOURCE_ROW_ID_COLUMN]).await;
-        assert_eq!(ids.len(), 3);
-        assert_eq!(ids.iter().collect::<HashSet<_>>().len(), 3, "{ids:?}");
-    }
-
-    #[tokio::test]
-    async fn a_grouped_view_is_planned_at_declaration() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = grouped_source(&conn).await;
-        for sql in [
-            // x is neither a key nor aggregated
-            "SELECT k, x FROM src GROUP BY k",
-            "SELECT k, random() AS r FROM src GROUP BY k",
-            "SELECT k, count(*) AS n FROM src GROUP BY missing",
-            "SELECT k, count(*) AS __source_row_id FROM src GROUP BY k",
-            "SELECT k, count(*) AS k FROM src GROUP BY k",
-        ] {
-            assert!(declare(&source, "v", sql).await.is_err(), "{sql}");
-        }
-        let mut grouped = crate::materialized_view::prepare_definition(
-            &source,
-            MaterializedViewDefinition::from_sql("SELECT k, count(*) AS n FROM src GROUP BY k")
-                .unwrap(),
-        )
-        .await
-        .unwrap();
-        assert!(grouped.input_column("x").is_err());
-    }
-
-    /// Twenty nonzero vectors in two clusters far apart, ids 0-9 and 10-19.
-    /// Nonzero so every vector has a cosine direction.
-    pub(in crate::materialized_view) async fn clustered_source(conn: &Connection) -> Table {
-        use arrow_array::types::Float32Type;
-        use arrow_array::{FixedSizeListArray, Int32Array};
-
-        let vectors = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
-            (0..20).map(|i| {
-                let base = if i < 10 { 0.0 } else { 100.0 };
-                Some(vec![Some(base + 1.0 + i as f32 * 0.1), Some(base)])
-            }),
-            2,
-        );
-        let batch = RecordBatch::try_from_iter(vec![
-            (
-                "id",
-                Arc::new(Int32Array::from_iter_values(0..20)) as arrow_array::ArrayRef,
-            ),
-            ("vec", Arc::new(vectors) as arrow_array::ArrayRef),
-        ])
-        .unwrap();
-        conn.create_table("images", batch)
-            .write_options(crate::materialized_view::tests::stable_row_ids())
-            .execute()
-            .await
-            .unwrap()
-    }
-
-    #[tokio::test]
-    async fn ivf_partition_groups_by_the_index_on_the_column() {
-        use crate::index::vector::IvfFlatIndexBuilder;
-
-        const BUCKETS: &str = "SELECT ivf_partition(vec) AS bucket, count(*) AS n, \
-             min(id) AS lo, max(id) AS hi FROM images GROUP BY ivf_partition(vec)";
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = clustered_source(&conn).await;
-        let unindexed = declare(&source, "buckets", BUCKETS).await.unwrap_err();
-        assert!(
-            unindexed.to_string().contains("IVF vector index"),
-            "{unindexed}"
-        );
-
-        source
-            .create_index(
-                &["vec"],
-                Index::IvfFlat(IvfFlatIndexBuilder::default().num_partitions(2)),
-            )
-            .execute()
-            .await
-            .unwrap();
-        let view = declare(&source, "buckets", BUCKETS).await.unwrap();
-        view.refresh().execute().await.unwrap();
-        assert_eq!(
-            rows(view.table(), &["n", "lo", "hi"]).await,
-            ["10 0 9", "10 10 19"]
-        );
-        let buckets = rows(view.table(), &["bucket"]).await;
-        assert_eq!(buckets, ["0", "1"]);
-    }
-
-    #[tokio::test]
-    async fn ivf_partition_takes_a_column() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = clustered_source(&conn).await;
-        for sql in [
-            "SELECT ivf_partition(id) AS b, count(*) AS n FROM images GROUP BY ivf_partition(id)",
-            "SELECT ivf_partition(vec, vec) AS b, count(*) AS n FROM images \
-             GROUP BY ivf_partition(vec, vec)",
-        ] {
-            assert!(declare(&source, "v", sql).await.is_err(), "{sql}");
-        }
-    }
-
-    /// A cosine index assigns by L2 over unit vectors, as lance's own index
-    /// path does; forwarding cosine to the assigner panics.
-    #[tokio::test]
-    async fn ivf_partition_supports_cosine_indices() {
-        use crate::index::vector::IvfFlatIndexBuilder;
-
-        const BUCKETS: &str = "SELECT ivf_partition(vec) AS bucket, count(*) AS n, \
-             min(id) AS lo, max(id) AS hi FROM images GROUP BY ivf_partition(vec)";
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = clustered_source(&conn).await;
-        source
-            .create_index(
-                &["vec"],
-                Index::IvfFlat(
-                    IvfFlatIndexBuilder::default()
-                        .num_partitions(2)
-                        .distance_type(crate::DistanceType::Cosine),
-                ),
-            )
-            .execute()
-            .await
-            .unwrap();
-        let view = declare(&source, "cosine_buckets", BUCKETS).await.unwrap();
-        view.refresh().execute().await.unwrap();
-        assert_eq!(
-            rows(view.table(), &["n", "lo", "hi"]).await,
-            ["10 0 9", "10 10 19"]
-        );
-    }
-
-    /// Segments of one index trained separately carry different centroids;
-    /// grouping by one of them would bucket the other segments' rows wrongly.
-    #[tokio::test]
-    async fn ivf_partition_refuses_segments_with_different_models() {
-        use arrow_array::types::Float32Type;
-        use arrow_array::{ArrayRef, FixedSizeListArray, Int32Array};
-        use lance::index::DatasetIndexExt;
-        use lance::index::vector::VectorIndexParams;
-        use lance_index::IndexType;
-
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = clustered_source(&conn).await;
-        // A second fragment far from the first, so each segment trains its own centroids.
-        let far = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
-            (0..20).map(|i| Some(vec![Some(-500.0 - i as f32), Some(-500.0)])),
-            2,
-        );
-        source
-            .add(
-                RecordBatch::try_from_iter(vec![
-                    (
-                        "id",
-                        Arc::new(Int32Array::from_iter_values(20..40)) as ArrayRef,
-                    ),
-                    ("vec", Arc::new(far) as ArrayRef),
-                ])
-                .unwrap(),
-            )
-            .execute()
-            .await
-            .unwrap();
-        let dataset = source.as_native().unwrap().dataset.get().await.unwrap();
-        let mut dataset = dataset.as_ref().clone();
-        let params = VectorIndexParams::ivf_flat(2, lance_linalg::distance::DistanceType::L2);
-        let mut segments = Vec::new();
-        for fragment in dataset.get_fragments() {
-            let segment = dataset
-                .create_index_builder(&["vec"], IndexType::Vector, &params)
-                .name("shared".to_string())
-                .fragments(vec![fragment.id() as u32])
-                .execute_uncommitted()
-                .await
-                .unwrap();
-            segments.push(segment);
-        }
-        dataset
-            .commit_existing_index_segments("shared", "vec", segments)
-            .await
-            .unwrap();
-        assert_eq!(dataset.load_indices().await.unwrap().len(), 2);
-
-        let source = conn.open_table("images").execute().await.unwrap();
-        let error = declare(
-            &source,
-            "buckets",
-            "SELECT ivf_partition(vec) AS b, count(*) AS n FROM images GROUP BY ivf_partition(vec)",
-        )
-        .await
-        .unwrap_err();
-        assert!(error.to_string().contains("trained separately"), "{error}");
-    }
-
-    /// A vector the assigner cannot place has no bucket: it is grouped under
-    /// NULL, never under partition 0.
-    #[tokio::test]
-    async fn ivf_partition_leaves_an_unassignable_vector_unbucketed() {
-        use crate::index::vector::IvfFlatIndexBuilder;
-        use arrow_array::types::Float32Type;
-        use arrow_array::{ArrayRef, FixedSizeListArray, Int32Array};
-
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = clustered_source(&conn).await;
-        source
-            .create_index(
-                &["vec"],
-                Index::IvfFlat(
-                    IvfFlatIndexBuilder::default()
-                        .num_partitions(2)
-                        .distance_type(crate::DistanceType::Cosine),
-                ),
-            )
-            .execute()
-            .await
-            .unwrap();
-        // The zero vector has no direction, so cosine cannot place it.
-        let zero = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
-            [Some(vec![Some(0.0), Some(0.0)])],
-            2,
-        );
-        source
-            .add(
-                RecordBatch::try_from_iter(vec![
-                    ("id", Arc::new(Int32Array::from(vec![20])) as ArrayRef),
-                    ("vec", Arc::new(zero) as ArrayRef),
-                ])
-                .unwrap(),
-            )
-            .execute()
-            .await
-            .unwrap();
-        let view = declare(
-            &source,
-            "buckets",
-            "SELECT ivf_partition(vec) AS bucket, count(*) AS n, min(id) AS lo, max(id) AS hi \
-             FROM images GROUP BY ivf_partition(vec)",
-        )
-        .await
-        .unwrap();
-        view.refresh().execute().await.unwrap();
-        // ArrayFormatter renders NULL as the empty string; partition numbers
-        // are the index's own and not asserted.
-        let groups = rows(view.table(), &["bucket", "n", "lo", "hi"]).await;
-        let (unbucketed, bucketed): (Vec<_>, Vec<_>) =
-            groups.iter().partition(|row| row.starts_with(' '));
-        assert_eq!(unbucketed, [" 1 20 20"], "{groups:?}");
-        let mut clusters: Vec<&str> = bucketed
-            .iter()
-            .map(|row| row.split_once(' ').unwrap().1)
-            .collect();
-        clusters.sort();
-        assert_eq!(clusters, ["10 0 9", "10 10 19"], "{groups:?}");
-    }
-
-    /// Byte vectors group by a Hamming index, the phash case. The centroids
-    /// are given, as lance's own tests do: two-means over a handful of
-    /// hashes can converge with one partition empty.
-    #[tokio::test]
-    async fn ivf_partition_groups_byte_vectors_by_hamming() {
-        use arrow_array::types::UInt8Type;
-        use arrow_array::{ArrayRef, FixedSizeListArray, Int32Array};
-        use lance::index::DatasetIndexExt;
-        use lance::index::vector::VectorIndexParams;
-        use lance_index::IndexType;
-        use lance_index::vector::ivf::IvfBuildParams;
-
-        let hashes = FixedSizeListArray::from_iter_primitive::<UInt8Type, _, _>(
-            (0..20u8).map(|i| {
-                let base = if i < 10 { 0x00 } else { 0xff };
-                Some(vec![
-                    Some(base ^ (i % 4)),
-                    Some(base),
-                    Some(base),
-                    Some(base),
-                ])
-            }),
-            4,
-        );
-        let batch = RecordBatch::try_from_iter(vec![
-            (
-                "id",
-                Arc::new(Int32Array::from_iter_values(0..20)) as ArrayRef,
-            ),
-            ("phash", Arc::new(hashes) as ArrayRef),
-        ])
-        .unwrap();
-        let conn = connect("memory://").execute().await.unwrap();
-        let source = conn
-            .create_table("images", batch)
-            .write_options(crate::materialized_view::tests::stable_row_ids())
-            .execute()
-            .await
-            .unwrap();
-        let centroids = FixedSizeListArray::from_iter_primitive::<UInt8Type, _, _>(
-            [Some(vec![Some(0); 4]), Some(vec![Some(0xff); 4])],
-            4,
-        );
-        let params = VectorIndexParams::with_ivf_flat_params(
-            lance_linalg::distance::DistanceType::Hamming,
-            IvfBuildParams::try_with_centroids(2, Arc::new(centroids)).unwrap(),
-        );
-        let mut dataset = source
             .as_native()
             .unwrap()
             .dataset
@@ -4806,19 +609,63 @@ pub(crate) mod tests {
             .unwrap()
             .as_ref()
             .clone();
-        dataset
-            .create_index(&["phash"], IndexType::Vector, None, &params, true)
+        assert!(planned.get_fragments().is_empty());
+
+        let (entered_tx, entered_rx) = tokio::sync::oneshot::channel();
+        let (release_tx, release_rx) = tokio::sync::oneshot::channel();
+        let loser_batch = result_batch.clone();
+        let blocked = futures::stream::once(async move {
+            entered_tx.send(()).unwrap();
+            release_rx.await.unwrap();
+            Ok::<_, DataFusionError>(loser_batch)
+        });
+        let stream = Box::pin(RecordBatchStreamAdapter::new(schema.clone(), blocked));
+        let loser_table = view.table().clone();
+        let loser =
+            tokio::spawn(
+                async move { replace_materialized_view_fragments(&loser_table, stream).await },
+            );
+        entered_rx.await.unwrap();
+
+        let winner_stream: SendableRecordBatchStream = Box::pin(RecordBatchStreamAdapter::new(
+            schema,
+            futures::stream::iter([Ok(result_batch)]),
+        ));
+        let planned = Arc::new(planned);
+        let staged = InsertBuilder::new(WriteDestination::Dataset(planned.clone()))
+            .with_params(&WriteParams {
+                mode: WriteMode::Append,
+                ..Default::default()
+            })
+            .execute_uncommitted_stream(winner_stream)
             .await
             .unwrap();
-        let source = conn.open_table("images").execute().await.unwrap();
+        let Operation::Append { fragments } = staged.operation else {
+            panic!("expected append staging");
+        };
+        let filter = refresh_conflict_filter(&planned).unwrap();
+        CommitBuilder::new(WriteDestination::Dataset(planned.clone()))
+            .execute(Transaction::new(
+                planned.version().version,
+                Operation::Update {
+                    removed_fragment_ids: Vec::new(),
+                    updated_fragments: Vec::new(),
+                    new_fragments: fragments,
+                    fields_modified: Vec::new(),
+                    compacted_sstables: Vec::new(),
+                    fields_for_preserving_frag_bitmap: Vec::new(),
+                    update_mode: None,
+                    inserted_rows_filter: Some(filter),
+                    updated_fragment_offsets: None,
+                },
+                None,
+            ))
+            .await
+            .unwrap();
+        release_tx.send(()).unwrap();
 
-        const BUCKETS: &str = "SELECT ivf_partition(phash) AS bucket, count(*) AS n, \
-             min(id) AS lo, max(id) AS hi FROM images GROUP BY ivf_partition(phash)";
-        let view = declare(&source, "buckets", BUCKETS).await.unwrap();
-        view.refresh().execute().await.unwrap();
-        assert_eq!(
-            rows(view.table(), &["bucket", "n", "lo", "hi"]).await,
-            ["0 10 0 9", "1 10 10 19"]
-        );
+        loser.await.unwrap().unwrap_err();
+        let current = connection.open_table("copy").execute().await.unwrap();
+        assert_eq!(current.count_rows(None).await.unwrap(), 3);
     }
 }
diff --git a/rust/lancedb/src/materialized_view/vector_dedup.rs b/rust/lancedb/src/materialized_view/vector_dedup.rs
deleted file mode 100644
index 38c007076..000000000
--- a/rust/lancedb/src/materialized_view/vector_dedup.rs
+++ /dev/null
@@ -1,636 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-//! Two dependent waves in the ordinary refresh fleet: index partition selection,
-//! then source fragment materialization. Only the latter publishes data files.
-
-use std::collections::BTreeMap;
-use std::io::Cursor;
-use std::sync::Arc;
-
-use arrow_array::{Array, RecordBatch, UInt64Array};
-use arrow_schema::{DataType, Field, Schema, SchemaRef};
-use datafusion::prelude::{SessionConfig, SessionContext, col};
-use datafusion_common::DataFusionError;
-use datafusion_execution::{
-    SendableRecordBatchStream, TaskContext, runtime_env::RuntimeEnvBuilder,
-};
-use datafusion_physical_plan::{ExecutionPlan, stream::RecordBatchStreamAdapter};
-use futures::TryStreamExt;
-use lance::Dataset;
-use lance::dataset::rowids::{get_row_id_index, load_row_id_sequence};
-use lance::dataset::scanner::{RowAddrMask, RowAddrTreeMap};
-use lance::dataset::transaction::Operation;
-use lance::dataset::{InsertBuilder, WriteDestination, WriteMode, WriteParams};
-use lance_core::datatypes::BlobHandling;
-use lance_core::utils::address::RowAddress;
-use lance_datafusion::exec::{OneShotExec, SessionContextExt};
-use lance_table::format::Fragment;
-use roaring::{RoaringBitmap, RoaringTreemap};
-use serde::{Deserialize, Serialize};
-use sha2::{Digest, Sha256};
-use uuid::Uuid;
-
-use super::duplicate_pairs::{self, DuplicatePairsRefreshPlan, VectorSourceKind};
-use super::{GroupedRefreshPlan, RefreshMaterializedViewResult};
-use crate::table::Table;
-use crate::table::datafusion::udtf::duplicate_pairs::{DuplicatePairsExec, plan_duplicate_pairs};
-use crate::{Error, Result};
-
-const POLICY_VERSION: u32 = 1;
-const SORT_MEMORY_BYTES: usize = 256 * 1024 * 1024;
-const MATERIALIZE_BATCH_ROWS: usize = 1024;
-const MASK_FILE: &str = "dedup.bitmap";
-const MASK_MAGIC: &[u8] = b"LANCEDB_DEDUP_1";
-
-fn invalid(message: impl Into<String>) -> Error {
-    Error::InvalidInput {
-        message: message.into(),
-    }
-}
-fn io_error(error: std::io::Error) -> Error {
-    invalid(format!("invalid dedup bitmap: {error}"))
-}
-
-/// Immutable snapshot, partition tasks and source fragments for both waves.
-#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
-#[serde(deny_unknown_fields)]
-pub struct VectorDedupPlan {
-    pub snapshot: GroupedRefreshPlan,
-    native: DuplicatePairsRefreshPlan,
-    source_fragments: Vec<u32>,
-    policy_version: u32,
-}
-impl VectorDedupPlan {
-    pub(super) fn dependencies(&self) -> u32 {
-        self.native.snapshot.units
-    }
-    fn identity(&self) -> Result<String> {
-        Ok(format!(
-            "{:x}",
-            Sha256::digest(serde_json::to_vec(self).map_err(|e| invalid(e.to_string()))?)
-        ))
-    }
-}
-
-/// Small receipt for a durable selection mask. Row IDs and pairs are not stored
-/// in the host's task registry. UUID directories use ordinary index-file GC.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-#[serde(deny_unknown_fields)]
-struct MaskFile {
-    source_fragment: u32,
-    object_id: Uuid,
-    bytes: usize,
-    digest: String,
-}
-
-/// A selection task produces masks; a materialization task produces data files.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-#[serde(deny_unknown_fields)]
-pub struct WrittenDedupUnit {
-    unit: u32,
-    plan_id: String,
-    masks: Vec<MaskFile>,
-    pub(super) fragments: Vec<Fragment>,
-    pub(super) rows: u64,
-}
-
-pub(super) async fn plan_refresh(view: &Table, pinned: Option<u64>) -> Result<VectorDedupPlan> {
-    let native = duplicate_pairs::plan_refresh(view, pinned).await?;
-    let (_, _, source) = duplicate_pairs::open_planned(view, &native).await?;
-    let source_fragments = source
-        .get_fragments()
-        .iter()
-        .map(|f| f.id() as u32)
-        .collect::<Vec<_>>();
-    let mut snapshot = native.snapshot.clone();
-    snapshot.units = snapshot
-        .units
-        .checked_add(
-            u32::try_from(source_fragments.len())
-                .map_err(|_| invalid("too many source fragments"))?,
-        )
-        .ok_or_else(|| invalid("too many dedup refresh units"))?;
-    Ok(VectorDedupPlan {
-        snapshot,
-        native,
-        source_fragments,
-        policy_version: POLICY_VERSION,
-    })
-}
-
-async fn open(
-    view: &Table,
-    plan: &VectorDedupPlan,
-) -> Result<(Dataset, super::MaterializedViewDefinition, Arc<Dataset>)> {
-    let (view_ds, definition, source) = duplicate_pairs::open_planned(view, &plan.native).await?;
-    let mut snapshot = plan.native.snapshot.clone();
-    snapshot.units = snapshot
-        .units
-        .checked_add(
-            u32::try_from(plan.source_fragments.len())
-                .map_err(|_| invalid("too many dedup fragments"))?,
-        )
-        .ok_or_else(|| invalid("too many dedup units"))?;
-    if plan.policy_version != POLICY_VERSION
-        || snapshot != plan.snapshot
-        || definition.vector_source.as_ref().map(|s| s.kind) != Some(VectorSourceKind::Dedup)
-        || source
-            .get_fragments()
-            .iter()
-            .map(|f| f.id() as u32)
-            .collect::<Vec<_>>()
-            != plan.source_fragments
-    {
-        return Err(invalid("dedup task belongs to another snapshot or policy"));
-    }
-    Ok((view_ds, definition, source))
-}
-
-fn edge_schema() -> SchemaRef {
-    Arc::new(Schema::new(vec![
-        Field::new("a", DataType::UInt64, false),
-        Field::new("b", DataType::UInt64, false),
-    ]))
-}
-
-/// Sorting canonical edges makes the first unremoved endpoint the smallest
-/// unassigned representative. The sort spills; only vertex masks stay in RAM.
-async fn select_representatives(
-    stream: SendableRecordBatchStream,
-    memory: usize,
-) -> Result<(RoaringTreemap, RoaringTreemap)> {
-    let schema = edge_schema();
-    let batch_schema = schema.clone();
-    let canonical = stream
-        .map_ok(move |batch| {
-            let a = batch
-                .column(0)
-                .as_any()
-                .downcast_ref::<UInt64Array>()
-                .expect("native row id");
-            let b = batch
-                .column(1)
-                .as_any()
-                .downcast_ref::<UInt64Array>()
-                .expect("native row id");
-            RecordBatch::try_new(
-                batch_schema.clone(),
-                vec![
-                    Arc::new(UInt64Array::from_iter_values(
-                        a.values().iter().zip(b.values()).map(|(&a, &b)| a.min(b)),
-                    )),
-                    Arc::new(UInt64Array::from_iter_values(
-                        a.values().iter().zip(b.values()).map(|(&a, &b)| a.max(b)),
-                    )),
-                ],
-            )
-            .map_err(DataFusionError::from)
-        })
-        .and_then(futures::future::ready);
-    let canonical = Box::pin(RecordBatchStreamAdapter::new(schema, canonical));
-    let mut config = SessionConfig::default().with_batch_size(MATERIALIZE_BATCH_ROWS);
-    // Leave enough room to merge spilled runs while the final in-memory run
-    // is still resident. Small batches also bound each merge input buffer.
-    config.options_mut().execution.sort_spill_reservation_bytes = memory / 2;
-    let ctx = SessionContext::new_with_config_rt(
-        config,
-        RuntimeEnvBuilder::new()
-            .with_memory_limit(memory, 1.0)
-            .build_arc()?,
-    );
-    let mut sorted = ctx
-        .read_one_shot(canonical)?
-        .sort_by(vec![col("a"), col("b")])?
-        .execute_stream()
-        .await?;
-    let mut removed = RoaringTreemap::new();
-    let mut representatives = RoaringTreemap::new();
-    while let Some(batch) = sorted.try_next().await? {
-        let a = batch
-            .column(0)
-            .as_any()
-            .downcast_ref::<UInt64Array>()
-            .expect("sorted id");
-        let b = batch
-            .column(1)
-            .as_any()
-            .downcast_ref::<UInt64Array>()
-            .expect("sorted id");
-        for (&a, &b) in a.values().iter().zip(b.values()) {
-            if a != b && !removed.contains(a) && !removed.contains(b) {
-                representatives.insert(a);
-                removed.insert(b);
-            }
-        }
-    }
-    Ok((removed, representatives))
-}
-
-#[derive(Default)]
-struct Masks {
-    removed: RoaringBitmap,
-    representatives: RoaringBitmap,
-}
-
-async fn persist_masks(
-    view: &Dataset,
-    source: &Dataset,
-    removed: RoaringTreemap,
-    representatives: RoaringTreemap,
-) -> Result<Vec<MaskFile>> {
-    let row_index = get_row_id_index(source).await?;
-    let mut masks: BTreeMap<u32, Masks> = BTreeMap::new();
-    for (ids, is_removed) in [(removed, true), (representatives, false)] {
-        for id in ids {
-            let address = match &row_index {
-                Some(index) => index
-                    .get(id)?
-                    .ok_or_else(|| invalid("pair row id missing from pinned source"))?,
-                None => RowAddress::new_from_u64(id),
-            };
-            let mask = masks.entry(address.fragment_id()).or_default();
-            if is_removed {
-                mask.removed.insert(address.row_offset());
-            } else {
-                mask.representatives.insert(address.row_offset());
-            }
-        }
-    }
-    let store = view.object_store(None).await?;
-    let mut files = Vec::new();
-    for (source_fragment, mask) in masks {
-        if source.get_fragment(source_fragment as usize).is_none() {
-            return Err(invalid("pair row address is outside the pinned snapshot"));
-        }
-        let mut bytes = MASK_MAGIC.to_vec();
-        mask.removed.serialize_into(&mut bytes).map_err(io_error)?;
-        mask.representatives
-            .serialize_into(&mut bytes)
-            .map_err(io_error)?;
-        let object_id = Uuid::new_v4();
-        store
-            .put(
-                &view
-                    .indices_dir()
-                    .join(object_id.to_string())
-                    .join(MASK_FILE),
-                &bytes,
-            )
-            .await?;
-        files.push(MaskFile {
-            source_fragment,
-            object_id,
-            bytes: bytes.len(),
-            digest: format!("{:x}", Sha256::digest(&bytes)),
-        });
-    }
-    Ok(files)
-}
-
-fn validate_receipts<'a>(
-    plan: &VectorDedupPlan,
-    receipts: &'a [WrittenDedupUnit],
-    count: u32,
-) -> Result<Vec<&'a WrittenDedupUnit>> {
-    let identity = plan.identity()?;
-    let mut ordered = vec![None; count as usize];
-    for receipt in receipts {
-        if receipt.plan_id != identity {
-            return Err(invalid("dedup receipt belongs to another plan"));
-        }
-        let slot = ordered
-            .get_mut(receipt.unit as usize)
-            .ok_or_else(|| invalid("unexpected dedup receipt"))?;
-        if slot.is_some() {
-            return Err(invalid("duplicate dedup receipt"));
-        }
-        if receipt.unit < plan.dependencies() {
-            if !receipt.fragments.is_empty()
-                || receipt.rows != 0
-                || receipt
-                    .masks
-                    .iter()
-                    .any(|m| !plan.source_fragments.contains(&m.source_fragment))
-            {
-                return Err(invalid("invalid selection receipt"));
-            }
-        } else if !receipt.masks.is_empty() {
-            return Err(invalid("invalid materialization receipt"));
-        }
-        *slot = Some(receipt);
-    }
-    ordered
-        .into_iter()
-        .collect::<Option<Vec<_>>>()
-        .ok_or_else(|| invalid("missing dedup dependency receipt"))
-}
-
-async fn load_masks(
-    view: &Dataset,
-    source_fragment: u32,
-    dependencies: &[&WrittenDedupUnit],
-) -> Result<RoaringBitmap> {
-    let store = view.object_store(None).await?;
-    let mut masks = Masks::default();
-    for file in dependencies
-        .iter()
-        .flat_map(|u| &u.masks)
-        .filter(|m| m.source_fragment == source_fragment)
-    {
-        let reader = store
-            .open(
-                &view
-                    .indices_dir()
-                    .join(file.object_id.to_string())
-                    .join(MASK_FILE),
-            )
-            .await?;
-        if reader.size().await? != file.bytes {
-            return Err(invalid("dedup mask length mismatch"));
-        }
-        let bytes = reader.get_range(0..file.bytes).await?;
-        if !bytes.starts_with(MASK_MAGIC) || format!("{:x}", Sha256::digest(&bytes)) != file.digest
-        {
-            return Err(invalid("dedup mask checksum mismatch"));
-        }
-        let mut cursor = Cursor::new(&bytes[MASK_MAGIC.len()..]);
-        masks.removed |= RoaringBitmap::deserialize_from(&mut cursor).map_err(io_error)?;
-        masks.representatives |= RoaringBitmap::deserialize_from(&mut cursor).map_err(io_error)?;
-        if cursor.position() as usize != bytes.len() - MASK_MAGIC.len() {
-            return Err(invalid("dedup mask has trailing data"));
-        }
-    }
-    // Native index scopes normally have disjoint rows. Fail closed if an
-    // overlapping scope would delete a representative used by another scope.
-    if !masks.removed.is_disjoint(&masks.representatives) {
-        return Err(invalid(
-            "overlapping index scopes disagree on retained representatives",
-        ));
-    }
-    Ok(masks.removed)
-}
-
-pub(super) async fn write_unit(
-    view: &Table,
-    unit: u32,
-    plan: &VectorDedupPlan,
-    dependencies: &[WrittenDedupUnit],
-) -> Result<WrittenDedupUnit> {
-    let (view_ds, definition, source) = open(view, plan).await?;
-    let mut written = WrittenDedupUnit {
-        unit,
-        plan_id: plan.identity()?,
-        masks: Vec::new(),
-        fragments: Vec::new(),
-        rows: 0,
-    };
-    if unit < plan.dependencies() {
-        let config = definition
-            .vector_source
-            .as_ref()
-            .expect("dedup source")
-            .config()?;
-        let exec = DuplicatePairsExec::try_new(
-            source.clone(),
-            config,
-            vec![plan.native.tasks[unit as usize].clone()],
-        )?;
-        let stream = exec.execute(0, Arc::new(TaskContext::default()))?;
-        let (removed, representatives) = select_representatives(stream, SORT_MEMORY_BYTES).await?;
-        written.masks = persist_masks(&view_ds, &source, removed, representatives).await?;
-        return Ok(written);
-    }
-    let source_fragment = *plan
-        .source_fragments
-        .get((unit - plan.dependencies()) as usize)
-        .ok_or_else(|| invalid("dedup unit is outside the plan"))?;
-    let dependencies = validate_receipts(plan, dependencies, plan.dependencies())?;
-    let removed = load_masks(&view_ds, source_fragment, &dependencies).await?;
-    let fragment = source
-        .get_fragment(source_fragment as usize)
-        .ok_or_else(|| invalid("source fragment missing"))?;
-    // The scanner prefilter uses snapshot row IDs. Our persisted masks use
-    // physical offsets so each writer only loads its own fragment's artifacts.
-    let blocked = if source.manifest.uses_stable_row_ids() {
-        let sequence = load_row_id_sequence(&source, fragment.metadata()).await?;
-        if removed
-            .max()
-            .is_some_and(|offset| offset as u64 >= sequence.len())
-        {
-            return Err(invalid("dedup mask offset is outside the source fragment"));
-        }
-        RowAddrTreeMap::from_iter(sequence.select(removed.iter().map(|offset| offset as usize)))
-    } else {
-        RowAddrTreeMap::from_iter(
-            removed
-                .iter()
-                .map(|offset| u64::from(RowAddress::new_from_parts(source_fragment, offset))),
-        )
-    };
-    let mut scanner = source.scan();
-    scanner
-        .with_fragments(vec![fragment.metadata().clone()])
-        .with_row_addr_prefilter(RowAddrMask::from_block(blocked))
-        .blob_handling(BlobHandling::AllBinary)
-        .batch_size(MATERIALIZE_BATCH_ROWS)
-        .strict_batch_size(true);
-    let stream = scanner.try_into_stream().await?;
-    let schema = lance_io::stream::RecordBatchStream::schema(&stream);
-    let rows = Arc::new(std::sync::atomic::AtomicU64::new(0));
-    let count = rows.clone();
-    let stream = stream
-        .map_err(|e| DataFusionError::External(Box::new(e)))
-        .inspect_ok(move |batch| {
-            count.fetch_add(
-                batch.num_rows() as u64,
-                std::sync::atomic::Ordering::Relaxed,
-            );
-        });
-    let stream: SendableRecordBatchStream = Box::pin(RecordBatchStreamAdapter::new(schema, stream));
-    // Materialized binary payloads must become the destination's logical blob
-    // structs before the Lance writer encodes them. Reuse the normal add path's
-    // coercions, including nulls and nested fields.
-    let coerced = crate::table::datafusion::cast::cast_to_table_schema(
-        Arc::new(OneShotExec::new(stream)),
-        &Schema::from(view_ds.schema()),
-    )?;
-    let stream = coerced.execute(0, Arc::new(TaskContext::default()))?;
-    let txn = InsertBuilder::new(WriteDestination::Dataset(Arc::new(view_ds)))
-        .with_params(&WriteParams {
-            mode: WriteMode::Append,
-            ..Default::default()
-        })
-        .execute_uncommitted_stream(stream)
-        .await?;
-    let Operation::Append { fragments } = txn.operation else {
-        return Err(invalid(
-            "dedup materialization did not stage append fragments",
-        ));
-    };
-    written.fragments = fragments;
-    written.rows = rows.load(std::sync::atomic::Ordering::Relaxed);
-    Ok(written)
-}
-
-pub(super) async fn commit(
-    view: &Table,
-    plan: &VectorDedupPlan,
-    units: Vec<WrittenDedupUnit>,
-    expected: Option<&str>,
-) -> Result<RefreshMaterializedViewResult> {
-    let (_, definition, source) = open(view, plan).await?;
-    let config = definition
-        .vector_source
-        .as_ref()
-        .expect("dedup source")
-        .config()?;
-    if plan_duplicate_pairs(source.clone(), &config).await? != plan.native.tasks {
-        return Err(invalid("dedup plan does not cover the pinned index"));
-    }
-    validate_receipts(plan, &units, plan.snapshot.units)?;
-    let rows = units.iter().map(|u| u.rows).sum();
-    let fragments = units.into_iter().flat_map(|u| u.fragments).collect();
-    super::grouped_units::commit_fragments(
-        view,
-        &plan.snapshot,
-        &definition,
-        source.manifest.timestamp_nanos,
-        fragments,
-        rows,
-        expected,
-    )
-    .await
-}
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    fn edges(values: &[(u64, u64)], batch_size: usize) -> SendableRecordBatchStream {
-        let schema = edge_schema();
-        let batches = values
-            .chunks(batch_size)
-            .map(|edges| {
-                Ok(RecordBatch::try_new(
-                    schema.clone(),
-                    vec![
-                        Arc::new(UInt64Array::from_iter_values(edges.iter().map(|e| e.0))),
-                        Arc::new(UInt64Array::from_iter_values(edges.iter().map(|e| e.1))),
-                    ],
-                )
-                .unwrap())
-            })
-            .collect::<Vec<_>>();
-        Box::pin(RecordBatchStreamAdapter::new(
-            schema,
-            futures::stream::iter(batches),
-        ))
-    }
-
-    #[tokio::test]
-    async fn direct_representatives_handle_chains_orientation_and_uint64() {
-        let high = u64::MAX - 2;
-        let pairs = [
-            (2, 1),
-            (1, 0),
-            (high + 2, high + 1),
-            (high, high + 1),
-            (1, 0),
-        ];
-        let (removed, representatives) =
-            select_representatives(edges(&pairs, 1), SORT_MEMORY_BYTES)
-                .await
-                .unwrap();
-        assert_eq!(removed.iter().collect::<Vec<_>>(), [1, high + 1]);
-        assert_eq!(representatives.iter().collect::<Vec<_>>(), [0, high]);
-    }
-
-    #[tokio::test]
-    async fn masks_reject_overlapping_representatives_and_corrupt_artifacts() {
-        let dir = tempfile::tempdir().unwrap();
-        let schema = Arc::new(Schema::new(vec![Field::new("id", DataType::UInt64, false)]));
-        let batch = RecordBatch::try_new(
-            schema.clone(),
-            vec![Arc::new(UInt64Array::from(vec![0, 1, 2]))],
-        )
-        .unwrap();
-        let ds = Dataset::write(
-            arrow_array::RecordBatchIterator::new(vec![Ok(batch)], schema),
-            dir.path().join("source.lance").to_str().unwrap(),
-            None,
-        )
-        .await
-        .unwrap();
-        let first = persist_masks(
-            &ds,
-            &ds,
-            [1].into_iter().collect(),
-            [0].into_iter().collect(),
-        )
-        .await
-        .unwrap();
-        let second = persist_masks(
-            &ds,
-            &ds,
-            [2].into_iter().collect(),
-            [1].into_iter().collect(),
-        )
-        .await
-        .unwrap();
-        let receipt = |masks| WrittenDedupUnit {
-            unit: 0,
-            plan_id: "test".into(),
-            masks,
-            fragments: vec![],
-            rows: 0,
-        };
-        let first = receipt(first);
-        let second = receipt(second);
-        assert_eq!(
-            load_masks(&ds, 0, &[&first]).await.unwrap(),
-            [1].into_iter().collect()
-        );
-        assert!(
-            load_masks(&ds, 0, &[&first, &second])
-                .await
-                .unwrap_err()
-                .to_string()
-                .contains("overlapping")
-        );
-        let file = &first.masks[0];
-        ds.object_store(None)
-            .await
-            .unwrap()
-            .put(
-                &ds.indices_dir()
-                    .join(file.object_id.to_string())
-                    .join(MASK_FILE),
-                &vec![0; file.bytes],
-            )
-            .await
-            .unwrap();
-        assert!(
-            load_masks(&ds, 0, &[&first])
-                .await
-                .unwrap_err()
-                .to_string()
-                .contains("checksum")
-        );
-    }
-
-    #[tokio::test]
-    async fn external_sort_handles_more_edges_than_its_budget() {
-        let pairs = (0..262144)
-            .rev()
-            .map(|i| ((i + 1) as u64, i as u64))
-            .collect::<Vec<_>>();
-        let (removed, reps) = select_representatives(edges(&pairs, 4096), 2 * 1024 * 1024)
-            .await
-            .unwrap();
-        assert_eq!(removed.len(), 131072);
-        assert_eq!(reps.len(), 131072);
-        assert!(removed.iter().all(|id| id % 2 == 1));
-        assert!(reps.iter().all(|id| id % 2 == 0));
-    }
-}
diff --git a/rust/lancedb/src/query.rs b/rust/lancedb/src/query.rs
index c8b19f013..5f072cad6 100644
--- a/rust/lancedb/src/query.rs
+++ b/rust/lancedb/src/query.rs
@@ -11,7 +11,7 @@ use arrow_array::{
     Array, Float16Array, Float32Array, Float64Array, RecordBatch, UInt64Array,
     cast::AsArray,
     make_array,
-    types::{Int64Type, UInt64Type},
+    types::{Float32Type, Int64Type, UInt64Type},
 };
 use arrow_schema::{DataType, SchemaRef};
 use datafusion_common::{DataFusionError, Result as DataFusionResult};
@@ -547,22 +547,22 @@ pub trait QueryBase {
     /// Control MemWAL read routing for this query.
     ///
     /// By default (unset), when the table carries a MemWAL write spec (see
-    /// [`crate::Table::set_lsm_write_spec`]), reads are routed through the LSM
+    /// [`crate::Table::enable_memwal`]), reads are routed through the LSM
     /// scanner so they also return data written via the `merge_insert` LSM path
     /// that has not yet been compacted into the base table (active/frozen
-    /// memtables and flushed generations); a table without a spec reads the base
+    /// memtables and SSTables); a table without a spec reads the base
     /// table.
     ///
-    /// - `use_lsm(true)` forces LSM routing and errors if the table has no
+    /// - `use_memwal(true)` forces MemWAL routing and errors if the table has no
     ///   MemWAL write spec.
-    /// - `use_lsm(false)` bypasses the MemWAL and reads the base table only,
+    /// - `use_memwal(false)` bypasses the MemWAL and reads the base table only,
     ///   even when a spec is present.
     ///
     /// Note: the LSM scanner does not support every query shape (e.g. reranking,
     /// hybrid search, `order_by`). On a MemWAL table those shapes error unless
-    /// `use_lsm(false)` is set, because a base-only read would silently
+    /// `use_memwal(false)` is set, because a base-only read would silently
     /// exclude un-compacted MemWAL data.
-    fn use_lsm(self, enable: bool) -> Self;
+    fn use_memwal(self, enable: bool) -> Self;
 }
 
 pub trait HasQuery {
@@ -634,8 +634,8 @@ impl<T: HasQuery> QueryBase for T {
         self
     }
 
-    fn use_lsm(mut self, enable: bool) -> Self {
-        self.mut_query().use_lsm = Some(enable);
+    fn use_memwal(mut self, enable: bool) -> Self {
+        self.mut_query().use_memwal = Some(enable);
         self
     }
 }
@@ -900,17 +900,17 @@ pub struct QueryRequest {
 
     /// Controls MemWAL read routing. When unset (the default), a query against a
     /// table that carries a MemWAL write spec (see
-    /// [`crate::Table::set_lsm_write_spec`]) is routed through the LSM scanner so
+    /// [`crate::Table::enable_memwal`]) is routed through the LSM scanner so
     /// it also sees data written via the `merge_insert` LSM path that has not yet
     /// been compacted into the base table — the active and frozen in-memory
-    /// memtables and the flushed (L0) generations, deduplicated by primary key
+    /// memtables and the SSTables, deduplicated by primary key
     /// against the base table (newest generation wins); a table without a spec
     /// reads the base table.
     ///
-    /// - `Some(true)` forces LSM routing and errors if the table has no MemWAL
+    /// - `Some(true)` forces MemWAL routing and errors if the table has no MemWAL
     ///   write spec.
     /// - `Some(false)` reads only the base table, bypassing the MemWAL.
-    pub use_lsm: Option<bool>,
+    pub use_memwal: Option<bool>,
 }
 
 impl Default for QueryRequest {
@@ -930,7 +930,7 @@ impl Default for QueryRequest {
             norm: None,
             disable_scoring_autoprojection: false,
             order_by: None,
-            use_lsm: None,
+            use_memwal: None,
         }
     }
 }
@@ -1024,6 +1024,8 @@ impl Query {
     ///
     /// By default, there is no embedding model, and the input should be
     /// vector/slice of floats.
+    /// NaN and infinite components are rejected with [`Error::InvalidInput`]
+    /// after conversion to Float32, for both local and remote tables.
     ///
     /// If there is only one vector column (a column whose data type is a
     /// fixed size list of floats) then the column does not need to be specified.
@@ -1050,8 +1052,7 @@ impl Query {
     /// * `vector` - The vector that will be used for search.
     pub fn nearest_to(self, vector: impl IntoQueryVector) -> Result<VectorQuery> {
         let mut vector_query = self.into_vector();
-        let query_vector = vector.to_query_vector(&DataType::Float32, "default")?;
-        vector_query.request.query_vector.push(query_vector);
+        vector_query.request.add_query_vector(vector)?;
 
         if vector_query.request.base.limit.is_none() {
             vector_query.request.base.limit = Some(DEFAULT_TOP_K);
@@ -1113,10 +1114,24 @@ pub struct VectorQueryRequest {
     pub column: Option<String>,
     /// The vector(s) to search for
     pub query_vector: Vec<Arc<dyn Array>>,
-    /// The minimum number of partitions to search
-    pub minimum_nprobes: usize,
-    /// The maximum number of partitions to search
+    /// The legacy number of partitions to search.
+    ///
+    /// Lance sets both probe bounds to this value. Explicit bounds can override it.
+    pub nprobes: Option<usize>,
+    /// The minimum number of partitions to search.
+    ///
+    /// If unset, Lance's default is used.
+    pub minimum_nprobes: Option<usize>,
+    /// The maximum number of partitions to search.
+    ///
+    /// If unset, Lance's default is used. Zero represents an explicitly unbounded
+    /// maximum on request and wire representations.
     pub maximum_nprobes: Option<usize>,
+    /// Optional initial IVF search effort in [0, 1].
+    ///
+    /// None preserves Auto and fixed `nprobes`; any explicit value overrides
+    /// `nprobes` while retaining explicit minimum and maximum bounds.
+    pub search_effort: Option<f64>,
     /// The lower bound (inclusive) of the distance to search for.
     pub lower_bound: Option<f32>,
     /// The upper bound (exclusive) of the distance to search for.
@@ -1140,8 +1155,10 @@ impl Default for VectorQueryRequest {
             base: QueryRequest::default(),
             column: None,
             query_vector: Vec::new(),
-            minimum_nprobes: 20,
-            maximum_nprobes: Some(20),
+            nprobes: None,
+            minimum_nprobes: None,
+            maximum_nprobes: None,
+            search_effort: None,
             lower_bound: None,
             upper_bound: None,
             ef: None,
@@ -1154,6 +1171,34 @@ impl Default for VectorQueryRequest {
 }
 
 impl VectorQueryRequest {
+    pub(crate) fn validate_search_effort(&self) -> Result<()> {
+        if let Some(effort) = self.search_effort
+            && (!effort.is_finite() || !(0.0..=1.0).contains(&effort))
+        {
+            return Err(Error::InvalidInput {
+                message: format!("search_effort must be finite and in [0, 1], got {effort}"),
+            });
+        }
+        Ok(())
+    }
+
+    fn add_query_vector(&mut self, vector: impl IntoQueryVector) -> Result<()> {
+        let vector = vector.to_query_vector(&DataType::Float32, "default")?;
+        let array =
+            vector
+                .as_primitive_opt::<Float32Type>()
+                .ok_or_else(|| Error::InvalidInput {
+                    message: "VectorQuery vector must be of type Float32".into(),
+                })?;
+        if array.values().iter().any(|value| !value.is_finite()) {
+            return Err(Error::InvalidInput {
+                message: "query vector must contain only finite values".into(),
+            });
+        }
+        self.query_vector.push(vector);
+        Ok(())
+    }
+
     pub fn from_plain_query(query: QueryRequest) -> Self {
         Self {
             base: query,
@@ -1222,13 +1267,15 @@ impl VectorQuery {
     /// The output data will contain an additional column `query_index` which
     /// will contain the index of the query vector that was used to generate the
     /// result.
+    ///
+    /// NaN and infinite components are rejected with [`Error::InvalidInput`]
+    /// after conversion to Float32, for both local and remote tables.
     pub fn add_query_vector(mut self, vector: impl IntoQueryVector) -> Result<Self> {
-        let query_vector = vector.to_query_vector(&DataType::Float32, "default")?;
-        self.request.query_vector.push(query_vector);
+        self.request.add_query_vector(vector)?;
         Ok(self)
     }
 
-    /// Set the number of partitions to search (probe)
+    /// Set the legacy IVF probe parameter
     ///
     /// This argument is only used when the vector column has an IVF PQ index.
     /// If there is no index then this value is ignored.
@@ -1238,24 +1285,56 @@ impl VectorQuery {
     ///
     /// The partition whose centroids are closest to the query vector will be
     /// exhaustiely searched to find matches.  This parameter controls how many
-    /// partitions should be searched.
+    /// partitions may be searched.
     ///
     /// Increasing this value will increase the recall of your query but will
-    /// also increase the latency of your query.  The default value is 20.  This
-    /// default is good for many cases but the best value to use will depend on
-    /// your data and the recall that you need to achieve.
+    /// also increase the latency of your query. If this method is not called,
+    /// Lance's adaptive probe defaults are used.
     ///
     /// For best results we recommend tuning this parameter with a benchmark against
     /// your actual data to find the smallest possible value that will still give
     /// you the desired recall.
     ///
-    /// This method sets both the minimum and maximum number of partitions to search.
-    /// For more fine-grained control see [`VectorQuery::minimum_nprobes`] and
+    /// LanceDB retains this as `nprobes` through local and remote request
+    /// construction. Lance sets both probe bounds to this value only when
+    /// [`Self::search_effort`] is unset. For more
+    /// fine-grained control see [`VectorQuery::minimum_nprobes`] and
     /// [`VectorQuery::maximum_nprobes`].
-    pub fn nprobes(mut self, nprobes: usize) -> Self {
-        self.request.minimum_nprobes = nprobes;
-        self.request.maximum_nprobes = Some(nprobes);
-        self
+    ///
+    /// Returns an error if `nprobes` is not greater than 0.
+    pub fn nprobes(mut self, nprobes: usize) -> Result<Self> {
+        if nprobes == 0 {
+            return Err(Error::InvalidInput {
+                message: "nprobes must be greater than 0".to_string(),
+            });
+        }
+        self.request.nprobes = Some(nprobes);
+        Ok(self)
+    }
+
+    /// Set the initial IVF search effort to a finite value in [0, 1].
+    ///
+    /// None (the default) resolves to 0.5, preserving Auto probing. At 0 the
+    /// search starts at the caller minimum; at 1 it searches all partitions
+    /// within the caller maximum. Intermediate values interpolate around Auto.
+    /// Later probing may continue when too few candidates are found.
+    /// Any explicit value, including 0.5, overrides [`Self::nprobes`] regardless
+    /// of setter order. Explicit minimum and maximum bounds still apply.
+    /// This does not change refinement or HNSW ef and is not a recall guarantee.
+    /// Lance's MemWAL LSM scanner does not yet support this option.
+    ///
+    /// ```
+    /// # use lancedb::{Table, query::{QueryBase, ExecutableQuery}};
+    /// # async fn example(table: &Table) -> lancedb::Result<()> {
+    /// let results = table.query().nearest_to([0.1, 0.2].as_slice())?
+    ///     .search_effort(0.75)?.execute().await?;
+    /// # Ok(())
+    /// # }
+    /// ```
+    pub fn search_effort(mut self, search_effort: impl Into<Option<f64>>) -> Result<Self> {
+        self.request.search_effort = search_effort.into();
+        self.request.validate_search_effort()?;
+        Ok(self)
     }
 
     /// Set the minimum number of partitions to search
@@ -1276,6 +1355,7 @@ impl VectorQuery {
             });
         }
         if let Some(maximum_nprobes) = self.request.maximum_nprobes
+            && maximum_nprobes != 0
             && minimum_nprobes > maximum_nprobes
         {
             return Err(Error::InvalidInput {
@@ -1283,7 +1363,7 @@ impl VectorQuery {
                     .to_string(),
             });
         }
-        self.request.minimum_nprobes = minimum_nprobes;
+        self.request.minimum_nprobes = Some(minimum_nprobes);
         Ok(self)
     }
 
@@ -1296,11 +1376,13 @@ impl VectorQuery {
     ///
     /// If this value is greater than minimum_nprobes then the excess partitions will
     /// only be searched if the initial search does not return enough results.
+    /// The bound applies per index segment to initial and later probing without
+    /// disabling Auto probing.
     ///
     /// This can be useful when there is a narrow filter to allow these queries to
     /// spend more time searching and avoid potential false negatives.
     ///
-    /// Set to None to search all partitions, if needed, to satisfy the limit
+    /// Set to None to use Lance's default maximum probe behavior.
     pub fn maximum_nprobes(mut self, maximum_nprobes: Option<usize>) -> Result<Self> {
         if let Some(maximum_nprobes) = maximum_nprobes {
             if maximum_nprobes == 0 {
@@ -1308,14 +1390,18 @@ impl VectorQuery {
                     message: "maximum_nprobes must be greater than 0".to_string(),
                 });
             }
-            if maximum_nprobes < self.request.minimum_nprobes {
+            if let Some(minimum_nprobes) = self.request.minimum_nprobes
+                && maximum_nprobes < minimum_nprobes
+            {
                 return Err(Error::InvalidInput {
                     message: "maximum_nprobes must be greater than or equal to minimum_nprobes"
                         .to_string(),
                 });
             }
         }
-        self.request.maximum_nprobes = maximum_nprobes;
+        // Preserve an explicit reset separately from an untouched maximum. The
+        // request protocols already use zero to represent an unbounded maximum.
+        self.request.maximum_nprobes = Some(maximum_nprobes.unwrap_or(0));
         Ok(self)
     }
 
@@ -2329,6 +2415,69 @@ mod tests {
 
     use crate::{Table, connect, database::CreateTableMode, index::Index};
 
+    #[tokio::test]
+    async fn test_query_vector_non_finite() {
+        let conn = connect("memory://non_finite_vectors")
+            .execute()
+            .await
+            .unwrap();
+        let table = conn
+            .create_table("my_table", make_test_batches())
+            .execute()
+            .await
+            .unwrap();
+        let assert_invalid = |result: Result<VectorQuery>| {
+            let err = result.unwrap_err();
+            assert!(matches!(err, Error::InvalidInput { .. }));
+            assert!(err.to_string().contains("only finite values"));
+        };
+
+        // Float64 values can be finite before conversion but overflow Float32.
+        for value in [
+            f64::NAN,
+            f64::INFINITY,
+            f64::NEG_INFINITY,
+            f64::MAX,
+            f64::MIN,
+        ] {
+            assert_invalid(table.query().nearest_to(&[0.1, value as f32]));
+            assert_invalid(table.query().nearest_to(vec![0.1, value]));
+            assert_invalid(
+                table
+                    .query()
+                    .nearest_to(&[0.1, 0.2])
+                    .unwrap()
+                    .add_query_vector(vec![0.1, value]),
+            );
+
+            let arrays: [Arc<dyn Array>; 3] = [
+                Arc::new(Float16Array::from(vec![
+                    f16::from_f32(0.1),
+                    f16::from_f64(value),
+                ])),
+                Arc::new(Float32Array::from(vec![0.1, value as f32])),
+                Arc::new(Float64Array::from(vec![0.1, value])),
+            ];
+            for array in arrays {
+                assert_invalid(table.query().nearest_to(array.clone()));
+                assert_invalid(
+                    table
+                        .query()
+                        .nearest_to(&[0.1, 0.2])
+                        .unwrap()
+                        .add_query_vector(array),
+                );
+            }
+        }
+
+        table
+            .query()
+            .nearest_to(&[f32::MIN, f32::MAX])
+            .unwrap()
+            .add_query_vector(&[0.0, -0.0])
+            .unwrap();
+    }
+
     #[tokio::test]
     async fn test_setters_getters() {
         let batches = make_test_batches();
@@ -2341,6 +2490,29 @@ mod tests {
 
         let vector = Float32Array::from_iter_values([0.1, 0.2]);
         let query = table.query().nearest_to(&[0.1, 0.2]).unwrap();
+        assert_eq!(query.request.nprobes, None);
+        assert_eq!(query.request.minimum_nprobes, None);
+        assert_eq!(query.request.maximum_nprobes, None);
+        assert_eq!(query.request.search_effort, None);
+
+        let effort_query = query
+            .clone()
+            .nprobes(20)
+            .unwrap()
+            .search_effort(0.5)
+            .unwrap();
+        assert_eq!(effort_query.request.search_effort, Some(0.5));
+        assert_eq!(effort_query.request.nprobes, Some(20));
+        assert_eq!(effort_query.request.minimum_nprobes, None);
+        assert_eq!(effort_query.request.maximum_nprobes, None);
+        assert_eq!(
+            effort_query
+                .search_effort(None)
+                .unwrap()
+                .request
+                .search_effort,
+            None
+        );
         assert_eq!(
             *query
                 .request
@@ -2352,6 +2524,37 @@ mod tests {
             vector
         );
 
+        let minimum_only_query = query.clone().minimum_nprobes(20).unwrap();
+        assert_eq!(minimum_only_query.request.minimum_nprobes, Some(20));
+        assert_eq!(minimum_only_query.request.maximum_nprobes, None);
+
+        let maximum_only_query = query.maximum_nprobes(Some(20)).unwrap();
+        assert_eq!(maximum_only_query.request.minimum_nprobes, None);
+        assert_eq!(maximum_only_query.request.maximum_nprobes, Some(20));
+
+        let explicitly_unbounded_query = table
+            .query()
+            .nearest_to(&[0.1, 0.2])
+            .unwrap()
+            .nprobes(20)
+            .unwrap()
+            .maximum_nprobes(None)
+            .unwrap();
+        assert_eq!(explicitly_unbounded_query.request.nprobes, Some(20));
+        assert_eq!(explicitly_unbounded_query.request.maximum_nprobes, Some(0));
+
+        let bounded_query = table
+            .query()
+            .nearest_to(&[0.1, 0.2])
+            .unwrap()
+            .minimum_nprobes(5)
+            .unwrap()
+            .nprobes(20)
+            .unwrap();
+        assert_eq!(bounded_query.request.nprobes, Some(20));
+        assert_eq!(bounded_query.request.minimum_nprobes, Some(5));
+        assert_eq!(bounded_query.request.maximum_nprobes, None);
+
         let new_vector = Float32Array::from_iter_values([9.8, 8.7]);
 
         let query = table
@@ -2360,7 +2563,8 @@ mod tests {
             .offset(1)
             .nearest_to(&[9.8, 8.7])
             .unwrap()
-            .nprobes(1000)
+            .nprobes(20)
+            .unwrap()
             .postfilter()
             .distance_type(DistanceType::Cosine)
             .approx_mode(ApproxMode::Accurate)
@@ -2378,14 +2582,40 @@ mod tests {
         );
         assert_eq!(query.request.base.limit.unwrap(), 100);
         assert_eq!(query.request.base.offset.unwrap(), 1);
-        assert_eq!(query.request.minimum_nprobes, 1000);
-        assert_eq!(query.request.maximum_nprobes, Some(1000));
+        assert_eq!(query.request.nprobes, Some(20));
+        assert_eq!(query.request.minimum_nprobes, None);
+        assert_eq!(query.request.maximum_nprobes, None);
         assert!(query.request.use_index);
         assert_eq!(query.request.distance_type, Some(DistanceType::Cosine));
         assert_eq!(query.request.approx_mode, Some(ApproxMode::Accurate));
         assert_eq!(query.request.refine_factor, Some(999));
     }
 
+    #[tokio::test]
+    async fn test_nprobes_validation() {
+        let conn = connect("memory://").execute().await.unwrap();
+        let table = conn
+            .create_table("my_table", make_test_batches())
+            .execute()
+            .await
+            .unwrap();
+        let query = table.query().nearest_to(&[0.1, 0.2]).unwrap();
+
+        assert!(matches!(
+            query.clone().nprobes(0),
+            Err(Error::InvalidInput { message }) if message == "nprobes must be greater than 0"
+        ));
+
+        // Legacy probe counts remain independent until Lance resolves them.
+        let mut query = query;
+        for nprobes in [30, 1, 50] {
+            query = query.nprobes(nprobes).unwrap();
+            assert_eq!(query.request.nprobes, Some(nprobes));
+            assert_eq!(query.request.minimum_nprobes, None);
+            assert_eq!(query.request.maximum_nprobes, None);
+        }
+    }
+
     #[test]
     fn test_approx_mode_serde_parse_default_and_display() {
         assert_eq!(ApproxMode::default(), ApproxMode::Normal);
@@ -3576,8 +3806,12 @@ mod tests {
         assert_eq!(results[0].num_columns(), 1);
     }
 
+    #[rstest::rstest]
+    #[case::single_row(1)]
+    #[case::two_rows(2)]
+    #[case::three_rows(3)]
     #[tokio::test]
-    async fn test_take_offsets_preserves_duplicate_multiplicity() {
+    async fn test_take_offsets_preserves_duplicate_multiplicity(#[case] max_batch_length: u32) {
         let tmp_dir = tempdir().unwrap();
         let table = make_test_table(&tmp_dir).await;
 
@@ -3585,7 +3819,7 @@ mod tests {
             .take_offsets(vec![5, 1, 5, 17])
             .select(Select::Columns(vec!["id".to_string()]))
             .execute_with_options(QueryExecutionOptions {
-                max_batch_length: 2,
+                max_batch_length,
                 ..Default::default()
             })
             .await
@@ -3594,7 +3828,11 @@ mod tests {
             .await
             .unwrap();
 
-        assert_eq!(results.len(), 2);
+        assert!(
+            results
+                .iter()
+                .all(|batch| batch.num_rows() <= max_batch_length as usize)
+        );
         assert!(results.iter().all(|batch| batch.num_columns() == 1));
         let mut ids = results
             .iter()
diff --git a/rust/lancedb/src/query/wal_fusion.rs b/rust/lancedb/src/query/wal_fusion.rs
index b438f6150..e8b6b3269 100644
--- a/rust/lancedb/src/query/wal_fusion.rs
+++ b/rust/lancedb/src/query/wal_fusion.rs
@@ -54,7 +54,7 @@ fn wal_row_id_unsupported() -> Error {
     Error::NotSupported {
         message: "hybrid search on a MemWAL table cannot return _rowid: the fresh tier has no \
                   stable row id, and the ids the fusion joins on are synthesized from the \
-                  primary key. Set use_lsm(false) to read the base table only (results will \
+                  primary key. Set use_memwal(false) to read the base table only (results will \
                   exclude un-compacted MemWAL data)"
             .to_string(),
     }
@@ -80,7 +80,7 @@ where
 {
     // A previous query on this table was already refused, so skip straight to
     // the key.
-    if query.request.base.use_lsm != Some(false) && query.parent.hybrid_pk_fusion_learned() {
+    if query.request.base.use_memwal != Some(false) && query.parent.hybrid_pk_fusion_learned() {
         return run(Some(PkFusion::resolve(query).await?)).await;
     }
     match run(None).await {
diff --git a/rust/lancedb/src/remote.rs b/rust/lancedb/src/remote.rs
index bc534c0f4..a6b2f0e62 100644
--- a/rust/lancedb/src/remote.rs
+++ b/rust/lancedb/src/remote.rs
@@ -6,6 +6,7 @@
 //! building client/server applications with LanceDB or as a client for some
 //! other custom LanceDB service.
 
+mod authz;
 pub mod catalog;
 pub(crate) mod client;
 pub(crate) mod db;
diff --git a/rust/lancedb/src/remote/authz.rs b/rust/lancedb/src/remote/authz.rs
new file mode 100644
index 000000000..88c2841ce
--- /dev/null
+++ b/rust/lancedb/src/remote/authz.rs
@@ -0,0 +1,254 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use super::client::{HttpSend, RequestResultExt, RestfulLanceDbClient, Sender};
+use crate::Result;
+use crate::authz::*;
+use serde::{Serialize, de::DeserializeOwned};
+
+pub struct RemoteAuthorization<S: HttpSend = Sender> {
+    pub(crate) client: RestfulLanceDbClient<S>,
+}
+
+impl<S: HttpSend> std::fmt::Debug for RemoteAuthorization<S> {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        f.debug_struct("RemoteAuthorization")
+            .finish_non_exhaustive()
+    }
+}
+
+impl<S: HttpSend> RemoteAuthorization<S> {
+    async fn post<T: Serialize + Sync>(
+        &self,
+        route: &str,
+        request: &T,
+    ) -> Result<(String, reqwest::Response)> {
+        // Structured objects can be constructed with invalid names. Serialize
+        // before building the request so validation errors remain local errors.
+        let body = serde_json::to_value(request).map_err(|error| crate::Error::InvalidInput {
+            message: error.to_string(),
+        })?;
+        let request = self
+            .client
+            .post(&format!("/admin/authz/{route}"))
+            .json(&body);
+        // Every subject selector can contain a secret. Never log these bodies,
+        // and never replay an ACL mutation after an ambiguous response.
+        let (id, response) = self.client.send_suppressing_body(request).await?;
+        let response = self.client.check_response(&id, response).await?;
+        Ok((id, response))
+    }
+
+    async fn read<T: Serialize + Sync, R: DeserializeOwned>(
+        &self,
+        route: &str,
+        request: &T,
+    ) -> Result<R> {
+        let (id, response) = self.post(route, request).await?;
+        response.json().await.err_to_http(id)
+    }
+}
+
+#[async_trait::async_trait]
+impl<S: HttpSend> Authorization for RemoteAuthorization<S> {
+    async fn list_acls(
+        &self,
+        request: ListAccessControlEntriesRequest,
+    ) -> Result<ListAccessControlEntriesResponse> {
+        self.read("acl/list", &request).await
+    }
+    async fn add_acl(&self, request: AccessControlEntryRequest) -> Result<()> {
+        self.post("acl/add", &request).await?;
+        Ok(())
+    }
+    async fn delete_acl(&self, request: AccessControlEntryRequest) -> Result<()> {
+        self.post("acl/delete", &request).await?;
+        Ok(())
+    }
+    async fn list_role_bindings(
+        &self,
+        request: ListRoleBindingsRequest,
+    ) -> Result<ListRoleBindingsResponse> {
+        request.validate()?;
+        self.read("role_binding/list", &request).await
+    }
+    async fn add_role_binding(&self, request: RoleBindingRequest) -> Result<()> {
+        request.validate()?;
+        self.post("role_binding/add", &request).await?;
+        Ok(())
+    }
+    async fn delete_role_binding(&self, request: RoleBindingRequest) -> Result<()> {
+        request.validate()?;
+        self.post("role_binding/delete", &request).await?;
+        Ok(())
+    }
+    async fn get_principal(&self, request: GetPrincipalRequest) -> Result<PrincipalInfo> {
+        request.validate()?;
+        self.read("principal", &request).await
+    }
+    async fn get_group(&self, request: GetGroupRequest) -> Result<GroupInfo> {
+        request.validate()?;
+        self.read("group", &request).await
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::remote::client::test_utils::client_with_handler;
+    use serde_json::json;
+    use std::sync::{
+        Arc,
+        atomic::{AtomicUsize, Ordering},
+    };
+
+    #[tokio::test]
+    async fn list_preserves_wire_identity_and_continuation() {
+        let authz = RemoteAuthorization {
+            client: client_with_handler(|request| {
+                assert_eq!(request.url().path(), "/admin/authz/acl/list");
+                let body: serde_json::Value =
+                    serde_json::from_slice(request.body().unwrap().as_bytes().unwrap()).unwrap();
+                assert_eq!(
+                    body,
+                    json!({"object": "table:tenant/db:a$b$events", "subject": "G:Engineering", "max_results": 100, "continuation_token": "opaque/token"})
+                );
+                http::Response::builder().status(200).body(json!({
+                    "entries": [{"object": "table:tenant/db:a$b$events", "subject_id": "g:team-id", "privilege": "PRIVILEGE20"}],
+                    "continuation_token": "next/token"
+                }).to_string()).unwrap()
+            }),
+        };
+        let result = authz
+            .list_acls(ListAccessControlEntriesRequest {
+                object: Some("table:tenant/db:a$b$events".parse().unwrap()),
+                subject: Some(Subject::group_name("Engineering").unwrap()),
+                max_results: Some(100),
+                continuation_token: Some("opaque/token".into()),
+            })
+            .await
+            .unwrap();
+        assert_eq!(result.continuation_token.as_deref(), Some("next/token"));
+        assert_eq!(
+            result.entries[0].subject_id,
+            Subject::group_id("team-id").unwrap()
+        );
+        assert_eq!(result.entries[0].subject_name, None);
+        assert_eq!(
+            result.entries[0].privilege,
+            "PRIVILEGE20".parse::<Privilege>().unwrap()
+        );
+    }
+
+    #[tokio::test]
+    async fn acl_failure_is_not_retried_or_reclassified() {
+        for status in [404, 409, 503] {
+            let calls = Arc::new(AtomicUsize::new(0));
+            let count = calls.clone();
+            let authz = RemoteAuthorization {
+                client: client_with_handler(move |request| {
+                    count.fetch_add(1, Ordering::SeqCst);
+                    let body: serde_json::Value =
+                        serde_json::from_slice(request.body().unwrap().as_bytes().unwrap())
+                            .unwrap();
+                    assert_eq!(body["privilege"], "USAGE");
+                    http::Response::builder()
+                        .status(status)
+                        .body("failure")
+                        .unwrap()
+                }),
+            };
+            let result = authz
+                .add_acl(AccessControlEntryRequest {
+                    object: Object::Database(DatabaseObject::new("db".into())),
+                    subject: Subject::principal_api_key("secret").unwrap(),
+                    privilege: Privilege::Usage,
+                })
+                .await;
+            let Err(crate::Error::Http {
+                status_code,
+                request_id,
+                ..
+            }) = result
+            else {
+                panic!("expected Http error")
+            };
+            assert_eq!(status_code.unwrap().as_u16(), status);
+            assert!(!request_id.is_empty());
+            assert_eq!(calls.load(Ordering::SeqCst), 1);
+        }
+    }
+
+    #[tokio::test]
+    async fn rejects_invalid_objects_before_sending() {
+        let authz = RemoteAuthorization {
+            client: client_with_handler(|_| -> http::Response<String> {
+                panic!("invalid objects must not send a request")
+            }),
+        };
+        let request = AccessControlEntryRequest::new(
+            Object::Database(DatabaseObject::new("bad:database".into())),
+            Subject::principal_api_key("secret").unwrap(),
+            Privilege::Usage,
+        );
+        for result in [
+            authz.add_acl(request.clone()).await,
+            authz.delete_acl(request.clone()).await,
+            authz
+                .list_acls(ListAccessControlEntriesRequest {
+                    object: Some(request.object),
+                    ..Default::default()
+                })
+                .await
+                .map(|_| ()),
+        ] {
+            assert!(matches!(result, Err(crate::Error::InvalidInput { .. })));
+            assert!(!result.unwrap_err().to_string().contains("secret"));
+        }
+    }
+
+    #[tokio::test]
+    async fn rejects_incompatible_subjects_before_sending() {
+        let authz = RemoteAuthorization {
+            client: client_with_handler(|_| -> http::Response<String> {
+                panic!("invalid selectors must not send a request")
+            }),
+        };
+        let request = RoleBindingRequest {
+            subject: Subject::role("reader").unwrap(),
+            role: "writer".into(),
+        };
+        assert!(matches!(
+            authz.add_role_binding(request.clone()).await,
+            Err(crate::Error::InvalidInput { .. })
+        ));
+        assert!(matches!(
+            authz.delete_role_binding(request).await,
+            Err(crate::Error::InvalidInput { .. })
+        ));
+        let request = ListRoleBindingsRequest {
+            subject: Some(Subject::role("reader").unwrap()),
+            ..Default::default()
+        };
+        assert!(matches!(
+            authz.list_role_bindings(request).await,
+            Err(crate::Error::InvalidInput { .. })
+        ));
+        assert!(matches!(
+            authz
+                .get_principal(GetPrincipalRequest {
+                    principal: Subject::group_id("group").unwrap()
+                })
+                .await,
+            Err(crate::Error::InvalidInput { .. })
+        ));
+        assert!(matches!(
+            authz
+                .get_group(GetGroupRequest {
+                    group: Subject::principal_api_key("secret").unwrap()
+                })
+                .await,
+            Err(crate::Error::InvalidInput { .. })
+        ));
+    }
+}
diff --git a/rust/lancedb/src/remote/catalog.rs b/rust/lancedb/src/remote/catalog.rs
index 1c0129889..df3782368 100644
--- a/rust/lancedb/src/remote/catalog.rs
+++ b/rust/lancedb/src/remote/catalog.rs
@@ -19,6 +19,7 @@ use crate::catalog::{
     ListDatabasesResponse,
 };
 use crate::database::Database;
+use crate::utils::validate_database_name;
 use crate::{Error, Result};
 
 /// Authentication and client settings shared by a catalog and its databases.
@@ -154,29 +155,6 @@ impl RemoteCatalog {
         })
     }
 
-    fn validate_name(&self, name: &str) -> Result<()> {
-        let delimiter = self
-            .options
-            .client_config
-            .id_delimiter
-            .as_deref()
-            .unwrap_or("$");
-        if name.is_empty()
-            || name.trim() != name
-            || !name.is_ascii()
-            || name.chars().any(char::is_control)
-            || name.contains(delimiter)
-            || matches!(name, "." | "..")
-        {
-            return Err(Error::InvalidInput {
-                message: format!(
-                    "Invalid database name '{name}': expected a nonempty ASCII name without surrounding whitespace, control characters, or namespace delimiter '{delimiter}'"
-                ),
-            });
-        }
-        Ok(())
-    }
-
     fn database(&self, name: &str) -> Result<Arc<dyn Database>> {
         Ok(Arc::new(RemoteDatabase::for_catalog(
             &self.endpoint,
@@ -198,12 +176,16 @@ impl RemoteCatalog {
 
 #[async_trait]
 impl Catalog for RemoteCatalog {
+    fn authz(&self) -> Result<Arc<dyn crate::authz::Authorization>> {
+        Ok(self.root.authz())
+    }
+
     fn uri(&self) -> &str {
         &self.endpoint
     }
 
     async fn create_database(&self, request: CreateDatabaseRequest) -> Result<Arc<dyn Database>> {
-        self.validate_name(&request.name)?;
+        validate_database_name(&request.name)?;
         self.root
             .create_namespace(CreateNamespaceRequest {
                 id: Some(vec![request.name.clone()]),
@@ -231,7 +213,7 @@ impl Catalog for RemoteCatalog {
     }
 
     async fn drop_database(&self, request: DropDatabaseRequest) -> Result<()> {
-        self.validate_name(&request.name)?;
+        validate_database_name(&request.name)?;
         let result = self
             .root
             .drop_namespace(DropNamespaceRequest {
@@ -286,7 +268,7 @@ impl Catalog for RemoteCatalog {
     }
 
     async fn connect_database(&self, name: &str) -> Result<Arc<dyn Database>> {
-        self.validate_name(name)?;
+        validate_database_name(name)?;
         self.root
             .describe_namespace(DescribeNamespaceRequest {
                 id: Some(vec![name.into()]),
@@ -416,6 +398,7 @@ mod tests {
             RemoteCatalog::try_new(&endpoint, options).unwrap(),
         ));
         let page = catalog
+            .catalog()
             .list_databases(ListDatabasesRequest::default().limit(1).page_token("a/b"))
             .await
             .unwrap();
@@ -443,6 +426,7 @@ mod tests {
             .unwrap();
         assert!(
             catalog
+                .catalog()
                 .list_databases(ListDatabasesRequest::default())
                 .await
                 .unwrap()
@@ -538,6 +522,128 @@ mod tests {
         );
     }
 
+    #[tokio::test]
+    async fn database_names_follow_pages_lazily() {
+        use futures::TryStreamExt;
+
+        let (endpoint, task) = server(vec![
+            (
+                200,
+                json!({"namespaces": ["first", "second"], "page_token": "a/b"}),
+            ),
+            (200, json!({"namespaces": [], "page_token": "next"})),
+            (200, json!({"namespaces": ["last"], "page_token": ""})),
+        ])
+        .await;
+        let catalog = CatalogConnection::new(Arc::new(
+            RemoteCatalog::try_new(endpoint, RemoteCatalogOptions::default()).unwrap(),
+        ));
+        let mut names = catalog.list_databases(None, None);
+        drop(catalog);
+        assert_eq!(names.num_page_results(), 0);
+        assert_eq!(names.page_token(), None);
+        assert_eq!(names.try_next().await.unwrap().as_deref(), Some("first"));
+        assert_eq!(names.num_page_results(), 1);
+        assert_eq!(names.page_token(), Some("a/b"));
+        assert_eq!(names.try_next().await.unwrap().as_deref(), Some("second"));
+        assert_eq!(names.num_page_results(), 0);
+        assert_eq!(names.page_token(), Some("a/b"));
+        assert!(!task.is_finished());
+        assert_eq!(names.try_next().await.unwrap().as_deref(), Some("last"));
+        assert_eq!(names.try_next().await.unwrap(), None);
+        assert_eq!(names.try_next().await.unwrap(), None);
+        let requests = task.await.unwrap();
+        assert_eq!(requests[0].line, "GET /v1/namespace/$/list HTTP/1.1");
+        assert_eq!(
+            requests[1].line,
+            "GET /v1/namespace/$/list?page_token=a%2Fb HTTP/1.1"
+        );
+        assert_eq!(
+            requests[2].line,
+            "GET /v1/namespace/$/list?page_token=next HTTP/1.1"
+        );
+    }
+
+    #[tokio::test]
+    async fn database_names_stop_after_error() {
+        use futures::StreamExt;
+
+        let (endpoint, task) = server(vec![
+            (200, json!({"namespaces": ["first"], "page_token": "next"})),
+            (401, json!({"error": "unauthorized"})),
+        ])
+        .await;
+        let catalog = CatalogConnection::new(Arc::new(
+            RemoteCatalog::try_new(endpoint, RemoteCatalogOptions::default()).unwrap(),
+        ));
+        let mut names = catalog.list_databases(None, None);
+        assert_eq!(names.next().await.unwrap().unwrap(), "first");
+        assert!(names.next().await.unwrap().is_err());
+        assert_eq!(names.num_page_results(), 0);
+        assert_eq!(names.page_token(), Some("next"));
+        assert!(names.next().await.is_none());
+        task.await.unwrap();
+    }
+
+    #[tokio::test]
+    async fn database_names_can_resume_with_page_limit() {
+        use futures::TryStreamExt;
+        let (endpoint, task) = server(vec![
+            (
+                200,
+                json!({"namespaces": ["first", "second"], "page_token": "next/token"}),
+            ),
+            (200, json!({"namespaces": ["third", "fourth"]})),
+        ])
+        .await;
+        let catalog = CatalogConnection::new(Arc::new(
+            RemoteCatalog::try_new(endpoint, RemoteCatalogOptions::default()).unwrap(),
+        ));
+        let mut names = catalog.list_databases(Some("start/token".into()), Some(2));
+        assert_eq!(names.page_token(), Some("start/token"));
+        assert_eq!(names.num_page_results(), 0);
+        assert_eq!(names.try_next().await.unwrap().as_deref(), Some("first"));
+        assert_eq!(names.page_token(), Some("next/token"));
+        assert_eq!(names.num_page_results(), 1);
+        assert_eq!(names.try_next().await.unwrap().as_deref(), Some("second"));
+        assert_eq!(names.num_page_results(), 0);
+        let token = names.page_token().map(str::to_owned);
+        drop(names);
+        let mut resumed = catalog.list_databases(token, Some(2));
+        assert_eq!(resumed.try_next().await.unwrap().as_deref(), Some("third"));
+        assert_eq!(resumed.page_token(), None);
+        assert_eq!(resumed.num_page_results(), 1);
+        assert_eq!(resumed.try_next().await.unwrap().as_deref(), Some("fourth"));
+        assert_eq!(resumed.num_page_results(), 0);
+        assert_eq!(resumed.try_next().await.unwrap(), None);
+        let requests = task.await.unwrap();
+        assert_eq!(
+            requests[0].line,
+            "GET /v1/namespace/$/list?limit=2&page_token=start%2Ftoken HTTP/1.1"
+        );
+        assert_eq!(
+            requests[1].line,
+            "GET /v1/namespace/$/list?limit=2&page_token=next%2Ftoken HTTP/1.1"
+        );
+    }
+
+    #[tokio::test]
+    async fn database_names_reject_invalid_page_limit() {
+        use futures::TryStreamExt;
+        let catalog = CatalogConnection::new(Arc::new(
+            RemoteCatalog::try_new("http://127.0.0.1:1", RemoteCatalogOptions::default()).unwrap(),
+        ));
+        for limit in [0, u32::MAX] {
+            let mut names = catalog.list_databases(None, Some(limit));
+            assert!(matches!(
+                names.try_next().await,
+                Err(Error::InvalidInput { .. })
+            ));
+            assert_eq!(names.num_page_results(), 0);
+            assert_eq!(names.try_next().await.unwrap(), None);
+        }
+    }
+
     #[tokio::test]
     async fn catalog_validates_before_sending_requests() {
         for endpoint in [
@@ -552,7 +658,24 @@ mod tests {
         }
         let catalog =
             RemoteCatalog::try_new("http://127.0.0.1:1", RemoteCatalogOptions::default()).unwrap();
-        for name in ["", "a$b", "\r\ninjected", "..", "café", " padded "] {
+        for name in [
+            "",
+            "/",
+            "//",
+            "a$b",
+            "\r\ninjected",
+            "..",
+            "café",
+            " padded ",
+            "a b",
+            "a?b",
+            "/db",
+            "db/",
+            "a//b",
+            "a/../b",
+            "a/./b",
+            &"a".repeat(65),
+        ] {
             assert!(matches!(
                 catalog.create_database(name.into()).await,
                 Err(Error::InvalidInput { .. })
diff --git a/rust/lancedb/src/remote/client.rs b/rust/lancedb/src/remote/client.rs
index 705506705..783d2ef84 100644
--- a/rust/lancedb/src/remote/client.rs
+++ b/rust/lancedb/src/remote/client.rs
@@ -8,12 +8,15 @@ use reqwest::{
     header::{HeaderMap, HeaderValue},
 };
 use std::{collections::HashMap, future::Future, str::FromStr, sync::Arc, time::Duration};
+use tokio::sync::Semaphore;
 
 use crate::error::{Error, Result};
 use crate::remote::db::RemoteOptions;
 use crate::remote::retry::{ResolvedRetryConfig, RetryCounter};
+use crate::utils::validate_database_name;
 
 const REQUEST_ID_HEADER: HeaderName = HeaderName::from_static("x-request-id");
+const DEFAULT_BLOB_REQUEST_CONCURRENCY: usize = 8;
 
 pub fn redact_sensitive_headers(headers: &mut HeaderMap) {
     const SENSITIVE_HEADERS: [&str; 5] = [
@@ -85,6 +88,11 @@ const DEFAULT_MAX_REQUEST_DURATION_DIVISOR: u32 = 2;
 pub struct ClientConfig {
     pub timeout_config: TimeoutConfig,
     pub retry_config: RetryConfig,
+    /// Maximum concurrent blob HTTP requests for this connection, shared by all
+    /// tables and blob handles. Defaults to 8. The environment variable
+    /// `LANCE_CLIENT_BLOB_REQUEST_CONCURRENCY` can also set this limit.
+    /// The value must be greater than zero.
+    pub blob_request_concurrency: Option<usize>,
     /// User agent to use for requests. The default provides the library
     /// name and version.
     pub user_agent: String,
@@ -139,6 +147,7 @@ impl std::fmt::Debug for ClientConfig {
         f.debug_struct("ClientConfig")
             .field("timeout_config", &self.timeout_config)
             .field("retry_config", &self.retry_config)
+            .field("blob_request_concurrency", &self.blob_request_concurrency)
             .field("user_agent", &self.user_agent)
             .field("extra_headers", &self.extra_headers)
             .field("id_delimiter", &self.id_delimiter)
@@ -159,6 +168,7 @@ impl Default for ClientConfig {
         Self {
             timeout_config: TimeoutConfig::default(),
             retry_config: RetryConfig::default(),
+            blob_request_concurrency: None,
             user_agent: concat!("LanceDB-Rust-Client/", env!("CARGO_PKG_VERSION")).into(),
             extra_headers: HashMap::new(),
             id_delimiter: None,
@@ -307,6 +317,7 @@ pub struct RestfulLanceDbClient<S: HttpSend = Sender> {
     client: reqwest::Client,
     host: String,
     pub(crate) retry_config: ResolvedRetryConfig,
+    pub(crate) blob_request_semaphore: Arc<Semaphore>,
     pub(crate) sender: S,
     pub(crate) header_provider: Option<Arc<dyn HeaderProvider>>,
     /// Connection-level read consistency interval. Drives the
@@ -383,6 +394,7 @@ pub fn parse_db_url(db_url: &str) -> Result<ParsedDbUrl> {
             message: format!("Invalid encoded database name: {err}"),
         })?
         .into_owned();
+    validate_database_name(&db_name)?;
     let db_prefix = {
         let prefix = parsed_url.path().trim_start_matches('/');
         if prefix.is_empty() {
@@ -467,6 +479,11 @@ impl ClientConfig {
     /// mistake is reported where it was made rather than as a confusing
     /// response later. Public so a caller can ask without connecting.
     pub fn validate(&self) -> Result<()> {
+        if self.blob_request_concurrency == Some(0) {
+            return Err(Error::InvalidInput {
+                message: "blob_request_concurrency must be greater than zero".into(),
+            });
+        }
         if let Some(delimiter) = &self.id_delimiter {
             validate_id_delimiter(delimiter)?;
         }
@@ -502,6 +519,8 @@ impl RestfulLanceDbClient<Sender> {
         // Before anything is built from it, so the error names the caller's
         // configuration rather than a request.
         client_config.validate()?;
+        let blob_request_concurrency =
+            Self::resolve_blob_request_concurrency(client_config.blob_request_concurrency)?;
 
         // Get the timeouts
         let timeout =
@@ -601,6 +620,7 @@ impl RestfulLanceDbClient<Sender> {
             client,
             host,
             retry_config,
+            blob_request_semaphore: Arc::new(Semaphore::new(blob_request_concurrency)),
             sender: Sender,
             header_provider: client_config.header_provider,
             read_consistency_interval,
@@ -609,6 +629,26 @@ impl RestfulLanceDbClient<Sender> {
         })
     }
 
+    fn resolve_blob_request_concurrency(passed: Option<usize>) -> Result<usize> {
+        let value = if let Some(value) = passed {
+            value
+        } else if let Ok(env) = std::env::var("LANCE_CLIENT_BLOB_REQUEST_CONCURRENCY") {
+            env.parse::<usize>().map_err(|_| Error::InvalidInput {
+                message: format!(
+                    "LANCE_CLIENT_BLOB_REQUEST_CONCURRENCY must be a positive integer, got '{env}'"
+                ),
+            })?
+        } else {
+            DEFAULT_BLOB_REQUEST_CONCURRENCY
+        };
+        if value == 0 {
+            return Err(Error::InvalidInput {
+                message: "blob request concurrency must be greater than zero".into(),
+            });
+        }
+        Ok(value)
+    }
+
     /// Resolve the max bytes per insert request from config, environment, or the
     /// default. A value of `0` (from either source) disables request splitting.
     fn resolve_max_bytes_per_request(passed: Option<u64>) -> Result<Option<u64>> {
@@ -1104,6 +1144,7 @@ pub mod test_utils {
             client: reqwest::Client::new(),
             host: "http://localhost".to_string(),
             retry_config: RetryConfig::default().try_into().unwrap(),
+            blob_request_semaphore: Arc::new(Semaphore::new(DEFAULT_BLOB_REQUEST_CONCURRENCY)),
             sender: MockSender {
                 f: Arc::new(wrapper),
             },
@@ -1130,6 +1171,11 @@ pub mod test_utils {
             client: reqwest::Client::new(),
             host: "http://localhost".to_string(),
             retry_config: config.retry_config.try_into().unwrap(),
+            blob_request_semaphore: Arc::new(Semaphore::new(
+                config
+                    .blob_request_concurrency
+                    .unwrap_or(DEFAULT_BLOB_REQUEST_CONCURRENCY),
+            )),
             sender: MockSender {
                 f: Arc::new(wrapper),
             },
@@ -1181,6 +1227,21 @@ mod tests {
         .expect_err("a configured delimiter other than the supported one must be refused");
     }
 
+    #[test]
+    fn test_blob_request_concurrency_must_be_positive() {
+        let error = super::ClientConfig {
+            blob_request_concurrency: Some(0),
+            ..Default::default()
+        }
+        .validate()
+        .unwrap_err();
+        assert!(error.to_string().contains("blob_request_concurrency"));
+        assert_eq!(
+            RestfulLanceDbClient::<Sender>::resolve_blob_request_concurrency(Some(3)).unwrap(),
+            3
+        );
+    }
+
     use super::*;
     use serial_test::serial;
     use std::time::Duration;
@@ -1204,6 +1265,29 @@ mod tests {
         assert_eq!(parsed.db_prefix.as_deref(), Some("prefix"));
     }
 
+    #[test]
+    fn test_parse_database_uri_validates_decoded_name() {
+        for name in [
+            "a b",
+            "a$b",
+            "a?b",
+            "café",
+            "/db",
+            "db/",
+            "a//b",
+            "a/../b",
+            "a/./b",
+            &"a".repeat(65),
+        ] {
+            let uri = format!("db://{}", urlencoding::encode(name));
+            assert!(
+                matches!(parse_db_url(&uri), Err(Error::InvalidInput { .. })),
+                "accepted {name:?}"
+            );
+        }
+        assert!(parse_db_url(&format!("db://{}", "a".repeat(64))).is_ok());
+    }
+
     #[test]
     fn test_timeout_config_default() {
         let config = TimeoutConfig::default();
@@ -1490,6 +1574,7 @@ mod tests {
             client: reqwest::Client::new(),
             host: "https://example.com".to_string(),
             retry_config: RetryConfig::default().try_into().unwrap(),
+            blob_request_semaphore: Arc::new(Semaphore::new(DEFAULT_BLOB_REQUEST_CONCURRENCY)),
             sender: Sender,
             header_provider: Some(Arc::new(provider) as Arc<dyn HeaderProvider>),
             read_consistency_interval: None,
@@ -1528,6 +1613,7 @@ mod tests {
             client: reqwest::Client::new(),
             host: "https://example.com".to_string(),
             retry_config: RetryConfig::default().try_into().unwrap(),
+            blob_request_semaphore: Arc::new(Semaphore::new(DEFAULT_BLOB_REQUEST_CONCURRENCY)),
             sender: Sender,
             header_provider: Some(Arc::new(provider) as Arc<dyn HeaderProvider>),
             read_consistency_interval: None,
@@ -1592,6 +1678,7 @@ mod tests {
             client: reqwest::Client::new(),
             host: "https://example.com".to_string(),
             retry_config: RetryConfig::default().try_into().unwrap(),
+            blob_request_semaphore: Arc::new(Semaphore::new(DEFAULT_BLOB_REQUEST_CONCURRENCY)),
             sender: Sender,
             header_provider: Some(Arc::new(provider) as Arc<dyn HeaderProvider>),
             read_consistency_interval: None,
diff --git a/rust/lancedb/src/remote/db.rs b/rust/lancedb/src/remote/db.rs
index 39bb5a528..a0f4aa953 100644
--- a/rust/lancedb/src/remote/db.rs
+++ b/rust/lancedb/src/remote/db.rs
@@ -20,8 +20,8 @@ use lance_namespace::models::{
 
 use crate::Error;
 use crate::database::{
-    CloneTableRequest, CreateTableMode, CreateTableRequest, Database, DatabaseOptions, JobInfo,
-    OpenTableRequest, ReadConsistency, TableNamesRequest,
+    CloneTableRequest, CreateTableMode, CreateTableRequest, Database, DatabaseOptions,
+    ExecuteQueryRequest, JobInfo, OpenTableRequest, ReadConsistency, TableNamesRequest,
 };
 use crate::error::Result;
 use crate::function::{
@@ -29,13 +29,17 @@ use crate::function::{
     PythonRuntimeSpec,
 };
 use crate::job::Job;
+use crate::listing::{ListingOptions, ListingPage};
 use crate::materialized_view::CreateMaterializedViewRequest;
 use crate::remote::job::{PauseJobResponse, RemoteJob, ResumeJobResponse, job_state_to_client};
 use crate::remote::util::stream_as_body;
 use crate::secrets::SecretBinding;
 use crate::secrets::SecretInfo;
 use crate::table::BaseTable;
-use crate::utils::{reject_relative_segment, validate_table_name};
+use crate::utils::{
+    validate_database_name, validate_function_name, validate_namespace, validate_secret_name,
+    validate_table_name, validate_view_name,
+};
 use crate::view::ViewDescription;
 
 use super::client::{
@@ -47,6 +51,10 @@ use super::table::RemoteTable;
 use super::util::parse_server_version;
 use super::{ARROW_STREAM_CONTENT_TYPE, extract_job_id};
 
+fn quote_sql_identifier(identifier: &str) -> String {
+    format!("\"{}\"", identifier.replace('"', "\"\""))
+}
+
 // Request structure for the remote clone table API
 #[derive(serde::Serialize)]
 struct RemoteCloneTableRequest {
@@ -225,6 +233,7 @@ pub struct RemoteDatabase<S: HttpSend = Sender> {
     /// TLS configuration for mTLS support
     tls_config: Option<super::client::TlsConfig>,
     sql_client: Option<SqlClient>,
+    is_catalog_root: bool,
 }
 
 #[derive(Clone)]
@@ -288,6 +297,12 @@ pub struct RemoteHostOverrides {
 }
 
 impl RemoteDatabase {
+    pub(crate) fn authz(&self) -> Arc<dyn crate::authz::Authorization> {
+        Arc::new(super::authz::RemoteAuthorization {
+            client: self.client.clone(),
+        })
+    }
+
     pub(crate) fn try_new(
         uri: &str,
         api_key: &str,
@@ -315,6 +330,9 @@ impl RemoteDatabase {
         name: Option<&str>,
         options: &super::catalog::RemoteCatalogOptions,
     ) -> Result<Self> {
+        if let Some(name) = name {
+            validate_database_name(name)?;
+        }
         let scope = super::catalog::ScopedHeaderProvider {
             provider: options.client_config.header_provider.clone(),
             database: name.map(str::to_string),
@@ -343,6 +361,7 @@ impl RemoteDatabase {
         )?;
         if name.is_none() {
             db.sql_client = None;
+            db.is_catalog_root = true;
         }
         Ok(db)
     }
@@ -417,11 +436,38 @@ impl RemoteDatabase {
             namespace_context_provider,
             tls_config: client_config.tls_config,
             sql_client: Some(sql_client),
+            is_catalog_root: false,
         })
     }
 }
 
 impl<S: HttpSend> RemoteDatabase<S> {
+    // A catalog's root namespaces are database names, which may contain slashes.
+    fn namespace_identifier(&self, namespace: &[String]) -> Result<String> {
+        match namespace.first() {
+            None => Ok(ID_DELIMITER.to_string()),
+            Some(first) => {
+                if self.is_catalog_root {
+                    // When dealing with the catalog root, the first component is a
+                    // database name. Those need to be validated differently because
+                    // they can contain slashes. Also, we need to use percent encoding
+                    // for the slashes.
+                    validate_database_name(first)?;
+                    validate_namespace(&namespace[1..])?;
+                    Ok(namespace
+                        .iter()
+                        .map(|component| urlencoding::encode(component).into_owned())
+                        .collect::<Vec<_>>()
+                        .join(ID_DELIMITER))
+                } else {
+                    // If we're not dealing with the catalog root, build the namespace identifier in the
+                    // usual way.
+                    build_namespace_identifier(namespace)
+                }
+            }
+        }
+    }
+
     /// Post a request whose body carries a credential.
     ///
     /// Shared by the create and alter verbs, which declare their own request
@@ -532,6 +578,7 @@ mod test_utils {
                 namespace_context_provider: None,
                 tls_config: None,
                 sql_client: None,
+                is_catalog_root: false,
             }
         }
 
@@ -555,6 +602,7 @@ mod test_utils {
                 namespace_context_provider,
                 tls_config: config.tls_config.clone(),
                 sql_client: None,
+                is_catalog_root: false,
             }
         }
     }
@@ -576,93 +624,60 @@ impl From<&CreateTableMode> for &'static str {
     }
 }
 
-/// The path segment addressing one object: its namespace path and its name.
-///
-/// One builder for tables, Secrets, Functions and materialized views: the
-/// identifier grammar belongs to the namespace spec, not to an object type. An
-/// empty path addresses an object with no namespace.
-///
-/// Components are checked for addressability, not a character set. The name's
-/// grammar is the caller's, so a table reports [`Error::InvalidTableName`], a
-/// Function admits names a table may not, and a catalog database carries the
-/// `/` that [`RemoteCatalog`] allows.
-///
-/// [`RemoteCatalog`]: super::catalog::RemoteCatalog
-fn build_object_identifier(what: &str, name: &str, namespace: &[String]) -> Result<String> {
-    for segment in namespace {
-        reject_unaddressable_component("namespace segment", segment)?;
+fn build_compound_identifier(name: &str, namespace: &[String]) -> Result<String> {
+    validate_namespace(namespace)?;
+    let mut identifier = namespace.join("$");
+    if !namespace.is_empty() {
+        identifier.push('$');
     }
-    reject_unaddressable_component(what, name)?;
-    Ok(join_identifier(
-        namespace.iter().map(String::as_str).chain([name]),
-    ))
+    identifier.push_str(name);
+    Ok(identifier)
 }
 
-/// What a component may not be if the join is to survive being split back
-/// apart: empty, a segment URL parsing resolves away, or the delimiter itself.
-///
-/// Each erases a boundary no encoding of the joined form recovers. `["prod",
-/// ""]` joins to `prod$`, which reads back as `["prod"]`, so a drop reaches the
-/// parent of the namespace the caller named.
-///
-/// Not a character set: per-component percent-encoding makes the wider set
-/// safe, since a `/` in a name reaches the service as `%2F`, still one
-/// segment.
-fn reject_unaddressable_component(what: &str, value: &str) -> Result<()> {
-    if value.is_empty() {
-        return Err(Error::InvalidInput {
-            message: format!(
-                "{what} must not be empty: the identifier would carry two delimiters in a row, \
-                 and splitting it back apart would name a different object"
-            ),
-        });
-    }
-    reject_relative_segment(what, value)?;
-    if value.contains(ID_DELIMITER) {
-        return Err(Error::InvalidInput {
-            message: format!(
-                "{what} '{value}' contains the identifier delimiter '{ID_DELIMITER}', so the \
-                 namespace path and the name it joins could not be told apart"
-            ),
-        });
-    }
-    Ok(())
-}
-
-/// The path segment addressing one table. A wrapper for the error type:
-/// callers match on [`Error::InvalidTableName`].
+/// Build a full path to a table. Note that we do not need to percent-encode
+/// this when using it in a URL because the validation functions ensure that
+/// only URL-safe characters are present.
 fn build_table_identifier(name: &str, namespace: &[String]) -> Result<String> {
     validate_table_name(name)?;
-    build_object_identifier("table name", name, namespace)
+    build_compound_identifier(name, namespace)
 }
 
-/// Join components into the `{id}` a route addresses: each percent-encoded,
-/// then joined by the delimiter.
-///
-/// Per component rather than over the joined string, so the delimiter stays a
-/// delimiter and nothing inside a component can end the path segment.
-///
-/// A second line, not the first: a component from the object charset is all
-/// unreserved and encodes to itself, so the route reads as the caller wrote it.
-/// It does not cover `.` and `..`, which are unreserved too and resolve away
-/// after decoding -- [`build_object_identifier`] refuses those.
-fn join_identifier<'a>(components: impl Iterator<Item = &'a str>) -> String {
-    components
-        .map(|component| urlencoding::encode(component).into_owned())
-        .collect::<Vec<_>>()
-        .join(ID_DELIMITER)
-}
-
-/// The path segment addressing one namespace.
+/// Translate an array of namespace components into a namespace string.
+/// Note that we do not need to percent-encode this when using it in a URL
+/// because validate_namespace ensures that only URL-safe characters are
+/// present.
 fn build_namespace_identifier(namespace: &[String]) -> Result<String> {
-    for segment in namespace {
-        reject_unaddressable_component("namespace segment", segment)?;
-    }
+    validate_namespace(namespace)?;
     if namespace.is_empty() {
         // According to the namespace spec, use delimiter to represent root namespace
-        return Ok(ID_DELIMITER.to_string());
+        Ok(ID_DELIMITER.to_string())
+    } else {
+        Ok(namespace.join(ID_DELIMITER))
     }
-    Ok(join_identifier(namespace.iter().map(String::as_str)))
+}
+
+/// Build a full path to a function. Note that we do not need to percent-encode
+/// this when using it in a URL because the validation functions ensure that
+/// only URL-safe characters are present.
+fn build_function_identifier(name: &str, namespace: &[String]) -> Result<String> {
+    validate_function_name(name)?;
+    build_compound_identifier(name, namespace)
+}
+
+/// Build a full path to a secret. Note that we do not need to percent-encode
+/// this when using it in a URL because the validation functions ensure that
+/// only URL-safe characters are present.
+fn build_secret_identifier(name: &str, namespace: &[String]) -> Result<String> {
+    validate_secret_name(name)?;
+    build_compound_identifier(name, namespace)
+}
+
+/// Build a full path to a view. Note that we do not need to percent-encode
+/// this when using it in a URL because the validation functions ensure that
+/// only URL-safe characters are present.
+fn build_view_identifier(name: &str, namespace: &[String]) -> Result<String> {
+    validate_view_name(name)?;
+    build_compound_identifier(name, namespace)
 }
 
 /// Build a secure cache key using length prefixes.
@@ -839,10 +854,6 @@ struct RemoteListViewsResponse {
     page_token: Option<String>,
 }
 
-/// Bound on `list_jobs` page walking; a warning is logged when the listing
-/// is truncated at this many pages.
-const MAX_LIST_JOBS_PAGES: usize = 100;
-
 #[async_trait]
 impl<S: HttpSend> Database for RemoteDatabase<S> {
     fn uri(&self) -> &str {
@@ -860,42 +871,22 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
         &self,
         request: CreateMaterializedViewRequest,
     ) -> Result<Job> {
-        let identifier = build_table_identifier(&request.name, &request.namespace_path)?;
-        let req = self
-            .client
-            .post(&format!("/v1/materialized_view/{identifier}/create"))
-            .json(&serde_json::json!({
-                "query": request.query,
-                "with_no_data": request.with_no_data,
-            }));
-        let (request_id, response) = self.client.send(req).await?;
-        let response = self.client.check_response(&request_id, response).await?;
-        let status = response.status();
-        let body = response.text().await.err_to_http(request_id.clone())?;
-        let job_id = extract_job_id(&body);
-
         if request.with_no_data {
-            return Ok(match job_id {
-                Some(job_id) => Job::new(Box::new(RemoteJob::new(self.client.clone(), job_id))),
-                None => Job::new_done(),
+            return Err(Error::NotSupported {
+                message: "remote materialized views are always populated by their SQL job"
+                    .to_string(),
             });
         }
-        if status != StatusCode::ACCEPTED {
-            return Err(Error::Http {
-                source: "materialized-view creation with data must return 202 Accepted".into(),
-                request_id,
-                status_code: Some(status),
-            });
-        }
-        let job_id = job_id.ok_or_else(|| Error::Http {
-            source: "materialized-view creation response did not contain a valid job_id".into(),
-            request_id,
-            status_code: Some(status),
+        let client = self.sql_client.clone().ok_or_else(|| Error::NotSupported {
+            message: "SQL is unavailable for this remote database client".to_string(),
         })?;
-        Ok(Job::new(Box::new(RemoteJob::new(
-            self.client.clone(),
-            job_id,
-        ))))
+        let namespace = request.namespace_path;
+        let statement = format!(
+            "CREATE MATERIALIZED VIEW {} AS {}",
+            quote_sql_identifier(&request.name),
+            request.query
+        );
+        Ok(client.submit_as_job(statement, namespace, || async { Ok(()) }))
     }
 
     async fn drop_materialized_view_async(
@@ -935,52 +926,36 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
         Ok(job)
     }
 
-    async fn list_materialized_views(&self, namespace_path: &[String]) -> Result<Vec<String>> {
-        #[derive(serde::Deserialize)]
-        struct ListMaterializedViewsResponse {
-            #[serde(default)]
-            views: Vec<String>,
-            #[serde(default)]
-            page_token: Option<String>,
-        }
-
+    async fn list_materialized_views(
+        &self,
+        namespace_path: &[String],
+        options: ListingOptions,
+    ) -> Result<ListingPage<String>> {
         let namespace_id = build_namespace_identifier(namespace_path)?;
         let path = format!("/v1/namespace/{namespace_id}/materialized_view/list");
-        let mut views = Vec::new();
-        let mut page_token: Option<String> = None;
-        let mut seen_page_tokens = HashSet::new();
-        loop {
-            let mut req = self.client.get(&path);
-            if let Some(token) = &page_token {
-                req = req.query(&[("page_token", token)]);
-            }
-            let (request_id, response) = self.client.send(req).await?;
-            let response = self.client.check_response(&request_id, response).await?;
-            let status = response.status();
-            let response: ListMaterializedViewsResponse =
-                response.json().await.err_to_http(request_id.clone())?;
-            views.extend(response.views);
-            let Some(next_page_token) = response.page_token.filter(|token| !token.is_empty())
-            else {
-                break;
-            };
-            if !seen_page_tokens.insert(next_page_token.clone()) {
-                return Err(Error::Http {
-                    source: "Materialized-view listing response repeated a page_token".into(),
-                    request_id,
-                    status_code: Some(status),
-                });
-            }
-            page_token = Some(next_page_token);
+        let mut req = self.client.get(&path);
+        if let Some(token) = options.page_token {
+            req = req.query(&[("page_token", token)]);
         }
-        Ok(views)
+        if let Some(limit) = options.page_limit {
+            req = req.query(&[("limit", limit)]);
+        }
+        let (request_id, response) = self.client.send(req).await?;
+        let response = self.client.check_response(&request_id, response).await?;
+        let response: RemoteListViewsResponse = response.json().await.err_to_http(request_id)?;
+        let items = response.views;
+        Ok(ListingPage {
+            items,
+            page_token: response.page_token,
+        })
     }
 
     async fn create_function_async(
         &self,
         request: FunctionRegistrationRequest,
+        namespace_path: &[String],
     ) -> Result<Job<FunctionVersion>> {
-        let function_id = build_object_identifier("Function name", &request.name, &[])?;
+        let function_id = build_function_identifier(&request.name, namespace_path)?;
         let req = self
             .client
             .post(&format!("/v1/function/{function_id}/create"))
@@ -1005,8 +980,13 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
         ))))
     }
 
-    async fn get_function(&self, name: &str, version: &str) -> Result<FunctionVersion> {
-        let function_id = build_object_identifier("Function name", name, &[])?;
+    async fn get_function(
+        &self,
+        name: &str,
+        version: &str,
+        namespace_path: &[String],
+    ) -> Result<FunctionVersion> {
+        let function_id = build_function_identifier(name, namespace_path)?;
         let req = self
             .client
             .post(&format!("/v1/function/{function_id}/describe"))
@@ -1018,53 +998,55 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
         response.json().await.err_to_http(request_id)
     }
 
-    async fn list_functions(&self) -> Result<Vec<FunctionVersion>> {
-        let namespace_id = build_namespace_identifier(&[])?;
+    async fn list_functions(
+        &self,
+        namespace_path: &[String],
+        options: ListingOptions,
+    ) -> Result<ListingPage<crate::function::FunctionVersion>> {
+        let namespace_id = build_namespace_identifier(namespace_path)?;
         let path = format!("/v1/namespace/{namespace_id}/function/list");
-        let mut functions = Vec::new();
-        let mut page_token: Option<String> = None;
-        let mut seen_page_tokens = HashSet::new();
-        loop {
-            let mut req = self
-                .client
-                .get(&path)
-                .query(&[("include_definition", true)]);
-            if let Some(token) = &page_token {
-                req = req.query(&[("page_token", token)]);
-            }
-            let (request_id, response) = self.client.send(req).await?;
-            let response = self.client.check_response(&request_id, response).await?;
-            let status = response.status();
-            let response: RemoteListFunctionsResponse =
-                response.json().await.err_to_http(request_id.clone())?;
-            functions.extend(
-                response
-                    .functions
-                    .into_iter()
-                    .map(|listed| listed.definition),
-            );
-            let Some(next_page_token) = response.page_token.filter(|token| !token.is_empty())
-            else {
-                break;
-            };
-            if !seen_page_tokens.insert(next_page_token.clone()) {
-                return Err(Error::Http {
-                    source: "Function listing response repeated a page_token".into(),
-                    request_id,
-                    status_code: Some(status),
-                });
-            }
-            page_token = Some(next_page_token);
+        let mut req = self.client.get(&path);
+        if let Some(token) = options.page_token {
+            req = req.query(&[("page_token", token)]);
         }
-        Ok(functions)
+        if let Some(limit) = options.page_limit {
+            req = req.query(&[("limit", limit)]);
+        }
+        req = req.query(&[("include_definition", true)]);
+        let (request_id, response) = self.client.send(req).await?;
+        let response = self.client.check_response(&request_id, response).await?;
+        let response: RemoteListFunctionsResponse =
+            response.json().await.err_to_http(request_id)?;
+        let items = response
+            .functions
+            .into_iter()
+            .map(|listed| listed.definition)
+            .collect();
+        Ok(ListingPage {
+            items,
+            page_token: response.page_token,
+        })
     }
 
-    async fn drop_function(&self, name: &str, version: &str) -> Result<bool> {
-        Ok(self.drop_function_async(name, version).await?.0)
+    async fn drop_function(
+        &self,
+        name: &str,
+        version: &str,
+        namespace_path: &[String],
+    ) -> Result<bool> {
+        Ok(self
+            .drop_function_async(name, version, namespace_path)
+            .await?
+            .0)
     }
 
-    async fn drop_function_async(&self, name: &str, version: &str) -> Result<(bool, Job)> {
-        let function_id = build_object_identifier("Function name", name, &[])?;
+    async fn drop_function_async(
+        &self,
+        name: &str,
+        version: &str,
+        namespace_path: &[String],
+    ) -> Result<(bool, Job)> {
+        let function_id = build_function_identifier(name, namespace_path)?;
         let req = self
             .client
             .post(&format!("/v1/function/{function_id}/drop"))
@@ -1109,7 +1091,7 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
         value: &str,
         namespace_path: &[String],
     ) -> Result<()> {
-        let secret_id = build_object_identifier("Secret name", name, namespace_path)?;
+        let secret_id = build_secret_identifier(name, namespace_path)?;
         self.post_secret_write(
             &format!("/v1/secret/{secret_id}/create"),
             &RemoteCreateSecretRequest { value },
@@ -1118,7 +1100,7 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
     }
 
     async fn alter_secret(&self, name: &str, value: &str, namespace_path: &[String]) -> Result<()> {
-        let secret_id = build_object_identifier("Secret name", name, namespace_path)?;
+        let secret_id = build_secret_identifier(name, namespace_path)?;
         self.post_secret_write(
             &format!("/v1/secret/{secret_id}/alter"),
             &RemoteAlterSecretRequest { value },
@@ -1126,41 +1108,36 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
         .await
     }
 
-    async fn list_secrets(&self, namespace_path: &[String]) -> Result<Vec<String>> {
+    async fn list_secrets(
+        &self,
+        namespace_path: &[String],
+        options: ListingOptions,
+    ) -> Result<ListingPage<String>> {
         let namespace_id = build_namespace_identifier(namespace_path)?;
         let path = format!("/v1/namespace/{namespace_id}/secret/list");
-        let mut names = Vec::new();
-        let mut page_token: Option<String> = None;
-        let mut seen_page_tokens = HashSet::new();
-        loop {
-            let mut req = self.client.get(&path);
-            if let Some(token) = &page_token {
-                req = req.query(&[("page_token", token)]);
-            }
-            let (request_id, response) = self.client.send(req).await?;
-            let response = self.client.check_response(&request_id, response).await?;
-            let status = response.status();
-            let response: RemoteListSecretsResponse =
-                response.json().await.err_to_http(request_id.clone())?;
-            names.extend(response.secrets.into_iter().map(|secret| secret.name));
-            let Some(next_page_token) = response.page_token.filter(|token| !token.is_empty())
-            else {
-                break;
-            };
-            if !seen_page_tokens.insert(next_page_token.clone()) {
-                return Err(Error::Http {
-                    source: "Secret listing response repeated a page_token".into(),
-                    request_id,
-                    status_code: Some(status),
-                });
-            }
-            page_token = Some(next_page_token);
+        let mut req = self.client.get(&path);
+        if let Some(token) = options.page_token {
+            req = req.query(&[("page_token", token)]);
         }
-        Ok(names)
+        if let Some(limit) = options.page_limit {
+            req = req.query(&[("limit", limit)]);
+        }
+        let (request_id, response) = self.client.send(req).await?;
+        let response = self.client.check_response(&request_id, response).await?;
+        let response: RemoteListSecretsResponse = response.json().await.err_to_http(request_id)?;
+        let items = response
+            .secrets
+            .into_iter()
+            .map(|secret| secret.name)
+            .collect();
+        Ok(ListingPage {
+            items,
+            page_token: response.page_token,
+        })
     }
 
     async fn drop_secret(&self, name: &str, namespace_path: &[String]) -> Result<()> {
-        let secret_id = build_object_identifier("Secret name", name, namespace_path)?;
+        let secret_id = build_secret_identifier(name, namespace_path)?;
         let req = self.client.post(&format!("/v1/secret/{secret_id}/drop"));
         let (request_id, response) = self.client.send(req).await?;
         self.client.check_response(&request_id, response).await?;
@@ -1168,7 +1145,7 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
     }
 
     async fn describe_secret(&self, name: &str, namespace_path: &[String]) -> Result<SecretInfo> {
-        let secret_id = build_object_identifier("Secret name", name, namespace_path)?;
+        let secret_id = build_secret_identifier(name, namespace_path)?;
         let req = self
             .client
             .post(&format!("/v1/secret/{secret_id}/describe"));
@@ -1183,7 +1160,7 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
         query: &str,
         namespace_path: &[String],
     ) -> Result<ViewDescription> {
-        let view_id = build_object_identifier("View name", name, namespace_path)?;
+        let view_id = build_view_identifier(name, namespace_path)?;
         let req = self
             .client
             .post(&format!("/v1/view/{view_id}/create"))
@@ -1200,7 +1177,7 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
         name: &str,
         namespace_path: &[String],
     ) -> Result<ViewDescription> {
-        let view_id = build_object_identifier("View name", name, namespace_path)?;
+        let view_id = build_view_identifier(name, namespace_path)?;
         let req = self.client.post(&format!("/v1/view/{view_id}/describe"));
         let (request_id, response) = self.client.send(req).await?;
         let response = self.client.check_response(&request_id, response).await?;
@@ -1217,7 +1194,7 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
     }
 
     async fn drop_view_async(&self, name: &str, namespace_path: &[String]) -> Result<Job> {
-        let view_id = build_object_identifier("View name", name, namespace_path)?;
+        let view_id = build_view_identifier(name, namespace_path)?;
         let req = self.client.post(&format!("/v1/view/{view_id}/drop"));
         let (request_id, response) = self.client.send(req).await?;
         let response = self.client.check_response(&request_id, response).await?;
@@ -1245,37 +1222,28 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
         }
     }
 
-    async fn list_views(&self, namespace_path: &[String]) -> Result<Vec<String>> {
+    async fn list_views(
+        &self,
+        namespace_path: &[String],
+        options: ListingOptions,
+    ) -> Result<ListingPage<String>> {
         let namespace_id = build_namespace_identifier(namespace_path)?;
         let path = format!("/v1/namespace/{namespace_id}/view/list");
-        let mut views = Vec::new();
-        let mut page_token: Option<String> = None;
-        let mut seen_page_tokens = HashSet::new();
-        loop {
-            let mut req = self.client.get(&path);
-            if let Some(token) = &page_token {
-                req = req.query(&[("page_token", token)]);
-            }
-            let (request_id, response) = self.client.send(req).await?;
-            let response = self.client.check_response(&request_id, response).await?;
-            let status = response.status();
-            let response: RemoteListViewsResponse =
-                response.json().await.err_to_http(request_id.clone())?;
-            views.extend(response.views);
-            let Some(next_page_token) = response.page_token.filter(|token| !token.is_empty())
-            else {
-                break;
-            };
-            if !seen_page_tokens.insert(next_page_token.clone()) {
-                return Err(Error::Http {
-                    source: "View listing response repeated a page_token".into(),
-                    request_id,
-                    status_code: Some(status),
-                });
-            }
-            page_token = Some(next_page_token);
+        let mut req = self.client.get(&path);
+        if let Some(token) = options.page_token {
+            req = req.query(&[("page_token", token)]);
         }
-        Ok(views)
+        if let Some(limit) = options.page_limit {
+            req = req.query(&[("limit", limit)]);
+        }
+        let (request_id, response) = self.client.send(req).await?;
+        let response = self.client.check_response(&request_id, response).await?;
+        let response: RemoteListViewsResponse = response.json().await.err_to_http(request_id)?;
+        let items = response.views;
+        Ok(ListingPage {
+            items,
+            page_token: response.page_token,
+        })
     }
 
     async fn open_job(&self, job_id: &str) -> Result<Job> {
@@ -1292,47 +1260,33 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
         }
     }
 
-    async fn list_jobs(&self) -> Result<Vec<JobInfo>> {
-        let mut out = Vec::new();
-        let mut page_token: Option<String> = None;
-        let mut seen_page_tokens = HashSet::new();
-        for page in 0..MAX_LIST_JOBS_PAGES {
-            let mut body = serde_json::json!({});
-            if let Some(token) = &page_token {
-                body["page_token"] = serde_json::Value::String(token.clone());
-            }
-            let req = self.client.post("/v1/jobs/list").json(&body);
-            let (request_id, rsp) = self.client.send(req).await?;
-            let rsp = self.client.check_response(&request_id, rsp).await?;
-            let status = rsp.status();
-            let body: RemoteListJobsResponse = rsp.json().await.err_to_http(request_id.clone())?;
-            out.extend(body.jobs.into_iter().map(|row| JobInfo {
+    async fn list_jobs(&self, options: ListingOptions) -> Result<ListingPage<JobInfo>> {
+        let mut body = serde_json::json!({});
+        if let Some(token) = options.page_token {
+            body["page_token"] = token.into();
+        }
+        if let Some(limit) = options.page_limit {
+            body["limit"] = limit.into();
+        }
+        let req = self.client.post("/v1/jobs/list").json(&body);
+        let (request_id, rsp) = self.client.send(req).await?;
+        let rsp = self.client.check_response(&request_id, rsp).await?;
+        let response: RemoteListJobsResponse = rsp.json().await.err_to_http(request_id)?;
+        let items = response
+            .jobs
+            .into_iter()
+            .map(|row| JobInfo {
                 job_id: row.job_id,
                 table: row.table,
                 job_type: row.job_type,
                 state: job_state_to_client(&row.state),
                 created_at_millis: row.created_at_millis,
-            }));
-            let Some(next_page_token) = body.page_token.filter(|token| !token.is_empty()) else {
-                break;
-            };
-            if !seen_page_tokens.insert(next_page_token.clone()) {
-                return Err(Error::Http {
-                    source: "Job listing response repeated a page_token".into(),
-                    request_id,
-                    status_code: Some(status),
-                });
-            }
-            page_token = Some(next_page_token);
-            if page + 1 == MAX_LIST_JOBS_PAGES {
-                log::warn!(
-                    "list_jobs truncated after {} pages ({} jobs)",
-                    MAX_LIST_JOBS_PAGES,
-                    out.len()
-                );
-            }
-        }
-        Ok(out)
+            })
+            .collect();
+        Ok(ListingPage {
+            items,
+            page_token: response.page_token,
+        })
     }
 
     async fn cancel_job(&self, job_id: &str) -> Result<bool> {
@@ -1385,18 +1339,14 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
         })
     }
 
-    async fn execute_query_async(
-        &self,
-        query: &str,
-        default_namespace_path: &[String],
-    ) -> Result<crate::sql::Query> {
+    async fn execute_query_async(&self, request: ExecuteQueryRequest) -> Result<crate::sql::Query> {
         let client = self
             .sql_client
             .as_ref()
             .ok_or_else(|| Error::NotSupported {
                 message: "SQL is unavailable for this remote database client".to_string(),
             })?;
-        client.submit(query, default_namespace_path).await
+        client.execute(request).await
     }
 
     async fn describe_query(&self, query_id: uuid::Uuid) -> Result<crate::sql::QueryDescription> {
@@ -1410,25 +1360,49 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
     }
 
     async fn table_names(&self, request: TableNamesRequest) -> Result<Vec<String>> {
+        // A zero limit is an empty listing, even on servers that reject limit=0.
+        if request.limit == Some(0) {
+            return Ok(Vec::new());
+        }
         let (tables, version) = if request.namespace_path.is_empty() {
             // The flat route resumes after a table name and orders by name, which is exactly
-            // what `start_after` means, so the server does the paging.
-            let mut req = self.client.get("/v1/table/");
-            if let Some(limit) = request.limit {
-                req = req.query(&[("limit", limit)]);
+            // what `start_after` means. Subsequent cursors come from the server and may be
+            // opaque. Follow them until the requested limit or the end of the listing.
+            let mut tables = Vec::new();
+            let mut version = None;
+            let mut page_token = request.start_after.clone();
+            let mut seen_tokens = HashSet::new();
+            if let Some(ref token) = page_token {
+                seen_tokens.insert(token.clone());
             }
-            if let Some(ref start_after) = request.start_after {
-                req = req.query(&[("page_token", start_after)]);
+            loop {
+                let mut req = self.client.get("/v1/table/");
+                if let Some(limit) = request.limit {
+                    req = req.query(&[("limit", limit as usize - tables.len())]);
+                }
+                if let Some(ref token) = page_token {
+                    req = req.query(&[("page_token", token)]);
+                }
+                let (request_id, rsp) = self.client.send_with_retry(req, None, true).await?;
+                let rsp = self.client.check_response(&request_id, rsp).await?;
+                if version.is_none() {
+                    version = Some(parse_server_version(&request_id, &rsp)?);
+                }
+                let response: ListTablesResponse = rsp.json().await.err_to_http(request_id)?;
+                tables.extend(response.tables);
+                if let Some(limit) = request.limit
+                    && tables.len() >= limit as usize
+                {
+                    tables.truncate(limit as usize);
+                    break;
+                }
+                // Empty or repeated tokens must not restart the listing or loop forever.
+                match response.page_token.filter(|token| !token.is_empty()) {
+                    Some(token) if seen_tokens.insert(token.clone()) => page_token = Some(token),
+                    _ => break,
+                }
             }
-            let (request_id, rsp) = self.client.send_with_retry(req, None, true).await?;
-            let rsp = self.client.check_response(&request_id, rsp).await?;
-            let version = parse_server_version(&request_id, &rsp)?;
-            let tables = rsp
-                .json::<ListTablesResponse>()
-                .await
-                .err_to_http(request_id)?
-                .tables;
-            (tables, version)
+            (tables, version.unwrap_or_default())
         } else {
             self.table_names_in_namespace(&request).await?
         };
@@ -1443,7 +1417,7 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
 
     async fn list_tables(&self, request: ListTablesRequest) -> Result<ListTablesResponse> {
         let namespace_parts = request.id.as_deref().unwrap_or(&[]);
-        let namespace_id = build_namespace_identifier(namespace_parts)?;
+        let namespace_id = self.namespace_identifier(namespace_parts)?;
         let mut req = self
             .client
             .get(&format!("/v1/namespace/{}/table/list", namespace_id));
@@ -1472,6 +1446,7 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
     }
 
     async fn create_table(&self, mut request: CreateTableRequest) -> Result<Arc<dyn BaseTable>> {
+        let data_schema = request.data.schema();
         let body = stream_as_body(request.data.scan_as_stream())?;
 
         let identifier = build_table_identifier(&request.name, &request.namespace_path)?;
@@ -1502,7 +1477,17 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
                             managed_versioning: None,
                         };
                         let req = (callback)(req);
-                        self.open_table(req).await
+                        let table = self.open_table(req).await?;
+                        let table_schema = table.schema().await?;
+
+                        if table_schema.as_ref() != data_schema.as_ref() {
+                            return Err(Error::Schema {
+                                message: "Provided schema does not match existing table schema"
+                                    .to_string(),
+                            });
+                        }
+
+                        Ok(table)
                     }
 
                     // This should not happen, as we explicitly set the mode to overwrite and the server
@@ -1528,12 +1513,13 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
         let version = parse_server_version(&request_id, &rsp)?;
         let table_identifier = build_table_identifier(&request.name, &request.namespace_path)?;
         let cache_key = build_cache_key(&request.name, &request.namespace_path);
-        let table = Arc::new(RemoteTable::new(
+        let table = Arc::new(RemoteTable::new_with_sql_client(
             self.client.clone(),
             request.name.clone(),
             request.namespace_path.clone(),
             table_identifier,
             version.clone(),
+            self.sql_client.clone(),
         ));
         self.table_cache.insert(cache_key, version).await;
 
@@ -1570,12 +1556,13 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
 
         let version = parse_server_version(&request_id, &rsp)?;
         let cache_key = build_cache_key(&request.target_table_name, &request.target_namespace_path);
-        let table = Arc::new(RemoteTable::new(
+        let table = Arc::new(RemoteTable::new_with_sql_client(
             self.client.clone(),
             request.target_table_name.clone(),
             request.target_namespace_path.clone(),
             table_identifier,
             version.clone(),
+            self.sql_client.clone(),
         ));
         self.table_cache.insert(cache_key, version).await;
 
@@ -1588,12 +1575,13 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
 
         // Every open gets its own checkout, schema cache, and freshness state.
         if let Some(version) = self.table_cache.get(&cache_key).await {
-            Ok(Arc::new(RemoteTable::new(
+            Ok(Arc::new(RemoteTable::new_with_sql_client(
                 self.client.clone(),
                 request.name,
                 request.namespace_path,
                 identifier,
                 version,
+                self.sql_client.clone(),
             )))
         } else {
             // Describe the table to confirm it exists before moving on.
@@ -1606,12 +1594,13 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
             let rsp = self.client.check_response(&request_id, rsp).await?;
             let version = parse_server_version(&request_id, &rsp)?;
             let describe_body = rsp.text().await.ok();
-            let table = Arc::new(RemoteTable::new(
+            let table = Arc::new(RemoteTable::new_with_sql_client(
                 self.client.clone(),
                 request.name.clone(),
                 request.namespace_path.clone(),
                 identifier,
                 version.clone(),
+                self.sql_client.clone(),
             ));
             // This describe already carries the schema, so hand it to the table
             // instead of making the first schema read fetch it again. A version or
@@ -1695,7 +1684,7 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
         request: ListNamespacesRequest,
     ) -> Result<ListNamespacesResponse> {
         let namespace_parts = request.id.as_deref().unwrap_or(&[]);
-        let namespace_id = build_namespace_identifier(namespace_parts)?;
+        let namespace_id = self.namespace_identifier(namespace_parts)?;
         let mut req = self
             .client
             .get(&format!("/v1/namespace/{}/list", namespace_id));
@@ -1717,7 +1706,7 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
         request: CreateNamespaceRequest,
     ) -> Result<CreateNamespaceResponse> {
         let namespace_parts = request.id.as_deref().unwrap_or(&[]);
-        let namespace_id = build_namespace_identifier(namespace_parts)?;
+        let namespace_id = self.namespace_identifier(namespace_parts)?;
         let mut req = self
             .client
             .post(&format!("/v1/namespace/{}/create", namespace_id));
@@ -1748,7 +1737,7 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
 
     async fn drop_namespace(&self, request: DropNamespaceRequest) -> Result<DropNamespaceResponse> {
         let namespace_parts = request.id.as_deref().unwrap_or(&[]);
-        let namespace_id = build_namespace_identifier(namespace_parts)?;
+        let namespace_id = self.namespace_identifier(namespace_parts)?;
         let mut req = self
             .client
             .post(&format!("/v1/namespace/{}/drop", namespace_id));
@@ -1782,7 +1771,7 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
         request: DescribeNamespaceRequest,
     ) -> Result<DescribeNamespaceResponse> {
         let namespace_parts = request.id.as_deref().unwrap_or(&[]);
-        let namespace_id = build_namespace_identifier(namespace_parts)?;
+        let namespace_id = self.namespace_identifier(namespace_parts)?;
         let req = self
             .client
             .post(&format!("/v1/namespace/{}/describe", namespace_id))
@@ -1887,6 +1876,8 @@ impl From<StorageOptions> for RemoteOptions {
 #[cfg(test)]
 mod tests {
     use super::{NamespaceHeaderProviderContext, build_cache_key};
+    use crate::listing::{Listing, ListingOptions};
+    use futures::TryStreamExt;
     use std::collections::HashMap;
     use std::sync::atomic::{AtomicUsize, Ordering};
     use std::sync::{Arc, OnceLock};
@@ -1894,6 +1885,7 @@ mod tests {
     use arrow_array::{Int32Array, RecordBatch};
     use arrow_schema::{DataType, Field, Schema};
     use lance_namespace_impls::{DynamicContextProvider, OperationInfo};
+    use rstest::rstest;
 
     use crate::connection::ConnectBuilder;
     use crate::database::Database;
@@ -1905,6 +1897,40 @@ mod tests {
         remote::{ARROW_STREAM_CONTENT_TYPE, ClientConfig, HeaderProvider, JSON_CONTENT_TYPE},
     };
 
+    #[tokio::test]
+    async fn test_catalog_namespace_identifier_validates_all_namespace_components() {
+        let mut db = super::RemoteDatabase::new_mock(|_| -> http::Response<String> {
+            panic!("identifier validation must not send a request")
+        });
+        db.is_catalog_root = true;
+
+        for (path, expected) in [
+            (vec![], "$"),
+            (vec!["team/search"], "team%2Fsearch"),
+            (vec!["team/search", "ns"], "team%2Fsearch$ns"),
+            (vec!["team/search", "ns", "child"], "team%2Fsearch$ns$child"),
+        ] {
+            let path = path.into_iter().map(String::from).collect::<Vec<_>>();
+            assert_eq!(db.namespace_identifier(&path).unwrap(), expected);
+        }
+
+        for path in [
+            vec!["../search"],
+            vec!["team/search", "bad/name"],
+            vec!["team/search", "ns", "bad/name"],
+            vec!["team/search", "public", "child"],
+        ] {
+            let path = path.into_iter().map(String::from).collect::<Vec<_>>();
+            assert!(
+                matches!(
+                    db.namespace_identifier(&path),
+                    Err(Error::InvalidInput { .. })
+                ),
+                "accepted {path:?}"
+            );
+        }
+    }
+
     #[test]
     fn test_cache_key_security() {
         // Test that cache keys are unique regardless of delimiter manipulation
@@ -1938,29 +1964,11 @@ mod tests {
     }
 
     #[tokio::test]
-    async fn test_create_materialized_view_uses_item_route_and_job() {
-        let db = super::RemoteDatabase::new_mock(|request| {
-            assert_eq!(request.method(), "POST");
-            assert_eq!(
-                request.url().path(),
-                "/v1/materialized_view/analytics$adults/create"
-            );
-            let body = request
-                .body()
-                .and_then(reqwest::Body::as_bytes)
-                .and_then(|bytes| serde_json::from_slice::<serde_json::Value>(bytes).ok())
-                .unwrap();
-            assert_eq!(
-                body["query"],
-                "SELECT age AS \"age\" FROM \"raw\".\"people\" WHERE age >= 18 LIMIT 10"
-            );
-            assert_eq!(body["with_no_data"], false);
-            http::Response::builder()
-                .status(202)
-                .body(serde_json::json!({"job_id": "j1-mv-create"}).to_string())
-                .unwrap()
+    async fn test_create_materialized_view_requires_sql_client() {
+        let db = super::RemoteDatabase::new_mock(|request| -> http::Response<String> {
+            panic!("unexpected REST request: {}", request.url().path())
         });
-        let job = db
+        let error = db
             .create_materialized_view_async(CreateMaterializedViewRequest {
                 name: "adults".into(),
                 namespace_path: vec!["analytics".into()],
@@ -1969,8 +1977,8 @@ mod tests {
                 with_no_data: false,
             })
             .await
-            .unwrap();
-        assert_eq!(job.id(), Some("j1-mv-create"));
+            .unwrap_err();
+        assert!(error.to_string().contains("SQL is unavailable"));
     }
 
     #[tokio::test]
@@ -2004,7 +2012,7 @@ mod tests {
                 .body(serde_json::json!({"dropped": true, "job_id": "j1-fn-drop"}).to_string())
                 .unwrap()
         });
-        let (dropped, job) = db.drop_function_async("embed", "1").await.unwrap();
+        let (dropped, job) = db.drop_function_async("embed", "1", &[]).await.unwrap();
         assert!(dropped);
         assert_eq!(job.id(), Some("j1-fn-drop"));
     }
@@ -2019,7 +2027,7 @@ mod tests {
                 .body(serde_json::json!({"dropped": false}).to_string())
                 .unwrap()
         });
-        let (dropped, job) = db.drop_function_async("embed", "1").await.unwrap();
+        let (dropped, job) = db.drop_function_async("embed", "1", &[]).await.unwrap();
         assert!(!dropped);
         assert_eq!(job.id(), None);
         assert_eq!(job.status().await.unwrap(), "finished");
@@ -2036,7 +2044,11 @@ mod tests {
             let db = super::RemoteDatabase::new_mock(move |_| {
                 http::Response::builder().status(202).body(body).unwrap()
             });
-            let error = db.drop_function_async("embed", "1").await.err().unwrap();
+            let error = db
+                .drop_function_async("embed", "1", &[])
+                .await
+                .err()
+                .unwrap();
             assert!(error.to_string().contains("valid job_id"));
         }
     }
@@ -2118,9 +2130,19 @@ mod tests {
             }
         });
         assert_eq!(
-            db.list_materialized_views(&["analytics".into()])
-                .await
-                .unwrap(),
+            Listing::new(Default::default(), {
+                let db = Arc::new(db);
+                move |options| {
+                    let db = db.clone();
+                    async move {
+                        db.list_materialized_views(&["analytics".into()], options)
+                            .await
+                    }
+                }
+            })
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap(),
             ["adults"]
         );
     }
@@ -2182,6 +2204,133 @@ mod tests {
         assert_eq!(names, vec!["table1", "table2"]);
     }
 
+    #[rstest::rstest]
+    #[case(None, None)]
+    #[case(Some("t009"), None)]
+    #[case(None, Some(200))]
+    #[case(Some("t009"), Some(15))]
+    #[tokio::test]
+    async fn test_table_names_follows_server_pages(
+        #[case] start_after: Option<&str>,
+        #[case] limit: Option<u32>,
+    ) {
+        let conn = Connection::new_with_handler(|request| {
+            assert_eq!(request.method(), &reqwest::Method::GET);
+            assert_eq!(request.url().path(), "/v1/table/");
+            let query: HashMap<_, _> = request.url().query_pairs().collect();
+            let start = query
+                .get("page_token")
+                .map(|token| token[1..4].parse::<usize>().unwrap() + 1)
+                .unwrap_or(0);
+            let limit = query
+                .get("limit")
+                .map(|limit| limit.parse::<usize>().unwrap())
+                .unwrap_or(10);
+            // A server may return a short page even when more tables remain.
+            let end = (start + limit.min(10)).min(130);
+            let tables: Vec<_> = (start..end).map(|i| format!("t{i:03}")).collect();
+            let page_token = (end < 130).then(|| format!("t{:03}.lance/", end - 1));
+            http::Response::builder()
+                .status(200)
+                .body(serde_json::json!({"tables": tables, "page_token": page_token}).to_string())
+                .unwrap()
+        });
+        let mut op = conn.table_names();
+        if let Some(start_after) = start_after {
+            op = op.start_after(start_after);
+        }
+        if let Some(limit) = limit {
+            op = op.limit(limit);
+        }
+        let start = if start_after.is_some() { 10 } else { 0 };
+        let end = limit.map(|limit| (start + limit).min(130)).unwrap_or(130);
+        let expected: Vec<_> = (start..end).map(|i| format!("t{i:03}")).collect();
+        assert_eq!(op.execute().await.unwrap(), expected);
+    }
+
+    #[rstest::rstest]
+    #[case(vec![])]
+    #[case(vec!["ns".to_string()])]
+    #[tokio::test]
+    async fn test_table_listing_zero_limit_never_sends_a_request(#[case] namespace: Vec<String>) {
+        let conn = Connection::new_with_handler(|_| -> http::Response<String> {
+            panic!("a zero limit must not be sent to the server")
+        });
+        assert!(
+            conn.table_names()
+                .namespace(namespace.clone())
+                .limit(0)
+                .execute()
+                .await
+                .unwrap()
+                .is_empty()
+        );
+        let page = conn
+            .list_tables(lance_namespace::models::ListTablesRequest {
+                id: Some(namespace),
+                limit: Some(0),
+                ..Default::default()
+            })
+            .await
+            .unwrap();
+        assert!(page.tables.is_empty());
+        assert!(page.page_token.is_none());
+    }
+
+    #[rstest::rstest]
+    #[case(vec![], "/v1/namespace/$/table/list")]
+    #[case(vec!["ns".to_string()], "/v1/namespace/ns/table/list")]
+    #[tokio::test]
+    async fn test_list_tables_default_page_size(
+        #[case] namespace: Vec<String>,
+        #[case] path: &'static str,
+    ) {
+        let conn = Connection::new_with_handler(move |request| {
+            assert_eq!(request.url().path(), path);
+            let query: HashMap<_, _> = request.url().query_pairs().collect();
+            assert_eq!(query.get("limit").map(|limit| limit.as_ref()), Some("100"));
+            let start = match query.get("page_token") {
+                None => 0,
+                Some(token) => {
+                    assert_eq!(token, "opaque-token");
+                    100
+                }
+            };
+            let end = (start + 100).min(130);
+            let tables: Vec<_> = (start..end).map(|i| format!("t{i:03}")).collect();
+            http::Response::builder()
+                .status(200)
+                .body(
+                    serde_json::json!({
+                        "tables": tables,
+                        "page_token": (end < 130).then_some("opaque-token")
+                    })
+                    .to_string(),
+                )
+                .unwrap()
+        });
+        let first = conn
+            .list_tables(lance_namespace::models::ListTablesRequest {
+                id: Some(namespace.clone()),
+                ..Default::default()
+            })
+            .await
+            .unwrap();
+        assert_eq!(first.tables.len(), 100);
+        assert_eq!(first.page_token.as_deref(), Some("opaque-token"));
+        let second = conn
+            .list_tables(lance_namespace::models::ListTablesRequest {
+                id: Some(namespace),
+                page_token: first.page_token,
+                ..Default::default()
+            })
+            .await
+            .unwrap();
+        let expected: Vec<_> = (100..130).map(|i| format!("t{i:03}")).collect();
+        assert_eq!(second.tables, expected);
+        assert!(second.page_token.is_none());
+    }
+
     #[tokio::test]
     async fn test_table_names_in_a_namespace_never_invents_a_page_token() {
         // The namespace route's token belongs to the store, so `table_names` cannot build one
@@ -2227,8 +2376,11 @@ mod tests {
         assert_eq!(names, vec!["widgets"]);
     }
 
+    #[rstest::rstest]
+    #[case(vec![])]
+    #[case(vec!["ns".to_string()])]
     #[tokio::test]
-    async fn test_table_names_in_a_namespace_stops_on_a_repeated_token() {
+    async fn test_table_names_stops_on_a_repeated_token(#[case] namespace: Vec<String>) {
         // A server that handed back the token it was given would never finish the walk.
         let conn = Connection::new_with_handler(|_request| {
             http::Response::builder()
@@ -2239,7 +2391,7 @@ mod tests {
 
         let names = conn
             .table_names()
-            .namespace(vec!["ns".to_string()])
+            .namespace(namespace)
             .execute()
             .await
             .unwrap();
@@ -2248,8 +2400,11 @@ mod tests {
         assert_eq!(names, vec!["a", "a"]);
     }
 
+    #[rstest::rstest]
+    #[case(vec![])]
+    #[case(vec!["ns".to_string()])]
     #[tokio::test]
-    async fn test_table_names_in_a_namespace_stops_on_an_empty_token() {
+    async fn test_table_names_stops_on_an_empty_token(#[case] namespace: Vec<String>) {
         // An empty token ends the listing. Sending it back would ask a server that reads it
         // as "start from the beginning" for the first page a second time, and every name on
         // that page would be collected twice.
@@ -2269,7 +2424,7 @@ mod tests {
 
         let names = conn
             .table_names()
-            .namespace(vec!["ns".to_string()])
+            .namespace(namespace)
             .execute()
             .await
             .unwrap();
@@ -2515,6 +2670,75 @@ mod tests {
         );
     }
 
+    #[rstest]
+    #[case::matching(Field::new("a", DataType::Int32, false), true)]
+    #[case::different_name(Field::new("x", DataType::Int32, false), false)]
+    #[case::different_type(Field::new("a", DataType::Int64, false), false)]
+    #[case::different_nullability(Field::new("a", DataType::Int32, true), false)]
+    #[tokio::test]
+    async fn test_create_table_exist_ok_validates_schema(
+        #[case] existing_field: Field,
+        #[case] matches: bool,
+        #[values(false, true)] empty: bool,
+        #[values(false, true)] cached: bool,
+    ) {
+        let existing_schema = Schema::new(vec![existing_field]);
+        let description = serde_json::json!({
+            "version": 1,
+            "schema": lance::arrow::json::JsonSchema::try_from(&existing_schema).unwrap(),
+        })
+        .to_string();
+        let mut db = super::RemoteDatabase::new_mock(move |request| {
+            assert_eq!(request.method(), &reqwest::Method::POST);
+            match request.url().path() {
+                "/v1/table/table1/create/" => {
+                    assert_eq!(request.url().query(), Some("mode=exist_ok"));
+                    http::Response::builder()
+                        .status(400)
+                        .body("Table table1 already exists".to_string())
+                        .unwrap()
+                }
+                "/v1/table/table1/describe/" => http::Response::builder()
+                    .status(200)
+                    .body(description.clone())
+                    .unwrap(),
+                path => panic!("unexpected path: {path}"),
+            }
+        });
+        db.table_cache = moka::future::Cache::new(10);
+        let conn = Connection::new(
+            Arc::new(db),
+            Arc::new(crate::embeddings::MemoryRegistry::new()),
+        );
+        if cached {
+            conn.open_table("table1").execute().await.unwrap();
+        }
+
+        let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Int32, false)]));
+        let builder = if empty {
+            conn.create_empty_table("table1", schema.clone())
+        } else {
+            let data = RecordBatch::try_new(
+                schema.clone(),
+                vec![Arc::new(Int32Array::from(vec![1, 2, 3]))],
+            )
+            .unwrap();
+            conn.create_table("table1", data)
+        };
+        let result = builder
+            .mode(CreateTableMode::exist_ok(|b| b))
+            .execute()
+            .await;
+        if matches {
+            let table = result.unwrap();
+            assert_eq!(table.name(), "table1");
+            assert_eq!(table.schema().await.unwrap(), schema);
+        } else {
+            assert!(matches!(result, Err(Error::Schema { message })
+                if message == "Provided schema does not match existing table schema"));
+        }
+    }
+
     #[tokio::test]
     async fn test_create_table_modes() {
         let test_cases = [
@@ -2554,7 +2778,14 @@ mod tests {
                 .status(400)
                 .body("Table table1 already exists")
                 .unwrap(),
-            "/v1/table/table1/describe/" => http::Response::builder().status(200).body("").unwrap(),
+            "/v1/table/table1/describe/" => http::Response::builder()
+                .status(200)
+                .body(
+                    r#"{"version": 1, "schema": {"fields": [
+                        {"name": "a", "type": {"type": "int32"}, "nullable": false}
+                    ]}}"#,
+                )
+                .unwrap(),
             _ => {
                 panic!("unexpected path: {:?}", request.url().path());
             }
@@ -3453,6 +3684,110 @@ mod tests {
         }
     }
 
+    #[tokio::test]
+    async fn test_resource_listings_are_lazy_and_forward_pagination() {
+        async fn check<T: Unpin + std::fmt::Debug>(mut items: Listing<T>, count: Arc<AtomicUsize>) {
+            assert_eq!(count.load(Ordering::SeqCst), 0);
+            assert_eq!(items.num_page_results(), 0);
+            assert_eq!(items.page_token(), Some("start/token"));
+            assert!(items.try_next().await.unwrap().is_some());
+            assert_eq!(items.num_page_results(), 1);
+            assert_eq!(items.page_token(), Some("empty"));
+            assert!(items.try_next().await.unwrap().is_some());
+            assert_eq!(count.load(Ordering::SeqCst), 1);
+            assert!(items.try_next().await.unwrap().is_some());
+            assert_eq!(count.load(Ordering::SeqCst), 3);
+            assert_eq!(items.num_page_results(), 1);
+            assert_eq!(items.page_token(), None);
+            assert!(items.try_next().await.unwrap().is_some());
+            assert!(items.try_next().await.unwrap().is_none());
+            assert!(items.try_next().await.unwrap().is_none());
+        }
+        for resource in ["secret", "view", "function", "materialized_view", "jobs"] {
+            let count = Arc::new(AtomicUsize::new(0));
+            let conn = Connection::new_with_handler({
+                let count = count.clone();
+                move |request| {
+                    let page = count.fetch_add(1, Ordering::SeqCst);
+                    assert!(page < 3);
+                    let token = ["start/token", "empty", "last"][page];
+                    if resource == "jobs" {
+                        assert_eq!(request.url().path(), "/v1/jobs/list");
+                        let body: serde_json::Value =
+                            serde_json::from_slice(request.body().unwrap().as_bytes().unwrap())
+                                .unwrap();
+                        assert_eq!(body, serde_json::json!({"page_token": token, "limit": 2}));
+                    } else {
+                        let ns = if resource == "materialized_view" {
+                            "$"
+                        } else {
+                            "team"
+                        };
+                        assert_eq!(
+                            request.url().path(),
+                            format!("/v1/namespace/{ns}/{resource}/list")
+                        );
+                        let query = request.url().query_pairs().collect::<HashMap<_, _>>();
+                        assert_eq!(query.get("page_token").unwrap(), token);
+                        assert_eq!(query.get("limit").unwrap(), "2");
+                        if resource == "function" {
+                            assert_eq!(query.get("include_definition").unwrap(), "true");
+                        }
+                    }
+                    let (key, item) = match resource {
+                        "secret" => ("secrets", serde_json::json!({"name": "a"})),
+                        "jobs" => (
+                            "jobs",
+                            serde_json::json!({"job_id": "a", "table": "t", "job_type": "create_index", "state": "in_progress", "created_at_millis": 1}),
+                        ),
+                        "function" => (
+                            "functions",
+                            serde_json::json!({"name": "embed", "version": "1", "definition": serde_json::from_str::<serde_json::Value>(include_str!("../../tests/fixtures/first_class_functions/v1/remote_function_version.canonical.json")).unwrap()}),
+                        ),
+                        _ => ("views", serde_json::json!("a")),
+                    };
+                    let items = if page == 1 {
+                        vec![]
+                    } else {
+                        vec![item.clone(), item]
+                    };
+                    http::Response::builder().status(200).body(serde_json::json!({key: items, "page_token": (["empty", "last", ""][page])}).to_string()).unwrap()
+                }
+            });
+            let options = ListingOptions::default()
+                .page_token("start/token")
+                .page_limit(2);
+            let namespace = vec!["team".to_owned()];
+            match resource {
+                "secret" => check(conn.list_secrets(&namespace, options), count).await,
+                "view" => check(conn.list_views(&namespace, options), count).await,
+                "function" => check(conn.list_functions(&namespace, options), count).await,
+                "materialized_view" => check(conn.list_materialized_views(options), count).await,
+                _ => check(conn.list_jobs(options), count).await,
+            }
+        }
+    }
+
+    #[tokio::test]
+    async fn test_list_jobs_does_not_truncate_after_100_pages() {
+        let conn = Connection::new_with_handler(|request| {
+            let body: serde_json::Value =
+                serde_json::from_slice(request.body().unwrap().as_bytes().unwrap()).unwrap();
+            let page: u32 = body["page_token"].as_str().unwrap_or("0").parse().unwrap();
+            http::Response::builder().status(200).body(serde_json::json!({
+                "jobs": [{"job_id": page.to_string(), "table": "t", "job_type": "create_index", "state": "in_progress", "created_at_millis": 1}],
+                "page_token": (page < 110).then(|| (page + 1).to_string()),
+            }).to_string()).unwrap()
+        });
+        let jobs = conn
+            .list_jobs(Default::default())
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        assert_eq!(jobs.len(), 111);
+        assert_eq!(jobs.last().unwrap().job_id, "110");
+    }
+
     #[tokio::test]
     async fn test_list_jobs_paginates() {
         let page = Arc::new(AtomicUsize::new(0));
@@ -3482,7 +3817,11 @@ mod tests {
                 }
             }
         });
-        let jobs = conn.list_jobs().await.unwrap();
+        let jobs = conn
+            .list_jobs(Default::default())
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
         assert_eq!(jobs.len(), 3);
         assert_eq!(jobs[0].job_id, "job-1");
         assert_eq!(jobs[0].table, "t1");
@@ -3511,17 +3850,14 @@ mod tests {
                 .unwrap()
         });
 
-        let error = conn.list_jobs().await.unwrap_err();
+        let mut items = conn.list_jobs(Default::default());
+        let error = (&mut items).try_collect::<Vec<_>>().await.unwrap_err();
         assert!(
-            matches!(
-                &error,
-                Error::Http {
-                    status_code: Some(http::StatusCode::OK),
-                    ..
-                }
-            ),
+            matches!(&error, Error::Runtime { message } if message.contains("repeated a page_token")),
             "got {error:?}"
         );
+        assert!(items.page_token().is_some());
+        assert!(items.try_next().await.unwrap().is_none());
         assert_eq!(requests.load(Ordering::SeqCst), 2);
     }
 
@@ -3753,7 +4089,7 @@ mod tests {
                 .expect_err("an illegal component must be refused");
             assert!(!*reached.lock().unwrap(), "{name:?} reached the transport");
             assert!(
-                error.to_string().contains("Secret name"),
+                error.to_string().contains("secret name"),
                 "{name:?}: {error}"
             );
         }
@@ -3784,16 +4120,19 @@ mod tests {
                 .await
                 .expect_err("a dot-only name must be refused");
             assert!(
-                by_name.to_string().contains("relative path segments"),
+                by_name.to_string().contains("Illegal secret name"),
                 "{by_name}"
             );
 
             let by_segment = conn
-                .list_secrets(&[component.to_string()])
+                .list_secrets(&[component.to_string()], Default::default())
+                .try_collect::<Vec<_>>()
                 .await
                 .expect_err("a dot-only namespace segment must be refused");
             assert!(
-                by_segment.to_string().contains("relative path segments"),
+                by_segment
+                    .to_string()
+                    .contains("Illegal namespace component"),
                 "{by_segment}"
             );
             assert!(
@@ -3872,28 +4211,20 @@ mod tests {
         }
     }
 
-    /// A segment outside the table charset still addresses one segment: the
-    /// service decides whether it may exist, and percent-encoding is what keeps
-    /// the question reaching the right route. A catalog database is named this
-    /// way.
+    /// Slashes belong to catalog database names, not table namespace segments.
     #[tokio::test]
-    async fn test_a_namespace_segment_outside_the_charset_is_encoded_not_refused() {
-        use std::sync::{Arc, Mutex};
-        let seen = Arc::new(Mutex::new(String::new()));
-        let path = seen.clone();
-        let conn = Connection::new_with_handler(move |request| {
-            *path.lock().unwrap() = request.url().path().to_string();
-            http::Response::builder().status(200).body("{}").unwrap()
+    async fn test_a_namespace_segment_outside_the_charset_is_refused() {
+        let conn = Connection::new_with_handler(|_| -> http::Response<String> {
+            panic!("invalid namespace reached the transport")
         });
-        conn.drop_table("t", &["team/search".to_string()])
-            .await
-            .unwrap();
-        assert_eq!(*seen.lock().unwrap(), "/v1/table/team%2Fsearch$t/drop/");
+        assert!(matches!(
+            conn.drop_table("t", &["team/search".to_string()]).await,
+            Err(Error::InvalidInput { .. })
+        ));
     }
 
-    /// A Function name is percent-encoded, which covers everything but the
-    /// relative segment: `..` is unreserved, so it survives encoding and is
-    /// then resolved away, posting a registration body to `/v1/create`.
+    /// Reject relative segments and their encoded forms before sending a request,
+    /// so a function name cannot change the route through URL normalization.
     #[tokio::test]
     async fn test_a_relative_segment_function_name_is_refused() {
         use std::sync::{Arc, Mutex};
@@ -3905,13 +4236,10 @@ mod tests {
                 http::Response::builder().status(200).body("{}").unwrap()
             });
             let error = conn
-                .drop_function(name, "fv_1")
+                .drop_function(name, "fv_1", &[])
                 .await
                 .expect_err("a dot-only Function name must be refused");
-            assert!(
-                error.to_string().contains("relative path segments"),
-                "{error}"
-            );
+            assert!(matches!(error, Error::InvalidInput { .. }), "{error}");
             assert!(!*reached.lock().unwrap(), "{name:?} reached the transport");
         }
     }
@@ -3988,7 +4316,10 @@ mod tests {
             http::Response::builder().status(200).body(body).unwrap()
         });
         assert_eq!(
-            conn.list_secrets(&[]).await.unwrap(),
+            conn.list_secrets(&[], Default::default())
+                .try_collect::<Vec<_>>()
+                .await
+                .unwrap(),
             vec!["openai-prod".to_string(), "hf-prod".to_string()]
         );
     }
@@ -4003,7 +4334,11 @@ mod tests {
                 .body(r#"{"secrets":[{"name":"openai-prod"}],"page_token":"same"}"#)
                 .unwrap()
         });
-        let error = conn.list_secrets(&[]).await.unwrap_err();
+        let error = conn
+            .list_secrets(&[], Default::default())
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap_err();
         assert!(
             error.to_string().contains("repeated a page_token"),
             "{error}"
@@ -4066,9 +4401,13 @@ mod tests {
                 .body(r#"{"secrets":[]}"#)
                 .unwrap()
         });
-        conn.list_secrets(&["prod".to_string(), "vision".to_string()])
-            .await
-            .unwrap();
+        conn.list_secrets(
+            &["prod".to_string(), "vision".to_string()],
+            Default::default(),
+        )
+        .try_collect::<Vec<_>>()
+        .await
+        .unwrap();
     }
 
     /// A view description carries the schema in the namespace spec's JSON
@@ -4233,7 +4572,10 @@ mod tests {
             http::Response::builder().status(200).body(body).unwrap()
         });
         assert_eq!(
-            conn.list_views(&["analytics".into()]).await.unwrap(),
+            conn.list_views(&["analytics".into()], Default::default())
+                .try_collect::<Vec<_>>()
+                .await
+                .unwrap(),
             vec!["adults".to_string()]
         );
     }
@@ -4248,7 +4590,11 @@ mod tests {
                 .body(r#"{"views":["adults"],"page_token":"same"}"#)
                 .unwrap()
         });
-        let error = conn.list_views(&[]).await.unwrap_err();
+        let error = conn
+            .list_views(&[], Default::default())
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap_err();
         assert!(
             error.to_string().contains("repeated a page_token"),
             "{error}"
@@ -4274,7 +4620,7 @@ mod tests {
             };
             // A view is not a table, so the refusal says so.
             assert!(
-                error.contains("view name") || error.contains("view namespace path segment"),
+                error.contains("view name") || error.contains("namespace component"),
                 "{error}"
             );
         }
@@ -4307,7 +4653,7 @@ mod tests {
             path => panic!("unexpected path: {path}"),
         });
         let request = crate::function::FunctionRegistrationRequest::from_json(REQUEST).unwrap();
-        let job = conn.create_function_async(request).await.unwrap();
+        let job = conn.create_function_async(request, &[]).await.unwrap();
         assert_eq!(job.id(), Some("job-function-1"));
         let version = job.wait().await.unwrap();
         assert_eq!(version.name(), "embed");
@@ -4327,7 +4673,7 @@ mod tests {
             assert_eq!(body, serde_json::json!({"version": "1"}));
             http::Response::builder().status(200).body(VERSION).unwrap()
         });
-        let version = conn.get_function("embed", "1").await.unwrap();
+        let version = conn.get_function("embed", "1", &[]).await.unwrap();
         assert_eq!(version.name(), "embed");
         assert_eq!(version.version(), "1");
     }
@@ -4370,7 +4716,11 @@ mod tests {
                 }
             }
         });
-        let functions = conn.list_functions().await.unwrap();
+        let functions = conn
+            .list_functions(&[], Default::default())
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
         assert_eq!(functions.len(), 1);
         assert_eq!(functions[0].name(), "embed");
         assert_eq!(functions[0].version(), "1");
@@ -4393,7 +4743,11 @@ mod tests {
                 .unwrap()
         });
 
-        let functions = conn.list_functions().await.unwrap();
+        let functions = conn
+            .list_functions(&[], Default::default())
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
         assert!(functions.is_empty());
         assert_eq!(requests.load(Ordering::SeqCst), 1);
     }
@@ -4434,17 +4788,14 @@ mod tests {
                 .unwrap()
         });
 
-        let error = conn.list_functions().await.unwrap_err();
+        let mut items = conn.list_functions(&[], Default::default());
+        let error = (&mut items).try_collect::<Vec<_>>().await.unwrap_err();
         assert!(
-            matches!(
-                &error,
-                Error::Http {
-                    status_code: Some(http::StatusCode::OK),
-                    ..
-                }
-            ),
+            matches!(&error, Error::Runtime { message } if message.contains("repeated a page_token")),
             "got {error:?}"
         );
+        assert!(items.page_token().is_some());
+        assert!(items.try_next().await.unwrap().is_none());
         assert_eq!(requests.load(Ordering::SeqCst), 3);
     }
 
@@ -4461,7 +4812,93 @@ mod tests {
                 .body(r#"{"dropped":false}"#)
                 .unwrap()
         });
-        assert!(!conn.drop_function("embed", "1").await.unwrap());
+        assert!(!conn.drop_function("embed", "1", &[]).await.unwrap());
+    }
+
+    /// A Function's namespace is addressed in the path the way a Secret's is:
+    /// every route takes the joined identifier and no body carries the
+    /// namespace, so a namespaced request differs from a root one only in its
+    /// path.
+    #[tokio::test]
+    async fn test_a_function_namespace_path_is_addressed_in_the_path() {
+        const REQUEST: &str = include_str!(
+            "../../tests/fixtures/first_class_functions/v1/remote_function_registration_request.json"
+        );
+        const VERSION: &str = include_str!(
+            "../../tests/fixtures/first_class_functions/v1/remote_function_version.canonical.json"
+        );
+        let namespace = ["analytics".to_string(), "features".to_string()];
+        let mut expected_create: serde_json::Value = serde_json::from_str(REQUEST).unwrap();
+        expected_create.as_object_mut().unwrap().remove("name");
+        let conn = Connection::new_with_handler(move |request| {
+            let body = request
+                .body()
+                .and_then(|body| body.as_bytes())
+                .map(|bytes| serde_json::from_slice::<serde_json::Value>(bytes).unwrap());
+            match request.url().path() {
+                "/v1/function/analytics$features$normalize_score/create" => {
+                    assert_eq!(body.unwrap(), expected_create);
+                    http::Response::builder()
+                        .status(202)
+                        .body(r#"{"job_id":"job-function-1"}"#.to_string())
+                        .unwrap()
+                }
+                "/v1/function/analytics$features$embed/describe" => {
+                    assert_eq!(body.unwrap(), serde_json::json!({"version": "1"}));
+                    http::Response::builder()
+                        .status(200)
+                        .body(VERSION.to_string())
+                        .unwrap()
+                }
+                "/v1/function/analytics$features$embed/drop" => {
+                    assert_eq!(body.unwrap(), serde_json::json!({"version": "1"}));
+                    http::Response::builder()
+                        .status(200)
+                        .body(r#"{"dropped":true}"#.to_string())
+                        .unwrap()
+                }
+                // Listing is namespace-scoped, so the namespace is the whole
+                // identifier.
+                "/v1/namespace/analytics$features/function/list" => http::Response::builder()
+                    .status(200)
+                    .body(r#"{"functions":[]}"#.to_string())
+                    .unwrap(),
+                path => panic!("unexpected path: {path}"),
+            }
+        });
+        let request = crate::function::FunctionRegistrationRequest::from_json(REQUEST).unwrap();
+        let job = conn
+            .create_function_async(request, &namespace)
+            .await
+            .unwrap();
+        assert_eq!(job.id(), Some("job-function-1"));
+        conn.get_function("embed", "1", &namespace).await.unwrap();
+        assert!(
+            conn.list_functions(&namespace, Default::default())
+                .try_collect::<Vec<_>>()
+                .await
+                .unwrap()
+                .is_empty()
+        );
+        assert!(conn.drop_function("embed", "1", &namespace).await.unwrap());
+    }
+
+    /// Each namespace segment is checked before a request is built, so an
+    /// empty segment cannot collapse the identifier onto the parent namespace.
+    #[tokio::test]
+    async fn test_an_unaddressable_function_namespace_segment_is_refused() {
+        let conn = Connection::new_with_handler(|request| -> http::Response<String> {
+            panic!("reached the transport: {}", request.url().path())
+        });
+        let namespace = ["analytics".to_string(), String::new()];
+        assert!(conn.get_function("embed", "1", &namespace).await.is_err());
+        assert!(
+            conn.list_functions(&namespace, Default::default())
+                .try_collect::<Vec<_>>()
+                .await
+                .is_err()
+        );
+        assert!(conn.drop_function("embed", "1", &namespace).await.is_err());
     }
 
     #[tokio::test]
diff --git a/rust/lancedb/src/remote/retry.rs b/rust/lancedb/src/remote/retry.rs
index 1a354eebb..19aa301e4 100644
--- a/rust/lancedb/src/remote/retry.rs
+++ b/rust/lancedb/src/remote/retry.rs
@@ -198,6 +198,29 @@ mod tests {
         assert_eq!(counter.connect_failures, 0);
     }
 
+    #[test]
+    fn retry_limit_message_includes_last_status_error() {
+        let config = test_config();
+        let mut counter = RetryCounter::new(&config, "test".to_string());
+        for _ in 0..2 {
+            counter
+                .increment_request_failures(crate::Error::Http {
+                    source: "502 Bad Gateway".into(),
+                    request_id: "test".to_string(),
+                    status_code: Some(reqwest::StatusCode::BAD_GATEWAY),
+                })
+                .unwrap();
+        }
+        let error = counter
+            .increment_request_failures(crate::Error::Http {
+                source: "last response: 502 Bad Gateway".into(),
+                request_id: "test".to_string(),
+                status_code: Some(reqwest::StatusCode::BAD_GATEWAY),
+            })
+            .unwrap_err();
+        assert!(error.to_string().contains("last response: 502 Bad Gateway"));
+    }
+
     #[tokio::test]
     async fn test_increment_from_error_respects_global_limits() {
         // If request_failures is already at max, a connect error should still
diff --git a/rust/lancedb/src/remote/sql.rs b/rust/lancedb/src/remote/sql.rs
index 489158922..a58a9afad 100644
--- a/rust/lancedb/src/remote/sql.rs
+++ b/rust/lancedb/src/remote/sql.rs
@@ -3,6 +3,7 @@
 
 use std::collections::HashMap;
 use std::fs;
+use std::future::{Future, pending};
 use std::sync::atomic::{AtomicBool, Ordering};
 use std::sync::{Arc, Mutex as StdMutex, OnceLock};
 use std::time::{Duration, Instant};
@@ -20,16 +21,23 @@ use arrow_schema::{Schema, SchemaRef};
 use futures::TryStreamExt;
 use http::header::{HeaderMap, HeaderName, HeaderValue};
 use prost::Message;
-use tokio::sync::{Mutex, Notify, OnceCell, mpsc};
+use serde::{Serialize, de::DeserializeOwned};
+use tokio::sync::{Mutex, Notify, OnceCell, mpsc, watch};
 use tonic::transport::{Certificate, Channel, ClientTlsConfig, Endpoint, Identity};
 use uuid::Uuid;
 
 use crate::arrow::{SendableRecordBatchStream, SimpleRecordBatchStream};
+use crate::database::ExecuteQueryRequest;
 use crate::error::{Error, Result};
+use crate::job::Job;
 use crate::remote::client::{ClientConfig, TlsConfig};
 use crate::remote::retry::ResolvedRetryConfig;
 use crate::sql::{Query, QueryDescription, QueryHandle, QueryStatus};
 
+mod exchange;
+
+use exchange::{ExchangeQuery, ExchangeQueryHandle};
+
 const DEFAULT_SQL_PORT: u16 = 10025;
 const DEFAULT_SQL_TLS_PORT: u16 = 10026;
 const DEFAULT_CONNECT_TIMEOUT: Duration = Duration::from_secs(120);
@@ -182,6 +190,23 @@ impl SqlClient {
         }
     }
 
+    pub(super) async fn execute(&self, request: ExecuteQueryRequest) -> Result<Query> {
+        match request.parameters {
+            Some(parameters) => {
+                self.submit_with_parameters(
+                    &request.query,
+                    parameters,
+                    &request.default_namespace_path,
+                )
+                .await
+            }
+            None => {
+                self.submit(&request.query, &request.default_namespace_path)
+                    .await
+            }
+        }
+    }
+
     pub(super) async fn submit(
         &self,
         query: &str,
@@ -203,12 +228,78 @@ impl SqlClient {
                 default_namespace_path.to_vec(),
                 poll_info,
             )?);
-            self.queries.insert(query_id, query.clone());
+            self.queries
+                .insert(query_id, RegisteredQuery::Polled(query.clone()));
             Ok(Query::new(Arc::new(RemoteQueryHandle::new(query))))
         })
         .await
     }
 
+    pub(super) fn submit_as_job<T, F, Fut>(
+        &self,
+        statement: String,
+        namespace: Vec<String>,
+        finish: F,
+    ) -> Job<T>
+    where
+        T: Clone + Serialize + DeserializeOwned + Send + Sync + 'static,
+        F: FnOnce() -> Fut + Send + 'static,
+        Fut: Future<Output = Result<T>> + Send + 'static,
+    {
+        let client = self.clone();
+        let (query_tx, query_rx) = watch::channel(None::<Arc<Query>>);
+        let task = tokio::spawn(async move {
+            let query = Arc::new(client.submit(&statement, &namespace).await?);
+            query_tx.send_replace(Some(query.clone()));
+            let mut reader = query.reader().await?;
+            while reader.try_next().await?.is_some() {}
+            finish().await
+        });
+        Job::spawned_with_cancellation(task, move || {
+            let mut query_rx = query_rx.clone();
+            async move {
+                loop {
+                    let query = query_rx.borrow().clone();
+                    if let Some(query) = query {
+                        return query.cancel().await;
+                    }
+                    if query_rx.changed().await.is_err() {
+                        pending::<()>().await;
+                    }
+                }
+            }
+        })
+    }
+
+    /// Values cannot ride on a poll, so a statement that has them is sent on
+    /// its own exchange instead.
+    pub(super) async fn submit_with_parameters(
+        &self,
+        query: &str,
+        parameters: RecordBatch,
+        default_namespace_path: &[String],
+    ) -> Result<Query> {
+        let timeout = self.inner.overall_timeout()?;
+        with_overall_timeout(timeout, "SQL query submission", async {
+            validate_namespace_path(default_namespace_path)?;
+            let query_id = Uuid::now_v7();
+            let query = Arc::new(
+                ExchangeQuery::open(
+                    query_id,
+                    self.inner.clone(),
+                    query,
+                    parameters,
+                    default_namespace_path,
+                )
+                .await?,
+            );
+            self.queries
+                .insert(query_id, RegisteredQuery::Exchanged(query.clone()));
+            Ok(Query::new(Arc::new(ExchangeQueryHandle::new(query))))
+        })
+        .await
+    }
+
     pub(super) async fn describe(&self, query_id: Uuid) -> Result<QueryDescription> {
         let query = self
             .queries
@@ -491,8 +582,40 @@ impl SqlClientInner {
     }
 }
 
+/// A submitted query as the connection remembers it.
+#[derive(Clone)]
+enum RegisteredQuery {
+    Polled(Arc<RemoteQuery>),
+    Exchanged(Arc<ExchangeQuery>),
+}
+
+impl RegisteredQuery {
+    fn touch(&self) {
+        match self {
+            Self::Polled(query) => query.touch(),
+            Self::Exchanged(query) => query.touch(),
+        }
+    }
+
+    /// Whether the registry may forget this query. It is abandoned when the
+    /// registry holds the only reference to it.
+    fn registry_expired(&self) -> bool {
+        match self {
+            Self::Polled(query) => query.registry_expired(Arc::strong_count(query) == 1),
+            Self::Exchanged(query) => query.registry_expired(Arc::strong_count(query) == 1),
+        }
+    }
+
+    async fn describe(&self) -> Result<QueryDescription> {
+        match self {
+            Self::Polled(query) => query.describe().await,
+            Self::Exchanged(query) => query.describe(),
+        }
+    }
+}
+
 struct QueryRegistry {
-    queries: StdMutex<HashMap<Uuid, Arc<RemoteQuery>>>,
+    queries: StdMutex<HashMap<Uuid, RegisteredQuery>>,
 }
 
 impl QueryRegistry {
@@ -502,12 +625,12 @@ impl QueryRegistry {
         }
     }
 
-    fn insert(&self, id: Uuid, query: Arc<RemoteQuery>) {
+    fn insert(&self, id: Uuid, query: RegisteredQuery) {
         self.remove_expired();
         self.queries.lock().unwrap().insert(id, query);
     }
 
-    fn get(&self, id: Uuid) -> Option<Arc<RemoteQuery>> {
+    fn get(&self, id: Uuid) -> Option<RegisteredQuery> {
         self.remove_expired();
         let query = self.queries.lock().unwrap().get(&id).cloned();
         if let Some(query) = &query {
@@ -520,7 +643,7 @@ impl QueryRegistry {
         self.queries
             .lock()
             .unwrap()
-            .retain(|_, query| !query.registry_expired(Arc::strong_count(query) == 1));
+            .retain(|_, query| !query.registry_expired());
     }
 }
 
diff --git a/rust/lancedb/src/remote/sql/exchange.rs b/rust/lancedb/src/remote/sql/exchange.rs
new file mode 100644
index 000000000..523aaf5d7
--- /dev/null
+++ b/rust/lancedb/src/remote/sql/exchange.rs
@@ -0,0 +1,435 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+//! SQL statements sent with parameters.
+//!
+//! A statement without parameters is polled: `PollFlightInfo` starts it
+//! detached from the connection and `DoGet` reads its endpoints. Neither call
+//! carries Arrow data, so a statement's values would have to be spelled into
+//! its text. A statement with parameters is sent on one `DoExchange` instead:
+//! the command and the parameter schema, then one row, and the result rows
+//! come back on the same call. The call is the statement's lifetime, so
+//! cancelling it -- or dropping the handle before reading -- stops the
+//! statement on the server.
+
+use std::sync::atomic::{AtomicBool, Ordering};
+use std::sync::{Arc, Mutex as StdMutex, OnceLock};
+use std::time::Instant;
+
+use arrow_array::RecordBatch;
+use arrow_flight::FlightDescriptor;
+use arrow_flight::encode::FlightDataEncoderBuilder;
+use arrow_flight::sql::{CommandStatementQuery, ProstMessageExt};
+use arrow_schema::SchemaRef;
+use prost::Message;
+use tokio::sync::{Notify, mpsc};
+use uuid::Uuid;
+
+use super::{
+    ABANDONED_QUERY_RETENTION, DEFAULT_READ_TIMEOUT, ResultEndpointStream, ResultStartGuard,
+    SqlClientInner, TERMINAL_QUERY_RETENTION, resolve_timeout, sql_error, with_overall_timeout,
+};
+use crate::arrow::{SendableRecordBatchStream, SimpleRecordBatchStream};
+use crate::error::{Error, Result};
+use crate::sql::{QueryDescription, QueryHandle, QueryStatus};
+
+#[derive(Clone, Debug, PartialEq, Eq)]
+enum Lifecycle {
+    Running,
+    /// Every row has been received.
+    Finished,
+    Cancelled,
+    Failed(String),
+}
+
+/// A failed statement, keeping the message `describe` reports it with -- not
+/// `Runtime error: …` again when `describe` wraps it in a runtime error.
+fn failure(error: &Error) -> Lifecycle {
+    Lifecycle::Failed(match error {
+        Error::Runtime { message } => message.clone(),
+        other => other.to_string(),
+    })
+}
+
+/// A statement running on its own `DoExchange` call.
+pub(super) struct ExchangeQuery {
+    id: Uuid,
+    client: Arc<SqlClientInner>,
+    /// The call's rows, until a reader takes them or a cancellation drops them.
+    response: StdMutex<Option<ResultEndpointStream>>,
+    lifecycle: StdMutex<Lifecycle>,
+    cancelled: Notify,
+    terminal_at: OnceLock<Instant>,
+    last_accessed: StdMutex<Instant>,
+}
+
+struct PreparedExchange {
+    schema: SchemaRef,
+    first: Option<RecordBatch>,
+    rest: Option<ResultEndpointStream>,
+}
+
+/// The call's rows while a reader waits for their first batch.
+///
+/// A reader whose future is dropped mid-wait -- its caller gave up, or its
+/// task was cancelled -- puts them back, so the next reader finds the query as
+/// this one did. A query cancelled meanwhile has its rows dropped instead,
+/// which ends the call.
+struct Borrowed<'a> {
+    query: &'a ExchangeQuery,
+    rows: Option<ResultEndpointStream>,
+}
+
+impl Borrowed<'_> {
+    fn get(&mut self) -> &mut ResultEndpointStream {
+        self.rows.as_mut().expect("rows are borrowed until kept")
+    }
+
+    fn keep(mut self) -> ResultEndpointStream {
+        self.rows.take().expect("rows are borrowed until kept")
+    }
+}
+
+impl Drop for Borrowed<'_> {
+    fn drop(&mut self) {
+        let Some(rows) = self.rows.take() else {
+            return;
+        };
+        // Checked under the response lock: `cancel` settles first and takes
+        // the response after, so rows put back here are never missed by it.
+        let mut response = self.query.response.lock().unwrap();
+        if *self.query.lifecycle.lock().unwrap() == Lifecycle::Running {
+            *response = Some(rows);
+        }
+    }
+}
+
+impl ExchangeQuery {
+    /// Send the statement and its parameters, returning once the server has
+    /// planned it: a statement it refuses is refused here, as a polled one is
+    /// on its first poll.
+    pub(super) async fn open(
+        id: Uuid,
+        client: Arc<SqlClientInner>,
+        query: &str,
+        parameters: RecordBatch,
+        default_namespace_path: &[String],
+    ) -> Result<Self> {
+        if parameters.num_rows() != 1 {
+            return Err(Error::InvalidInput {
+                message: format!(
+                    "SQL query parameters must be exactly one row, got {}",
+                    parameters.num_rows()
+                ),
+            });
+        }
+        let request_id = Uuid::new_v4().to_string();
+        let read_timeout = resolve_timeout(
+            client.client_config.timeout_config.read_timeout,
+            "LANCE_CLIENT_READ_TIMEOUT",
+            Some(DEFAULT_READ_TIMEOUT),
+        )?
+        .unwrap();
+        let descriptor = FlightDescriptor::new_cmd(
+            CommandStatementQuery {
+                query: query.to_string(),
+                transaction_id: None,
+            }
+            .as_any()
+            .encode_to_vec(),
+        );
+        // The descriptor rides on the schema message, and the row follows it.
+        let request = FlightDataEncoderBuilder::new()
+            .with_flight_descriptor(Some(descriptor))
+            .with_schema(parameters.schema())
+            .build(futures::stream::iter([Ok(parameters)]));
+        let mut flight = client
+            .client_with_headers(default_namespace_path, &request_id)
+            .await?;
+        let stream = tokio::time::timeout(read_timeout, flight.do_exchange(request))
+            .await
+            .map_err(|_| sql_error(&request_id, "SQL query submission timed out"))?
+            .map_err(|err| sql_error(&request_id, err))?;
+        Ok(Self {
+            id,
+            client,
+            response: StdMutex::new(Some(ResultEndpointStream {
+                stream,
+                request_id,
+                read_timeout,
+            })),
+            lifecycle: StdMutex::new(Lifecycle::Running),
+            cancelled: Notify::new(),
+            terminal_at: OnceLock::new(),
+            last_accessed: StdMutex::new(Instant::now()),
+        })
+    }
+
+    pub(super) fn touch(&self) {
+        *self.last_accessed.lock().unwrap() = Instant::now();
+    }
+
+    pub(super) fn registry_expired(&self, abandoned: bool) -> bool {
+        if let Some(finished) = self.terminal_at.get() {
+            return finished.elapsed() >= TERMINAL_QUERY_RETENTION;
+        }
+        abandoned && self.last_accessed.lock().unwrap().elapsed() >= ABANDONED_QUERY_RETENTION
+    }
+
+    pub(super) fn describe(&self) -> Result<QueryDescription> {
+        self.touch();
+        let status = match &*self.lifecycle.lock().unwrap() {
+            Lifecycle::Running => QueryStatus::Running,
+            Lifecycle::Finished => QueryStatus::Finished,
+            Lifecycle::Cancelled => QueryStatus::Cancelled,
+            Lifecycle::Failed(message) => {
+                return Err(Error::Runtime {
+                    message: message.clone(),
+                });
+            }
+        };
+        Ok(QueryDescription {
+            id: self.id,
+            status,
+            progress: (status == QueryStatus::Finished).then_some(1.0),
+            expires_at: None,
+        })
+    }
+
+    /// Stop the statement. Idempotent, and a no-op once it has ended.
+    pub(super) fn cancel(&self) {
+        self.touch();
+        let settled = self.settle(Lifecycle::Cancelled);
+        // Unread rows are dropped here, which cancels the call; a query that
+        // already ended has none. A reader's stream is dropped by its task
+        // when it sees the cancellation.
+        self.response.lock().unwrap().take();
+        if settled {
+            self.cancelled.notify_waiters();
+        }
+    }
+
+    /// Move a running statement to `state`. `true` if this call did.
+    fn settle(&self, state: Lifecycle) -> bool {
+        let mut lifecycle = self.lifecycle.lock().unwrap();
+        if *lifecycle != Lifecycle::Running {
+            return false;
+        }
+        *lifecycle = state;
+        drop(lifecycle);
+        let _ = self.terminal_at.set(Instant::now());
+        true
+    }
+
+    fn is_cancelled(&self) -> bool {
+        *self.lifecycle.lock().unwrap() == Lifecycle::Cancelled
+    }
+
+    fn cancelled_error(&self) -> Error {
+        Error::JobCancelled {
+            job_id: Some(self.id.to_string()),
+        }
+    }
+
+    async fn wait_for_cancellation(&self) {
+        loop {
+            let cancelled = self.cancelled.notified();
+            if self.is_cancelled() {
+                return;
+            }
+            cancelled.await;
+        }
+    }
+
+    /// Take the rows and wait for the first batch, which carries the schema.
+    async fn prepare(&self) -> Result<PreparedExchange> {
+        let Some(rows) = self.response.lock().unwrap().take() else {
+            // Taken by an earlier attempt that failed, or dropped by a cancel.
+            return Err(match &*self.lifecycle.lock().unwrap() {
+                Lifecycle::Failed(message) => Error::Runtime {
+                    message: message.clone(),
+                },
+                _ => self.cancelled_error(),
+            });
+        };
+        let mut rows = Borrowed {
+            query: self,
+            rows: Some(rows),
+        };
+        let first = tokio::select! {
+            biased;
+            _ = self.wait_for_cancellation() => return Err(self.cancelled_error()),
+            result = rows.get().next_batch() => result,
+        };
+        // Read or failed, the rows are this attempt's now: a failed read is
+        // not worth putting back.
+        let rows = rows.keep();
+        let first = first?;
+        let schema = first
+            .as_ref()
+            .map(RecordBatch::schema)
+            .or_else(|| rows.stream.schema().cloned())
+            .ok_or_else(|| Error::Runtime {
+                message: "SQL result stream did not include a schema".to_string(),
+            })?;
+        if first.is_none() {
+            self.settle(Lifecycle::Finished);
+        }
+        Ok(PreparedExchange {
+            schema,
+            rest: first.is_some().then_some(rows),
+            first,
+        })
+    }
+
+    async fn run(
+        self: Arc<Self>,
+        mut prepared: PreparedExchange,
+        sender: mpsc::Sender<Result<RecordBatch>>,
+    ) -> Result<()> {
+        if let Some(batch) = prepared.first.take()
+            && !self.send(&sender, batch).await?
+        {
+            return Ok(());
+        }
+        let Some(mut rows) = prepared.rest.take() else {
+            return Ok(());
+        };
+        loop {
+            let batch = tokio::select! {
+                biased;
+                // The reader was dropped: the rows go with this task, which
+                // ends the call.
+                _ = sender.closed() => {
+                    self.settle(Lifecycle::Cancelled);
+                    return Ok(());
+                }
+                _ = self.wait_for_cancellation() => return Err(self.cancelled_error()),
+                result = rows.next_batch() => result,
+            };
+            match batch {
+                Ok(Some(batch)) => {
+                    if !self.send(&sender, batch).await? {
+                        return Ok(());
+                    }
+                }
+                Ok(None) => {
+                    self.settle(Lifecycle::Finished);
+                    return Ok(());
+                }
+                Err(error) => {
+                    self.settle(failure(&error));
+                    return Err(error);
+                }
+            }
+        }
+    }
+
+    async fn send(
+        &self,
+        sender: &mpsc::Sender<Result<RecordBatch>>,
+        batch: RecordBatch,
+    ) -> Result<bool> {
+        tokio::select! {
+            biased;
+            _ = self.wait_for_cancellation() => Err(self.cancelled_error()),
+            result = sender.send(Ok(batch)) => {
+                if result.is_err() {
+                    self.settle(Lifecycle::Cancelled);
+                }
+                Ok(result.is_ok())
+            }
+        }
+    }
+}
+
+pub(super) struct ExchangeQueryHandle {
+    query: Arc<ExchangeQuery>,
+    result_started: AtomicBool,
+}
+
+impl ExchangeQueryHandle {
+    pub(super) fn new(query: Arc<ExchangeQuery>) -> Self {
+        Self {
+            query,
+            result_started: AtomicBool::new(false),
+        }
+    }
+}
+
+impl Drop for ExchangeQueryHandle {
+    // Rows nobody took are rows nobody will read. Keeping the call open would
+    // hold the statement running on the server, stalled on flow control,
+    // until the registry forgot it.
+    fn drop(&mut self) {
+        if self.query.response.lock().unwrap().is_some() {
+            self.query.cancel();
+        }
+    }
+}
+
+#[async_trait::async_trait]
+impl QueryHandle for ExchangeQueryHandle {
+    fn id(&self) -> Uuid {
+        self.query.touch();
+        self.query.id
+    }
+
+    async fn describe(&self) -> Result<QueryDescription> {
+        self.query.describe()
+    }
+
+    async fn reader(&self) -> Result<SendableRecordBatchStream> {
+        let timeout = self.query.client.overall_timeout()?;
+        if self
+            .result_started
+            .compare_exchange(false, true, Ordering::SeqCst, Ordering::SeqCst)
+            .is_err()
+        {
+            return Err(Error::Runtime {
+                message: "SQL query results can only be consumed once".to_string(),
+            });
+        }
+        let result_start = ResultStartGuard::new(&self.result_started);
+        self.query.touch();
+        let started = Instant::now();
+        let prepared =
+            match with_overall_timeout(timeout, "SQL query result", self.query.prepare()).await {
+                Ok(prepared) => prepared,
+                Err(error) => {
+                    // A failed read or the timeout ends the query, and with it
+                    // the call, including rows a timed-out wait put back.
+                    self.query.settle(failure(&error));
+                    self.query.response.lock().unwrap().take();
+                    return Err(error);
+                }
+            };
+        let remaining_timeout = timeout.map(|timeout| timeout.saturating_sub(started.elapsed()));
+        let schema = prepared.schema.clone();
+        let (sender, receiver) = mpsc::channel(2);
+        let error_sender = sender.clone();
+        let query = self.query.clone();
+        tokio::spawn(async move {
+            let result = with_overall_timeout(
+                remaining_timeout,
+                "SQL query result",
+                query.clone().run(prepared, sender),
+            )
+            .await;
+            if let Err(error) = result {
+                query.settle(failure(&error));
+                let _ = error_sender.send(Err(error)).await;
+            }
+        });
+        let stream = futures::stream::unfold(receiver, |mut receiver| async move {
+            receiver.recv().await.map(|item| (item, receiver))
+        });
+        result_start.commit();
+        Ok(Box::pin(SimpleRecordBatchStream::new(stream, schema)))
+    }
+
+    async fn cancel(&self) -> Result<()> {
+        self.query.cancel();
+        Ok(())
+    }
+}
diff --git a/rust/lancedb/src/remote/sql_test.rs b/rust/lancedb/src/remote/sql_test.rs
index d20ba495f..79ca339db 100644
--- a/rust/lancedb/src/remote/sql_test.rs
+++ b/rust/lancedb/src/remote/sql_test.rs
@@ -4,7 +4,8 @@
 use std::sync::atomic::AtomicUsize;
 
 use arrow_array::builder::StringDictionaryBuilder;
-use arrow_array::{Array, Int64Array, StringArray, types::Int32Type};
+use arrow_array::types::{Float32Type, Int32Type};
+use arrow_array::{Array, ArrayRef, FixedSizeListArray, Float32Array, Int64Array, StringArray};
 use arrow_flight::encode::FlightDataEncoderBuilder;
 use arrow_flight::flight_service_server::{FlightService, FlightServiceServer};
 use arrow_flight::sql::{Any, CommandStatementQuery};
@@ -15,13 +16,17 @@ use arrow_flight::{
 use arrow_schema::{DataType, Field, Schema};
 use futures::stream::BoxStream;
 use futures::{StreamExt, TryStreamExt};
+use tonic::metadata::MetadataMap;
 use tonic::{Request, Response, Status, Streaming};
 
 use super::*;
 use crate::database::Database;
 use crate::remote::RemoteCatalogOptions;
 use crate::remote::client::HeaderProvider;
+use crate::remote::client::test_utils::client_with_handler;
 use crate::remote::db::RemoteDatabase;
+use crate::remote::table::RemoteTable;
+use crate::table::BaseTable;
 
 #[derive(Debug, Default)]
 struct DelayedHeaderProvider {
@@ -60,6 +65,9 @@ struct CapturedHeaders {
     database_prefix: String,
 }
 
+/// Every exchange's statement and the parameter row it arrived with.
+type ReceivedExchanges = Arc<std::sync::Mutex<Vec<(String, Option<RecordBatch>)>>>;
+
 #[derive(Clone)]
 struct TestSqlService {
     query_count: Arc<AtomicUsize>,
@@ -73,11 +81,47 @@ struct TestSqlService {
     first_continuation_count: Arc<AtomicUsize>,
     transient_poll_failures: Arc<AtomicUsize>,
     headers: Arc<std::sync::Mutex<Vec<CapturedHeaders>>>,
+    gate_refresh_release: Arc<Notify>,
+    exchanges: ReceivedExchanges,
+    /// How many exchange responses have been dropped, finished or not.
+    exchange_drops: Arc<AtomicUsize>,
     result: RecordBatch,
     large_result: RecordBatch,
     dictionary_result: RecordBatch,
 }
 
+/// Counts a response stream being dropped, which is how a server sees a call
+/// end -- by the client cancelling it or by the rows running out.
+struct DropSignal(Arc<AtomicUsize>);
+
+impl Drop for DropSignal {
+    fn drop(&mut self) {
+        self.0.fetch_add(1, Ordering::SeqCst);
+    }
+}
+
+impl TestSqlService {
+    fn capture_headers(&self, metadata: &MetadataMap) {
+        let header = |name| {
+            metadata
+                .get(name)
+                .and_then(|value| value.to_str().ok())
+                .unwrap()
+                .to_string()
+        };
+        self.headers.lock().unwrap().push(CapturedHeaders {
+            database: header("database"),
+            namespace_path: header("namespace-path"),
+            request_id: header("x-request-id"),
+            api_key: header("x-api-key"),
+            database_prefix: metadata
+                .get("x-lancedb-database-prefix")
+                .map(|value| value.to_str().unwrap().to_string())
+                .unwrap_or_default(),
+        });
+    }
+}
+
 impl Default for TestSqlService {
     fn default() -> Self {
         let schema = Arc::new(Schema::new(vec![Field::new(
@@ -121,6 +165,9 @@ impl Default for TestSqlService {
             first_continuation_count: Arc::new(AtomicUsize::new(0)),
             transient_poll_failures: Arc::new(AtomicUsize::new(0)),
             headers: Arc::new(std::sync::Mutex::new(Vec::new())),
+            gate_refresh_release: Arc::new(Notify::new()),
+            exchanges: Arc::new(std::sync::Mutex::new(Vec::new())),
+            exchange_drops: Arc::new(AtomicUsize::new(0)),
             result,
             large_result,
             dictionary_result,
@@ -163,24 +210,7 @@ impl FlightService for TestSqlService {
         &self,
         request: Request<arrow_flight::FlightDescriptor>,
     ) -> std::result::Result<Response<PollInfo>, Status> {
-        let metadata = request.metadata();
-        let header = |name| {
-            metadata
-                .get(name)
-                .and_then(|value| value.to_str().ok())
-                .unwrap()
-                .to_string()
-        };
-        self.headers.lock().unwrap().push(CapturedHeaders {
-            database: header("database"),
-            namespace_path: header("namespace-path"),
-            request_id: header("x-request-id"),
-            api_key: header("x-api-key"),
-            database_prefix: metadata
-                .get("x-lancedb-database-prefix")
-                .map(|value| value.to_str().unwrap().to_string())
-                .unwrap_or_default(),
-        });
+        self.capture_headers(request.metadata());
 
         let command = Any::decode(request.get_ref().cmd.as_ref())
             .ok()
@@ -279,6 +309,7 @@ impl FlightService for TestSqlService {
     ) -> std::result::Result<Response<<Self as FlightService>::DoGetStream>, Status> {
         self.do_get_count.fetch_add(1, Ordering::SeqCst);
         let ticket = request.get_ref().ticket.as_ref();
+        let gate_refresh = ticket == b"REFRESH MATERIALIZED VIEW \"cancel_view\"";
         let empty = ticket == b"SELECT empty";
         let slow = ticket == b"SELECT slow get";
         let large = ticket == b"SELECT large message";
@@ -290,7 +321,11 @@ impl FlightService for TestSqlService {
             self.result.clone()
         };
         let schema = result.schema();
+        let gate_refresh_release = self.gate_refresh_release.clone();
         let input = futures::stream::once(async move {
+            if gate_refresh {
+                gate_refresh_release.notified().await;
+            }
             if slow {
                 tokio::time::sleep(Duration::from_millis(250)).await;
             }
@@ -384,11 +419,336 @@ impl FlightService for TestSqlService {
         Err(Status::unimplemented("list_actions"))
     }
 
+    /// Answers with the parameter row it was sent, so a test can compare what
+    /// came back with what went out.
     async fn do_exchange(
         &self,
-        _request: Request<Streaming<FlightData>>,
+        request: Request<Streaming<FlightData>>,
     ) -> std::result::Result<Response<Self::DoExchangeStream>, Status> {
-        Err(Status::unimplemented("do_exchange"))
+        self.capture_headers(request.metadata());
+        let mut messages = request.into_inner();
+        let first = messages
+            .message()
+            .await?
+            .ok_or_else(|| Status::invalid_argument("empty exchange"))?;
+        let query = first
+            .flight_descriptor
+            .as_ref()
+            .and_then(|descriptor| Any::decode(descriptor.cmd.as_ref()).ok())
+            .and_then(|any| any.unpack::<CommandStatementQuery>().ok().flatten())
+            .map(|command| command.query)
+            .ok_or_else(|| Status::invalid_argument("exchange without a statement"))?;
+        let data = futures::stream::once(async move { Ok(first) })
+            .chain(messages)
+            .map_err(|status| FlightError::Tonic(Box::new(status)));
+        let parameters = FlightRecordBatchStream::new_from_flight_data(data)
+            .try_next()
+            .await
+            .map_err(|error| Status::invalid_argument(error.to_string()))?;
+        self.exchanges
+            .lock()
+            .unwrap()
+            .push((query.clone(), parameters.clone()));
+        if query == "SELECT exchange refused" {
+            return Err(Status::invalid_argument(
+                "parameter 1 is not referenced by the statement",
+            ));
+        }
+        if query == "SELECT exchange stall" {
+            return Ok(Response::new(futures::stream::pending().boxed()));
+        }
+        let rows =
+            parameters.ok_or_else(|| Status::invalid_argument("exchange without parameters"))?;
+        let schema = rows.schema();
+        let rest: BoxStream<'static, arrow_flight::error::Result<RecordBatch>> =
+            if query == "SELECT exchange slow" {
+                futures::stream::pending().boxed()
+            } else {
+                futures::stream::empty().boxed()
+            };
+        let delay = if query == "SELECT exchange delayed" {
+            Duration::from_millis(300)
+        } else {
+            Duration::ZERO
+        };
+        let dropped = DropSignal(self.exchange_drops.clone());
+        let stream = FlightDataEncoderBuilder::new()
+            .with_schema(schema)
+            .build(
+                futures::stream::once(async move {
+                    tokio::time::sleep(delay).await;
+                    Ok(rows)
+                })
+                .chain(rest),
+            )
+            .map_err(Status::from)
+            .map(move |message| {
+                let _dropped = &dropped;
+                message
+            });
+        Ok(Response::new(Box::pin(stream)))
+    }
+}
+
+fn parameter_row() -> RecordBatch {
+    let vector = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+        [Some([0.1_f32, 1e-45, -0.0].map(Some))],
+        3,
+    );
+    RecordBatch::try_from_iter([
+        ("vector", Arc::new(vector) as ArrayRef),
+        ("k", Arc::new(Int64Array::from(vec![10])) as ArrayRef),
+    ])
+    .unwrap()
+}
+
+fn parameterized(query: &str, parameters: RecordBatch) -> ExecuteQueryRequest {
+    ExecuteQueryRequest {
+        parameters: Some(parameters),
+        ..ExecuteQueryRequest::new(query)
+    }
+}
+
+async fn start_sql_service(
+    service: TestSqlService,
+    client_config: ClientConfig,
+) -> (SqlClient, tokio::sync::oneshot::Sender<()>) {
+    let listener = tokio::net::TcpListener::bind("127.0.0.1:0").await.unwrap();
+    let address = listener.local_addr().unwrap();
+    let incoming = futures::stream::try_unfold(listener, |listener| async {
+        let (socket, _) = listener.accept().await?;
+        Ok::<_, std::io::Error>(Some((socket, listener)))
+    });
+    let (shutdown_tx, shutdown_rx) = tokio::sync::oneshot::channel();
+    tokio::spawn(
+        tonic::transport::Server::builder()
+            .add_service(FlightServiceServer::new(service))
+            .serve_with_incoming_shutdown(incoming, async {
+                let _ = shutdown_rx.await;
+            }),
+    );
+    let client = SqlClient::new(
+        "analytics".to_string(),
+        None,
+        "test-key".to_string(),
+        None,
+        Some(format!("grpc://{address}")),
+        client_config,
+    );
+    (client, shutdown_tx)
+}
+
+async fn wait_for(count: &AtomicUsize, expected: usize, what: &str) {
+    tokio::time::timeout(Duration::from_secs(5), async {
+        while count.load(Ordering::SeqCst) < expected {
+            tokio::time::sleep(Duration::from_millis(5)).await;
+        }
+    })
+    .await
+    .unwrap_or_else(|_| panic!("{what}"));
+}
+
+/// The statement and its row go out on one exchange -- no poll, no ticket --
+/// and the row arrives as exactly the bytes that were sent.
+#[tokio::test]
+async fn parameters_travel_on_one_exchange() {
+    let service = TestSqlService::default();
+    let exchanges = service.exchanges.clone();
+    let query_count = service.query_count.clone();
+    let do_get_count = service.do_get_count.clone();
+    let headers = service.headers.clone();
+    let (client, _shutdown) = start_sql_service(service, ClientConfig::default()).await;
+
+    let parameters = parameter_row();
+    let query = client
+        .execute(ExecuteQueryRequest {
+            default_namespace_path: vec!["events".to_string(), "raw".to_string()],
+            ..parameterized("SELECT echo", parameters.clone())
+        })
+        .await
+        .unwrap();
+    assert_eq!(query.describe().await.unwrap().status, QueryStatus::Running);
+    let rows = collect_result(&query).await.unwrap();
+    assert_eq!(rows, vec![parameters.clone()]);
+    let echoed = rows[0]
+        .column(0)
+        .as_any()
+        .downcast_ref::<FixedSizeListArray>()
+        .unwrap()
+        .values()
+        .as_any()
+        .downcast_ref::<Float32Array>()
+        .unwrap()
+        .values()
+        .iter()
+        .map(|value| value.to_bits())
+        .collect::<Vec<_>>();
+    assert_eq!(
+        echoed,
+        [0.1_f32, 1e-45, -0.0].map(f32::to_bits),
+        "every float keeps its exact bits, the subnormal and the sign of zero included"
+    );
+
+    let finished = query.describe().await.unwrap();
+    assert_eq!(finished.status, QueryStatus::Finished);
+    assert_eq!(finished.progress, Some(1.0));
+    assert_eq!(
+        client.describe(query.id()).await.unwrap().status,
+        QueryStatus::Finished
+    );
+    assert!(query.reader().await.is_err());
+
+    assert_eq!(
+        *exchanges.lock().unwrap(),
+        vec![("SELECT echo".to_string(), Some(parameters))]
+    );
+    assert_eq!(query_count.load(Ordering::SeqCst), 0);
+    assert_eq!(do_get_count.load(Ordering::SeqCst), 0);
+    let headers = headers.lock().unwrap();
+    assert_eq!(headers.len(), 1);
+    assert_eq!(headers[0].database, "analytics");
+    assert_eq!(headers[0].namespace_path, "events$raw");
+    assert_eq!(headers[0].api_key, "test-key");
+}
+
+/// The server plans before it answers, so a refusal is the submission's.
+#[tokio::test]
+async fn a_refused_statement_fails_its_submission() {
+    let (client, _shutdown) =
+        start_sql_service(TestSqlService::default(), ClientConfig::default()).await;
+    let refused = client
+        .execute(parameterized("SELECT exchange refused", parameter_row()))
+        .await
+        .unwrap_err();
+    assert!(
+        refused
+            .to_string()
+            .contains("not referenced by the statement"),
+        "{refused}"
+    );
+
+    let two_rows =
+        RecordBatch::try_from_iter([("k", Arc::new(Int64Array::from(vec![1, 2])) as ArrayRef)])
+            .unwrap();
+    assert!(matches!(
+        client.execute(parameterized("SELECT echo", two_rows)).await,
+        Err(Error::InvalidInput { .. })
+    ));
+}
+
+/// The call is the statement's lifetime: cancelling a read, or dropping a
+/// handle nobody read, ends the call and so the work behind it.
+#[tokio::test]
+async fn cancelling_or_abandoning_an_exchange_ends_the_call() {
+    let service = TestSqlService::default();
+    let drops = service.exchange_drops.clone();
+    let (client, _shutdown) = start_sql_service(service, ClientConfig::default()).await;
+
+    let reading = client
+        .execute(parameterized("SELECT exchange slow", parameter_row()))
+        .await
+        .unwrap();
+    let mut rows = reading.reader().await.unwrap();
+    assert_eq!(rows.try_next().await.unwrap().unwrap(), parameter_row());
+    reading.cancel().await.unwrap();
+    assert!(matches!(
+        tokio::time::timeout(Duration::from_secs(5), rows.try_next())
+            .await
+            .expect("cancellation must wake the reader"),
+        Err(Error::JobCancelled { .. })
+    ));
+    assert_eq!(
+        reading.describe().await.unwrap().status,
+        QueryStatus::Cancelled
+    );
+    wait_for(&drops, 1, "cancelling must end the call on the server").await;
+    reading.cancel().await.unwrap();
+
+    let unread = client
+        .execute(parameterized("SELECT exchange slow", parameter_row()))
+        .await
+        .unwrap();
+    let id = unread.id();
+    drop(unread);
+    wait_for(&drops, 2, "dropping an unread handle must end the call").await;
+    assert_eq!(
+        client.describe(id).await.unwrap().status,
+        QueryStatus::Cancelled
+    );
+
+    let dropped_reader = client
+        .execute(parameterized("SELECT exchange slow", parameter_row()))
+        .await
+        .unwrap();
+    let rows = dropped_reader.reader().await.unwrap();
+    drop(rows);
+    wait_for(&drops, 3, "dropping a reader must end the call").await;
+}
+
+/// A reader abandoned before the first batch -- its task cancelled -- must
+/// leave the rows for the next reader, as a polled query does, rather than
+/// take the call down with it.
+#[tokio::test]
+async fn an_abandoned_reader_leaves_the_rows_for_the_next_one() {
+    let service = TestSqlService::default();
+    let drops = service.exchange_drops.clone();
+    let (client, _shutdown) = start_sql_service(service, ClientConfig::default()).await;
+
+    let delayed = Arc::new(
+        client
+            .execute(parameterized("SELECT exchange delayed", parameter_row()))
+            .await
+            .unwrap(),
+    );
+    let abandoned = {
+        let delayed = delayed.clone();
+        tokio::spawn(async move { delayed.reader().await.map(|_| ()) })
+    };
+    tokio::time::sleep(Duration::from_millis(50)).await;
+    abandoned.abort();
+    assert!(abandoned.await.unwrap_err().is_cancelled());
+    assert_eq!(
+        delayed.describe().await.unwrap().status,
+        QueryStatus::Running
+    );
+    assert_eq!(drops.load(Ordering::SeqCst), 0, "the call must survive");
+
+    assert_eq!(
+        collect_result(&delayed).await.unwrap(),
+        vec![parameter_row()]
+    );
+    assert_eq!(
+        delayed.describe().await.unwrap().status,
+        QueryStatus::Finished
+    );
+}
+
+/// A reader that gives up before the first batch has already taken the rows,
+/// so the query must say it failed rather than report `Running` for a call
+/// that is gone.
+#[tokio::test]
+async fn a_reader_that_times_out_reports_the_failure() {
+    let mut client_config = ClientConfig::default();
+    client_config.timeout_config.timeout = Some(Duration::from_millis(200));
+    let (client, _shutdown) = start_sql_service(TestSqlService::default(), client_config).await;
+
+    let stalled = client
+        .execute(parameterized("SELECT exchange stall", parameter_row()))
+        .await
+        .unwrap();
+    assert_overall_timeout(stalled.reader().await, "result");
+    let Err(retried) = stalled.reader().await else {
+        panic!("the rows are gone, so a second reader cannot succeed");
+    };
+    for error in [
+        stalled.describe().await.unwrap_err(),
+        client.describe(stalled.id()).await.unwrap_err(),
+        retried,
+    ] {
+        assert!(
+            matches!(&error, Error::Runtime { message } if message == "SQL query result timed out"),
+            "{error}"
+        );
     }
 }
 
@@ -420,7 +780,10 @@ async fn catalog_connections_use_explicit_sql_endpoint_and_database_scope() {
         let database =
             RemoteDatabase::for_catalog("https://catalog.example", Some(name), &options).unwrap();
         let query = database
-            .execute_query_async("SELECT 42", &[])
+            .execute_query_async(ExecuteQueryRequest {
+                default_namespace_path: vec![],
+                ..ExecuteQueryRequest::new("SELECT 42")
+            })
             .await
             .unwrap();
         assert_eq!(
@@ -446,6 +809,147 @@ async fn catalog_connections_use_explicit_sql_endpoint_and_database_scope() {
     server.await.unwrap().unwrap();
 }
 
+#[tokio::test]
+async fn remote_refresh_twice_keeps_latest_selector() {
+    let listener = tokio::net::TcpListener::bind("127.0.0.1:0").await.unwrap();
+    let address = listener.local_addr().unwrap();
+    let incoming = futures::stream::try_unfold(listener, |listener| async {
+        let (socket, _) = listener.accept().await?;
+        Ok::<_, std::io::Error>(Some((socket, listener)))
+    });
+    let service = TestSqlService::default();
+    let (shutdown_tx, shutdown_rx) = tokio::sync::oneshot::channel();
+    let server = tokio::spawn(
+        tonic::transport::Server::builder()
+            .add_service(FlightServiceServer::new(service))
+            .serve_with_incoming_shutdown(incoming, async {
+                let _ = shutdown_rx.await;
+            }),
+    );
+    let sql_client = SqlClient::new(
+        "analytics".into(),
+        None,
+        "test-key".into(),
+        None,
+        Some(format!("grpc://{address}")),
+        ClientConfig::default(),
+    );
+    let selectors = Arc::new(std::sync::Mutex::new(Vec::new()));
+    let captured = selectors.clone();
+    let client = client_with_handler(move |request| {
+        let body = request
+            .body()
+            .and_then(reqwest::Body::as_bytes)
+            .and_then(|bytes| serde_json::from_slice::<serde_json::Value>(bytes).ok())
+            .unwrap();
+        captured.lock().unwrap().push(body["version"].clone());
+        let text = match request.url().path() {
+            "/v1/table/ns$view/describe/" => r#"{"version": 42, "schema": {"fields": []}}"#,
+            "/v1/table/ns$view/count_rows/" => "2",
+            other => panic!("unexpected REST request: {other}"),
+        };
+        http::Response::builder()
+            .status(200)
+            .body(text.to_string())
+            .unwrap()
+    });
+    let table = RemoteTable::new_with_sql_client(
+        client,
+        "view".into(),
+        vec!["ns".into()],
+        "ns$view".into(),
+        Default::default(),
+        Some(sql_client),
+    );
+
+    let first = table
+        .refresh_materialized_view_async(None)
+        .await
+        .unwrap()
+        .wait()
+        .await
+        .unwrap();
+    let second = table
+        .refresh_materialized_view_async(None)
+        .await
+        .unwrap()
+        .wait()
+        .await
+        .unwrap();
+
+    assert_eq!(first.version, 42);
+    assert_eq!(second.version, 42);
+    assert!(
+        selectors
+            .lock()
+            .unwrap()
+            .iter()
+            .all(serde_json::Value::is_null)
+    );
+    shutdown_tx.send(()).unwrap();
+    server.await.unwrap().unwrap();
+}
+
+#[tokio::test]
+async fn cancelling_remote_refresh_cancels_sql_query() {
+    let listener = tokio::net::TcpListener::bind("127.0.0.1:0").await.unwrap();
+    let address = listener.local_addr().unwrap();
+    let incoming = futures::stream::try_unfold(listener, |listener| async {
+        let (socket, _) = listener.accept().await?;
+        Ok::<_, std::io::Error>(Some((socket, listener)))
+    });
+    let service = TestSqlService::default();
+    let cancel_count = service.cancel_count.clone();
+    let do_get_count = service.do_get_count.clone();
+    let gate_refresh_release = service.gate_refresh_release.clone();
+    let (shutdown_tx, shutdown_rx) = tokio::sync::oneshot::channel();
+    let server = tokio::spawn(
+        tonic::transport::Server::builder()
+            .add_service(FlightServiceServer::new(service))
+            .serve_with_incoming_shutdown(incoming, async {
+                let _ = shutdown_rx.await;
+            }),
+    );
+    let sql_client = SqlClient::new(
+        "analytics".into(),
+        None,
+        "test-key".into(),
+        None,
+        Some(format!("grpc://{address}")),
+        ClientConfig::default(),
+    );
+    let client = client_with_handler(|request| -> http::Response<String> {
+        panic!(
+            "cancelled refresh must not read result metadata: {}",
+            request.url().path()
+        );
+    });
+    let table = RemoteTable::new_with_sql_client(
+        client,
+        "cancel_view".into(),
+        vec![],
+        "cancel_view".into(),
+        Default::default(),
+        Some(sql_client),
+    );
+    let job = table.refresh_materialized_view_async(None).await.unwrap();
+    tokio::time::timeout(Duration::from_secs(10), async {
+        while do_get_count.load(Ordering::SeqCst) == 0 {
+            tokio::task::yield_now().await;
+        }
+    })
+    .await
+    .unwrap();
+
+    job.cancel().await.unwrap();
+    assert!(matches!(job.wait().await, Err(Error::JobCancelled { .. })));
+    assert_eq!(cancel_count.load(Ordering::SeqCst), 1);
+
+    gate_refresh_release.notify_one();
+    shutdown_tx.send(()).unwrap();
+    server.await.unwrap().unwrap();
+}
+
 #[tokio::test]
 async fn submits_polls_fetches_cancels_and_reuses_client() {
     let listener = std::net::TcpListener::bind("127.0.0.1:0").unwrap();
@@ -662,7 +1166,11 @@ async fn submits_polls_fetches_cancels_and_reuses_client() {
         .submit("SELECT slow", &["public".to_string()])
         .await
         .unwrap();
-    let tracked_dropped_reader = client.queries.get(dropped_reader.id()).unwrap();
+    let Some(RegisteredQuery::Polled(tracked_dropped_reader)) =
+        client.queries.get(dropped_reader.id())
+    else {
+        panic!("a submitted statement is registered as a polled query");
+    };
     let continuation_count_before = first_continuation_count.load(Ordering::SeqCst);
     let dropped_result_stream = dropped_reader.reader().await.unwrap();
     tokio::time::timeout(Duration::from_millis(100), async {
@@ -956,8 +1464,11 @@ async fn submits_polls_fetches_cancels_and_reuses_client() {
             )
             .unwrap(),
         );
-        registry.insert(id, query.clone());
-        assert!(Arc::ptr_eq(&registry.get(id).unwrap(), &query));
+        registry.insert(id, RegisteredQuery::Polled(query.clone()));
+        let Some(RegisteredQuery::Polled(found)) = registry.get(id) else {
+            panic!("the registry returns the query it was given");
+        };
+        assert!(Arc::ptr_eq(&found, &query));
     }
 
     let expired_id = Uuid::now_v7();
@@ -974,7 +1485,7 @@ async fn submits_polls_fetches_cancels_and_reuses_client() {
         )
         .unwrap(),
     );
-    registry.insert(expired_id, expired_query);
+    registry.insert(expired_id, RegisteredQuery::Polled(expired_query));
     assert!(registry.get(expired_id).is_none());
 
     let stale_id = Uuid::now_v7();
@@ -991,7 +1502,7 @@ async fn submits_polls_fetches_cancels_and_reuses_client() {
         .unwrap(),
     );
     *stale_query.last_accessed.lock().unwrap() = Instant::now() - ABANDONED_QUERY_RETENTION;
-    registry.insert(stale_id, stale_query.clone());
+    registry.insert(stale_id, RegisteredQuery::Polled(stale_query.clone()));
     drop(stale_query);
     assert!(registry.get(stale_id).is_none());
 
diff --git a/rust/lancedb/src/remote/table.rs b/rust/lancedb/src/remote/table.rs
index 195c18ea5..57843ca55 100644
--- a/rust/lancedb/src/remote/table.rs
+++ b/rust/lancedb/src/remote/table.rs
@@ -8,6 +8,7 @@ use self::insert::{RemoteWriteExec, WriteOp};
 use super::client::RequestResultExt;
 use super::client::{HttpSend, RestfulLanceDbClient, Sender};
 use super::db::ServerVersion;
+use super::sql::SqlClient;
 use super::{ARROW_FILE_CONTENT_TYPE, ARROW_STREAM_CONTENT_TYPE, extract_job_id};
 use crate::blob::BlobFile;
 use crate::data::scannable::{PeekedScannable, Scannable, estimate_write_partitions};
@@ -18,7 +19,7 @@ use crate::index::scalar::FtsQuery;
 use crate::index::waiter::wait_for_index;
 use crate::job::Job;
 use crate::materialized_view::{
-    MaterializedViewDefinition, MaterializedViewInfo, RefreshMaterializedViewResult, ViewProjection,
+    MaterializedViewDefinition, MaterializedViewInfo, RefreshMaterializedViewResult,
 };
 use crate::query::wal_fusion::PkFusionMemory; // WAL-PK-FUSION: delete.
 use crate::query::{QueryFilter, QueryRequest, Select, VectorQueryRequest};
@@ -29,13 +30,14 @@ use crate::table::BranchDiff;
 use crate::table::CherryPickResult;
 use crate::table::DeleteResult;
 use crate::table::DropColumnsResult;
-use crate::table::LsmStats;
-use crate::table::LsmWriteSpec;
+use crate::table::MemWalStats;
+use crate::table::MemWalWriteSpec;
 use crate::table::MergeResult;
 use crate::table::Tags;
 use crate::table::UpdateResult;
-use crate::table::lsm_stats::GetLsmStatsResponse;
+use crate::table::memwal_stats::GetMemWalStatsResponse;
 use crate::table::merge::MergeFilter;
+use crate::table::primary_key;
 use crate::table::query::create_multi_vector_plan;
 use crate::table::write_progress::FinishOnDrop;
 use crate::table::{
@@ -46,6 +48,7 @@ use crate::utils::background_cache::BackgroundCache;
 use crate::utils::{
     MaxBatchLengthStream, TimeoutStream, public_fts_field_path_by_id, resolve_arrow_field_path,
     resolve_arrow_fts_field_path, supported_btree_data_type, supported_vector_data_type,
+    validate_fts_field,
 };
 use crate::{DistanceType, Error};
 use crate::{
@@ -72,10 +75,10 @@ use lance::arrow::json::{JsonDataType, JsonSchema};
 use lance::dataset::refs::TagContents;
 use lance::dataset::scanner::DatasetRecordBatchStream;
 use lance::dataset::{ColumnAlteration, NewColumnTransform, Version};
+use lance_core::datatypes::Schema as LanceSchema;
 use lance_datafusion::exec::{OneShotExec, execute_plan};
 use reqwest::{RequestBuilder, Response};
 use serde::{Deserialize, Serialize};
-use serde_json::Number;
 use std::collections::{HashMap, HashSet};
 use std::io::Cursor;
 use std::pin::Pin;
@@ -96,6 +99,8 @@ const SCHEMA_SELECTOR_CHANGED: &str = "table selector changed while fetching sch
 
 fn fts_query_requires_document_granularity_support(query: &FtsQuery) -> bool {
     match query {
+        // Combined-fields queries do not expose a document granularity option.
+        FtsQuery::CombinedFields(_) => false,
         FtsQuery::Match(query) => query
             .document_granularity
             .is_some_and(|granularity| granularity.is_list_element()),
@@ -262,7 +267,6 @@ struct FreshnessJob<S: HttpSend> {
 enum TrackedJobResult {
     None,
     RefreshColumn,
-    MaterializedView,
 }
 
 #[async_trait]
@@ -303,11 +307,6 @@ impl<S: HttpSend> crate::job::JobHandle for FreshnessJob<S> {
                                 })
                         })
                 }),
-                TrackedJobResult::MaterializedView => result.value().and_then(|value| {
-                    serde_json::from_value::<RefreshMaterializedViewResult>(value.clone())
-                        .ok()
-                        .map(|result| result.version)
-                }),
             }
             .filter(|version| *version != 0);
             if let Some(version) = result_version {
@@ -345,6 +344,10 @@ fn compute_min_timestamp(
     }
 }
 
+fn quote_sql_identifier(identifier: &str) -> String {
+    format!("\"{}\"", identifier.replace('"', "\"\""))
+}
+
 fn freshness_headers_snapshot(
     freshness: &Mutex<FreshnessState>,
     interval: Option<Duration>,
@@ -494,6 +497,7 @@ pub struct RemoteTable<S: HttpSend = Sender> {
     namespace: Vec<String>,
     identifier: String,
     server_version: ServerVersion,
+    sql_client: Option<SqlClient>,
 
     version: Arc<RwLock<Option<u64>>>,
     location: RwLock<Option<String>>,
@@ -595,6 +599,7 @@ impl<S: HttpSend> RemoteTable<S> {
             Index::BloomFilter(p) => ("BLOOM_FILTER", Some(to_json(p)?)),
             Index::RTree(p) => ("RTREE", Some(to_json(p)?)),
             Index::FTS(p) => {
+                validate_fts_field(&field)?;
                 let mut params = to_json(p)?;
                 if p.get_document_granularity().is_list_element() {
                     params["document_granularity"] = "list_element".into();
@@ -618,9 +623,9 @@ impl<S: HttpSend> RemoteTable<S> {
                     });
                 }
             }
-            _ => {
+            Index::IvfHnswPq(_) => {
                 return Err(Error::NotSupported {
-                    message: "Index type not supported".into(),
+                    message: "IVF_HNSW_PQ is not supported for remote tables. Please use IVF_HNSW_SQ instead.".into(),
                 });
             }
         };
@@ -652,12 +657,13 @@ impl<S: HttpSend> RemoteTable<S> {
         Ok(job_id)
     }
 
-    pub fn new(
+    pub(super) fn new_with_sql_client(
         client: RestfulLanceDbClient<S>,
         name: String,
         namespace: Vec<String>,
         identifier: String,
         server_version: ServerVersion,
+        sql_client: Option<SqlClient>,
     ) -> Self {
         Self {
             client,
@@ -665,6 +671,7 @@ impl<S: HttpSend> RemoteTable<S> {
             namespace,
             identifier,
             server_version,
+            sql_client,
             version: Arc::new(RwLock::new(None)),
             location: RwLock::new(None),
             schema_cache: BackgroundCache::new(SCHEMA_CACHE_TTL, SCHEMA_CACHE_REFRESH_WINDOW),
@@ -699,6 +706,7 @@ impl<S: HttpSend> RemoteTable<S> {
             namespace: self.namespace.clone(),
             identifier: self.identifier.clone(),
             server_version: self.server_version.clone(),
+            sql_client: self.sql_client.clone(),
             version: Arc::new(RwLock::new(None)),
             location: RwLock::new(None),
             schema_cache: BackgroundCache::new(SCHEMA_CACHE_TTL, SCHEMA_CACHE_REFRESH_WINDOW),
@@ -1012,10 +1020,10 @@ impl<S: HttpSend> RemoteTable<S> {
     ) -> Result<()> {
         params.check_filter()?;
         body["prefilter"] = params.prefilter.into();
-        // Only forward use_lsm when explicitly set; a server that predates it
+        // Only forward use_memwal when explicitly set; a server that predates it
         // ignores the field and routes as it would by default.
-        if let Some(use_lsm) = params.use_lsm {
-            body["use_lsm"] = serde_json::Value::Bool(use_lsm);
+        if let Some(use_memwal) = params.use_memwal {
+            body["use_memwal"] = serde_json::Value::Bool(use_memwal);
         }
         if let Some(offset) = params.offset {
             body["offset"] = serde_json::Value::Number(serde_json::Number::from(offset));
@@ -1051,6 +1059,8 @@ impl<S: HttpSend> RemoteTable<S> {
                 );
             }
             Select::Dynamic(pairs) => {
+                // Keep the caller's alias order in the JSON object sent to the server.
+                // serde_json's preserve_order feature is required here.
                 let alias_map =
                     serde_json::Map::from_iter(pairs.iter().map(|(name, expr)| {
                         (name.clone(), serde_json::Value::String(expr.clone()))
@@ -1080,7 +1090,7 @@ impl<S: HttpSend> RemoteTable<S> {
         if let Some(full_text_search) = &params.full_text_search {
             if full_text_search.wand_factor.is_some() {
                 return Err(Error::NotSupported {
-                    message: "Wand factor is not yet supported in LanceDB Cloud".into(),
+                    message: "Wand factor is not supported for remote tables.".into(),
                 });
             }
 
@@ -1131,6 +1141,7 @@ impl<S: HttpSend> RemoteTable<S> {
         mut body: serde_json::Value,
         query: &VectorQueryRequest,
     ) -> Result<Vec<serde_json::Value>> {
+        query.validate_search_effort()?;
         self.apply_query_params(&mut body, &query.base)?;
 
         // Apply general parameters, before we dispatch based on number of query vectors.
@@ -1140,17 +1151,17 @@ impl<S: HttpSend> RemoteTable<S> {
         if let Some(approx_mode) = query.approx_mode {
             body["approx_mode"] = serde_json::json!(approx_mode);
         }
-        // In 0.23.1 we migrated from `nprobes` to `minimum_nprobes` and `maximum_nprobes`.
-        // Old client / new server: since minimum_nprobes is missing, fallback to nprobes
-        // New client / old server: old server will only see nprobes, make sure to set both
-        //                          nprobes and minimum_nprobes
-        // New client / new server: since minimum_nprobes is present, server can ignore nprobes
-        body["nprobes"] = query.minimum_nprobes.into();
-        body["minimum_nprobes"] = query.minimum_nprobes.into();
+        if let Some(nprobes) = query.nprobes {
+            body["nprobes"] = nprobes.into();
+        }
+        if let Some(minimum_nprobes) = query.minimum_nprobes {
+            body["minimum_nprobes"] = minimum_nprobes.into();
+        }
         if let Some(maximum_nprobes) = query.maximum_nprobes {
             body["maximum_nprobes"] = maximum_nprobes.into();
-        } else {
-            body["maximum_nprobes"] = serde_json::Value::Number(Number::from_u128(0).unwrap())
+        }
+        if let Some(search_effort) = query.search_effort {
+            body["search_effort"] = search_effort.into();
         }
         body["lower_bound"] = query.lower_bound.into();
         body["upper_bound"] = query.upper_bound.into();
@@ -1175,11 +1186,14 @@ impl<S: HttpSend> RemoteTable<S> {
                             .values()
                             .iter()
                             .map(|v| {
-                                serde_json::Value::Number(
-                                    serde_json::Number::from_f64(*v as f64).unwrap(),
-                                )
+                                serde_json::Number::from_f64(*v as f64)
+                                    .map(serde_json::Value::Number)
+                                    .ok_or_else(|| Error::InvalidInput {
+                                        message: "query vector must contain only finite values"
+                                            .into(),
+                                    })
                             })
-                            .collect(),
+                            .collect::<Result<Vec<_>>>()?,
                     ))
                 }
                 _ => Err(Error::InvalidInput {
@@ -1392,24 +1406,29 @@ impl<S: HttpSend> RemoteTable<S> {
         });
         let streams = futures::future::try_join_all(futures);
 
-        if let Some(timeout) = options.timeout {
+        let streams = if let Some(timeout) = options.timeout {
             let timeout_future = tokio::time::sleep(timeout);
             tokio::pin!(timeout_future);
             tokio::pin!(streams);
             tokio::select! {
                 _ = &mut timeout_future => {
-                    Err(Error::Other {
+                    return Err(Error::Other {
                         message: format!("Query timeout after {} ms", timeout.as_millis()),
                         source: None,
                     })
                 }
                 result = &mut streams => {
-                    Ok(result?)
+                    result?
                 }
             }
         } else {
-            Ok(streams.await?)
-        }
+            streams.await?
+        };
+
+        Ok(streams
+            .into_iter()
+            .map(|stream| restore_projection_order(stream, &query.base().select))
+            .collect())
     }
 
     fn prepare_query_bodies(
@@ -1451,6 +1470,52 @@ impl<S: HttpSend> RemoteTable<S> {
     }
 }
 
+fn restore_projection_order(
+    stream: Pin<Box<dyn RecordBatchStream + Send>>,
+    select: &Select,
+) -> Pin<Box<dyn RecordBatchStream + Send>> {
+    let pairs = match select {
+        Select::Dynamic(pairs) => pairs.iter().map(|(name, _)| name).collect::<Vec<_>>(),
+        Select::Expr(pairs) => pairs.iter().map(|(name, _)| name).collect::<Vec<_>>(),
+        _ => return stream,
+    };
+
+    let schema = stream.schema();
+    let mut remaining = (0..schema.fields().len()).collect::<Vec<_>>();
+    let mut indices = Vec::with_capacity(remaining.len());
+    for name in pairs {
+        if let Some(position) = remaining
+            .iter()
+            .position(|&index| schema.field(index).name() == name)
+        {
+            indices.push(remaining.remove(position));
+        }
+    }
+    indices.extend(remaining);
+    if indices
+        .iter()
+        .enumerate()
+        .all(|(index, &field)| index == field)
+    {
+        return stream;
+    }
+
+    // Older servers may sort alias maps even when the request preserves their
+    // order. Reorder the schema and every batch while keeping server-added fields.
+    let fields = indices
+        .iter()
+        .map(|&index| schema.field(index).clone())
+        .collect::<Vec<_>>();
+    let schema = Arc::new(arrow_schema::Schema::new_with_metadata(
+        fields,
+        schema.metadata().clone(),
+    ));
+    let batches = stream.map(move |batch| {
+        batch.and_then(|batch| batch.project(&indices).map_err(DataFusionError::from))
+    });
+    Box::pin(RecordBatchStreamAdapter::new(schema, batches))
+}
+
 #[derive(Deserialize)]
 struct TableDescription {
     version: u64,
@@ -1617,6 +1682,7 @@ mod test_utils {
                 namespace: vec![],
                 identifier: name,
                 server_version: version.map(ServerVersion).unwrap_or_default(),
+                sql_client: None,
                 version: Arc::new(RwLock::new(None)),
                 location: RwLock::new(None),
                 schema_cache: BackgroundCache::new(SCHEMA_CACHE_TTL, SCHEMA_CACHE_REFRESH_WINDOW),
@@ -1642,6 +1708,7 @@ mod test_utils {
                 namespace: vec![],
                 identifier: name,
                 server_version: ServerVersion::default(),
+                sql_client: None,
                 version: Arc::new(RwLock::new(None)),
                 location: RwLock::new(None),
                 schema_cache: BackgroundCache::new(SCHEMA_CACHE_TTL, SCHEMA_CACHE_REFRESH_WINDOW),
@@ -1676,6 +1743,7 @@ mod test_utils {
                 namespace: vec![],
                 identifier: name,
                 server_version: version.map(ServerVersion).unwrap_or_default(),
+                sql_client: None,
                 version: Arc::new(RwLock::new(None)),
                 location: RwLock::new(None),
                 schema_cache: BackgroundCache::new(SCHEMA_CACHE_TTL, SCHEMA_CACHE_REFRESH_WINDOW),
@@ -2104,29 +2172,9 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
         &self.identifier
     }
     async fn materialized_view_info(&self) -> Result<MaterializedViewInfo> {
-        #[derive(Deserialize)]
-        struct Projection {
-            output_column: String,
-            expression: String,
-        }
-
         #[derive(Deserialize)]
         struct DescribeMaterializedViewResponse {
-            source_table: String,
-            #[serde(default)]
-            source_namespace: Vec<String>,
-            #[serde(default)]
-            projections: Vec<Projection>,
-            #[serde(default)]
-            filter: Option<String>,
-            #[serde(default)]
-            limit: Option<u64>,
-            /// The defining query, once the server describes a view by it;
-            /// takes precedence over the structured fields.
-            #[serde(default)]
-            query: Option<String>,
-            #[serde(default)]
-            incarnation: Option<String>,
+            query: String,
         }
 
         let request = self.client.post(&format!(
@@ -2137,79 +2185,64 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
         let response = self.check_table_response(&request_id, response).await?;
         let response: DescribeMaterializedViewResponse =
             response.json().await.err_to_http(request_id)?;
-        let definition = match response.query {
-            Some(query) => MaterializedViewDefinition::from_sql(&query)?,
-            None => MaterializedViewDefinition {
-                vector_source: None,
-                source_table: response.source_table,
-                source_namespace: response.source_namespace,
-                lateral: None,
-                projections: response
-                    .projections
-                    .into_iter()
-                    .map(|projection| ViewProjection {
-                        output: projection.output_column,
-                        expression: projection.expression,
-                    })
-                    .collect(),
-                filter: response.filter,
-                group_by: Vec::new(),
-                limit: response.limit,
-            },
-        };
+        let parsed_definition = MaterializedViewDefinition::from_sql(&response.query).ok();
         Ok(MaterializedViewInfo {
-            definition,
-            incarnation: response.incarnation,
+            definition_sql: response.query,
+            parsed_definition,
         })
     }
 
     async fn refresh_materialized_view_async(
         &self,
-        full: bool,
         source_version: Option<u64>,
-        expected_incarnation: Option<&str>,
     ) -> Result<Job<RefreshMaterializedViewResult>> {
         self.check_mutable().await?;
-        let mut body = serde_json::json!({ "full": full });
-        if let Some(source_version) = source_version {
-            body["source_version"] = source_version.into();
-        }
-        if let Some(expected_incarnation) = expected_incarnation {
-            body["expected_incarnation"] = expected_incarnation.into();
-        }
-        let request = self
-            .client
-            .post(&format!(
-                "/v1/materialized_view/{}/refresh",
-                self.identifier
-            ))
-            .json(&body);
-        let freshness_request = self.snapshot_freshness_headers();
-        let (request_id, response) = self
-            .send_with_freshness(request, true, freshness_request)
-            .await?;
-        let response = self.check_table_response(&request_id, response).await?;
-        let status = response.status();
-        let body = response.text().await.err_to_http(request_id.clone())?;
-        if status != StatusCode::ACCEPTED {
-            return Err(Error::Http {
-                source: "materialized-view refresh must return 202 Accepted".into(),
-                request_id,
-                status_code: Some(status),
+        if source_version.is_some() {
+            return Err(Error::NotSupported {
+                message: "source-version pinning is available only for local materialized views"
+                    .to_string(),
             });
         }
-        let job_id = extract_job_id(&body).ok_or_else(|| Error::Http {
-            source: "materialized-view refresh response did not contain a valid job_id".into(),
-            request_id,
-            status_code: Some(status),
+        let sql_client = self.sql_client.clone().ok_or_else(|| Error::NotSupported {
+            message: "SQL is unavailable for this remote table client".to_string(),
         })?;
-        Ok(Job::new_typed(Box::new(FreshnessJob {
-            inner: RemoteJob::new(self.client.clone(), job_id),
-            freshness: self.freshness.clone(),
-            version: self.version.clone(),
-            tracked_result: TrackedJobResult::MaterializedView,
-            freshness_request,
-        })))
+        let statement = format!(
+            "REFRESH MATERIALIZED VIEW {}",
+            quote_sql_identifier(&self.name)
+        );
+        let namespace = self.namespace.clone();
+        let client = self.client.clone();
+        let name = self.name.clone();
+        let identifier = self.identifier.clone();
+        let server_version = self.server_version.clone();
+        let freshness = self.freshness.clone();
+        let freshness_request = self.snapshot_freshness_headers();
+        let job_sql_client = sql_client.clone();
+        Ok(
+            sql_client.submit_as_job(statement, namespace.clone(), move || async move {
+                let table = Self::new_with_sql_client(
+                    client,
+                    name,
+                    namespace,
+                    identifier,
+                    server_version,
+                    Some(job_sql_client),
+                );
+                table.checkout_latest().await?;
+                let version = table.version().await?;
+                let rows_written =
+                    u64::try_from(table.count_rows(None).await?).map_err(|_| Error::Runtime {
+                        message: "materialized-view row count exceeds u64".to_string(),
+                    })?;
+                freshness_request.observe_version(&freshness, version);
+                Ok(RefreshMaterializedViewResult {
+                    mode: crate::materialized_view::RefreshMode::Rebuild,
+                    rows_written,
+                    source_version: 0,
+                    version,
+                })
+            }),
+        )
     }
     async fn query_snapshot(&self) -> Result<Arc<dyn BaseTable>> {
         let description = self.describe().await?;
@@ -2299,11 +2332,14 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
         Ok(Some(Arc::new(snapshot)))
     }
     async fn restore(&self) -> Result<()> {
+        let read_snapshot = self.snapshot_read_state().await;
+        let version = read_snapshot.version.ok_or_else(|| Error::InvalidInput {
+            message: "you must run checkout before running restore".to_string(),
+        })?;
         let mut request = self
             .client
             .post(&format!("/v1/table/{}/restore/", self.identifier));
-        let read_snapshot = self.snapshot_read_state().await;
-        let mut body = serde_json::json!({ "version": read_snapshot.version });
+        let mut body = serde_json::json!({ "version": version });
         self.apply_branch_body(&mut body);
         request = request.json(&body);
 
@@ -2846,6 +2882,16 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
         self.fetch_blobs_impl(column, row_ids).await
     }
 
+    async fn fetch_blob_ranges(
+        &self,
+        _column: &str,
+        _requests: &[crate::blob::BlobRangeRequest],
+    ) -> Result<LargeBinaryArray> {
+        Err(Error::NotSupported {
+            message: "fetch_blob_ranges is not supported for remote tables.".into(),
+        })
+    }
+
     async fn fetch_blob_files(
         &self,
         column: &str,
@@ -3099,7 +3145,13 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
         // loop can re-execute the plan (and re-stream the body) on each retry.
         // This mirrors the old `send_streaming(with_retry=true)` path, which
         // likewise buffered the reader to support retries.
-        let batches = new_data.collect::<std::result::Result<Vec<_>, _>>()?;
+        let schema = RecordBatchReader::schema(new_data.as_ref());
+        let mut batches = new_data.collect::<std::result::Result<Vec<_>, _>>()?;
+        // An empty reader still carries a schema. Keep it in an empty batch so
+        // the buffered source remains scannable and can be replayed on retries.
+        if batches.is_empty() {
+            batches.push(RecordBatch::new_empty(schema));
+        }
         let source: Box<dyn Scannable> = Box::new(batches);
         let rescannable = source.rescannable();
         let input: Arc<dyn ExecutionPlan> =
@@ -3152,48 +3204,66 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
         }
     }
 
-    async fn set_unenforced_primary_key(&self, _columns: &[&str]) -> Result<()> {
-        Err(Error::NotSupported {
-            message: "set_unenforced_primary_key is not supported on LanceDB cloud.".into(),
-        })
+    /// The unenforced primary key is Lance schema field metadata, so this
+    /// installs it through the `update_field_metadata` endpoint. The commit
+    /// layer behind that endpoint is what actually installs and validates the
+    /// key, exactly as on a native table; the checks here only fail fast with
+    /// the same messages a native table gives.
+    async fn set_unenforced_primary_key(&self, columns: &[&str]) -> Result<()> {
+        self.check_mutable().await?;
+
+        let arrow_schema = self.schema().await?;
+        let schema = LanceSchema::try_from(arrow_schema.as_ref()).map_err(|e| Error::Schema {
+            message: format!("Invalid schema: {}", e),
+        })?;
+        primary_key::validate(&schema, columns)?;
+
+        self.update_field_metadata(&[FieldMetadataUpdate {
+            path: columns[0].to_string(),
+            metadata: primary_key::install_edit(),
+            replace: false,
+        }])
+        .await?;
+        Ok(())
     }
 
-    async fn flush_lsm(&self) -> Result<()> {
+    async fn flush_memwal(&self) -> Result<()> {
         let request = self
             .client
-            .post(&format!("/v1/table/{}/flush_lsm/", self.identifier));
+            .post(&format!("/v1/table/{}/flush_memwal/", self.identifier));
         self.send_lsm_route(request).await?;
         Ok(())
     }
 
-    async fn compact_lsm(&self) -> Result<()> {
+    async fn compact_memwal(&self) -> Result<()> {
         let request = self
             .client
-            .post(&format!("/v1/table/{}/compact_lsm/", self.identifier));
+            .post(&format!("/v1/table/{}/compact_memwal/", self.identifier));
         self.send_lsm_route(request).await?;
         Ok(())
     }
 
-    async fn get_lsm_stats(&self, include_generation_rows: bool) -> Result<Option<LsmStats>> {
-        // Read-semantics POST, like `get_lsm_write_spec`.
+    async fn get_memwal_stats(&self, include_sstable_rows: bool) -> Result<Option<MemWalStats>> {
+        // Read-semantics POST, like `get_memwal_spec`.
         let request = self
             .client
-            .post(&format!("/v1/table/{}/get_lsm_stats/", self.identifier))
+            .post(&format!("/v1/table/{}/get_memwal_stats/", self.identifier))
             .json(&serde_json::json!({
-                "include_generation_rows": include_generation_rows,
+                "include_sstable_rows": include_sstable_rows,
             }));
         let (request_id, response) = self.send_lsm_route(request).await?;
         let body = response.text().await.err_to_http(request_id.clone())?;
-        let parsed: GetLsmStatsResponse = serde_json::from_str(&body).map_err(|e| Error::Http {
-            source: format!("Failed to parse get_lsm_stats response: {e}").into(),
-            request_id,
-            status_code: None,
-        })?;
-        // `null` — and only — when the table has no LSM write path.
-        Ok(parsed.lsm_stats)
+        let parsed: GetMemWalStatsResponse =
+            serde_json::from_str(&body).map_err(|e| Error::Http {
+                source: format!("Failed to parse get_memwal_stats response: {e}").into(),
+                request_id,
+                status_code: None,
+            })?;
+        // `null` — and only — when the table has no MemWAL write path.
+        Ok(parsed.memwal_stats)
     }
 
-    async fn set_lsm_write_spec(&self, spec: LsmWriteSpec) -> Result<()> {
+    async fn enable_memwal(&self, spec: MemWalWriteSpec) -> Result<()> {
         self.check_mutable().await?;
 
         // Map the spec onto the server's request DTO. `sharding` is internally
@@ -3201,7 +3271,7 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
         // `maintained_indexes` asks the server to resolve every maintainable
         // index at HEAD; a list is verbatim, an empty one meaning none.
         let sharding = match &spec {
-            LsmWriteSpec::Bucket {
+            MemWalWriteSpec::Bucket {
                 column,
                 num_buckets,
                 ..
@@ -3210,11 +3280,11 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
                 "column": column,
                 "num_buckets": num_buckets,
             }),
-            LsmWriteSpec::Identity { column, .. } => serde_json::json!({
+            MemWalWriteSpec::Identity { column, .. } => serde_json::json!({
                 "mode": "identity",
                 "column": column,
             }),
-            LsmWriteSpec::Unsharded { .. } => serde_json::json!({ "mode": "unsharded" }),
+            MemWalWriteSpec::Unsharded { .. } => serde_json::json!({ "mode": "unsharded" }),
         };
         let body = serde_json::json!({
             "sharding": sharding,
@@ -3224,45 +3294,40 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
 
         let request = self
             .client
-            .post(&format!(
-                "/v1/table/{}/set_lsm_write_spec/",
-                self.identifier
-            ))
+            .post(&format!("/v1/table/{}/enable_memwal/", self.identifier))
             .json(&body);
         let (request_id, response) = self.send(request, true).await?;
         self.check_table_response(&request_id, response).await?;
         Ok(())
     }
 
-    async fn unset_lsm_write_spec(&self) -> Result<()> {
+    async fn disable_memwal(&self) -> Result<()> {
         self.check_mutable().await?;
-        let request = self.client.post(&format!(
-            "/v1/table/{}/unset_lsm_write_spec/",
-            self.identifier
-        ));
+        let request = self
+            .client
+            .post(&format!("/v1/table/{}/disable_memwal/", self.identifier));
         let (request_id, response) = self.send(request, true).await?;
         self.check_table_response(&request_id, response).await?;
         self.wal_pk_fusion.forget(); // WAL-PK-FUSION: delete.
         Ok(())
     }
 
-    async fn get_lsm_write_spec(&self) -> Result<Option<LsmWriteSpec>> {
+    async fn get_memwal_spec(&self) -> Result<Option<MemWalWriteSpec>> {
         // Read counterpart to set/unset, resolved server-side against HEAD. The
         // server reads the spec from the `__lance_mem_wal` system index (shard
         // column mapped from its Lance field id against the current schema) and
         // re-encodes it into the same sophon-owned shape the set endpoint
-        // accepts — no lance/lancedb types cross the wire. `lsm_write_spec` is
-        // null when the LSM write path is not enabled for the table.
-        let request = self.client.post(&format!(
-            "/v1/table/{}/get_lsm_write_spec/",
-            self.identifier
-        ));
+        // accepts — no lance/lancedb types cross the wire. `memwal_spec` is
+        // null when the MemWAL write path is not enabled for the table.
+        let request = self
+            .client
+            .post(&format!("/v1/table/{}/get_memwal_spec/", self.identifier));
         let (request_id, response) = self.send(request, true).await?;
         let response = self.check_table_response(&request_id, response).await?;
         let body = response.text().await.err_to_http(request_id.clone())?;
 
         // Mirror of sophon's `Sharding` (internally tagged on `mode`) and
-        // `LsmWriteSpecBody` / `GetLsmWriteSpecResponse`.
+        // `MemWalWriteSpecBody` / `GetMemWalSpecResponse`.
         #[derive(Deserialize)]
         #[serde(tag = "mode", rename_all = "snake_case")]
         enum Sharding {
@@ -3271,27 +3336,28 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
             Identity { column: String },
         }
         #[derive(Deserialize)]
-        struct LsmWriteSpecBody {
+        struct MemWalWriteSpecBody {
             sharding: Sharding,
+            /// `null` selects every index the table has; `[]` selects none.
             #[serde(default)]
-            maintained_indexes: Vec<String>,
+            maintained_indexes: Option<Vec<String>>,
             #[serde(default)]
             writer_config_defaults: std::collections::HashMap<String, String>,
         }
         #[derive(Deserialize)]
-        struct GetLsmWriteSpecResponse {
-            lsm_write_spec: Option<LsmWriteSpecBody>,
+        struct GetMemWalSpecResponse {
+            memwal_spec: Option<MemWalWriteSpecBody>,
         }
 
-        let parsed: GetLsmWriteSpecResponse =
+        let parsed: GetMemWalSpecResponse =
             serde_json::from_str(&body).map_err(|e| Error::Http {
-                source: format!("Failed to parse get_lsm_write_spec response: {}", e).into(),
+                source: format!("Failed to parse get_memwal_spec response: {}", e).into(),
                 request_id,
                 status_code: None,
             })?;
 
-        let Some(body) = parsed.lsm_write_spec else {
-            // The LSM write path is not enabled for this table.
+        let Some(body) = parsed.memwal_spec else {
+            // The MemWAL write path is not enabled for this table.
             return Ok(None);
         };
 
@@ -3299,9 +3365,9 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
             Sharding::Bucket {
                 column,
                 num_buckets,
-            } => LsmWriteSpec::bucket(column, num_buckets),
-            Sharding::Identity { column } => LsmWriteSpec::identity(column),
-            Sharding::Unsharded => LsmWriteSpec::unsharded(),
+            } => MemWalWriteSpec::bucket(column, num_buckets),
+            Sharding::Identity { column } => MemWalWriteSpec::identity(column),
+            Sharding::Unsharded => MemWalWriteSpec::unsharded(),
         }
         .with_maintained_indexes(body.maintained_indexes)
         .with_writer_config_defaults(body.writer_config_defaults);
@@ -3345,7 +3411,7 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
     async fn optimize(&self, _action: OptimizeAction) -> Result<OptimizeStats> {
         self.check_mutable().await?;
         Err(Error::NotSupported {
-            message: "optimize is not supported on LanceDB cloud.".into(),
+            message: "optimize is not supported for remote tables.".into(),
         })
     }
     async fn add_columns(
@@ -3515,7 +3581,12 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
         let (request_id, response) = self
             .send_with_freshness(request, true, freshness_request)
             .await?;
-        let response = self.check_table_response(&request_id, response).await?;
+        // A Function declaration can return 404 for the Function rather than the table.
+        let response = self
+            .client
+            .check_response(&request_id, response)
+            .await
+            .inspect_err(|error| self.handle_error_invalidation(error))?;
         let body = response.text().await.err_to_http(request_id.clone())?;
 
         if body.trim().is_empty() {
@@ -3555,7 +3626,12 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
             .post(&format!("/v1/table/{}/backfill_column", self.identifier))
             .json(&body);
         let (request_id, response) = self.send(request, true).await?;
-        let response = self.check_table_response(&request_id, response).await?;
+        // Preserve dependency errors: a deleted bound Function also returns 404.
+        let response = self
+            .client
+            .check_response(&request_id, response)
+            .await
+            .inspect_err(|error| self.handle_error_invalidation(error))?;
         let body = response.text().await.err_to_http(request_id.clone())?;
 
         #[derive(serde::Deserialize)]
@@ -3894,10 +3970,10 @@ pub struct MergeInsertRequest {
     // (the default is true)
     #[serde(skip_serializing_if = "is_true")]
     use_index: bool,
-    // Only serialize use_lsm when explicitly set (Some); a server that predates
+    // Only serialize use_memwal when explicitly set (Some); a server that predates
     // it ignores the field and routes as it would by default.
     #[serde(skip_serializing_if = "Option::is_none")]
-    use_lsm: Option<bool>,
+    use_memwal: Option<bool>,
 }
 
 impl MergeInsertRequest {
@@ -3935,22 +4011,14 @@ impl TryFrom<MergeInsertBuilder> for MergeInsertRequest {
 
         let when_matched_update_all_filt = match value.when_matched_update_all_filt {
             Some(MergeFilter::Sql(sql)) => Some(sql),
-            Some(MergeFilter::Expr(_)) => {
-                return Err(Error::NotSupported {
-                    message: "DataFusion expressions are not supported on remote tables".into(),
-                });
-            }
+            Some(MergeFilter::Expr(expr)) => Some(expr_to_sql_string(&expr)?),
             None => None,
         };
 
         let when_not_matched_by_source_delete_filt =
             match value.when_not_matched_by_source_delete_filt {
                 Some(MergeFilter::Sql(sql)) => Some(sql),
-                Some(MergeFilter::Expr(_)) => {
-                    return Err(Error::NotSupported {
-                        message: "DataFusion expressions are not supported on remote tables".into(),
-                    });
-                }
+                Some(MergeFilter::Expr(expr)) => Some(expr_to_sql_string(&expr)?),
                 None => None,
             };
 
@@ -3963,7 +4031,7 @@ impl TryFrom<MergeInsertBuilder> for MergeInsertRequest {
             when_not_matched_by_source_delete_filt,
             // Only serialize use_index when it's false for backwards compatibility
             use_index: value.use_index,
-            use_lsm: value.use_lsm,
+            use_memwal: value.use_memwal,
         })
     }
 }
@@ -4049,7 +4117,7 @@ mod tests {
             ))
         };
 
-        // All endpoints should translate 404 to TableNotFound.
+        // These table operations should translate 404 to TableNotFound.
         let results: Vec<BoxFuture<'_, Result<()>>> = vec![
             Box::pin(table.version().map_ok(|_| ())),
             Box::pin(table.schema().map_ok(|_| ())),
@@ -4630,10 +4698,18 @@ mod tests {
     }
 
     #[rstest]
-    #[case(true)]
-    #[case(false)]
+    #[case::old_server("", 0, 0)]
+    #[case::rows_updated(r#"{"rows_updated": 5, "version": 43}"#, 5, 43)]
+    #[case::updated_rows(r#"{"updated_rows": 5, "version": 43}"#, 5, 43)]
+    #[case::zero_updated_rows(r#"{"updated_rows": 0, "version": 43}"#, 0, 43)]
+    #[case::missing_row_count(r#"{"version": 43}"#, 0, 43)]
     #[tokio::test]
-    async fn test_update(#[case] old_server: bool) {
+    async fn test_update(
+        #[case] response_body: &'static str,
+        #[case] expected_rows_updated: u64,
+        #[case] expected_version: u64,
+        #[values(true, false)] filtered: bool,
+    ) {
         let table = Table::new_with_handler("my_table", move |request| {
             if request.url().path() == "/v1/table/my_table/update/" {
                 assert_eq!(request.method(), "POST");
@@ -4658,32 +4734,48 @@ mod tests {
                     assert_eq!(col_name, "b");
                     assert_eq!(expression, "b - 1");
 
-                    let only_if = value.get("predicate").unwrap().as_str().unwrap();
-                    assert_eq!(only_if, "`B` > 10");
+                    assert_eq!(
+                        value.get("predicate").unwrap(),
+                        &serde_json::json!(if filtered { Some("`B` > 10") } else { None })
+                    );
                 }
 
-                if old_server {
-                    http::Response::builder().status(200).body("").unwrap()
-                } else {
-                    http::Response::builder()
-                        .status(200)
-                        .body(r#"{"rows_updated": 5, "version": 43}"#)
-                        .unwrap()
-                }
+                http::Response::builder()
+                    .status(200)
+                    .body(response_body)
+                    .unwrap()
             } else {
                 panic!("Unexpected request path: {}", request.url().path());
             }
         });
 
-        let update = table
-            .update()
-            .column("a", "a + 1")
-            .column("b", "b - 1")
-            .only_if(r#""B" > 10"#);
+        let mut update = table.update().column("a", "a + 1").column("b", "b - 1");
+        if filtered {
+            update = update.only_if(r#""B" > 10"#);
+        }
         let result = table.base_table().update(update).await.unwrap();
 
-        assert_eq!(result.version, if old_server { 0 } else { 43 });
-        assert_eq!(result.rows_updated, if old_server { 0 } else { 5 });
+        assert_eq!(result.version, expected_version);
+        assert_eq!(result.rows_updated, expected_rows_updated);
+    }
+
+    #[tokio::test]
+    async fn test_alter_columns_rejects_missing_changes_before_request() {
+        let table = Table::new_with_handler::<String>("my_table", |request| {
+            panic!("Unexpected request: {}", request.url().path())
+        });
+
+        for alterations in [
+            vec![ColumnAlteration::new("id".into())],
+            vec![
+                ColumnAlteration::new("id".into()).rename("new_id".into()),
+                ColumnAlteration::new("id".into()),
+            ],
+        ] {
+            let err = table.alter_columns(&alterations).await.unwrap_err();
+            assert!(matches!(err, Error::InvalidInput { .. }), "got {err:?}");
+            assert!(err.to_string().contains("path 'id'"));
+        }
     }
 
     #[rstest]
@@ -4799,6 +4891,167 @@ mod tests {
         }
     }
 
+    #[rstest]
+    #[case::sql(false, false)]
+    #[case::expr_update(true, false)]
+    #[case::expr_delete(false, true)]
+    #[case::expr_both(true, true)]
+    #[tokio::test]
+    async fn test_merge_insert_filter_expressions(
+        #[case] update_expr: bool,
+        #[case] delete_expr: bool,
+    ) {
+        use datafusion_expr::{col, lit};
+
+        let batch = record_batch!(("id", Int32, [0, 1]), ("v", Int32, [100, 110])).unwrap();
+        let data: Box<dyn RecordBatchReader + Send> = Box::new(RecordBatchIterator::new(
+            [Ok(batch.clone())],
+            batch.schema(),
+        ));
+
+        let table = Table::new_with_handler("my_table", move |request| {
+            assert_eq!(request.method(), "POST");
+            assert_eq!(request.url().path(), "/v1/table/my_table/merge_insert/");
+            let params = request.url().query_pairs().collect::<HashMap<_, _>>();
+            assert_eq!(params["on"], "id");
+            assert_eq!(params["when_matched_update_all"], "true");
+            assert_eq!(params["when_not_matched_insert_all"], "false");
+            assert_eq!(params["when_not_matched_by_source_delete"], "true");
+            assert_eq!(
+                params["when_matched_update_all_filt"],
+                if update_expr {
+                    "(`target`.v < `source`.v)"
+                } else {
+                    "target.v < source.v"
+                }
+            );
+            assert_eq!(
+                params["when_not_matched_by_source_delete_filt"],
+                if delete_expr { "(id > 3)" } else { "id > 3" }
+            );
+            http::Response::builder()
+                .status(200)
+                .body(r#"{"version": 2, "num_updated_rows": 2, "num_inserted_rows": 0, "num_deleted_rows": 2}"#)
+                .unwrap()
+        });
+
+        let mut merge = table.merge_insert(&["id"]);
+        if update_expr {
+            merge.when_matched_update_all_expr(col("target.v").lt(col("source.v")));
+        } else {
+            merge.when_matched_update_all(Some("target.v < source.v".into()));
+        }
+        if delete_expr {
+            merge.when_not_matched_by_source_delete_expr(col("id").gt(lit(3)));
+        } else {
+            merge.when_not_matched_by_source_delete(Some("id > 3".into()));
+        }
+
+        let result = merge.execute(data).await.unwrap();
+        assert_eq!(result.version, 2);
+        assert_eq!(result.num_updated_rows, 2);
+        assert_eq!(result.num_inserted_rows, 0);
+        assert_eq!(result.num_deleted_rows, 2);
+    }
+
+    #[rstest]
+    #[case::no_batches_insert(false, false)]
+    #[case::empty_batch_insert(true, false)]
+    #[case::no_batches_delete(false, true)]
+    #[case::empty_batch_delete(true, true)]
+    #[tokio::test]
+    async fn test_merge_insert_empty_source(
+        #[case] has_batch: bool,
+        #[case] delete_unmatched: bool,
+    ) {
+        let mut fields = vec![Field::new("id", DataType::Int64, false)];
+        if !delete_unmatched {
+            fields.extend([
+                Field::new("k", DataType::Int64, false),
+                Field::new(
+                    "vector",
+                    DataType::FixedSizeList(
+                        Arc::new(Field::new("item", DataType::Float32, true)),
+                        2,
+                    ),
+                    true,
+                ),
+                Field::new("s", DataType::Utf8, true),
+            ]);
+        }
+        let schema = Arc::new(Schema::new_with_metadata(
+            fields,
+            HashMap::from([("source".to_string(), "empty".to_string())]),
+        ));
+        let batches = if has_batch {
+            vec![Ok(RecordBatch::new_empty(schema.clone()))]
+        } else {
+            vec![]
+        };
+        let data: Box<dyn RecordBatchReader + Send> =
+            Box::new(RecordBatchIterator::new(batches, schema.clone()));
+        let attempts = Arc::new(AtomicUsize::new(0));
+        let attempts_ref = attempts.clone();
+        let num_deleted_rows = if delete_unmatched { 3 } else { 0 };
+
+        let table = Table::new_with_handler("my_table", move |request| {
+            assert_eq!(request.method(), "POST");
+            assert_eq!(request.url().path(), "/v1/table/my_table/merge_insert/");
+            assert_eq!(request.headers()[CONTENT_TYPE], ARROW_STREAM_CONTENT_TYPE);
+            let params = request.url().query_pairs().collect::<HashMap<_, _>>();
+            assert_eq!(params["on"], "id");
+            assert_eq!(
+                params["when_not_matched_insert_all"],
+                (!delete_unmatched).to_string()
+            );
+            assert_eq!(
+                params["when_not_matched_by_source_delete"],
+                delete_unmatched.to_string()
+            );
+
+            let body = request.body().unwrap().as_bytes().unwrap();
+            let reader = StreamReader::try_new(Cursor::new(body), None).unwrap();
+            assert_eq!(reader.schema(), schema);
+            for batch in reader {
+                assert_eq!(batch.unwrap().num_rows(), 0);
+            }
+
+            // The empty source must retain its schema when replayed after a conflict.
+            if attempts_ref.fetch_add(1, Ordering::SeqCst) == 0 {
+                http::Response::builder()
+                    .status(409)
+                    .body(String::new())
+                    .unwrap()
+            } else {
+                http::Response::builder()
+                    .status(200)
+                    .body(
+                        json!({
+                            "version": 43,
+                            "num_deleted_rows": num_deleted_rows,
+                            "num_inserted_rows": 0,
+                            "num_updated_rows": 0,
+                        })
+                        .to_string(),
+                    )
+                    .unwrap()
+            }
+        });
+
+        let mut merge = table.merge_insert(&["id"]);
+        if delete_unmatched {
+            merge.when_not_matched_by_source_delete(None);
+        } else {
+            merge.when_not_matched_insert_all();
+        }
+        let result = merge.execute(data).await.unwrap();
+        assert_eq!(attempts.load(Ordering::SeqCst), 2);
+        assert_eq!(result.version, 43);
+        assert_eq!(result.num_deleted_rows, num_deleted_rows);
+        assert_eq!(result.num_inserted_rows, 0);
+        assert_eq!(result.num_updated_rows, 0);
+    }
+
     #[tokio::test]
     async fn test_merge_insert_composite_key() {
         let batch = RecordBatch::try_new(
@@ -5066,6 +5319,14 @@ mod tests {
         assert_eq!(data[0].as_ref().unwrap(), &expected_data);
     }
 
+    fn schema_describe_response(schema: &Schema) -> http::Response<String> {
+        let json_schema = JsonSchema::try_from(schema).unwrap();
+        http::Response::builder()
+            .status(200)
+            .body(serde_json::json!({ "version": 1, "schema": json_schema }).to_string())
+            .unwrap()
+    }
+
     fn blob_describe_response() -> http::Response<String> {
         let schema = Schema::new(vec![
             Field::new("id", DataType::Int64, false),
@@ -5073,11 +5334,7 @@ mod tests {
             Field::new("caption", DataType::Utf8, true),
             crate::blob("thumbnail", true),
         ]);
-        let json_schema = JsonSchema::try_from(&schema).unwrap();
-        http::Response::builder()
-            .status(200)
-            .body(serde_json::json!({ "version": 1, "schema": json_schema }).to_string())
-            .unwrap()
+        schema_describe_response(&schema)
     }
 
     #[rstest]
@@ -5096,6 +5353,95 @@ mod tests {
         assert_eq!(columns, vec!["image".to_string(), "thumbnail".to_string()]);
     }
 
+    #[tokio::test]
+    async fn test_list_blob_paths_are_not_advertised_or_fetchable() {
+        let schema = Schema::new(vec![
+            crate::blob("thumbnail", true),
+            Field::new(
+                "imgs",
+                DataType::List(Arc::new(crate::blob("item", true))),
+                true,
+            ),
+        ]);
+        let table = Table::new_with_handler_version(
+            "my_table",
+            semver::Version::new(0, 5, 0),
+            move |request| match request.url().path() {
+                "/v1/table/my_table/describe/" => schema_describe_response(&schema),
+                "/v1/table/my_table/fetch_blobs/" | "/v1/table/my_table/query/" => {
+                    http::Response::builder()
+                        .status(400)
+                        .body("Invalid input, column 'imgs' is not a blob column".to_string())
+                        .unwrap()
+                }
+                path if path.starts_with("/v1/table/my_table/blob/") => http::Response::builder()
+                    .status(400)
+                    .body("Invalid input, column 'imgs' is not a blob column".to_string())
+                    .unwrap(),
+                path => panic!("unexpected request: {path}"),
+            },
+        );
+
+        assert_eq!(table.blob_columns().await.unwrap(), vec!["thumbnail"]);
+        for column in ["imgs", "imgs.item"] {
+            let err = table.fetch_blobs(column, &[10]).await.unwrap_err();
+            assert!(
+                err.to_string()
+                    .contains("blobs inside lists cannot be fetched")
+            );
+            let err = table.fetch_blob_files(column, &[10]).await.unwrap_err();
+            assert!(
+                err.to_string()
+                    .contains("blobs inside lists cannot be fetched")
+            );
+        }
+    }
+
+    #[tokio::test]
+    async fn test_fetch_blob_files_explains_uncached_list_descriptors() {
+        let list_field = Field::new(
+            "imgs",
+            DataType::List(Arc::new(crate::blob("item", true))),
+            true,
+        );
+        let schema = Schema::new(vec![list_field.clone()]);
+        let batch = RecordBatch::try_new(
+            Arc::new(Schema::new(vec![
+                list_field.clone(),
+                Field::new(lance::dataset::ROW_ID, DataType::UInt64, false),
+            ])),
+            vec![
+                arrow_array::new_null_array(list_field.data_type(), 1),
+                Arc::new(arrow_array::UInt64Array::from(vec![10])),
+            ],
+        )
+        .unwrap();
+        let table = Table::new_with_handler_version(
+            "my_table",
+            semver::Version::new(0, 5, 0),
+            move |request| match request.url().path() {
+                "/v1/table/my_table/describe/" => {
+                    schema_describe_response(&schema).map(|body| body.into_bytes())
+                }
+                "/v1/table/my_table/query/" => http::Response::builder()
+                    .status(200)
+                    .header(CONTENT_TYPE, ARROW_STREAM_CONTENT_TYPE)
+                    .header(VERSION_HEADER, "1")
+                    .body(write_ipc_stream_uncompressed(&batch))
+                    .unwrap(),
+                path => panic!("unexpected request: {path}"),
+            },
+        );
+
+        let error = table.fetch_blob_files("imgs", &[10]).await.unwrap_err();
+        assert!(matches!(error, Error::InvalidInput { .. }));
+        assert!(
+            error
+                .to_string()
+                .contains("blobs inside lists cannot be fetched")
+        );
+    }
+
     #[tokio::test]
     async fn test_fetch_blobs_decodes_null_aligned_bytes() {
         let mut builder = LargeBinaryBuilder::new();
@@ -5326,9 +5672,10 @@ mod tests {
                     "bytes=0-0" => http::Response::builder()
                         .status(206)
                         .header(reqwest::header::CONTENT_RANGE, "bytes 0-0/10")
+                        .header(VERSION_HEADER, "42")
                         .body(b"0".to_vec())
                         .unwrap(),
-                    "bytes=0-" => http::Response::builder()
+                    "bytes=0-9" => http::Response::builder()
                         .status(206)
                         .header(reqwest::header::CONTENT_RANGE, "bytes 0-9/10")
                         .body(b"0123456789".to_vec())
@@ -5350,21 +5697,35 @@ mod tests {
         assert_eq!(blobs.value(4), b"bbb");
         let requests = requests.lock().unwrap();
         assert!(requests.contains(&"GET bytes=0-0".to_string()));
-        assert!(requests.contains(&"GET bytes=0-".to_string()));
+        assert!(requests.contains(&"GET bytes=0-9".to_string()));
     }
 
     #[tokio::test]
     async fn test_fetch_blobs_does_not_split_unrelated_bad_requests() {
         let requests = Arc::new(AtomicUsize::new(0));
         let seen = requests.clone();
-        let table =
-            Table::new_with_handler_version("my_table", semver::Version::new(0, 5, 0), move |_| {
-                seen.fetch_add(1, Ordering::SeqCst);
-                http::Response::builder()
-                    .status(400)
-                    .body(b"unknown blob column".to_vec())
-                    .unwrap()
-            });
+        let table = Table::new_with_handler_version(
+            "my_table",
+            semver::Version::new(0, 5, 0),
+            move |request| match request.url().path() {
+                "/v1/table/my_table/describe/" => {
+                    schema_describe_response(&Schema::new(vec![Field::new(
+                        "id",
+                        DataType::Int64,
+                        false,
+                    )]))
+                    .map(|body| body.into_bytes())
+                }
+                "/v1/table/my_table/fetch_blobs/" => {
+                    seen.fetch_add(1, Ordering::SeqCst);
+                    http::Response::builder()
+                        .status(400)
+                        .body(b"unknown blob column".to_vec())
+                        .unwrap()
+                }
+                path => panic!("unexpected request: {path}"),
+            },
+        );
 
         assert_fetch_blobs_http_error(
             table.fetch_blobs("missing", &[10, 20]).await.unwrap_err(),
@@ -5527,10 +5888,18 @@ mod tests {
     #[tokio::test]
     async fn test_fetch_blobs_sends_a_nested_column_as_a_dotted_path() {
         let ipc = one_row_blob_ipc_stream("info.blob");
+        let schema = Schema::new(vec![Field::new(
+            "info",
+            DataType::Struct(vec![crate::blob("blob", true)].into()),
+            true,
+        )]);
         let table = Table::new_with_handler_version(
             "my_table",
             semver::Version::new(0, 5, 0),
             move |request| {
+                if request.url().path() == "/v1/table/my_table/describe/" {
+                    return schema_describe_response(&schema).map(|body| body.into_bytes());
+                }
                 let body = request_body_json(&request);
                 assert_eq!(body["column"], "info.blob");
                 http::Response::builder()
@@ -5574,6 +5943,68 @@ mod tests {
         }
     }
 
+    #[tokio::test]
+    async fn test_remote_unsupported_operations() {
+        let table = Table::new_with_handler("my_table", |_| -> http::Response<String> {
+            panic!("unsupported operations must not send requests to the server");
+        });
+        let mut fts = FullTextSearchQuery::new("test".into());
+        fts.wand_factor = Some(1.0);
+        let results = [
+            (
+                "optimize",
+                table.optimize(OptimizeAction::All).await.map(|_| ()),
+            ),
+            (
+                "fetch_blob_ranges",
+                table.fetch_blob_ranges("image", []).await.map(|_| ()),
+            ),
+            (
+                "Wand factor",
+                table
+                    .query()
+                    .full_text_search(fts)
+                    .execute()
+                    .await
+                    .map(|_| ()),
+            ),
+        ];
+        for (operation, result) in results {
+            match result.unwrap_err() {
+                Error::NotSupported { message } => assert_eq!(
+                    message,
+                    format!("{operation} is not supported for remote tables.")
+                ),
+                error => panic!("expected not-supported error, got {error}"),
+            }
+        }
+    }
+
+    #[tokio::test]
+    async fn test_remote_unsupported_hnsw_pq() {
+        let table = Table::new_with_handler("my_table", |request| {
+            assert_eq!(request.url().path(), "/v1/table/my_table/describe/");
+            let schema = Schema::new(vec![Field::new(
+                "vector",
+                DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 2),
+                false,
+            )]);
+            http::Response::builder()
+                .status(200)
+                .body(describe_response(&schema))
+                .unwrap()
+        });
+        let error = table
+            .create_index(&["vector"], Index::IvfHnswPq(Default::default()))
+            .execute()
+            .await
+            .unwrap_err();
+        assert_not_supported_error(
+            error,
+            "IVF_HNSW_PQ is not supported for remote tables. Please use IVF_HNSW_SQ instead.",
+        );
+    }
+
     #[tokio::test]
     async fn test_fetch_blobs_rejects_missing_column() {
         let batch = RecordBatch::try_new(
@@ -5687,12 +6118,12 @@ mod tests {
 
         assert_not_supported_error(
             table.fetch_blobs("image", &[1]).await.unwrap_err(),
-            "fetch_blobs",
+            "fetch_blobs is not supported by this LanceDB server.",
         );
 
         assert_not_supported_error(
             table.fetch_blob_files("image", &[1]).await.unwrap_err(),
-            "requires LanceDB Cloud server 0.5.0 or newer",
+            "requires LanceDB server 0.5.0 or newer",
         );
     }
 
@@ -5776,6 +6207,47 @@ mod tests {
         assert_eq!(blobs.value(0), b"alpha");
     }
 
+    #[rstest]
+    #[case(DEFAULT_SERVER_VERSION.clone())]
+    #[case(semver::Version::new(0, 2, 0))]
+    #[tokio::test]
+    async fn test_query_vector_non_finite(#[case] version: semver::Version) {
+        let table =
+            Table::new_with_handler_version("my_table", version, |_| -> http::Response<String> {
+                panic!("non-finite vectors must be rejected before sending a request")
+            });
+
+        for value in [f32::NAN, f32::INFINITY, f32::NEG_INFINITY] {
+            // Requests built without the query builder must also return an
+            // error, including a non-finite vector later in a batch.
+            for batched in [false, true] {
+                let mut request = table
+                    .query()
+                    .nearest_to(&[0.1, 0.2])
+                    .unwrap()
+                    .into_request();
+                if !batched {
+                    request.query_vector.clear();
+                }
+                request
+                    .query_vector
+                    .push(Arc::new(arrow_array::Float32Array::from(vec![0.1, value])));
+                let result = table
+                    .base_table()
+                    .query(
+                        &AnyQuery::VectorQuery(request),
+                        QueryExecutionOptions::default(),
+                    )
+                    .await;
+                let Err(err) = result else {
+                    panic!("non-finite query vector unexpectedly succeeded")
+                };
+                assert!(matches!(err, Error::InvalidInput { .. }));
+                assert!(err.to_string().contains("only finite values"));
+            }
+        }
+    }
+
     #[tokio::test]
     async fn test_query_vector_default_values() {
         let expected_data = RecordBatch::try_new(
@@ -5797,9 +6269,6 @@ mod tests {
             let body: serde_json::Value = serde_json::from_slice(body).unwrap();
             let mut expected_body = serde_json::json!({
                 "prefilter": true,
-                "nprobes": 20,
-                "minimum_nprobes": 20,
-                "maximum_nprobes": 20,
                 "lower_bound": Option::<f32>::None,
                 "upper_bound": Option::<f32>::None,
                 "k": 10,
@@ -5831,7 +6300,7 @@ mod tests {
     }
 
     #[tokio::test]
-    async fn test_query_vector_approx_mode_sent_when_set() {
+    async fn test_query_vector_minimum_only_and_approx_mode() {
         let expected_data = RecordBatch::try_new(
             Arc::new(Schema::new(vec![Field::new("a", DataType::Int32, false)])),
             vec![Arc::new(Int32Array::from(vec![1, 2, 3]))],
@@ -5851,9 +6320,7 @@ mod tests {
             let body: serde_json::Value = serde_json::from_slice(body).unwrap();
             let mut expected_body = serde_json::json!({
                 "prefilter": true,
-                "nprobes": 20,
-                "minimum_nprobes": 20,
-                "maximum_nprobes": 20,
+                "minimum_nprobes": 5,
                 "approx_mode": "accurate",
                 "lower_bound": Option::<f32>::None,
                 "upper_bound": Option::<f32>::None,
@@ -5877,6 +6344,8 @@ mod tests {
             .query()
             .nearest_to(vec![0.1, 0.2, 0.3])
             .unwrap()
+            .minimum_nprobes(5)
+            .unwrap()
             .approx_mode(crate::ApproxMode::Accurate)
             .execute()
             .await;
@@ -5885,6 +6354,39 @@ mod tests {
         assert_eq!(data[0].as_ref().unwrap(), &expected_data);
     }
 
+    #[tokio::test]
+    async fn test_query_vector_explicitly_unbounded_maximum() {
+        let table = Table::new_with_handler("my_table", |request| {
+            let body = request.body().unwrap().as_bytes().unwrap();
+            let body: serde_json::Value = serde_json::from_slice(body).unwrap();
+            assert_eq!(body["nprobes"], 5);
+            assert_eq!(body["maximum_nprobes"], 0);
+
+            let data = RecordBatch::try_new(
+                Arc::new(Schema::new(vec![Field::new("a", DataType::Int32, false)])),
+                vec![Arc::new(Int32Array::from(vec![1]))],
+            )
+            .unwrap();
+            http::Response::builder()
+                .status(200)
+                .header(CONTENT_TYPE, ARROW_FILE_CONTENT_TYPE)
+                .body(write_ipc_file(&data))
+                .unwrap()
+        });
+
+        table
+            .query()
+            .nearest_to(vec![0.1, 0.2, 0.3])
+            .unwrap()
+            .nprobes(5)
+            .unwrap()
+            .maximum_nprobes(None)
+            .unwrap()
+            .execute()
+            .await
+            .unwrap();
+    }
+
     #[tokio::test]
     async fn test_query_fts_default_values() {
         let expected_data = RecordBatch::try_new(
@@ -5934,6 +6436,93 @@ mod tests {
         assert_eq!(data[0].as_ref().unwrap(), &expected_data);
     }
 
+    #[tokio::test]
+    async fn test_query_vector_nprobes_zero() {
+        let table = Table::new_with_handler::<&str>("my_table", |_| {
+            panic!("invalid nprobes must be rejected before sending a request")
+        });
+        let result = table
+            .query()
+            .nearest_to(vec![0.1, 0.2, 0.3])
+            .unwrap()
+            .nprobes(0);
+        assert!(matches!(
+            result,
+            Err(Error::InvalidInput { message }) if message == "nprobes must be greater than 0"
+        ));
+    }
+
+    #[rstest]
+    #[case::unset(None)]
+    #[case::minimum(Some(0.0))]
+    #[case::low(Some(0.25))]
+    #[case::auto(Some(0.5))]
+    #[case::high(Some(0.75))]
+    #[case::maximum(Some(1.0))]
+    #[tokio::test]
+    async fn test_query_vector_search_effort_wire(#[case] effort: Option<f64>) {
+        let table = Table::new_with_handler("my_table", move |request| {
+            let body = request_body_json(&request);
+            assert_eq!(body["nprobes"], 7);
+            assert_eq!(body["minimum_nprobes"], 2);
+            assert_eq!(body["maximum_nprobes"], 8);
+            if let Some(effort) = effort {
+                assert_eq!(body["search_effort"], effort);
+            } else {
+                assert!(body.get("search_effort").is_none());
+            }
+            let batch = RecordBatch::try_new(
+                Arc::new(Schema::new(vec![Field::new("id", DataType::Int32, false)])),
+                vec![Arc::new(Int32Array::from(vec![1]))],
+            )
+            .unwrap();
+            http::Response::builder()
+                .status(200)
+                .header(CONTENT_TYPE, ARROW_FILE_CONTENT_TYPE)
+                .body(write_ipc_file(&batch))
+                .unwrap()
+        });
+        let batches = table
+            .query()
+            .nearest_to(vec![0.1, 0.2])
+            .unwrap()
+            .search_effort(effort)
+            .unwrap()
+            .nprobes(7)
+            .unwrap()
+            .minimum_nprobes(2)
+            .unwrap()
+            .maximum_nprobes(Some(8))
+            .unwrap()
+            .execute()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        assert_eq!(batches[0].num_rows(), 1);
+    }
+
+    #[rstest]
+    #[case::negative(-0.1)]
+    #[case::too_large(1.1)]
+    #[case::nan(f64::NAN)]
+    #[case::infinity(f64::INFINITY)]
+    #[case::negative_infinity(f64::NEG_INFINITY)]
+    #[tokio::test]
+    async fn test_query_vector_search_effort_invalid(#[case] effort: f64) {
+        let table = Table::new_with_handler::<&str>("my_table", |_| {
+            panic!("invalid search_effort must not send a request")
+        });
+        let result = table
+            .query()
+            .nearest_to(vec![0.1, 0.2])
+            .unwrap()
+            .search_effort(effort);
+        assert!(matches!(result, Err(Error::InvalidInput { message })
+            if message.contains("search_effort must be finite and in [0, 1]")));
+    }
+
     #[tokio::test]
     async fn test_query_vector_all_params() {
         let table = Table::new_with_handler("my_table", |request| {
@@ -5967,8 +6556,8 @@ mod tests {
                     }
                 ],
                 "nprobes": 12,
-                "minimum_nprobes": 12,
-                "maximum_nprobes": 12,
+                "minimum_nprobes": 3,
+                "maximum_nprobes": 10,
                 "lower_bound": Option::<f32>::None,
                 "upper_bound": Option::<f32>::None,
                 "ef": Option::<usize>::None,
@@ -6007,6 +6596,11 @@ mod tests {
             .postfilter()
             .distance_type(crate::DistanceType::Cosine)
             .nprobes(12)
+            .unwrap()
+            .minimum_nprobes(3)
+            .unwrap()
+            .maximum_nprobes(Some(10))
+            .unwrap()
             .refine_factor(2)
             .bypass_vector_index()
             .execute()
@@ -6014,6 +6608,72 @@ mod tests {
             .unwrap();
     }
 
+    #[rstest]
+    #[case::sql(Select::dynamic(&[("p", "x"), ("id", "id")]))]
+    #[case::typed(Select::expr_projection(&[
+        ("p", crate::expr::col("x")),
+        ("id", crate::expr::col("id")),
+    ]))]
+    #[tokio::test]
+    async fn test_query_dynamic_projection_order(#[case] select: Select) {
+        let table = Table::new_with_handler("my_table", |request| {
+            assert_eq!(request.url().path(), "/v1/table/my_table/query/");
+            let body: serde_json::Value =
+                serde_json::from_slice(request.body().unwrap().as_bytes().unwrap()).unwrap();
+            let aliases = body["columns"]
+                .as_object()
+                .unwrap()
+                .keys()
+                .map(String::as_str)
+                .collect::<Vec<_>>();
+            assert_eq!(aliases, ["p", "id"]);
+
+            // Simulate a server that returns aliases in alphabetical order.
+            let data = RecordBatch::try_new(
+                Arc::new(Schema::new(vec![
+                    Field::new("id", DataType::Int32, false),
+                    Field::new("p", DataType::Int32, false),
+                ])),
+                vec![
+                    Arc::new(Int32Array::from(vec![7])),
+                    Arc::new(Int32Array::from(vec![42])),
+                ],
+            )
+            .unwrap();
+            http::Response::builder()
+                .status(200)
+                .header(CONTENT_TYPE, ARROW_FILE_CONTENT_TYPE)
+                .body(write_ipc_file(&data))
+                .unwrap()
+        });
+
+        let batches = table
+            .query()
+            .select(select)
+            .execute()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        assert_eq!(batches.len(), 1);
+        let schema = batches[0].schema();
+        assert_eq!(
+            schema
+                .fields()
+                .iter()
+                .map(|field| field.name().as_str())
+                .collect::<Vec<_>>(),
+            ["p", "id"]
+        );
+        let projected = batches[0]
+            .column(0)
+            .as_any()
+            .downcast_ref::<Int32Array>()
+            .unwrap();
+        assert_eq!(projected.value(0), 42);
+    }
+
     #[tokio::test]
     async fn test_query_vector_nested_field_path() {
         let expected_data = RecordBatch::try_new(
@@ -6037,9 +6697,6 @@ mod tests {
                 "vector_column": "image.embedding",
                 "prefilter": true,
                 "k": 10,
-                "nprobes": 20,
-                "minimum_nprobes": 20,
-                "maximum_nprobes": 20,
                 "lower_bound": Option::<f32>::None,
                 "upper_bound": Option::<f32>::None,
                 "ef": Option::<usize>::None,
@@ -6338,6 +6995,53 @@ mod tests {
             .unwrap();
     }
 
+    #[tokio::test]
+    async fn test_query_combined_fields_uses_structured_fts() {
+        use lance_index::scalar::inverted::query::CombinedFieldsQuery;
+
+        let table =
+            Table::new_with_handler_version("my_table", semver::Version::new(0, 3, 0), |request| {
+                let body = request.body().unwrap().as_bytes().unwrap();
+                let body: serde_json::Value = serde_json::from_slice(body).unwrap();
+                assert_eq!(
+                    body["full_text_query"]["query"],
+                    serde_json::json!({
+                        "combined_fields": {
+                            "query": "hello world",
+                            "columns": ["title", "text"],
+                            "boost": [1.0, 1.0],
+                            "operator": "Or"
+                        }
+                    })
+                );
+
+                let data = RecordBatch::try_new(
+                    Arc::new(Schema::new(vec![Field::new("a", DataType::Int32, false)])),
+                    vec![Arc::new(Int32Array::from(vec![1]))],
+                )
+                .unwrap();
+                http::Response::builder()
+                    .status(200)
+                    .header(CONTENT_TYPE, ARROW_FILE_CONTENT_TYPE)
+                    .body(write_ipc_file(&data))
+                    .unwrap()
+            });
+
+        table
+            .query()
+            .full_text_search(FullTextSearchQuery::new_query(
+                CombinedFieldsQuery::try_new(
+                    "hello world".into(),
+                    vec!["title".into(), "text".into()],
+                )
+                .unwrap()
+                .into(),
+            ))
+            .execute()
+            .await
+            .unwrap();
+    }
+
     #[tokio::test]
     async fn test_query_row_document_granularity_uses_structured_fts() {
         let table =
@@ -6728,19 +7432,24 @@ mod tests {
         ];
 
         for (index_type, expected_body, index) in cases {
+            let data_type = match &index {
+                Index::FTS(params) if params.get_document_granularity().is_list_element() => {
+                    DataType::List(Arc::new(Field::new("item", DataType::Utf8, true)))
+                }
+                Index::FTS(_) => DataType::Utf8,
+                _ => DataType::Int32,
+            };
+            let schema = Schema::new(vec![Field::new("a", data_type, false)]);
             let table = Table::new_with_handler_version(
                 "my_table",
                 semver::Version::new(0, 6, 0),
                 move |request| {
                     assert_eq!(request.method(), "POST");
                     match request.url().path() {
-                        "/v1/table/my_table/describe/" => {
-                            let schema = Schema::new(vec![Field::new("a", DataType::Int32, false)]);
-                            http::Response::builder()
-                                .status(200)
-                                .body(describe_response(&schema))
-                                .unwrap()
-                        }
+                        "/v1/table/my_table/describe/" => http::Response::builder()
+                            .status(200)
+                            .body(describe_response(&schema))
+                            .unwrap(),
                         "/v1/table/my_table/create_index/" => {
                             assert_eq!(
                                 request.headers().get("Content-Type").unwrap(),
@@ -7986,6 +8695,43 @@ mod tests {
             .unwrap();
     }
 
+    #[tokio::test]
+    async fn test_restore_requires_checkout() {
+        let request_count = Arc::new(AtomicUsize::new(0));
+        let request_count_clone = request_count.clone();
+        let table = Table::new_with_handler("my_table", move |request| {
+            request_count_clone.fetch_add(1, Ordering::SeqCst);
+            assert_eq!(request_body_json(&request)["version"], 42);
+            let body = match request.url().path() {
+                "/v1/table/my_table/describe/" => r#"{"version":42,"schema":{"fields":[]}}"#,
+                "/v1/table/my_table/restore/" => r#"{"version":43}"#,
+                path => panic!("unexpected request path: {path}"),
+            };
+            http::Response::builder().status(200).body(body).unwrap()
+        });
+
+        let err = table.restore().await.unwrap_err();
+        assert!(matches!(err, Error::InvalidInput { message }
+            if message == "you must run checkout before running restore"));
+        assert_eq!(request_count.load(Ordering::SeqCst), 0);
+
+        table.checkout(42).await.unwrap();
+        table.checkout_latest().await.unwrap();
+        let err = table.restore().await.unwrap_err();
+        assert!(matches!(err, Error::InvalidInput { message }
+            if message == "you must run checkout before running restore"));
+        assert_eq!(request_count.load(Ordering::SeqCst), 1);
+
+        table.checkout(42).await.unwrap();
+        table.restore().await.unwrap();
+        assert_eq!(request_count.load(Ordering::SeqCst), 3);
+
+        let err = table.restore().await.unwrap_err();
+        assert!(matches!(err, Error::InvalidInput { message }
+            if message == "you must run checkout before running restore"));
+        assert_eq!(request_count.load(Ordering::SeqCst), 3);
+    }
+
     #[tokio::test]
     async fn test_fails_if_checkout_version_doesnt_exist() {
         let table = Table::new_with_handler("my_table", |request| {
@@ -8603,6 +9349,73 @@ mod tests {
         );
     }
 
+    #[rstest]
+    #[case(false, "Function is unavailable")]
+    #[case(true, "Function name was not found")]
+    #[tokio::test]
+    async fn test_function_column_operations_preserve_dependency_not_found(
+        #[case] declare_column: bool,
+        #[case] message: &'static str,
+    ) {
+        let schema_requests = Arc::new(AtomicUsize::new(0));
+        let requests = schema_requests.clone();
+        let table = Table::new_with_handler("my_table", move |request| {
+            match request.url().path() {
+                "/v1/table/my_table/describe/" => {
+                    requests.fetch_add(1, Ordering::SeqCst);
+                    http::Response::builder()
+                        .status(200)
+                        .body(
+                            r#"{"version":1,"schema":{"fields":[{"name":"description","nullable":true,"type":{"type":"string"}}]}}"#.to_string(),
+                        )
+                        .unwrap()
+                }
+                "/v1/table/my_table/backfill_column" | "/v1/table/my_table/add_columns/" => {
+                    http::Response::builder()
+                        .status(404)
+                        .body(json!({"code": 4, "error": message}).to_string())
+                        .unwrap()
+                }
+                path => panic!("unexpected request: {path}"),
+            }
+        });
+        table.schema().await.unwrap();
+        assert_eq!(schema_requests.load(Ordering::SeqCst), 1);
+
+        let error = if declare_column {
+            let fixture: serde_json::Value = serde_json::from_str(include_str!(
+                "../../tests/fixtures/first_class_functions/v1/remote_fixed_size_declaration_request.json"
+            ))
+            .unwrap();
+            let application = crate::function::FunctionApplication::from_json(
+                &fixture["function"]["application"].to_string(),
+            )
+            .unwrap();
+            table
+                .add_columns()
+                .function_as("embedding", application)
+                .execute()
+                .await
+                .unwrap_err()
+        } else {
+            table.refresh_column_async("embedding").await.unwrap_err()
+        };
+        let Error::Http {
+            source,
+            status_code,
+            request_id,
+        } = error
+        else {
+            panic!("dependency 404 was misclassified: {error:?}");
+        };
+        assert_eq!(status_code, Some(StatusCode::NOT_FOUND));
+        assert!(source.to_string().contains(message));
+        assert!(!request_id.is_empty());
+
+        table.schema().await.unwrap();
+        assert_eq!(schema_requests.load(Ordering::SeqCst), 2);
+    }
+
     /// The error listing is table-addressed with optional job and column
     /// filters, mirroring the server's SQL surface, and the two non-record
     /// signals come back as their own fields rather than as rows.
@@ -9193,13 +10006,10 @@ mod tests {
     }
 
     #[tokio::test]
-    async fn test_set_lsm_write_spec_unsharded() {
+    async fn test_enable_memwal_unsharded() {
         let table = Table::new_with_handler("my_table", |request| {
             assert_eq!(request.method(), "POST");
-            assert_eq!(
-                request.url().path(),
-                "/v1/table/my_table/set_lsm_write_spec/"
-            );
+            assert_eq!(request.url().path(), "/v1/table/my_table/enable_memwal/");
             let body = request.body().unwrap().as_bytes().unwrap();
             let body: serde_json::Value = serde_json::from_slice(body).unwrap();
             assert_eq!(body["sharding"], serde_json::json!({ "mode": "unsharded" }));
@@ -9213,20 +10023,17 @@ mod tests {
                 .body(r#"{"maintained_indexes":["id_idx"]}"#)
                 .unwrap()
         });
-        let spec = crate::table::LsmWriteSpec::unsharded()
+        let spec = crate::table::MemWalWriteSpec::unsharded()
             .with_maintained_indexes(vec!["id_idx".to_string()])
             .with_writer_config_defaults([("max_memtable_rows", "1000")]);
-        table.set_lsm_write_spec(spec).await.unwrap();
+        table.enable_memwal(spec).await.unwrap();
     }
 
     #[tokio::test]
-    async fn test_set_lsm_write_spec_bucket() {
+    async fn test_enable_memwal_bucket() {
         let table = Table::new_with_handler("my_table", |request| {
             assert_eq!(request.method(), "POST");
-            assert_eq!(
-                request.url().path(),
-                "/v1/table/my_table/set_lsm_write_spec/"
-            );
+            assert_eq!(request.url().path(), "/v1/table/my_table/enable_memwal/");
             let body = request.body().unwrap().as_bytes().unwrap();
             let body: serde_json::Value = serde_json::from_slice(body).unwrap();
             assert_eq!(
@@ -9238,14 +10045,14 @@ mod tests {
             http::Response::builder().status(200).body("{}").unwrap()
         });
         table
-            .set_lsm_write_spec(crate::table::LsmWriteSpec::bucket("id", 16))
+            .enable_memwal(crate::table::MemWalWriteSpec::bucket("id", 16))
             .await
             .unwrap();
     }
 
     /// `[]` (none) must stay distinguishable on the wire from null (all).
     #[tokio::test]
-    async fn test_set_lsm_write_spec_no_maintained_indexes() {
+    async fn test_enable_memwal_no_maintained_indexes() {
         let table = Table::new_with_handler("my_table", |request| {
             let body = request.body().unwrap().as_bytes().unwrap();
             let body: serde_json::Value = serde_json::from_slice(body).unwrap();
@@ -9253,21 +10060,18 @@ mod tests {
             http::Response::builder().status(200).body("{}").unwrap()
         });
         table
-            .set_lsm_write_spec(
-                crate::table::LsmWriteSpec::bucket("id", 16).with_maintained_indexes(Vec::new()),
+            .enable_memwal(
+                crate::table::MemWalWriteSpec::bucket("id", 16).with_maintained_indexes(Vec::new()),
             )
             .await
             .unwrap();
     }
 
     #[tokio::test]
-    async fn test_set_lsm_write_spec_identity() {
+    async fn test_enable_memwal_identity() {
         let table = Table::new_with_handler("my_table", |request| {
             assert_eq!(request.method(), "POST");
-            assert_eq!(
-                request.url().path(),
-                "/v1/table/my_table/set_lsm_write_spec/"
-            );
+            assert_eq!(request.url().path(), "/v1/table/my_table/enable_memwal/");
             let body = request.body().unwrap().as_bytes().unwrap();
             let body: serde_json::Value = serde_json::from_slice(body).unwrap();
             assert_eq!(
@@ -9277,38 +10081,32 @@ mod tests {
             http::Response::builder().status(200).body("{}").unwrap()
         });
         table
-            .set_lsm_write_spec(crate::table::LsmWriteSpec::identity("tenant"))
+            .enable_memwal(crate::table::MemWalWriteSpec::identity("tenant"))
             .await
             .unwrap();
     }
 
     #[tokio::test]
-    async fn test_unset_lsm_write_spec() {
+    async fn test_disable_memwal() {
         let table = Table::new_with_handler("my_table", |request| {
             assert_eq!(request.method(), "POST");
-            assert_eq!(
-                request.url().path(),
-                "/v1/table/my_table/unset_lsm_write_spec/"
-            );
+            assert_eq!(request.url().path(), "/v1/table/my_table/disable_memwal/");
             http::Response::builder().status(200).body("{}").unwrap()
         });
-        table.unset_lsm_write_spec().await.unwrap();
+        table.disable_memwal().await.unwrap();
     }
 
     #[tokio::test]
-    async fn test_get_lsm_write_spec() {
+    async fn test_get_memwal_spec() {
         let table = Table::new_with_handler("my_table", |request| {
             assert_eq!(request.method(), "POST");
-            assert_eq!(
-                request.url().path(),
-                "/v1/table/my_table/get_lsm_write_spec/"
-            );
+            assert_eq!(request.url().path(), "/v1/table/my_table/get_memwal_spec/");
 
             // The server resolves the spec and re-encodes it into the same
             // sophon-owned shape the set endpoint accepts (`Sharding` internally
-            // tagged on `mode`, wrapped in `lsm_write_spec`).
+            // tagged on `mode`, wrapped in `memwal_spec`).
             let response = serde_json::json!({
-                "lsm_write_spec": {
+                "memwal_spec": {
                     "sharding": { "mode": "bucket", "column": "id", "num_buckets": 4 },
                     "maintained_indexes": ["id_idx"],
                     "writer_config_defaults": { "durable_write": "false" },
@@ -9321,12 +10119,12 @@ mod tests {
         });
 
         let spec = table
-            .get_lsm_write_spec()
+            .get_memwal_spec()
             .await
             .unwrap()
             .expect("a spec should be reported");
         match spec {
-            crate::table::LsmWriteSpec::Bucket {
+            crate::table::MemWalWriteSpec::Bucket {
                 column,
                 num_buckets,
                 maintained_indexes,
@@ -9346,36 +10144,70 @@ mod tests {
         }
     }
 
+    /// Every selection reads back as the server reported it: every index
+    /// (`null`), none (`[]`), and a named list.
+    #[rstest::rstest]
+    #[case::every_index(serde_json::Value::Null, None)]
+    #[case::no_index(serde_json::json!([]), Some(vec![]))]
+    #[case::named(serde_json::json!(["id_idx"]), Some(vec!["id_idx".to_string()]))]
     #[tokio::test]
-    async fn test_get_lsm_write_spec_absent() {
-        let table = Table::new_with_handler("my_table", |request| {
-            assert_eq!(
-                request.url().path(),
-                "/v1/table/my_table/get_lsm_write_spec/"
-            );
-            // Null spec → the LSM write path is not enabled.
-            let response = serde_json::json!({ "lsm_write_spec": null });
+    async fn test_get_memwal_spec_round_trips_the_selection(
+        #[case] reported: serde_json::Value,
+        #[case] expected: Option<Vec<String>>,
+    ) {
+        let table = Table::new_with_handler("my_table", move |_| {
+            let response = serde_json::json!({
+                "memwal_spec": {
+                    "sharding": { "mode": "unsharded" },
+                    "maintained_indexes": reported,
+                    "writer_config_defaults": {},
+                }
+            });
             http::Response::builder()
                 .status(200)
                 .body(response.to_string())
                 .unwrap()
         });
-        assert!(table.get_lsm_write_spec().await.unwrap().is_none());
+
+        let spec = table
+            .get_memwal_spec()
+            .await
+            .unwrap()
+            .expect("a spec should be reported");
+        assert_eq!(
+            spec.maintained_indexes().map(<[String]>::to_vec),
+            expected,
+            "the selection the server reported must survive the read"
+        );
     }
 
-    /// Build a `get_lsm_stats` body for one bucket holding `generations`.
-    fn stats_body(generations: &[u64], compacting: bool) -> String {
+    #[tokio::test]
+    async fn test_get_memwal_spec_absent() {
+        let table = Table::new_with_handler("my_table", |request| {
+            assert_eq!(request.url().path(), "/v1/table/my_table/get_memwal_spec/");
+            // Null spec → the MemWAL write path is not enabled.
+            let response = serde_json::json!({ "memwal_spec": null });
+            http::Response::builder()
+                .status(200)
+                .body(response.to_string())
+                .unwrap()
+        });
+        assert!(table.get_memwal_spec().await.unwrap().is_none());
+    }
+
+    /// Build a `get_memwal_stats` body for one table shard holding `sstables`.
+    fn stats_body(sstables: &[u64], compacting: bool) -> String {
         serde_json::json!({
-            "lsm_stats": {
-                "buckets": [{
+            "memwal_stats": {
+                "table_shards": [{
                     "shard_id": "b0",
                     "status": "Active",
                     "writer_epoch": 1,
                     "manifest_version": 1,
-                    "current_generation": generations.iter().max().copied().unwrap_or(0) + 1,
+                    "current_generation": sstables.iter().max().copied().unwrap_or(0) + 1,
                     "replay_after_wal_entry_position": 0,
                     "wal_entry_position_last_seen": 0,
-                    "generations": generations.iter()
+                    "sstables": sstables.iter()
                         .map(|g| serde_json::json!({ "generation": g, "bytes": 1 }))
                         .collect::<Vec<_>>(),
                     "compacting": compacting,
@@ -9386,7 +10218,7 @@ mod tests {
         .to_string()
     }
 
-    /// `flush_lsm` / `compact_lsm` answer 202 with no body at all.
+    /// `flush_memwal` / `compact_memwal` answer 202 with no body at all.
     fn accepted() -> http::Response<String> {
         http::Response::builder()
             .status(202)
@@ -9398,7 +10230,7 @@ mod tests {
         http::Response::builder().status(200).body(body).unwrap()
     }
 
-    /// A flush landing in an empty L0 finishes on the opening stats read
+    /// A flush landing in an empty SSTable tier finishes on the opening stats read
     /// alone. Asserting zero compacts is the point: "it returned Ok" is also
     /// true of a loop that ran a pointless pass.
     #[tokio::test(start_paused = true)]
@@ -9407,18 +10239,18 @@ mod tests {
         let seen = compacts.clone();
         let table = Table::new_with_handler("my_table", move |request| {
             let path = request.url().path().to_string();
-            if path.contains("compact_lsm") {
+            if path.contains("compact_memwal") {
                 seen.fetch_add(1, std::sync::atomic::Ordering::SeqCst);
                 panic!("an already-converged table must issue no compact calls");
             }
-            if path.contains("flush_lsm") {
+            if path.contains("flush_memwal") {
                 return accepted();
             }
-            assert_eq!(path, "/v1/table/my_table/get_lsm_stats/");
+            assert_eq!(path, "/v1/table/my_table/get_memwal_stats/");
             ok_json(stats_body(&[], false))
         });
 
-        table.checkpoint_lsm().await.unwrap();
+        table.checkpoint_memwal().await.unwrap();
         assert_eq!(compacts.load(std::sync::atomic::Ordering::SeqCst), 0);
     }
 
@@ -9430,10 +10262,10 @@ mod tests {
         let seen = compacts.clone();
         let table = Table::new_with_handler("my_table", move |request| {
             let path = request.url().path().to_string();
-            if path.contains("flush_lsm") {
+            if path.contains("flush_memwal") {
                 return accepted();
             }
-            if path.contains("compact_lsm") {
+            if path.contains("compact_memwal") {
                 seen.fetch_add(1, std::sync::atomic::Ordering::SeqCst);
                 return accepted();
             }
@@ -9443,7 +10275,7 @@ mod tests {
             ok_json(stats_body(&left, false))
         });
 
-        table.checkpoint_lsm().await.unwrap();
+        table.checkpoint_memwal().await.unwrap();
         assert_eq!(
             compacts.load(std::sync::atomic::Ordering::SeqCst),
             3,
@@ -9452,7 +10284,7 @@ mod tests {
     }
 
     /// Generations created *during* the checkpoint are not waited on, which
-    /// is what lets the loop terminate on a table taking writes where "L0 is
+    /// is what lets the loop terminate on a table taking writes where "the SSTable tier is
     /// empty" never becomes true.
     #[tokio::test(start_paused = true)]
     async fn test_checkpoint_ignores_generations_created_while_it_runs() {
@@ -9460,10 +10292,10 @@ mod tests {
         let seen = compacts.clone();
         let table = Table::new_with_handler("my_table", move |request| {
             let path = request.url().path().to_string();
-            if path.contains("flush_lsm") {
+            if path.contains("flush_memwal") {
                 return accepted();
             }
-            if path.contains("compact_lsm") {
+            if path.contains("compact_memwal") {
                 seen.fetch_add(1, std::sync::atomic::Ordering::SeqCst);
                 return accepted();
             }
@@ -9477,11 +10309,11 @@ mod tests {
             ok_json(body)
         });
 
-        table.checkpoint_lsm().await.unwrap();
+        table.checkpoint_memwal().await.unwrap();
         assert_eq!(
             compacts.load(std::sync::atomic::Ordering::SeqCst),
             1,
-            "the loop must not chase generations written after it started"
+            "the loop must not chase SSTables written after it started"
         );
     }
 
@@ -9494,11 +10326,11 @@ mod tests {
         let seen = compacts.clone();
         let table = Table::new_with_handler("my_table", move |request| {
             let path = request.url().path().to_string();
-            if path.contains("flush_lsm") {
+            if path.contains("flush_memwal") {
                 return accepted();
             }
-            if path.contains("compact_lsm") {
-                // First two triggers: every bucket already latched.
+            if path.contains("compact_memwal") {
+                // First two triggers: every table shard already latched.
                 if seen.fetch_add(1, std::sync::atomic::Ordering::SeqCst) < 2 {
                     return http::Response::builder()
                         .status(429)
@@ -9519,7 +10351,7 @@ mod tests {
         });
 
         table
-            .checkpoint_lsm()
+            .checkpoint_memwal()
             .await
             .expect("contention must not abort the checkpoint");
         assert_eq!(
@@ -9539,7 +10371,7 @@ mod tests {
         let seen = polls.clone();
         let table = Table::new_with_handler("my_table", move |request| {
             let path = request.url().path().to_string();
-            if path.contains("flush_lsm") || path.contains("compact_lsm") {
+            if path.contains("flush_memwal") || path.contains("compact_memwal") {
                 return accepted();
             }
             // The opening read lands; the next two polls are latched out.
@@ -9554,7 +10386,7 @@ mod tests {
         });
 
         table
-            .checkpoint_lsm()
+            .checkpoint_memwal()
             .await
             .expect("a contended poll must be retried, not surfaced");
         assert_eq!(
@@ -9574,7 +10406,7 @@ mod tests {
         let seen = flushes.clone();
         let table = Table::new_with_handler("my_table", move |request| {
             let path = request.url().path().to_string();
-            if path.contains("flush_lsm") {
+            if path.contains("flush_memwal") {
                 if seen.fetch_add(1, std::sync::atomic::Ordering::SeqCst) < 5 {
                     return http::Response::builder()
                         .status(429)
@@ -9583,20 +10415,20 @@ mod tests {
                 }
                 return accepted();
             }
-            if path.contains("compact_lsm") {
+            if path.contains("compact_memwal") {
                 return accepted();
             }
             ok_json(stats_body(&[], false))
         });
 
         table
-            .checkpoint_lsm()
+            .checkpoint_memwal()
             .await
             .expect("contention must not be reported as a lost claim");
         assert_eq!(
             flushes.load(std::sync::atomic::Ordering::SeqCst),
             6,
-            "five retries against one seal, then it lands"
+            "five retries against one freeze, then it lands"
         );
     }
 
@@ -9615,7 +10447,7 @@ mod tests {
                 .unwrap()
         });
 
-        let err = table.checkpoint_lsm().await.unwrap_err();
+        let err = table.checkpoint_memwal().await.unwrap_err();
         assert!(
             matches!(&err, Error::Http { status_code: Some(s), .. } if s.as_u16() == 429),
             "the fault that spent the budget must be the one reported: {err:?}"
@@ -9645,7 +10477,7 @@ mod tests {
                 .unwrap()
         });
 
-        let err = table.checkpoint_lsm().await.unwrap_err();
+        let err = table.checkpoint_memwal().await.unwrap_err();
         let message = err.to_string();
         assert!(
             matches!(&err, Error::Http { status_code: Some(s), .. } if s.as_u16() == 503),
@@ -9665,7 +10497,7 @@ mod tests {
     /// A long stall with nothing compacting must keep waiting, not fail. The
     /// client cannot judge this: a checkpoint queued behind unrelated tables
     /// on the pod-wide compactor pool reports exactly these numbers — flat
-    /// generations, an idle latch — as one whose merges are failing. The
+    /// SSTable counts, an idle latch — as one whose merges are failing. The
     /// deadline is the caller's.
     #[tokio::test(start_paused = true)]
     async fn test_checkpoint_waits_out_a_long_stall_rather_than_failing() {
@@ -9673,7 +10505,7 @@ mod tests {
         let seen = polls.clone();
         let table = Table::new_with_handler("my_table", move |request| {
             let path = request.url().path().to_string();
-            if path.contains("flush_lsm") || path.contains("compact_lsm") {
+            if path.contains("flush_memwal") || path.contains("compact_memwal") {
                 return accepted();
             }
             // Flat for far longer than any bound this loop ever had, with
@@ -9683,7 +10515,7 @@ mod tests {
         });
 
         table
-            .checkpoint_lsm()
+            .checkpoint_memwal()
             .await
             .expect("a stall is the server being slow, not the client's call to make");
         assert!(
@@ -9692,7 +10524,7 @@ mod tests {
         );
     }
 
-    /// A pass already owns the latch on every outstanding bucket, so the loop
+    /// A pass already owns the latch on every outstanding table shard, so the loop
     /// waits rather than piling on triggers it would only refuse. This is the
     /// sole thing `compacting` is read for.
     #[tokio::test(start_paused = true)]
@@ -9703,10 +10535,10 @@ mod tests {
         let seen_compacts = compacts.clone();
         let table = Table::new_with_handler("my_table", move |request| {
             let path = request.url().path().to_string();
-            if path.contains("flush_lsm") {
+            if path.contains("flush_memwal") {
                 return accepted();
             }
-            if path.contains("compact_lsm") {
+            if path.contains("compact_memwal") {
                 seen_compacts.fetch_add(1, std::sync::atomic::Ordering::SeqCst);
                 return accepted();
             }
@@ -9720,32 +10552,32 @@ mod tests {
         });
 
         table
-            .checkpoint_lsm()
+            .checkpoint_memwal()
             .await
             .expect("a running pass is progress, not a stall");
         assert_eq!(
             compacts.load(std::sync::atomic::Ordering::SeqCst),
             0,
-            "never trigger against a bucket already compacting"
+            "never trigger against a table shard already compacting"
         );
     }
 
     /// WAL off ⇒ `None`; WAL on ⇒ a fully populated `Some` with no field
-    /// defaulting to a zero it did not measure. `include_generation_rows`
+    /// defaulting to a zero it did not measure. `include_sstable_rows`
     /// rides in the body and is off unless asked for.
     #[tokio::test]
-    async fn test_get_lsm_stats_round_trip() {
+    async fn test_get_memwal_stats_round_trip() {
         let table = Table::new_with_handler("my_table", |request| {
-            assert_eq!(request.url().path(), "/v1/table/my_table/get_lsm_stats/");
+            assert_eq!(request.url().path(), "/v1/table/my_table/get_memwal_stats/");
             let body = request.body().unwrap().as_bytes().unwrap();
             let body: serde_json::Value = serde_json::from_slice(body).unwrap();
             assert_eq!(
-                body["include_generation_rows"], true,
+                body["include_sstable_rows"], true,
                 "the flag must reach the server, not be silently dropped"
             );
             let response = serde_json::json!({
-                "lsm_stats": {
-                    "buckets": [{
+                "memwal_stats": {
+                    "table_shards": [{
                         "shard_id": "b0",
                         "status": "Active",
                         "writer_epoch": 3,
@@ -9753,7 +10585,7 @@ mod tests {
                         "current_generation": 9,
                         "replay_after_wal_entry_position": 100,
                         "wal_entry_position_last_seen": 140,
-                        "generations": [{ "generation": 8, "bytes": 4096, "rows": 30 }],
+                        "sstables": [{ "generation": 8, "bytes": 4096, "rows": 30 }],
                         "compacting": false,
                         "memtables": [
                             { "generation": 9, "rows": 12, "bytes": 900, "batches": 2,
@@ -9769,19 +10601,19 @@ mod tests {
         });
 
         let stats = table
-            .get_lsm_stats(true)
+            .get_memwal_stats(true)
             .await
             .unwrap()
             .expect("a WAL-backed table reports Some");
-        let bucket = &stats.buckets[0];
-        assert_eq!(bucket.replay_after_wal_entry_position, 100);
-        assert_eq!(bucket.wal_entry_position_last_seen, 140);
-        assert!(!bucket.compacting);
-        assert_eq!(bucket.generations[0].generation, 8);
-        assert_eq!(bucket.generations[0].rows, Some(30));
+        let table_shard = &stats.table_shards[0];
+        assert_eq!(table_shard.replay_after_wal_entry_position, 100);
+        assert_eq!(table_shard.wal_entry_position_last_seen, 140);
+        assert!(!table_shard.compacting);
+        assert_eq!(table_shard.sstables[0].generation, 8);
+        assert_eq!(table_shard.sstables[0].rows, Some(30));
         // The line that answers "why is my fresh-tier vector search
         // brute-force" — an absent index name is the whole explanation.
-        let memtables = bucket.memtables.as_ref().unwrap();
+        let memtables = table_shard.memtables.as_ref().unwrap();
         assert_eq!(memtables[0].indexes, vec!["vec_idx".to_string()]);
     }
 
@@ -9801,7 +10633,7 @@ mod tests {
                 .unwrap()
         });
 
-        let err = table.checkpoint_lsm().await.unwrap_err();
+        let err = table.checkpoint_memwal().await.unwrap_err();
         assert!(
             matches!(err, Error::TableNotFound { .. }),
             "a missing table must say so: {err:?}"
@@ -9822,12 +10654,12 @@ mod tests {
         let table = Table::new_with_handler("my_table", move |request| {
             let path = request.url().path().to_string();
             let n = seen.fetch_add(1, std::sync::atomic::Ordering::SeqCst);
-            if path.contains("flush_lsm") {
+            if path.contains("flush_memwal") {
                 // First flush lands; the claim is then lost, and the
                 // re-issued flush succeeds.
                 return accepted();
             }
-            if path.contains("compact_lsm") {
+            if path.contains("compact_memwal") {
                 if n < 4 {
                     return http::Response::builder()
                         .status(421)
@@ -9840,20 +10672,20 @@ mod tests {
         });
 
         table
-            .checkpoint_lsm()
+            .checkpoint_memwal()
             .await
             .expect("a lost claim must be recovered by re-flushing, not surfaced");
     }
 
     #[tokio::test]
-    async fn test_get_lsm_stats_absent_when_wal_off() {
+    async fn test_get_memwal_stats_absent_when_wal_off() {
         let table = Table::new_with_handler("my_table", |_request| {
             http::Response::builder()
                 .status(200)
-                .body(serde_json::json!({ "lsm_stats": null }).to_string())
+                .body(serde_json::json!({ "memwal_stats": null }).to_string())
                 .unwrap()
         });
-        assert!(table.get_lsm_stats(false).await.unwrap().is_none());
+        assert!(table.get_memwal_stats(false).await.unwrap().is_none());
     }
 
     #[tokio::test]
@@ -10711,22 +11543,24 @@ mod tests {
             }
         });
 
+        table.checkout(1).await.unwrap();
+
         // First schema call
         let schema1 = table.schema().await.unwrap();
-        assert_eq!(call_count.load(Ordering::SeqCst), 1);
+        assert_eq!(call_count.load(Ordering::SeqCst), 2);
 
         // Second schema call uses cache
         let schema2 = table.schema().await.unwrap();
         assert_eq!(Arc::as_ptr(&schema2), Arc::as_ptr(&schema1));
-        assert_eq!(call_count.load(Ordering::SeqCst), 1);
+        assert_eq!(call_count.load(Ordering::SeqCst), 2);
 
         // Restore operation
-        let _ = table.restore().await;
+        table.restore().await.unwrap();
 
         // Schema call after restore should re-fetch (cache invalidated)
         let schema3 = table.schema().await.unwrap();
         assert_ne!(Arc::as_ptr(&schema3), Arc::as_ptr(&schema1));
-        assert_eq!(call_count.load(Ordering::SeqCst), 2);
+        assert_eq!(call_count.load(Ordering::SeqCst), 3);
     }
 
     /// Test that centralized error handling invalidates cache on query errors
@@ -12524,6 +13358,120 @@ mod tests {
         assert_eq!(result.version, 7);
     }
 
+    /// The unenforced primary key is field metadata, so the remote table
+    /// installs it through the `update_field_metadata` endpoint.
+    #[tokio::test]
+    async fn test_set_unenforced_primary_key() {
+        let table = Table::new_with_handler("my_table", |request| {
+            assert_eq!(request.method(), "POST");
+            match request.url().path() {
+                "/v1/table/my_table/describe/" => {
+                    let schema = Schema::new(vec![
+                        Field::new("id", DataType::Int64, false),
+                        Field::new("name", DataType::Utf8, true),
+                    ]);
+                    http::Response::builder()
+                        .status(200)
+                        .body(describe_response(&schema))
+                        .unwrap()
+                }
+                "/v1/table/my_table/update_field_metadata/" => {
+                    let body = request_body_json(&request);
+                    assert_eq!(body["updates"].as_array().unwrap().len(), 1);
+                    let update = &body["updates"][0];
+                    assert_eq!(update["path"], "id");
+                    assert_eq!(update["replace"], json!(false));
+                    assert_eq!(
+                        update["metadata"]["lance-schema:unenforced-primary-key:position"],
+                        "1"
+                    );
+                    assert_eq!(
+                        update["metadata"]["lance-schema:unenforced-primary-key"],
+                        json!(null)
+                    );
+                    http::Response::builder()
+                        .status(200)
+                        .body(r#"{"version": 3, "fields": {}}"#.to_string())
+                        .unwrap()
+                }
+                path => panic!("Unexpected path: {}", path),
+            }
+        });
+
+        table.set_unenforced_primary_key(["id"]).await.unwrap();
+    }
+
+    /// Requests the native table rejects are rejected here too, before any
+    /// write reaches the server.
+    #[tokio::test]
+    async fn test_set_unenforced_primary_key_rejects_invalid_requests() {
+        let table = Table::new_with_handler("my_table", |request| match request.url().path() {
+            "/v1/table/my_table/describe/" => {
+                let schema = Schema::new(vec![
+                    Field::new("id", DataType::Int64, false),
+                    Field::new("score", DataType::Float32, true),
+                ]);
+                http::Response::builder()
+                    .status(200)
+                    .body(describe_response(&schema))
+                    .unwrap()
+            }
+            path => panic!("Unexpected path: {}", path),
+        });
+
+        for columns in [
+            vec![],
+            vec!["id", "score"],
+            vec!["nonexistent"],
+            vec!["score"],
+        ] {
+            let err = table
+                .set_unenforced_primary_key(columns.clone())
+                .await
+                .unwrap_err();
+            assert!(
+                matches!(err, Error::InvalidInput { .. }),
+                "unexpected error for {:?}: {:?}",
+                columns,
+                err
+            );
+        }
+    }
+
+    /// The key is immutable once set, and the schema the server already
+    /// reports is enough to say so.
+    #[tokio::test]
+    async fn test_set_unenforced_primary_key_already_set() {
+        let table = Table::new_with_handler("my_table", |request| match request.url().path() {
+            "/v1/table/my_table/describe/" => {
+                let schema = Schema::new(vec![
+                    Field::new("id", DataType::Int64, false).with_metadata(HashMap::from([(
+                        "lance-schema:unenforced-primary-key:position".to_string(),
+                        "1".to_string(),
+                    )])),
+                    Field::new("name", DataType::Utf8, false),
+                ]);
+                http::Response::builder()
+                    .status(200)
+                    .body(describe_response(&schema))
+                    .unwrap()
+            }
+            path => panic!("Unexpected path: {}", path),
+        });
+
+        for column in ["name", "id"] {
+            let err = table
+                .set_unenforced_primary_key([column])
+                .await
+                .unwrap_err();
+            assert!(
+                err.to_string().contains("already set"),
+                "unexpected error: {:?}",
+                err
+            );
+        }
+    }
+
     // ----- Branch support -----
 
     /// Parse a request's in-memory JSON body. Only valid for JSON-body ops
@@ -12709,54 +13657,16 @@ mod tests {
     }
 
     #[tokio::test]
-    async fn test_materialized_view_describe_and_refresh() {
+    async fn test_materialized_view_describe_and_refresh_requires_sql() {
+        const QUERY: &str =
+            "SELECT s.x, d.label FROM analytics.source s JOIN analytics.dim d ON s.id = d.id";
         let table = Table::new_with_handler("my_table", |request| match request.url().path() {
             "/v1/materialized_view/my_table/describe" => http::Response::builder()
                 .status(200)
                 .body(
                     json!({
                         "name": "my_table",
-                        "source_table": "source",
-                        "source_namespace": ["analytics"],
-                        "projections": [{
-                            "output_column": "double_x",
-                            "expression": "x * 2"
-                        }],
-                        "filter": "x > 0",
-                        "limit": 10,
-                        "inputs": ["x"],
-                        "incarnation": "inc-1"
-                    })
-                    .to_string(),
-                )
-                .unwrap(),
-            "/v1/materialized_view/my_table/refresh" => {
-                assert_eq!(request.method(), "POST");
-                assert_eq!(
-                    request_body_json(&request),
-                    json!({
-                        "full": true,
-                        "source_version": 7,
-                        "expected_incarnation": "inc-1"
-                    })
-                );
-                http::Response::builder()
-                    .status(202)
-                    .body(json!({"job_id": "j1-mv-refresh"}).to_string())
-                    .unwrap()
-            }
-            "/v1/jobs/describe" => http::Response::builder()
-                .status(200)
-                .body(
-                    json!({
-                        "job_id": "j1-mv-refresh",
-                        "job_state": "DONE",
-                        "result": {
-                            "mode": "rebuild",
-                            "rows_written": 2,
-                            "source_version": 7,
-                            "version": 9
-                        }
+                        "query": QUERY
                     })
                     .to_string(),
                 )
@@ -12764,21 +13674,10 @@ mod tests {
             path => panic!("unexpected request: {path}"),
         });
         let view = crate::MaterializedView::from_table(table).await.unwrap();
-        assert_eq!(view.definition().source_table, "source");
-        assert_eq!(view.definition().source_namespace, ["analytics"]);
-        assert_eq!(view.incarnation(), Some("inc-1"));
-
-        let result = view
-            .refresh()
-            .full(true)
-            .source_version(7)
-            .expect_incarnation("inc-1")
-            .execute()
-            .await
-            .unwrap();
-        assert_eq!(result.mode, crate::RefreshMode::Rebuild);
-        assert_eq!(result.rows_written, 2);
-        assert_eq!(result.version, 9);
+        assert_eq!(view.definition_sql(), QUERY);
+        assert!(view.definition().is_err());
+        let error = view.refresh().execute().await.unwrap_err();
+        assert!(error.to_string().contains("SQL is unavailable"));
     }
 
     #[tokio::test]
@@ -13596,8 +14495,17 @@ mod tests {
                     .status(200)
                     .body("{}".to_string())
                     .unwrap(),
+                "/v1/table/my_table/describe/" => {
+                    assert_eq!(request_body_json(&request)["branch"], "exp");
+                    assert_eq!(request_body_json(&request)["version"], 1);
+                    http::Response::builder()
+                        .status(200)
+                        .body(r#"{"version":1,"schema":{"fields":[]}}"#.to_string())
+                        .unwrap()
+                }
                 "/v1/table/my_table/restore/" => {
                     assert_eq!(request_body_json(&request)["branch"], "exp");
+                    assert_eq!(request_body_json(&request)["version"], 1);
                     http::Response::builder()
                         .status(200)
                         .body(r#"{"version":1}"#.to_string())
@@ -13609,6 +14517,7 @@ mod tests {
             .create_branch("exp", Ref::Version(None, None))
             .await
             .unwrap();
+        branch.checkout(1).await.unwrap();
         branch.restore().await.unwrap();
     }
 
@@ -13795,7 +14704,7 @@ mod tests {
 
     /// Verbatim from sophon's `LSM_WITH_ROW_ID_UNSUPPORTED`; the fallback keys
     /// off this sentence, so a test that paraphrased it would prove nothing.
-    const WAL_ROW_ID_REFUSAL_BODY: &str = r#"{"code":13,"error":"Bad request: the MemWAL LSM scanner does not support with_row_id (the LSM scanner exposes _rowaddr, not a stable _rowid); set use_lsm(false) to read the base table only (results will exclude un-compacted MemWAL data)"}"#;
+    const WAL_ROW_ID_REFUSAL_BODY: &str = r#"{"code":13,"error":"Bad request: the MemWAL LSM scanner does not support with_row_id (the LSM scanner exposes _rowaddr, not a stable _rowid); set use_memwal(false) to read the base table only (results will exclude un-compacted MemWAL data)"}"#;
 
     use lance::dataset::ROW_ID;
 
@@ -14054,7 +14963,7 @@ mod tests {
         // Translated, not passed through: the caller gets the reason and the
         // way out, which a bare 400 does not carry.
         assert!(matches!(err, Error::NotSupported { .. }), "{err:?}");
-        assert!(err.to_string().contains("use_lsm(false)"), "{err}");
+        assert!(err.to_string().contains("use_memwal(false)"), "{err}");
         assert!(
             bodies
                 .lock()
@@ -14064,4 +14973,18 @@ mod tests {
             "no retry should have been attempted"
         );
     }
+
+    #[tokio::test]
+    async fn test_stats_num_deleted_rows_is_none_when_absent() {
+        // The stats endpoint does not report num_deleted_rows, so the response
+        // must still parse and value set to None.
+        let body = r#"{"total_bytes":1,"num_rows":3,"num_indices":0,"fragment_stats":{"num_fragments":1,"num_small_fragments":0,"lengths":{"min":3,"max":3,"mean":3,"p25":3,"p50":3,"p75":3,"p99":3}}}"#;
+        let table = Table::new_with_handler("my_table", move |_| {
+            http::Response::builder()
+                .status(200)
+                .body(body.to_string())
+                .unwrap()
+        });
+        assert_eq!(table.stats().await.unwrap().num_deleted_rows, None);
+    }
 }
diff --git a/rust/lancedb/src/remote/table/blobs.rs b/rust/lancedb/src/remote/table/blobs.rs
index 29023eeed..fdcb8f440 100644
--- a/rust/lancedb/src/remote/table/blobs.rs
+++ b/rust/lancedb/src/remote/table/blobs.rs
@@ -3,26 +3,32 @@
 
 //! Cloud blob column listing, whole-byte fetch, and seekable HTTP byte-range handles.
 
+use std::collections::HashMap;
 use std::ops::Range;
 use std::sync::Arc;
 use std::sync::atomic::{AtomicBool, Ordering};
 use std::time::Duration;
 
-use arrow_array::{Array, LargeBinaryArray};
+use arrow_array::{Array, LargeBinaryArray, StructArray, UInt8Array, UInt64Array};
 use arrow_schema::DataType;
-use bytes::{Bytes, BytesMut};
+use bytes::Bytes;
+use datafusion_expr::{col, lit};
 use futures::{StreamExt, TryStreamExt};
+use lance::dataset::ROW_ID;
+use lance_core::datatypes::BlobKind;
 use reqwest::{Response, StatusCode, header};
-use tokio::sync::Mutex;
+use tokio::sync::{Mutex, OwnedSemaphorePermit};
 
 use crate::Error;
 use crate::blob::BlobFile;
 use crate::error::Result;
+use crate::query::{QueryFilter, QueryRequest, Select};
 use crate::remote::client::{HttpSend, RequestResultExt, RestfulLanceDbClient};
-use crate::table::BaseTable;
+use crate::table::{AnyQuery, BaseTable};
 
 use super::{
-    FreshnessHeaders, FreshnessState, ReadSnapshot, RemoteTable, freshness_headers_snapshot,
+    FreshnessHeaders, FreshnessState, ReadSnapshot, RemoteTable, VERSION_HEADER,
+    freshness_headers_snapshot,
 };
 
 // The Cloud route rejects larger row-id lists. Its separate 64 MiB byte limit
@@ -50,7 +56,27 @@ trait BlobRangeRequester: Send + Sync + std::fmt::Debug {
         &self,
         range_header: &str,
         mode: RangeRequestMode,
-    ) -> Result<(String, Response)>;
+        version: Option<u64>,
+    ) -> Result<BlobRangeResponse>;
+}
+
+/// Keep the connection slot until the response body has been consumed or dropped.
+#[derive(Debug)]
+struct BlobRangeResponse {
+    request_id: String,
+    response: Response,
+    _permit: Option<OwnedSemaphorePermit>,
+}
+
+#[cfg(test)]
+impl From<(String, Response)> for BlobRangeResponse {
+    fn from((request_id, response): (String, Response)) -> Self {
+        Self {
+            request_id,
+            response,
+            _permit: None,
+        }
+    }
 }
 
 #[derive(Debug)]
@@ -71,13 +97,21 @@ impl<S: HttpSend> BlobRangeRequester for TableBlobRangeRequester<S> {
         &self,
         range_header: &str,
         mode: RangeRequestMode,
-    ) -> Result<(String, Response)> {
+        version: Option<u64>,
+    ) -> Result<BlobRangeResponse> {
+        let permit = self
+            .client
+            .blob_request_semaphore
+            .clone()
+            .acquire_owned()
+            .await
+            .expect("blob request semaphore is never closed");
         let freshness_request =
             freshness_headers_snapshot(&self.freshness, self.read_consistency_interval);
         let mut request = freshness_request
             .apply(self.client.get(&self.path))
             .header(header::RANGE, range_header);
-        if let Some(version) = self.version {
+        if let Some(version) = version.or(self.version) {
             request = request.query(&[("version", version)]);
         }
         if let Some(branch) = &self.branch {
@@ -88,56 +122,55 @@ impl<S: HttpSend> BlobRangeRequester for TableBlobRangeRequester<S> {
         if response.status() == StatusCode::RANGE_NOT_SATISFIABLE
             && matches!(mode, RangeRequestMode::SizeProbe)
         {
-            return Ok((request_id, response));
+            return Ok(BlobRangeResponse {
+                request_id,
+                response,
+                _permit: Some(permit),
+            });
         }
         let response = self.client.check_response(&request_id, response).await?;
         freshness_request.observe_headers(&self.freshness, response.headers());
         self.parent_freshness_request
             .observe_headers(&self.parent_freshness, response.headers());
-        Ok((request_id, response))
+        Ok(BlobRangeResponse {
+            request_id,
+            response,
+            _permit: Some(permit),
+        })
     }
 }
 
-#[derive(Debug)]
-struct SequentialResponse {
-    response: Response,
-    request_id: String,
-    buffered: Bytes,
-}
-
 #[derive(Debug, Default)]
 struct RemoteBlobState {
     cursor: u64,
-    sequential_response: Option<SequentialResponse>,
 }
 
 /// Seekable Cloud blob handle over HTTP Range.
+///
+/// Handles read the table version used to fetch their descriptor or probe their size.
 #[derive(Debug)]
 pub struct RemoteBlobFile {
     requester: Arc<dyn BlobRangeRequester>,
     state: Mutex<RemoteBlobState>,
     closed: AtomicBool,
     size: u64,
+    version: Option<u64>,
 }
 
 impl RemoteBlobFile {
-    fn new(requester: Arc<dyn BlobRangeRequester>, size: u64) -> Self {
+    fn new(requester: Arc<dyn BlobRangeRequester>, size: u64, version: Option<u64>) -> Self {
         Self {
             requester,
             state: Mutex::new(RemoteBlobState::default()),
             closed: AtomicBool::new(false),
             size,
+            version,
         }
     }
 
     /// Close the handle without waiting for an in-flight read.
     pub(crate) async fn close(&self) -> lance_core::Result<()> {
         self.closed.store(true, Ordering::Release);
-        // Drop a retained response when the state lock is immediately available.
-        // A reader holding the lock drops it instead once it observes the flag.
-        if let Ok(mut state) = self.state.try_lock() {
-            state.sequential_response = None;
-        }
         Ok(())
     }
 
@@ -173,9 +206,13 @@ impl RemoteBlobFile {
             return Ok(Bytes::new());
         }
         let range_header = format!("bytes={}-{}", range.start, range.end - 1);
-        let (request_id, response) = self
+        let BlobRangeResponse {
+            request_id,
+            response,
+            _permit,
+        } = self
             .requester
-            .request_range(&range_header, RangeRequestMode::DataRead)
+            .request_range(&range_header, RangeRequestMode::DataRead, self.version)
             .await
             .map_err(remote_blob_error)?;
         self.ensure_open()?;
@@ -240,62 +277,20 @@ impl RemoteBlobFile {
             return Ok(Bytes::new());
         }
 
-        // Remove the retained response from shared state before awaiting. Failed or
-        // cancelled reads leave the committed cursor unchanged and force the next
-        // read to open a fresh response.
-        let mut sequential_response = state.sequential_response.take();
-        let mut cursor = state.cursor;
-        let mut output = BytesMut::with_capacity(target_len);
-        while output.len() < target_len {
-            if sequential_response.is_none() {
-                let range_header = format!("bytes={cursor}-");
-                let (request_id, response) = self
-                    .requester
-                    .request_range(&range_header, RangeRequestMode::DataRead)
-                    .await
-                    .map_err(remote_blob_error)?;
-                self.ensure_open()?;
-                validate_partial_response(&response, cursor..self.size, self.size)?;
-                sequential_response = Some(SequentialResponse {
-                    response,
-                    request_id,
-                    buffered: Bytes::new(),
-                });
-            }
-
-            let needed = target_len - output.len();
-            let active = sequential_response.as_mut().unwrap();
-            if !active.buffered.is_empty() {
-                let take = needed.min(active.buffered.len());
-                output.extend_from_slice(&active.buffered.split_to(take));
-                cursor += take as u64;
-                continue;
-            }
-            let chunk = active
-                .response
-                .chunk()
-                .await
-                .err_to_http(active.request_id.clone())
-                .map_err(remote_blob_error)?;
-            self.ensure_open()?;
-            let chunk = chunk.ok_or_else(|| {
-                remote_blob_error("response ended before the requested blob range")
-            })?;
-            active.buffered = chunk;
-        }
+        // A partial read must release its HTTP response before returning. Keeping
+        // it open across calls would hold a connection slot indefinitely and
+        // prevent other handles from making progress.
+        let end = state.cursor + target_len as u64;
+        let bytes = self.read_range(state.cursor..end).await?;
         self.ensure_open()?;
-        state.cursor = cursor;
-        if state.cursor < self.size {
-            state.sequential_response = sequential_response;
-        }
-        Ok(output.freeze())
+        state.cursor = end;
+        Ok(bytes)
     }
 
     pub(crate) async fn seek(&self, new_cursor: u64) -> lance_core::Result<()> {
         self.ensure_open()?;
         let mut state = self.state.lock().await;
         self.ensure_open()?;
-        state.sequential_response = None;
         state.cursor = new_cursor;
         Ok(())
     }
@@ -363,6 +358,34 @@ fn validate_partial_response(
 }
 
 impl<S: HttpSend> RemoteTable<S> {
+    async fn reject_list_blob_path_if_known(&self, column: &str) -> Result<()> {
+        // A dotted path needs client-side validation because the server may
+        // report an internal type mismatch when the path crosses a list. Use
+        // an already cached schema for top-level list fields as well.
+        if column.contains('.') || self.schema_cache.try_get().is_some() {
+            let schema = self.schema().await?;
+            crate::blob::reject_list_blob_column(schema.as_ref(), column)?;
+        }
+        Ok(())
+    }
+
+    async fn explain_list_blob_bad_request(&self, column: &str, error: Error) -> Error {
+        let is_bad_request = match &error {
+            Error::Http {
+                status_code: Some(code),
+                ..
+            } => *code == StatusCode::BAD_REQUEST || *code == StatusCode::UNPROCESSABLE_ENTITY,
+            _ => false,
+        };
+        if is_bad_request
+            && let Ok(schema) = self.schema().await
+            && let Err(list_error) = crate::blob::reject_list_blob_column(schema.as_ref(), column)
+        {
+            return list_error;
+        }
+        error
+    }
+
     /// Blob v2 columns are marked in field metadata, which `describe` returns. Reading
     /// them from the cached schema needs no route of its own and no version gate.
     pub(super) async fn blob_columns_impl(&self) -> Result<Vec<String>> {
@@ -375,13 +398,14 @@ impl<S: HttpSend> RemoteTable<S> {
         column: &str,
         row_ids: &[u64],
     ) -> Result<LargeBinaryArray> {
+        self.reject_list_blob_path_if_known(column).await?;
         // Empty requests do not require blob-route support.
         if row_ids.is_empty() {
             return Ok(LargeBinaryArray::from(Vec::<Option<&[u8]>>::new()));
         }
         if !self.server_version.support_blobs() {
             return Err(Error::NotSupported {
-                message: "fetch_blobs is not supported on this LanceDB Cloud server".into(),
+                message: "fetch_blobs is not supported by this LanceDB server.".into(),
             });
         }
         let mut read_snapshot = self.snapshot_read_state().await;
@@ -454,7 +478,10 @@ impl<S: HttpSend> RemoteTable<S> {
         let (request_id, response) = self
             .send_with_freshness(request, true, read_snapshot.freshness)
             .await?;
-        let mut stream = self.read_arrow_response(&request_id, response).await?;
+        let mut stream = match self.read_arrow_response(&request_id, response).await {
+            Ok(stream) => stream,
+            Err(error) => return Err(self.explain_list_blob_bad_request(column, error).await),
+        };
 
         let mut blob_chunks: Vec<Arc<dyn Array>> = Vec::new();
         while let Some(batch) = stream.try_next().await? {
@@ -513,55 +540,284 @@ impl<S: HttpSend> RemoteTable<S> {
 
     /// Open seekable handles for `row_ids`.
     ///
-    /// Each non-null row is probed once to determine its size.
+    /// Descriptors for the requested rows come from one `_rowid` take query per
+    /// [`MAX_FETCH_BLOBS_ROW_IDS`] unique row ids, all read at one exact table
+    /// version. Each handle is sized from its descriptor and reads that
+    /// version. Only external blobs whose descriptor records no size are
+    /// probed, because their length is the whole object's.
     pub(super) async fn fetch_blob_files_impl(
         &self,
         column: &str,
         row_ids: &[u64],
     ) -> Result<Vec<Option<BlobFile>>> {
+        self.reject_list_blob_path_if_known(column).await?;
         // Empty requests do not require blob-route support.
         if row_ids.is_empty() {
             return Ok(Vec::new());
         }
-        if !self.server_version.support_blobs() {
-            return Err(Error::NotSupported {
-                message: "fetch_blob_files requires LanceDB Cloud server 0.5.0 or newer".into(),
-            });
-        }
+        self.ensure_blob_files_supported()?;
 
-        let read_snapshot = self.snapshot_read_state().await;
-        self.fetch_blob_files_with_snapshot(column, row_ids, read_snapshot)
+        let mut read_snapshot = self.snapshot_read_state().await;
+        let sizes = match self
+            .take_blob_descriptor_sizes(column, row_ids, &mut read_snapshot)
             .await
+        {
+            Ok(sizes) => sizes,
+            Err(error) => return Err(self.explain_list_blob_bad_request(column, error).await),
+        };
+        let version = read_snapshot.version;
+
+        let mut files = Vec::with_capacity(row_ids.len());
+        let mut probe_indices = Vec::new();
+        let mut probe_requesters = Vec::new();
+        for (index, row_id) in row_ids.iter().enumerate() {
+            match sizes[row_id] {
+                DescriptorSize::Null => files.push(None),
+                DescriptorSize::Known(size) => {
+                    let requester = self.blob_range_requester(column, *row_id, &read_snapshot);
+                    files.push(Some(RemoteBlobFile::new(requester, size, version).into()));
+                }
+                DescriptorSize::Unresolved => {
+                    files.push(None);
+                    probe_indices.push(index);
+                    probe_requesters.push(self.blob_range_requester(
+                        column,
+                        *row_id,
+                        &read_snapshot,
+                    ));
+                }
+            }
+        }
+        let probed = match probe_blob_files(probe_requesters).await {
+            Ok(files) => files,
+            Err(error) => return Err(self.explain_list_blob_bad_request(column, error).await),
+        };
+        for (index, file) in probe_indices.into_iter().zip(probed) {
+            files[index] = file;
+        }
+        Ok(files)
     }
 
+    /// Probe each row once for its size.
+    ///
+    /// Serves callers that must not issue a query, such as a single blob that
+    /// the whole-byte route could not return.
     async fn fetch_blob_files_with_snapshot(
         &self,
         column: &str,
         row_ids: &[u64],
         read_snapshot: ReadSnapshot,
     ) -> Result<Vec<Option<BlobFile>>> {
-        let encoded_column = urlencoding::encode(column);
         let requesters = row_ids
             .iter()
-            .map(|row_id| {
-                let path = format!(
-                    "/v1/table/{}/blob/{encoded_column}/{row_id}/bytes",
-                    self.identifier
-                );
-                let requester: Arc<dyn BlobRangeRequester> = Arc::new(TableBlobRangeRequester {
-                    client: self.client.clone(),
-                    path,
-                    version: read_snapshot.version,
-                    branch: self.branch.clone(),
-                    freshness: Arc::new(std::sync::Mutex::new(read_snapshot.freshness_state)),
-                    parent_freshness: self.freshness.clone(),
-                    parent_freshness_request: read_snapshot.freshness,
-                    read_consistency_interval: self.client.read_consistency_interval,
-                });
-                requester
-            })
+            .map(|row_id| self.blob_range_requester(column, *row_id, &read_snapshot))
             .collect();
-        probe_blob_files(requesters).await
+        match probe_blob_files(requesters).await {
+            Ok(files) => Ok(files),
+            Err(error) => Err(self.explain_list_blob_bad_request(column, error).await),
+        }
+    }
+
+    /// Read the blob descriptor of every unique row in `row_ids`.
+    ///
+    /// Pins `read_snapshot` to the version the first take read, so later chunks
+    /// and the returned handles all see one table version.
+    async fn take_blob_descriptor_sizes(
+        &self,
+        column: &str,
+        row_ids: &[u64],
+        read_snapshot: &mut ReadSnapshot,
+    ) -> Result<HashMap<u64, DescriptorSize>> {
+        let mut unique_row_ids = row_ids.to_vec();
+        unique_row_ids.sort_unstable();
+        unique_row_ids.dedup();
+
+        let mut sizes = HashMap::with_capacity(unique_row_ids.len());
+        for chunk in unique_row_ids.chunks(MAX_FETCH_BLOBS_ROW_IDS) {
+            let (mut chunk_sizes, response_version) = self
+                .take_blob_descriptor_chunk(column, chunk, *read_snapshot)
+                .await?;
+            if read_snapshot.version.is_none() {
+                match response_version {
+                    Some(version) => read_snapshot.version = Some(version),
+                    None => {
+                        // A server that does not report its read version may have read
+                        // an older version than a describe now returns, so the take is
+                        // repeated at the described version.
+                        read_snapshot.version =
+                            Some(self.describe_read_snapshot(*read_snapshot).await?.version);
+                        (chunk_sizes, _) = self
+                            .take_blob_descriptor_chunk(column, chunk, *read_snapshot)
+                            .await?;
+                    }
+                }
+            }
+            sizes.extend(chunk_sizes);
+        }
+        let found = unique_row_ids
+            .iter()
+            .filter(|row_id| sizes.contains_key(row_id))
+            .count();
+        if found < unique_row_ids.len() {
+            return Err(Error::InvalidInput {
+                message: format!(
+                    "blob read for column '{column}' requested {} row ids but only {found} exist \
+                     in the table; pass row ids collected from this table",
+                    unique_row_ids.len()
+                ),
+            });
+        }
+        Ok(sizes)
+    }
+
+    /// Take one chunk of descriptors, also returning the version the server
+    /// reported reading.
+    async fn take_blob_descriptor_chunk(
+        &self,
+        column: &str,
+        row_ids: &[u64],
+        read_snapshot: ReadSnapshot,
+    ) -> Result<(HashMap<u64, DescriptorSize>, Option<u64>)> {
+        let query = AnyQuery::Query(QueryRequest {
+            filter: Some(QueryFilter::Datafusion(
+                col(ROW_ID).in_list(row_ids.iter().map(|id| lit(*id)).collect(), false),
+            )),
+            select: Select::columns(&[column]),
+            with_row_id: true,
+            // Row ids name base-table rows, and the MemWAL scanner has no stable `_rowid`.
+            use_memwal: Some(false),
+            ..Default::default()
+        });
+        let body = self
+            .prepare_query_bodies(&query, read_snapshot.version)?
+            .pop()
+            .expect("a plain query has one body");
+        let request = self
+            .client
+            .post(&format!("/v1/table/{}/query/", self.identifier))
+            .json(&body);
+        let (request_id, response) = self
+            .send_with_freshness(request, true, read_snapshot.freshness)
+            .await?;
+        let response_version = response
+            .headers()
+            .get(&VERSION_HEADER)
+            .and_then(|value| value.to_str().ok())
+            .and_then(|value| value.parse().ok());
+        let mut stream = self.read_arrow_response(&request_id, response).await?;
+
+        let mut sizes = HashMap::with_capacity(row_ids.len());
+        let invalid_response = |message: String| Error::Http {
+            source: message.into(),
+            request_id: request_id.clone(),
+            status_code: None,
+        };
+        while let Some(batch) = stream.try_next().await? {
+            let taken_row_ids = batch
+                .column_by_name(ROW_ID)
+                .and_then(|array| array.as_any().downcast_ref::<UInt64Array>())
+                .ok_or_else(|| {
+                    invalid_response(format!("blob descriptor take is missing UInt64 '{ROW_ID}'"))
+                })?;
+            let values = batch.column_by_name(column).ok_or_else(|| {
+                invalid_response(format!("blob descriptor take is missing '{column}'"))
+            })?;
+            // An uncached top-level list can reach the descriptor query. Explain
+            // that unsupported path before the generic descriptor type check.
+            if matches!(
+                values.data_type(),
+                DataType::List(_) | DataType::LargeList(_) | DataType::FixedSizeList(_, _)
+            ) && let Ok(schema) = self.schema().await
+            {
+                crate::blob::reject_list_blob_column(schema.as_ref(), column)?;
+            }
+            // Only blob v2 columns read back as descriptors.
+            let descriptors = values
+                .as_any()
+                .downcast_ref::<StructArray>()
+                .and_then(BlobDescriptorSizes::try_new)
+                .ok_or_else(|| Error::InvalidInput {
+                    message: format!("column '{column}' is not a blob column"),
+                })?;
+            for (index, row_id) in taken_row_ids.values().iter().enumerate() {
+                sizes.insert(*row_id, descriptors.size(index)?);
+            }
+        }
+        Ok((sizes, response_version))
+    }
+
+    fn ensure_blob_files_supported(&self) -> Result<()> {
+        if self.server_version.support_blobs() {
+            Ok(())
+        } else {
+            Err(Error::NotSupported {
+                message: "fetch_blob_files requires LanceDB server 0.5.0 or newer.".into(),
+            })
+        }
+    }
+
+    fn blob_range_requester(
+        &self,
+        column: &str,
+        row_id: u64,
+        read_snapshot: &ReadSnapshot,
+    ) -> Arc<dyn BlobRangeRequester> {
+        let path = format!(
+            "/v1/table/{}/blob/{}/{row_id}/bytes",
+            self.identifier,
+            urlencoding::encode(column)
+        );
+        Arc::new(TableBlobRangeRequester {
+            client: self.client.clone(),
+            path,
+            version: read_snapshot.version,
+            branch: self.branch.clone(),
+            freshness: Arc::new(std::sync::Mutex::new(read_snapshot.freshness_state)),
+            parent_freshness: self.freshness.clone(),
+            parent_freshness_request: read_snapshot.freshness,
+            read_consistency_interval: self.client.read_consistency_interval,
+        })
+    }
+}
+
+/// What a blob v2 descriptor says about a row's size.
+#[derive(Debug, Clone, Copy)]
+enum DescriptorSize {
+    Null,
+    Known(u64),
+    /// An external blob that recorded no size. Its length is the whole
+    /// object's, which only the server can resolve.
+    Unresolved,
+}
+
+/// The `kind` and `size` children of a blob v2 descriptor array.
+struct BlobDescriptorSizes<'a> {
+    descriptors: &'a StructArray,
+    kinds: &'a UInt8Array,
+    sizes: &'a UInt64Array,
+}
+
+impl<'a> BlobDescriptorSizes<'a> {
+    fn try_new(descriptors: &'a StructArray) -> Option<Self> {
+        let child = |name: &str| descriptors.column_by_name(name);
+        Some(Self {
+            descriptors,
+            kinds: child("kind")?.as_any().downcast_ref::<UInt8Array>()?,
+            sizes: child("size")?.as_any().downcast_ref::<UInt64Array>()?,
+        })
+    }
+
+    fn size(&self, index: usize) -> Result<DescriptorSize> {
+        if self.descriptors.is_null(index) || self.kinds.is_null(index) {
+            return Ok(DescriptorSize::Null);
+        }
+        let kind = BlobKind::try_from(self.kinds.value(index))?;
+        let size = self.sizes.value(index);
+        Ok(if kind == BlobKind::External && size == 0 {
+            DescriptorSize::Unresolved
+        } else {
+            DescriptorSize::Known(size)
+        })
     }
 }
 
@@ -579,12 +835,29 @@ async fn probe_blob_files(
 
 const BLOB_REQUEST_CONCURRENCY: usize = 8;
 
+fn probe_blob_version(response: &Response, request_id: &str) -> Result<u64> {
+    response
+        .headers()
+        .get(&VERSION_HEADER)
+        .and_then(|value| value.to_str().ok())
+        .and_then(|value| value.parse().ok())
+        .ok_or_else(|| Error::Http {
+            source: "blob size probe returned a missing or invalid x-lancedb-version header".into(),
+            request_id: request_id.to_string(),
+            status_code: Some(response.status()),
+        })
+}
+
 /// Probe one blob's size.
 ///
 /// `204` represents null. `416` with `bytes */0` represents an empty blob.
 async fn probe_blob_file(requester: Arc<dyn BlobRangeRequester>) -> Result<Option<BlobFile>> {
-    let (request_id, response) = requester
-        .request_range("bytes=0-0", RangeRequestMode::SizeProbe)
+    let BlobRangeResponse {
+        request_id,
+        response,
+        _permit,
+    } = requester
+        .request_range("bytes=0-0", RangeRequestMode::SizeProbe, None)
         .await?;
     match response.status() {
         StatusCode::NO_CONTENT => {
@@ -617,7 +890,8 @@ async fn probe_blob_file(requester: Arc<dyn BlobRangeRequester>) -> Result<Optio
                     });
                 }
             }
-            Ok(Some(RemoteBlobFile::new(requester, 0).into()))
+            // An empty handle never makes a data range request, so no version is needed.
+            Ok(Some(RemoteBlobFile::new(requester, 0, None).into()))
         }
         StatusCode::PARTIAL_CONTENT => {
             let size = response
@@ -633,6 +907,7 @@ async fn probe_blob_file(requester: Arc<dyn BlobRangeRequester>) -> Result<Optio
                     request_id: request_id.clone(),
                     status_code: Some(StatusCode::PARTIAL_CONTENT),
                 })?;
+            let version = probe_blob_version(&response, &request_id)?;
             let probe_body = response.bytes().await.err_to_http(request_id.clone())?;
             if probe_body.len() != 1 {
                 return Err(Error::Http {
@@ -645,7 +920,9 @@ async fn probe_blob_file(requester: Arc<dyn BlobRangeRequester>) -> Result<Optio
                     status_code: Some(StatusCode::PARTIAL_CONTENT),
                 });
             }
-            Ok(Some(RemoteBlobFile::new(requester, size).into()))
+            Ok(Some(
+                RemoteBlobFile::new(requester, size, Some(version)).into(),
+            ))
         }
         status => Err(Error::Http {
             source: format!("blob size probe expected HTTP 206 Partial Content, got {status}")
@@ -658,13 +935,15 @@ async fn probe_blob_file(requester: Arc<dyn BlobRangeRequester>) -> Result<Optio
 
 #[cfg(test)]
 mod tests {
-    use std::sync::atomic::{AtomicUsize, Ordering};
+    use std::sync::atomic::{AtomicBool, AtomicUsize, Ordering};
     use std::sync::{Arc, Mutex as StdMutex};
     use std::time::Duration;
 
     use reqwest::Request;
     use semver::Version;
 
+    use arrow_array::RecordBatch;
+
     use super::*;
 
     const PAYLOAD: &[u8] = b"0123456789abcdefghijklmnopqrstuvwxyz";
@@ -708,6 +987,7 @@ mod tests {
                 header::CONTENT_RANGE,
                 format!("bytes {start}-{end}/{}", payload.len()),
             )
+            .header(VERSION_HEADER, "5")
             .body(payload[start..=end].to_vec())
             .unwrap()
     }
@@ -747,10 +1027,7 @@ mod tests {
         let requests = Arc::new(StdMutex::new(Vec::new()));
         let table = mock_remote_blob_table(requests.clone());
 
-        let mut files = table
-            .fetch_blob_files_impl("image", &[10, 20])
-            .await
-            .unwrap();
+        let mut files = table.probe_files("image", &[10, 20]).await.unwrap();
 
         assert_eq!(files.len(), 2);
         assert!(files[1].is_none());
@@ -762,12 +1039,368 @@ mod tests {
         );
     }
 
+    /// Descriptors as a remote query returns them, one `(kind, size)` per row.
+    fn descriptors(rows: &[Option<(BlobKind, u64)>]) -> StructArray {
+        let kinds = rows
+            .iter()
+            .map(|row| row.map_or(0, |(kind, _)| kind as u8))
+            .collect::<UInt8Array>();
+        let sizes = rows
+            .iter()
+            .map(|row| row.map_or(0, |(_, size)| size))
+            .collect::<UInt64Array>();
+        let len = rows.len();
+        StructArray::new(
+            lance_core::datatypes::BLOB_V2_DESC_FIELDS.clone(),
+            vec![
+                Arc::new(kinds),
+                Arc::new(UInt64Array::from(vec![0; len])),
+                Arc::new(sizes),
+                Arc::new(arrow_array::UInt32Array::from(vec![0; len])),
+                Arc::new(arrow_array::StringArray::from(vec![""; len])),
+            ],
+            Some(rows.iter().map(Option::is_some).collect()),
+        )
+    }
+
+    impl<S: HttpSend> RemoteTable<S> {
+        /// Open handles through the per-row size probe alone.
+        async fn probe_files(
+            &self,
+            column: &str,
+            row_ids: &[u64],
+        ) -> Result<Vec<Option<BlobFile>>> {
+            let read_snapshot = self.snapshot_read_state().await;
+            self.fetch_blob_files_with_snapshot(column, row_ids, read_snapshot)
+                .await
+        }
+    }
+
+    /// A request the take mock received.
+    #[derive(Debug, Clone, PartialEq)]
+    enum TakeMockRequest {
+        Query {
+            row_ids: Vec<u64>,
+            version: Option<u64>,
+        },
+        Describe,
+        Range {
+            row_id: u64,
+            range: String,
+            version: Option<String>,
+        },
+    }
+
+    /// Row ids named by a `_rowid IN (...)` filter.
+    fn filter_row_ids(filter: &str) -> Vec<u64> {
+        let list = filter.split_once('(').unwrap().1.trim_end_matches(')');
+        list.split(',')
+            .map(|id| id.trim().parse().unwrap())
+            .collect()
+    }
+
+    /// A table whose query route answers `_rowid` takes from `rows` and whose
+    /// byte routes serve [`PAYLOAD`]. Takes return rows in reverse order, and
+    /// report `query_version` in `x-lancedb-version` when it is set.
+    fn mock_take_blob_table(
+        rows: Vec<(u64, Option<(BlobKind, u64)>)>,
+        query_version: Option<&'static str>,
+    ) -> (
+        RemoteTable<crate::remote::client::test_utils::MockSender>,
+        Arc<StdMutex<Vec<TakeMockRequest>>>,
+    ) {
+        let requests = Arc::new(StdMutex::new(Vec::new()));
+        let captured = requests.clone();
+        let table = RemoteTable::new_mock(
+            "my_table".to_string(),
+            move |request| {
+                let path = request.url().path().to_string();
+                if path == "/v1/table/my_table/query/" {
+                    let body: serde_json::Value =
+                        serde_json::from_slice(request.body().unwrap().as_bytes().unwrap())
+                            .unwrap();
+                    assert_eq!(body["columns"], serde_json::json!(["image"]));
+                    assert_eq!(body["with_row_id"], serde_json::json!(true));
+                    assert_eq!(body["use_memwal"], serde_json::json!(false));
+                    let requested = filter_row_ids(body["filter"].as_str().unwrap());
+                    captured.lock().unwrap().push(TakeMockRequest::Query {
+                        row_ids: requested.clone(),
+                        version: body["version"].as_u64(),
+                    });
+                    let taken = rows
+                        .iter()
+                        .rev()
+                        .filter(|(row_id, _)| requested.contains(row_id))
+                        .collect::<Vec<_>>();
+                    let batch = RecordBatch::try_from_iter([
+                        (
+                            "image",
+                            Arc::new(descriptors(
+                                &taken.iter().map(|(_, row)| *row).collect::<Vec<_>>(),
+                            )) as Arc<dyn Array>,
+                        ),
+                        (
+                            ROW_ID,
+                            Arc::new(UInt64Array::from_iter_values(
+                                taken.iter().map(|(row_id, _)| *row_id),
+                            )) as Arc<dyn Array>,
+                        ),
+                    ])
+                    .unwrap();
+                    let mut body = Vec::new();
+                    let mut writer =
+                        arrow_ipc::writer::FileWriter::try_new(&mut body, &batch.schema()).unwrap();
+                    writer.write(&batch).unwrap();
+                    writer.finish().unwrap();
+                    drop(writer);
+                    let mut response = http::Response::builder().status(200);
+                    if let Some(version) = query_version {
+                        response = response.header(VERSION_HEADER, version);
+                    }
+                    return response.body(body).unwrap();
+                }
+                if path == "/v1/table/my_table/describe/" {
+                    captured.lock().unwrap().push(TakeMockRequest::Describe);
+                    return http::Response::builder()
+                        .status(200)
+                        .body(r#"{"version":9,"schema":{"fields":[]}}"#.as_bytes().to_vec())
+                        .unwrap();
+                }
+                let row_id = path
+                    .strip_prefix("/v1/table/my_table/blob/image/")
+                    .and_then(|rest| rest.strip_suffix("/bytes"))
+                    .unwrap_or_else(|| panic!("unexpected path: {path}"))
+                    .parse()
+                    .unwrap();
+                captured.lock().unwrap().push(TakeMockRequest::Range {
+                    row_id,
+                    range: request.headers()[header::RANGE]
+                        .to_str()
+                        .unwrap()
+                        .to_string(),
+                    version: request
+                        .url()
+                        .query_pairs()
+                        .find(|(name, _)| name == "version")
+                        .map(|(_, value)| value.into_owned()),
+                });
+                range_response(&request, PAYLOAD)
+            },
+            Some(Version::new(0, 5, 0)),
+        );
+        (table, requests)
+    }
+
+    #[tokio::test]
+    async fn remote_blob_files_are_sized_from_one_descriptor_take() {
+        let payload_size = PAYLOAD.len() as u64;
+        let (table, requests) = mock_take_blob_table(
+            vec![
+                (10, Some((BlobKind::Inline, payload_size))),
+                (20, None),
+                (30, Some((BlobKind::Inline, 0))),
+                (40, Some((BlobKind::Packed, payload_size))),
+                (50, Some((BlobKind::Dedicated, payload_size))),
+                (60, Some((BlobKind::External, 0))),
+            ],
+            Some("7"),
+        );
+
+        // Reordered and duplicated row ids come back in request order.
+        let files = table
+            .fetch_blob_files_impl("image", &[60, 10, 20, 30, 40, 50, 10])
+            .await
+            .unwrap();
+
+        let sizes = files
+            .iter()
+            .map(|file| file.as_ref().map(BlobFile::size))
+            .collect::<Vec<_>>();
+        assert_eq!(
+            sizes,
+            [
+                Some(payload_size),
+                Some(payload_size),
+                None,
+                Some(0),
+                Some(payload_size),
+                Some(payload_size),
+                Some(payload_size),
+            ]
+        );
+        // One take, then a probe for the external blob that recorded no size.
+        assert_eq!(
+            requests.lock().unwrap().as_slice(),
+            [
+                TakeMockRequest::Query {
+                    row_ids: vec![10, 20, 30, 40, 50, 60],
+                    version: None,
+                },
+                TakeMockRequest::Range {
+                    row_id: 60,
+                    range: "bytes=0-0".into(),
+                    version: Some("7".into()),
+                },
+            ]
+        );
+
+        // Sized handles read the version the take read.
+        assert_eq!(
+            files[1].as_ref().unwrap().read_range(5..12).await.unwrap(),
+            &PAYLOAD[5..12]
+        );
+        assert!(files[3].as_ref().unwrap().read().await.unwrap().is_empty());
+        assert_eq!(
+            requests.lock().unwrap().last().unwrap(),
+            &TakeMockRequest::Range {
+                row_id: 10,
+                range: "bytes=5-11".into(),
+                version: Some("7".into()),
+            }
+        );
+    }
+
+    #[tokio::test]
+    async fn remote_blob_files_take_every_chunk_at_the_first_version() {
+        let row_ids = (0..MAX_FETCH_BLOBS_ROW_IDS as u64 + 1).collect::<Vec<_>>();
+        let (table, requests) = mock_take_blob_table(
+            row_ids.iter().map(|row_id| (*row_id, None)).collect(),
+            Some("7"),
+        );
+
+        let files = table
+            .fetch_blob_files_impl("image", &row_ids)
+            .await
+            .unwrap();
+
+        assert_eq!(files.len(), row_ids.len());
+        let versions = requests
+            .lock()
+            .unwrap()
+            .iter()
+            .map(|request| match request {
+                TakeMockRequest::Query { row_ids, version } => (row_ids.len(), *version),
+                other => panic!("unexpected request: {other:?}"),
+            })
+            .collect::<Vec<_>>();
+        assert_eq!(versions, [(MAX_FETCH_BLOBS_ROW_IDS, None), (1, Some(7))]);
+    }
+
+    #[tokio::test]
+    async fn remote_blob_files_retake_at_the_described_version_when_a_take_does_not_report_it() {
+        let payload_size = PAYLOAD.len() as u64;
+        let (table, requests) =
+            mock_take_blob_table(vec![(10, Some((BlobKind::Inline, payload_size)))], None);
+
+        let file = table
+            .fetch_blob_files_impl("image", &[10])
+            .await
+            .unwrap()
+            .pop()
+            .flatten()
+            .unwrap();
+        file.read_range(0..4).await.unwrap();
+
+        // The unversioned take is repeated at the described version.
+        assert_eq!(
+            requests.lock().unwrap().as_slice(),
+            [
+                TakeMockRequest::Query {
+                    row_ids: vec![10],
+                    version: None,
+                },
+                TakeMockRequest::Describe,
+                TakeMockRequest::Query {
+                    row_ids: vec![10],
+                    version: Some(9),
+                },
+                TakeMockRequest::Range {
+                    row_id: 10,
+                    range: "bytes=0-3".into(),
+                    version: Some("9".into()),
+                },
+            ]
+        );
+    }
+
+    #[tokio::test]
+    async fn remote_blob_files_take_at_the_checked_out_version() {
+        let payload_size = PAYLOAD.len() as u64;
+        let (table, requests) = mock_take_blob_table(
+            vec![(10, Some((BlobKind::Inline, payload_size)))],
+            Some("7"),
+        );
+        table.checkout(9).await.unwrap();
+        requests.lock().unwrap().clear();
+
+        table.fetch_blob_files_impl("image", &[10]).await.unwrap();
+
+        assert_eq!(
+            requests.lock().unwrap().as_slice(),
+            [TakeMockRequest::Query {
+                row_ids: vec![10],
+                version: Some(9),
+            }]
+        );
+    }
+
+    #[tokio::test]
+    async fn remote_blob_files_reject_row_ids_missing_from_the_take() {
+        let (table, requests) = mock_take_blob_table(vec![(10, None)], Some("7"));
+
+        let error = table
+            .fetch_blob_files_impl("image", &[10, 99])
+            .await
+            .unwrap_err();
+
+        assert!(matches!(error, Error::InvalidInput { .. }), "{error}");
+        assert!(error.to_string().contains("only 1 exist"), "{error}");
+        assert_eq!(requests.lock().unwrap().len(), 1);
+    }
+
+    #[tokio::test]
+    async fn remote_blob_files_reject_a_non_blob_column() {
+        let table = RemoteTable::new_mock(
+            "my_table".to_string(),
+            |request| {
+                assert_eq!(request.url().path(), "/v1/table/my_table/query/");
+                let batch = RecordBatch::try_from_iter([
+                    (
+                        "id",
+                        Arc::new(arrow_array::Int64Array::from(vec![1])) as Arc<dyn Array>,
+                    ),
+                    (
+                        ROW_ID,
+                        Arc::new(UInt64Array::from(vec![10])) as Arc<dyn Array>,
+                    ),
+                ])
+                .unwrap();
+                let mut body = Vec::new();
+                let mut writer =
+                    arrow_ipc::writer::FileWriter::try_new(&mut body, &batch.schema()).unwrap();
+                writer.write(&batch).unwrap();
+                writer.finish().unwrap();
+                drop(writer);
+                http::Response::builder().status(200).body(body).unwrap()
+            },
+            Some(Version::new(0, 5, 0)),
+        );
+
+        let error = table.fetch_blob_files_impl("id", &[10]).await.unwrap_err();
+
+        assert!(matches!(error, Error::InvalidInput { .. }), "{error}");
+        assert!(
+            error.to_string().contains("'id' is not a blob column"),
+            "{error}"
+        );
+    }
+
     #[tokio::test]
     async fn remote_blob_file_reads_the_requested_range() {
         let requests = Arc::new(StdMutex::new(Vec::new()));
         let table = mock_remote_blob_table(requests.clone());
         let file = table
-            .fetch_blob_files_impl("image", &[10])
+            .probe_files("image", &[10])
             .await
             .unwrap()
             .pop()
@@ -778,6 +1411,179 @@ mod tests {
         assert!(requests.lock().unwrap().contains(&"bytes=5-11".to_string()));
     }
 
+    #[tokio::test(flavor = "multi_thread", worker_threads = 8)]
+    async fn blob_requests_are_bounded_across_handles() {
+        let in_flight = Arc::new(AtomicUsize::new(0));
+        let max_in_flight = Arc::new(AtomicUsize::new(0));
+        let table = RemoteTable::new_mock_with_version_and_config(
+            "my_table".to_string(),
+            {
+                let in_flight = in_flight.clone();
+                let max_in_flight = max_in_flight.clone();
+                move |request| {
+                    let range = request.headers().get(header::RANGE).unwrap();
+                    if range != "bytes=0-0" {
+                        let now = in_flight.fetch_add(1, Ordering::SeqCst) + 1;
+                        max_in_flight.fetch_max(now, Ordering::SeqCst);
+                        std::thread::sleep(Duration::from_millis(20));
+                        in_flight.fetch_sub(1, Ordering::SeqCst);
+                    }
+                    range_response(&request, PAYLOAD)
+                }
+            },
+            Some(Version::new(0, 5, 0)),
+            crate::remote::ClientConfig {
+                blob_request_concurrency: Some(3),
+                ..Default::default()
+            },
+        );
+        let files = table
+            .probe_files("image", &(0..16).collect::<Vec<_>>())
+            .await
+            .unwrap();
+        let start = Arc::new(tokio::sync::Barrier::new(files.len() + 1));
+        let reads = files.into_iter().map(|file| {
+            let start = start.clone();
+            tokio::spawn(async move {
+                start.wait().await;
+                file.unwrap().read_range(1..2).await
+            })
+        });
+        let reads = reads.collect::<Vec<_>>();
+        start.wait().await;
+        for result in futures::future::join_all(reads).await {
+            assert_eq!(result.unwrap().unwrap(), &PAYLOAD[1..2]);
+        }
+        let max = max_in_flight.load(Ordering::SeqCst);
+        assert!(max > 1, "reads never overlapped");
+        assert!(
+            max <= 3,
+            "{max} blob requests exceeded the configured limit"
+        );
+    }
+
+    #[tokio::test]
+    async fn partial_reads_release_slots_for_other_handles() {
+        let table = RemoteTable::new_mock_with_version_and_config(
+            "my_table".to_string(),
+            |request| range_response(&request, PAYLOAD),
+            Some(Version::new(0, 5, 0)),
+            crate::remote::ClientConfig {
+                blob_request_concurrency: Some(1),
+                ..Default::default()
+            },
+        );
+        let files = table.probe_files("image", &[10, 20]).await.unwrap();
+        let reads = files
+            .iter()
+            .map(|file| file.as_ref().unwrap().read_up_to(1));
+        let results =
+            tokio::time::timeout(Duration::from_secs(1), futures::future::join_all(reads))
+                .await
+                .expect("an idle partial response held the only connection slot");
+        for result in results {
+            assert_eq!(result.unwrap(), &PAYLOAD[..1]);
+        }
+    }
+
+    #[tokio::test]
+    async fn blob_retry_error_includes_last_status() {
+        let table = RemoteTable::new_mock_with_version_and_config(
+            "my_table".to_string(),
+            |request| {
+                if request.headers().get(header::RANGE).unwrap() == "bytes=0-0" {
+                    range_response(&request, PAYLOAD)
+                } else {
+                    http::Response::builder()
+                        .status(StatusCode::SERVICE_UNAVAILABLE)
+                        .body(Vec::new())
+                        .unwrap()
+                }
+            },
+            Some(Version::new(0, 5, 0)),
+            crate::remote::ClientConfig {
+                retry_config: crate::remote::RetryConfig {
+                    retries: Some(1),
+                    ..Default::default()
+                },
+                ..Default::default()
+            },
+        );
+        let file = table.probe_files("image", &[10]).await.unwrap();
+        let error = file[0]
+            .as_ref()
+            .unwrap()
+            .read_range(1..2)
+            .await
+            .unwrap_err();
+        assert!(error.to_string().contains("503"), "got: {error}");
+    }
+
+    #[tokio::test]
+    async fn remote_blob_file_reads_the_probed_version_after_table_changes() {
+        let changed = Arc::new(AtomicBool::new(false));
+        let requests = Arc::new(StdMutex::new(Vec::new()));
+        let table = RemoteTable::new_mock(
+            "my_table".to_string(),
+            {
+                let changed = changed.clone();
+                let requests = requests.clone();
+                move |request| {
+                    let range = request
+                        .headers()
+                        .get(header::RANGE)
+                        .unwrap()
+                        .to_str()
+                        .unwrap();
+                    let version = request
+                        .url()
+                        .query_pairs()
+                        .find(|(name, _)| name == "version")
+                        .map(|(_, value)| value.into_owned());
+                    requests
+                        .lock()
+                        .unwrap()
+                        .push((range.to_string(), version.clone()));
+                    if changed.load(Ordering::SeqCst) && version.as_deref() != Some("5") {
+                        return http::Response::builder()
+                            .status(StatusCode::BAD_REQUEST)
+                            .body(b"row id is absent from the latest version".to_vec())
+                            .unwrap();
+                    }
+                    range_response(&request, PAYLOAD)
+                }
+            },
+            Some(Version::new(0, 5, 0)),
+        );
+
+        let file = table
+            .probe_files("image", &[10])
+            .await
+            .unwrap()
+            .pop()
+            .flatten()
+            .unwrap();
+        changed.store(true, Ordering::SeqCst);
+
+        assert_eq!(file.read_range(5..9).await.unwrap(), &PAYLOAD[5..9]);
+        assert_eq!(file.read_up_to(4).await.unwrap(), &PAYLOAD[..4]);
+        file.seek(10).await.unwrap();
+        assert_eq!(file.read().await.unwrap(), &PAYLOAD[10..]);
+
+        assert_eq!(
+            requests.lock().unwrap().as_slice(),
+            [
+                ("bytes=0-0".to_string(), None),
+                ("bytes=5-8".to_string(), Some("5".to_string())),
+                ("bytes=0-3".to_string(), Some("5".to_string())),
+                (
+                    format!("bytes=10-{}", PAYLOAD.len() - 1),
+                    Some("5".to_string())
+                ),
+            ]
+        );
+    }
+
     #[tokio::test]
     async fn remote_blob_file_keeps_the_open_timeline_after_parent_checkout() {
         let range_requests = Arc::new(StdMutex::new(Vec::new()));
@@ -803,7 +1609,7 @@ mod tests {
 
         table.checkout(5).await.unwrap();
         let file = table
-            .fetch_blob_files_impl("image", &[10])
+            .probe_files("image", &[10])
             .await
             .unwrap()
             .pop()
@@ -819,11 +1625,11 @@ mod tests {
     }
 
     #[tokio::test]
-    async fn remote_blob_file_reuses_sequential_response_until_seek() {
+    async fn remote_blob_file_releases_response_after_each_partial_read() {
         let requests = Arc::new(StdMutex::new(Vec::new()));
         let table = mock_remote_blob_table(requests.clone());
         let file = table
-            .fetch_blob_files_impl("image", &[10])
+            .probe_files("image", &[10])
             .await
             .unwrap()
             .pop()
@@ -837,26 +1643,25 @@ mod tests {
         assert_eq!(file.tell().await.unwrap(), 24);
         assert_eq!(
             requests.lock().unwrap().as_slice(),
-            ["bytes=0-0", "bytes=0-", "bytes=20-"]
+            ["bytes=0-0", "bytes=0-3", "bytes=4-6", "bytes=20-23"]
         );
     }
 
     #[tokio::test]
-    async fn remote_blob_files_return_a_handle_for_an_empty_blob() {
+    async fn remote_blob_files_allow_an_empty_blob_without_a_version_header() {
         let requests = Arc::new(StdMutex::new(Vec::new()));
         let table = mock_remote_blob_table(requests.clone());
 
-        let mut files = table
-            .fetch_blob_files_impl("image", &[10, 20, 30])
-            .await
-            .unwrap();
+        let mut files = table.probe_files("image", &[10, 20, 30]).await.unwrap();
 
         assert_eq!(files.len(), 3);
         assert!(files[1].is_none());
         let empty = files[2].take().unwrap();
         assert_eq!(empty.size(), 0);
+        let probe_requests = requests.lock().unwrap().len();
         assert!(empty.read_range(0..0).await.unwrap().is_empty());
         assert!(empty.read().await.unwrap().is_empty());
+        assert_eq!(requests.lock().unwrap().len(), probe_requests);
         let nonempty = files[0].take().unwrap();
         assert_eq!(nonempty.size(), PAYLOAD.len() as u64);
     }
@@ -875,10 +1680,7 @@ mod tests {
             Some(Version::new(0, 5, 0)),
         );
 
-        let error = table
-            .fetch_blob_files_impl("image", &[10])
-            .await
-            .unwrap_err();
+        let error = table.probe_files("image", &[10]).await.unwrap_err();
         assert!(
             error
                 .to_string()
@@ -902,10 +1704,7 @@ mod tests {
             Some(Version::new(0, 5, 0)),
         );
 
-        let error = table
-            .fetch_blob_files_impl("image", &[10])
-            .await
-            .unwrap_err();
+        let error = table.probe_files("image", &[10]).await.unwrap_err();
         assert!(
             error
                 .to_string()
@@ -914,6 +1713,27 @@ mod tests {
         );
     }
 
+    #[tokio::test]
+    async fn remote_blob_files_reject_a_probe_without_a_version() {
+        let table = RemoteTable::new_mock(
+            "my_table".to_string(),
+            |request| {
+                let mut response = range_response(&request, PAYLOAD);
+                response.headers_mut().remove(VERSION_HEADER);
+                response
+            },
+            Some(Version::new(0, 5, 0)),
+        );
+
+        let error = table.probe_files("image", &[10]).await.unwrap_err();
+        assert!(
+            error
+                .to_string()
+                .contains("missing or invalid x-lancedb-version"),
+            "got: {error}"
+        );
+    }
+
     #[tokio::test]
     async fn remote_blob_files_reject_unsupported_server_version() {
         let table = RemoteTable::new_mock(
@@ -963,7 +1783,7 @@ mod tests {
             Some(Version::new(0, 5, 0)),
         );
         let file = table
-            .fetch_blob_files_impl("image", &[10])
+            .probe_files("image", &[10])
             .await
             .unwrap()
             .pop()
@@ -1003,7 +1823,7 @@ mod tests {
             Some(Version::new(0, 5, 0)),
         );
         let file = table
-            .fetch_blob_files_impl("image", &[10])
+            .probe_files("image", &[10])
             .await
             .unwrap()
             .pop()
@@ -1019,11 +1839,11 @@ mod tests {
 
     #[tokio::test]
     async fn remote_blob_file_failed_read_preserves_cursor_and_retries_fresh() {
-        let sequential_requests = Arc::new(AtomicUsize::new(0));
+        let read_requests = Arc::new(AtomicUsize::new(0));
         let table = RemoteTable::new_mock(
             "my_table".to_string(),
             {
-                let sequential_requests = sequential_requests.clone();
+                let read_requests = read_requests.clone();
                 move |request| {
                     let range = request
                         .headers()
@@ -1035,15 +1855,14 @@ mod tests {
                     if range == "bytes=0-0" {
                         return range_response(&request, PAYLOAD);
                     }
-                    let attempt = sequential_requests.fetch_add(1, Ordering::SeqCst);
+                    let attempt = read_requests.fetch_add(1, Ordering::SeqCst);
                     if attempt == 0 {
-                        // End the response five bytes early to simulate a truncated
-                        // sequential read.
+                        // Return too few bytes for the requested range.
                         return http::Response::builder()
                             .status(StatusCode::PARTIAL_CONTENT)
                             .header(
                                 header::CONTENT_RANGE,
-                                format!("bytes 0-{}/{}", PAYLOAD.len() - 1, PAYLOAD.len()),
+                                format!("bytes 0-9/{}", PAYLOAD.len()),
                             )
                             .body(PAYLOAD[..5].to_vec())
                             .unwrap();
@@ -1054,7 +1873,7 @@ mod tests {
             Some(Version::new(0, 5, 0)),
         );
         let file = table
-            .fetch_blob_files_impl("image", &[10])
+            .probe_files("image", &[10])
             .await
             .unwrap()
             .pop()
@@ -1063,9 +1882,7 @@ mod tests {
 
         let error = file.read_up_to(10).await.unwrap_err();
         assert!(
-            error
-                .to_string()
-                .contains("response ended before the requested blob range"),
+            error.to_string().contains("returned 5 bytes, expected 10"),
             "got: {error}"
         );
         assert_eq!(file.tell().await.unwrap(), 0);
@@ -1073,7 +1890,7 @@ mod tests {
         // Retry from the last committed cursor with a fresh request.
         let retried = file.read_up_to(4).await.unwrap();
         assert_eq!(retried, b"0123".as_slice());
-        assert_eq!(sequential_requests.load(Ordering::SeqCst), 2);
+        assert_eq!(read_requests.load(Ordering::SeqCst), 2);
     }
 
     #[tokio::test]
@@ -1081,7 +1898,7 @@ mod tests {
         let requests = Arc::new(StdMutex::new(Vec::new()));
         let table = mock_remote_blob_table(requests.clone());
         let file = table
-            .fetch_blob_files_impl("image", &[10])
+            .probe_files("image", &[10])
             .await
             .unwrap()
             .pop()
@@ -1105,7 +1922,8 @@ mod tests {
             &self,
             range_header: &str,
             _mode: RangeRequestMode,
-        ) -> Result<(String, Response)> {
+            _version: Option<u64>,
+        ) -> Result<BlobRangeResponse> {
             assert_eq!(range_header, "bytes=0-0");
             let now = self.in_flight.fetch_add(1, Ordering::SeqCst) + 1;
             self.max_in_flight.fetch_max(now, Ordering::SeqCst);
@@ -1119,9 +1937,10 @@ mod tests {
                     header::CONTENT_RANGE,
                     format!("bytes 0-0/{}", 100 + self.index),
                 )
+                .header(VERSION_HEADER, "5")
                 .body(vec![0u8])
                 .unwrap();
-            Ok((format!("probe-{}", self.index), Response::from(response)))
+            Ok((format!("probe-{}", self.index), Response::from(response)).into())
         }
     }
 
@@ -1158,7 +1977,7 @@ mod tests {
         let requests = Arc::new(StdMutex::new(Vec::new()));
         let table = mock_remote_blob_table(requests.clone());
 
-        let mut files = table.fetch_blob_files_impl("image", &[10]).await.unwrap();
+        let mut files = table.probe_files("image", &[10]).await.unwrap();
         let file = files.remove(0).unwrap();
 
         assert_eq!(file.size(), PAYLOAD.len() as u64);
@@ -1173,7 +1992,7 @@ mod tests {
         let requests = Arc::new(StdMutex::new(Vec::new()));
         let table = mock_remote_blob_table(requests.clone());
 
-        let mut files = table.fetch_blob_files_impl("image", &[10]).await.unwrap();
+        let mut files = table.probe_files("image", &[10]).await.unwrap();
         let file = files.remove(0).unwrap();
         let probe_requests = requests.lock().unwrap().len();
 
@@ -1198,7 +2017,7 @@ mod tests {
         let requests = Arc::new(StdMutex::new(Vec::new()));
         let table = mock_remote_blob_table(requests.clone());
 
-        let mut files = table.fetch_blob_files_impl("image", &[10]).await.unwrap();
+        let mut files = table.probe_files("image", &[10]).await.unwrap();
         let file = files.remove(0).unwrap();
         let probe_requests = requests.lock().unwrap().len();
 
@@ -1237,6 +2056,7 @@ mod tests {
                             header::CONTENT_RANGE,
                             format!("bytes 0-0/{}", PAYLOAD.len()),
                         )
+                        .header(VERSION_HEADER, "5")
                         .body(vec![PAYLOAD[0]])
                         .unwrap();
                 }
@@ -1249,7 +2069,7 @@ mod tests {
             Some(Version::new(0, 5, 0)),
         );
 
-        let mut files = table.fetch_blob_files_impl("image", &[10]).await.unwrap();
+        let mut files = table.probe_files("image", &[10]).await.unwrap();
         let file = files.remove(0).unwrap();
 
         let error = file.read_range(1..3).await.unwrap_err();
@@ -1283,7 +2103,8 @@ mod tests {
             &self,
             range_header: &str,
             _mode: RangeRequestMode,
-        ) -> Result<(String, Response)> {
+            _version: Option<u64>,
+        ) -> Result<BlobRangeResponse> {
             let now = self.in_flight.fetch_add(1, Ordering::SeqCst) + 1;
             self.max_in_flight.fetch_max(now, Ordering::SeqCst);
             tokio::time::sleep(Duration::from_millis(5)).await;
@@ -1303,7 +2124,7 @@ mod tests {
                 )
                 .body(PAYLOAD[start..=end].to_vec())
                 .unwrap();
-            Ok(("range".to_string(), Response::from(response)))
+            Ok(("range".to_string(), Response::from(response)).into())
         }
     }
 
@@ -1315,7 +2136,7 @@ mod tests {
             in_flight,
             max_in_flight: max_in_flight.clone(),
         });
-        let file = RemoteBlobFile::new(requester, PAYLOAD.len() as u64);
+        let file = RemoteBlobFile::new(requester, PAYLOAD.len() as u64, Some(5));
 
         let ranges: Vec<_> = (0..16u64).map(|start| start..start + 2).collect();
         let output = file.read_ranges(&ranges).await.unwrap();
@@ -1339,7 +2160,7 @@ mod tests {
         let requests = Arc::new(StdMutex::new(Vec::new()));
         let table = mock_remote_blob_table(requests.clone());
         let file = table
-            .fetch_blob_files_impl("image", &[10])
+            .probe_files("image", &[10])
             .await
             .unwrap()
             .pop()
@@ -1372,6 +2193,7 @@ mod tests {
                             header::CONTENT_RANGE,
                             format!("bytes 0-0/{}", PAYLOAD.len()),
                         )
+                        .header(VERSION_HEADER, "5")
                         .body(vec![PAYLOAD[0]])
                         .unwrap();
                 }
@@ -1384,7 +2206,7 @@ mod tests {
             Some(Version::new(0, 5, 0)),
         );
 
-        let mut files = table.fetch_blob_files_impl("image", &[10]).await.unwrap();
+        let mut files = table.probe_files("image", &[10]).await.unwrap();
         let file = files.remove(0).unwrap();
         let error = file.read_range(1..3).await.unwrap_err();
         assert!(error.to_string().contains("206"), "got: {error}");
@@ -1402,8 +2224,9 @@ mod tests {
             &self,
             range_header: &str,
             _mode: RangeRequestMode,
-        ) -> Result<(String, Response)> {
-            if range_header.ends_with('-') {
+            _version: Option<u64>,
+        ) -> Result<BlobRangeResponse> {
+            if range_header == "bytes=0-3" {
                 self.started.notify_one();
                 self.release.wait().await;
             }
@@ -1415,7 +2238,7 @@ mod tests {
                 )
                 .body(PAYLOAD.to_vec())
                 .unwrap();
-            Ok(("hung".to_string(), Response::from(response)))
+            Ok(("hung".to_string(), Response::from(response)).into())
         }
     }
 
@@ -1427,7 +2250,11 @@ mod tests {
             release: release.clone(),
             started: started.clone(),
         });
-        let file = Arc::new(RemoteBlobFile::new(requester, PAYLOAD.len() as u64));
+        let file = Arc::new(RemoteBlobFile::new(
+            requester,
+            PAYLOAD.len() as u64,
+            Some(5),
+        ));
 
         let reader = {
             let file = file.clone();
diff --git a/rust/lancedb/src/remote/table/insert.rs b/rust/lancedb/src/remote/table/insert.rs
index eef1d8e42..3a089eb95 100644
--- a/rust/lancedb/src/remote/table/insert.rs
+++ b/rust/lancedb/src/remote/table/insert.rs
@@ -1497,7 +1497,7 @@ mod tests {
             when_not_matched_by_source_delete: false,
             when_not_matched_by_source_delete_filt: None,
             use_index: true,
-            use_lsm: None,
+            use_memwal: None,
         };
 
         let input: Arc<dyn ExecutionPlan> = Arc::new(ErroringExec::new());
diff --git a/rust/lancedb/src/sql.rs b/rust/lancedb/src/sql.rs
index 7c040c51b..c2ef13ba5 100644
--- a/rust/lancedb/src/sql.rs
+++ b/rust/lancedb/src/sql.rs
@@ -17,6 +17,10 @@ pub enum QueryStatus {
     /// The server is still executing the query.
     Running,
     /// The server has made the complete result available.
+    ///
+    /// A query submitted with parameters streams its rows on the call that
+    /// runs it, so it reports `Finished` only once its reader has received
+    /// the last batch. Read it rather than waiting for this status.
     Finished,
     /// The server accepted cancellation but has not confirmed it yet.
     Cancelling,
@@ -60,7 +64,9 @@ pub(crate) trait QueryHandle: Send + Sync {
 /// A handle to a submitted SQL query.
 ///
 /// The handle can be inspected, opened as an Arrow reader, or cancelled.
-/// Dropping it does not cancel the server-side query.
+/// Dropping it does not cancel the server-side query, unless the query was
+/// submitted with parameters: those run on the call that returns their rows,
+/// so dropping the handle before reading, or dropping the reader, stops them.
 /// Identifier lookup is scoped to the connection that submitted the query and
 /// is not a durable resume mechanism.
 pub struct Query {
@@ -88,6 +94,11 @@ impl Query {
     }
 
     /// Get a point-in-time description of the query.
+    ///
+    /// For a query submitted with parameters the status follows its result
+    /// stream: it stays [`QueryStatus::Running`] until the reader has received
+    /// every row, so open [`Self::reader`] directly instead of polling for
+    /// [`QueryStatus::Finished`] first.
     pub async fn describe(&self) -> Result<QueryDescription> {
         self.handle.describe().await
     }
diff --git a/rust/lancedb/src/table.rs b/rust/lancedb/src/table.rs
index 7bcc7b5d6..6af2385ed 100644
--- a/rust/lancedb/src/table.rs
+++ b/rust/lancedb/src/table.rs
@@ -19,6 +19,9 @@ pub use lance::dataset::ReadParams;
 pub use lance::dataset::Version;
 use lance::dataset::WriteMode;
 use lance::dataset::builder::DatasetBuilder;
+pub use lance::dataset::mem_wal::MemIndexRegistry;
+/// The traits a memtable index plugin implements; see [`MemIndexRegistry`].
+pub use lance::dataset::mem_wal::index as mem_index;
 use lance::dataset::{InsertBuilder, WriteParams};
 use lance::index::DatasetIndexExt;
 use lance::index::scalar::load_segment_params;
@@ -77,10 +80,10 @@ pub mod datafusion;
 pub(crate) mod dataset;
 pub mod delete;
 pub mod freshness;
-pub mod lsm_stats;
+pub mod memwal_stats;
 pub mod merge;
 pub mod optimize;
-mod primary_key;
+pub(crate) mod primary_key;
 pub mod query;
 pub mod refresh;
 pub mod schema_evolution;
@@ -104,7 +107,7 @@ use futures::future::join_all;
 pub use lance::dataset::refs::{BranchContents, Ref, TagContents, Tags as LanceTags};
 pub use lance::dataset::scanner::DatasetRecordBatchStream;
 pub use lance_index::optimize::OptimizeOptions;
-pub use lsm_stats::{BucketStats, GenerationStats, LsmStats, MemtableStats};
+pub use memwal_stats::{MemWalStats, MemtableStats, SsTableStats, TableShardStats};
 pub use optimize::{
     CompactionMode, CompactionOptions, IndexRemapMode, OptimizeAction, OptimizeStats,
 };
@@ -326,19 +329,20 @@ pub use self::merge::MergeResult;
 /// Specification selecting Lance's MemWAL LSM-style write path for
 /// `merge_insert`.
 ///
-/// Construct via [`LsmWriteSpec::bucket`], [`LsmWriteSpec::identity`], or
-/// [`LsmWriteSpec::unsharded`], then optionally chain
-/// [`LsmWriteSpec::with_maintained_indexes`] (indexes the MemWAL keeps up to
-/// date) and [`LsmWriteSpec::with_writer_config_defaults`] (default
+/// Construct via [`MemWalWriteSpec::bucket`], [`MemWalWriteSpec::identity`], or
+/// [`MemWalWriteSpec::unsharded`], then optionally chain
+/// [`MemWalWriteSpec::with_maintained_indexes`] (indexes the MemWAL keeps up to
+/// date) and [`MemWalWriteSpec::with_writer_config_defaults`] (default
 /// `ShardWriter` configuration recorded in the MemWAL index).
 ///
-/// A fresh spec maintains every index on the table, resolved on install.
+/// A fresh spec maintains every index on the table, including ones created
+/// after it is installed.
 ///
-/// Install a spec with [`Table::set_lsm_write_spec`] and remove it with
-/// [`Table::unset_lsm_write_spec`]. The actual `merge_insert` dispatch
+/// Install a spec with [`Table::enable_memwal`] and remove it with
+/// [`Table::disable_memwal`]. The actual `merge_insert` dispatch
 /// onto the MemWAL writer is a follow-up.
 #[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
-pub enum LsmWriteSpec {
+pub enum MemWalWriteSpec {
     /// Hash-bucket sharding by a scalar column.
     ///
     /// `column` must be a non-nested column with a supported scalar type.
@@ -350,9 +354,8 @@ pub enum LsmWriteSpec {
         num_buckets: u32,
         /// Indexes the MemWAL maintains in-memory as rows are appended.
         ///
-        /// `None` means every index it can maintain, resolved on install — a
-        /// snapshot, so indexes created later need the spec unset and re-set.
-        /// `Some([])` maintains nothing.
+        /// `None` means every index the table has, including ones created
+        /// later. `Some([])` maintains nothing.
         maintained_indexes: Option<Vec<String>>,
         /// Default `ShardWriter` configuration recorded in the MemWAL index.
         writer_config_defaults: HashMap<String, String>,
@@ -365,9 +368,8 @@ pub enum LsmWriteSpec {
         column: String,
         /// Indexes the MemWAL maintains in-memory as rows are appended.
         ///
-        /// `None` means every index it can maintain, resolved on install — a
-        /// snapshot, so indexes created later need the spec unset and re-set.
-        /// `Some([])` maintains nothing.
+        /// `None` means every index the table has, including ones created
+        /// later. `Some([])` maintains nothing.
         maintained_indexes: Option<Vec<String>>,
         /// Default `ShardWriter` configuration recorded in the MemWAL index.
         writer_config_defaults: HashMap<String, String>,
@@ -376,16 +378,15 @@ pub enum LsmWriteSpec {
     Unsharded {
         /// Indexes the MemWAL maintains in-memory as rows are appended.
         ///
-        /// `None` means every index it can maintain, resolved on install — a
-        /// snapshot, so indexes created later need the spec unset and re-set.
-        /// `Some([])` maintains nothing.
+        /// `None` means every index the table has, including ones created
+        /// later. `Some([])` maintains nothing.
         maintained_indexes: Option<Vec<String>>,
         /// Default `ShardWriter` configuration recorded in the MemWAL index.
         writer_config_defaults: HashMap<String, String>,
     },
 }
 
-impl LsmWriteSpec {
+impl MemWalWriteSpec {
     /// Construct a hash-bucket sharding spec maintaining every index on the table.
     pub fn bucket(column: impl Into<String>, num_buckets: u32) -> Self {
         Self::Bucket {
@@ -402,7 +403,7 @@ impl LsmWriteSpec {
     /// `column` must be a deterministic function of the unenforced primary
     /// key: every row with a given primary key must always produce the same
     /// `column` value. MemWAL dedups upserts by primary key but tracks
-    /// generations per shard, so if the same key is written with two
+    /// SSTables per shard, so if the same key is written with two
     /// different `column` values its versions land in different shards and a
     /// stale value can win. Typically `column` is the primary key itself, or
     /// a stable attribute of it (e.g. a tenant id).
@@ -424,19 +425,21 @@ impl LsmWriteSpec {
 
     /// Set which indexes the MemWAL maintains.
     ///
-    /// `None` (the default) resolves to every index on the table at install,
-    /// failing if one cannot be maintained — name the set to install anyway. A
-    /// list is verbatim: each name must already exist and be maintainable, and
-    /// an empty list maintains nothing.
+    /// `None` (the default) is every index on the table, re-read as the table
+    /// changes, so an index created later is maintained too; one of a kind the
+    /// MemWAL cannot mirror is skipped rather than failing the table. A list is
+    /// verbatim: each name must already exist and be maintainable, and an empty
+    /// list maintains nothing. The maintainable kinds are Lance's built-ins plus
+    /// any added with [`NativeTable::set_mem_index_registry`].
     ///
     /// ```
-    /// # use lancedb::table::LsmWriteSpec;
-    /// // Every index the table has when the spec is installed:
-    /// LsmWriteSpec::unsharded().with_maintained_indexes(None);
+    /// # use lancedb::table::MemWalWriteSpec;
+    /// // Every index the table has, now and later:
+    /// MemWalWriteSpec::unsharded().with_maintained_indexes(None);
     /// // Exactly these:
-    /// LsmWriteSpec::unsharded().with_maintained_indexes(vec!["id_idx".to_string()]);
+    /// MemWalWriteSpec::unsharded().with_maintained_indexes(vec!["id_idx".to_string()]);
     /// // None at all:
-    /// LsmWriteSpec::unsharded().with_maintained_indexes(Vec::new());
+    /// MemWalWriteSpec::unsharded().with_maintained_indexes(Vec::new());
     /// ```
     pub fn with_maintained_indexes(mut self, indexes: impl Into<Option<Vec<String>>>) -> Self {
         let indexes = indexes.into();
@@ -566,7 +569,7 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
     fn id(&self) -> &str;
     /// Get the arrow [Schema] of the table.
     async fn schema(&self) -> Result<SchemaRef>;
-    /// Read this table's materialized-view definition and incarnation.
+    /// Read this table's materialized-view definition.
     #[doc(hidden)]
     async fn materialized_view_info(
         &self,
@@ -581,9 +584,7 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
     #[doc(hidden)]
     async fn refresh_materialized_view_async(
         &self,
-        _full: bool,
         _source_version: Option<u64>,
-        _expected_incarnation: Option<&str>,
     ) -> Result<Job<crate::materialized_view::RefreshMaterializedViewResult>> {
         Err(Error::NotSupported {
             message: "remote materialized-view refresh is not supported on this table type".into(),
@@ -675,37 +676,37 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
             message: "set_unenforced_primary_key is not supported on this table type".into(),
         })
     }
-    /// Install an [`LsmWriteSpec`] on this table.
+    /// Install an [`MemWalWriteSpec`] on this table.
     ///
     /// The spec selects Lance's MemWAL LSM-style write path for future
     /// `merge_insert` calls.
     ///
     /// The default implementation returns `NotSupported`. Implementations
-    /// that support the MemWAL LSM write path must override this.
-    async fn set_lsm_write_spec(&self, _spec: LsmWriteSpec) -> Result<()> {
+    /// that support the MemWAL write path must override this.
+    async fn enable_memwal(&self, _spec: MemWalWriteSpec) -> Result<()> {
         Err(Error::NotSupported {
-            message: "set_lsm_write_spec is not supported on this table type".into(),
+            message: "enable_memwal is not supported on this table type".into(),
         })
     }
-    /// Remove the [`LsmWriteSpec`] from this table.
+    /// Remove the [`MemWalWriteSpec`] from this table.
     ///
     /// This is a no-op if no spec is currently set.
     ///
     /// The default implementation returns `NotSupported`. Implementations
-    /// that support the MemWAL LSM write path must override this.
-    async fn unset_lsm_write_spec(&self) -> Result<()> {
+    /// that support the MemWAL write path must override this.
+    async fn disable_memwal(&self) -> Result<()> {
         Err(Error::NotSupported {
-            message: "unset_lsm_write_spec is not supported on this table type".into(),
+            message: "disable_memwal is not supported on this table type".into(),
         })
     }
-    /// Read the [`LsmWriteSpec`] currently installed on this table, returning
-    /// `None` when the MemWAL LSM write path is not enabled.
+    /// Read the [`MemWalWriteSpec`] currently installed on this table, returning
+    /// `None` when the MemWAL write path is not enabled.
     ///
     /// The default implementation returns `NotSupported`. Implementations that
-    /// support the MemWAL LSM write path must override this.
-    async fn get_lsm_write_spec(&self) -> Result<Option<LsmWriteSpec>> {
+    /// support the MemWAL write path must override this.
+    async fn get_memwal_spec(&self) -> Result<Option<MemWalWriteSpec>> {
         Err(Error::NotSupported {
-            message: "get_lsm_write_spec is not supported on this table type".into(),
+            message: "get_memwal_spec is not supported on this table type".into(),
         })
     }
     /// Whether a hybrid query on this table has already been told it cannot
@@ -733,36 +734,36 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
     /// the only cost of being late to notice is a base-only read that is still
     /// correct.
     fn note_hybrid_pk_fusion(&self) {}
-    /// Seal every bucket's active memtable into L0.
+    /// Freeze every table shard's active memtable into an SSTable.
     ///
     /// The default implementation returns `NotSupported`.
-    async fn flush_lsm(&self) -> Result<()> {
+    async fn flush_memwal(&self) -> Result<()> {
         Err(Error::NotSupported {
-            message: "flush_lsm is not supported on this table type".into(),
+            message: "flush_memwal is not supported on this table type".into(),
         })
     }
-    /// Trigger a background L0 → base compaction pass per bucket.
+    /// Trigger a background SSTable compaction pass per table shard.
     ///
     /// The default implementation returns `NotSupported`.
-    async fn compact_lsm(&self) -> Result<()> {
+    async fn compact_memwal(&self) -> Result<()> {
         Err(Error::NotSupported {
-            message: "compact_lsm is not supported on this table type".into(),
+            message: "compact_memwal is not supported on this table type".into(),
         })
     }
-    /// Read live LSM state, or `None` when the LSM write path is not
+    /// Read live MemWAL state, or `None` when the MemWAL write path is not
     /// enabled for this table.
     ///
     /// The default implementation returns `NotSupported`.
-    async fn get_lsm_stats(&self, _include_generation_rows: bool) -> Result<Option<LsmStats>> {
+    async fn get_memwal_stats(&self, _include_sstable_rows: bool) -> Result<Option<MemWalStats>> {
         Err(Error::NotSupported {
-            message: "get_lsm_stats is not supported on this table type".into(),
+            message: "get_memwal_stats is not supported on this table type".into(),
         })
     }
     /// Drain and close any cached MemWAL shard writers for this table.
     ///
     /// The default implementation is a no-op; table types that maintain
     /// MemWAL shard writers override it.
-    async fn close_lsm_writers(&self) -> Result<()> {
+    async fn close_memwal_writers(&self) -> Result<()> {
         Ok(())
     }
     /// Names of the blob v2 columns in this table, in declaration order.
@@ -1242,10 +1243,11 @@ impl Table {
         self.inner.count_rows(filter).await
     }
 
-    /// Names of the blob v2 columns in this table, in declaration order.
+    /// Names of the row-addressable blob v2 columns, in declaration order.
     ///
-    /// Nested blobs use dotted paths (e.g. `info.blob`). Returns
-    /// [`Error::NotSupported`] on table types without blob support.
+    /// Blobs nested in structs use dotted paths (e.g. `info.blob`). Blobs
+    /// inside lists are omitted because row ids cannot identify their elements.
+    /// Returns [`Error::NotSupported`] on table types without blob support.
     pub async fn blob_columns(&self) -> Result<Vec<String>> {
         self.inner.blob_columns().await
     }
@@ -1260,7 +1262,8 @@ impl Table {
     /// [`Self::fetch_blob_files`] for large selections.
     ///
     /// `_rowid` values stay valid after compaction when the table has stable
-    /// row ids.
+    /// row ids. One missing or deleted row id rejects the whole request; no
+    /// partial results are returned.
     ///
     /// ```
     /// use arrow_array::UInt64Array;
@@ -1284,9 +1287,10 @@ impl Table {
     /// # }
     /// ```
     ///
-    /// Returns [`Error::InvalidInput`] when the column does not exist or is
-    /// not a blob v2 column, and [`Error::NotSupported`] on table types
-    /// without blob support.
+    /// Blobs inside lists cannot be fetched by row id. Returns
+    /// [`Error::InvalidInput`] for those paths, when the column does not exist,
+    /// or when it is not a blob v2 column. Returns [`Error::NotSupported`] on
+    /// table types without blob support.
     pub async fn fetch_blobs(
         &self,
         column: impl AsRef<str>,
@@ -1304,7 +1308,8 @@ impl Table {
     /// non-null blobs produce empty byte strings.
     ///
     /// `_rowid` values stay valid after compaction when the table has stable
-    /// row ids.
+    /// row ids. One missing or deleted row id rejects the whole request; no
+    /// partial results are returned.
     ///
     /// ```
     /// use lancedb::blob::BlobRangeRequest;
@@ -1343,9 +1348,13 @@ impl Table {
     ///
     /// Same length and order as `row_ids`. Null rows are `None`. Bytes are not
     /// read from disk until a call to [`BlobFile::read`].
+    /// Remote blob requests share a connection-wide concurrency limit. Set
+    /// `ClientConfig::blob_request_concurrency` or the
+    /// `LANCE_CLIENT_BLOB_REQUEST_CONCURRENCY` environment variable to tune it.
     ///
     /// `_rowid` values stay valid after compaction when the table has stable
-    /// row ids.
+    /// row ids. One missing or deleted row id rejects the whole request; no
+    /// partial handles are returned.
     ///
     /// ```
     /// # use lancedb::Table;
@@ -1677,6 +1686,7 @@ impl Table {
     ///     .unwrap()
     ///     .refine_factor(5)
     ///     .nprobes(10)
+    ///     .unwrap()
     ///     .execute()
     ///     .await
     ///     .unwrap();
@@ -1922,11 +1932,28 @@ impl Table {
         self.inner.function_errors(&request).await
     }
 
-    /// Change a column's name or nullability.
+    /// Change a column's name, data type, or nullability.
+    ///
+    /// Each alteration must specify at least one of `rename`, `data_type`, or
+    /// `nullable`. Returns [`Error::InvalidInput`] before applying any changes
+    /// if an alteration does not specify any of these fields.
     pub async fn alter_columns(
         &self,
         alterations: &[ColumnAlteration],
     ) -> Result<AlterColumnsResult> {
+        for alteration in alterations {
+            if alteration.rename.is_none()
+                && alteration.nullable.is_none()
+                && alteration.data_type.is_none()
+            {
+                return Err(Error::InvalidInput {
+                    message: format!(
+                        "One of rename, nullable or data_type must be specified for path '{}'",
+                        alteration.path
+                    ),
+                });
+            }
+        }
         self.inner.alter_columns(alterations).await
     }
 
@@ -1981,69 +2008,77 @@ impl Table {
         self.inner.set_unenforced_primary_key(&borrowed).await
     }
 
-    /// Install an [`LsmWriteSpec`] on this table, selecting Lance's MemWAL
+    /// Install an [`MemWalWriteSpec`] on this table, selecting Lance's MemWAL
     /// LSM-style write path for future `merge_insert` calls.
     ///
-    /// [`LsmWriteSpec`] chooses one of three sharding strategies:
+    /// [`MemWalWriteSpec`] chooses one of three sharding strategies:
     ///
-    /// - [`LsmWriteSpec::bucket`] — hash-bucket writes by a scalar column.
-    /// - [`LsmWriteSpec::identity`] — shard by the raw value of a scalar column.
-    /// - [`LsmWriteSpec::unsharded`] — route every write to a single shard.
+    /// - [`MemWalWriteSpec::bucket`] — hash-bucket writes by a scalar column.
+    /// - [`MemWalWriteSpec::identity`] — shard by the raw value of a scalar column.
+    /// - [`MemWalWriteSpec::unsharded`] — route every write to a single shard.
+    ///
+    /// A table carries one spec: this fails while one is installed, since the
+    /// SSTables already written were homed under it.
+    /// [`Table::disable_memwal`] removes one.
     ///
     /// # Example
     ///
     /// ```
-    /// # use lancedb::table::{LsmWriteSpec, Table};
+    /// # use lancedb::table::{MemWalWriteSpec, Table};
     /// # async fn example(table: &Table) -> Result<(), Box<dyn std::error::Error>> {
     /// table
-    ///     .set_lsm_write_spec(
-    ///         LsmWriteSpec::bucket("id", 16).with_maintained_indexes(vec!["id_idx".to_string()]),
+    ///     .enable_memwal(
+    ///         MemWalWriteSpec::bucket("id", 16).with_maintained_indexes(vec!["id_idx".to_string()]),
     ///     )
     ///     .await?;
     /// # Ok(())
     /// # }
     /// ```
-    pub async fn set_lsm_write_spec(&self, spec: LsmWriteSpec) -> Result<()> {
-        self.inner.set_lsm_write_spec(spec).await
+    pub async fn enable_memwal(&self, spec: MemWalWriteSpec) -> Result<()> {
+        self.inner.enable_memwal(spec).await
     }
 
-    /// Remove the [`LsmWriteSpec`] from this table, reverting to the standard
+    /// Remove the [`MemWalWriteSpec`] from this table, reverting to the standard
     /// `merge_insert` write path.
     ///
     /// Errors if no spec is currently set.
-    pub async fn unset_lsm_write_spec(&self) -> Result<()> {
-        self.inner.unset_lsm_write_spec().await
+    pub async fn disable_memwal(&self) -> Result<()> {
+        self.inner.disable_memwal().await
     }
 
-    /// Read the [`LsmWriteSpec`] currently installed on this table.
+    /// Read the [`MemWalWriteSpec`] currently installed on this table.
     ///
-    /// Returns `Ok(None)` when the MemWAL LSM write path is not enabled (no
-    /// spec has been set, or it was removed with [`Table::unset_lsm_write_spec`]).
-    /// The returned spec mirrors what was passed to
-    /// [`Table::set_lsm_write_spec`], except that
-    /// [`LsmWriteSpec::maintained_indexes`] always reports the concrete list
-    /// resolved when the spec was set — `None` never round-trips.
+    /// `Ok(None)` means the MemWAL write path is not enabled at all — no spec has
+    /// been set, or one was removed with [`Table::disable_memwal`]. That
+    /// is a different answer from a spec whose
+    /// [`MemWalWriteSpec::maintained_indexes`] is `None`, which is an installed
+    /// spec selecting indexes automatically.
+    ///
+    /// The spec read back is the one that was installed, selection included:
+    /// `None` maintains every supported index the table has now or gains
+    /// later, `[]` maintains none, and a non-empty list maintains exactly
+    /// those. All three round-trip.
     ///
     /// # Example
     ///
     /// ```
     /// # use lancedb::table::Table;
     /// # async fn example(table: &Table) -> Result<(), Box<dyn std::error::Error>> {
-    /// if let Some(spec) = table.get_lsm_write_spec().await? {
-    ///     println!("LSM write path enabled: {:?}", spec);
+    /// if let Some(spec) = table.get_memwal_spec().await? {
+    ///     println!("MemWAL write path enabled: {:?}", spec);
     /// }
     /// # Ok(())
     /// # }
     /// ```
-    pub async fn get_lsm_write_spec(&self) -> Result<Option<LsmWriteSpec>> {
-        self.inner.get_lsm_write_spec().await
+    pub async fn get_memwal_spec(&self) -> Result<Option<MemWalWriteSpec>> {
+        self.inner.get_memwal_spec().await
     }
 
-    /// Converge this table's LSM write path into its base table.
+    /// Converge this table's MemWAL write path into its base table.
     ///
-    /// One `flush` to seal every memtable into L0, then compaction triggers
+    /// One `flush` to freeze every memtable into an SSTable, then compaction triggers
     /// until every generation that existed at that moment has reached base.
-    /// The loop runs client-side, reading progress from `get_lsm_stats`, so
+    /// The loop runs client-side, reading progress from `get_memwal_stats`, so
     /// there is no held socket and nothing to reconcile if you drop this
     /// future partway through.
     ///
@@ -2053,7 +2088,7 @@ impl Table {
     /// already-converged table costs two round trips and triggers nothing.
     ///
     /// **No deadline, and the caller owns that.** It returns when the target
-    /// generations are gone, propagates a terminal server fault, and
+    /// SSTables are gone, propagates a terminal server fault, and
     /// otherwise waits however long the server takes. A slow table and a
     /// stuck one are the same picture from here: the compactor pool is shared
     /// across every table on the node, so a checkpoint queued behind
@@ -2066,66 +2101,69 @@ impl Table {
     /// ```no_run
     /// # use lancedb::Table;
     /// # async fn example(table: &Table) -> Result<(), Box<dyn std::error::Error>> {
-    /// let before = table.get_lsm_stats(false).await?;
-    /// table.checkpoint_lsm().await?;
-    /// let after = table.get_lsm_stats(false).await?;
+    /// let before = table.get_memwal_stats(false).await?;
+    /// table.checkpoint_memwal().await?;
+    /// let after = table.get_memwal_stats(false).await?;
     /// # Ok(())
     /// # }
     /// ```
-    pub async fn checkpoint_lsm(&self) -> Result<()> {
-        checkpoint::checkpoint_lsm(self).await
+    pub async fn checkpoint_memwal(&self) -> Result<()> {
+        checkpoint::checkpoint_memwal(self).await
     }
 
-    /// Seal every bucket's active memtable into L0 without touching the
+    /// Freeze every table shard's active memtable into an SSTable without touching the
     /// base table.
     ///
-    /// Independently useful: flushing makes memtable rows readable from L0 at
+    /// Independently useful: flushing makes memtable rows readable from an SSTable at
     /// a lower per-query cost. On a node that has not claimed this table it
     /// claims it and replays the WAL log first — reporting "nothing to flush"
     /// without replaying would lie about durable data.
-    pub async fn flush_lsm(&self) -> Result<()> {
-        self.inner.flush_lsm().await
+    pub async fn flush_memwal(&self) -> Result<()> {
+        self.inner.flush_memwal().await
     }
 
-    /// Run one bounded L0 → base compaction pass per bucket, reporting what
+    /// Run one bounded SSTable compaction pass per table shard, reporting what
     /// it merged and what is left.
     ///
     /// One pass, not convergence: that bounds each request's cost and gives a
     /// caller driving its own cadence a progress signal per round trip.
-    pub async fn compact_lsm(&self) -> Result<()> {
-        self.inner.compact_lsm().await
+    pub async fn compact_memwal(&self) -> Result<()> {
+        self.inner.compact_memwal().await
     }
 
-    /// Read live per-bucket LSM state.
+    /// Read live per-table-shard MemWAL state.
     ///
-    /// Answers "how far behind is my fresh tier", "which bucket is hot", and
+    /// Answers "how far behind is my fresh tier", "which table shard is hot", and
     /// "why is my fresh-tier vector search brute-force". Mutates no table
     /// state, though on a node that has not claimed this table it claims it,
     /// exactly as a read would.
     ///
-    /// `include_generation_rows` reports a row count per L0 generation. Off by
+    /// `include_sstable_rows` reports a row count per SSTable. Off by
     /// default: each count opens an uncached Lance dataset, and
-    /// `checkpoint_lsm` polls this needing only generation numbers.
+    /// `checkpoint_memwal` polls this needing only generation numbers.
     ///
-    /// `Ok(None)` only when the LSM write path is not enabled, matching
-    /// [`Table::get_lsm_write_spec`]. Stats is fresh-tier only, so with the
+    /// `Ok(None)` only when the MemWAL write path is not enabled, matching
+    /// [`Table::get_memwal_spec`]. Stats is fresh-tier only, so with the
     /// WAL off there is no manifest to report and a struct of zeros would
     /// read as measurements.
     ///
     /// Do not build a checkpoint's termination on this: the completion
     /// predicate lives in the `flush` and `compact` responses.
-    pub async fn get_lsm_stats(&self, include_generation_rows: bool) -> Result<Option<LsmStats>> {
-        self.inner.get_lsm_stats(include_generation_rows).await
+    pub async fn get_memwal_stats(
+        &self,
+        include_sstable_rows: bool,
+    ) -> Result<Option<MemWalStats>> {
+        self.inner.get_memwal_stats(include_sstable_rows).await
     }
 
     /// Drain and close any cached MemWAL shard writers held for this table.
     ///
-    /// When an [`LsmWriteSpec`] is installed, `merge_insert` opens MemWAL shard
+    /// When an [`MemWalWriteSpec`] is installed, `merge_insert` opens MemWAL shard
     /// writers and caches them for reuse across calls. This closes them,
     /// flushing pending data; writers reopen lazily on the next `merge_insert`.
     /// It is a no-op when no writers are cached.
-    pub async fn close_lsm_writers(&self) -> Result<()> {
-        self.inner.close_lsm_writers().await
+    pub async fn close_memwal_writers(&self) -> Result<()> {
+        self.inner.close_memwal_writers().await
     }
 
     /// Retrieve the version of the table
@@ -2569,6 +2607,9 @@ pub struct NativeTable {
     pub(crate) pushdown_operations: HashSet<NamespaceClientPushdownOperation>,
     // Read-freshness baseline; `Some` only for namespace-backed tables.
     freshness: Option<TableFreshness>,
+    // Memtable index kinds this handle's LSM writes maintain, shared with its
+    // clones and branch handles.
+    mem_index_registry: Arc<std::sync::RwLock<MemIndexRegistry>>,
 }
 
 impl std::fmt::Debug for NativeTable {
@@ -2735,9 +2776,51 @@ impl NativeTable {
             namespace_client,
             pushdown_operations,
             freshness: None,
+            mem_index_registry: Default::default(),
         })
     }
 
+    /// Maintain the memtable index kinds in `registry` when this table writes
+    /// through its [`MemWalWriteSpec`], and accept them when
+    /// [`MemWalWriteSpec::with_maintained_indexes`] names one.
+    ///
+    /// The registry belongs to this handle, its clones and its branch handles,
+    /// and is not stored with the table: a handle opened separately starts from
+    /// Lance's built-ins. Set it before the handle's first LSM write; writers
+    /// already open keep the registry they were opened with. Local tables only,
+    /// reached through [`Table::as_native`].
+    ///
+    /// ```
+    /// # use std::sync::Arc;
+    /// # use lancedb::Table;
+    /// # use lancedb::table::{MemWalWriteSpec, MemIndexRegistry, mem_index::MemIndexPlugin};
+    /// # async fn example(
+    /// #     table: &Table,
+    /// #     plugin: Arc<dyn MemIndexPlugin>,
+    /// # ) -> Result<(), Box<dyn std::error::Error>> {
+    /// let registry = MemIndexRegistry::default().with_plugin(plugin)?;
+    /// if let Some(native) = table.as_native() {
+    ///     native.set_mem_index_registry(registry);
+    /// }
+    /// let spec = MemWalWriteSpec::unsharded().with_maintained_indexes(vec!["region_bitmap".to_string()]);
+    /// table.enable_memwal(spec).await?;
+    /// # Ok(())
+    /// # }
+    /// ```
+    pub fn set_mem_index_registry(&self, registry: MemIndexRegistry) {
+        *self
+            .mem_index_registry
+            .write()
+            .unwrap_or_else(std::sync::PoisonError::into_inner) = registry;
+    }
+
+    pub(crate) fn mem_index_registry(&self) -> MemIndexRegistry {
+        self.mem_index_registry
+            .read()
+            .unwrap_or_else(std::sync::PoisonError::into_inner)
+            .clone()
+    }
+
     /// Set the namespace client for server-side query execution.
     ///
     /// When set, queries will be executed on the namespace server instead of locally.
@@ -2765,6 +2848,7 @@ impl NativeTable {
             namespace_client: self.namespace_client.clone(),
             pushdown_operations: self.pushdown_operations.clone(),
             freshness: self.freshness.clone(),
+            mem_index_registry: self.mem_index_registry.clone(),
         }
     }
 
@@ -2872,6 +2956,7 @@ impl NativeTable {
             namespace_client: stored_namespace_client,
             pushdown_operations,
             freshness: None,
+            mem_index_registry: Default::default(),
         })
     }
 
@@ -2963,6 +3048,7 @@ impl NativeTable {
             namespace_client,
             pushdown_operations,
             freshness: None,
+            mem_index_registry: Default::default(),
         })
     }
 
@@ -3096,9 +3182,20 @@ impl NativeTable {
             namespace_client: stored_namespace_client,
             pushdown_operations,
             freshness: None,
+            mem_index_registry: Default::default(),
         })
     }
 
+    /// Refuse `operation` when this table is a materialized view.
+    pub(crate) async fn ensure_not_a_view(&self, operation: &str) -> Result<()> {
+        let dataset = self.dataset.get().await?;
+        crate::materialized_view::ensure_not_a_view(
+            &self.name,
+            &dataset.schema().metadata,
+            operation,
+        )
+    }
+
     /// Merge new data into this table.
     pub async fn merge(
         &mut self,
@@ -3107,6 +3204,7 @@ impl NativeTable {
         right_on: &str,
     ) -> Result<()> {
         self.dataset.ensure_mutable()?;
+        self.ensure_not_a_view("merge into").await?;
         let mut dataset = (*self.dataset.get().await?).clone();
         dataset.merge(batches, left_on, right_on).await?;
         self.dataset.update(dataset);
@@ -3409,6 +3507,7 @@ impl BaseTable for NativeTable {
         self.dataset.ensure_mutable()?;
         let ds_wrapper = self.dataset.clone();
         let ds = self.dataset.get().await?;
+        crate::materialized_view::ensure_not_a_view(&self.name, &ds.schema().metadata, "add to")?;
 
         let table_schema = Schema::from(&ds.schema().clone());
         computed_columns::ensure_supported_function_metadata(&table_schema)?;
@@ -3583,6 +3682,7 @@ impl BaseTable for NativeTable {
         new_data: Box<dyn RecordBatchReader + Send>,
     ) -> Result<MergeResult> {
         let source_schema = arrow_array::RecordBatchReader::schema(&new_data);
+        self.ensure_not_a_view("merge insert into").await?;
         computed_columns::ensure_not_written(
             &Schema::from(self.dataset.get().await?.schema()),
             source_schema.fields().iter().map(|f| f.name().as_str()),
@@ -3596,20 +3696,20 @@ impl BaseTable for NativeTable {
         primary_key::set_unenforced_primary_key(self, columns).await
     }
 
-    async fn set_lsm_write_spec(&self, spec: LsmWriteSpec) -> Result<()> {
-        merge::lsm::set_lsm_write_spec(self, spec).await
+    async fn enable_memwal(&self, spec: MemWalWriteSpec) -> Result<()> {
+        merge::lsm::enable_memwal(self, spec).await
     }
 
-    async fn unset_lsm_write_spec(&self) -> Result<()> {
-        merge::lsm::unset_lsm_write_spec(self).await
+    async fn disable_memwal(&self) -> Result<()> {
+        merge::lsm::disable_memwal(self).await
     }
 
-    async fn get_lsm_write_spec(&self) -> Result<Option<LsmWriteSpec>> {
-        merge::lsm::get_lsm_write_spec(self).await
+    async fn get_memwal_spec(&self) -> Result<Option<MemWalWriteSpec>> {
+        merge::lsm::get_memwal_spec(self).await
     }
 
-    async fn close_lsm_writers(&self) -> Result<()> {
-        merge::lsm::close_lsm_writers(self).await
+    async fn close_memwal_writers(&self) -> Result<()> {
+        merge::lsm::close_memwal_writers(self).await
     }
 
     async fn blob_columns(&self) -> Result<Vec<String>> {
@@ -3793,7 +3893,20 @@ impl BaseTable for NativeTable {
             let Some(segment) = segments.first() else {
                 continue;
             };
-            let params = load_segment_params(&dataset, segment).await?;
+            // The listing itself only needs the manifest. Missing index files must
+            // not hide every other index, or callers cannot find the one to repair.
+            let params = match load_segment_params(&dataset, segment).await {
+                Ok(params) => params,
+                Err(err) => {
+                    log::warn!(
+                        "Failed to read full text search configuration for index '{}': {}",
+                        index.name,
+                        err
+                    );
+                    index.index_details = None;
+                    continue;
+                }
+            };
             let details = serde_json::to_string(&params).map_err(|source| Error::Other {
                 message: format!(
                     "Failed to serialize full text search configuration for index '{}'",
@@ -3922,6 +4035,7 @@ impl BaseTable for NativeTable {
         let num_rows = self.count_rows(None).await?;
         let num_indices = self.list_indices().await?.len();
         let ds = self.dataset.get().await?;
+        let num_deleted_rows = Some(ds.count_deleted_rows().await?);
         // Sizes come from the manifest. Summing per-field `bytes_on_disk` instead
         // would open every data file to read its column metadata, which costs one
         // IO per fragment and reports 0 for legacy v1 storage.
@@ -3986,6 +4100,7 @@ impl BaseTable for NativeTable {
         let stats = TableStatistics {
             total_bytes,
             num_rows,
+            num_deleted_rows,
             num_indices,
             fragment_stats: frag_stats,
         };
@@ -3998,6 +4113,11 @@ impl BaseTable for NativeTable {
         write_params: WriteParams,
     ) -> Result<Arc<dyn datafusion_physical_plan::ExecutionPlan>> {
         let ds = self.dataset.get().await?;
+        crate::materialized_view::ensure_not_a_view(
+            &self.name,
+            &ds.schema().metadata,
+            "insert into",
+        )?;
         let dataset = Arc::new((*ds).clone());
         Ok(Arc::new(datafusion::insert::InsertExec::new(
             self.dataset.clone(),
@@ -4022,6 +4142,20 @@ pub struct TableStatistics {
     /// The number of rows in the table
     pub num_rows: usize,
 
+    /// The number of rows marked as deleted across all fragments of the table
+    ///
+    /// Deleted rows are only marked in deletion files and still occupy space on
+    /// disk until the table is compacted, so a large value here is a good
+    /// indication that [`Table::optimize`] should be called.
+    ///
+    /// These rows are not included in [`Self::num_rows`]. Fragments in which
+    /// every row was deleted are dropped outright rather than kept with a
+    /// deletion file, so their rows are not counted here either.
+    ///
+    /// `None` means the backend did not report a deletion count.
+    #[serde(default)]
+    pub num_deleted_rows: Option<usize>,
+
     /// The number of indices in the table
     pub num_indices: usize,
 
@@ -5381,7 +5515,7 @@ mod tests {
     }
 
     #[tokio::test]
-    async fn test_set_lsm_write_spec() {
+    async fn test_enable_memwal() {
         use arrow_array::StringArray;
         use lance::dataset::mem_wal::DatasetMemWalExt;
 
@@ -5413,7 +5547,7 @@ mod tests {
         // Reject num_buckets out of range.
         for bad in [0u32, 1025] {
             let err = table
-                .set_lsm_write_spec(LsmWriteSpec::bucket("id", bad))
+                .enable_memwal(MemWalWriteSpec::bucket("id", bad))
                 .await
                 .expect_err("should reject");
             assert!(matches!(err, Error::InvalidInput { .. }), "got {:?}", err);
@@ -5421,7 +5555,7 @@ mod tests {
 
         // Happy path: install spec; verify MemWAL details record it.
         table
-            .set_lsm_write_spec(LsmWriteSpec::bucket("id", 4))
+            .enable_memwal(MemWalWriteSpec::bucket("id", 4))
             .await
             .unwrap();
 
@@ -5447,14 +5581,14 @@ mod tests {
 
         // Mutation rejected.
         let err = table
-            .set_lsm_write_spec(LsmWriteSpec::bucket("id", 8))
+            .enable_memwal(MemWalWriteSpec::bucket("id", 8))
             .await
             .expect_err("mutation should be rejected");
         assert!(matches!(err, Error::InvalidInput { .. }), "got {:?}", err);
     }
 
     #[tokio::test]
-    async fn test_set_lsm_write_spec_unsharded() {
+    async fn test_enable_memwal_unsharded() {
         use lance::dataset::mem_wal::DatasetMemWalExt;
 
         let tmp_dir = tempdir().unwrap();
@@ -5476,7 +5610,7 @@ mod tests {
         let table = conn.create_table("t", reader).execute().await.unwrap();
 
         table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .enable_memwal(MemWalWriteSpec::unsharded())
             .await
             .unwrap();
 
@@ -5494,7 +5628,7 @@ mod tests {
     }
 
     #[tokio::test]
-    async fn test_set_lsm_write_spec_identity() {
+    async fn test_enable_memwal_identity() {
         use lance::dataset::mem_wal::DatasetMemWalExt;
 
         let tmp_dir = tempdir().unwrap();
@@ -5522,8 +5656,8 @@ mod tests {
         let table = conn.create_table("t", reader).execute().await.unwrap();
 
         table
-            .set_lsm_write_spec(
-                LsmWriteSpec::identity("region")
+            .enable_memwal(
+                MemWalWriteSpec::identity("region")
                     .with_writer_config_defaults([("durable_write", "false")]),
             )
             .await
@@ -5551,7 +5685,7 @@ mod tests {
     }
 
     #[tokio::test]
-    async fn test_unset_lsm_write_spec() {
+    async fn test_disable_memwal() {
         use lance::dataset::mem_wal::DatasetMemWalExt;
 
         let tmp_dir = tempdir().unwrap();
@@ -5573,11 +5707,11 @@ mod tests {
         let table = conn.create_table("t", reader).execute().await.unwrap();
 
         // unset errors when no spec is set.
-        table.unset_lsm_write_spec().await.unwrap_err();
+        table.disable_memwal().await.unwrap_err();
 
         // Install a spec, then unset it.
         table
-            .set_lsm_write_spec(LsmWriteSpec::bucket("id", 4))
+            .enable_memwal(MemWalWriteSpec::bucket("id", 4))
             .await
             .unwrap();
         {
@@ -5585,16 +5719,16 @@ mod tests {
             assert!(dataset.mem_wal_index_details().await.unwrap().is_some());
         }
 
-        table.unset_lsm_write_spec().await.unwrap();
+        table.disable_memwal().await.unwrap();
         {
             let dataset = table.as_native().unwrap().dataset.get().await.unwrap();
             assert!(dataset.mem_wal_index_details().await.unwrap().is_none());
         }
 
         // A second unset errors; a fresh spec can still be installed afterwards.
-        table.unset_lsm_write_spec().await.unwrap_err();
+        table.disable_memwal().await.unwrap_err();
         table
-            .set_lsm_write_spec(LsmWriteSpec::bucket("id", 8))
+            .enable_memwal(MemWalWriteSpec::bucket("id", 8))
             .await
             .unwrap();
         {
@@ -5604,7 +5738,7 @@ mod tests {
     }
 
     #[tokio::test]
-    async fn test_get_lsm_write_spec() {
+    async fn test_get_memwal_spec() {
         let tmp_dir = tempdir().unwrap();
         let uri = tmp_dir.path().to_str().unwrap();
 
@@ -5630,7 +5764,7 @@ mod tests {
         let table = conn.create_table("t", reader).execute().await.unwrap();
 
         // No spec installed yet.
-        assert_eq!(table.get_lsm_write_spec().await.unwrap(), None);
+        assert_eq!(table.get_memwal_spec().await.unwrap(), None);
 
         // A real scalar index is needed to name it as a maintained index.
         table
@@ -5642,42 +5776,35 @@ mod tests {
 
         // Bucket spec round-trips exactly, including the routing column (recovered
         // from its field id), maintained indexes, and writer config defaults.
-        let spec = LsmWriteSpec::bucket("id", 4)
+        let spec = MemWalWriteSpec::bucket("id", 4)
             .with_maintained_indexes(vec![idx_name.clone()])
             .with_writer_config_defaults([("durable_write", "false")]);
-        table.set_lsm_write_spec(spec.clone()).await.unwrap();
-        assert_eq!(table.get_lsm_write_spec().await.unwrap(), Some(spec));
+        table.enable_memwal(spec.clone()).await.unwrap();
+        assert_eq!(table.get_memwal_spec().await.unwrap(), Some(spec));
 
         // After unset, no spec is reported.
-        table.unset_lsm_write_spec().await.unwrap();
-        assert_eq!(table.get_lsm_write_spec().await.unwrap(), None);
+        table.disable_memwal().await.unwrap();
+        assert_eq!(table.get_memwal_spec().await.unwrap(), None);
 
         // Identity sharding round-trips (column recovered from the schema).
-        // A spec left at its default maintains every index on the table, so it
-        // reads back naming the one on the table rather than as "infer".
-        let spec = LsmWriteSpec::identity("region");
-        table.set_lsm_write_spec(spec.clone()).await.unwrap();
-        assert_eq!(
-            table.get_lsm_write_spec().await.unwrap(),
-            Some(spec.with_maintained_indexes(vec![idx_name.clone()]))
-        );
-        table.unset_lsm_write_spec().await.unwrap();
+        // A spec left at its default round-trips as the default: what is stored
+        // is the intent to maintain everything, not the set it resolves to now.
+        let spec = MemWalWriteSpec::identity("region");
+        table.enable_memwal(spec.clone()).await.unwrap();
+        assert_eq!(table.get_memwal_spec().await.unwrap(), Some(spec));
+        table.disable_memwal().await.unwrap();
 
         // Unsharded round-trips (no routing column).
-        let spec = LsmWriteSpec::unsharded();
-        table.set_lsm_write_spec(spec.clone()).await.unwrap();
-        assert_eq!(
-            table.get_lsm_write_spec().await.unwrap(),
-            Some(spec.with_maintained_indexes(vec![idx_name]))
-        );
+        let spec = MemWalWriteSpec::unsharded();
+        table.enable_memwal(spec.clone()).await.unwrap();
+        assert_eq!(table.get_memwal_spec().await.unwrap(), Some(spec));
     }
 
-    /// The maintained set defaults to every index on the table, resolved at
-    /// install. An index the memtable cannot build fails the install rather
-    /// than being dropped: maintaining it would take the table offline for
-    /// writes, dropping it would hide that from the caller.
+    /// The maintained set defaults to every index on the table. A named index
+    /// the memtable cannot build fails the install, because the caller asked
+    /// for it; an unnamed one is skipped, because it arrived by existing.
     #[tokio::test]
-    async fn test_set_lsm_write_spec_infers_maintained_indexes() {
+    async fn test_enable_memwal_infers_maintained_indexes() {
         let tmp_dir = tempdir().unwrap();
         let uri = tmp_dir.path().to_str().unwrap();
 
@@ -5717,8 +5844,9 @@ mod tests {
 
         // Explicitly naming the bitmap index fails before anything commits.
         let err = table
-            .set_lsm_write_spec(
-                LsmWriteSpec::unsharded().with_maintained_indexes(vec!["tag_bitmap".to_string()]),
+            .enable_memwal(
+                MemWalWriteSpec::unsharded()
+                    .with_maintained_indexes(vec!["tag_bitmap".to_string()]),
             )
             .await
             .unwrap_err();
@@ -5726,30 +5854,38 @@ mod tests {
             matches!(err, Error::InvalidInput { ref message } if message.contains("tag_bitmap")),
             "expected the bitmap index to be rejected, got {err:?}"
         );
-        assert_eq!(table.get_lsm_write_spec().await.unwrap(), None);
+        assert_eq!(table.get_memwal_spec().await.unwrap(), None);
 
-        // The default covers every index, so the bitmap fails it too.
-        let err = table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
-            .await
-            .unwrap_err();
-        assert!(
-            matches!(err, Error::InvalidInput { ref message }
-                if message.contains("tag_bitmap") && message.contains("maintained_indexes")),
-            "expected the inferred set to be rejected, got {err:?}"
-        );
-        assert_eq!(table.get_lsm_write_spec().await.unwrap(), None);
-
-        // Naming the maintainable subset installs.
+        // The default names nothing, so the bitmap is skipped rather than
+        // refusing the table: it is maintained by existing, not by being asked
+        // for. The intent is what is stored, not the set it resolves to today.
         table
-            .set_lsm_write_spec(
-                LsmWriteSpec::unsharded().with_maintained_indexes(vec!["id_btree".to_string()]),
+            .enable_memwal(MemWalWriteSpec::unsharded())
+            .await
+            .unwrap();
+        assert_eq!(
+            table
+                .get_memwal_spec()
+                .await
+                .unwrap()
+                .unwrap()
+                .maintained_indexes(),
+            None,
+            "an unnamed set is stored as the intent to maintain everything"
+        );
+
+        // Narrowing to a named subset: unset first, since an installed spec
+        // cannot be set over.
+        table.disable_memwal().await.unwrap();
+        table
+            .enable_memwal(
+                MemWalWriteSpec::unsharded().with_maintained_indexes(vec!["id_btree".to_string()]),
             )
             .await
             .unwrap();
         assert_eq!(
             table
-                .get_lsm_write_spec()
+                .get_memwal_spec()
                 .await
                 .unwrap()
                 .unwrap()
@@ -5758,20 +5894,42 @@ mod tests {
         );
 
         // Opting out entirely is distinct from the default.
-        table.unset_lsm_write_spec().await.unwrap();
+        table.disable_memwal().await.unwrap();
         table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded().with_maintained_indexes(Vec::new()))
+            .enable_memwal(MemWalWriteSpec::unsharded().with_maintained_indexes(Vec::new()))
             .await
             .unwrap();
         assert_eq!(
             table
-                .get_lsm_write_spec()
+                .get_memwal_spec()
                 .await
                 .unwrap()
                 .unwrap()
                 .maintained_indexes(),
             Some([].as_slice())
         );
+
+        // An installed spec is never set over, whatever the new one asks for,
+        // and the refused call leaves it alone.
+        let err = table
+            .enable_memwal(MemWalWriteSpec::bucket("id", 4))
+            .await
+            .unwrap_err();
+        assert!(
+            matches!(err, Error::InvalidInput { ref message }
+                if message.contains("already set")),
+            "expected the repeat to be refused, got {err:?}"
+        );
+        assert_eq!(
+            table
+                .get_memwal_spec()
+                .await
+                .unwrap()
+                .unwrap()
+                .maintained_indexes(),
+            Some([].as_slice()),
+            "a refused call changes nothing"
+        );
     }
 
     #[tokio::test]
@@ -5827,6 +5985,7 @@ mod tests {
             res,
             TableStatistics {
                 num_rows: 250,
+                num_deleted_rows: Some(0),
                 num_indices: 0,
                 total_bytes: 8969,
                 fragment_stats: FragmentStatistics {
@@ -5850,6 +6009,7 @@ mod tests {
             res,
             TableStatistics {
                 num_rows: 0,
+                num_deleted_rows: Some(0),
                 num_indices: 0,
                 total_bytes: 0,
                 fragment_stats: FragmentStatistics {
@@ -6067,4 +6227,60 @@ mod tests {
             stats.fragment_stats.num_fragments
         );
     }
+
+    #[tokio::test]
+    pub async fn test_stats_num_deleted_rows() {
+        let tmp_dir = tempdir().unwrap();
+        let uri = tmp_dir.path().to_str().unwrap();
+
+        let conn = ConnectBuilder::new(uri).execute().await.unwrap();
+
+        let schema = Arc::new(Schema::new(vec![Field::new("id", DataType::Int32, false)]));
+        let batch = |ids: Vec<i32>| {
+            RecordBatch::try_new(schema.clone(), vec![Arc::new(Int32Array::from(ids))]).unwrap()
+        };
+
+        let table = conn
+            .create_table("test_stats_deletions", batch(vec![0, 1]))
+            .execute()
+            .await
+            .unwrap();
+        // A second fragment, holding only rows that the delete below removes.
+        table.add(batch(vec![1])).execute().await.unwrap();
+        table.add(batch(vec![1, 2, 3])).execute().await.unwrap();
+
+        let res = table.stats().await.unwrap();
+        assert_eq!(res.num_deleted_rows, Some(0));
+        assert_eq!(res.fragment_stats.num_fragments, 3);
+
+        table.delete("id = 1").await.unwrap();
+
+        let res = table.stats().await.unwrap();
+        // 6 rows are inserted over 3 fragments
+        // 3 rows with id = 1 are deleted (one in each fragment), and that empties the 2nd
+        // fragment, which is dropped outright rather than kept with a deletion
+        // file, so only the row marked in the surviving fragment are counted.
+        assert_eq!(res.num_deleted_rows, Some(2));
+        assert_eq!(res.num_rows, 3);
+        assert_eq!(res.fragment_stats.num_fragments, 2);
+        // The marked row is are physically there, so it counts towards the
+        // 3rd fragment length even though it is excluded from num_rows.
+        assert_eq!(res.fragment_stats.lengths.max, 3);
+
+        // Compaction materializes the deletions.
+        table
+            .optimize(OptimizeAction::Compact {
+                options: CompactionOptions {
+                    target_rows_per_fragment: 1000,
+                    ..Default::default()
+                },
+                remap_options: None,
+            })
+            .await
+            .unwrap();
+
+        let res = table.stats().await.unwrap();
+        assert_eq!(res.num_deleted_rows, Some(0));
+        assert_eq!(res.num_rows, 3);
+    }
 }
diff --git a/rust/lancedb/src/table/checkpoint.rs b/rust/lancedb/src/table/checkpoint.rs
index bb76604ed..c9a2f60d6 100644
--- a/rust/lancedb/src/table/checkpoint.rs
+++ b/rust/lancedb/src/table/checkpoint.rs
@@ -1,18 +1,18 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-//! Converging a table's LSM write path into its base table.
+//! Converging a table's MemWAL write path into its base table.
 //!
-//! `checkpoint_lsm` seals once, then triggers compaction and watches
-//! generation numbers until the L0 that existed at the start is gone.
+//! `checkpoint_memwal` freezes once, then triggers compaction and watches
+//! generation numbers until the SSTables that existed at the start are gone.
 //!
-//! The loop runs in the client, not the server: `compact_lsm` dispatches a
+//! The loop runs in the client, not the server: `compact_memwal` dispatches a
 //! pass and returns, so nothing holds a socket and a client can vanish
 //! mid-operation with nothing to reconcile. Completion is read from
 //! generation numbers in the shard manifest — durable state, unlike a count
 //! in a compact response, which a concurrent write invalidates.
 //!
-//! The target set is fixed at the start, so generations created *during* the
+//! The target set is fixed at the start, so SSTables created *during* the
 //! checkpoint are ignored. That is what lets it terminate under write load,
 //! and what makes it best-effort: it converges the fresh tier as of some
 //! instant. Idempotent, abandonable at any point, safe on a cadence.
@@ -68,7 +68,7 @@ fn is_lost_claim(e: &Error) -> bool {
     status_of(e) == Some(421)
 }
 
-/// Interval between `get_lsm_stats` polls. One interval is roughly one
+/// Interval between `get_memwal_stats` polls. One interval is roughly one
 /// compaction pass, the granularity at which the answer can change.
 ///
 /// Fixed rather than configurable, matching `wait_for_index`. It costs
@@ -149,15 +149,15 @@ where
     }
 }
 
-/// Drive [`Table::checkpoint_lsm`]: seal once, fix the target watermark
-/// from the resulting L0, then trigger and poll until it drains.
-pub(crate) async fn checkpoint_lsm(table: &Table) -> Result<()> {
+/// Drive [`Table::checkpoint_memwal`]: freeze once, fix the target watermark
+/// from the resulting SSTables, then trigger and poll until they drain.
+pub(crate) async fn checkpoint_memwal(table: &Table) -> Result<()> {
     for reissue in 0..=MAX_REISSUES {
-        // The seal turns everything written before this call into a
-        // generation, so the watermark has to be read after it. Idempotent:
-        // sealing an empty memtable is a no-op, so a re-issue does not churn
-        // empty generations.
-        match issue(|| table.flush_lsm()).await? {
+        // The freeze turns everything written before this call into an
+        // SSTable, so the watermark has to be read after it. Idempotent:
+        // freezing an empty memtable is a no-op, so a re-issue does not churn
+        // empty SSTables.
+        match issue(|| table.flush_memwal()).await? {
             Attempt::Ok(()) => {}
             Attempt::ReissueFromFlush => {
                 backoff(reissue).await;
@@ -165,7 +165,7 @@ pub(crate) async fn checkpoint_lsm(table: &Table) -> Result<()> {
             }
         }
 
-        let stats = match issue(|| table.get_lsm_stats(false)).await? {
+        let stats = match issue(|| table.get_memwal_stats(false)).await? {
             Attempt::Ok(stats) => stats,
             Attempt::ReissueFromFlush => {
                 backoff(reissue).await;
@@ -173,13 +173,13 @@ pub(crate) async fn checkpoint_lsm(table: &Table) -> Result<()> {
             }
         };
         let Some(stats) = stats else {
-            // Not WAL-backed; `flush_lsm` would have errored first but for a race.
+            // Not WAL-backed; `flush_memwal` would have errored first but for a race.
             return Ok(());
         };
         let targets: HashMap<String, u64> = stats
-            .buckets
+            .table_shards
             .iter()
-            .filter_map(|b| Some((b.shard_id.clone(), b.newest_generation()?)))
+            .filter_map(|b| Some((b.shard_id.clone(), b.newest_sstable_generation()?)))
             .collect();
         if targets.is_empty() {
             return Ok(());
@@ -194,18 +194,18 @@ pub(crate) async fn checkpoint_lsm(table: &Table) -> Result<()> {
         }
     }
     Err(Error::Runtime {
-        message: "checkpoint_lsm: the owning node kept losing its claim; \
+        message: "checkpoint_memwal: the owning node kept losing its claim; \
                   re-issued from flush the maximum number of times"
             .into(),
     })
 }
 
-/// Trigger and poll until no bucket holds a generation at or below its
+/// Trigger and poll until no table shard holds an SSTable generation at or below its
 /// target.
 ///
 /// No liveness bound, deliberately. The pod-wide compactor pool (a semaphore
 /// of 2 by default, shared across every table on the node) is taken *inside*
-/// the pass, after the bucket latch, so a checkpoint queued behind unrelated
+/// the pass, after the table shard latch, so a checkpoint queued behind unrelated
 /// tables is indistinguishable from one that is merging. An idle-poll counter
 /// here could only ever have fired on a table that would have finished.
 async fn drain_to_targets(
@@ -213,24 +213,24 @@ async fn drain_to_targets(
     targets: &HashMap<String, u64>,
 ) -> Result<CheckpointOutcome> {
     loop {
-        let stats = match issue(|| table.get_lsm_stats(false)).await? {
+        let stats = match issue(|| table.get_memwal_stats(false)).await? {
             Attempt::Ok(stats) => stats,
             Attempt::ReissueFromFlush => return Ok(CheckpointOutcome::ReissueFromFlush),
         };
         let Some(stats) = stats else {
             return Ok(CheckpointOutcome::Done);
         };
-        // `compacting` is the bucket's compaction latch, held from dispatch
+        // `compacting` is the table shard's compaction latch, held from dispatch
         // until the pass ends — including while it waits on the pod-wide
-        // permit. So it answers one question only: do not pile on. Buckets
-        // with nothing outstanding are skipped, not counted as idle.
+        // permit. So it answers one question only: do not pile on. Table
+        // shards with nothing outstanding are skipped, not counted as idle.
         let mut outstanding = 0;
         let mut all_compacting = true;
-        for b in &stats.buckets {
+        for b in &stats.table_shards {
             let Some(target) = targets.get(&b.shard_id) else {
                 continue;
             };
-            let n = b.outstanding_generations(*target);
+            let n = b.outstanding_sstables(*target);
             if n > 0 {
                 outstanding += n;
                 all_compacting &= b.compacting;
@@ -241,11 +241,11 @@ async fn drain_to_targets(
         }
 
         if !all_compacting {
-            match table.compact_lsm().await {
+            match table.compact_memwal().await {
                 Ok(()) => {}
                 Err(e) if is_lost_claim(&e) => return Ok(CheckpointOutcome::ReissueFromFlush),
                 Err(e) if !is_retryable(&e) => return Err(e),
-                // A 429 here means the server could latch no bucket at all,
+                // A 429 here means the server could latch no table shard at all,
                 // which the poll above already handles. Not retried in place:
                 // the latch it would contend for is the one doing the work, so
                 // fall through and re-read — `POLL_INTERVAL` is the backoff.
diff --git a/rust/lancedb/src/table/computed_columns.rs b/rust/lancedb/src/table/computed_columns.rs
index 98b4e1627..563f185b6 100644
--- a/rust/lancedb/src/table/computed_columns.rs
+++ b/rust/lancedb/src/table/computed_columns.rs
@@ -388,7 +388,7 @@ pub(crate) fn ensure_supported_function_metadata(schema: &ArrowSchema) -> Result
             }
         }
     }
-    Ok(())
+    ensure_computed_inputs_acyclic(schema, &bindings)
 }
 
 /// Refuse `operation` outright on a table with a Function binding. For
@@ -704,13 +704,7 @@ fn ensure_known_binding_shape(value: &Value) -> Result<()> {
         object
             .get("function")
             .ok_or_else(|| invalid_function("Function binding is missing its exact version"))?,
-        &[
-            "name",
-            "object_id",
-            "location",
-            "version",
-            "manifest_digest",
-        ],
+        crate::function::FUNCTION_VERSION_REF_FIELDS,
         "version reference",
     )?;
     for input in object
@@ -791,6 +785,157 @@ fn resolve_field_path<'a>(schema: &'a ArrowSchema, path: &str) -> Result<Resolve
     Ok(ResolvedFieldPath { root, leaf })
 }
 
+/// Whether a Function input rooted at `root` reads a computed column,
+/// refusing a computed column a Function cannot read.
+///
+/// A computed input is filled by its own refresh, so until then it holds
+/// placeholder nulls. A Function refresh treats a null computed input as not
+/// yet computed: it skips the row and leaves its outputs null for a later
+/// refresh, after the input is filled. A computed input that is null by
+/// definition therefore leaves the row unfilled too, which is the price of
+/// not tracking fill state per row.
+pub fn function_input_is_computed(root: &ArrowField, path: &str) -> Result<bool> {
+    if root
+        .metadata()
+        .get(COMPUTED_COLUMN_META_KEY)
+        .map(String::as_str)
+        != Some("true")
+    {
+        return Ok(false);
+    }
+    match computed_column_from_field(root).map(|column| column.kind) {
+        Some(ComputedColumnKind::Sql { .. }) => Ok(true),
+        Some(ComputedColumnKind::Function { output_ordinal, .. })
+            if output_ordinal != FUNCTION_ASSIGNMENT_OUTPUT_ORDINAL =>
+        {
+            Ok(true)
+        }
+        Some(ComputedColumnKind::Function { .. }) => Err(invalid_function(format!(
+            "Function input '{path}' is an internal Function assignment column"
+        ))),
+        Some(ComputedColumnKind::Unrecognized { kind }) => Err(Error::NotSupported {
+            message: format!(
+                "Function input '{path}' is computed by '{kind}', which this version of \
+                 lancedb cannot fill"
+            ),
+        }),
+        None => Err(invalid_function(format!(
+            "Function input '{path}' carries an incomplete computed-column declaration"
+        ))),
+    }
+}
+
+/// Nullability of the parameter a Function input binds: the source field's,
+/// except that a computed source never passes a null (see
+/// [`function_input_is_computed`]).
+fn function_input_nullability(resolved: &ResolvedFieldPath<'_>, path: &str) -> Result<bool> {
+    Ok(!function_input_is_computed(resolved.root, path)? && resolved.leaf.is_nullable())
+}
+
+/// The metadata a Function parameter carries from the column it binds: the
+/// column's semantic Arrow metadata, such as a Blob v2 extension, without its
+/// computed-column declaration or refresh stamps. A binding's input schema is
+/// immutable, and a refresh of a computed input rewrites those stamps, so
+/// they cannot be part of it. A plain column carries none of them, so its
+/// parameter keeps every key.
+pub fn function_input_metadata(field: &ArrowField) -> HashMap<String, String> {
+    field
+        .metadata()
+        .iter()
+        .filter(|(key, _)| !is_declaration_key(key))
+        .map(|(key, value)| (key.clone(), value.clone()))
+        .collect()
+}
+
+/// The input paths of `binding` that read a computed column. A Function
+/// refresh computes a row only where every one of them holds a value; see
+/// [`function_input_is_computed`].
+pub fn computed_function_inputs(
+    schema: &ArrowSchema,
+    binding: &FunctionBinding,
+) -> Result<Vec<String>> {
+    let mut computed = Vec::new();
+    for input in binding.inputs() {
+        let resolved = resolve_field_path(schema, &input.field_path)?;
+        if function_input_is_computed(resolved.root, &input.field_path)? {
+            computed.push(input.field_path.clone());
+        }
+    }
+    Ok(computed)
+}
+
+/// Refuse a schema whose computed columns read each other in a cycle: no
+/// refresh order could fill any column on it. A declaration can only read
+/// columns that already exist, so only a schema supplied whole can hold one.
+fn ensure_computed_inputs_acyclic(
+    schema: &ArrowSchema,
+    bindings: &[FunctionBinding],
+) -> Result<()> {
+    let columns = computed_columns(schema);
+    let declared = columns
+        .iter()
+        .map(|column| column.name.as_str())
+        .collect::<HashSet<_>>();
+    let bindings_by_id = bindings
+        .iter()
+        .map(|binding| (binding.binding_id(), binding))
+        .collect::<HashMap<_, _>>();
+    let mut reads: HashMap<&str, BTreeSet<String>> = HashMap::new();
+    for column in &columns {
+        let paths = match &column.kind {
+            ComputedColumnKind::Function { binding_id, .. } => bindings_by_id
+                .get(binding_id.as_str())
+                .map(|binding| {
+                    binding
+                        .inputs()
+                        .iter()
+                        .map(|input| input.field_path.clone())
+                        .collect::<Vec<_>>()
+                })
+                .unwrap_or_default(),
+            _ => column.inputs.clone(),
+        };
+        let roots = paths
+            .iter()
+            .map(|path| root(path))
+            .filter(|root| declared.contains(root.as_str()))
+            .collect();
+        reads.insert(column.name.as_str(), roots);
+    }
+
+    #[derive(Clone, Copy, PartialEq)]
+    enum Visit {
+        Open,
+        Done,
+    }
+    fn visit<'a>(
+        column: &'a str,
+        reads: &'a HashMap<&str, BTreeSet<String>>,
+        state: &mut HashMap<&'a str, Visit>,
+    ) -> Result<()> {
+        match state.get(column) {
+            Some(Visit::Done) => return Ok(()),
+            Some(Visit::Open) => {
+                return Err(Error::InvalidInput {
+                    message: format!("computed column '{column}' depends on itself"),
+                });
+            }
+            None => {}
+        }
+        state.insert(column, Visit::Open);
+        for input in reads.get(column).into_iter().flatten() {
+            visit(input.as_str(), reads, state)?;
+        }
+        state.insert(column, Visit::Done);
+        Ok(())
+    }
+    let mut state = HashMap::new();
+    for column in &columns {
+        visit(column.name.as_str(), &reads, &mut state)?;
+    }
+    Ok(())
+}
+
 fn canonical_input_arrow_type(field: &JsonArrowField) -> Result<String> {
     let is_blob_v2 = field
         .metadata
@@ -1010,21 +1155,21 @@ fn ensure_binding_matches_schema(schema: &ArrowSchema, binding: &FunctionBinding
     let mut input_fields = Vec::with_capacity(binding.inputs().len());
     for input in binding.inputs() {
         let resolved = resolve_field_path(schema, &input.field_path)?;
+        let computed = function_input_is_computed(resolved.root, &input.field_path)?;
         let field = resolved.leaf;
-        if field
-            .metadata()
-            .get(COMPUTED_COLUMN_META_KEY)
-            .map(String::as_str)
-            == Some("true")
-        {
+        if computed && input.nullable {
             return Err(invalid_function(format!(
-                "Function input '{}' is computed",
-                input.field_path
+                "Function input '{}' is computed, so parameter '{}' in binding '{}' must be \
+                 non-nullable: a refresh passes it only rows where the input holds a value",
+                input.field_path,
+                input.parameter,
+                binding.binding_id()
             )));
         }
         // A non-null source is within a nullable parameter's domain. The
         // reverse can pass nulls to a Function that does not accept them.
-        if field.is_nullable() && !input.nullable {
+        // A computed source never passes one: see `function_input_nullability`.
+        if !computed && field.is_nullable() && !input.nullable {
             return Err(invalid_function(format!(
                 "Function input column '{}' is nullable, but parameter '{}' in binding '{}' is non-nullable",
                 input.field_path,
@@ -1037,7 +1182,7 @@ fn ensure_binding_matches_schema(schema: &ArrowSchema, binding: &FunctionBinding
             field.data_type().clone(),
             input.nullable,
         )
-        .with_metadata(field.metadata().clone());
+        .with_metadata(function_input_metadata(field));
         let json = lance_namespace::schema::arrow_schema_to_json(&ArrowSchema::new(vec![
             parameter_field.clone(),
         ]))
@@ -1263,24 +1408,11 @@ pub(crate) fn plan_function_application(
             ))
         })?;
         let resolved = resolve_field_path(schema, path)?;
-        if resolved
-            .root
-            .metadata()
-            .get(COMPUTED_COLUMN_META_KEY)
-            .map(String::as_str)
-            == Some("true")
-        {
-            return Err(invalid_function(format!(
-                "Function input '{path}' is computed; computed-on-computed bindings are not supported"
-            )));
-        }
+        let nullable = function_input_nullability(&resolved, path)?;
         let field = resolved.leaf;
-        let parameter_field = ArrowField::new(
-            input.parameter.clone(),
-            field.data_type().clone(),
-            field.is_nullable(),
-        )
-        .with_metadata(field.metadata().clone());
+        let parameter_field =
+            ArrowField::new(input.parameter.clone(), field.data_type().clone(), nullable)
+                .with_metadata(function_input_metadata(field));
         let input_schema = lance_namespace::schema::arrow_schema_to_json(&ArrowSchema::new(vec![
             parameter_field.clone(),
         ]))
@@ -1290,7 +1422,7 @@ pub(crate) fn plan_function_application(
             parameter: input.parameter.clone(),
             field_path: path.to_string(),
             arrow_type: canonical_input_arrow_type(&json_field)?,
-            nullable: field.is_nullable(),
+            nullable,
         });
         input_fields.push(parameter_field);
     }
@@ -1555,9 +1687,9 @@ pub(crate) fn ensure_batch_writes_no_computed_values(
 /// Validate every computed-column declaration `schema` carries against the
 /// schema itself: every field with declaration metadata is a complete
 /// declaration, a SQL declaration re-plans to the field it declares, a
-/// Function declaration satisfies the binding contract, and no declaration
-/// reads another computed column. What passes here is what `refresh_column`
-/// can execute.
+/// Function declaration satisfies the binding contract, and no SQL
+/// declaration reads another computed column. What passes here is what
+/// `refresh_column` can execute.
 pub(crate) fn ensure_declarations_are_planned(schema: &ArrowSchema) -> Result<()> {
     let invalid = |message: String| Error::InvalidInput { message };
     // A field with any declaration key is a declaration; a partial one is
@@ -1616,28 +1748,8 @@ pub(crate) fn ensure_declarations_are_planned(schema: &ArrowSchema) -> Result<()
                     )));
                 }
             }
-            ComputedColumnKind::Function { binding_id, .. } => {
-                // The binding validator resolves each input's leaf; the
-                // no-computed-input rule is about the root it hangs from.
-                let bindings = function_bindings(schema)?;
-                let Some(binding) = bindings.iter().find(|b| b.binding_id() == binding_id) else {
-                    continue; // reported by the binding validator below
-                };
-                // Roots come from the canonical path parser: a quoted
-                // top-level name may itself contain a dot.
-                if let Some(input) = binding
-                    .inputs()
-                    .iter()
-                    .filter_map(|input| resolve_field_path(schema, &input.field_path).ok())
-                    .map(|resolved| resolved.root.name().as_str())
-                    .find(|r| declared.contains(*r))
-                {
-                    return Err(invalid(format!(
-                        "computed column '{}' reads computed column '{input}'",
-                        column.name
-                    )));
-                }
-            }
+            // The binding validator below checks what a Function reads.
+            ComputedColumnKind::Function { .. } => {}
             ComputedColumnKind::Unrecognized { kind } => {
                 return Err(Error::NotSupported {
                     message: format!(
@@ -2085,7 +2197,7 @@ pub(crate) fn plan(schema: SchemaRef, columns: &[(String, String)]) -> Result<Ve
 /// every declaration. For callers that stage declarations behind other work
 /// and need those rejections before any of it lands.
 ///
-/// Only the schema is consulted. Declaring also refuses a table with an LSM
+/// Only the schema is consulted. Declaring also refuses a table with a MemWAL
 /// write spec or retained SSTables; that is table state, checked at commit.
 ///
 /// ```
@@ -2913,7 +3025,7 @@ mod tests {
     /// The gate's reproducer: the reciprocal of the declare-under-spec check.
     #[tokio::test]
     async fn test_installing_an_lsm_spec_over_computed_columns_is_refused() {
-        use crate::table::LsmWriteSpec;
+        use crate::table::MemWalWriteSpec;
 
         let tmp_dir = tempfile::tempdir().unwrap();
         let conn = connect(tmp_dir.path().to_str().unwrap())
@@ -2941,14 +3053,14 @@ mod tests {
             .unwrap();
 
         let err = table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .enable_memwal(MemWalWriteSpec::unsharded())
             .await
             .unwrap_err();
         assert!(
             matches!(&err, Error::NotSupported { message } if message.contains("computed")),
             "{err:?}"
         );
-        assert!(table.get_lsm_write_spec().await.unwrap().is_none());
+        assert!(table.get_memwal_spec().await.unwrap().is_none());
     }
 
     /// The gate's reproducer: declaration metadata is admitted only through
@@ -3121,10 +3233,10 @@ mod tests {
         );
     }
 
-    /// Stale handles must not commit the computed/LSM state in either order.
+    /// Stale handles must not commit the computed/MemWAL state in either order.
     #[tokio::test]
     async fn test_stale_handles_cannot_mix_computed_and_lsm() {
-        use crate::table::LsmWriteSpec;
+        use crate::table::MemWalWriteSpec;
 
         let tmp_dir = tempfile::tempdir().unwrap();
         let uri = tmp_dir.path().to_str().unwrap();
@@ -3148,7 +3260,7 @@ mod tests {
             .await
             .unwrap();
         let err = stale
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .enable_memwal(MemWalWriteSpec::unsharded())
             .await
             .unwrap_err();
         assert!(matches!(err, Error::NotSupported { .. }), "install won");
@@ -3163,7 +3275,7 @@ mod tests {
         table.set_unenforced_primary_key(["x"]).await.unwrap();
         let stale = conn.open_table("mix2").execute().await.unwrap();
         table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .enable_memwal(MemWalWriteSpec::unsharded())
             .await
             .unwrap();
         let err = add_computed(&stale, &[("doubled".into(), "x * 2".into())])
@@ -3177,7 +3289,7 @@ mod tests {
     /// flag is the durable marker; declaration refuses on it.
     #[tokio::test]
     async fn test_unset_with_retained_lsm_rows_cannot_admit_a_declaration() {
-        use crate::table::LsmWriteSpec;
+        use crate::table::MemWalWriteSpec;
         use arrow_array::{Int64Array, RecordBatchIterator};
 
         let tmp_dir = tempfile::tempdir().unwrap();
@@ -3204,7 +3316,7 @@ mod tests {
             .unwrap();
         table.set_unenforced_primary_key(["id"]).await.unwrap();
         table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .enable_memwal(MemWalWriteSpec::unsharded())
             .await
             .unwrap();
 
@@ -3212,12 +3324,12 @@ mod tests {
         merge
             .when_matched_update_all(None)
             .when_not_matched_insert_all()
-            .use_lsm(true);
+            .use_memwal(true);
         merge
             .execute(Box::new(RecordBatchIterator::new(vec![Ok(batch)], schema)))
             .await
             .unwrap();
-        table.unset_lsm_write_spec().await.unwrap();
+        table.disable_memwal().await.unwrap();
 
         let err = add_computed(&table, &[("doubled".into(), "value * 2".into())])
             .await
@@ -3620,12 +3732,7 @@ mod tests {
             "title",
             DataType::Struct(vec![ArrowField::new("value", DataType::Utf8, true)].into()),
             true,
-        )
-        .with_metadata(HashMap::from([
-            (COMPUTED_COLUMN_META_KEY.to_string(), "true".to_string()),
-            (KIND_META_KEY.to_string(), SQL_KIND.to_string()),
-            (EXPRESSION_META_KEY.to_string(), "title".to_string()),
-        ]));
+        );
         let mut fields = vec![title, ArrowField::new("body", DataType::Utf8, true)];
         fields.extend(binding.outputs().iter().map(|output| {
             let data_type = match output.arrow_type.as_str() {
@@ -3703,11 +3810,11 @@ mod tests {
             }"#,
         )
         .unwrap();
-        let err = plan_function_application(&reopened, &dependent_application, Some("dependent"))
-            .unwrap_err();
-        assert!(
-            matches!(&err, Error::InvalidInput { message } if message.contains("computed-on-computed"))
-        );
+        let dependent =
+            plan_function_application(&reopened, &dependent_application, Some("dependent"))
+                .unwrap();
+        assert_eq!(dependent.input_bindings[0].field_path, "search_text");
+        assert!(!dependent.input_bindings[0].nullable);
         let plan = plan_function_application(
             &reopened,
             &named_struct_application(
@@ -3760,6 +3867,20 @@ mod tests {
         ensure_known_binding_shape(&raw_binding).unwrap();
     }
 
+    /// A binding names its Function by parts, so one declared against a
+    /// namespaced Function is part of the known contract too.
+    #[test]
+    fn test_namespaced_bindings_are_a_known_shape() {
+        let mut raw_binding: Value = serde_json::from_str(include_str!(
+            "../../tests/fixtures/first_class_functions/v1/remote_initialized_function_binding.json"
+        ))
+        .unwrap();
+        raw_binding["function"]["namespace_path"] = serde_json::json!(["analytics", "features"]);
+        ensure_known_binding_shape(&raw_binding).unwrap();
+        let binding = FunctionBinding::from_json(&raw_binding.to_string()).unwrap();
+        assert_eq!(binding.function().namespace_path, ["analytics", "features"]);
+    }
+
     #[test]
     fn test_named_struct_can_be_kept_as_one_nullable_physical_column() {
         let application = named_struct_application("{}");
@@ -4227,60 +4348,315 @@ mod tests {
     }
 
     #[test]
-    fn test_function_inputs_use_paths_and_cannot_be_computed() {
-        let mut schema = function_input_schema();
-        let plan =
-            plan_function_application(&schema, &named_struct_application("{}"), None).unwrap();
+    fn test_function_inputs_use_paths() {
+        let plan = plan_function_application(
+            &function_input_schema(),
+            &named_struct_application("{}"),
+            None,
+        )
+        .unwrap();
         assert_eq!(plan.input_bindings[0].field_path, "title");
         assert_eq!(plan.input_bindings[1].field_path, "body");
+        assert!(plan.input_bindings.iter().all(|input| input.nullable));
+    }
 
-        let title = schema
-            .field(0)
-            .as_ref()
-            .clone()
-            .with_metadata(HashMap::from([
-                (COMPUTED_COLUMN_META_KEY.to_string(), "true".to_string()),
-                (KIND_META_KEY.to_string(), SQL_KIND.to_string()),
-                (EXPRESSION_META_KEY.to_string(), "title".to_string()),
-            ]));
-        schema = ArrowSchema::new(vec![title, schema.field(1).as_ref().clone()]);
-        let err =
-            plan_function_application(&schema, &named_struct_application("{}"), None).unwrap_err();
+    /// Declare `plan` on `schema` the way the table service persists it: the
+    /// binding in schema metadata, one nullable field per output.
+    fn declare_plan(
+        schema: &ArrowSchema,
+        plan: &FunctionDeclarationPlan,
+        binding_id: &str,
+    ) -> ArrowSchema {
+        let mut raw = serde_json::to_value(binding_from_plan(plan)).unwrap();
+        raw["binding_id"] = Value::String(binding_id.to_string());
+        let binding: FunctionBinding = serde_json::from_value(raw).unwrap();
+        let inputs = binding
+            .inputs()
+            .iter()
+            .map(|input| input.field_path.clone())
+            .collect::<Vec<_>>();
+        let mut fields = schema
+            .fields()
+            .iter()
+            .map(|field| field.as_ref().clone())
+            .collect::<Vec<_>>();
+        for (output, field) in binding.outputs().iter().zip(&plan.output_schema.fields) {
+            let field = lance_namespace::schema::convert_json_arrow_field(field).unwrap();
+            fields.push(field.with_metadata(function_computed_column_metadata(
+                binding_id,
+                output.output_ordinal,
+                &inputs,
+            )));
+        }
+        let mut bindings = function_bindings(schema).unwrap();
+        bindings.push(binding);
+        ArrowSchema::new_with_metadata(
+            fields,
+            HashMap::from([(
+                FUNCTION_BINDINGS_META_KEY.to_string(),
+                function_bindings_metadata(&bindings).unwrap(),
+            )]),
+        )
+    }
+
+    fn sql_computed_field(name: &str, data_type: DataType, expression: &str) -> ArrowField {
+        ArrowField::new(name, data_type, true)
+            .with_metadata(computed_column_metadata(expression, &["title".to_string()]))
+    }
+
+    #[test]
+    fn test_function_reads_a_function_output() {
+        let upstream = plan_function_application(
+            &function_input_schema(),
+            &named_struct_application("{}"),
+            None,
+        )
+        .unwrap();
+        let schema = declare_plan(&function_input_schema(), &upstream, "fb_upstream");
+
+        let downstream = plan_function_application(
+            &schema,
+            &single_input_application("normalized_text"),
+            Some("inspected"),
+        )
+        .unwrap();
+        // The refresh skips a row whose computed input is still null, so the
+        // Function only ever sees a value.
+        assert!(!downstream.input_bindings[0].nullable);
+        assert!(!downstream.input_schema.fields[0].nullable);
+
+        let schema = declare_plan(&schema, &downstream, "fb_downstream");
+        ensure_supported_function_metadata(&schema).unwrap();
+        ensure_declarations_are_planned(&schema).unwrap();
+        let bindings = function_bindings(&schema).unwrap();
         assert!(
-            matches!(&err, Error::InvalidInput { message } if message.contains("computed-on-computed"))
+            computed_function_inputs(&schema, &bindings[0])
+                .unwrap()
+                .is_empty()
+        );
+        assert_eq!(
+            computed_function_inputs(&schema, &bindings[1]).unwrap(),
+            ["normalized_text"]
         );
 
-        let nested_title = ArrowField::new(
+        // An upstream output a binding reads cannot be dropped out from under it.
+        let unbinding =
+            plan_function_unbinding(&schema, &["normalized_text", "token_count"]).unwrap();
+        assert!(
+            unbinding
+                .ensure_retained_unaffected("drop", ["normalized_text"])
+                .is_err()
+        );
+        let unbinding =
+            plan_function_unbinding(&schema, &["normalized_text", "token_count", "inspected"])
+                .unwrap();
+        assert!(unbinding.retained.is_empty());
+    }
+
+    #[test]
+    fn test_function_reads_a_sql_computed_column_and_nested_path() {
+        let title = ArrowField::new(
             "title",
             DataType::Struct(vec![ArrowField::new("value", DataType::Utf8, true)].into()),
             true,
-        )
-        .with_metadata(HashMap::from([
-            (COMPUTED_COLUMN_META_KEY.to_string(), "true".to_string()),
-            (KIND_META_KEY.to_string(), SQL_KIND.to_string()),
-            (
-                EXPRESSION_META_KEY.to_string(),
-                "struct('value')".to_string(),
+        );
+        let schema = ArrowSchema::new(vec![
+            title,
+            sql_computed_field("upper_title", DataType::Utf8, "upper(title.value)"),
+            sql_computed_field(
+                "wrapped",
+                DataType::Struct(vec![ArrowField::new("value", DataType::Utf8, true)].into()),
+                "named_struct('value', title.value)",
             ),
-        ]));
-        let nested_schema = ArrowSchema::new(vec![nested_title, schema.field(1).as_ref().clone()]);
-        let nested_application = FunctionApplication::from_json(
-            r#"{
-                "function":{"name":"text_features","version":"1","object_id":"fixture","location":"memory:///fixture","manifest_digest":"sha256:7e22f815b6648e14f093a3979a8e5a2082fa773ebe1ec84b135cae7e84d6f8e6"},
-                "inputs":[
-                    {"parameter":"title","kind":"column","value":{"path":"title.value"}},
-                    {"parameter":"body","kind":"column","value":{"path":"body"}}
-                ],
-                "output":{"kind":"named_struct","fields":[
-                    {"name":"normalized_text","arrow_type":"utf8","nullable":false},
-                    {"name":"token_count","arrow_type":"int64","nullable":false}
-                ]}
-            }"#,
+        ]);
+        for path in ["upper_title", "wrapped.value"] {
+            let plan =
+                plan_function_application(&schema, &single_input_application(path), Some("out"))
+                    .unwrap();
+            assert_eq!(plan.input_bindings[0].field_path, path);
+            assert!(!plan.input_bindings[0].nullable, "{path}");
+        }
+    }
+
+    /// A refresh of a computed input rewrites its freshness stamps; the
+    /// downstream binding's immutable input schema must not record them, or
+    /// the first upstream refresh strands the table.
+    #[tokio::test]
+    async fn test_computed_input_survives_upstream_refresh() {
+        use arrow_array::{Int32Array, Int64Array, RecordBatch};
+
+        let base = ArrowSchema::new(vec![ArrowField::new("x", DataType::Int32, true)]);
+        let mut fields = base.fields().to_vec();
+        fields.extend(
+            plan(Arc::new(base), &[("doubled".into(), "x * 2".into())])
+                .unwrap()
+                .into_iter()
+                .map(Arc::new),
+        );
+        let schema = ArrowSchema::new(fields);
+        let downstream = plan_function_application(
+            &schema,
+            &single_input_application("doubled"),
+            Some("inspected"),
         )
         .unwrap();
-        let err = plan_function_application(&nested_schema, &nested_application, None).unwrap_err();
         assert!(
-            matches!(&err, Error::InvalidInput { message } if message.contains("computed-on-computed"))
+            downstream.input_schema.fields[0]
+                .metadata
+                .as_ref()
+                .is_none_or(|metadata| !metadata.keys().any(|key| is_declaration_key(key)))
+        );
+        let schema = Arc::new(declare_plan(&schema, &downstream, "fb_downstream"));
+        ensure_declarations_are_planned(&schema).unwrap();
+        let batch = RecordBatch::try_new(
+            schema,
+            vec![
+                Arc::new(Int32Array::from(vec![1])),
+                Arc::new(Int32Array::from(vec![None])),
+                Arc::new(Int64Array::from(vec![None])),
+            ],
+        )
+        .unwrap();
+        let conn = connect("memory://").execute().await.unwrap();
+        let table = conn
+            .create_table("upstream_refresh", batch)
+            .execute()
+            .await
+            .unwrap();
+        assert_eq!(
+            table.refresh_column("doubled").await.unwrap().rows_filled,
+            1
+        );
+
+        ensure_supported_function_metadata(&table.schema().await.unwrap()).unwrap();
+        table
+            .add(record_batch!(("x", Int32, [2])).unwrap())
+            .execute()
+            .await
+            .unwrap();
+        table.drop_columns(&["inspected"]).await.unwrap();
+    }
+
+    #[test]
+    fn test_a_computed_input_must_bind_a_non_nullable_parameter() {
+        let upstream = plan_function_application(
+            &function_input_schema(),
+            &named_struct_application("{}"),
+            None,
+        )
+        .unwrap();
+        let schema = declare_plan(&function_input_schema(), &upstream, "fb_upstream");
+        let mut downstream = plan_function_application(
+            &schema,
+            &single_input_application("normalized_text"),
+            Some("inspected"),
+        )
+        .unwrap();
+        downstream.input_bindings[0].nullable = true;
+        downstream.input_schema.fields[0].nullable = true;
+        let schema = declare_plan(&schema, &downstream, "fb_downstream");
+        let err = ensure_supported_function_metadata(&schema).unwrap_err();
+        assert!(
+            matches!(&err, Error::InvalidInput { message } if message.contains("non-nullable")),
+            "{err:?}"
+        );
+    }
+
+    #[test]
+    fn test_function_cannot_read_an_unreadable_computed_column() {
+        // A schema carrying either of these fails validation before any input
+        // is resolved, so the rule is exercised on the field itself.
+        let unknown =
+            ArrowField::new("future", DataType::Utf8, true).with_metadata(HashMap::from([
+                (COMPUTED_COLUMN_META_KEY.to_string(), "true".to_string()),
+                (KIND_META_KEY.to_string(), "from_the_future".to_string()),
+                (INPUTS_META_KEY.to_string(), "[]".to_string()),
+            ]));
+        let err = function_input_is_computed(&unknown, "future").unwrap_err();
+        assert!(matches!(err, Error::NotSupported { .. }), "{err:?}");
+        let incomplete = ArrowField::new("partial", DataType::Utf8, true).with_metadata(
+            HashMap::from([(COMPUTED_COLUMN_META_KEY.to_string(), "true".to_string())]),
+        );
+        let err = function_input_is_computed(&incomplete, "partial").unwrap_err();
+        assert!(
+            matches!(&err, Error::InvalidInput { message } if message.contains("incomplete")),
+            "{err:?}"
+        );
+
+        let mut raw_binding: Value = serde_json::from_str(include_str!(
+            "../../tests/fixtures/first_class_functions/v1/remote_function_binding.json"
+        ))
+        .unwrap();
+        raw_binding["outputs"][0]["nullable"] = Value::Bool(true);
+        raw_binding["outputs"][1]["nullable"] = Value::Bool(true);
+        raw_binding["assignment"] = serde_json::json!({
+            "output_name": "__function_assignment_fb_01K3TEXT",
+            "output_field_id": -1,
+        });
+        raw_binding["output_schema"]["fields"]
+            .as_array_mut()
+            .unwrap()
+            .push(serde_json::json!({
+                "name": "__function_assignment_fb_01K3TEXT",
+                "nullable": true,
+                "type": {"type": "bool"},
+            }));
+        let binding: FunctionBinding = serde_json::from_value(raw_binding).unwrap();
+        let schema = ArrowSchema::new_with_metadata(
+            valid_function_binding_schema(true, true, &binding)
+                .fields()
+                .to_vec(),
+            HashMap::from([(
+                FUNCTION_BINDINGS_META_KEY.to_string(),
+                function_bindings_metadata(std::slice::from_ref(&binding)).unwrap(),
+            )]),
+        );
+        let err = plan_function_application(
+            &schema,
+            &single_input_application("__function_assignment_fb_01K3TEXT"),
+            Some("out"),
+        )
+        .unwrap_err();
+        assert!(
+            matches!(&err, Error::InvalidInput { message } if message.contains("assignment")),
+            "{err:?}"
+        );
+    }
+
+    #[test]
+    fn test_computed_columns_cannot_read_each_other_in_a_cycle() {
+        let upstream = plan_function_application(
+            &function_input_schema(),
+            &named_struct_application("{}"),
+            None,
+        )
+        .unwrap();
+        let schema = declare_plan(&function_input_schema(), &upstream, "fb_upstream");
+        let downstream = plan_function_application(
+            &schema,
+            &single_input_application("normalized_text"),
+            Some("inspected"),
+        )
+        .unwrap();
+        let schema = declare_plan(&schema, &downstream, "fb_downstream");
+
+        // Rewire the upstream binding to read the downstream output: a graph
+        // only a whole-schema write could carry. Field metadata is irrelevant;
+        // a Function column's inputs are its binding's.
+        let mut bindings = function_bindings(&schema)
+            .unwrap()
+            .into_iter()
+            .map(|binding| serde_json::to_value(binding).unwrap())
+            .collect::<Vec<_>>();
+        bindings[0]["inputs"][0]["field_path"] = Value::String("inspected".to_string());
+        let bindings = bindings
+            .into_iter()
+            .map(|binding| serde_json::from_value(binding).unwrap())
+            .collect::<Vec<FunctionBinding>>();
+        let err = ensure_computed_inputs_acyclic(&schema, &bindings).unwrap_err();
+        assert!(
+            matches!(&err, Error::InvalidInput { message } if message.contains("depends on itself")),
+            "{err:?}"
         );
     }
 }
diff --git a/rust/lancedb/src/table/create_index.rs b/rust/lancedb/src/table/create_index.rs
index 0a0d00da1..3ac759bdd 100644
--- a/rust/lancedb/src/table/create_index.rs
+++ b/rust/lancedb/src/table/create_index.rs
@@ -29,8 +29,8 @@ use crate::index::Index;
 use crate::index::vector::{VectorIndex, suggested_num_sub_vectors};
 use crate::utils::{
     resolve_lance_fts_field_path, supported_bitmap_data_type, supported_btree_data_type,
-    supported_fm_data_type, supported_fts_data_type, supported_label_list_data_type,
-    supported_vector_data_type, supported_zonemap_data_type,
+    supported_fm_data_type, supported_label_list_data_type, supported_vector_data_type,
+    supported_zonemap_data_type, validate_fts_field,
 };
 
 use super::NativeTable;
@@ -160,6 +160,12 @@ impl NativeTable {
             builder = builder.name(name);
         }
         builder.await?;
+        // A writer already open was built before this index existed, so it has
+        // to pick it up here or a read needing it is refused until it reopens.
+        self.dataset
+            .shard_writer()
+            .refresh_maintained_indexes(&dataset)
+            .await;
         self.dataset.update(dataset);
         Ok(())
     }
@@ -286,7 +292,7 @@ impl NativeTable {
                 ))
             }
             Index::FTS(fts_opts) => {
-                Self::validate_index_type(field, "FTS", supported_fts_data_type)?;
+                validate_fts_field(field)?;
                 Ok(Box::new(fts_opts))
             }
             Index::IvfFlat(index) => {
@@ -2078,6 +2084,50 @@ mod tests {
         assert_eq!(index.columns, vec!["tags".to_string()]);
     }
 
+    #[tokio::test]
+    async fn test_list_indices_with_missing_fts_index_files() {
+        let tmp_dir = tempdir().unwrap();
+        let uri = tmp_dir.path().to_str().unwrap();
+        let conn = connect(uri).execute().await.unwrap();
+        let batch = record_batch!(
+            ("id", Int32, [1, 2, 3]),
+            ("text", Utf8, ["alpha", "beta", "gamma"])
+        )
+        .unwrap();
+        let table = conn.create_table("t", batch).execute().await.unwrap();
+        table
+            .create_index(&["text"], Index::FTS(FtsIndexBuilder::default()))
+            .execute()
+            .await
+            .unwrap();
+        table
+            .create_index(&["id"], Index::BTree(BTreeIndexBuilder::default()))
+            .execute()
+            .await
+            .unwrap();
+
+        let fts_uuid = table
+            .list_indices()
+            .await
+            .unwrap()
+            .into_iter()
+            .find(|index| index.name == "text_idx")
+            .and_then(|index| index.index_uuid)
+            .unwrap();
+        std::fs::remove_dir_all(tmp_dir.path().join("t.lance/_indices").join(fts_uuid)).unwrap();
+
+        // A new connection, so the index cache cannot serve the deleted files.
+        let conn = connect(uri).execute().await.unwrap();
+        let table = conn.open_table("t").execute().await.unwrap();
+        let mut indices = table.list_indices().await.unwrap();
+        indices.sort_by(|a, b| a.name.cmp(&b.name));
+        let names = indices.iter().map(|i| i.name.as_str()).collect::<Vec<_>>();
+        assert_eq!(names, vec!["id_idx", "text_idx"]);
+        assert_eq!(indices[1].index_type, crate::index::IndexType::FTS);
+        assert_eq!(indices[1].index_details, None);
+        assert!(indices[0].index_details.is_some());
+    }
+
     #[tokio::test]
     async fn test_create_inverted_index() {
         let conn = connect("memory://").execute().await.unwrap();
diff --git a/rust/lancedb/src/table/datafusion.rs b/rust/lancedb/src/table/datafusion.rs
index 85366910b..8bb3d51cb 100644
--- a/rust/lancedb/src/table/datafusion.rs
+++ b/rust/lancedb/src/table/datafusion.rs
@@ -20,7 +20,8 @@ use datafusion_common::{DataFusionError, Result as DataFusionResult, Statistics}
 use datafusion_execution::{SendableRecordBatchStream, TaskContext};
 use datafusion_expr::{Expr, TableProviderFilterPushDown, TableType, dml::InsertOp};
 use datafusion_physical_plan::{
-    DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties, stream::RecordBatchStreamAdapter,
+    DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties, execution_plan::CardinalityEffect,
+    stream::RecordBatchStreamAdapter,
 };
 use futures::{TryFutureExt, TryStreamExt};
 use lance::dataset::{WriteMode, WriteParams};
@@ -123,12 +124,11 @@ impl ExecutionPlan for MetadataEraserExec {
         children: Vec<Arc<dyn ExecutionPlan>>,
     ) -> DataFusionResult<Arc<dyn ExecutionPlan>> {
         assert_eq!(children.len(), 1);
-        let new_properties = Self::compute_properties_from_input(&children[0], &self.schema);
-        Ok(Arc::new(Self {
-            input: children[0].clone(),
-            schema: self.schema.clone(),
-            properties: new_properties,
-        }))
+        Ok(Arc::new(Self::new(children[0].clone())))
+    }
+
+    fn cardinality_effect(&self) -> CardinalityEffect {
+        CardinalityEffect::Equal
     }
 
     fn execute(
@@ -166,6 +166,7 @@ pub struct BaseTableAdapter {
     table: Arc<dyn BaseTable>,
     schema: Arc<ArrowSchema>,
     fts_query: Option<FullTextSearchQuery>,
+    use_memwal: Option<bool>,
 }
 
 impl BaseTableAdapter {
@@ -181,9 +182,16 @@ impl BaseTableAdapter {
             table,
             schema: Arc::new(schema),
             fts_query: None,
+            use_memwal: None,
         })
     }
 
+    /// Set the MemWAL read routing of every scan; see [`QueryRequest::use_memwal`].
+    pub fn with_use_memwal(mut self, use_memwal: Option<bool>) -> Self {
+        self.use_memwal = use_memwal;
+        self
+    }
+
     /// Create a new adapter with an FTS query applied.
     pub fn with_fts_query(&self, fts_query: FullTextSearchQuery) -> Self {
         // Add _score column to the schema
@@ -196,6 +204,7 @@ impl BaseTableAdapter {
             table: self.table.clone(),
             schema,
             fts_query: Some(fts_query),
+            use_memwal: self.use_memwal,
         }
     }
 }
@@ -223,6 +232,7 @@ impl TableProvider for BaseTableAdapter {
         let mut query = QueryRequest {
             full_text_search: self.fts_query.clone(),
             disable_scoring_autoprojection: disable_scoring,
+            use_memwal: self.use_memwal,
             ..Default::default()
         };
 
@@ -668,4 +678,97 @@ pub mod tests {
 
         assert!(matches!(partition_stats.num_rows, Precision::Exact(10)));
     }
+
+    #[tokio::test]
+    async fn test_count_pushdown_through_metadata_eraser() {
+        let fixture = TestFixture::new().await;
+        let state = datafusion::execution::SessionStateBuilder::new()
+            .with_default_features()
+            .with_physical_optimizer_rule(Arc::new(lance::io::exec::count_pushdown::CountPushdown))
+            .build();
+        let ctx = SessionContext::new_with_state(state);
+        ctx.register_table("foo", fixture.adapter.clone()).unwrap();
+
+        let df = ctx
+            .sql("SELECT COUNT(*) FROM foo WHERE indexed > 2")
+            .await
+            .unwrap();
+        let plan = df.clone().create_physical_plan().await.unwrap();
+        let plan = datafusion_physical_plan::displayable(plan.as_ref())
+            .indent(true)
+            .to_string();
+        assert!(plan.contains("CountFromMask"), "{plan}");
+
+        let batches = df.collect().await.unwrap();
+        assert_eq!(
+            batches[0]
+                .column(0)
+                .as_primitive::<arrow_array::types::Int64Type>()
+                .value(0),
+            7
+        );
+    }
+
+    #[tokio::test]
+    async fn test_topk_late_materialization_over_table_adapter() {
+        use lance::io::exec::topk_late_materialization::TopKLateMaterialization;
+
+        let fixture = TestFixture::new().await;
+        let run = |rule: Option<TopKLateMaterialization>| {
+            let adapter = fixture.adapter2.clone();
+            async move {
+                let mut state =
+                    datafusion::execution::SessionStateBuilder::new().with_default_features();
+                if let Some(rule) = rule {
+                    state = state.with_physical_optimizer_rule(Arc::new(rule));
+                }
+                let ctx = SessionContext::new_with_state(state.build());
+                ctx.register_table("tbl2", adapter).unwrap();
+                let df = ctx
+                    .sql("SELECT ints, strings, jsons FROM tbl2 WHERE floats > 10 ORDER BY ints DESC LIMIT 5")
+                    .await
+                    .unwrap();
+                let plan = df.clone().create_physical_plan().await.unwrap();
+                let plan = datafusion_physical_plan::displayable(plan.as_ref())
+                    .indent(true)
+                    .to_string();
+                (plan, df.collect().await.unwrap())
+            }
+        };
+
+        let (plan, expected) = run(None).await;
+        assert!(!plan.contains("Take"), "{plan}");
+
+        // The sort lands below `MetadataEraserExec`, so the rule needs no
+        // certification for it.
+        let (plan, batches) = run(Some(TopKLateMaterialization::new())).await;
+        assert!(plan.contains("Take"), "{plan}");
+        assert_eq!(batches, expected);
+        assert!(batches[0].schema().metadata().is_empty());
+    }
+
+    #[tokio::test]
+    async fn test_metadata_eraser_with_new_children_takes_child_schema() {
+        let fixture = TestFixture::new().await;
+        let ctx = SessionContext::new();
+        let scan = |adapter: Arc<BaseTableAdapter>, name: &str| {
+            LogicalPlanBuilder::scan(name, provider_as_source(adapter), None)
+                .unwrap()
+                .build()
+                .unwrap()
+        };
+        let foo = ctx
+            .state()
+            .create_physical_plan(&scan(fixture.adapter.clone(), "foo"))
+            .await
+            .unwrap();
+        let tbl2 = ctx
+            .state()
+            .create_physical_plan(&scan(fixture.adapter2.clone(), "tbl2"))
+            .await
+            .unwrap();
+
+        let rewired = foo.with_new_children(vec![tbl2.clone()]).unwrap();
+        assert_eq!(rewired.schema(), tbl2.schema());
+    }
 }
diff --git a/rust/lancedb/src/table/datafusion/blob_coerce.rs b/rust/lancedb/src/table/datafusion/blob_coerce.rs
index 3b79ec022..80b6cd6fc 100644
--- a/rust/lancedb/src/table/datafusion/blob_coerce.rs
+++ b/rust/lancedb/src/table/datafusion/blob_coerce.rs
@@ -9,16 +9,22 @@ use std::fmt;
 use std::hash::{Hash, Hasher};
 use std::sync::Arc;
 
-use arrow_array::{Array, BooleanArray, RecordBatch};
+use arrow_array::{
+    Array, ArrayRef, BooleanArray, FixedSizeListArray, LargeListArray, ListArray, RecordBatch,
+};
+use arrow_buffer::OffsetBuffer;
+use arrow_cast::cast;
 use arrow_schema::{DataType, Field, FieldRef, Fields, Schema};
 use arrow_select::nullif::nullif;
 use datafusion::functions::core::{get_field, named_struct};
-use datafusion_common::ScalarValue;
 use datafusion_common::config::ConfigOptions;
+use datafusion_common::{DataFusionError, ScalarValue};
 use datafusion_expr::ColumnarValue;
 use datafusion_physical_expr::ScalarFunctionExpr;
 use datafusion_physical_expr::expressions::{CastExpr, Literal};
 use datafusion_physical_plan::PhysicalExpr;
+use datafusion_physical_plan::expressions::Column;
+use lance_arrow::FieldExt;
 
 use crate::error::{Error, Result};
 
@@ -157,6 +163,198 @@ pub(super) fn coerce_blob_expr(
     Ok((expr, table_field.clone()))
 }
 
+/// Apply the normal blob coercion to list values while retaining the list's
+/// offsets and validity. A child batch lets the existing blob expression handle
+/// binary, null, and descriptor inputs exactly as it does for top-level fields.
+pub(super) fn coerce_blob_list_expr(
+    input_expr: Arc<dyn PhysicalExpr>,
+    input_field: &Field,
+    table_field: &FieldRef,
+    config: &Arc<ConfigOptions>,
+) -> Result<Option<(Arc<dyn PhysicalExpr>, FieldRef)>> {
+    let (DataType::List(input_item)
+    | DataType::LargeList(input_item)
+    | DataType::FixedSizeList(input_item, _)) = input_field.data_type()
+    else {
+        return Ok(None);
+    };
+    let (DataType::List(table_item)
+    | DataType::LargeList(table_item)
+    | DataType::FixedSizeList(table_item, _)) = table_field.data_type()
+    else {
+        return Ok(None);
+    };
+    if !table_item.is_blob_v2() || input_item == table_item {
+        return Ok(None);
+    }
+
+    let (item_expr, _) = coerce_blob_expr(
+        Arc::new(Column::new(input_item.name(), 0)),
+        input_item,
+        table_item,
+        config,
+    )?;
+    let expr: Arc<dyn PhysicalExpr> = Arc::new(CoerceBlobList {
+        source: input_expr,
+        input_item: input_item.clone(),
+        item_expr,
+        field: table_field.clone(),
+    });
+    Ok(Some((expr, table_field.clone())))
+}
+
+#[derive(Debug, Clone)]
+struct CoerceBlobList {
+    source: Arc<dyn PhysicalExpr>,
+    input_item: FieldRef,
+    item_expr: Arc<dyn PhysicalExpr>,
+    field: FieldRef,
+}
+
+impl fmt::Display for CoerceBlobList {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        write!(f, "coerce_blob_list({})", self.source)
+    }
+}
+
+impl PartialEq for CoerceBlobList {
+    fn eq(&self, other: &Self) -> bool {
+        self.source.eq(&other.source)
+            && self.input_item == other.input_item
+            && self.item_expr.eq(&other.item_expr)
+            && self.field == other.field
+    }
+}
+
+impl Eq for CoerceBlobList {}
+
+impl Hash for CoerceBlobList {
+    fn hash<H: Hasher>(&self, state: &mut H) {
+        self.source.hash(state);
+        self.input_item.hash(state);
+        self.item_expr.hash(state);
+        self.field.hash(state);
+    }
+}
+
+impl PhysicalExpr for CoerceBlobList {
+    fn return_field(&self, _input_schema: &Schema) -> datafusion_common::Result<FieldRef> {
+        Ok(self.field.clone())
+    }
+
+    fn nullable(&self, _input_schema: &Schema) -> datafusion_common::Result<bool> {
+        Ok(self.field.is_nullable())
+    }
+
+    fn evaluate(&self, batch: &RecordBatch) -> datafusion_common::Result<ColumnarValue> {
+        let source = self.source.evaluate(batch)?.into_array(batch.num_rows())?;
+        // First convert the outer list kind, if needed, while keeping its binary
+        // item type. PyArrow commonly infers List even for a LargeList column.
+        let intermediate_type = match self.field.data_type() {
+            DataType::List(_) => DataType::List(self.input_item.clone()),
+            DataType::LargeList(_) => DataType::LargeList(self.input_item.clone()),
+            DataType::FixedSizeList(_, size) => {
+                DataType::FixedSizeList(self.input_item.clone(), *size)
+            }
+            _ => unreachable!("validated list type when building the expression"),
+        };
+        let source = if source.data_type() == &intermediate_type {
+            source
+        } else {
+            cast(source.as_ref(), &intermediate_type)?
+        };
+        let values = match source.data_type() {
+            DataType::List(_) => {
+                let list = source.as_any().downcast_ref::<ListArray>().unwrap();
+                let first = list.offsets()[0] as usize;
+                let last = *list.offsets().last().unwrap() as usize;
+                list.values().slice(first, last - first)
+            }
+            DataType::LargeList(_) => {
+                let list = source.as_any().downcast_ref::<LargeListArray>().unwrap();
+                let first = list.offsets()[0] as usize;
+                let last = *list.offsets().last().unwrap() as usize;
+                list.values().slice(first, last - first)
+            }
+            DataType::FixedSizeList(_, _) => source
+                .as_any()
+                .downcast_ref::<FixedSizeListArray>()
+                .unwrap()
+                .values()
+                .clone(),
+            other => {
+                return Err(DataFusionError::Internal(format!(
+                    "coerce_blob_list expected a list, got {other}"
+                )));
+            }
+        };
+        let item_batch = RecordBatch::try_new(
+            Arc::new(Schema::new(vec![self.input_item.clone()])),
+            vec![values.clone()],
+        )?;
+        let coerced = self
+            .item_expr
+            .evaluate(&item_batch)?
+            .into_array(values.len())?;
+
+        let rebuilt: ArrayRef = match (source.data_type(), self.field.data_type()) {
+            (DataType::List(_), DataType::List(item)) => {
+                let list = source.as_any().downcast_ref::<ListArray>().unwrap();
+                Arc::new(ListArray::try_new(
+                    item.clone(),
+                    OffsetBuffer::from_lengths(list.offsets().lengths()),
+                    coerced,
+                    list.nulls().cloned(),
+                )?)
+            }
+            (DataType::LargeList(_), DataType::LargeList(item)) => {
+                let list = source.as_any().downcast_ref::<LargeListArray>().unwrap();
+                Arc::new(LargeListArray::try_new(
+                    item.clone(),
+                    OffsetBuffer::from_lengths(list.offsets().lengths()),
+                    coerced,
+                    list.nulls().cloned(),
+                )?)
+            }
+            (DataType::FixedSizeList(_, _), DataType::FixedSizeList(item, size)) => {
+                let list = source
+                    .as_any()
+                    .downcast_ref::<FixedSizeListArray>()
+                    .unwrap();
+                Arc::new(FixedSizeListArray::try_new_with_length(
+                    item.clone(),
+                    *size,
+                    coerced,
+                    list.nulls().cloned(),
+                    list.len(),
+                )?)
+            }
+            _ => unreachable!("validated list types when building the expression"),
+        };
+        Ok(ColumnarValue::Array(rebuilt))
+    }
+
+    fn children(&self) -> Vec<&Arc<dyn PhysicalExpr>> {
+        vec![&self.source]
+    }
+
+    fn with_new_children(
+        self: Arc<Self>,
+        children: Vec<Arc<dyn PhysicalExpr>>,
+    ) -> datafusion_common::Result<Arc<dyn PhysicalExpr>> {
+        Ok(Arc::new(Self {
+            source: children[0].clone(),
+            input_item: self.input_item.clone(),
+            item_expr: self.item_expr.clone(),
+            field: self.field.clone(),
+        }))
+    }
+
+    fn fmt_sql(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        write!(f, "{self}")
+    }
+}
+
 /// Carries the source column's nullity onto the struct built for it.
 ///
 /// This is its own expression rather than a `CASE` because the projection
@@ -253,9 +451,11 @@ mod tests {
     use super::*;
     use crate::blob::blob;
     use arrow_array::{
-        Array, ArrayRef, BinaryArray, BinaryViewArray, Int32Array, Int64Array, LargeBinaryArray,
-        NullArray, RecordBatch, StringArray, StringViewArray, StructArray, UInt8Array, UInt64Array,
+        Array, ArrayRef, BinaryArray, BinaryViewArray, FixedSizeListArray, Int32Array, Int64Array,
+        LargeBinaryArray, LargeListArray, ListArray, NullArray, RecordBatch, StringArray,
+        StringViewArray, StructArray, UInt8Array, UInt64Array,
     };
+    use arrow_buffer::{NullBuffer, OffsetBuffer};
     use arrow_schema::Schema;
     use datafusion::prelude::SessionContext;
     use datafusion_catalog::MemTable;
@@ -366,6 +566,207 @@ mod tests {
         );
     }
 
+    #[tokio::test]
+    async fn binary_list_items_coerce_to_blobs_without_losing_list_shape() {
+        for kind in [
+            "list",
+            "large_list",
+            "large_list_from_list",
+            "fixed_size_list",
+        ] {
+            let input_item = Arc::new(Field::new("item", DataType::Binary, true));
+            let table_item = Arc::new(wide_blob_field("item"));
+            let values: ArrayRef = Arc::new(BinaryArray::from_iter(vec![
+                Some(b"a".as_slice()),
+                None,
+                Some(b"".as_slice()),
+                Some(b"ignored".as_slice()),
+                None,
+                None,
+            ]));
+            let (input_type, table_type, list): (DataType, DataType, ArrayRef) = match kind {
+                "list" => (
+                    DataType::List(input_item.clone()),
+                    DataType::List(table_item.clone()),
+                    Arc::new(ListArray::new(
+                        input_item,
+                        OffsetBuffer::new(vec![0, 3, 3].into()),
+                        values,
+                        Some(NullBuffer::from(vec![true, false])),
+                    )),
+                ),
+                "large_list" => (
+                    DataType::LargeList(input_item.clone()),
+                    DataType::LargeList(table_item.clone()),
+                    Arc::new(LargeListArray::new(
+                        input_item,
+                        OffsetBuffer::new(vec![0_i64, 3, 3].into()),
+                        values,
+                        Some(NullBuffer::from(vec![true, false])),
+                    )),
+                ),
+                "large_list_from_list" => (
+                    DataType::List(input_item.clone()),
+                    DataType::LargeList(table_item.clone()),
+                    Arc::new(ListArray::new(
+                        input_item,
+                        OffsetBuffer::new(vec![0, 3, 3].into()),
+                        values,
+                        Some(NullBuffer::from(vec![true, false])),
+                    )),
+                ),
+                "fixed_size_list" => (
+                    DataType::FixedSizeList(input_item.clone(), 3),
+                    DataType::FixedSizeList(table_item.clone(), 3),
+                    Arc::new(FixedSizeListArray::new(
+                        input_item,
+                        3,
+                        values,
+                        Some(NullBuffer::from(vec![true, false])),
+                    )),
+                ),
+                _ => unreachable!(),
+            };
+            let batch = batch_with_image(Field::new("image", input_type, true), list);
+            let schema = Schema::new(vec![
+                Field::new("id", DataType::Int64, false),
+                Field::new("image", table_type, true),
+            ]);
+            let coerced = coerce(batch, &schema).await;
+            let image = coerced.column_by_name("image").unwrap();
+            let output_schema = coerced.schema();
+            let (DataType::List(item_field)
+            | DataType::LargeList(item_field)
+            | DataType::FixedSizeList(item_field, _)) =
+                output_schema.field_with_name("image").unwrap().data_type()
+            else {
+                unreachable!()
+            };
+            assert!(item_field.is_blob_v2(), "{kind} lost blob metadata");
+            let (offsets, items): (Vec<i64>, &StructArray) = match kind {
+                "list" => {
+                    let array = image.as_any().downcast_ref::<ListArray>().unwrap();
+                    assert!(array.is_null(1));
+                    (
+                        array.offsets().iter().map(|v| i64::from(*v)).collect(),
+                        array.values().as_any().downcast_ref().unwrap(),
+                    )
+                }
+                "large_list" | "large_list_from_list" => {
+                    let array = image.as_any().downcast_ref::<LargeListArray>().unwrap();
+                    assert!(array.is_null(1));
+                    (
+                        array.offsets().iter().copied().collect(),
+                        array.values().as_any().downcast_ref().unwrap(),
+                    )
+                }
+                "fixed_size_list" => {
+                    let array = image.as_any().downcast_ref::<FixedSizeListArray>().unwrap();
+                    assert!(array.is_null(1));
+                    (
+                        vec![0, 3, 6],
+                        array.values().as_any().downcast_ref().unwrap(),
+                    )
+                }
+                _ => unreachable!(),
+            };
+            assert_eq!(
+                offsets,
+                if kind == "fixed_size_list" {
+                    vec![0, 3, 6]
+                } else {
+                    vec![0, 3, 3]
+                }
+            );
+            assert_eq!(items.len(), if kind == "fixed_size_list" { 6 } else { 3 });
+            let data: &LargeBinaryArray = items
+                .column_by_name("data")
+                .unwrap()
+                .as_any()
+                .downcast_ref()
+                .unwrap();
+            assert_eq!(data.value(0), b"a");
+            assert!(items.is_null(1));
+            assert_eq!(data.value(2), b"");
+            assert!(!items.is_null(2));
+        }
+    }
+
+    #[tokio::test]
+    async fn sliced_binary_lists_only_coerce_visible_items() {
+        for large in [false, true] {
+            let input_item = Arc::new(Field::new("item", DataType::Binary, true));
+            let table_item = Arc::new(wide_blob_field("item"));
+            let values: ArrayRef = Arc::new(BinaryArray::from_iter(vec![
+                Some(b"before".as_slice()),
+                Some(b"a".as_slice()),
+                None,
+                Some(b"after".as_slice()),
+                Some(b"tail".as_slice()),
+            ]));
+            let (input_type, table_type, list): (DataType, DataType, ArrayRef) = if large {
+                (
+                    DataType::LargeList(input_item.clone()),
+                    DataType::LargeList(table_item),
+                    Arc::new(
+                        LargeListArray::new(
+                            input_item,
+                            OffsetBuffer::new(vec![0_i64, 1, 3, 4, 5].into()),
+                            values,
+                            None,
+                        )
+                        .slice(1, 2),
+                    ),
+                )
+            } else {
+                (
+                    DataType::List(input_item.clone()),
+                    DataType::List(table_item),
+                    Arc::new(
+                        ListArray::new(
+                            input_item,
+                            OffsetBuffer::new(vec![0, 1, 3, 4, 5].into()),
+                            values,
+                            None,
+                        )
+                        .slice(1, 2),
+                    ),
+                )
+            };
+            let batch = batch_with_image(Field::new("image", input_type, true), list);
+            let schema = Schema::new(vec![
+                Field::new("id", DataType::Int64, false),
+                Field::new("image", table_type, true),
+            ]);
+            let coerced = coerce(batch, &schema).await;
+            let image = coerced.column_by_name("image").unwrap();
+            let (offsets, items): (Vec<i64>, &StructArray) = if large {
+                let list = image.as_any().downcast_ref::<LargeListArray>().unwrap();
+                (
+                    list.offsets().iter().copied().collect(),
+                    list.values().as_any().downcast_ref().unwrap(),
+                )
+            } else {
+                let list = image.as_any().downcast_ref::<ListArray>().unwrap();
+                (
+                    list.offsets().iter().map(|v| i64::from(*v)).collect(),
+                    list.values().as_any().downcast_ref().unwrap(),
+                )
+            };
+            assert_eq!(offsets, [0, 2, 3]);
+            assert_eq!(items.len(), 3);
+            let data: &LargeBinaryArray = items
+                .column_by_name("data")
+                .unwrap()
+                .as_any()
+                .downcast_ref()
+                .unwrap();
+            assert_eq!(data.value(0), b"a");
+            assert!(items.is_null(1));
+            assert_eq!(data.value(2), b"after");
+        }
+    }
+
     #[tokio::test]
     async fn binary_view_coerces_to_declared_blob_struct() {
         let batch = batch_with_image(
diff --git a/rust/lancedb/src/table/datafusion/cast.rs b/rust/lancedb/src/table/datafusion/cast.rs
index 2f3f8b868..063572cbe 100644
--- a/rust/lancedb/src/table/datafusion/cast.rs
+++ b/rust/lancedb/src/table/datafusion/cast.rs
@@ -20,7 +20,7 @@ use datafusion_physical_plan::{ExecutionPlan, PhysicalExpr};
 use lance_arrow::json::{ARROW_JSON_EXT_NAME, has_json_fields, is_arrow_json_field, is_json_field};
 use lance_arrow::{ARROW_EXT_NAME_KEY, FieldExt};
 
-use super::blob_coerce::coerce_blob_expr;
+use super::blob_coerce::{coerce_blob_expr, coerce_blob_list_expr};
 use crate::{Error, Result};
 
 pub fn cast_to_table_schema(
@@ -113,6 +113,14 @@ fn build_field_exprs(
             continue;
         }
 
+        // Blob values in lists need the same coercion as top-level blob columns.
+        if let Some(coerced) =
+            coerce_blob_list_expr(input_expr.clone(), input_field, table_field, &config)?
+        {
+            result.push(coerced);
+            continue;
+        }
+
         // A column whose values are all null infers as `Null` (pyarrow does this for a list of
         // dicts), so there is no input type to cast from. Emit typed nulls carrying the table
         // field verbatim: a plain cast would drop the field metadata, and extension columns
diff --git a/rust/lancedb/src/table/datafusion/udtf.rs b/rust/lancedb/src/table/datafusion/udtf.rs
index a7a72f800..53eb55fcc 100644
--- a/rust/lancedb/src/table/datafusion/udtf.rs
+++ b/rust/lancedb/src/table/datafusion/udtf.rs
@@ -6,3 +6,5 @@
 pub mod fts;
 
 pub mod duplicate_pairs;
+
+pub mod vector_query;
diff --git a/rust/lancedb/src/table/datafusion/udtf/duplicate_pairs.rs b/rust/lancedb/src/table/datafusion/udtf/duplicate_pairs.rs
index 47a35cbd2..20de93148 100644
--- a/rust/lancedb/src/table/datafusion/udtf/duplicate_pairs.rs
+++ b/rust/lancedb/src/table/datafusion/udtf/duplicate_pairs.rs
@@ -330,6 +330,96 @@ pub async fn plan_duplicate_pairs(
     Ok(tasks)
 }
 
+/// Optional query columns. The default preserves the primitive's three-column
+/// schema; SQL convenience calls include physical scope identity and may map
+/// endpoints to an application key without fetching source vectors.
+#[derive(Debug, Clone, Default, PartialEq, Eq)]
+pub struct DuplicatePairsOutput {
+    pub scope: bool,
+    pub id_column: Option<String>,
+}
+
+impl DuplicatePairsOutput {
+    pub fn schema(&self, dataset: &Dataset) -> Result<SchemaRef> {
+        let mut fields = duplicate_pair_schema().fields().to_vec();
+        if self.scope {
+            fields.extend([
+                Arc::new(Field::new("segment_id", DataType::Utf8, false)),
+                Arc::new(Field::new("partition_id", DataType::UInt64, false)),
+                Arc::new(Field::new("dataset_version", DataType::UInt64, false)),
+            ]);
+        }
+        if let Some(column) = &self.id_column {
+            let schema = Schema::from(dataset.schema());
+            let field = schema.field_with_name(column)?;
+            if column.contains('.')
+                || matches!(
+                    field.data_type(),
+                    DataType::FixedSizeList(..)
+                        | DataType::List(..)
+                        | DataType::LargeList(..)
+                        | DataType::Struct(..)
+                        | DataType::Map(..)
+                )
+            {
+                return plan_err!("id_column must be a top-level scalar column");
+            }
+            fields.push(Arc::new(Field::new(
+                "id_a",
+                field.data_type().clone(),
+                field.is_nullable(),
+            )));
+            fields.push(Arc::new(Field::new(
+                "id_b",
+                field.data_type().clone(),
+                field.is_nullable(),
+            )));
+        }
+        Ok(Arc::new(Schema::new(fields)))
+    }
+
+    async fn enrich(
+        &self,
+        batch: arrow_array::RecordBatch,
+        dataset: &Dataset,
+        task: &DuplicatePairTask,
+    ) -> Result<arrow_array::RecordBatch> {
+        use arrow_array::{StringArray, UInt64Array};
+        let mut columns = batch.columns().to_vec();
+        let n = batch.num_rows();
+        if self.scope {
+            columns.extend([
+                Arc::new(StringArray::from(vec![task.segment_id.to_string(); n]))
+                    as arrow_array::ArrayRef,
+                Arc::new(UInt64Array::from(vec![task.partition_id as u64; n])),
+                Arc::new(UInt64Array::from(vec![dataset.version().version; n])),
+            ]);
+        }
+        if let Some(column) = &self.id_column {
+            for endpoint in 0..2 {
+                let ids = batch
+                    .column(endpoint)
+                    .as_any()
+                    .downcast_ref::<UInt64Array>()
+                    .expect("native row IDs");
+                let projection = lance::dataset::ProjectionRequest::from_columns(
+                    [column.as_str()],
+                    dataset.schema(),
+                );
+                let values = dataset.take_rows(ids.values(), projection).await?;
+                if values.num_rows() != n {
+                    return plan_err!("pair row ID is missing from the pinned snapshot");
+                }
+                columns.push(values.column(0).clone());
+            }
+        }
+        Ok(arrow_array::RecordBatch::try_new(
+            self.schema(dataset)?,
+            columns,
+        )?)
+    }
+}
+
 /// Streaming native operator over partition descriptors, never source-vector
 /// rows. Assigned descriptors are consumed sequentially; distributed hosts send
 /// one descriptor per worker request. No Python or per-vector ANN calls.
@@ -341,6 +431,7 @@ pub struct DuplicatePairsExec {
     tasks: Vec<DuplicatePairTask>,
     properties: Arc<PlanProperties>,
     metrics: ExecutionPlanMetricsSet,
+    output: DuplicatePairsOutput,
 }
 
 impl DuplicatePairsExec {
@@ -374,8 +465,23 @@ impl DuplicatePairsExec {
             tasks,
             properties,
             metrics: ExecutionPlanMetricsSet::new(),
+            output: DuplicatePairsOutput::default(),
         })
     }
+    /// Configure optional output fields before dispatching this plan.
+    pub fn with_output(mut self, output: DuplicatePairsOutput) -> Result<Self> {
+        self.properties = Arc::new(PlanProperties::new(
+            EquivalenceProperties::new(output.schema(&self.dataset)?),
+            Partitioning::UnknownPartitioning(1),
+            EmissionType::Incremental,
+            Boundedness::Bounded,
+        ));
+        self.output = output;
+        Ok(self)
+    }
+    pub fn output(&self) -> &DuplicatePairsOutput {
+        &self.output
+    }
     pub fn dataset(&self) -> &Arc<Dataset> {
         &self.dataset
     }
@@ -430,6 +536,7 @@ impl ExecutionPlan for DuplicatePairsExec {
         if partition != 0 {
             return plan_err!("duplicate-pair output partition out of bounds");
         }
+        let output = self.output.clone();
         let tasks = self.tasks.clone();
         let dataset = self.dataset.clone();
         let config = self.config.clone();
@@ -467,6 +574,7 @@ impl ExecutionPlan for DuplicatePairsExec {
         // into one-task PE requests before execution.
         let stream = stream::iter(tasks)
             .then(move |task| {
+                let output = output.clone();
                 let dataset = dataset.clone();
                 let config = config.clone();
                 let reservation = reservation.clone();
@@ -474,7 +582,7 @@ impl ExecutionPlan for DuplicatePairsExec {
                 let completed = completed.clone();
                 async move {
                     let reader = lance::index::vector::dedup::find_duplicate_pairs_in_partition_with_options(
-                        dataset,
+                        dataset.clone(),
                         &config.column,
                         task.segment_id,
                         task.partition_id,
@@ -506,15 +614,19 @@ impl ExecutionPlan for DuplicatePairsExec {
                             }
                         },
                     );
-                    Ok::<_, DataFusionError>(Box::pin(RecordBatchStreamAdapter::new(
-                        duplicate_pair_schema(),
-                        measured,
-                    )) as SendableRecordBatchStream)
+                    let schema = output.schema(&dataset)?;
+                    let measured = measured.and_then(move |batch| {
+                        let output = output.clone();
+                        let dataset = dataset.clone();
+                        let task = task.clone();
+                        async move { output.enrich(batch, &dataset, &task).await }
+                    });
+                    Ok::<_, DataFusionError>(Box::pin(RecordBatchStreamAdapter::new(schema, measured)) as SendableRecordBatchStream)
                 }
             })
             .try_flatten();
         Ok(Box::pin(RecordBatchStreamAdapter::new(
-            duplicate_pair_schema(),
+            self.schema(),
             stream,
         )))
     }
diff --git a/rust/lancedb/src/table/datafusion/udtf/vector_query.rs b/rust/lancedb/src/table/datafusion/udtf/vector_query.rs
new file mode 100644
index 000000000..543a59589
--- /dev/null
+++ b/rust/lancedb/src/table/datafusion/udtf/vector_query.rs
@@ -0,0 +1,431 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+//! Composable index-backed pair and dedup relations. Hosts bind a snapshot once
+//! during planning, distribute the pair child, and use their ordinary SQL sink.
+
+use std::sync::Arc;
+
+use arrow_array::{RecordBatch, UInt64Array};
+use arrow_schema::{DataType, Field, Schema, SchemaRef};
+use async_trait::async_trait;
+use datafusion::prelude::{SessionConfig, SessionContext, col};
+use datafusion_catalog::{Session, TableProvider};
+use datafusion_common::{DataFusionError, Result, plan_err};
+use datafusion_execution::{
+    SendableRecordBatchStream, TaskContext, runtime_env::RuntimeEnvBuilder,
+};
+use datafusion_expr::{Expr, TableType};
+use datafusion_physical_expr::{EquivalenceProperties, Partitioning};
+use datafusion_physical_plan::{
+    DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties,
+    coalesce_partitions::CoalescePartitionsExec,
+    empty::EmptyExec,
+    execution_plan::{Boundedness, EmissionType},
+    projection::ProjectionExec,
+    stream::RecordBatchStreamAdapter,
+};
+use futures::{TryStreamExt, stream};
+use lance::dataset::scanner::{RowAddrMask, RowAddrTreeMap};
+use lance::{Dataset, index::DatasetIndexInternalExt};
+use lance_core::datatypes::BlobHandling;
+use lance_datafusion::exec::SessionContextExt;
+use lance_index::metrics::NoOpMetricsCollector;
+use roaring::RoaringTreemap;
+
+use super::duplicate_pairs::{
+    DuplicatePairTask, DuplicatePairsConfig, DuplicatePairsExec, DuplicatePairsOutput,
+    plan_duplicate_pairs,
+};
+use crate::table::{NativeTableExt, datafusion::BaseTableAdapter};
+
+const REPRESENTATIVE_SORT_MEMORY_BYTES: usize = 256 * 1024 * 1024;
+const MATERIALIZE_BATCH_ROWS: usize = 1024;
+
+fn edge_schema() -> SchemaRef {
+    Arc::new(Schema::new(vec![
+        Field::new("a", DataType::UInt64, false),
+        Field::new("b", DataType::UInt64, false),
+    ]))
+}
+
+/// Sorting canonical edges makes the first unremoved endpoint the smallest
+/// unassigned representative. The sort spills; only vertex masks stay in RAM.
+async fn select_representatives(
+    stream: SendableRecordBatchStream,
+    memory: usize,
+) -> crate::Result<(RoaringTreemap, RoaringTreemap)> {
+    let schema = edge_schema();
+    let batch_schema = schema.clone();
+    let canonical = stream
+        .map_ok(move |batch| {
+            let a = batch
+                .column(0)
+                .as_any()
+                .downcast_ref::<UInt64Array>()
+                .expect("native row id");
+            let b = batch
+                .column(1)
+                .as_any()
+                .downcast_ref::<UInt64Array>()
+                .expect("native row id");
+            RecordBatch::try_new(
+                batch_schema.clone(),
+                vec![
+                    Arc::new(UInt64Array::from_iter_values(
+                        a.values().iter().zip(b.values()).map(|(&a, &b)| a.min(b)),
+                    )),
+                    Arc::new(UInt64Array::from_iter_values(
+                        a.values().iter().zip(b.values()).map(|(&a, &b)| a.max(b)),
+                    )),
+                ],
+            )
+            .map_err(DataFusionError::from)
+        })
+        .and_then(futures::future::ready);
+    let canonical = Box::pin(RecordBatchStreamAdapter::new(schema, canonical));
+    let mut config = SessionConfig::default().with_batch_size(MATERIALIZE_BATCH_ROWS);
+    config.options_mut().execution.sort_spill_reservation_bytes = memory / 2;
+    let ctx = SessionContext::new_with_config_rt(
+        config,
+        RuntimeEnvBuilder::new()
+            .with_memory_limit(memory, 1.0)
+            .build_arc()?,
+    );
+    let mut sorted = ctx
+        .read_one_shot(canonical)?
+        .sort_by(vec![col("a"), col("b")])?
+        .execute_stream()
+        .await?;
+    let mut removed = RoaringTreemap::new();
+    let mut representatives = RoaringTreemap::new();
+    while let Some(batch) = sorted.try_next().await? {
+        let a = batch
+            .column(0)
+            .as_any()
+            .downcast_ref::<UInt64Array>()
+            .expect("sorted id");
+        let b = batch
+            .column(1)
+            .as_any()
+            .downcast_ref::<UInt64Array>()
+            .expect("sorted id");
+        for (&a, &b) in a.values().iter().zip(b.values()) {
+            if a != b && !removed.contains(a) && !removed.contains(b) {
+                representatives.insert(a);
+                removed.insert(b);
+            }
+        }
+    }
+    Ok((removed, representatives))
+}
+
+/// Select nonempty physical IVF scopes before dispatch, never by filtering the
+/// resulting pairs. A count spans all segments; partition IDs are segment-local.
+#[derive(Debug, Clone, Default)]
+pub enum PartitionSelection {
+    #[default]
+    All,
+    First {
+        count: usize,
+    },
+    Random {
+        count: usize,
+        seed: u64,
+    },
+}
+
+/// Query-only options. Snapshot binding is separate so multiple calls in one
+/// statement can share the same immutable dataset.
+#[derive(Debug, Clone)]
+pub struct VectorQueryOptions {
+    pub column: String,
+    pub distance_threshold: f32,
+    pub selection: PartitionSelection,
+    pub output: DuplicatePairsOutput,
+    pub dedup: bool,
+}
+
+impl BaseTableAdapter {
+    /// Open a retained version, or refresh and capture the latest manifest once.
+    /// No worker is allowed to independently resolve "latest".
+    pub async fn vector_query_snapshot(&self, version: Option<u64>) -> Result<Arc<Dataset>> {
+        let native = self.table.as_native().ok_or_else(|| {
+            datafusion_common::DataFusionError::Plan(
+                "vector queries require a native indexed table".into(),
+            )
+        })?;
+        if version == Some(0) {
+            return plan_err!("dataset_version must be positive");
+        }
+        let current = native
+            .dataset
+            .get()
+            .await
+            .map_err(|e| datafusion_common::DataFusionError::External(Box::new(e)))?;
+        Ok(match version {
+            Some(version) => Arc::new(current.checkout_version(version).await?),
+            None => {
+                // Do not change a shared table handle's pinned version.
+                let mut latest = (*current).clone();
+                latest.checkout_latest().await?;
+                Arc::new(latest)
+            }
+        })
+    }
+}
+
+/// A table provider bound to one source snapshot. The host retains the user's
+/// unbound SQL for subsequent refreshes; this provider lives only for this run.
+#[derive(Debug)]
+pub struct VectorQueryTable {
+    dataset: Arc<Dataset>,
+    config: DuplicatePairsConfig,
+    tasks: Vec<DuplicatePairTask>,
+    options: VectorQueryOptions,
+    schema: SchemaRef,
+}
+
+impl VectorQueryTable {
+    pub async fn try_new(dataset: Arc<Dataset>, options: VectorQueryOptions) -> Result<Self> {
+        if options.dedup
+            && (!matches!(options.selection, PartitionSelection::All)
+                || options.output.id_column.is_some())
+        {
+            return plan_err!(
+                "vector_dedup requires all partitions and returns original rows; sampling and id_column are pair-query options"
+            );
+        }
+        let config = DuplicatePairsConfig {
+            dataset_version: dataset.version().version,
+            column: options.column.clone(),
+            distance_threshold: options.distance_threshold,
+        };
+        let mut tasks = plan_duplicate_pairs(dataset.clone(), &config).await?;
+        // Canonical scope order is independent of metadata enumeration order.
+        tasks.sort_by_key(|task| (task.segment_id, task.partition_id));
+        let mut nonempty = Vec::new();
+        for group in tasks.chunk_by(|a, b| a.segment_id == b.segment_id) {
+            let index = dataset
+                .open_vector_index(&config.column, &group[0].segment_id, &NoOpMetricsCollector)
+                .await?;
+            nonempty.extend(
+                group
+                    .iter()
+                    .filter(|task| index.partition_size(task.partition_id) > 0)
+                    .cloned(),
+            );
+        }
+        match options.selection {
+            PartitionSelection::All => {}
+            PartitionSelection::First { count } | PartitionSelection::Random { count, .. }
+                if count == 0 =>
+            {
+                return plan_err!("partition count must be positive");
+            }
+            PartitionSelection::First { count } => nonempty.truncate(count),
+            PartitionSelection::Random { count, seed } => {
+                // A single segment matches ivf_partition's SplitMix64 sampling.
+                // With multiple segments the canonical scope ordinal extends the
+                // same policy without conflating equal partition IDs.
+                let segments = tasks
+                    .iter()
+                    .map(|t| t.segment_id)
+                    .collect::<std::collections::BTreeSet<_>>()
+                    .len();
+                let mut ranked = nonempty
+                    .into_iter()
+                    .enumerate()
+                    .map(|(ordinal, task)| {
+                        let key = if segments == 1 {
+                            task.partition_id as u64
+                        } else {
+                            ordinal as u64
+                        };
+                        (sample_rank(seed, key), task)
+                    })
+                    .collect::<Vec<_>>();
+                ranked.sort_by_key(|(rank, task)| (*rank, task.segment_id, task.partition_id));
+                ranked.truncate(count);
+                nonempty = ranked.into_iter().map(|(_, task)| task).collect();
+                nonempty.sort_by_key(|task| (task.segment_id, task.partition_id));
+            }
+        }
+        let schema = if options.dedup {
+            let mut scanner = dataset.scan();
+            scanner.blob_handling(BlobHandling::AllBinary);
+            scanner.schema().await?
+        } else {
+            options.output.schema(&dataset)?
+        };
+        Ok(Self {
+            dataset,
+            config,
+            tasks: nonempty,
+            options,
+            schema,
+        })
+    }
+}
+
+fn sample_rank(seed: u64, ordinal: u64) -> u64 {
+    let mut value = seed.wrapping_add(ordinal).wrapping_add(0x9E3779B97F4A7C15);
+    value = (value ^ (value >> 30)).wrapping_mul(0xBF58476D1CE4E5B9);
+    value = (value ^ (value >> 27)).wrapping_mul(0x94D049BB133111EB);
+    value ^ (value >> 31)
+}
+
+#[async_trait]
+impl TableProvider for VectorQueryTable {
+    fn schema(&self) -> SchemaRef {
+        self.schema.clone()
+    }
+    fn table_type(&self) -> TableType {
+        TableType::View
+    }
+    async fn scan(
+        &self,
+        _state: &dyn Session,
+        projection: Option<&Vec<usize>>,
+        _filters: &[Expr],
+        _limit: Option<usize>,
+    ) -> Result<Arc<dyn ExecutionPlan>> {
+        let output = if self.options.dedup {
+            DuplicatePairsOutput::default()
+        } else {
+            self.options.output.clone()
+        };
+        let pairs: Arc<dyn ExecutionPlan> = if self.tasks.is_empty() {
+            Arc::new(EmptyExec::new(output.schema(&self.dataset)?))
+        } else {
+            Arc::new(
+                DuplicatePairsExec::try_new(
+                    self.dataset.clone(),
+                    self.config.clone(),
+                    self.tasks.clone(),
+                )?
+                .with_output(output)?,
+            )
+        };
+        let plan: Arc<dyn ExecutionPlan> = if self.options.dedup {
+            Arc::new(VectorDedupExec::new(
+                self.dataset.clone(),
+                pairs,
+                self.schema.clone(),
+            ))
+        } else {
+            pairs
+        };
+        if let Some(indices) = projection {
+            let expressions: Vec<_> = indices
+                .iter()
+                .map(|&i| {
+                    (
+                        Arc::new(datafusion_physical_expr::expressions::Column::new(
+                            self.schema.field(i).name(),
+                            i,
+                        ))
+                            as Arc<dyn datafusion_physical_expr::PhysicalExpr>,
+                        self.schema.field(i).name().clone(),
+                    )
+                })
+                .collect();
+            Ok(Arc::new(ProjectionExec::try_new(expressions, plan)?))
+        } else {
+            Ok(plan)
+        }
+    }
+}
+
+/// Greedy direct representatives over the complete pair relation. Sorting spills
+/// to disk; only vertex masks are retained. This has no durable checkpoint and
+/// publishes nothing: failure is handled by the enclosing query/MV transaction.
+#[derive(Debug)]
+pub struct VectorDedupExec {
+    dataset: Arc<Dataset>,
+    pairs: Arc<dyn ExecutionPlan>,
+    properties: Arc<PlanProperties>,
+}
+
+impl VectorDedupExec {
+    fn new(dataset: Arc<Dataset>, pairs: Arc<dyn ExecutionPlan>, schema: SchemaRef) -> Self {
+        Self {
+            dataset,
+            pairs,
+            properties: Arc::new(PlanProperties::new(
+                EquivalenceProperties::new(schema),
+                Partitioning::UnknownPartitioning(1),
+                EmissionType::Final,
+                Boundedness::Bounded,
+            )),
+        }
+    }
+}
+
+impl DisplayAs for VectorDedupExec {
+    fn fmt_as(&self, _: DisplayFormatType, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(
+            f,
+            "VectorDedupExec: source_version={}, policy=direct_representative",
+            self.dataset.version().version
+        )
+    }
+}
+
+impl ExecutionPlan for VectorDedupExec {
+    fn name(&self) -> &str {
+        "VectorDedupExec"
+    }
+    fn properties(&self) -> &Arc<PlanProperties> {
+        &self.properties
+    }
+    fn children(&self) -> Vec<&Arc<dyn ExecutionPlan>> {
+        vec![&self.pairs]
+    }
+    fn with_new_children(
+        self: Arc<Self>,
+        children: Vec<Arc<dyn ExecutionPlan>>,
+    ) -> Result<Arc<dyn ExecutionPlan>> {
+        if children.len() != 1 {
+            return plan_err!("VectorDedupExec requires one pair input");
+        }
+        Ok(Arc::new(Self::new(
+            self.dataset.clone(),
+            children[0].clone(),
+            self.schema(),
+        )))
+    }
+    fn execute(
+        &self,
+        partition: usize,
+        context: Arc<TaskContext>,
+    ) -> Result<SendableRecordBatchStream> {
+        if partition != 0 {
+            return plan_err!("invalid dedup output partition");
+        }
+        let dataset = self.dataset.clone();
+        let pairs = CoalescePartitionsExec::new(self.pairs.clone()).execute(0, context)?;
+        let stream = stream::once(async move {
+            let (removed, _) = select_representatives(pairs, REPRESENTATIVE_SORT_MEMORY_BYTES)
+                .await
+                .map_err(|e| datafusion_common::DataFusionError::External(Box::new(e)))?;
+            let mut scanner = dataset.scan();
+            scanner
+                .with_row_id_prefilter(RowAddrMask::from_block(RowAddrTreeMap::from_iter(removed)))
+                .blob_handling(BlobHandling::AllBinary)
+                .batch_size(MATERIALIZE_BATCH_ROWS)
+                .strict_batch_size(true);
+            Ok::<_, datafusion_common::DataFusionError>(
+                scanner
+                    .try_into_stream()
+                    .await?
+                    .map_err(datafusion_common::DataFusionError::from),
+            )
+        })
+        .try_flatten();
+        Ok(Box::pin(RecordBatchStreamAdapter::new(
+            self.schema(),
+            stream,
+        )))
+    }
+}
diff --git a/rust/lancedb/src/table/delete.rs b/rust/lancedb/src/table/delete.rs
index cb1da03ae..b9e598f3b 100644
--- a/rust/lancedb/src/table/delete.rs
+++ b/rust/lancedb/src/table/delete.rs
@@ -29,6 +29,7 @@ pub(crate) async fn execute_delete(
     predicate: Predicate<'_>,
 ) -> Result<DeleteResult> {
     table.dataset.ensure_mutable()?;
+    table.ensure_not_a_view("delete from").await?;
     match predicate {
         Predicate::String(s) => {
             let predicate = crate::expr::canonicalize_sql_predicate(s)?;
diff --git a/rust/lancedb/src/table/lsm_stats.rs b/rust/lancedb/src/table/memwal_stats.rs
similarity index 56%
rename from rust/lancedb/src/table/lsm_stats.rs
rename to rust/lancedb/src/table/memwal_stats.rs
index 953aea90f..d9747d257 100644
--- a/rust/lancedb/src/table/lsm_stats.rs
+++ b/rust/lancedb/src/table/memwal_stats.rs
@@ -1,21 +1,21 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-//! Live per-bucket LSM state — the shape [`crate::Table::get_lsm_stats`]
+//! Live per-table-shard MemWAL state — the shape [`crate::Table::get_memwal_stats`]
 //! returns and [`super::checkpoint`] polls.
 //!
-//! Nothing here is derived: sums and differences (total L0 bytes, WAL lag)
+//! Nothing here is derived: sums and differences (total SSTable bytes, WAL lag)
 //! are the caller's to compute. There is no "WAL is off" shape — that case is
 //! `None`, because a struct of zeros would read as measurements.
 
 use serde::Deserialize;
 
-/// One flushed L0 generation.
+/// One SSTable.
 #[derive(Debug, Clone, Deserialize)]
-pub struct GenerationStats {
+pub struct SsTableStats {
     pub generation: u64,
     pub bytes: u64,
-    /// Present only when `include_generation_rows` was requested. Off by
+    /// Present only when `include_sstable_rows` was requested. Off by
     /// default because each count opens an uncached Lance dataset, and the
     /// checkpoint loop polls this route needing only generation numbers.
     #[serde(default)]
@@ -34,11 +34,11 @@ pub struct MemtableStats {
     pub indexes: Vec<String>,
 }
 
-/// Live state of one bucket. A table is N buckets on one node; flattening to
-/// a single number hides the one hot bucket that is usually why someone
+/// Live state of one table shard. A table is N table shards on one node; flattening to
+/// a single number hides the one hot table shard that is usually why someone
 /// opened this endpoint.
 #[derive(Debug, Clone, Deserialize)]
-pub struct BucketStats {
+pub struct TableShardStats {
     pub shard_id: String,
     /// `Active` | `Sealed` (drop-table 2PC in flight).
     pub status: String,
@@ -47,68 +47,68 @@ pub struct BucketStats {
     pub current_generation: u64,
     pub replay_after_wal_entry_position: u64,
     pub wal_entry_position_last_seen: u64,
-    pub generations: Vec<GenerationStats>,
-    /// Whether a pass owns this bucket's compaction latch right now. Says *a*
+    pub sstables: Vec<SsTableStats>,
+    /// Whether a pass owns this table shard's compaction latch right now. Says *a*
     /// driver is running, not *whose*, and the latch is held from dispatch —
     /// including while the pass queues for a pod-wide compactor permit. Read
     /// it as "do not pile on", never as "mine is progressing".
     pub compacting: bool,
-    /// Oldest first, active last. Absent for a `Sealed` bucket, whose
+    /// Oldest first, active last. Absent for a `Sealed` table shard, whose
     /// in-memory state is torn down.
     #[serde(default)]
     pub memtables: Option<Vec<MemtableStats>>,
 }
 
-impl BucketStats {
-    /// The newest flushed generation, or `None` when L0 is empty.
-    pub(crate) fn newest_generation(&self) -> Option<u64> {
-        self.generations.iter().map(|g| g.generation).max()
+impl TableShardStats {
+    /// The newest SSTable generation, or `None` when the tier is empty.
+    pub(crate) fn newest_sstable_generation(&self) -> Option<u64> {
+        self.sstables.iter().map(|g| g.generation).max()
     }
 
-    /// How many generations at or below `target` are still in L0.
+    /// How many SSTables at or below `target` are still uncompacted.
     ///
     /// A count, not a boolean: one pass drains a bounded prefix rather than
     /// the whole target set, so a boolean would read as "no progress" for
     /// every pass but the last. Compaction drains oldest-first, so this
     /// decreases monotonically.
-    pub(crate) fn outstanding_generations(&self, target: u64) -> usize {
-        self.generations
+    pub(crate) fn outstanding_sstables(&self, target: u64) -> usize {
+        self.sstables
             .iter()
             .filter(|g| g.generation <= target)
             .count()
     }
 }
 
-/// Live LSM state, one entry per bucket.
+/// Live MemWAL state, one entry per table shard.
 #[derive(Debug, Clone, Deserialize)]
-pub struct LsmStats {
-    pub buckets: Vec<BucketStats>,
+pub struct MemWalStats {
+    pub table_shards: Vec<TableShardStats>,
 }
 
-/// Server-side JSON envelope for `get_lsm_stats`. `lsm_stats` is null when
-/// the table has no LSM write path.
+/// Server-side JSON envelope for `get_memwal_stats`. `memwal_stats` is null when
+/// the table has no MemWAL write path.
 #[derive(Debug, Deserialize)]
-pub(crate) struct GetLsmStatsResponse {
+pub(crate) struct GetMemWalStatsResponse {
     #[serde(default)]
-    pub lsm_stats: Option<LsmStats>,
+    pub memwal_stats: Option<MemWalStats>,
 }
 
 #[cfg(test)]
 mod tests {
     use super::*;
 
-    fn bucket(shard: &str, generations: &[u64], compacting: bool) -> BucketStats {
-        BucketStats {
+    fn table_shard(shard: &str, sstables: &[u64], compacting: bool) -> TableShardStats {
+        TableShardStats {
             shard_id: shard.into(),
             status: "Active".into(),
             writer_epoch: 1,
             manifest_version: 1,
-            current_generation: generations.iter().max().copied().unwrap_or(0) + 1,
+            current_generation: sstables.iter().max().copied().unwrap_or(0) + 1,
             replay_after_wal_entry_position: 0,
             wal_entry_position_last_seen: 0,
-            generations: generations
+            sstables: sstables
                 .iter()
-                .map(|g| GenerationStats {
+                .map(|g| SsTableStats {
                     generation: *g,
                     bytes: 1,
                     rows: None,
@@ -123,40 +123,46 @@ mod tests {
     /// generation created after it must not hold the loop open — that is why
     /// the predicate terminates under write load.
     #[test]
-    fn newer_generations_do_not_extend_the_target() {
-        let start = bucket("b0", &[7, 8], false);
-        let target = start.newest_generation().expect("L0 is non-empty");
+    fn newer_sstables_do_not_extend_the_target() {
+        let start = table_shard("b0", &[7, 8], false);
+        let target = start
+            .newest_sstable_generation()
+            .expect("the SSTable tier is non-empty");
         assert_eq!(target, 8);
 
         // Compaction drained 7 and 8; 9 and 10 arrived while it ran.
-        let later = bucket("b0", &[9, 10], false);
+        let later = table_shard("b0", &[9, 10], false);
         assert_eq!(
-            later.outstanding_generations(target),
+            later.outstanding_sstables(target),
             0,
-            "generations above the target are somebody else's problem"
+            "sstables above the target are somebody else's problem"
         );
 
         // Still holding 8 means still outstanding.
         assert_eq!(
-            bucket("b0", &[8, 9], false).outstanding_generations(target),
+            table_shard("b0", &[8, 9], false).outstanding_sstables(target),
             1
         );
     }
 
-    /// The metric counts generations, not buckets: a pass drains a bounded
-    /// prefix, so one bucket going 3 → 2 → 1 → 0 is three steps.
+    /// The metric counts SSTables, not table shards: a pass drains a bounded
+    /// prefix, so one table shard going 3 → 2 → 1 → 0 is three steps.
     #[test]
-    fn progress_is_measured_in_generations() {
+    fn progress_is_measured_in_sstables() {
         let target = 3;
         let counts: Vec<usize> = [&[1u64, 2, 3][..], &[2, 3][..], &[3][..], &[][..]]
             .iter()
-            .map(|gens| bucket("b0", gens, false).outstanding_generations(target))
+            .map(|gens| table_shard("b0", gens, false).outstanding_sstables(target))
             .collect();
         assert_eq!(counts, vec![3, 2, 1, 0]);
     }
 
     #[test]
-    fn empty_l0_has_no_target() {
-        assert!(bucket("b0", &[], false).newest_generation().is_none());
+    fn an_empty_sstable_tier_has_no_target() {
+        assert!(
+            table_shard("b0", &[], false)
+                .newest_sstable_generation()
+                .is_none()
+        );
     }
 }
diff --git a/rust/lancedb/src/table/merge.rs b/rust/lancedb/src/table/merge.rs
index 982818ff6..66ec48baf 100644
--- a/rust/lancedb/src/table/merge.rs
+++ b/rust/lancedb/src/table/merge.rs
@@ -47,7 +47,7 @@ pub struct MergeResult {
     /// Total number of rows written.
     ///
     /// On the standard `merge_insert` path this equals
-    /// `num_inserted_rows + num_updated_rows`. On the MemWAL LSM write path the
+    /// `num_inserted_rows + num_updated_rows`. On the MemWAL write path the
     /// insert/update breakdown is not known until compaction; in that mode
     /// `num_inserted_rows`, `num_updated_rows`, `num_deleted_rows`, `version`
     /// and `num_attempts` are all `0` and this field holds the total number of
@@ -76,7 +76,7 @@ pub struct MergeInsertBuilder {
     pub(crate) when_not_matched_by_source_delete_filt: Option<MergeFilter>,
     pub(crate) timeout: Option<Duration>,
     pub(crate) use_index: bool,
-    pub(crate) use_lsm: Option<bool>,
+    pub(crate) use_memwal: Option<bool>,
     pub(crate) validate_single_shard: bool,
 }
 
@@ -92,7 +92,7 @@ impl MergeInsertBuilder {
             when_not_matched_by_source_delete_filt: None,
             timeout: None,
             use_index: true,
-            use_lsm: None,
+            use_memwal: None,
             validate_single_shard: true,
         }
     }
@@ -193,27 +193,27 @@ impl MergeInsertBuilder {
     /// Control MemWAL routing for this `merge_insert`.
     ///
     /// By default (unset), a `merge_insert` on a table with an
-    /// [`LsmWriteSpec`](super::LsmWriteSpec) installed is routed through Lance's
+    /// [`MemWalWriteSpec`](super::MemWalWriteSpec) installed is routed through Lance's
     /// MemWAL shard writer; a table without one uses the standard path.
     ///
-    /// - `use_lsm(true)` forces MemWAL routing and errors if the table has no
-    ///   LSM write spec.
-    /// - `use_lsm(false)` forces the standard write path even when a spec is set.
-    pub fn use_lsm(&mut self, enable: bool) -> &mut Self {
-        self.use_lsm = Some(enable);
+    /// - `use_memwal(true)` forces MemWAL routing and errors if the table has no
+    ///   MemWAL write spec.
+    /// - `use_memwal(false)` forces the standard write path even when a spec is set.
+    pub fn use_memwal(&mut self, enable: bool) -> &mut Self {
+        self.use_memwal = Some(enable);
         self
     }
 
     /// Controls how an LSM `merge_insert` checks that its input targets a
     /// single shard.
     ///
-    /// When a table has an LSM write spec, every row in a `merge_insert` call
+    /// When a table has a MemWAL write spec, every row in a `merge_insert` call
     /// must route to the same shard. When `true` (the default), every row is
     /// inspected to verify this. When `false`, only the first row is inspected
     /// and the shard it routes to is used for the whole input — a faster path
     /// for callers that have already pre-sharded their input.
     ///
-    /// Has no effect on tables without an LSM write spec.
+    /// Has no effect on tables without a MemWAL write spec.
     pub fn validate_single_shard(&mut self, validate_single_shard: bool) -> &mut Self {
         self.validate_single_shard = validate_single_shard;
         self
@@ -890,7 +890,7 @@ mod lsm_tests {
 
     use crate::connect;
     use crate::error::Error;
-    use crate::table::{LsmWriteSpec, Table};
+    use crate::table::{MemWalWriteSpec, Table};
 
     /// A reader of `[id: Int64, value: Int64]` rows; `value` is `0..n`.
     fn id_value_reader(ids: Vec<i64>) -> Box<dyn RecordBatchReader + Send> {
@@ -974,7 +974,7 @@ mod lsm_tests {
         let table = id_value_table(&dir).await;
         // num_buckets = 1: every row routes to the single bucket.
         table
-            .set_lsm_write_spec(LsmWriteSpec::bucket("id", 1))
+            .enable_memwal(MemWalWriteSpec::bucket("id", 1))
             .await
             .unwrap();
 
@@ -1001,7 +1001,7 @@ mod lsm_tests {
         let dir = tempdir().unwrap();
         let table = id_value_table(&dir).await;
         table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .enable_memwal(MemWalWriteSpec::unsharded())
             .await
             .unwrap();
 
@@ -1030,7 +1030,7 @@ mod lsm_tests {
             .unwrap();
         table.set_unenforced_primary_key(["id"]).await.unwrap();
         table
-            .set_lsm_write_spec(LsmWriteSpec::identity("region"))
+            .enable_memwal(MemWalWriteSpec::identity("region"))
             .await
             .unwrap();
 
@@ -1047,18 +1047,18 @@ mod lsm_tests {
     }
 
     #[tokio::test]
-    async fn lsm_merge_insert_use_lsm_false_falls_back() {
+    async fn lsm_merge_insert_use_memwal_false_falls_back() {
         let dir = tempdir().unwrap();
         let table = id_value_table(&dir).await;
         table
-            .set_lsm_write_spec(LsmWriteSpec::bucket("id", 1))
+            .enable_memwal(MemWalWriteSpec::bucket("id", 1))
             .await
             .unwrap();
 
-        // use_lsm(false) opts out: the standard path runs and commits even though
+        // use_memwal(false) opts out: the standard path runs and commits even though
         // a spec is installed.
         let mut builder = table.merge_insert(&["id"]);
-        builder.when_not_matched_insert_all().use_lsm(false);
+        builder.when_not_matched_insert_all().use_memwal(false);
         let result = builder
             .execute(id_value_reader(vec![3, 4, 5]))
             .await
@@ -1069,17 +1069,17 @@ mod lsm_tests {
     }
 
     #[tokio::test]
-    async fn lsm_merge_insert_use_lsm_true_without_spec_errors() {
+    async fn lsm_merge_insert_use_memwal_true_without_spec_errors() {
         let dir = tempdir().unwrap();
         let table = id_value_table(&dir).await;
 
-        // use_lsm(true) demands MemWAL routing; without a write spec it errors
+        // use_memwal(true) demands MemWAL routing; without a write spec it errors
         // rather than silently falling back to the standard path.
         let mut builder = table.merge_insert(&["id"]);
         builder
             .when_matched_update_all(None)
             .when_not_matched_insert_all()
-            .use_lsm(true);
+            .use_memwal(true);
         let err = builder
             .execute(id_value_reader(vec![3, 4, 5]))
             .await
@@ -1092,7 +1092,7 @@ mod lsm_tests {
         let dir = tempdir().unwrap();
         let table = id_value_table(&dir).await;
         table
-            .set_lsm_write_spec(LsmWriteSpec::bucket("id", 1))
+            .enable_memwal(MemWalWriteSpec::bucket("id", 1))
             .await
             .unwrap();
 
@@ -1109,7 +1109,7 @@ mod lsm_tests {
         let dir = tempdir().unwrap();
         let table = id_value_table(&dir).await;
         table
-            .set_lsm_write_spec(LsmWriteSpec::bucket("id", 1))
+            .enable_memwal(MemWalWriteSpec::bucket("id", 1))
             .await
             .unwrap();
 
@@ -1125,7 +1125,7 @@ mod lsm_tests {
         let dir = tempdir().unwrap();
         let table = id_value_table(&dir).await;
         table
-            .set_lsm_write_spec(LsmWriteSpec::bucket("id", 1))
+            .enable_memwal(MemWalWriteSpec::bucket("id", 1))
             .await
             .unwrap();
 
@@ -1135,7 +1135,7 @@ mod lsm_tests {
             .when_not_matched_insert_all();
         builder.execute(id_value_reader(vec![7, 8])).await.unwrap();
 
-        table.close_lsm_writers().await.unwrap();
+        table.close_memwal_writers().await.unwrap();
 
         // The writer reopens lazily on the next merge_insert.
         let mut builder = table.merge_insert(&[]);
@@ -1160,7 +1160,7 @@ mod lsm_tests {
             .unwrap();
         table.set_unenforced_primary_key(["id"]).await.unwrap();
         table
-            .set_lsm_write_spec(LsmWriteSpec::identity("region"))
+            .enable_memwal(MemWalWriteSpec::identity("region"))
             .await
             .unwrap();
 
@@ -1197,10 +1197,10 @@ mod lsm_tests {
     #[tokio::test]
     async fn lsm_merge_insert_no_spec_uses_standard_path() {
         let dir = tempdir().unwrap();
-        // id_value_table sets a primary key but no LSM write spec.
+        // id_value_table sets a primary key but no MemWAL write spec.
         let table = id_value_table(&dir).await;
 
-        // Without a spec, a default merge_insert (use_lsm unset) simply uses
+        // Without a spec, a default merge_insert (use_memwal unset) simply uses
         // the standard path and commits — no opt-out required, no error.
         let mut builder = table.merge_insert(&["id"]);
         builder
@@ -1228,7 +1228,7 @@ mod lsm_tests {
             .unwrap();
         table.set_unenforced_primary_key(["id"]).await.unwrap();
         table
-            .set_lsm_write_spec(LsmWriteSpec::identity("region"))
+            .enable_memwal(MemWalWriteSpec::identity("region"))
             .await
             .unwrap();
 
@@ -1254,7 +1254,7 @@ mod lsm_tests {
         assert!(matches!(err, Error::InvalidInput { .. }), "got {err:?}");
 
         // After closing the writer, a different shard can be written.
-        table.close_lsm_writers().await.unwrap();
+        table.close_memwal_writers().await.unwrap();
         let mut builder = table.merge_insert(&[]);
         builder
             .when_matched_update_all(None)
@@ -1266,7 +1266,7 @@ mod lsm_tests {
     }
 
     // ---------------------------------------------------------------------
-    // LSM read path
+    // MemWAL read path
     // ---------------------------------------------------------------------
 
     use crate::arrow::SendableRecordBatchStream;
@@ -1296,6 +1296,21 @@ mod lsm_tests {
         rows
     }
 
+    async fn collect_ids(stream: SendableRecordBatchStream) -> Vec<i64> {
+        let batches: Vec<_> = stream.try_collect().await.unwrap();
+        batches
+            .iter()
+            .flat_map(|batch| {
+                batch
+                    .column_by_name("id")
+                    .unwrap()
+                    .as_primitive::<Int64Type>()
+                    .values()
+                    .to_vec()
+            })
+            .collect()
+    }
+
     /// Upsert `ids` (value = 0..n) through the LSM `merge_insert` path.
     async fn lsm_upsert(table: &Table, ids: Vec<i64>) {
         let mut builder = table.merge_insert(&[]);
@@ -1310,7 +1325,7 @@ mod lsm_tests {
         let dir = tempdir().unwrap();
         let table = id_value_table(&dir).await; // base: ids 1,2,3 (value 0,1,2)
         table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .enable_memwal(MemWalWriteSpec::unsharded())
             .await
             .unwrap();
 
@@ -1325,8 +1340,8 @@ mod lsm_tests {
             vec![1, 2, 3, 4, 5]
         );
 
-        // use_lsm(false) bypasses the MemWAL and reads the base table only.
-        let base_only = table.query().use_lsm(false).execute().await.unwrap();
+        // use_memwal(false) bypasses the MemWAL and reads the base table only.
+        let base_only = table.query().use_memwal(false).execute().await.unwrap();
         let rows = collect_id_value(base_only).await;
         assert_eq!(
             rows.iter().map(|(id, _)| *id).collect::<Vec<_>>(),
@@ -1334,12 +1349,47 @@ mod lsm_tests {
         );
     }
 
+    #[tokio::test]
+    async fn base_table_adapter_use_memwal_false_scans_the_base_only() {
+        use crate::table::datafusion::BaseTableAdapter;
+        use datafusion::prelude::SessionContext;
+
+        let dir = tempdir().unwrap();
+        let table = id_value_table(&dir).await; // base: ids 1,2,3
+        table
+            .enable_memwal(MemWalWriteSpec::unsharded())
+            .await
+            .unwrap();
+        // Ids 4,5 land in the active memtable, not the base table.
+        lsm_upsert(&table, vec![4, 5]).await;
+
+        let adapter = BaseTableAdapter::try_new(table.base_table().clone())
+            .await
+            .unwrap()
+            .with_use_memwal(Some(false));
+        let ctx = SessionContext::new();
+        ctx.register_table("base_only", Arc::new(adapter)).unwrap();
+        let batches = ctx
+            .sql("SELECT id FROM base_only")
+            .await
+            .unwrap()
+            .collect()
+            .await
+            .unwrap();
+        let mut ids: Vec<i64> = batches
+            .iter()
+            .flat_map(|b| b.column(0).as_primitive::<Int64Type>().values().to_vec())
+            .collect();
+        ids.sort();
+        assert_eq!(ids, vec![1, 2, 3]);
+    }
+
     #[tokio::test]
     async fn query_snapshot_preserves_lsm_read_semantics() {
         let dir = tempdir().unwrap();
         let table = id_value_table(&dir).await;
         table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .enable_memwal(MemWalWriteSpec::unsharded())
             .await
             .unwrap();
         lsm_upsert(&table, vec![4, 5]).await;
@@ -1357,7 +1407,7 @@ mod lsm_tests {
         let dir = tempdir().unwrap();
         let table = id_value_table(&dir).await;
         table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .enable_memwal(MemWalWriteSpec::unsharded())
             .await
             .unwrap();
         lsm_upsert(&table, vec![4]).await;
@@ -1377,7 +1427,7 @@ mod lsm_tests {
         let dir = tempdir().unwrap();
         let table = id_value_table(&dir).await; // base: id 2 -> value 1
         table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .enable_memwal(MemWalWriteSpec::unsharded())
             .await
             .unwrap();
 
@@ -1395,7 +1445,7 @@ mod lsm_tests {
         let dir = tempdir().unwrap();
         let table = id_value_table(&dir).await;
         table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .enable_memwal(MemWalWriteSpec::unsharded())
             .await
             .unwrap();
         lsm_upsert(&table, vec![2, 3, 4]).await; // id 2 -> value 0 (shadows base)
@@ -1410,14 +1460,14 @@ mod lsm_tests {
         let dir = tempdir().unwrap();
         let table = id_value_table(&dir).await;
         table
-            .set_lsm_write_spec(LsmWriteSpec::bucket("id", 8))
+            .enable_memwal(MemWalWriteSpec::bucket("id", 8))
             .await
             .unwrap();
 
-        // Two single-row upserts that route to (likely) different buckets; each
+        // Two single-row upserts that route to (likely) different shards; each
         // closes the writer so the next opens a fresh shard.
         lsm_upsert(&table, vec![10]).await;
-        table.close_lsm_writers().await.unwrap();
+        table.close_memwal_writers().await.unwrap();
         lsm_upsert(&table, vec![11]).await;
 
         let lsm = table.query().execute().await.unwrap();
@@ -1432,14 +1482,14 @@ mod lsm_tests {
         let dir = tempdir().unwrap();
         let table = id_value_table(&dir).await;
         table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .enable_memwal(MemWalWriteSpec::unsharded())
             .await
             .unwrap();
         lsm_upsert(&table, vec![4, 5]).await;
         // close flushes the active memtable to an on-disk generation and drops
         // the cached writer; the read must still see those rows via the shard
         // manifest snapshot.
-        table.close_lsm_writers().await.unwrap();
+        table.close_memwal_writers().await.unwrap();
 
         let lsm = table.query().execute().await.unwrap();
         let ids: Vec<i64> = collect_id_value(lsm)
@@ -1453,11 +1503,11 @@ mod lsm_tests {
     #[tokio::test]
     async fn lsm_read_without_spec_reads_base() {
         let dir = tempdir().unwrap();
-        let table = id_value_table(&dir).await; // no LSM write spec
+        let table = id_value_table(&dir).await; // no MemWAL write spec
 
         // With no spec installed there is nothing to route: the default read and
-        // an explicit use_lsm(false) both read the base table without error.
-        for query in [table.query(), table.query().use_lsm(false)] {
+        // an explicit use_memwal(false) both read the base table without error.
+        for query in [table.query(), table.query().use_memwal(false)] {
             let rows = collect_id_value(query.execute().await.unwrap()).await;
             assert_eq!(
                 rows.iter().map(|(id, _)| *id).collect::<Vec<_>>(),
@@ -1467,11 +1517,11 @@ mod lsm_tests {
     }
 
     #[tokio::test]
-    async fn lsm_read_unsupported_shape_errors_without_use_lsm_false() {
+    async fn lsm_read_unsupported_shape_errors_without_use_memwal_false() {
         let dir = tempdir().unwrap();
         let table = id_value_table(&dir).await;
         table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .enable_memwal(MemWalWriteSpec::unsharded())
             .await
             .unwrap();
         lsm_upsert(&table, vec![4]).await;
@@ -1488,12 +1538,12 @@ mod lsm_tests {
             .expect("unsupported shape on a MemWAL table must error");
         assert!(matches!(err, Error::NotSupported { .. }), "got {err:?}");
 
-        // use_lsm(false) is the escape hatch: it reads the base table only.
+        // use_memwal(false) is the escape hatch: it reads the base table only.
         let rows = collect_id_value(
             table
                 .query()
                 .with_row_id()
-                .use_lsm(false)
+                .use_memwal(false)
                 .execute()
                 .await
                 .unwrap(),
@@ -1505,6 +1555,30 @@ mod lsm_tests {
         );
     }
 
+    /// A table with `id`/`text` and a primary key, ready for a MemWAL write spec.
+    async fn lsm_text_table(dir: &tempfile::TempDir) -> crate::Table {
+        let conn = connect(dir.path().to_str().unwrap())
+            .execute()
+            .await
+            .unwrap();
+        let table = conn
+            .create_table("t", id_text_reader(vec![(1, "alpha")]))
+            .execute()
+            .await
+            .unwrap();
+        table.set_unenforced_primary_key(["id"]).await.unwrap();
+        table
+    }
+
+    /// Upsert `rows` through the MemWAL write path.
+    async fn upsert_text(table: &crate::Table, rows: Vec<(i64, &str)>) {
+        let mut builder = table.merge_insert(&[]);
+        builder
+            .when_matched_update_all(None)
+            .when_not_matched_insert_all();
+        builder.execute(id_text_reader(rows)).await.unwrap();
+    }
+
     /// A reader of `[id: Int64, text: Utf8]` rows.
     fn id_text_reader(rows: Vec<(i64, &str)>) -> Box<dyn RecordBatchReader + Send> {
         let schema = Arc::new(Schema::new(vec![
@@ -1524,6 +1598,165 @@ mod lsm_tests {
         Box::new(RecordBatchIterator::new(vec![Ok(batch)], schema))
     }
 
+    /// Under the default set, an index created while a writer is open reaches
+    /// that writer.
+    ///
+    /// The default maintains every index the table has, so creating one changes
+    /// what the table maintains without anyone touching the spec. A writer left
+    /// on its old configs would keep building MemTables the new index does not
+    /// cover.
+    #[tokio::test]
+    async fn lsm_default_set_picks_up_an_index_created_while_writing() {
+        use crate::index::Index;
+        use lance_index::scalar::FullTextSearchQuery;
+
+        let dir = tempdir().unwrap();
+        let table = lsm_text_table(&dir).await;
+        // The default set, on a table with no FTS index yet.
+        table
+            .enable_memwal(MemWalWriteSpec::unsharded())
+            .await
+            .unwrap();
+
+        // Opens the writer, whose MemTable carries no FTS index.
+        upsert_text(&table, vec![(99, "zebra")]).await;
+
+        // Created with the writer still open and never reopened.
+        table
+            .create_index(&["text"], Index::FTS(Default::default()))
+            .execute()
+            .await
+            .unwrap();
+
+        upsert_text(&table, vec![(100, "zebra stripes")]).await;
+
+        let query = FullTextSearchQuery::new("stripes".to_string())
+            .with_column("text".to_string())
+            .unwrap();
+        let batches = table
+            .query()
+            .full_text_search(query)
+            .execute()
+            .await
+            .expect("the created index reached the open writer")
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        let found: usize = batches.iter().map(|b| b.num_rows()).sum();
+        assert_eq!(found, 1, "the row written after create_index must be found");
+    }
+
+    /// A spec is replaced by unsetting it and setting the new one.
+    ///
+    /// `disable_memwal` drains the open writer, so the rows it held
+    /// survive the replacement, and the writer the next write opens builds its
+    /// MemTables from the new set.
+    #[tokio::test]
+    async fn lsm_spec_is_replaced_by_unsetting_it_first() {
+        use crate::index::Index;
+        use lance_index::scalar::FullTextSearchQuery;
+
+        let dir = tempdir().unwrap();
+        let table = lsm_text_table(&dir).await;
+        table
+            .create_index(&["text"], Index::FTS(Default::default()))
+            .execute()
+            .await
+            .unwrap();
+        let fts_index = table.list_indices().await.unwrap()[0].name.clone();
+
+        // Installed maintaining nothing, then written to: the writer opens with
+        // no FTS index.
+        table
+            .enable_memwal(MemWalWriteSpec::unsharded().with_maintained_indexes(Vec::new()))
+            .await
+            .unwrap();
+        upsert_text(&table, vec![(99, "zebra")]).await;
+
+        // Setting over an installed spec is refused; unset, then set.
+        let err = table
+            .enable_memwal(
+                MemWalWriteSpec::unsharded().with_maintained_indexes(vec![fts_index.clone()]),
+            )
+            .await
+            .expect_err("an installed spec cannot be set over");
+        assert!(
+            err.to_string().contains("already set"),
+            "unexpected error: {err}"
+        );
+        table.disable_memwal().await.unwrap();
+        table
+            .enable_memwal(MemWalWriteSpec::unsharded().with_maintained_indexes(vec![fts_index]))
+            .await
+            .unwrap();
+
+        // A row written after the replacement is answered by the index the new
+        // writer maintains.
+        upsert_text(&table, vec![(100, "zebra stripes")]).await;
+
+        let query = FullTextSearchQuery::new("stripes".to_string())
+            .with_column("text".to_string())
+            .unwrap();
+        let batches = table
+            .query()
+            .full_text_search(query)
+            .execute()
+            .await
+            .expect("the replaced spec maintains the index")
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        let found: usize = batches.iter().map(|b| b.num_rows()).sum();
+        assert_eq!(
+            found, 1,
+            "the row written after the replacement must be found"
+        );
+    }
+
+    /// A row written before the index exists is searchable once it does.
+    ///
+    /// The MemTable holding it was built without the index. Creating the index
+    /// replaces the writer's configs, which freezes that MemTable and waits for
+    /// its flush, so no resident MemTable is left that cannot answer the read.
+    #[tokio::test]
+    async fn lsm_a_row_written_before_the_index_is_searchable_after_it() {
+        use crate::index::Index;
+        use lance_index::scalar::FullTextSearchQuery;
+
+        let dir = tempdir().unwrap();
+        let table = lsm_text_table(&dir).await;
+        // No index yet, and the default set: maintain whatever the table has.
+        table
+            .enable_memwal(MemWalWriteSpec::unsharded())
+            .await
+            .unwrap();
+
+        // Opens the writer, whose MemTable therefore carries no FTS index.
+        upsert_text(&table, vec![(99, "zebra")]).await;
+
+        // Built afterwards: the spec now resolves to it, the MemTable does not.
+        table
+            .create_index(&["text"], Index::FTS(Default::default()))
+            .execute()
+            .await
+            .unwrap();
+
+        let query = FullTextSearchQuery::new("zebra".to_string())
+            .with_column("text".to_string())
+            .unwrap();
+        let batches = table
+            .query()
+            .full_text_search(query)
+            .execute()
+            .await
+            .expect("no resident MemTable is left that cannot answer")
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        let found: usize = batches.iter().map(|b| b.num_rows()).sum();
+        assert_eq!(found, 1, "the row written before the index must be found");
+    }
+
     #[tokio::test]
     async fn lsm_read_full_text_search() {
         use crate::index::Index;
@@ -1550,7 +1783,7 @@ mod lsm_tests {
             .unwrap();
         let fts_index = table.list_indices().await.unwrap()[0].name.clone();
         table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded().with_maintained_indexes(vec![fts_index]))
+            .enable_memwal(MemWalWriteSpec::unsharded().with_maintained_indexes(vec![fts_index]))
             .await
             .unwrap();
 
@@ -1584,12 +1817,16 @@ mod lsm_tests {
         assert_eq!(rows, 1, "LSM FTS must still see base rows");
     }
 
-    #[tokio::test]
-    async fn lsm_read_vector_search() {
+    /// A table whose vector index the LSM maintains: base rows filled with
+    /// their id, and one memtable row filled with 1000, nearest to `[1000; 8]`.
+    /// `registry` replaces the table's memtable kinds.
+    async fn lsm_vector_table(
+        dir: &std::path::Path,
+        registry: Option<lance::dataset::mem_wal::MemIndexRegistry>,
+    ) -> Table {
         use crate::index::Index;
         use crate::index::vector::IvfPqIndexBuilder;
         use arrow::array::{FixedSizeListBuilder, Float32Builder};
-        use arrow::datatypes::Int64Type;
 
         const DIM: i32 = 8;
         const N: i64 = 256;
@@ -1618,12 +1855,7 @@ mod lsm_tests {
             .unwrap()
         };
 
-        let dir = tempdir().unwrap();
-        let conn = connect(dir.path().to_str().unwrap())
-            .execute()
-            .await
-            .unwrap();
-        // Base rows fill each vector with its own id (0..256); all far from 1000.
+        let conn = connect(dir.to_str().unwrap()).execute().await.unwrap();
         let base = make_batch((0..N).map(|i| (i, i as f32)).collect());
         let base_reader: Box<dyn RecordBatchReader + Send> =
             Box::new(RecordBatchIterator::new(vec![Ok(base)], schema.clone()));
@@ -1634,20 +1866,22 @@ mod lsm_tests {
                 &["vec"],
                 Index::IvfPq(
                     IvfPqIndexBuilder::default()
-                        .num_partitions(1)
+                        .num_partitions(8)
                         .num_sub_vectors(2),
                 ),
             )
             .execute()
             .await
             .unwrap();
+        if let Some(registry) = registry {
+            table.as_native().unwrap().set_mem_index_registry(registry);
+        }
         let vec_index = table.list_indices().await.unwrap()[0].name.clone();
         table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded().with_maintained_indexes(vec![vec_index]))
+            .enable_memwal(MemWalWriteSpec::unsharded().with_maintained_indexes(vec![vec_index]))
             .await
             .unwrap();
 
-        // Insert a vector (filled with 1000) that is nearest to the query.
         let mut builder = table.merge_insert(&[]);
         builder
             .when_matched_update_all(None)
@@ -1657,32 +1891,511 @@ mod lsm_tests {
             schema.clone(),
         ));
         builder.execute(insert_reader).await.unwrap();
+        table
+    }
 
-        // KNN near [1000; DIM]: the default (auto-routed) read surfaces the
-        // memtable row.
-        let stream = table
-            .query()
-            .nearest_to(&[1000.0_f32; 8])
-            .unwrap()
-            .limit(1)
-            .execute()
-            .await
-            .unwrap();
-        let batches: Vec<_> = stream.try_collect().await.unwrap();
-        let ids: Vec<i64> = batches
-            .iter()
-            .flat_map(|b| {
-                b.column_by_name("id")
-                    .unwrap()
-                    .as_primitive::<Int64Type>()
-                    .values()
-                    .to_vec()
-            })
-            .collect();
+    /// The ids of the nearest row to `[1000; 8]` through the default read.
+    async fn nearest_ids(table: &Table) -> Vec<i64> {
+        let query = table.query().nearest_to(&[1000.0_f32; 8]).unwrap().limit(1);
+        collect_ids(query.execute().await.unwrap()).await
+    }
+
+    #[tokio::test]
+    async fn lsm_read_vector_search() {
+        let dir = tempdir().unwrap();
+        let table = lsm_vector_table(dir.path(), None).await;
         assert_eq!(
-            ids,
+            nearest_ids(&table).await,
             vec![9999],
             "LSM vector search must rank the memtable row first"
         );
+
+        let base_query = table
+            .query()
+            .nearest_to(&[0.0_f32; 8])
+            .unwrap()
+            .only_if("id = 255")
+            .limit(1);
+        for query in [
+            base_query.clone(),
+            base_query.clone().nprobes(8).unwrap(),
+            base_query.clone().minimum_nprobes(8).unwrap(),
+            base_query.clone().maximum_nprobes(Some(8)).unwrap(),
+            base_query
+                .nprobes(1)
+                .unwrap()
+                .maximum_nprobes(None)
+                .unwrap(),
+        ] {
+            let ids = collect_ids(query.execute().await.unwrap()).await;
+            assert_eq!(ids, vec![255]);
+        }
+    }
+
+    /// The built-in vector plugin, except that its index answers no search.
+    #[derive(Debug)]
+    struct AnswersNothing;
+
+    #[derive(Debug)]
+    struct AnswersNothingIndex(Arc<dyn lance::dataset::mem_wal::index::MemIndex>);
+
+    fn vector_plugin() -> Arc<dyn lance::dataset::mem_wal::index::MemIndexPlugin> {
+        Arc::new(lance::dataset::mem_wal::index::HnswMemIndexPlugin)
+    }
+
+    #[async_trait::async_trait]
+    impl lance::dataset::mem_wal::index::MemIndexPlugin for AnswersNothing {
+        fn name(&self) -> &str {
+            "AnswersNothing"
+        }
+        fn details_message(&self) -> &str {
+            "VectorIndexDetails"
+        }
+        fn flush_index_type(&self) -> lance_index::IndexType {
+            vector_plugin().flush_index_type()
+        }
+        fn training_criteria(&self) -> lance_index::scalar::registry::TrainingCriteria {
+            vector_plugin().training_criteria()
+        }
+        async fn resolve(
+            &self,
+            ctx: &lance::dataset::mem_wal::index::ResolveContext<'_>,
+        ) -> lance_core::Result<lance::dataset::mem_wal::index::ResolvedIndex> {
+            vector_plugin().resolve(ctx).await
+        }
+        fn validate(
+            &self,
+            ctx: &lance::dataset::mem_wal::index::MemIndexBuildContext<'_>,
+        ) -> lance_core::Result<()> {
+            vector_plugin().validate(ctx)
+        }
+        fn create(
+            &self,
+            ctx: &lance::dataset::mem_wal::index::MemIndexBuildContext<'_>,
+        ) -> lance_core::Result<Arc<dyn lance::dataset::mem_wal::index::MemIndex>> {
+            Ok(Arc::new(AnswersNothingIndex(vector_plugin().create(ctx)?)))
+        }
+    }
+
+    #[async_trait::async_trait]
+    impl lance::dataset::mem_wal::index::MemIndex for AnswersNothingIndex {
+        fn columns(&self) -> &[String] {
+            self.0.columns()
+        }
+        fn can_answer(&self, _query: &dyn lance::dataset::mem_wal::index::MemQuery) -> bool {
+            false
+        }
+        fn insert(&self, batch: &RecordBatch, row_offset: u64) -> lance_core::Result<()> {
+            self.0.insert(batch, row_offset)
+        }
+        fn insert_batches(
+            &self,
+            batches: &[lance::dataset::mem_wal::write::StoredBatch],
+        ) -> lance_core::Result<()> {
+            self.0.insert_batches(batches)
+        }
+        fn resident_bytes(&self) -> usize {
+            self.0.resident_bytes()
+        }
+        fn search(
+            &self,
+            _query: &dyn lance::dataset::mem_wal::index::MemQuery,
+            _ctx: &lance::dataset::mem_wal::index::SearchContext,
+        ) -> lance_core::Result<Option<lance::dataset::mem_wal::index::MemMatches>> {
+            Ok(None)
+        }
+        async fn flush(
+            &self,
+            ctx: &lance::dataset::mem_wal::index::FlushContext<'_>,
+        ) -> lance_core::Result<lance::dataset::mem_wal::index::FlushOutcome> {
+            self.0.flush(ctx).await
+        }
+    }
+
+    /// A resident memtable whose vector index answers nothing still serves an
+    /// LSM vector search: Lance compares every row instead.
+    #[tokio::test]
+    async fn lsm_vector_search_compares_every_row_when_no_resident_index_answers() {
+        let dir = tempdir().unwrap();
+        let mut registry = lance::dataset::mem_wal::MemIndexRegistry::default();
+        registry.replace_plugin(Arc::new(AnswersNothing)).unwrap();
+        let table = lsm_vector_table(dir.path(), Some(registry)).await;
+        assert_eq!(nearest_ids(&table).await, vec![9999]);
+
+        // The second page starts after the memtable row.
+        let page = table
+            .query()
+            .nearest_to(&[1000.0_f32; 8])
+            .unwrap()
+            .limit(2)
+            .offset(1);
+        assert_eq!(
+            collect_ids(page.execute().await.unwrap()).await,
+            vec![255, 254]
+        );
+    }
+
+    /// Claims the base table's bitmap index and keeps it as a B-tree.
+    #[derive(Debug)]
+    struct BitmapAsBTree;
+
+    #[derive(Debug)]
+    struct BitmapAsBTreeIndex(lance::dataset::mem_wal::index::BTreeMemIndex);
+
+    fn btree_plugin() -> Arc<dyn lance::dataset::mem_wal::index::MemIndexPlugin> {
+        lance::dataset::mem_wal::MemIndexRegistry::default()
+            .plugin_for_details_url("/lance.table.BTreeIndexDetails")
+            .unwrap()
+            .clone()
+    }
+
+    #[async_trait::async_trait]
+    impl lance::dataset::mem_wal::index::MemIndexPlugin for BitmapAsBTree {
+        fn name(&self) -> &str {
+            "BitmapAsBTree"
+        }
+        fn details_message(&self) -> &str {
+            "BitmapIndexDetails"
+        }
+        fn flush_index_type(&self) -> lance_index::IndexType {
+            lance_index::IndexType::Bitmap
+        }
+        fn training_criteria(&self) -> lance_index::scalar::registry::TrainingCriteria {
+            use lance_index::scalar::registry::{TrainingCriteria, TrainingOrdering};
+            TrainingCriteria::new(TrainingOrdering::Values).with_row_id()
+        }
+        fn validate(
+            &self,
+            ctx: &lance::dataset::mem_wal::index::MemIndexBuildContext<'_>,
+        ) -> lance_core::Result<()> {
+            btree_plugin().validate(ctx)
+        }
+        fn create(
+            &self,
+            ctx: &lance::dataset::mem_wal::index::MemIndexBuildContext<'_>,
+        ) -> lance_core::Result<Arc<dyn lance::dataset::mem_wal::index::MemIndex>> {
+            let (column, field_id) = ctx.single_column()?;
+            Ok(Arc::new(BitmapAsBTreeIndex(
+                lance::dataset::mem_wal::index::BTreeMemIndex::new(field_id, column.to_string()),
+            )))
+        }
+    }
+
+    #[async_trait::async_trait]
+    impl lance::dataset::mem_wal::index::MemIndex for BitmapAsBTreeIndex {
+        fn columns(&self) -> &[String] {
+            self.0.columns()
+        }
+        fn can_answer(&self, query: &dyn lance::dataset::mem_wal::index::MemQuery) -> bool {
+            self.0.can_answer(query)
+        }
+        fn insert(&self, batch: &RecordBatch, row_offset: u64) -> lance_core::Result<()> {
+            self.0.insert(batch, row_offset)
+        }
+        fn resident_bytes(&self) -> usize {
+            self.0.resident_bytes()
+        }
+        fn search(
+            &self,
+            query: &dyn lance::dataset::mem_wal::index::MemQuery,
+            ctx: &lance::dataset::mem_wal::index::SearchContext,
+        ) -> lance_core::Result<Option<lance::dataset::mem_wal::index::MemMatches>> {
+            self.0.search(query, ctx)
+        }
+        async fn flush(
+            &self,
+            ctx: &lance::dataset::mem_wal::index::FlushContext<'_>,
+        ) -> lance_core::Result<lance::dataset::mem_wal::index::FlushOutcome> {
+            // BTree now trains on row addresses, while Bitmap still takes row IDs.
+            // These coincide in the flushed generation, which uses physical IDs.
+            assert!(!ctx.generation()?.dataset.manifest().uses_stable_row_ids());
+            let batches = self
+                .0
+                .to_training_batches(ctx.batch_size)?
+                .into_iter()
+                .map(|batch| {
+                    assert_eq!(batch.schema().field(1).name(), lance_core::ROW_ADDR);
+                    let schema = Arc::new(Schema::new(vec![
+                        batch.schema().field(0).clone(),
+                        Field::new(lance_core::ROW_ID, DataType::UInt64, false),
+                    ]));
+                    RecordBatch::try_new(schema, batch.columns().to_vec())
+                })
+                .collect::<std::result::Result<Vec<_>, _>>()?;
+            Ok(lance::dataset::mem_wal::index::FlushOutcome::from_batches(
+                batches,
+            ))
+        }
+    }
+
+    /// A kind Lance has no memtable index for is refused when named until the
+    /// table's registry maintains it; then it is maintained, flushed into each
+    /// SSTable, and answers searches on the SSTables.
+    #[tokio::test]
+    async fn lsm_maintains_an_index_kind_from_the_tables_registry() {
+        let dir = tempdir().unwrap();
+        let conn = connect(dir.path().to_str().unwrap())
+            .execute()
+            .await
+            .unwrap();
+        let table = conn
+            .create_table("t", id_region_reader(vec![(1, "us"), (2, "eu"), (3, "us")]))
+            .execute()
+            .await
+            .unwrap();
+        table.set_unenforced_primary_key(["id"]).await.unwrap();
+        table
+            .create_index(&["region"], crate::index::Index::Bitmap(Default::default()))
+            .name("region_bitmap".to_string())
+            .execute()
+            .await
+            .unwrap();
+        let spec = || {
+            MemWalWriteSpec::unsharded().with_maintained_indexes(vec!["region_bitmap".to_string()])
+        };
+
+        let refused = table.enable_memwal(spec()).await.unwrap_err();
+        assert!(
+            refused.to_string().contains("region_bitmap")
+                && refused
+                    .to_string()
+                    .contains("no registered plugin maintains"),
+            "{refused:?}"
+        );
+
+        let registry = lance::dataset::mem_wal::MemIndexRegistry::default()
+            .with_plugin(Arc::new(BitmapAsBTree))
+            .unwrap();
+        table.as_native().unwrap().set_mem_index_registry(registry);
+        table.enable_memwal(spec()).await.unwrap();
+
+        // The registry is not stored with the table.
+        let other = conn.open_table("t").execute().await.unwrap();
+        let mut builder = other.merge_insert(&["id"]);
+        builder
+            .when_matched_update_all(None)
+            .when_not_matched_insert_all();
+        let refused = builder
+            .execute(id_region_reader(vec![(20, "us")]))
+            .await
+            .unwrap_err();
+        assert!(
+            refused
+                .to_string()
+                .contains("no registered plugin maintains"),
+            "{refused:?}"
+        );
+
+        let mut builder = table.merge_insert(&["id"]);
+        builder
+            .when_matched_update_all(None)
+            .when_not_matched_insert_all();
+        builder
+            .execute(id_region_reader(vec![
+                (10, "fresh"),
+                (11, "fresh"),
+                (12, "eu"),
+            ]))
+            .await
+            .unwrap();
+
+        let fresh = || async {
+            let query = table.query().only_if("region = 'fresh'");
+            let mut ids = collect_ids(query.execute().await.unwrap()).await;
+            ids.sort_unstable();
+            ids
+        };
+        assert_eq!(fresh().await, vec![10, 11]);
+        let (_, _, memtables) = table
+            .as_native()
+            .unwrap()
+            .dataset
+            .shard_writer()
+            .read_snapshot()
+            .await
+            .unwrap()
+            .expect("the merge opened a writer");
+        let active = memtables.expect("the writer keeps memtables").active;
+        assert!(
+            active
+                .index_store
+                .index_names()
+                .contains(&"region_bitmap".to_string()),
+            "the memtable maintains the plugin's index: {:?}",
+            active.index_store.index_names()
+        );
+
+        table.close_memwal_writers().await.unwrap();
+        assert_eq!(fresh().await, vec![10, 11]);
+        assert_sstables_answer_through_region_bitmap(&table, vec![10, 11]).await;
+    }
+
+    /// The SSTables each shard's manifest publishes.
+    async fn published_sstables(table: &Table) -> Vec<lance::Dataset> {
+        use lance::dataset::mem_wal::ShardManifestStore;
+
+        let uri = table.uri().await.unwrap();
+        let (store, base) = lance_io::object_store::ObjectStore::from_uri(&uri)
+            .await
+            .unwrap();
+        let mem_wal = std::path::Path::new(&uri).join("_mem_wal");
+        let mut sstables = Vec::new();
+        for shard in std::fs::read_dir(&mem_wal).unwrap() {
+            let Ok(shard_id) = shard.unwrap().file_name().to_string_lossy().parse() else {
+                continue;
+            };
+            let manifest = ShardManifestStore::new(store.clone(), &base, shard_id, 2)
+                .latest()
+                .await
+                .unwrap()
+                .expect("a shard directory has a manifest");
+            for sstable in manifest.sstables {
+                let path = mem_wal.join(shard_id.to_string()).join(&sstable.path);
+                sstables.push(lance::Dataset::open(path.to_str().unwrap()).await.unwrap());
+            }
+        }
+        sstables
+    }
+
+    /// Every published SSTable carries `region_bitmap` as a bitmap index on
+    /// `region`, and a search for `region = 'fresh'` on it uses that index.
+    async fn assert_sstables_answer_through_region_bitmap(table: &Table, fresh: Vec<i64>) {
+        use lance::index::DatasetIndexExt;
+
+        let sstables = published_sstables(table).await;
+        assert!(
+            !sstables.is_empty(),
+            "closing the writer flushed an SSTable"
+        );
+        let mut found = Vec::new();
+        for sstable in sstables {
+            let region = sstable.schema().field("region").unwrap().id;
+            let indices = sstable.load_indices().await.unwrap();
+            let index = indices
+                .iter()
+                .find(|index| index.name == "region_bitmap")
+                .expect("the SSTable carries the index the plugin trained");
+            assert_eq!(index.fields, vec![region]);
+            assert!(
+                index
+                    .index_details
+                    .as_ref()
+                    .is_some_and(|details| details.type_url.ends_with("BitmapIndexDetails")),
+                "{:?}",
+                index.index_details
+            );
+
+            let mut scan = sstable.scan();
+            scan.filter("region = 'fresh'").unwrap();
+            let plan = scan.explain_plan(false).await.unwrap();
+            assert!(plan.contains("ScalarIndexQuery"), "{plan}");
+            let batch = scan.try_into_batch().await.unwrap();
+            found.extend(
+                batch["id"]
+                    .as_primitive::<Int64Type>()
+                    .values()
+                    .iter()
+                    .copied(),
+            );
+        }
+        found.sort_unstable();
+        assert_eq!(found, fresh);
+    }
+
+    /// Under the default set, a kind only the registry maintains is picked up
+    /// when its index is created after the writer opened, and the table's
+    /// clones share the registry.
+    #[tokio::test]
+    async fn lsm_default_set_maintains_a_registry_kind_created_while_writing() {
+        let dir = tempdir().unwrap();
+        let conn = connect(dir.path().to_str().unwrap())
+            .execute()
+            .await
+            .unwrap();
+        let table = conn
+            .create_table("t", id_region_reader(vec![(1, "us"), (2, "eu")]))
+            .execute()
+            .await
+            .unwrap();
+        table.set_unenforced_primary_key(["id"]).await.unwrap();
+        let registry = lance::dataset::mem_wal::MemIndexRegistry::default()
+            .with_plugin(Arc::new(BitmapAsBTree))
+            .unwrap();
+        table.as_native().unwrap().set_mem_index_registry(registry);
+        let clone = table.clone();
+        assert!(
+            clone
+                .as_native()
+                .unwrap()
+                .mem_index_registry()
+                .plugin_for_details_url("/lance.table.BitmapIndexDetails")
+                .is_some(),
+            "a clone shares the handle's registry"
+        );
+        table
+            .enable_memwal(MemWalWriteSpec::unsharded())
+            .await
+            .unwrap();
+
+        let upsert = |rows: Vec<(i64, &'static str)>| {
+            let table = table.clone();
+            async move {
+                let mut builder = table.merge_insert(&["id"]);
+                builder
+                    .when_matched_update_all(None)
+                    .when_not_matched_insert_all();
+                builder.execute(id_region_reader(rows)).await.unwrap();
+            }
+        };
+        // Opens the writer before the table has a bitmap index.
+        upsert(vec![(3, "us")]).await;
+        table
+            .create_index(&["region"], crate::index::Index::Bitmap(Default::default()))
+            .name("region_bitmap".to_string())
+            .execute()
+            .await
+            .unwrap();
+        upsert(vec![(10, "fresh"), (11, "fresh")]).await;
+
+        let (_, _, memtables) = table
+            .as_native()
+            .unwrap()
+            .dataset
+            .shard_writer()
+            .read_snapshot()
+            .await
+            .unwrap()
+            .expect("the merge opened a writer");
+        let active = memtables.expect("the writer keeps memtables").active;
+        assert!(
+            active
+                .index_store
+                .index_names()
+                .contains(&"region_bitmap".to_string()),
+            "the open writer picked up the new index: {:?}",
+            active.index_store.index_names()
+        );
+
+        table.close_memwal_writers().await.unwrap();
+        // Only the SSTable written after the index exists carries it.
+        let sstables = published_sstables(&table).await;
+        let mut carrying = 0;
+        for sstable in &sstables {
+            use lance::index::DatasetIndexExt;
+            if sstable
+                .load_indices()
+                .await
+                .unwrap()
+                .iter()
+                .any(|index| index.name == "region_bitmap")
+            {
+                carrying += 1;
+            }
+        }
+        assert!(
+            carrying >= 1,
+            "{} SSTables, none carry the index",
+            sstables.len()
+        );
     }
 }
diff --git a/rust/lancedb/src/table/merge/lsm.rs b/rust/lancedb/src/table/merge/lsm.rs
index fc925c014..d26be28cc 100644
--- a/rust/lancedb/src/table/merge/lsm.rs
+++ b/rust/lancedb/src/table/merge/lsm.rs
@@ -1,10 +1,10 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-//! MemWAL LSM write path for `merge_insert`.
+//! MemWAL write path for `merge_insert`.
 //!
-//! [`set_lsm_write_spec`] installs an [`LsmWriteSpec`] on a table by creating
-//! Lance's MemWAL index; [`unset_lsm_write_spec`] removes it. Once a spec is
+//! [`enable_memwal`] installs an [`MemWalWriteSpec`] on a table by creating
+//! Lance's MemWAL index; [`disable_memwal`] removes it. Once a spec is
 //! installed, `merge_insert` upsert calls are dispatched through Lance's MemWAL
 //! `ShardWriter` (LSM-style append) instead of the standard merge path — see
 //! [`lsm_dispatch_decision`] and [`execute_lsm_merge_insert`].
@@ -14,7 +14,7 @@
 //! unsharded). [`MergeInsertBuilder::validate_single_shard`] controls whether
 //! every row is checked or only the first. A dataset writes to one shard at a
 //! time; its writer is cached in the [`ShardWriterCache`] held alongside the
-//! dataset, and [`close_lsm_writers`] closes it.
+//! dataset, and [`close_memwal_writers`] closes it.
 
 use std::collections::HashMap;
 use std::sync::Arc;
@@ -29,7 +29,7 @@ use arrow_schema::{DataType, Schema as ArrowSchema, SchemaRef};
 use lance::Dataset;
 use lance::dataset::mem_wal::{
     DatasetMemWalExt, ShardWriter, ShardWriterConfig, evaluate_sharding_spec,
-    validate_maintained_indexes,
+    validate_maintained_indexes_with,
 };
 use lance::index::DatasetIndexExt;
 use lance_core::datatypes::Schema as LanceSchema;
@@ -40,9 +40,9 @@ use uuid::Uuid;
 use crate::error::{Error, Result};
 use crate::index::IndexConfig;
 use crate::table::merge::{MergeInsertBuilder, MergeResult};
-use crate::table::{BaseTable, LsmWriteSpec, NativeTable};
+use crate::table::{BaseTable, MemWalWriteSpec, NativeTable};
 
-/// Spec id of the sole sharding spec installed by [`set_lsm_write_spec`].
+/// Spec id of the sole sharding spec installed by [`enable_memwal`].
 /// Must match Lance's `InitializeMemWalBuilder` (`SHARDING_SPEC_ID`).
 const SHARDING_SPEC_ID: u32 = 1;
 
@@ -61,37 +61,53 @@ const NUM_BUCKETS_PARAM: &str = "num_buckets";
 const SHARD_NAMESPACE: Uuid = Uuid::from_u128(0x4c53_4d57_5249_5445_5f53_4841_5244_3031);
 
 // =============================================================================
-// set_lsm_write_spec
+// enable_memwal
 // =============================================================================
 
-/// Install an [`LsmWriteSpec`] on the table.
+/// Install an [`MemWalWriteSpec`] on the table.
 ///
 /// The bucket / identity / unsharded sharding spec is constructed and validated
 /// by Lance's
 /// [`InitializeMemWalBuilder`](lance::dataset::mem_wal::InitializeMemWalBuilder).
 #[allow(clippy::redundant_pub_crate)]
-pub(crate) async fn set_lsm_write_spec(table: &NativeTable, spec: LsmWriteSpec) -> Result<()> {
+pub(crate) async fn enable_memwal(table: &NativeTable, spec: MemWalWriteSpec) -> Result<()> {
     table.dataset.ensure_mutable()?;
 
     {
         let dataset = table.dataset.get().await?;
         if dataset.mem_wal_index_details().await?.is_some() {
             return Err(Error::InvalidInput {
-                message: "set_lsm_write_spec: an LSM write spec is already set on this table; mutation is not supported".into(),
+                message: "enable_memwal: a MemWAL write spec is already set on this \
+                          table and cannot be changed"
+                    .into(),
             });
         }
     }
 
-    // Before the builder borrows the dataset clone. `list_indices` merges an
-    // index's segments into one entry, so the result needs no dedup.
-    let maintained_indexes = {
-        let dataset = table.dataset.get().await?;
-        resolve_maintained_indexes(
-            &dataset,
-            &table.list_indices().await?,
-            spec.maintained_indexes(),
-        )
-        .await?
+    // A named set is checked against the table here, where the caller can still
+    // be told which names exist. An unnamed set is not resolved at all: it is an
+    // intent lance re-reads whenever it builds a MemTable, so an index created
+    // later is maintained without another call.
+    let maintained_indexes = match spec.maintained_indexes() {
+        Some(requested) => {
+            let indices = table.list_indices().await?;
+            for name in requested {
+                if !indices.iter().any(|index| &index.name == name) {
+                    return Err(Error::InvalidInput {
+                        message: format!(
+                            "maintained index '{}' does not exist on this table; it has {}",
+                            name,
+                            index_name_list(&indices),
+                        ),
+                    });
+                }
+            }
+            let dataset = table.dataset.get().await?;
+            validate_maintained_indexes_with(&dataset, requested, &table.mem_index_registry())
+                .await?;
+            Some(requested.to_vec())
+        }
+        None => None,
     };
 
     table.checkout_latest().await?;
@@ -99,21 +115,23 @@ pub(crate) async fn set_lsm_write_spec(table: &NativeTable, spec: LsmWriteSpec)
     let schema = arrow_schema::Schema::from(dataset.schema());
     if !crate::table::computed_columns::computed_columns(&schema).is_empty() {
         return Err(Error::NotSupported {
-            message: "an LSM write spec cannot be installed on a table with computed \
+            message: "a MemWAL write spec cannot be installed on a table with computed \
                       columns: rows in un-compacted tiers are invisible to refresh"
                 .into(),
         });
     }
     if crate::materialized_view::read_definition(&dataset.schema().metadata)?.is_some() {
         return Err(Error::NotSupported {
-            message: "an LSM write spec cannot be installed on a materialized view: \
+            message: "a MemWAL write spec cannot be installed on a materialized view: \
                       rows in un-compacted tiers are invisible to refresh"
                 .into(),
         });
     }
-    let mut builder = dataset.initialize_mem_wal();
+    let mut builder = dataset
+        .initialize_mem_wal()
+        .mem_index_registry(table.mem_index_registry());
     let writer_config_defaults = match spec {
-        LsmWriteSpec::Bucket {
+        MemWalWriteSpec::Bucket {
             column,
             num_buckets,
             writer_config_defaults,
@@ -122,7 +140,7 @@ pub(crate) async fn set_lsm_write_spec(table: &NativeTable, spec: LsmWriteSpec)
             builder = builder.bucket_sharding(column, num_buckets);
             writer_config_defaults
         }
-        LsmWriteSpec::Identity {
+        MemWalWriteSpec::Identity {
             column,
             writer_config_defaults,
             ..
@@ -130,7 +148,7 @@ pub(crate) async fn set_lsm_write_spec(table: &NativeTable, spec: LsmWriteSpec)
             builder = builder.identity_sharding(column);
             writer_config_defaults
         }
-        LsmWriteSpec::Unsharded {
+        MemWalWriteSpec::Unsharded {
             writer_config_defaults,
             ..
         } => {
@@ -138,7 +156,9 @@ pub(crate) async fn set_lsm_write_spec(table: &NativeTable, spec: LsmWriteSpec)
             writer_config_defaults
         }
     };
-    builder = builder.maintained_indexes(maintained_indexes);
+    if let Some(maintained_indexes) = maintained_indexes {
+        builder = builder.maintained_indexes(maintained_indexes);
+    }
     for (key, value) in writer_config_defaults {
         builder = builder.add_writer_config_default(key, value);
     }
@@ -147,48 +167,6 @@ pub(crate) async fn set_lsm_write_spec(table: &NativeTable, spec: LsmWriteSpec)
     Ok(())
 }
 
-/// Resolve a spec's maintained-index selection against `indices`, as reported
-/// by [`Table::list_indices`](crate::Table::list_indices).
-///
-/// `None` means every index on the table, snapshotted now. Lance validates
-/// either selection against its shard-writer rules, so a spec that installs is
-/// one the MemWAL can open.
-///
-/// An unmaintainable index fails an inferred set rather than being dropped from
-/// it — dropping would leave the caller believing it is maintained.
-async fn resolve_maintained_indexes(
-    dataset: &Dataset,
-    indices: &[IndexConfig],
-    requested: Option<&[String]>,
-) -> Result<Vec<String>> {
-    let Some(requested) = requested else {
-        let all: Vec<String> = indices.iter().map(|index| index.name.clone()).collect();
-        validate_maintained_indexes(dataset, &all)
-            .await
-            .map_err(|source| Error::InvalidInput {
-                message: format!(
-                    "cannot maintain every index on this table: {source}. Set \
-                     maintained_indexes explicitly to choose from {}",
-                    index_name_list(indices),
-                ),
-            })?;
-        return Ok(all);
-    };
-    for name in requested {
-        if !indices.iter().any(|index| &index.name == name) {
-            return Err(Error::InvalidInput {
-                message: format!(
-                    "maintained index '{}' does not exist on this table; it has {}",
-                    name,
-                    index_name_list(indices),
-                ),
-            });
-        }
-    }
-    validate_maintained_indexes(dataset, requested).await?;
-    Ok(requested.to_vec())
-}
-
 /// Index names for an error message.
 fn index_name_list(indices: &[IndexConfig]) -> String {
     if indices.is_empty() {
@@ -200,22 +178,22 @@ fn index_name_list(indices: &[IndexConfig]) -> String {
 }
 
 // =============================================================================
-// unset_lsm_write_spec
+// disable_memwal
 // =============================================================================
 
-/// Remove the [`LsmWriteSpec`] from the table by dropping the MemWAL index.
+/// Remove the [`MemWalWriteSpec`] from the table by dropping the MemWAL index.
 ///
 /// Any cached shard writers are drained and closed first. Errors if no spec is
 /// currently set.
 #[allow(clippy::redundant_pub_crate)]
-pub(crate) async fn unset_lsm_write_spec(table: &NativeTable) -> Result<()> {
+pub(crate) async fn disable_memwal(table: &NativeTable) -> Result<()> {
     table.dataset.ensure_mutable()?;
 
     {
         let dataset = table.dataset.get().await?;
         if dataset.mem_wal_index_details().await?.is_none() {
             return Err(Error::InvalidInput {
-                message: "unset_lsm_write_spec: no LSM write spec is set on this table".into(),
+                message: "disable_memwal: no MemWAL write spec is set on this table".into(),
             });
         }
     }
@@ -231,46 +209,46 @@ pub(crate) async fn unset_lsm_write_spec(table: &NativeTable) -> Result<()> {
 }
 
 // =============================================================================
-// get_lsm_write_spec
+// get_memwal_spec
 // =============================================================================
 
-/// Read the [`LsmWriteSpec`] currently installed on the table, if any.
+/// Read the [`MemWalWriteSpec`] currently installed on the table, if any.
 ///
 /// Reconstructs the spec from the MemWAL index's stored details — the same
-/// metadata [`set_lsm_write_spec`] writes — read directly via
+/// metadata [`enable_memwal`] writes — read directly via
 /// [`mem_wal_index_details`](DatasetMemWalExt::mem_wal_index_details) (the raw
 /// manifest record, not the `describe_indices` enrichment path, so it is
 /// unaffected by system-index filtering there). Returns `Ok(None)` when no
 /// MemWAL index is installed.
 #[allow(clippy::redundant_pub_crate)]
-pub(crate) async fn get_lsm_write_spec(table: &NativeTable) -> Result<Option<LsmWriteSpec>> {
+pub(crate) async fn get_memwal_spec(table: &NativeTable) -> Result<Option<MemWalWriteSpec>> {
     let dataset = table.dataset.get().await?;
     let Some(details) = dataset.mem_wal_index_details().await? else {
         return Ok(None);
     };
-    let spec = lsm_write_spec_from_details(&details, dataset.schema())?;
+    let spec = memwal_spec_from_details(&details, dataset.schema())?;
     Ok(Some(spec))
 }
 
-/// Reconstruct the public [`LsmWriteSpec`] from stored MemWAL index details.
+/// Reconstruct the public [`MemWalWriteSpec`] from stored MemWAL index details.
 ///
 /// The sharding transform and its parameters recover the mode; the routing
 /// column is resolved from the sharding field's source id back to its schema
 /// name (only bucket / identity carry a column). `maintained_indexes` and
 /// `writer_config_defaults` are copied verbatim, so the result round-trips
-/// what `set_lsm_write_spec` installed.
-fn lsm_write_spec_from_details(
+/// what `enable_memwal` installed.
+fn memwal_spec_from_details(
     details: &MemWalIndexDetails,
     schema: &LanceSchema,
-) -> Result<LsmWriteSpec> {
+) -> Result<MemWalWriteSpec> {
     let spec = details
         .sharding_specs
         .first()
         .ok_or_else(|| Error::Runtime {
-            message: "get_lsm_write_spec: MemWAL index has no sharding spec".to_string(),
+            message: "get_memwal_spec: MemWAL index has no sharding spec".to_string(),
         })?;
     let field = spec.fields.first().ok_or_else(|| Error::Runtime {
-        message: "get_lsm_write_spec: MemWAL index has an empty sharding spec".to_string(),
+        message: "get_memwal_spec: MemWAL index has an empty sharding spec".to_string(),
     })?;
 
     let base = match field.transform.as_deref() {
@@ -281,16 +259,16 @@ fn lsm_write_spec_from_details(
                 .and_then(|s| s.parse::<u32>().ok())
                 .filter(|n| *n > 0)
                 .ok_or_else(|| Error::Runtime {
-                    message: "get_lsm_write_spec: MemWAL bucket spec has a missing or invalid num_buckets parameter".to_string(),
+                    message: "get_memwal_spec: MemWAL bucket spec has a missing or invalid num_buckets parameter".to_string(),
                 })?;
-            LsmWriteSpec::bucket(sharding_column(field, schema)?, num_buckets)
+            MemWalWriteSpec::bucket(sharding_column(field, schema)?, num_buckets)
         }
-        Some(IDENTITY_TRANSFORM) => LsmWriteSpec::identity(sharding_column(field, schema)?),
-        Some(UNSHARDED_TRANSFORM) => LsmWriteSpec::unsharded(),
+        Some(IDENTITY_TRANSFORM) => MemWalWriteSpec::identity(sharding_column(field, schema)?),
+        Some(UNSHARDED_TRANSFORM) => MemWalWriteSpec::unsharded(),
         other => {
             return Err(Error::Runtime {
                 message: format!(
-                    "get_lsm_write_spec: MemWAL index has an unsupported sharding transform {:?}",
+                    "get_memwal_spec: MemWAL index has an unsupported sharding transform {:?}",
                     other
                 ),
             });
@@ -298,36 +276,38 @@ fn lsm_write_spec_from_details(
     };
 
     Ok(base
-        .with_maintained_indexes(details.maintained_indexes.clone())
+        .with_maintained_indexes(
+            (!details.maintain_all_indexes).then(|| details.maintained_indexes.clone()),
+        )
         .with_writer_config_defaults(details.writer_config_defaults.clone()))
 }
 
 /// Resolve the single routing column name from a sharding field's source id.
 ///
-/// `set_lsm_write_spec` records the shard column by its Lance field id, so the
+/// `enable_memwal` records the shard column by its Lance field id, so the
 /// name is recovered by looking that id up in the current schema.
 fn sharding_column(field: &ShardingField, schema: &LanceSchema) -> Result<String> {
     let source_id = *field.source_ids.first().ok_or_else(|| Error::Runtime {
-        message: "get_lsm_write_spec: sharding field has no source column".to_string(),
+        message: "get_memwal_spec: sharding field has no source column".to_string(),
     })?;
     schema
         .field_by_id(source_id)
         .map(|f| f.name.clone())
         .ok_or_else(|| Error::Runtime {
             message: format!(
-                "get_lsm_write_spec: sharding source field id {} not found in schema",
+                "get_memwal_spec: sharding source field id {} not found in schema",
                 source_id
             ),
         })
 }
 
 // =============================================================================
-// close_lsm_writers
+// close_memwal_writers
 // =============================================================================
 
 /// Drain and close every cached MemWAL shard writer for the table.
 #[allow(clippy::redundant_pub_crate)]
-pub(crate) async fn close_lsm_writers(table: &NativeTable) -> Result<()> {
+pub(crate) async fn close_memwal_writers(table: &NativeTable) -> Result<()> {
     table.dataset.shard_writer().drain_and_close().await
 }
 
@@ -341,7 +321,7 @@ pub(crate) async fn close_lsm_writers(table: &NativeTable) -> Result<()> {
 /// so the writer lives where the dataset lives — cached for the session and
 /// reused across `merge_insert` calls. A dataset writes to one shard at a
 /// time; routing a `merge_insert` to a different shard requires closing the
-/// current writer first via [`close_lsm_writers`]. `ShardWriter::put` takes
+/// current writer first via [`close_memwal_writers`]. `ShardWriter::put` takes
 /// `&self`, so concurrent puts on the cached writer are safe; `close` consumes
 /// the writer, so the entry wraps it in `RwLock<Option<ShardWriter>>`.
 #[derive(Default)]
@@ -390,8 +370,8 @@ impl ShardWriterEntry {
     }
 
     /// The cached writer's latest in-memory manifest (current generation +
-    /// flushed generations). `Ok(None)` if the writer was already closed.
-    /// Used by the LSM read path to snapshot this shard authoritatively
+    /// SSTables). `Ok(None)` if the writer was already closed.
+    /// Used by the MemWAL read path to snapshot this shard authoritatively
     /// without re-reading the on-disk manifest.
     async fn manifest(&self) -> Result<Option<lance_index::mem_wal::ShardManifest>> {
         let guard = self.inner.read().await;
@@ -460,7 +440,56 @@ impl ShardWriterCache {
         Ok(entry)
     }
 
-    /// Snapshot the cached writer's shard for the LSM read path: its shard id,
+    /// Install `dataset`'s maintained index set on the cached writer, if one is
+    /// open, so a change reaches a table that is already being written to.
+    ///
+    /// Reported rather than propagated: the index is committed durably by the
+    /// time this runs, and the writer this process happens to hold may be fenced
+    /// or poisoned -- which is not a reason to fail creating an index. Any
+    /// writer opened afterwards reads the committed set.
+    ///
+    /// Holds the entry's read lock across the refresh, so a concurrent close
+    /// waits rather than retiring the writer mid-freeze. Writes take the same
+    /// lock first, so the order matches theirs.
+    pub(crate) async fn refresh_maintained_indexes(&self, dataset: &Dataset) {
+        let entry = {
+            let guard = self.slot.read().await;
+            guard.as_ref().map(|(_, entry)| entry.clone())
+        };
+        let Some(entry) = entry else {
+            return;
+        };
+        let guard = entry.inner.read().await;
+        let Some(writer) = guard.as_ref() else {
+            return;
+        };
+        // Replacing the configs freezes the active memtable so the next one carries
+        // the new indexes. The outgoing one keeps the set it was built with, so
+        // its flush has to land before a read: while it is still frozen, an
+        // indexed read sees a resident memtable that cannot answer and is
+        // refused.
+        let frozen = match dataset.refresh_mem_wal_index_configs(writer).await {
+            Ok(frozen) => frozen,
+            Err(error) => {
+                log::warn!(
+                    "the maintained index set changed but the open shard writer could \
+                     not be refreshed, so it keeps the set it opened with: {error}"
+                );
+                return;
+            }
+        };
+        if let Some(fence) = frozen
+            && let Err(error) = fence.wait().await
+        {
+            log::warn!(
+                "the shard writer took the new maintained index set, but the memtable \
+                 it froze was not flushed, so an indexed read stays refused until \
+                 it is: {error}"
+            );
+        }
+    }
+
+    /// Snapshot the cached writer's shard for the MemWAL read path: its shard id,
     /// authoritative in-memory manifest, and active + frozen memtable refs.
     /// Returns `None` when no writer is currently cached (e.g. nothing has been
     /// written this session, or the writer was closed).
@@ -508,7 +537,7 @@ fn check_shard_match(cached: Uuid, wanted: Uuid) -> Result<()> {
     }
     Err(Error::InvalidInput {
         message: format!(
-            "merge_insert: a shard writer is already open for shard {} but this input routes to shard {}; call close_lsm_writers before writing to a different shard",
+            "merge_insert: a shard writer is already open for shard {} but this input routes to shard {}; call close_memwal_writers before writing to a different shard",
             cached, wanted
         ),
     })
@@ -540,7 +569,7 @@ pub(crate) struct LsmPlan {
 /// Outcome of [`lsm_dispatch_decision`].
 #[allow(clippy::redundant_pub_crate)]
 pub(crate) enum LsmDispatch {
-    /// No LSM write spec applies; use the standard `merge_insert` path.
+    /// No MemWAL write spec applies; use the standard `merge_insert` path.
     Standard,
     /// Route the `merge_insert` through the MemWAL shard writer.
     Lsm(LsmPlan),
@@ -554,18 +583,18 @@ pub(crate) async fn lsm_dispatch_decision(
     params: &MergeInsertBuilder,
 ) -> Result<LsmDispatch> {
     // Explicit opt-out: use the standard path regardless of any installed spec.
-    if params.use_lsm == Some(false) {
+    if params.use_memwal == Some(false) {
         return Ok(LsmDispatch::Standard);
     }
 
     let dataset = table.dataset.get().await?;
     let Some(details) = dataset.mem_wal_index_details().await? else {
-        // No write spec installed. `use_lsm(true)` demanded MemWAL routing, so
+        // No write spec installed. `use_memwal(true)` demanded MemWAL routing, so
         // that is an error; otherwise fall back to the standard path.
-        if params.use_lsm == Some(true) {
+        if params.use_memwal == Some(true) {
             return Err(Error::InvalidInput {
-                message: "use_lsm(true) was set but the table has no MemWAL write spec; \
-                    install one with set_lsm_write_spec or leave use_lsm unset"
+                message: "use_memwal(true) was set but the table has no MemWAL write spec; \
+                    install one with enable_memwal or leave use_memwal unset"
                     .to_string(),
             });
         }
@@ -587,7 +616,7 @@ pub(crate) async fn lsm_dispatch_decision(
     if !params.on.is_empty() && params.on != pk_cols {
         return Err(Error::InvalidInput {
             message: format!(
-                "merge_insert: `on` columns {:?} must match the table's unenforced primary key {:?} when an LSM write spec is set; pass an empty `on` to default to the primary key",
+                "merge_insert: `on` columns {:?} must match the table's unenforced primary key {:?} when a MemWAL write spec is set; pass an empty `on` to default to the primary key",
                 params.on, pk_cols
             ),
         });
@@ -595,7 +624,7 @@ pub(crate) async fn lsm_dispatch_decision(
 
     if !is_upsert_only(params) {
         return Err(Error::InvalidInput {
-            message: "merge_insert: when an LSM write spec is set, only the upsert form (when_matched_update_all without a filter + when_not_matched_insert_all, no by-source delete) is supported; call use_lsm(false) to use the standard merge_insert path".to_string(),
+            message: "merge_insert: when a MemWAL write spec is set, only the upsert form (when_matched_update_all without a filter + when_not_matched_insert_all, no by-source delete) is supported; call use_memwal(false) to use the standard merge_insert path".to_string(),
         });
     }
 
@@ -699,7 +728,8 @@ pub(crate) async fn execute_lsm_merge_insert(
         return Ok(lsm_merge_result(0));
     };
 
-    let config = shard_writer_config_from_defaults(&plan.writer_config_defaults);
+    let mut config = shard_writer_config_from_defaults(&plan.writer_config_defaults);
+    config.mem_index_registry = table.mem_index_registry();
     let writer = table
         .dataset
         .shard_writer()
diff --git a/rust/lancedb/src/table/primary_key.rs b/rust/lancedb/src/table/primary_key.rs
index 8a7b48efa..d399155fd 100644
--- a/rust/lancedb/src/table/primary_key.rs
+++ b/rust/lancedb/src/table/primary_key.rs
@@ -11,24 +11,26 @@
 //! Only a single-column primary key is supported, and the key cannot be
 //! changed once set.
 
+use std::collections::HashMap;
+
 use arrow_schema::DataType;
-use lance_core::datatypes::{LANCE_UNENFORCED_PRIMARY_KEY, LANCE_UNENFORCED_PRIMARY_KEY_POSITION};
+use lance_core::datatypes::{
+    Field as LanceField, LANCE_UNENFORCED_PRIMARY_KEY, LANCE_UNENFORCED_PRIMARY_KEY_POSITION,
+    Schema as LanceSchema,
+};
 
 use crate::error::{Error, Result};
 use crate::table::NativeTable;
 
-/// Set the unenforced primary key on `table` to the single column in `columns`.
+/// Validate a `set_unenforced_primary_key` request against `schema`, returning
+/// the field the key would be installed on.
 ///
-/// Fails if `columns` is not exactly one column (compound primary keys are not
-/// supported), if the column does not exist or has an unsupported dtype, or if
-/// the table already has an unenforced primary key (changing the primary key
-/// is not supported).
-pub(super) async fn set_unenforced_primary_key(
-    table: &NativeTable,
-    columns: &[&str],
-) -> Result<()> {
-    table.dataset.ensure_mutable()?;
-
+/// Shared by [`NativeTable`] and the remote table so both reject the same
+/// requests with the same messages. Fails if `columns` is not exactly one
+/// column (compound primary keys are not supported), if the column does not
+/// exist or has an unsupported dtype, or if the table already has an
+/// unenforced primary key (changing the primary key is not supported).
+pub fn validate<'a>(schema: &'a LanceSchema, columns: &[&str]) -> Result<&'a LanceField> {
     if columns.is_empty() {
         return Err(Error::InvalidInput {
             message: "set_unenforced_primary_key: a column is required".into(),
@@ -44,43 +46,71 @@ pub(super) async fn set_unenforced_primary_key(
     }
     let column = columns[0];
 
+    // The primary key is immutable once set. The Lance commit layer is the
+    // source of truth for this (it also covers the concurrent-writer race);
+    // this check just fails fast with a clear message.
+    if !schema.unenforced_primary_key().is_empty() {
+        return Err(Error::InvalidInput {
+            message: "set_unenforced_primary_key: an unenforced primary key is already set on this table; changing it is not supported".into(),
+        });
+    }
+
+    let field = schema.field(column).ok_or_else(|| Error::InvalidInput {
+        message: format!(
+            "set_unenforced_primary_key: column '{}' not found on table",
+            column
+        ),
+    })?;
+    if !is_supported_pk_dtype(&field.data_type()) {
+        return Err(Error::InvalidInput {
+            message: format!(
+                "set_unenforced_primary_key: column '{}' has dtype {:?} which is not supported as a primary key. Supported: Int32, Int64, Utf8, LargeUtf8, Binary, LargeBinary, FixedSizeBinary",
+                column,
+                field.data_type()
+            ),
+        });
+    }
+    Ok(field)
+}
+
+/// The field metadata edit that installs the primary key on a field: keys to
+/// set (`Some`) or delete (`None`).
+///
+/// Position metadata is 1-indexed; `Schema::unenforced_primary_key` treats
+/// position 0 as a legacy "no specific position" fallback, so the legacy
+/// boolean key is cleared and only the position governs.
+pub fn install_edit() -> HashMap<String, Option<String>> {
+    HashMap::from([
+        (LANCE_UNENFORCED_PRIMARY_KEY.to_string(), None),
+        (
+            LANCE_UNENFORCED_PRIMARY_KEY_POSITION.to_string(),
+            Some("1".to_string()),
+        ),
+    ])
+}
+
+/// Set the unenforced primary key on `table` to the single column in `columns`.
+pub(super) async fn set_unenforced_primary_key(
+    table: &NativeTable,
+    columns: &[&str],
+) -> Result<()> {
+    table.dataset.ensure_mutable()?;
+
     let updates = {
         let dataset = table.dataset.get().await?;
-        let schema = dataset.schema();
+        let field = validate(dataset.schema(), columns)?;
 
-        // The primary key is immutable once set. The Lance commit layer is the
-        // source of truth for this (it also covers the concurrent-writer race);
-        // this check just fails fast with a clear message.
-        if !schema.unenforced_primary_key().is_empty() {
-            return Err(Error::InvalidInput {
-                message: "set_unenforced_primary_key: an unenforced primary key is already set on this table; changing it is not supported".into(),
-            });
-        }
-
-        let field = schema.field(column).ok_or_else(|| Error::InvalidInput {
-            message: format!(
-                "set_unenforced_primary_key: column '{}' not found on table",
-                column
-            ),
-        })?;
-        if !is_supported_pk_dtype(&field.data_type()) {
-            return Err(Error::InvalidInput {
-                message: format!(
-                    "set_unenforced_primary_key: column '{}' has dtype {:?} which is not supported as a primary key. Supported: Int32, Int64, Utf8, LargeUtf8, Binary, LargeBinary, FixedSizeBinary",
-                    column,
-                    field.data_type()
-                ),
-            });
-        }
-
-        // Position metadata is 1-indexed; `Schema::unenforced_primary_key`
-        // treats position 0 as a legacy "no specific position" fallback.
         let mut metadata = field.metadata.clone();
-        metadata.remove(LANCE_UNENFORCED_PRIMARY_KEY);
-        metadata.insert(
-            LANCE_UNENFORCED_PRIMARY_KEY_POSITION.to_string(),
-            "1".to_string(),
-        );
+        for (key, value) in install_edit() {
+            match value {
+                Some(value) => {
+                    metadata.insert(key, value);
+                }
+                None => {
+                    metadata.remove(&key);
+                }
+            }
+        }
         vec![(field_id_to_u32(field.id, &field.name)?, metadata)]
     };
 
diff --git a/rust/lancedb/src/table/query.rs b/rust/lancedb/src/table/query.rs
index d611fe8a5..90a1d3217 100644
--- a/rust/lancedb/src/table/query.rs
+++ b/rust/lancedb/src/table/query.rs
@@ -95,8 +95,8 @@ async fn can_execute_namespace_query(table: &NativeTable, query: &AnyQuery) -> R
         return Ok(false);
     }
     // A MemWAL write spec means reads auto-route through the LSM scanner in
-    // `create_plan` even when `use_lsm` is unset. The namespace request has no
-    // use_lsm field, so pushing the default query down would silently omit
+    // `create_plan` even when `use_memwal` is unset. The namespace request has no
+    // use_memwal field, so pushing the default query down would silently omit
     // un-compacted rows — force local execution whenever a spec is installed.
     let dataset = table.dataset.get().await?;
     if dataset.mem_wal_index_details().await?.is_some() {
@@ -106,19 +106,16 @@ async fn can_execute_namespace_query(table: &NativeTable, query: &AnyQuery) -> R
 }
 
 fn requires_local_namespace_execution(query: &AnyQuery) -> bool {
-    // The namespace QueryTable request has no approx_mode or use_lsm field yet, so
-    // pushing these down would silently ignore the user's setting. For use_lsm that
-    // is worse than a tuning miss: MemWAL read routing lives only in `create_plan`,
-    // so a pushed-down query would return stale base-only data with no error.
-    if query.base().use_lsm.is_some() || query.base().take_offsets.is_some() {
+    // The namespace QueryTable request cannot represent approx_mode, search_effort or use_memwal.
+    // For use_memwal that is worse than a tuning miss: MemWAL read routing lives only
+    // in `create_plan`, so a pushed-down query would return stale base-only data.
+    if query.base().use_memwal.is_some() || query.base().take_offsets.is_some() {
         return true;
     }
     matches!(
         query,
-        AnyQuery::VectorQuery(VectorQueryRequest {
-            approx_mode: Some(_),
-            ..
-        })
+        AnyQuery::VectorQuery(vector_query)
+            if vector_query.approx_mode.is_some() || vector_query.search_effort.is_some()
     )
 }
 
@@ -166,28 +163,29 @@ pub async fn create_plan(
         AnyQuery::Query(query) => VectorQueryRequest::from_plain_query(query),
     };
     query.base.check_filter()?;
+    query.validate_search_effort()?;
 
     let ds_ref = table.dataset.get().await?;
 
-    // MemWAL read routing driven by `use_lsm`:
+    // MemWAL read routing driven by `use_memwal`:
     //   * unset  — route through the LSM scanner iff the table carries a write spec
-    //   * Some(true)  — force LSM routing; error if the table has no write spec
+    //   * Some(true)  — force MemWAL routing; error if the table has no write spec
     //   * Some(false) — read the base table only, bypassing the MemWAL
     // The LSM scanner surfaces in-flight `merge_insert` data (active/frozen
-    // memtables + flushed generations); validation and dispatch live in `lsm`.
+    // memtables + SSTables); validation and dispatch live in `lsm`.
     let has_spec = ds_ref.mem_wal_index_details().await?.is_some();
-    let use_lsm = match query.base.use_lsm {
+    let use_memwal = match query.base.use_memwal {
         Some(true) if !has_spec => {
             return Err(Error::InvalidInput {
-                message: "use_lsm(true) was set but the table has no MemWAL write spec; \
-                    install one with set_lsm_write_spec or leave use_lsm unset"
+                message: "use_memwal(true) was set but the table has no MemWAL write spec; \
+                    install one with enable_memwal or leave use_memwal unset"
                     .to_string(),
             });
         }
         Some(enable) => enable,
         None => has_spec,
     };
-    if use_lsm {
+    if use_memwal {
         return lsm::create_lsm_plan(table, ds_ref, query).await;
     }
 
@@ -296,10 +294,24 @@ pub async fn create_plan(
             scanner.approx_mode(approx_mode.into());
         }
 
-        scanner.minimum_nprobes(query.minimum_nprobes);
-        if let Some(maximum_nprobes) = query.maximum_nprobes {
+        if query.search_effort.is_none()
+            && let Some(nprobes) = query.nprobes
+        {
+            if query.maximum_nprobes == Some(0) {
+                scanner.minimum_nprobes(nprobes);
+            } else {
+                scanner.nprobes(nprobes);
+            }
+        }
+        if let Some(minimum_nprobes) = query.minimum_nprobes {
+            scanner.minimum_nprobes(minimum_nprobes);
+        }
+        if let Some(maximum_nprobes) = query.maximum_nprobes.filter(|nprobes| *nprobes != 0) {
             scanner.maximum_nprobes(maximum_nprobes);
         }
+        if let Some(search_effort) = query.search_effort {
+            scanner.search_effort(search_effort)?;
+        }
     }
 
     // For a batch query, `nearest` already applies k to each query vector.
@@ -598,6 +610,11 @@ fn convert_to_namespace_query(query: &AnyQuery) -> Result<NsQueryTableRequest> {
             };
 
             // Check for unsupported features
+            if vq.search_effort.is_some() {
+                return Err(Error::NotSupported {
+                    message: "namespace QueryTable does not support search_effort; execute the query locally".to_string(),
+                });
+            }
             if vq.base.reranker.is_some() {
                 return Err(Error::NotSupported {
                     message: "Reranker is not supported for server-side queries".to_string(),
@@ -630,7 +647,9 @@ fn convert_to_namespace_query(query: &AnyQuery) -> Result<NsQueryTableRequest> {
                 columns,
                 offset: vq.base.offset.map(|o| o as i32),
                 distance_type: vq.distance_type.map(|dt| dt.to_string()),
-                nprobes: Some(vq.minimum_nprobes as i32),
+                nprobes: vq.nprobes.map(|nprobes| nprobes as i32),
+                minimum_nprobes: vq.minimum_nprobes.map(|nprobes| nprobes as i32),
+                maximum_nprobes: vq.maximum_nprobes.map(|nprobes| nprobes as i32),
                 ef: vq.ef.map(|e| e as i32),
                 refine_factor: vq.refine_factor.map(|r| r as i32),
                 lower_bound: vq.lower_bound,
@@ -919,7 +938,6 @@ mod tests {
             column: Some("vector".to_string()),
             // We cast here to satisfy the struct definition
             query_vector: vec![query_vector as Arc<dyn Array>],
-            minimum_nprobes: 20,
             distance_type: Some(crate::DistanceType::L2),
             ..Default::default()
         };
@@ -940,6 +958,7 @@ mod tests {
         );
         assert_eq!(ns_request.vector_column, Some("vector".to_string()));
         assert_eq!(ns_request.distance_type, Some("l2".to_string()));
+        assert_eq!(ns_request.nprobes, None);
 
         // Verify the vector data was extracted correctly
         assert!(ns_request.vector.single_vector.is_some());
@@ -949,6 +968,79 @@ mod tests {
         );
     }
 
+    #[test]
+    fn test_namespace_query_probe_pushdown_compatibility() {
+        let default_query = AnyQuery::VectorQuery(VectorQueryRequest::default());
+        assert!(!requires_local_namespace_execution(&default_query));
+
+        for effort in [0.0, 0.5, 1.0] {
+            let effort_query = AnyQuery::VectorQuery(VectorQueryRequest {
+                query_vector: vec![Arc::new(Float32Array::from(vec![1.0, 2.0]))],
+                search_effort: Some(effort),
+                ..Default::default()
+            });
+            assert!(requires_local_namespace_execution(&effort_query));
+            assert!(matches!(
+                convert_to_namespace_query(&effort_query),
+                Err(Error::NotSupported { message }) if message.contains("search_effort")
+            ));
+        }
+
+        let nprobes_query = AnyQuery::VectorQuery(VectorQueryRequest {
+            query_vector: vec![Arc::new(Float32Array::from(vec![1.0, 2.0]))],
+            nprobes: Some(20),
+            ..Default::default()
+        });
+        assert!(!requires_local_namespace_execution(&nprobes_query));
+        assert_eq!(
+            convert_to_namespace_query(&nprobes_query).unwrap().nprobes,
+            Some(20)
+        );
+
+        let minimum_query = AnyQuery::VectorQuery(VectorQueryRequest {
+            query_vector: vec![Arc::new(Float32Array::from(vec![1.0, 2.0]))],
+            minimum_nprobes: Some(5),
+            ..Default::default()
+        });
+        assert!(!requires_local_namespace_execution(&minimum_query));
+        let request = convert_to_namespace_query(&minimum_query).unwrap();
+        assert_eq!(request.minimum_nprobes, Some(5));
+        assert_eq!(request.maximum_nprobes, None);
+
+        let maximum_query = AnyQuery::VectorQuery(VectorQueryRequest {
+            query_vector: vec![Arc::new(Float32Array::from(vec![1.0, 2.0]))],
+            maximum_nprobes: Some(10),
+            ..Default::default()
+        });
+        assert!(!requires_local_namespace_execution(&maximum_query));
+        let request = convert_to_namespace_query(&maximum_query).unwrap();
+        assert_eq!(request.minimum_nprobes, None);
+        assert_eq!(request.maximum_nprobes, Some(10));
+
+        let exact_query = AnyQuery::VectorQuery(VectorQueryRequest {
+            query_vector: vec![Arc::new(Float32Array::from(vec![1.0, 2.0]))],
+            nprobes: Some(8),
+            minimum_nprobes: Some(20),
+            maximum_nprobes: Some(20),
+            ..Default::default()
+        });
+        assert!(!requires_local_namespace_execution(&exact_query));
+        let request = convert_to_namespace_query(&exact_query).unwrap();
+        assert_eq!(request.nprobes, Some(8));
+        assert_eq!(request.minimum_nprobes, Some(20));
+        assert_eq!(request.maximum_nprobes, Some(20));
+
+        let explicitly_unbounded_query = AnyQuery::VectorQuery(VectorQueryRequest {
+            query_vector: vec![Arc::new(Float32Array::from(vec![1.0, 2.0]))],
+            nprobes: Some(8),
+            maximum_nprobes: Some(0),
+            ..Default::default()
+        });
+        let request = convert_to_namespace_query(&explicitly_unbounded_query).unwrap();
+        assert_eq!(request.nprobes, Some(8));
+        assert_eq!(request.maximum_nprobes, Some(0));
+    }
+
     #[test]
     fn test_convert_to_namespace_query_plain_query() {
         let q = QueryRequest {
@@ -1103,7 +1195,7 @@ mod tests {
 
         table.set_unenforced_primary_key(["id"]).await.unwrap();
         table
-            .set_lsm_write_spec(crate::table::LsmWriteSpec::unsharded())
+            .enable_memwal(crate::table::MemWalWriteSpec::unsharded())
             .await
             .unwrap();
         let lsm_error = table
@@ -1303,7 +1395,7 @@ mod tests {
     }
 
     #[tokio::test]
-    async fn test_execute_query_use_lsm_with_namespace_pushdown_runs_locally() {
+    async fn test_execute_query_use_memwal_with_namespace_pushdown_runs_locally() {
         use crate::connect;
         use crate::table::query::execute_query;
         use arrow_array::{Int32Array, RecordBatch};
@@ -1326,7 +1418,7 @@ mod tests {
         .unwrap();
 
         let table = conn
-            .create_table("test_use_lsm_namespace_fallback", batch)
+            .create_table("test_use_memwal_namespace_fallback", batch)
             .execute()
             .await
             .unwrap();
@@ -1337,13 +1429,13 @@ mod tests {
             .pushdown_operations
             .insert(NamespaceClientPushdownOperation::QueryTable);
 
-        // `use_lsm` set (even to false) must force local execution — the namespace
-        // request has no use_lsm field, so a pushdown would silently ignore it.
+        // `use_memwal` set (even to false) must force local execution — the namespace
+        // request has no use_memwal field, so a pushdown would silently ignore it.
         let query_vector = Arc::new(Float32Array::from(vec![0.0, 0.0]));
         let query = AnyQuery::VectorQuery(VectorQueryRequest {
             base: QueryRequest {
                 limit: Some(1),
-                use_lsm: Some(false),
+                use_memwal: Some(false),
                 ..Default::default()
             },
             column: Some("vector".to_string()),
diff --git a/rust/lancedb/src/table/query/lsm.rs b/rust/lancedb/src/table/query/lsm.rs
index 86c1fe5f2..32966e4a6 100644
--- a/rust/lancedb/src/table/query/lsm.rs
+++ b/rust/lancedb/src/table/query/lsm.rs
@@ -1,14 +1,14 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-//! MemWAL LSM read path.
+//! MemWAL read path.
 //!
-//! When a table has an LSM write spec installed (see [`set_lsm_write_spec`]),
+//! When a table has a MemWAL write spec installed (see [`enable_memwal`]),
 //! reads are routed through Lance's [`LsmScanner`] instead of the plain
 //! base-table scan unless the query sets
-//! [`use_lsm(false)`](crate::query::QueryBase::use_lsm). This makes data
+//! [`use_memwal(false)`](crate::query::QueryBase::use_memwal). This makes data
 //! written via the LSM `merge_insert` path — which lives in the active/frozen
-//! in-memory memtables and the flushed SSTable generations until an external
+//! in-memory memtables and the SSTables until an external
 //! compaction merges it into the base table — visible to queries, deduplicated by
 //! primary key (newest generation wins).
 //!
@@ -17,10 +17,10 @@
 //! three run through a single [`LsmScanner`], so a `where` filter is honored as a
 //! prefilter uniformly — including for vector search, where `LsmScanner` threads
 //! it into the vector planner's prefilter. Shapes the LSM path cannot honor are
-//! rejected with [`Error::NotSupported`]; the caller must set `use_lsm(false)` to
+//! rejected with [`Error::NotSupported`]; the caller must set `use_memwal(false)` to
 //! run those against the base table.
 //!
-//! [`set_lsm_write_spec`]: crate::Table::set_lsm_write_spec
+//! [`enable_memwal`]: crate::Table::enable_memwal
 
 use std::collections::HashMap;
 use std::sync::Arc;
@@ -33,7 +33,7 @@ use datafusion_physical_plan::expressions::Column;
 use datafusion_physical_plan::projection::ProjectionExec;
 use datafusion_physical_plan::{ExecutionPlan, PhysicalExpr};
 use lance::Dataset;
-use lance::dataset::mem_wal::scanner::InMemoryMemTables;
+use lance::dataset::mem_wal::scanner::{InMemoryMemTableRef, InMemoryMemTables};
 use lance::dataset::mem_wal::{
     DatasetMemWalExt, LsmScanner, ShardManifestStore, ShardSnapshot, ShardWriterConfig,
 };
@@ -57,7 +57,7 @@ const LSM_OVERFETCH_FACTOR: f64 = 2.0;
 /// The caller guarantees `ds_ref` carries a MemWAL write spec (routing is decided
 /// in [`create_plan`](super::create_plan)). Errors with [`Error::NotSupported`]
 /// for query shapes the LSM scanner cannot honor — the caller must set
-/// `use_lsm(false)` to run those against the base table.
+/// `use_memwal(false)` to run those against the base table.
 pub(super) async fn create_lsm_plan(
     table: &NativeTable,
     ds_ref: Arc<Dataset>,
@@ -67,11 +67,11 @@ pub(super) async fn create_lsm_plan(
 
     // A time-traveled (checked-out) handle pins an older dataset version, but the
     // WAL manifests and cached writer expose current live state — mixing them would
-    // surface WAL rows written after the requested version. `use_lsm(false)` reads
+    // surface WAL rows written after the requested version. `use_memwal(false)` reads
     // the base table at the pinned version.
     if table.dataset.time_travel_version().is_some() {
         return Err(Error::NotSupported {
-            message: "the MemWAL LSM scanner cannot read from a time-traveled dataset version; set use_lsm(false) to read the base table at this version".to_string(),
+            message: "the MemWAL LSM scanner cannot read from a time-traveled dataset version; set use_memwal(false) to read the base table at this version".to_string(),
         });
     }
 
@@ -80,7 +80,7 @@ pub(super) async fn create_lsm_plan(
         .mem_wal_index_details()
         .await?
         .ok_or_else(|| Error::Runtime {
-            message: "the MemWAL LSM write spec disappeared during read planning".to_string(),
+            message: "the MemWAL write spec disappeared during read planning".to_string(),
         })?;
 
     let pk_columns = pk_columns(&ds_ref)?;
@@ -135,10 +135,10 @@ pub(super) async fn create_lsm_plan(
     restore_projection(plan, &query, &pk_columns)
 }
 
-/// Reject query shapes the LSM read path does not implement. On a MemWAL table
+/// Reject query shapes the MemWAL read path does not implement. On a MemWAL table
 /// reads route through the LSM scanner by default, so an unsupported shape is a
 /// hard error rather than a silent fallback to the base-only scan — which would
-/// exclude un-compacted MemWAL data. The caller must set `use_lsm(false)` to
+/// exclude un-compacted MemWAL data. The caller must set `use_memwal(false)` to
 /// run these against the base table, accepting that the results omit un-compacted
 /// MemWAL data.
 ///
@@ -148,7 +148,7 @@ fn reject_unsupported(query: &VectorQueryRequest) -> Result<()> {
     let unsupported = |what: &str| {
         Err(Error::NotSupported {
             message: format!(
-                "the MemWAL LSM scanner does not support {what}; set use_lsm(false) to read the base table only (results will exclude un-compacted MemWAL data)"
+                "the MemWAL LSM scanner does not support {what}; set use_memwal(false) to read the base table only (results will exclude un-compacted MemWAL data)"
             ),
         })
     };
@@ -170,10 +170,12 @@ fn reject_unsupported(query: &VectorQueryRequest) -> Result<()> {
     // Vector-only knobs the LSM scanner cannot honor. Both change results rather
     // than just recall, so error instead of silently ignoring them: distance_range
     // would return rows outside the bound, and use_index(false) asks for a
-    // brute-force search the index-only base arm can't do. (ef / approx_mode /
-    // maximum_nprobes are recall/speed knobs and are left to no-op — and
-    // maximum_nprobes defaults to Some, so it cannot be rejected on presence.)
+    // brute-force search the index-only base arm can't do. (ef / approx_mode are
+    // recall/speed knobs and are left to no-op.)
     if !query.query_vector.is_empty() {
+        if query.search_effort.is_some() {
+            return unsupported("search_effort on vector search");
+        }
         if query.lower_bound.is_some() || query.upper_bound.is_some() {
             return unsupported("distance_range on vector search");
         }
@@ -273,7 +275,7 @@ fn exclusion_watermarks(
     exclude
 }
 
-/// Assemble the per-shard snapshots (flushed SSTable generations) and the
+/// Assemble the per-shard snapshots (SSTables) and the
 /// in-memory memtables (active + frozen) for the table.
 ///
 /// Snapshots for all shards come from their on-disk manifests; for the shard
@@ -411,7 +413,7 @@ fn base_scanner(
             QueryFilter::Datafusion(expr) => scanner.filter_expr(expr.clone()),
             QueryFilter::Substrait(_) => {
                 return Err(Error::NotSupported {
-                    message: "the MemWAL LSM scanner does not support Substrait filters; set use_lsm(false) to read the base table only".to_string(),
+                    message: "the MemWAL LSM scanner does not support Substrait filters; set use_memwal(false) to read the base table only".to_string(),
                 });
             }
         };
@@ -456,11 +458,11 @@ async fn fts_plan(
     offset: Option<usize>,
 ) -> Result<Arc<dyn ExecutionPlan>> {
     // Pre-check for a lancedb-flavored error; `LsmScanner` also validates the
-    // single-column requirement, but without the `use_lsm(false)` guidance.
+    // single-column requirement, but without the `use_memwal(false)` guidance.
     let columns: Vec<String> = fts.columns().into_iter().collect();
     if columns.len() > 1 {
         return Err(Error::NotSupported {
-            message: "the MemWAL LSM scanner full-text search supports a single column; set use_lsm(false) to read the base table only".to_string(),
+            message: "the MemWAL LSM scanner full-text search supports a single column; set use_memwal(false) to read the base table only".to_string(),
         });
     }
     let column = columns.first().ok_or_else(|| Error::NotSupported {
@@ -468,20 +470,19 @@ async fn fts_plan(
             .to_string(),
     })?;
 
-    // Without a maintained in-memory FTS index for this column, the active memtable
-    // arm produces an empty plan (`active_source_can_execute_fts` returns false), so
-    // the search silently omits un-compacted documents. Reject rather than mislead.
-    if !index_maintained(
-        dataset,
-        column,
-        &details.maintained_indexes,
-        "InvertedIndexDetails",
-    )
-    .await?
+    // Two ways the fresh tier cannot answer: the spec maintains no FTS index for
+    // this column, or a resident MemTable was built before the index joined the
+    // set. Either leaves the memtable arm an empty plan
+    // (`active_source_can_execute_fts` returns false), so the search silently
+    // omits un-compacted documents. Reject rather than mislead.
+    if !index_maintained(dataset, column, details, "InvertedIndexDetails").await?
+        || !resident_memtables_carry(&in_memory, |memtable| {
+            !memtable.index_store.fts_granularities_on(column).is_empty()
+        })
     {
         return Err(Error::NotSupported {
             message: format!(
-                "the MemWAL LSM scanner full-text search requires the FTS index on '{column}' to be maintained by the write spec (LsmWriteSpec::with_maintained_indexes); otherwise un-compacted documents are omitted. set use_lsm(false) to read the base table only"
+                "the MemWAL LSM scanner full-text search requires the FTS index on '{column}' to be maintained by the write spec (MemWalWriteSpec::with_maintained_indexes) and carried by every resident MemTable; otherwise un-compacted documents are omitted. set use_memwal(false) to read the base table only"
             ),
         });
     }
@@ -493,6 +494,27 @@ async fn fts_plan(
     Ok(scanner.create_plan().await?)
 }
 
+/// Whether the spec maintains the index named `name`.
+///
+/// An unnamed set names nothing and covers everything, so the stored list alone
+/// cannot answer this.
+fn spec_maintains(details: &MemWalIndexDetails, name: &str) -> bool {
+    details.maintain_all_indexes || details.maintained_indexes.iter().any(|n| n == name)
+}
+
+/// Whether every resident MemTable carries the index an arm needs.
+///
+/// The spec is intent; a MemTable built before the index joined the set
+/// contributes nothing to an indexed read, so its rows would go missing.
+fn resident_memtables_carry(
+    in_memory: &HashMap<Uuid, InMemoryMemTables>,
+    carries: impl Fn(&InMemoryMemTableRef) -> bool,
+) -> bool {
+    in_memory
+        .values()
+        .all(|tables| carries(&tables.active) && tables.frozen.iter().all(&carries))
+}
+
 /// Whether an index of `type_url_suffix` covering `column` is in the MemWAL spec's
 /// maintained set. Only a maintained index has its catch-up tracked (so exclusion is
 /// gated correctly) and its in-memory arm kept current; an unmaintained base index
@@ -502,7 +524,7 @@ async fn fts_plan(
 async fn index_maintained(
     dataset: &Dataset,
     column: &str,
-    maintained: &[String],
+    details: &MemWalIndexDetails,
     type_url_suffix: &str,
 ) -> Result<bool> {
     use lance::index::DatasetIndexExt;
@@ -512,7 +534,7 @@ async fn index_maintained(
     let indices = dataset.load_indices().await?;
     Ok(indices.iter().any(|idx| {
         idx.fields.contains(&field.id)
-            && maintained.iter().any(|m| m == &idx.name)
+            && spec_maintains(details, &idx.name)
             && idx
                 .index_details
                 .as_ref()
@@ -583,9 +605,7 @@ async fn arm_maintained_index_names(
             })
             .map(|idx| idx.name.clone())
             .collect();
-        if let Some(name) =
-            resolve_single_index(segment_names, &details.maintained_indexes, arm, &column)?
-        {
+        if let Some(name) = resolve_single_index(segment_names, details, arm, &column)? {
             names.push(name);
         }
     }
@@ -604,7 +624,7 @@ async fn arm_maintained_index_names(
 /// compaction watermark).
 fn resolve_single_index(
     mut names: Vec<String>,
-    maintained: &[String],
+    details: &MemWalIndexDetails,
     arm: &str,
     column: &str,
 ) -> Result<Option<String>> {
@@ -613,14 +633,14 @@ fn resolve_single_index(
     if names.len() > 1 {
         return Err(Error::NotSupported {
             message: format!(
-                "the MemWAL LSM scanner cannot resolve the {arm} index catch-up watermark for '{column}': it has multiple {arm} indexes; set use_lsm(false) to read the base table only"
+                "the MemWAL LSM scanner cannot resolve the {arm} index catch-up watermark for '{column}': it has multiple {arm} indexes; set use_memwal(false) to read the base table only"
             ),
         });
     }
     Ok(names
         .into_iter()
         .next()
-        .filter(|name| maintained.contains(name)))
+        .filter(|name| spec_maintains(details, name)))
 }
 
 /// Drop the primary-key columns Lance appends internally for dedup when the user's
@@ -662,7 +682,7 @@ fn restore_projection(
 ///
 /// Note: the base and SSTable arms use `fast_search` (indexed data only), so a
 /// base-table row not covered by a vector index is invisible here — it surfaces
-/// only via the memtable or `use_lsm(false)`.
+/// only via the memtable or `use_memwal(false)`.
 #[allow(clippy::too_many_arguments)]
 async fn vector_plan(
     dataset: &Dataset,
@@ -688,21 +708,16 @@ async fn vector_plan(
         None => default_vector_column(&arrow_schema, Some(query_vector.len() as i32))?,
     };
 
-    // The base arm relies on the column's vector index (`fast_search`). Unless it is
-    // maintained, its catch-up is untracked and exclusion falls back to the
-    // compaction watermark — dropping compacted SSTables the (lagging) base index has
-    // not re-indexed. Reject rather than silently omit rows, mirroring the FTS arm.
-    if !index_maintained(
-        dataset,
-        &column,
-        &details.maintained_indexes,
-        "VectorIndexDetails",
-    )
-    .await?
-    {
+    // The base arm relies on the column's vector index (`fast_search`). Unmaintained,
+    // its catch-up is untracked and exclusion falls back to the compaction watermark,
+    // dropping compacted SSTables the lagging base index has not re-indexed.
+    // Reject rather than silently omit rows, mirroring the FTS arm.
+    // A resident MemTable needs no index of its own: one that cannot answer
+    // compares every row.
+    if !index_maintained(dataset, &column, details, "VectorIndexDetails").await? {
         return Err(Error::NotSupported {
             message: format!(
-                "the MemWAL LSM scanner requires the vector index on '{column}' to be maintained by the write spec (LsmWriteSpec::with_maintained_indexes); otherwise compacted rows not yet re-indexed are omitted. set use_lsm(false) to read the base table only"
+                "the MemWAL LSM scanner requires the vector index on '{column}' to be maintained by the write spec (MemWalWriteSpec::with_maintained_indexes); otherwise compacted rows not yet re-indexed are omitted. set use_memwal(false) to read the base table only"
             ),
         });
     }
@@ -711,7 +726,7 @@ async fn vector_plan(
     // clear error rather than failing deep in the planner.
     if is_binary_vector_column(&arrow_schema, &column) {
         return Err(Error::NotSupported {
-            message: "the MemWAL LSM scanner does not support binary (uint8) vector search; set use_lsm(false) to read the base table only".to_string(),
+            message: "the MemWAL LSM scanner does not support binary (uint8) vector search; set use_memwal(false) to read the base table only".to_string(),
         });
     }
 
@@ -724,8 +739,20 @@ async fn vector_plan(
     let mut scanner = base_scanner(dataset, query, pk_columns, snapshots, in_memory)?
         .with_overfetch_factor(LSM_OVERFETCH_FACTOR)
         .nearest(&column, query_vector.as_ref(), k)?
-        .nprobes(query.minimum_nprobes)
         .distance_metric(distance_type.into());
+    if let Some(nprobes) = query.nprobes {
+        scanner = if query.maximum_nprobes == Some(0) {
+            scanner.minimum_nprobes(nprobes)
+        } else {
+            scanner.nprobes(nprobes)
+        };
+    }
+    if let Some(minimum_nprobes) = query.minimum_nprobes {
+        scanner = scanner.minimum_nprobes(minimum_nprobes);
+    }
+    if let Some(maximum_nprobes) = query.maximum_nprobes.filter(|nprobes| *nprobes != 0) {
+        scanner = scanner.maximum_nprobes(maximum_nprobes);
+    }
     if let Some(refine_factor) = query.refine_factor {
         scanner = scanner.refine(refine_factor);
     }
@@ -777,11 +804,58 @@ mod tests {
     use super::*;
     use lance_index::mem_wal::{CompactedSsTable, IndexCatchupProgress};
 
+    #[test]
+    fn search_effort_is_not_silently_ignored() {
+        let query = VectorQueryRequest {
+            query_vector: vec![Arc::new(arrow_array::Float32Array::from(vec![1.0, 0.0]))],
+            search_effort: Some(0.5),
+            ..Default::default()
+        };
+        assert!(
+            matches!(reject_unsupported(&query), Err(Error::NotSupported { message })
+            if message.contains("search_effort") && message.contains("un-compacted MemWAL"))
+        );
+    }
+
+    #[test]
+    fn resident_fts_check_distinguishes_columns() {
+        use arrow_schema::Field;
+        use lance::dataset::mem_wal::index::{IndexStore, MemIndexSpec};
+
+        let schema = ArrowSchema::new(vec![
+            Field::new("id", DataType::Int32, false),
+            Field::new("text", DataType::Utf8, true),
+            Field::new(
+                "vector",
+                DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4),
+                true,
+            ),
+        ]);
+        let schema = lance_core::datatypes::Schema::try_from(&schema).unwrap();
+        let specs = vec![
+            MemIndexSpec::btree("id_idx", 0, "id"),
+            MemIndexSpec::fts("text_idx", 1, "text"),
+            MemIndexSpec::hnsw(
+                "vector_idx",
+                2,
+                "vector",
+                lance_linalg::distance::DistanceType::Cosine,
+            ),
+        ];
+        let store = IndexStore::from_specs(&specs, &schema, 1_000, 16).unwrap();
+        assert!(!store.fts_granularities_on("text").is_empty());
+        for column in ["id", "vector", "missing"] {
+            assert!(store.fts_granularities_on(column).is_empty());
+        }
+        let empty = IndexStore::from_specs(&[], &schema, 1_000, 16).unwrap();
+        assert!(empty.fts_granularities_on("text").is_empty());
+    }
+
     #[test]
     fn exclusion_watermark_gates_on_lagging_index_catchup() {
         let shard = Uuid::from_u128(1);
         let details = MemWalIndexDetails {
-            // Compaction has drained generations through 5 into the base table...
+            // Compaction has drained SSTables through generation 5 into the base table...
             compacted_sstables: vec![CompactedSsTable::new(shard, 5)],
             // ...but the FTS index has only caught up through generation 2.
             index_catchup: vec![IndexCatchupProgress::new(
@@ -888,7 +962,10 @@ mod tests {
 
     #[test]
     fn resolve_single_index_dedupes_segments() {
-        let maintained = vec!["fts_idx".to_string()];
+        let maintained = MemWalIndexDetails {
+            maintained_indexes: vec!["fts_idx".to_string()],
+            ..Default::default()
+        };
         // Two physical segments of ONE logical index must not count as "multiple".
         assert_eq!(
             resolve_single_index(
@@ -917,4 +994,33 @@ mod tests {
             None
         );
     }
+
+    /// A spec that maintains every index names none of them, so the stored list
+    /// is empty while every index on the table is in fact maintained. Reading
+    /// the list alone would refuse the read path on the default spec.
+    #[test]
+    fn an_unnamed_set_maintains_every_index() {
+        let all = MemWalIndexDetails {
+            maintained_indexes: Vec::new(),
+            maintain_all_indexes: true,
+            ..Default::default()
+        };
+        assert!(spec_maintains(&all, "fts_idx"));
+        assert_eq!(
+            resolve_single_index(vec!["fts_idx".to_string()], &all, "full-text", "text").unwrap(),
+            Some("fts_idx".to_string()),
+        );
+
+        // The same empty list without the flag maintains nothing.
+        let none = MemWalIndexDetails {
+            maintained_indexes: Vec::new(),
+            maintain_all_indexes: false,
+            ..Default::default()
+        };
+        assert!(!spec_maintains(&none, "fts_idx"));
+        assert_eq!(
+            resolve_single_index(vec!["fts_idx".to_string()], &none, "full-text", "text").unwrap(),
+            None,
+        );
+    }
 }
diff --git a/rust/lancedb/src/table/refresh.rs b/rust/lancedb/src/table/refresh.rs
index 0119ba36d..4274aaa6f 100644
--- a/rust/lancedb/src/table/refresh.rs
+++ b/rust/lancedb/src/table/refresh.rs
@@ -87,7 +87,7 @@ async fn execute_refresh_column_with_source(
     column: &str,
 ) -> Result<RefreshExecution> {
     table.dataset.ensure_mutable()?;
-    ensure_no_lsm_write_spec(table).await?;
+    ensure_no_memwal_spec(table).await?;
     let dataset = table.dataset.get().await?;
 
     let expression = declared_expression(&dataset, column)?;
@@ -303,7 +303,7 @@ pub(crate) async fn execute_refresh_column_async(
     // Validate before spawning so bad input is reported by this call rather
     // than only by the job.
     table.dataset.ensure_mutable()?;
-    ensure_no_lsm_write_spec(table).await?;
+    ensure_no_memwal_spec(table).await?;
     let dataset = table.dataset.get().await?;
     declared_expression(&dataset, column)?;
     drop(dataset);
@@ -323,12 +323,12 @@ pub(crate) async fn execute_refresh_column_async(
     })))
 }
 
-/// Refuse to refresh under an LSM write spec.
+/// Refuse to refresh under a MemWAL write spec.
 ///
 /// Refresh enumerates base fragments, and a write spec keeps visible rows in
 /// un-compacted MemWAL tiers it cannot reach -- success would silently omit
 /// readable rows.
-async fn ensure_no_lsm_write_spec(table: &NativeTable) -> Result<()> {
+async fn ensure_no_memwal_spec(table: &NativeTable) -> Result<()> {
     use lance::dataset::mem_wal::DatasetMemWalExt;
 
     // Unset drops the MemWAL index, so the spec alone stops describing a table
@@ -341,7 +341,7 @@ async fn ensure_no_lsm_write_spec(table: &NativeTable) -> Result<()> {
         .list_mem_wal_latest_shard_ids()
         .await?
         .is_empty();
-    if retained_sstables || table.get_lsm_write_spec().await?.is_some() {
+    if retained_sstables || table.get_memwal_spec().await?.is_some() {
         return Err(Error::NotSupported {
             message: "refresh_column is not supported on a table with an LSM write \
                       spec: rows in un-compacted tiers are invisible to refresh"
@@ -1637,7 +1637,7 @@ mod tests {
     /// writer left in that state.
     #[tokio::test]
     async fn test_refresh_refuses_a_foreign_lsm_state() {
-        use crate::table::LsmWriteSpec;
+        use crate::table::MemWalWriteSpec;
 
         let tmp_dir = tempfile::tempdir().unwrap();
         let conn = connect(tmp_dir.path().to_str().unwrap())
@@ -1655,7 +1655,7 @@ mod tests {
         let table = conn.create_table("lsm", batch).execute().await.unwrap();
         table.set_unenforced_primary_key(["x"]).await.unwrap();
         table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .enable_memwal(MemWalWriteSpec::unsharded())
             .await
             .unwrap();
         super::super::computed_columns::add_foreign_kind(&table, "doubled", "sql").await;
@@ -1673,7 +1673,7 @@ mod tests {
     /// flag still marks retained SSTable rows; refresh refuses on the flag.
     #[tokio::test]
     async fn test_refresh_refuses_retained_catchup_state() {
-        use crate::table::LsmWriteSpec;
+        use crate::table::MemWalWriteSpec;
 
         let tmp_dir = tempfile::tempdir().unwrap();
         let conn = connect(tmp_dir.path().to_str().unwrap())
@@ -1697,14 +1697,14 @@ mod tests {
             .unwrap();
         table.set_unenforced_primary_key(["x"]).await.unwrap();
         table
-            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .enable_memwal(MemWalWriteSpec::unsharded())
             .await
             .unwrap();
         let mut merge = table.merge_insert(&["x"]);
         merge
             .when_matched_update_all(None)
             .when_not_matched_insert_all()
-            .use_lsm(true);
+            .use_memwal(true);
         merge
             .execute(Box::new(arrow_array::RecordBatchIterator::new(
                 vec![Ok(batch)],
@@ -1712,7 +1712,7 @@ mod tests {
             )))
             .await
             .unwrap();
-        table.unset_lsm_write_spec().await.unwrap();
+        table.disable_memwal().await.unwrap();
         super::super::computed_columns::add_foreign_kind(&table, "doubled", "sql").await;
 
         let err = table.refresh_column("doubled").await.unwrap_err();
diff --git a/rust/lancedb/src/table/schema_evolution.rs b/rust/lancedb/src/table/schema_evolution.rs
index 4d0e2815d..1bb57d6c0 100644
--- a/rust/lancedb/src/table/schema_evolution.rs
+++ b/rust/lancedb/src/table/schema_evolution.rs
@@ -104,6 +104,7 @@ pub(crate) async fn execute_add_columns(
     transforms: NewColumnTransform,
     read_columns: Option<Vec<String>>,
 ) -> Result<AddColumnsResult> {
+    table.ensure_not_a_view("add columns to").await?;
     computed_columns::ensure_not_function_bound(
         table.schema().await?.as_ref(),
         "schema evolution",
@@ -130,7 +131,7 @@ pub(crate) async fn execute_declare(
 ) -> Result<AddColumnsResult> {
     use lance::dataset::mem_wal::DatasetMemWalExt;
 
-    // An LSM write spec keeps visible rows in tiers refresh cannot reach;
+    // A MemWAL write spec keeps visible rows in tiers refresh cannot reach;
     // checked against latest committed state, not this handle's snapshot.
     table.checkout_latest().await?;
     computed_columns::ensure_not_function_bound(
@@ -148,7 +149,7 @@ pub(crate) async fn execute_declare(
         .list_mem_wal_latest_shard_ids()
         .await?
         .is_empty();
-    if retained_sstables || table.get_lsm_write_spec().await?.is_some() {
+    if retained_sstables || table.get_memwal_spec().await?.is_some() {
         return Err(Error::NotSupported {
             message: "computed columns are not supported on a table with an LSM write \
                       spec: rows in un-compacted tiers are invisible to refresh"
@@ -202,6 +203,15 @@ pub(crate) async fn execute_alter_columns(
     table.dataset.ensure_mutable()?;
     let mut dataset = (*table.dataset.get().await?).clone();
     let schema = std::sync::Arc::new(ArrowSchema::from(dataset.schema()));
+    ensure_only_computed_columns_of_a_view(
+        table,
+        &dataset,
+        &schema,
+        "alter columns of",
+        alterations
+            .iter()
+            .map(|alteration| alteration.path.as_str()),
+    )?;
     // A Function binding stores its columns' exact fields, nullability
     // included, so every alteration of one counts, and a rename's target too.
     computed_columns::ensure_not_function_bound(
@@ -241,6 +251,13 @@ pub(crate) async fn execute_drop_columns(
     table.dataset.ensure_mutable()?;
     let mut dataset = (*table.dataset.get().await?).clone();
     let schema = std::sync::Arc::new(ArrowSchema::from(dataset.schema()));
+    ensure_only_computed_columns_of_a_view(
+        table,
+        &dataset,
+        &schema,
+        "drop columns of",
+        columns.iter().copied(),
+    )?;
     let unbinding = computed_columns::plan_function_unbinding(schema.as_ref(), columns)?;
 
     let mut names = columns.iter().map(|c| c.to_string()).collect::<Vec<_>>();
@@ -266,6 +283,30 @@ pub(crate) async fn execute_drop_columns(
     Ok(DropColumnsResult { version })
 }
 
+/// A view's stored columns are the definition's output, which refresh checks
+/// against the view's schema; only its computed columns may be altered or
+/// dropped.
+fn ensure_only_computed_columns_of_a_view<'a>(
+    table: &NativeTable,
+    dataset: &Dataset,
+    schema: &ArrowSchema,
+    operation: &str,
+    paths: impl IntoIterator<Item = &'a str>,
+) -> Result<()> {
+    let metadata = &dataset.schema().metadata;
+    if !metadata.contains_key(crate::materialized_view::DEFINITION_META_KEY) {
+        return Ok(());
+    }
+    let computed = computed_columns::computed_columns(schema);
+    if paths
+        .into_iter()
+        .all(|path| computed.iter().any(|column| column.name == path))
+    {
+        return Ok(());
+    }
+    crate::materialized_view::ensure_not_a_view(&table.name, metadata, operation)
+}
+
 /// Retire the bindings a drop covers, in the commit before it.
 ///
 /// A binding lives in three places -- the schema-metadata envelope, each
@@ -807,6 +848,29 @@ mod tests {
 
     // Alter Columns Tests
 
+    #[tokio::test]
+    async fn test_alter_columns_rejects_missing_changes() {
+        let conn = connect("memory://").execute().await.unwrap();
+        let batch = record_batch!(("id", Int32, [1, 2, 3])).unwrap();
+        let table = conn.create_table("test", batch).execute().await.unwrap();
+        let initial_version = table.version().await.unwrap();
+        let initial_schema = table.schema().await.unwrap();
+
+        for alterations in [
+            vec![ColumnAlteration::new("id".into())],
+            vec![
+                ColumnAlteration::new("id".into()).rename("new_id".into()),
+                ColumnAlteration::new("id".into()),
+            ],
+        ] {
+            let err = table.alter_columns(&alterations).await.unwrap_err();
+            assert!(matches!(err, Error::InvalidInput { .. }), "got {err:?}");
+            assert!(err.to_string().contains("path 'id'"));
+            assert_eq!(table.version().await.unwrap(), initial_version);
+            assert_eq!(table.schema().await.unwrap(), initial_schema);
+        }
+    }
+
     #[tokio::test]
     async fn test_alter_column_rename() {
         let conn = connect("memory://").execute().await.unwrap();
diff --git a/rust/lancedb/src/table/update.rs b/rust/lancedb/src/table/update.rs
index f10594f23..cec787ff8 100644
--- a/rust/lancedb/src/table/update.rs
+++ b/rust/lancedb/src/table/update.rs
@@ -13,7 +13,7 @@ use crate::Result;
 /// The result of an update operation
 #[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize, Default)]
 pub struct UpdateResult {
-    #[serde(default)]
+    #[serde(default, alias = "updated_rows")]
     pub rows_updated: u64,
     /// The commit version associated with the operation.
     #[serde(default)]
@@ -93,6 +93,7 @@ pub(crate) async fn execute_update(
 
     // 1. Snapshot the current dataset
     let dataset = table.dataset.get().await?;
+    crate::materialized_view::ensure_not_a_view(&table.name, &dataset.schema().metadata, "update")?;
     super::computed_columns::ensure_no_function_bindings_for_mutation(
         &arrow_schema::Schema::from(dataset.schema()),
         "update",
diff --git a/rust/lancedb/src/utils/mod.rs b/rust/lancedb/src/utils/mod.rs
index 52b7bd5e8..20f209f2e 100644
--- a/rust/lancedb/src/utils/mod.rs
+++ b/rust/lancedb/src/utils/mod.rs
@@ -19,15 +19,27 @@ use std::pin::Pin;
 use crate::error::{Error, Result};
 use datafusion_physical_plan::SendableRecordBatchStream;
 
-/// The characters any object name may contain: a table, a namespace segment, a
-/// Secret, a materialized view.
-///
-/// No positional rule on top of it -- a name may begin with `_`, `-` or `.`,
-/// as LanceDB namespaces already do. `.` and `..` are excluded separately, by
-/// [`reject_relative_segment`]: that is a property of where a name sits in a
-/// URL, not of the name. Length is the service's to bound.
-static OBJECT_NAME_REGEX: std::sync::LazyLock<regex::Regex> =
-    std::sync::LazyLock::new(|| regex::Regex::new(r"^[A-Za-z0-9_.\-]+$").unwrap());
+/// The maximum length of a table name. Table names are used in URLs and in the keys used with the
+/// object store.
+const MAX_TABLE_NAME_CHARS: usize = 128;
+
+/// The maximum length of a component of a namespace.
+const MAX_NAMESPACE_NAME_CHARS: usize = 64;
+
+/// The maximum number of namespace components that are allowed.
+const MAX_NAMESPACE_COMPONENTS: usize = 10;
+
+/// The maximum length of a view name.
+const MAX_VIEW_NAME_CHARS: usize = 128;
+
+/// The maximum length of a secret name.
+const MAX_SECRET_NAME_CHARS: usize = 128;
+
+/// The maximum length of a function name.
+const MAX_FUNCTION_NAME_CHARS: usize = 128;
+
+/// The maximum length of a database name.
+const MAX_DB_NAME_CHARS: usize = 64;
 
 pub trait PatchStoreParam {
     fn patch_with_store_wrapper(
@@ -86,129 +98,190 @@ impl PatchReadParam for ReadParams {
     }
 }
 
-/// The reason `.` and `..` are refused wherever a name becomes a path segment.
-const RELATIVE_SEGMENT_REASON: &str =
-    "'.' and '..' are read as relative path segments and cannot address an object";
-
-/// Whether URL parsing would resolve this component away rather than keep it.
-///
-/// Exactly `.` and `..`, and their percent-encoded spellings -- resolution
-/// happens after decoding, so `%2E%2E` collapses as surely as `..` does, and
-/// `drop_table("..")` would reach `/v1/drop/`. No wider than that: `...` is an
-/// ordinary segment that addresses fine.
-fn is_relative_segment(value: &str) -> bool {
-    let decoded = value.replace("%2e", ".").replace("%2E", ".");
-    decoded == "." || decoded == ".."
+/// Translate a string into something that can be safely logged. This involves removing control
+/// characters that could be used in an exploit like CVE-2003-0063.
+fn sanitize(input: &str) -> String {
+    input.chars().filter(|c| !c.is_control()).collect()
 }
 
-/// Refuse a path component that URL parsing resolves as a relative segment.
-///
-/// Reachable on its own for an identifier with no other validator: a Function
-/// name has no client-side grammar, so this is the only rule that applies.
-pub(crate) fn reject_relative_segment(what: &str, value: &str) -> Result<()> {
-    if is_relative_segment(value) {
-        return Err(Error::InvalidInput {
-            message: format!("invalid {what} '{value}': {RELATIVE_SEGMENT_REASON}"),
+pub fn validate_table_name(name: &str) -> Result<()> {
+    if name.is_empty() || name == "." || name == ".." {
+        return Err(Error::InvalidTableName {
+            name: name.to_string(),
+            reason: "Illegal table name.".to_string(),
+        });
+    }
+    if name.len() > MAX_TABLE_NAME_CHARS {
+        return Err(Error::InvalidTableName {
+            name: sanitize(name),
+            reason: format!(
+                "Table name cannot be more than {MAX_TABLE_NAME_CHARS} characters long."
+            ),
+        });
+    }
+    if !name
+        .chars()
+        .all(|c| c.is_ascii_alphanumeric() || c == '-' || c == '_' || c == '.')
+    {
+        return Err(Error::InvalidTableName {
+            name: sanitize(name),
+            reason: "Only alphanumeric characters, dashes, underscores, and\
+                periods are allowed in table names."
+                .to_string(),
         });
     }
     Ok(())
 }
 
-/// Every rule an object name obeys: non-empty, inside [`OBJECT_NAME_REGEX`],
-/// and addressable as a path segment.
-///
-/// Returns the reason rather than an [`Error`], because the error type is each
-/// API's own -- a table reports [`Error::InvalidTableName`], the rest
-/// [`Error::InvalidInput`]. Sharing the rules but not the error keeps a table,
-/// a namespace segment and a Secret from drifting apart.
-fn check_object_name(name: &str) -> std::result::Result<(), &'static str> {
-    if name.is_empty() {
-        return Err("it must not be empty");
+/// Validate that an input string contains alphanumerics, or dashes, underscores, and periods.
+fn validate_view_or_namespace_or_secret(input: &str, kind: &str, max_len: usize) -> Result<()> {
+    if input.is_empty() || input == "." || input == ".." {
+        return Err(Error::InvalidInput {
+            message: format!("Illegal {} '{}'.", kind, input),
+        });
     }
-    if !OBJECT_NAME_REGEX.is_match(name) {
-        return Err(
-            "it may contain only alphanumeric characters, underscores, hyphens and periods",
-        );
+    if input.len() > max_len {
+        return Err(Error::InvalidInput {
+            message: format!(
+                "Invalid {} '{}' is more than {} characters long.",
+                kind,
+                sanitize(input),
+                max_len
+            ),
+        });
     }
-    if is_relative_segment(name) {
-        return Err(RELATIVE_SEGMENT_REASON);
+    if !input
+        .chars()
+        .all(|c| c.is_ascii_alphanumeric() || c == '-' || c == '_' || c == '.')
+    {
+        return Err(Error::InvalidInput {
+            message: format!(
+                "Invalid {} '{}': only alphanumeric characters, \
+                         dashes, underscores, and periods are allowed.",
+                kind,
+                sanitize(input)
+            ),
+        });
     }
     Ok(())
 }
 
-/// Validate a table name.
-pub fn validate_table_name(name: &str) -> Result<()> {
-    check_object_name(name).map_err(|reason| Error::InvalidTableName {
-        name: name.to_string(),
-        reason: reason.to_string(),
-    })
-}
-
 /// Validate one component of a namespace path -- a single segment, not the
 /// whole path. [`validate_namespace`] covers a path.
-pub fn validate_namespace_name(name: &str) -> Result<()> {
-    check_object_name(name).map_err(|reason| Error::InvalidInput {
-        message: format!("invalid namespace name '{name}': {reason}"),
-    })
+pub fn validate_namespace_component(name: &str) -> Result<()> {
+    validate_view_or_namespace_or_secret(name, "namespace component", MAX_NAMESPACE_NAME_CHARS)
 }
 
-/// Validate one component of a Secret identifier: a Secret name, or one segment
-/// of the namespace path holding it.
-///
-/// The join decides identity, and the service only sees what the split
-/// produced. `"a$b"` is not a name the service accepts, but joined and split it
-/// reads as the namespace `a` and the name `b` -- a different Secret that may
-/// already exist. This is not a second opinion on the name; it is what lets the
-/// service have one.
-pub fn validate_secret_component(what: &str, value: &str) -> Result<()> {
-    check_object_name(value).map_err(|reason| Error::InvalidInput {
-        message: format!("invalid {what} '{value}': {reason}"),
-    })
+/// Validate all components of a namespace
+pub fn validate_namespace(namespace: &[String]) -> Result<()> {
+    if namespace.len() > 1 && namespace.first().map(String::as_str) == Some("public") {
+        return Err(Error::InvalidInput {
+            message: "Namespaces that begin with public cannot have multiple namespace components."
+                .to_string(),
+        });
+    }
+    if namespace.len() > MAX_NAMESPACE_COMPONENTS {
+        return Err(Error::InvalidInput {
+            message: format!(
+                "Cannot have a namespace with more than {MAX_NAMESPACE_COMPONENTS} components."
+            ),
+        });
+    }
+    for component in namespace {
+        validate_namespace_component(component)?;
+    }
+    Ok(())
+}
+
+/// Validate a secret name.
+pub fn validate_secret_name(name: &str) -> Result<()> {
+    validate_view_or_namespace_or_secret(name, "secret name", MAX_SECRET_NAME_CHARS)
 }
 
 /// Validate a Secret name and every segment of the namespace path holding it.
 pub fn validate_secret_reference(name: &str, namespace_path: &[String]) -> Result<()> {
-    for segment in namespace_path {
-        validate_secret_component("Secret namespace path segment", segment)?;
-    }
-    validate_secret_component("Secret name", name)
+    validate_namespace(namespace_path)?;
+    validate_secret_name(name)
+}
+
+pub fn validate_view_name(name: &str) -> Result<()> {
+    validate_view_or_namespace_or_secret(name, "view name", MAX_VIEW_NAME_CHARS)
 }
 
 /// Validate a view name and every segment of the namespace path holding it.
-///
-/// Worded for a view rather than deferring to [`validate_table_name`]: a view
-/// is not a table, and a caller who mistypes one should not be told their
-/// table name is invalid. The rule is the same one every object name follows,
-/// which is what lets the `$`-joined identifier split back apart.
 pub fn validate_view_reference(name: &str, namespace_path: &[String]) -> Result<()> {
-    for segment in namespace_path {
-        validate_view_component("view namespace path segment", segment)?;
+    validate_namespace(namespace_path)?;
+    validate_view_name(name)
+}
+
+/// Validate a function name. These do not allow dashes or periods.
+pub fn validate_function_name(name: &str) -> Result<()> {
+    if name.is_empty() {
+        return Err(Error::InvalidInput {
+            message: format!("Invalid function name '{}'.", name),
+        });
     }
-    validate_view_component("view name", name)
+    if name.len() > MAX_FUNCTION_NAME_CHARS {
+        return Err(Error::InvalidInput {
+            message: format!(
+                "Invalid function name '{}' is more than {} characters long.",
+                sanitize(name),
+                MAX_FUNCTION_NAME_CHARS
+            ),
+        });
+    }
+    if !name.chars().all(|c| c.is_ascii_alphanumeric() || c == '_') {
+        return Err(Error::InvalidInput {
+            message: format!(
+                "Invalid function name '{}': only alphanumeric \
+                              characters and underscores are allowed.",
+                sanitize(name)
+            ),
+        });
+    }
+    Ok(())
 }
 
-/// Validate one component of a view identifier: a view name, or one segment of
-/// the namespace path holding it.
-pub fn validate_view_component(what: &str, value: &str) -> Result<()> {
-    check_object_name(value).map_err(|reason| Error::InvalidInput {
-        message: format!("invalid {what} '{value}': {reason}"),
-    })
-}
-
-/// Validate all components of a namespace
-///
-/// Iterates through all namespace components and validates each one.
-/// Returns an error if any component is invalid.
-///
-/// # Arguments
-/// * `namespace` - The namespace components to validate
-///
-/// # Returns
-/// * `Ok(())` if all namespace components are valid
-/// * `Err(Error)` if any component is invalid
-pub fn validate_namespace(namespace: &[String]) -> Result<()> {
-    for component in namespace {
-        validate_namespace_name(component)?;
+/// Validate a database name, including slash-separated components.
+pub fn validate_database_name(name: &str) -> Result<()> {
+    if name.len() > MAX_DB_NAME_CHARS {
+        return Err(Error::InvalidInput {
+            message: format!(
+                "Invalid database name '{}' is more than {} characters long.",
+                sanitize(name),
+                MAX_DB_NAME_CHARS
+            ),
+        });
+    }
+    if !name
+        .chars()
+        .all(|c| c.is_ascii_alphanumeric() || c == '-' || c == '_' || c == '.' || c == '/')
+    {
+        return Err(Error::InvalidInput {
+            message: format!(
+                "Invalid database name '{}': only alphanumeric characters, dashes, \
+                              underscores, periods, and slashes are allowed.",
+                sanitize(name)
+            ),
+        });
+    }
+    for component in name.split('/') {
+        if component.is_empty() {
+            return Err(Error::InvalidInput {
+                message: format!(
+                    "Invalid database name '{}': invalid empty path component.",
+                    sanitize(name)
+                ),
+            });
+        }
+        if component == "." || component == ".." {
+            return Err(Error::InvalidInput {
+                message: format!(
+                    "Invalid database name '{}': illegal path component.",
+                    sanitize(name)
+                ),
+            });
+        }
     }
     Ok(())
 }
@@ -494,6 +567,26 @@ pub fn supported_fts_data_type(dtype: &DataType) -> bool {
     supported_fts_data_type_impl(dtype, false)
 }
 
+/// Validate FTS input without losing the logical type in field metadata.
+pub(crate) fn validate_fts_field(field: &Field) -> Result<()> {
+    if lance_arrow::json::is_json_field(field) || supported_fts_data_type(field.data_type()) {
+        return Ok(());
+    }
+    let logical_type = field
+        .metadata()
+        .get("ARROW:extension:name")
+        .map(|name| format!(" (logical type {name})"))
+        .unwrap_or_default();
+    Err(Error::Schema {
+        message: format!(
+            "A FTS index cannot be created on the field `{}` which has data type {}{}. FTS supports strings, lists of strings, and lance.json fields stored as LargeBinary; raw binary is not supported",
+            field.name(),
+            field.data_type(),
+            logical_type,
+        ),
+    })
+}
+
 fn supported_fts_data_type_impl(dtype: &DataType, in_list: bool) -> bool {
     match (dtype, in_list) {
         (DataType::Utf8 | DataType::LargeUtf8, _) => true,
@@ -707,6 +800,43 @@ mod tests {
 
     use super::*;
 
+    #[test]
+    fn test_fts_field_logical_type_validation() {
+        let json = lance_arrow::json::json_field("doc", true);
+        assert!(validate_fts_field(&json).is_ok());
+
+        for dtype in [DataType::Binary, DataType::LargeBinary] {
+            let raw = Field::new("doc", dtype.clone(), true);
+            let err = validate_fts_field(&raw).unwrap_err().to_string();
+            assert!(err.contains("raw binary is not supported"), "{err}");
+            assert!(err.contains("lance.json"), "{err}");
+
+            let other_extension = raw.with_metadata(std::collections::HashMap::from([(
+                "ARROW:extension:name".into(),
+                "other.json".into(),
+            )]));
+            let err = validate_fts_field(&other_extension)
+                .unwrap_err()
+                .to_string();
+            assert!(err.contains("logical type other.json"), "{err}");
+        }
+
+        // A JSON marker on the wrong binary storage type is insufficient.
+        let wrong_storage =
+            Field::new("doc", DataType::Binary, true).with_metadata(json.metadata().clone());
+        assert!(validate_fts_field(&wrong_storage).is_err());
+        for dtype in [DataType::Utf8, DataType::LargeUtf8] {
+            let text = Field::new("text", dtype, true);
+            assert!(validate_fts_field(&text).is_ok());
+            for list_type in [
+                DataType::List(Arc::new(text.clone())),
+                DataType::LargeList(Arc::new(text.clone())),
+            ] {
+                assert!(validate_fts_field(&Field::new("texts", list_type, true)).is_ok());
+            }
+        }
+    }
+
     #[test]
     fn test_public_fts_field_path_prefers_exact_case() {
         let text_list = || {
@@ -870,6 +1000,37 @@ mod tests {
         assert!(err.contains("text.embedding"));
     }
 
+    #[test]
+    fn test_validate_database_name() {
+        for name in [
+            "db",
+            "team/search",
+            "Team_1/db-2.3",
+            &"a".repeat(MAX_DB_NAME_CHARS),
+        ] {
+            assert!(validate_database_name(name).is_ok(), "rejected {name:?}");
+        }
+        for name in [
+            "",
+            "/",
+            "//",
+            ".",
+            "..",
+            "/db",
+            "db/",
+            "a//b",
+            "a/./b",
+            "a/../b",
+            "a$b",
+            "a b",
+            "a?b",
+            "café",
+            &"a".repeat(MAX_DB_NAME_CHARS + 1),
+        ] {
+            assert!(validate_database_name(name).is_err(), "accepted {name:?}");
+        }
+    }
+
     #[test]
     fn test_validate_table_name() {
         assert!(validate_table_name("my_table").is_ok());
@@ -890,27 +1051,27 @@ mod tests {
     }
 
     #[test]
-    fn test_validate_namespace_name() {
+    fn test_validate_namespace_component() {
         // Valid namespace names
-        assert!(validate_namespace_name("ns1").is_ok());
-        assert!(validate_namespace_name("namespace_123").is_ok());
-        assert!(validate_namespace_name("my-namespace").is_ok());
-        assert!(validate_namespace_name("my.namespace").is_ok());
-        assert!(validate_namespace_name("NS_1.2.3").is_ok());
-        assert!(validate_namespace_name("a").is_ok());
-        assert!(validate_namespace_name("123").is_ok());
-        assert!(validate_namespace_name("_underscore").is_ok());
-        assert!(validate_namespace_name("-hyphen").is_ok());
-        assert!(validate_namespace_name(".period").is_ok());
+        assert!(validate_namespace_component("ns1").is_ok());
+        assert!(validate_namespace_component("namespace_123").is_ok());
+        assert!(validate_namespace_component("my-namespace").is_ok());
+        assert!(validate_namespace_component("my.namespace").is_ok());
+        assert!(validate_namespace_component("NS_1.2.3").is_ok());
+        assert!(validate_namespace_component("a").is_ok());
+        assert!(validate_namespace_component("123").is_ok());
+        assert!(validate_namespace_component("_underscore").is_ok());
+        assert!(validate_namespace_component("-hyphen").is_ok());
+        assert!(validate_namespace_component(".period").is_ok());
 
         // Invalid namespace names
-        assert!(validate_namespace_name("").is_err());
-        assert!(validate_namespace_name("namespace with spaces").is_err());
-        assert!(validate_namespace_name("namespace/with/slashes").is_err());
-        assert!(validate_namespace_name("namespace\\with\\backslashes").is_err());
-        assert!(validate_namespace_name("namespace$with$delimiter").is_err());
-        assert!(validate_namespace_name("namespace@special").is_err());
-        assert!(validate_namespace_name("namespace#hash").is_err());
+        assert!(validate_namespace_component("").is_err());
+        assert!(validate_namespace_component("namespace with spaces").is_err());
+        assert!(validate_namespace_component("namespace/with/slashes").is_err());
+        assert!(validate_namespace_component("namespace\\with\\backslashes").is_err());
+        assert!(validate_namespace_component("namespace$with$delimiter").is_err());
+        assert!(validate_namespace_component("namespace@special").is_err());
+        assert!(validate_namespace_component("namespace#hash").is_err());
     }
 
     #[test]
@@ -926,6 +1087,9 @@ mod tests {
         // Empty namespace (root) is valid
         assert!(validate_namespace(&[]).is_ok());
 
+        // Valid public namespace (which is equivalent to the root namespace.)
+        assert!(validate_namespace(&["public".to_string()]).is_ok());
+
         // Invalid: contains empty component
         assert!(validate_namespace(&["ns1".to_string(), "".to_string()]).is_err());
 
@@ -937,6 +1101,9 @@ mod tests {
         assert!(validate_namespace(&["ns1".to_string(), "ns/2".to_string()]).is_err());
         assert!(validate_namespace(&["ns1".to_string(), "ns$2".to_string()]).is_err());
 
+        // Invalid: starts with public but contains other components.
+        assert!(validate_namespace(&["public".to_string(), "foo".to_string()]).is_err());
+
         // Valid: underscores, hyphens, and periods are allowed
         assert!(
             validate_namespace(&["ns_1".to_string(), "ns-2".to_string(), "ns.3".to_string()])
diff --git a/rust/lancedb/tests/blob_integration.rs b/rust/lancedb/tests/blob_integration.rs
index dd4051657..063690129 100644
--- a/rust/lancedb/tests/blob_integration.rs
+++ b/rust/lancedb/tests/blob_integration.rs
@@ -501,17 +501,23 @@ async fn collect_id_rowid(table: &Table) -> Result<Vec<(i64, u64)>> {
 fn assert_missing_blob_row_ids(err: &Error) {
     assert!(matches!(err, Error::InvalidInput { .. }), "got {err:?}");
     let message = err.to_string();
-    assert!(message.contains("row ids"), "{message}");
+    assert!(
+        message.contains("row ids but some do not exist in the table"),
+        "{message}"
+    );
     assert!(!message.contains("rowaddr"), "{message}");
     assert!(!message.contains("fragment"), "{message}");
 }
 
 async fn assert_fetch_apis_reject_missing_row_ids(table: &Table, row_ids: &[u64]) -> Result<()> {
+    let expected = format!("first missing row ids: [{}]", row_ids[0]);
     let err = table.fetch_blobs("image", row_ids).await.unwrap_err();
     assert_missing_blob_row_ids(&err);
+    assert!(err.to_string().contains(&expected), "{err}");
 
     let err = table.fetch_blob_files("image", row_ids).await.unwrap_err();
     assert_missing_blob_row_ids(&err);
+    assert!(err.to_string().contains(&expected), "{err}");
 
     let err = table
         .fetch_blob_ranges(
@@ -524,6 +530,7 @@ async fn assert_fetch_apis_reject_missing_row_ids(table: &Table, row_ids: &[u64]
         .await
         .unwrap_err();
     assert_missing_blob_row_ids(&err);
+    assert!(err.to_string().contains(&expected), "{err}");
     Ok(())
 }
 
@@ -611,6 +618,47 @@ async fn blob_columns_lists_nested_dotted_paths() -> Result<()> {
     Ok(())
 }
 
+#[tokio::test]
+async fn blob_columns_excludes_list_elements_and_fetches_explain_limitation() -> Result<()> {
+    let tmp = tempdir().unwrap();
+    let db = connect(tmp.path().to_str().unwrap()).execute().await?;
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int64, false),
+        blob("thumbnail", true),
+        Field::new("imgs", DataType::List(Arc::new(blob("item", true))), true),
+    ]));
+    let table = db
+        .create_empty_table("list_blobs", schema)
+        .execute()
+        .await?;
+
+    assert_eq!(table.blob_columns().await?, vec!["thumbnail"]);
+    for column in ["imgs", "imgs.item"] {
+        let err = table.fetch_blobs(column, &[0]).await.unwrap_err();
+        assert!(matches!(err, Error::InvalidInput { .. }));
+        assert!(
+            err.to_string()
+                .contains("blobs inside lists cannot be fetched")
+        );
+
+        let err = table.fetch_blob_files(column, &[0]).await.unwrap_err();
+        assert!(
+            err.to_string()
+                .contains("blobs inside lists cannot be fetched")
+        );
+
+        let err = table
+            .fetch_blob_ranges(column, [BlobRangeRequest::new(0, 0, 1)])
+            .await
+            .unwrap_err();
+        assert!(
+            err.to_string()
+                .contains("blobs inside lists cannot be fetched")
+        );
+    }
+    Ok(())
+}
+
 #[tokio::test]
 async fn blob_columns_lists_blob_fields_in_order() -> Result<()> {
     let tmp = tempdir().unwrap();
@@ -790,6 +838,30 @@ async fn fetch_blobs_out_of_range_id_errors_without_panic() -> Result<()> {
     Ok(())
 }
 
+#[tokio::test]
+async fn fetch_blob_apis_reject_row_past_fragment_end() -> Result<()> {
+    let tmp = tempdir().unwrap();
+    let db = connect(tmp.path().to_str().unwrap()).execute().await?;
+    let table =
+        create_inline_blob_table(&db, "t", &[1, 2, 3], &[Some(b"a"), Some(b"b"), Some(b"c")])
+            .await?;
+
+    assert_fetch_apis_reject_missing_row_ids(&table, &[3]).await?;
+    assert_fetch_apis_reject_missing_row_ids(&table, &[3, 0]).await
+}
+
+#[tokio::test]
+async fn fetch_blobs_identifies_multiple_missing_ids_without_partial_results() -> Result<()> {
+    let tmp = tempdir().unwrap();
+    let db = connect(tmp.path().to_str().unwrap()).execute().await?;
+    let table = create_inline_blob_table(&db, "t", &[1, 2], &[Some(b"a"), Some(b"b")]).await?;
+
+    let err = table.fetch_blobs("image", &[0, 2, 3]).await.unwrap_err();
+    assert_missing_blob_row_ids(&err);
+    assert!(err.to_string().contains("first missing row ids: [2, 3]"));
+    Ok(())
+}
+
 #[tokio::test]
 async fn fetch_blob_files_rejects_missing_fragment_row_addr() -> Result<()> {
     let tmp = tempdir().unwrap();
@@ -831,6 +903,23 @@ async fn fetch_blob_apis_reject_deleted_row_ids() -> Result<()> {
     assert_fetch_apis_reject_missing_row_ids(&table, &[deleted_row_addr, live_row_addr]).await
 }
 
+#[tokio::test]
+async fn fetch_blob_apis_reject_deleted_stable_row_ids() -> Result<()> {
+    let tmp = tempdir().unwrap();
+    let db = connect(tmp.path().to_str().unwrap())
+        .storage_option(OPT_NEW_TABLE_ENABLE_STABLE_ROW_IDS, "true")
+        .execute()
+        .await?;
+    let table = create_inline_blob_table(&db, "t", &[1, 2], &[Some(b"one"), Some(b"two")]).await?;
+    let pairs = collect_id_rowid(&table).await?;
+    let deleted_row_id = pairs.iter().find(|(id, _)| *id == 2).unwrap().1;
+    let live_row_id = pairs.iter().find(|(id, _)| *id == 1).unwrap().1;
+
+    table.delete("id = 2").await?;
+
+    assert_fetch_apis_reject_missing_row_ids(&table, &[deleted_row_id, live_row_id]).await
+}
+
 #[tokio::test]
 async fn fetch_blobs_rejects_non_blob_column() -> Result<()> {
     let tmp = tempdir().unwrap();
diff --git a/rust/lancedb/tests/duplicate_pairs.rs b/rust/lancedb/tests/duplicate_pairs.rs
index 910088bc8..63f1c9f5a 100644
--- a/rust/lancedb/tests/duplicate_pairs.rs
+++ b/rust/lancedb/tests/duplicate_pairs.rs
@@ -76,7 +76,7 @@ async fn sql_pairs_match_native_snapshot_and_partition_tasks() -> anyhow::Result
             .to_str()
             .unwrap()
             .to_string();
-        let n = 1025;
+        let n = 8193;
         let vectors = (0..n * 2)
             .flat_map(|i| {
                 let i = i % n;
@@ -196,6 +196,89 @@ async fn sql_pairs_match_native_snapshot_and_partition_tasks() -> anyhow::Result
             );
             let actual = ctx.sql(&sql).await?.collect().await?;
             assert_eq!(pairs(&actual), pairs(&expected));
+            use lancedb::table::datafusion::udtf::{
+                duplicate_pairs::DuplicatePairsOutput,
+                vector_query::{PartitionSelection, VectorQueryOptions, VectorQueryTable},
+            };
+            let query_options = VectorQueryOptions {
+                column: "vector".into(),
+                distance_threshold: threshold,
+                selection: PartitionSelection::All,
+                output: DuplicatePairsOutput {
+                    scope: true,
+                    id_column: Some("id".into()),
+                },
+                dedup: false,
+            };
+            let provider = VectorQueryTable::try_new(ds.clone(), query_options.clone()).await?;
+            let extended = ctx.read_table(Arc::new(provider))?.collect().await?;
+            assert_eq!(pairs(&extended), pairs(&expected));
+            for batch in &extended {
+                let rowids = batch
+                    .column_by_name("row_id_a")
+                    .unwrap()
+                    .as_any()
+                    .downcast_ref::<UInt64Array>()
+                    .unwrap();
+                let ids = batch
+                    .column_by_name("id_a")
+                    .unwrap()
+                    .as_any()
+                    .downcast_ref::<Int64Array>()
+                    .unwrap();
+                for i in 0..batch.num_rows() {
+                    let expected_id = (rowids.value(i) >> 32) as i64 * n as i64
+                        + (rowids.value(i) & 0xffffffff) as i64;
+                    assert_eq!(ids.value(i), expected_id);
+                }
+            }
+            let mut sampled_options = query_options.clone();
+            sampled_options.selection = PartitionSelection::Random { count: 1, seed: 42 };
+            let sample = ctx
+                .read_table(Arc::new(
+                    VectorQueryTable::try_new(ds.clone(), sampled_options.clone()).await?,
+                ))?
+                .collect()
+                .await?;
+            let replay = ctx
+                .read_table(Arc::new(
+                    VectorQueryTable::try_new(ds.clone(), sampled_options).await?,
+                ))?
+                .collect()
+                .await?;
+            assert_eq!(sample, replay);
+            let scopes = sample
+                .iter()
+                .flat_map(|batch| {
+                    let segments = batch
+                        .column_by_name("segment_id")
+                        .unwrap()
+                        .as_any()
+                        .downcast_ref::<arrow_array::StringArray>()
+                        .unwrap();
+                    let partitions = batch
+                        .column_by_name("partition_id")
+                        .unwrap()
+                        .as_any()
+                        .downcast_ref::<UInt64Array>()
+                        .unwrap();
+                    (0..batch.num_rows())
+                        .map(|i| (segments.value(i).to_string(), partitions.value(i)))
+                        .collect::<Vec<_>>()
+                })
+                .collect::<std::collections::BTreeSet<_>>();
+            assert!(scopes.len() <= 1);
+            let mut dedup_options = query_options;
+            dedup_options.dedup = true;
+            dedup_options.output = DuplicatePairsOutput::default();
+            let clean_query = ctx
+                .read_table(Arc::new(
+                    VectorQueryTable::try_new(ds.clone(), dedup_options).await?,
+                ))?
+                .select(vec![datafusion::prelude::col("id")])?
+                .collect()
+                .await?;
+
             assert_eq!(
                 pairs(&actual).len(),
                 match threshold {
@@ -206,127 +289,6 @@ async fn sql_pairs_match_native_snapshot_and_partition_tasks() -> anyhow::Result
             );
             let retry = ctx.sql(&sql).await?.collect().await?;
             assert_eq!(pairs(&retry), pairs(&actual));
-            // The same table function can be maintained through the MV unit
-            // lifecycle. Staging attempts do not make any rows visible.
-            use lancedb::materialized_view::{
-                MaterializedViewDefinition, commit_partitioned_refresh, plan_partitioned_refresh,
-                prepare_definition, write_refresh_partition,
-            };
-            let view_name = format!("pairs_{}", threshold.to_bits());
-            let view = prepare_definition(&table, MaterializedViewDefinition::from_sql(&sql)?)
-                .await?
-                .create(&view_name)
-                .await?;
-            assert_eq!(view.table().schema().await?.fields().len(), 3);
-            let plan = plan_partitioned_refresh(view.table(), Some(version))
-                .await?
-                .unwrap();
-            assert_eq!(plan.units() as usize, tasks.len());
-            let plan = serde_json::from_slice(&serde_json::to_vec(&plan)?)?;
-            let first = write_refresh_partition(view.table(), 0, &plan).await?;
-            assert_eq!(view.table().count_rows(None).await?, 0);
-            assert!(
-                commit_partitioned_refresh(
-                    view.table(),
-                    &plan,
-                    vec![first.clone(), first.clone()],
-                    view.incarnation()
-                )
-                .await
-                .is_err()
-            );
-            // Lost receipt: its files remain unreferenced; the retry writes
-            // another attempt, of which only one receipt is ever committed.
-            drop(write_refresh_partition(view.table(), 0, &plan).await?);
-            let mut units = vec![first];
-            for unit in 1..plan.units() {
-                units.push(write_refresh_partition(view.table(), unit, &plan).await?);
-            }
-            assert_eq!(view.table().count_rows(None).await?, 0);
-            let outcome =
-                commit_partitioned_refresh(view.table(), &plan, units.clone(), view.incarnation())
-                    .await?;
-            assert_eq!(outcome.rows_written as usize, pairs(&expected).len());
-            assert!(
-                commit_partitioned_refresh(view.table(), &plan, units, view.incarnation())
-                    .await
-                    .is_err()
-            );
-            use lancedb::query::ExecutableQuery;
-            let written = view
-                .table()
-                .query()
-                .execute()
-                .await?
-                .try_collect::<Vec<_>>()
-                .await?;
-            assert_eq!(pairs(&written), pairs(&expected));
-            // Repeating a refresh replaces the view rather than appending.
-            view.refresh().execute().await?;
-            assert_eq!(
-                view.table().count_rows(None).await? as usize,
-                pairs(&expected).len()
-            );
-            // The complete declarative pipeline keeps direct representatives
-            // and materializes original rows, including isolated source rows.
-            use lancedb::materialized_view::write_refresh_partition_with_inputs;
-            use lancedb::query::QueryBase;
-            let definition = MaterializedViewDefinition::from_sql(&format!(
-                "SELECT * FROM vector_dedup('source', {version}, 'vector', {threshold})"
-            ))?;
-            let clean = prepare_definition(&table, definition)
-                .await?
-                .create(&format!("clean_{}", threshold.to_bits()))
-                .await?;
-            let clean_plan = plan_partitioned_refresh(clean.table(), Some(version))
-                .await?
-                .unwrap();
-            assert_eq!(clean_plan.dependency_units() as usize, tasks.len());
-            assert_eq!(clean_plan.units() as usize, tasks.len() + 2);
-            let clean_plan: lancedb::materialized_view::PartitionedRefreshPlan =
-                serde_json::from_slice(&serde_json::to_vec(&clean_plan)?)?;
-            assert!(
-                write_refresh_partition(clean.table(), tasks.len() as u32, &clean_plan)
-                    .await
-                    .is_err()
-            );
-            let mut receipts = Vec::new();
-            for unit in 0..clean_plan.units() {
-                let dependencies = &receipts[..receipts.len().min(tasks.len())];
-                if unit == 0 || unit == tasks.len() as u32 {
-                    // Both selection and materialization can lose a receipt.
-                    drop(
-                        write_refresh_partition_with_inputs(
-                            clean.table(),
-                            unit,
-                            &clean_plan,
-                            dependencies,
-                        )
-                        .await?,
-                    );
-                }
-                let receipt = write_refresh_partition_with_inputs(
-                    clean.table(),
-                    unit,
-                    &clean_plan,
-                    dependencies,
-                )
-                .await?;
-                receipts.push(serde_json::from_slice(&serde_json::to_vec(&receipt)?)?);
-            }
-            assert_eq!(clean.table().count_rows(None).await?, 0);
-            assert!(
-                commit_partitioned_refresh(
-                    clean.table(),
-                    &clean_plan,
-                    receipts[..receipts.len() - 1].to_vec(),
-                    clean.incarnation()
-                )
-                .await
-                .is_err()
-            );
-            commit_partitioned_refresh(clean.table(), &clean_plan, receipts, clean.incarnation())
-                .await?;
             let mut edges = pairs(&expected)
                 .into_iter()
                 .map(|(a, b, _)| (a.min(b), a.max(b)))
@@ -366,36 +328,26 @@ async fn sql_pairs_match_native_snapshot_and_partition_tasks() -> anyhow::Result
                     }
                 }
             }
-            let kept = clean
-                .table()
-                .query()
-                .select(lancedb::query::Select::columns(&["id"]))
-                .execute()
-                .await?
-                .try_collect::<Vec<_>>()
-                .await?;
-            let mut actual_ids = kept
+            expected_ids.sort_unstable();
+            let mut query_ids = clean_query
                 .iter()
-                .flat_map(|b| {
-                    b.column(0)
+                .flat_map(|batch| {
+                    batch
+                        .column(0)
                         .as_any()
                         .downcast_ref::<Int64Array>()
                         .unwrap()
                         .values()
-                        .iter()
-                        .copied()
+                        .to_vec()
                 })
                 .collect::<Vec<_>>();
-            actual_ids.sort_unstable();
-            expected_ids.sort_unstable();
-            assert_eq!(actual_ids, expected_ids);
+            query_ids.sort_unstable();
+            assert_eq!(query_ids, expected_ids);
+
             if threshold == 1.0 {
-                assert!(actual_ids.contains(&0) && actual_ids.contains(&2));
-                assert!(!actual_ids.contains(&1), "A-B-C retains A and C");
+                assert!(query_ids.contains(&0) && query_ids.contains(&2));
+                assert!(!query_ids.contains(&1), "A-B-C retains A and C");
             }
-            clean.refresh().execute().await?;
-            assert_eq!(clean.table().count_rows(None).await?, expected_ids.len());
-            assert_eq!(table.count_rows(None).await?, n * 2);
             assert!(
                 DuplicatePairsExec::try_new(
                     ds.clone(),
@@ -429,10 +381,8 @@ async fn sql_pairs_match_native_snapshot_and_partition_tasks() -> anyhow::Result
 }
 
 #[tokio::test]
-async fn dedup_materializes_blob_payloads_null_vectors_and_snapshot_deletions() -> anyhow::Result<()>
-{
+async fn dedup_query_materializes_blob_payloads_and_null_vectors() -> anyhow::Result<()> {
     use arrow_array::{LargeBinaryArray, StringArray, StructArray, types::Float32Type};
-    use lancedb::materialized_view::{MaterializedViewDefinition, prepare_definition};
     for stable in [false, true] {
         let dir = tempfile::tempdir()?;
         let conn = lancedb::connect(dir.path().to_str().unwrap())
@@ -509,29 +459,75 @@ async fn dedup_materializes_blob_payloads_null_vectors_and_snapshot_deletions()
             true,
         )
         .await?;
-        let version = ds.version().version;
-        let definition = MaterializedViewDefinition::from_sql(&format!(
-            "SELECT * FROM vector_dedup('source', {version}, 'vector', 1)"
-        ))?;
-        let clean = prepare_definition(&table, definition)
-            .await?
-            .create("clean")
+        use lancedb::table::datafusion::udtf::{
+            duplicate_pairs::DuplicatePairsOutput,
+            vector_query::{PartitionSelection, VectorQueryOptions, VectorQueryTable},
+        };
+        let options = VectorQueryOptions {
+            column: "vector".into(),
+            distance_threshold: 1.0,
+            selection: PartitionSelection::All,
+            output: DuplicatePairsOutput::default(),
+            dedup: true,
+        };
+        let mapped = VectorQueryTable::try_new(
+            Arc::new(ds.clone()),
+            VectorQueryOptions {
+                dedup: false,
+                output: DuplicatePairsOutput {
+                    scope: true,
+                    id_column: Some("id".into()),
+                },
+                ..options.clone()
+            },
+        )
+        .await?;
+        let mapped = SessionContext::new()
+            .read_table(Arc::new(mapped))?
+            .collect()
             .await?;
-        // A later source delete does not change the declared result snapshot.
-        table.delete("id = 0").await?;
-        clean.refresh().execute().await?;
-        assert_eq!(clean.table().count_rows(None).await?, 4);
-        let output = Dataset::open(&clean.table().uri().await?).await?;
-        let batches = output
-            .scan()
-            .blob_handling(lance_core::datatypes::BlobHandling::AllBinary)
-            .try_into_stream()
-            .await?
-            .try_collect::<Vec<_>>()
+        let mut mapped_edges = Vec::new();
+        for batch in mapped {
+            let a = batch
+                .column_by_name("id_a")
+                .unwrap()
+                .as_any()
+                .downcast_ref::<Int64Array>()
+                .unwrap();
+            let b = batch
+                .column_by_name("id_b")
+                .unwrap()
+                .as_any()
+                .downcast_ref::<Int64Array>()
+                .unwrap();
+            for i in 0..batch.num_rows() {
+                mapped_edges.push((a.value(i).min(b.value(i)), a.value(i).max(b.value(i))));
+            }
+        }
+        mapped_edges.sort_unstable();
+        assert_eq!(mapped_edges, [(0, 1), (1, 2)]);
+        let provider = VectorQueryTable::try_new(Arc::new(ds.clone()), options).await?;
+        let query_rows = SessionContext::new()
+            .read_table(Arc::new(provider))?
+            .collect()
             .await?;
-        let mut ids = Vec::new();
-        for batch in batches {
-            let row_ids = batch
+        let mut query_ids = query_rows
+            .iter()
+            .flat_map(|batch| {
+                batch
+                    .column_by_name("id")
+                    .unwrap()
+                    .as_any()
+                    .downcast_ref::<Int64Array>()
+                    .unwrap()
+                    .values()
+                    .to_vec()
+            })
+            .collect::<Vec<_>>();
+        query_ids.sort_unstable();
+        assert_eq!(query_ids, [0, 2, 3, 4]);
+        for batch in &query_rows {
+            let ids = batch
                 .column_by_name("id")
                 .unwrap()
                 .as_any()
@@ -544,18 +540,9 @@ async fn dedup_materializes_blob_payloads_null_vectors_and_snapshot_deletions()
                 .downcast_ref::<LargeBinaryArray>()
                 .unwrap();
             for i in 0..batch.num_rows() {
-                let id = row_ids.value(i);
-                assert_eq!(images.value(i), vec![id as u8; 4096]);
-                ids.push(id);
+                assert_eq!(images.value(i), vec![ids.value(i) as u8; 4096]);
             }
         }
-        ids.sort_unstable();
-        assert_eq!(ids, [0, 2, 3, 4]);
-        assert_eq!(
-            table.count_rows(None).await?,
-            4,
-            "refresh must not mutate the source"
-        );
     }
     Ok(())
 }
diff --git a/rust/lancedb/tests/first_class_function_slice2.rs b/rust/lancedb/tests/first_class_function_slice2.rs
index 14ceb511d..380766e03 100644
--- a/rust/lancedb/tests/first_class_function_slice2.rs
+++ b/rust/lancedb/tests/first_class_function_slice2.rs
@@ -97,13 +97,16 @@ async fn local_function_catalog_operations_return_stable_not_supported() {
     ))
     .unwrap();
 
-    let create_error = connection.create_function_async(request).await.unwrap_err();
+    let create_error = connection
+        .create_function_async(request, &[])
+        .await
+        .unwrap_err();
     let lookup_error = connection
-        .get_function("normalize_score", "1")
+        .get_function("normalize_score", "1", &[])
         .await
         .unwrap_err();
     let drop_error = connection
-        .drop_function("normalize_score", "1")
+        .drop_function("normalize_score", "1", &[])
         .await
         .unwrap_err();
     for error in [create_error, lookup_error, drop_error] {
diff --git a/rust/lancedb/tests/fixtures/first_class_functions/v1/remote_function_job.json b/rust/lancedb/tests/fixtures/first_class_functions/v1/remote_function_job.json
index c6bbd83e3..e07c29f98 100644
--- a/rust/lancedb/tests/fixtures/first_class_functions/v1/remote_function_job.json
+++ b/rust/lancedb/tests/fixtures/first_class_functions/v1/remote_function_job.json
@@ -23,7 +23,7 @@
         "nullable": false
       }
     },
-    "created_at": "2026-08-21T00:00:00Z",
+    "created_at_millis": 1787270400000,
     "image": {
       "manifest_digest": "sha256:7e22f815b6648e14f093a3979a8e5a2082fa773ebe1ec84b135cae7e84d6f8e6",
       "descriptor": {
diff --git a/rust/lancedb/tests/fixtures/first_class_functions/v1/remote_function_version.canonical.json b/rust/lancedb/tests/fixtures/first_class_functions/v1/remote_function_version.canonical.json
index 63f3c824e..44a84b17c 100644
--- a/rust/lancedb/tests/fixtures/first_class_functions/v1/remote_function_version.canonical.json
+++ b/rust/lancedb/tests/fixtures/first_class_functions/v1/remote_function_version.canonical.json
@@ -1 +1 @@
-{"created_at":"2026-08-21T00:00:00Z","disabled":false,"image":{"descriptor":{"behavior":{"result_stability":"input_and_context","side_effects":"non_idempotent"},"entrypoint":"app.function:create","format_version":1,"interface":{"type":"lance.scalar","version":1},"python":{"abi_tag":"cp312","executable":"/usr/local/bin/python3","implementation":"cpython","import_paths":["/opt/function/code"],"version":"3.12.14"},"python_api":1,"requires":{"capabilities":[],"kernel_min":"4.18.0"},"schemas":{"initialization":"/opt/function/schemas/initialization.arrow","input":"/opt/function/schemas/input.arrow","output":"/opt/function/schemas/output.arrow"}},"manifest_digest":"sha256:7e22f815b6648e14f093a3979a8e5a2082fa773ebe1ec84b135cae7e84d6f8e6","source":false},"location":"memory:///fixture","metadata":{},"name":"embed","object_id":"fixture","secret_bindings":[{"kind":"env","secret_ref":{"name":"hf-prod"},"variable":"HF_TOKEN"}],"signature":{"inputs":[{"arrow_type":"utf8","name":"text","nullable":true}],"output":{"arrow_type":"list<float32>","kind":"scalar","nullable":false}},"version":"1"}
+{"created_at_millis":1787270400000,"disabled":false,"image":{"descriptor":{"behavior":{"result_stability":"input_and_context","side_effects":"non_idempotent"},"entrypoint":"app.function:create","format_version":1,"interface":{"type":"lance.scalar","version":1},"python":{"abi_tag":"cp312","executable":"/usr/local/bin/python3","implementation":"cpython","import_paths":["/opt/function/code"],"version":"3.12.14"},"python_api":1,"requires":{"capabilities":[],"kernel_min":"4.18.0"},"schemas":{"initialization":"/opt/function/schemas/initialization.arrow","input":"/opt/function/schemas/input.arrow","output":"/opt/function/schemas/output.arrow"}},"manifest_digest":"sha256:7e22f815b6648e14f093a3979a8e5a2082fa773ebe1ec84b135cae7e84d6f8e6","source":false},"location":"memory:///fixture","metadata":{},"name":"embed","object_id":"fixture","secret_bindings":[{"kind":"env","secret_ref":{"name":"hf-prod"},"variable":"HF_TOKEN"}],"signature":{"inputs":[{"arrow_type":"utf8","name":"text","nullable":true}],"output":{"arrow_type":"list<float32>","kind":"scalar","nullable":false}},"version":"1"}