feat(node)!: require Node >= 22 and drop npm lockfiles (#4074)

The bindings are built, installed and published with pnpm everywhere,
but a parallel npm dependency graph was still being maintained beside
it. This removes it, raises the supported Node floor to the versions we
actually test, and gives Dependabot the npm coverage it was missing.

## Dropping npm

`nodejs/package-lock.json` was regenerated by `ci/update_lockfiles.sh`
on every release commit and read by nothing — no workflow runs `npm ci`
or `npm install` in `nodejs/`, and npm never publishes a lockfile in a
package tarball. It could not even agree with the real install, since
npm does not see pnpm's `overrides`. Because GitHub's dependency graph
parses `package-lock.json`, it was also reporting vulnerabilities for a
tree we neither install nor ship.

`docs/package.json`, `docs/package-lock.json` and `docs/tsconfig.json`
go too. They depend on `file:../node` and
`file:../node/node_modules/apache-arrow` — the `node/` directory was
removed long ago — the tsconfig compiles `src/*.ts` where no TypeScript
files exist, and nothing installs any of it. `docs.yml` only referenced
the lockfile to configure an npm cache for an install it never ran.

Two `workflow_dispatch` workflows for regenerating those lockfiles are
removed as well. Both were already broken: they `uses:` composite
actions at `.github/workflows/update_package_lock{,_nodejs}` that do not
exist, so dispatching either failed immediately.

The remaining `npx` calls become direct `node_modules/.bin/...`
invocations. These were already running locally installed binaries
rather than resolving anything, but naming the binary removes the npm
CLI from the loop and does not depend on which Node version is active.
`dev.yml`'s commitlint check was the last place doing real npm
dependency resolution — an unpinned `npm install
@commitlint/config-conventional` that also bypassed the
`minimumReleaseAge` hold configured for `nodejs/` — and is now a pinned
`pnpm dlx`.

## Node support

Node 18 and 20 both reached end-of-life, in April 2025 and April 2026.
The matrix moves to 22, 24 and 26, and `engines` rises from `>= 18` to
`>= 22` so the declared floor is one the matrix actually covers. Node 22
is LTS until April 2027; 24 is LTS; 26 is Current and becomes LTS in
October 2026.

This also removes the reason the workflows reached for `npx` in the
first place: pnpm 11 requires Node >= 22.13, which every matrix version
now satisfies.

The prebuilt-binary smoke test in `npm-publish.yml` moves from Node 20
to Node 22 — the floor, where a napi ABI problem would surface first —
rather than fanning out across all three, to keep the publish matrix
from tripling.

## Dependabot

There were no npm-ecosystem entries at all, which is why the advisories
behind #4073 went unnoticed. Both pnpm lockfiles are now watched —
`nodejs/` and `nodejs/examples/`, which is a separate install — using
the same `lockfile-only` strategy as the existing cargo and pip entries,
so version ranges in `package.json` are left alone.

## Pre-commit biome

The hook ran `npx @biomejs/biome@1.8.3` while `nodejs/package.json`
resolved 1.9.4. The two disagree about formatting, so the hook rejected
code that `pnpm lint` accepts, and failed on unmodified `main` for
anyone touching `nodejs/`. It now uses the pnpm-managed biome, which
fixes the drift with no source changes.

## Testing

`dev.yml`'s commitlint job does not check out the repo, so it runs in an
empty workspace, and I could not verify `pnpm/action-setup` there
locally. It triggers on `pull_request_target`, so this PR exercises it
directly — worth confirming green before merge. I did verify the `pnpm
dlx` invocation itself locally: it accepts a conventional title and
rejects a non-conventional one with exit 1.

Node 26 is new enough that the examples job may surface gaps in prebuilt
native binaries (`onnxruntime-node`, `sharp`) before their maintainers
publish for it.

## Not included

`nodejs/examples/` still pins `sharp: "0.33.5"` and has its own audit
findings. Raising the Node floor unblocks that work — sharp 0.35
requires Node >= 20.9, which the matrix now satisfies — but it is a
dependency bump rather than tooling cleanup, so it is left separate.

## Breaking changes

`@lancedb/lancedb` now requires Node >= 22; previously >= 18. The
`@types/node` peer range moves from `>=18` to `>=22` to match. Users on
Node 18 or 20 must upgrade their runtime; both have been end-of-life for
some time. Existing installs are unaffected, since `engines` is only
checked on install.

---------

Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.38.0-beta.6"
+current_version = "0.38.0-beta.12"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

.github/dependabot.yml

@@ -44,3 +44,27 @@ updates:
       python-deps:
         patterns:
           - "*"
+
+  # The npm ecosystem covers pnpm lockfiles. There are two separate installs:
+  # the bindings themselves and the examples, which have their own lockfile.
+  # As with cargo and pip above, only bump the lockfile — the version ranges
+  # in package.json are our consumers' constraints, not ours.
+  - package-ecosystem: npm
+    directory: /nodejs
+    schedule:
+      interval: weekly
+    versioning-strategy: lockfile-only
+    groups:
+      nodejs-deps:
+        patterns:
+          - "*"
+
+  - package-ecosystem: npm
+    directory: /nodejs/examples
+    schedule:
+      interval: weekly
+    versioning-strategy: lockfile-only
+    groups:
+      nodejs-examples-deps:
+        patterns:
+          - "*"

.github/workflows/dev.yml

@@ -29,12 +29,14 @@ jobs:
     steps:
       - uses: actions/setup-node@v6
         with:
-          node-version: "18"
+          node-version: "24"
+      - uses: pnpm/action-setup@v6
+        with:
+          version: 11.1.1
       # These rules are disabled because Github will always ensure there
       # is a blank line between the title and the body and Github will
       # word wrap the description field to ensure a reasonable max line
       # length.
-      - run: npm install @commitlint/config-conventional
       - run: >
           echo 'module.exports = {
             "rules": {
@@ -43,7 +45,11 @@ jobs:
               "body-leading-blank": [0, "always"]
             }
           }' > .commitlintrc.js
-      - run: npx commitlint --extends @commitlint/config-conventional --verbose <<< $COMMIT_MSG
+      - run: >
+          pnpm dlx
+          --package @commitlint/cli@21.2.2
+          --package @commitlint/config-conventional@21.2.2
+          commitlint --extends @commitlint/config-conventional --verbose <<< $COMMIT_MSG
         env:
           COMMIT_MSG: >
             ${{ github.event.pull_request.title }}
@@ -54,7 +60,7 @@ jobs:
         with:
           script: |
             const message = `**ACTION NEEDED**
-              
+
               Lance follows the [Conventional Commits specification](https://www.conventionalcommits.org/en/v1.0.0/) for release automation.
 
               The PR title and description are used as the merge commit message.\

.github/workflows/docs-link-check.yml

@@ -56,7 +56,7 @@ jobs:
         uses: lycheeverse/lychee-action@e7477775783ea5526144ba13e8db5eec57747ce8 # v2.9.0
         with:
           # Restricted to http(s) on purpose. Much of docs/src is generated
-          # API reference (the js/ tree comes from `npm run docs` in nodejs)
+          # API reference (the js/ tree comes from `pnpm run docs` in nodejs)
           # and the hand-written pages use mkdocstrings cross-references and
           # nav-relative paths that only resolve in the site mkdocs builds,
           # not in this checkout, so relative links would be reported as

.github/workflows/docs.yml

@@ -55,9 +55,7 @@ jobs:
       - name: Set up node
         uses: actions/setup-node@v6
         with:
-          node-version: 20
-          cache: 'npm'
-          cache-dependency-path: docs/package-lock.json
+          node-version: 24
       - name: Install node dependencies
         working-directory: nodejs
         run: |

.github/workflows/nodejs.yml

@@ -47,9 +47,8 @@ jobs:
         version: 11.1.1
     - uses: actions/setup-node@v6
       with:
-        # pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
-        # in October. The library itself still supports Node >= 18
-        # (see test matrix below).
+        # Build on a supported LTS; the matrix job below covers every
+        # Node version the library claims to support.
         node-version: 24
         cache: 'pnpm'
         cache-dependency-path: nodejs/pnpm-lock.yaml
@@ -84,7 +83,7 @@ jobs:
     timeout-minutes: 30
     strategy:
       matrix:
-        node-version: [ "18", "20" ]
+        node-version: [ "22", "24", "26" ]
     runs-on: "ubuntu-22.04"
     defaults:
       run:
@@ -101,9 +100,9 @@ jobs:
     - uses: actions/setup-node@v6
       name: Setup Node.js 24 for build
       with:
-        # pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
-        # in October. Build/install runs on Node 24; tests run on the
-        # matrix version below using direct jest invocation.
+        # Build and install once on a fixed version so the generated docs
+        # are identical across matrix legs; the tests below then run on each
+        # supported Node version.
         node-version: 24
         cache: 'pnpm'
         cache-dependency-path: nodejs/pnpm-lock.yaml
@@ -152,9 +151,9 @@ jobs:
         S3_TEST: "1"
         # Newer @smithy/core uses dynamic ESM imports.
         NODE_OPTIONS: "--experimental-vm-modules"
-      # Invoke jest directly because pnpm 11 itself requires Node 22+
-      # while the matrix tests on older Node versions.
-      run: npx jest --verbose
+      # Invoke the installed jest binary directly; the pnpm shim is set up
+      # against the build-phase Node, not the version selected above.
+      run: node_modules/.bin/jest --verbose
     - name: Test examples
       working-directory: ./
       env:
@@ -164,7 +163,7 @@ jobs:
       run: |
         python ci/mock_openai.py &
         cd nodejs/examples
-        npx jest --testEnvironment jest-environment-node-single-context --verbose
+        node_modules/.bin/jest --testEnvironment jest-environment-node-single-context --verbose
   macos:
     timeout-minutes: 30
     # macos-15 ships a newer linker; the older macos-14 linker fails to insert
@@ -185,8 +184,7 @@ jobs:
         version: 11.1.1
     - uses: actions/setup-node@v6
       with:
-        # pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
-        # in October.
+        # pnpm 11 requires Node >= 22.13.
         node-version: 24
         cache: 'pnpm'
         cache-dependency-path: nodejs/pnpm-lock.yaml

.github/workflows/npm-publish.yml

@@ -40,40 +40,31 @@ jobs:
           - target: aarch64-apple-darwin
             host: macos-latest
             features: fp16kernels
+            # Fat LTO was ~111 of this job's ~113 minutes.
+            lto: thin
+            codegen_units: 16
             pre_build: |-
               brew install protobuf
-              # Fat LTO (the workspace default in .cargo/config.toml) is
-              # single-threaded and is the peak-memory step of the build. On
-              # this runner it accounted for ~111 of the job's ~113 minutes,
-              # making it the critical path of the entire publish pipeline.
-              # ThinLTO parallelizes it across the runner's cores, for a few
-              # percent of runtime performance.
-              export CARGO_PROFILE_RELEASE_LTO=thin
-              export CARGO_PROFILE_RELEASE_CODEGEN_UNITS=16
           - target: x86_64-pc-windows-msvc
             host: windows-2025
             features: ","
+            # The lower peak also keeps this on the standard 4-core runner.
+            lto: thin
+            codegen_units: 16
             pre_build: |-
               choco install --no-progress protoc ninja nasm
               tail -n 1000 /c/ProgramData/chocolatey/logs/chocolatey.log
               # There is an issue where choco doesn't add nasm to the path
               export PATH="$PATH:/c/Program Files/NASM"
               nasm -v
-              # See the ThinLTO note on aarch64-apple-darwin above. Keeping
-              # peak memory down is also what lets this run on the standard
-              # 4-core runner: the 8-core larger runner was only needed to
-              # stop fat LTO from OOMing rustc-LLVM.
-              export CARGO_PROFILE_RELEASE_LTO=thin
-              export CARGO_PROFILE_RELEASE_CODEGEN_UNITS=16
           - target: aarch64-pc-windows-msvc
             host: windows-2025
             features: ","
+            lto: thin
+            codegen_units: 16
             pre_build: |-
                 choco install --no-progress protoc
                 rustup target add aarch64-pc-windows-msvc
-                # See the ThinLTO note on aarch64-apple-darwin above.
-                export CARGO_PROFILE_RELEASE_LTO=thin
-                export CARGO_PROFILE_RELEASE_CODEGEN_UNITS=16
           - target: x86_64-unknown-linux-gnu
             host: ubuntu-latest
             features: fp16kernels
@@ -103,6 +94,14 @@ jobs:
             # https://github.com/napi-rs/napi-rs/blob/main/debian-aarch64.Dockerfile
             docker: ghcr.io/napi-rs/napi-rs/nodejs-rust:lts-debian-aarch64
             features: "fp16kernels"
+            # Fat LTO OOM-killed rustc every nightly; even with lld it peaked
+            # at 31391 MiB of the runner's 32 GiB.
+            lto: thin
+            codegen_units: 16
+            # arm64 Linux links through GNU `ld` where x86_64 defaults to
+            # `rust-lld`, which is why only arm64 OOM'd. lld cut the largest
+            # linker process 7.0 -> 4.0 GiB (lancedb/sophon#7313).
+            linker: /tmp/aarch64-lld-clang
             pre_build: |-
               set -e &&
               apt-get update &&
@@ -112,9 +111,30 @@ jobs:
               # AT_HWCAP2 (added in Linux 3.17). Define it for aws-lc-sys.
               export CFLAGS="$CFLAGS -DAT_HWCAP2=26" &&
               rustup target add aarch64-unknown-linux-gnu
+              # Not `&&`-chained: in dash, errexit does not fire for a
+              # non-final command in an `&&` list, so failures were ignored.
+              #
+              # A wrapper rather than `-C link-arg` because the per-target
+              # rustflags variable does not reach every unit that links, while
+              # the linker variable does. `clang` because GCC silently ignores
+              # `-fuse-ld=lld` unless built with lld support. Two echoes
+              # because printf's newline escape gets rewritten to `;` between
+              # here and the container.
+              echo '#!/bin/sh' > /tmp/aarch64-lld-clang
+              echo 'exec clang --target=aarch64-unknown-linux-gnu --sysroot=/usr/aarch64-unknown-linux-gnu/aarch64-unknown-linux-gnu/sysroot --gcc-toolchain=/usr/aarch64-unknown-linux-gnu -fuse-ld=lld "$@"' >> /tmp/aarch64-lld-clang
+              chmod 0755 /tmp/aarch64-lld-clang
+              # Fail now, not at the cdylib link ~30 minutes later. Linking at
+              # all also proves lld resolved; clang errors out when it cannot.
+              echo 'int main(void){return 0;}' > /tmp/probe.c
+              /tmp/aarch64-lld-clang /tmp/probe.c -o /tmp/probe
+              readelf -h /tmp/probe | grep AArch64
           - target: aarch64-unknown-linux-musl
             host: ubuntu-2404-8x-x64
             features: ","
+            # Fat LTO took the whole runner down. lld cannot help: it died
+            # inside rustc's LLVM, before any linker was spawned.
+            lto: thin
+            codegen_units: 16
             pre_build: |-
               set -e &&
               sudo apt-get update &&
@@ -123,6 +143,19 @@ jobs:
               export EXTRA_ARGS="-x"
     name: build - ${{ matrix.settings.target }}
     runs-on: ${{ matrix.settings.host }}
+    # On the job, not exported from `pre_build`: `Swatinem/rust-cache` hashes
+    # `CARGO_*` into its cache key before any step runs, so a step-local export
+    # leaves the key unchanged while cargo still rebuilds cold. The ThinLTO
+    # legs had been doing that every run.
+    #
+    # Not `RUSTFLAGS`: setting it, even to "", discards every config-file
+    # rustflag, silently dropping .cargo/config.toml's `target-cpu` and
+    # `target-feature` from the published binaries.
+    env:
+      CARGO_PROFILE_RELEASE_LTO: ${{ matrix.settings.lto || 'fat' }}
+      CARGO_PROFILE_RELEASE_CODEGEN_UNITS: ${{ matrix.settings.codegen_units || '1' }}
+      # Empty elsewhere: a per-target variable is only read for that triple.
+      CARGO_TARGET_AARCH64_UNKNOWN_LINUX_GNU_LINKER: ${{ matrix.settings.linker }}
     defaults:
       run:
         working-directory: nodejs
@@ -135,8 +168,7 @@ jobs:
       - name: Setup node
         uses: actions/setup-node@v6
         with:
-          # pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
-          # in October.
+          # pnpm 11 requires Node >= 22.13.
           node-version: 24
           cache: pnpm
           cache-dependency-path: nodejs/pnpm-lock.yaml
@@ -169,19 +201,15 @@ jobs:
           # creating ref). The nightly cadence also keeps entries inside
           # GitHub's 7-day eviction window, which a tag-only trigger would not.
           save-if: ${{ github.ref == 'refs/heads/main' }}
-      # Docker builds can use rust-cache too. `target/` already lives on the
-      # host because the whole workspace is bind-mounted into the container, and
-      # rust-cache's prune and save run host-side, so they can manage it -- which
-      # is what keeps the entry to dependency artifacts rather than a multi-GB
-      # copy of everything.
+      # Docker builds can use rust-cache too: the workspace is bind-mounted, so
+      # `target/` lives on the host and rust-cache's prune keeps the entry
+      # small.
       #
       # Two differences from the native builds. The container's CARGO_HOME is
-      # bind-mounted from `.cargo-cache` rather than the host's ~/.cargo, so that
-      # has to be cached explicitly. And the key is derived from the *host* rustc
-      # version, which is not the compiler that produced these artifacts; that is
-      # safe because cargo fingerprints the real compiler and rebuilds on a
-      # mismatch, it just means a base-image toolchain bump costs one cold build
-      # instead of invalidating the key.
+      # bind-mounted from `.cargo-cache` rather than ~/.cargo, so that is cached
+      # explicitly. And the key uses the *host* rustc version, not the compiler
+      # that built these artifacts -- safe, since cargo fingerprints the real
+      # one; a base-image bump just costs one cold build.
       - name: Cache cargo (docker builds)
         uses: Swatinem/rust-cache@v2
         if: ${{ matrix.settings.docker }}
@@ -210,14 +238,19 @@ jobs:
           # cache step above saves. Previously the registry mounts pointed at
           # `.cargo/...`, a path nothing cached, so the container re-downloaded
           # the whole crate registry on every run.
+          #
+          # `docker run` inherits nothing; `-e NAME` carries the job's `env:` in.
           options: "--user 0:0 -v ${{ github.workspace }}/.cargo-cache/git/db:/usr/local/cargo/git/db \
                     -v ${{ github.workspace }}/.cargo-cache/registry/cache:/usr/local/cargo/registry/cache \
                     -v ${{ github.workspace }}/.cargo-cache/registry/index:/usr/local/cargo/registry/index \
+                    -e CARGO_PROFILE_RELEASE_LTO \
+                    -e CARGO_PROFILE_RELEASE_CODEGEN_UNITS \
+                    -e CARGO_TARGET_AARCH64_UNKNOWN_LINUX_GNU_LINKER \
                     -v ${{ github.workspace }}:/build -w /build/nodejs"
           run: |
             set -e
             ${{ matrix.settings.pre_build }}
-            npx napi build --platform --release \
+            node_modules/.bin/napi build --platform --release \
               --features ${{ matrix.settings.features }} \
               --target ${{ matrix.settings.target }} \
               --dts ../lancedb/native.d.ts \
@@ -237,7 +270,7 @@ jobs:
       - name: Build
         run: |
           ${{ matrix.settings.pre_build }}
-          npx napi build --platform --release \
+          node_modules/.bin/napi build --platform --release \
               --features ${{ matrix.settings.features }} \
               --target ${{ matrix.settings.target }} \
               --dts ../lancedb/native.d.ts \
@@ -256,6 +289,18 @@ jobs:
         if: always()
         run: df -h
         shell: bash
+      - name: Report peak memory
+        if: always() && runner.os == 'Linux'
+        shell: bash
+        run: |
+          peak=$(find /sys/fs/cgroup -name memory.peak -readable \
+                 -exec cat {} + 2>/dev/null | sort -n | tail -1)
+          if [ -n "$peak" ]; then
+            echo "peak memory: $((peak / 1024 / 1024)) MiB"
+          else
+            echo "peak memory: unavailable (no readable cgroup v2 memory.peak)"
+          fi
+          free -g || true
       - name: Upload artifact
         uses: actions/upload-artifact@v7
         with:
@@ -293,7 +338,7 @@ jobs:
           - target: aarch64-unknown-linux-gnu
             host: ubuntu-2404-8x-arm64
         node:
-          - '20'
+          - '22'
     runs-on: ${{ matrix.settings.host }}
     defaults:
       run:
@@ -339,9 +384,9 @@ jobs:
       - name: Move built files
         run: cp dist/native.d.ts dist/native.js dist/*.node lancedb/
       - name: Test bindings
-        # Invoke jest directly because pnpm 11 itself requires Node 22+
-        # while the matrix tests on older Node versions.
-        run: npx jest --verbose
+        # Invoke the installed jest binary directly; the pnpm shim is set up
+        # against the install-phase Node, not the version selected above.
+        run: node_modules/.bin/jest --verbose
   publish:
     name: Publish
     runs-on: ubuntu-latest

.github/workflows/update_package_lock_run.yml

@@ -1,22 +0,0 @@
-name: Update package-lock.json
-
-on:
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-jobs:
-  publish:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v6
-        with:
-          ref: main
-          persist-credentials: false
-          fetch-depth: 0
-          lfs: true
-      - uses: ./.github/workflows/update_package_lock
-        with:
-          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}

.github/workflows/update_package_lock_run_nodejs.yml

@@ -1,22 +0,0 @@
-name: Update NodeJs package-lock.json
-
-on:
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-jobs:
-  publish:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v6
-        with:
-          ref: main
-          persist-credentials: false
-          fetch-depth: 0
-          lfs: true
-      - uses: ./.github/workflows/update_package_lock_nodejs
-        with:
-          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}

.pre-commit-config.yaml

@@ -20,7 +20,10 @@ repos:
     hooks:
       - id: local-biome-check
         name: biome check
-        entry: npx @biomejs/biome@1.8.3 check --config-path nodejs/biome.json nodejs/
+        # Use the biome from nodejs/package.json rather than a separately
+        # pinned one: the two drifted apart and disagreed on formatting, so
+        # this hook rejected code that `pnpm lint` accepted.
+        entry: nodejs/node_modules/.bin/biome check --config-path nodejs/biome.json nodejs/
         language: system
         types: [text]
         files: "nodejs/.*"

AGENTS.md

@@ -38,7 +38,7 @@ Before committing changes, run formatting for every language you touched. At min
 * Rust changes: run `cargo fmt --all`.
 * Python changes: run `ruff format .` and `ruff check .` from the repository root,
   and run targeted tests through `cd python && uv run ...`.
-* TypeScript changes: run the relevant `npm`/`pnpm` lint, format, build, and docs commands in `nodejs`.
+* TypeScript changes: run the relevant `pnpm` lint, format, build, and docs commands in `nodejs`.
 
 Before creating a PR, the exact value passed to `gh pr create --title` must follow
 Conventional Commits, such as `fix: support nested field paths in native index creation`
@@ -101,12 +101,12 @@ Python bindings changes:
 TypeScript bindings changes:
 
 1. Add napi-rs method binding on `Table` in `nodejs/src/table.rs`.
-2. Run `npm run build` to generate TypeScript definitions.
+2. Run `pnpm build` to generate TypeScript definitions.
 3. Add typescript method on abstract class `Table` in `nodejs/src/table.ts`.
 4. Add concrete method on `LocalTable` class in `nodejs/src/native_table.ts`.
     * Note: despite the name, this class is also used for remote tables.
 5. Add test in `nodejs/__test__/table.test.ts`.
-6. Run `npm run docs` to generate TypeScript documentation.
+6. Run `pnpm run docs` to generate TypeScript documentation.
 
 ## Python API reference
 

Cargo.lock

@@ -1597,9 +1597,9 @@ checksum = "613afe47fcd5fac7ccf1db93babcb082c5994d996f20b8b159f2ad1658eb5724"
 
 [[package]]
 name = "chacha20"
-version = "0.10.0"
+version = "0.10.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6f8d983286843e49675a4b7a2d174efe136dc93a18d69130dd18198a6c167601"
+checksum = "65c35e4b699c7e15ccbe7ee35c005e4fc0a278d22238a2857e6ce2dadeda1b06"
 dependencies = [
  "cfg-if 1.0.4",
  "cpufeatures 0.3.0",
@@ -3455,8 +3455,8 @@ checksum = "42703706b716c37f96a77aea830392ad231f44c9e9a67872fa5548707e11b11c"
 
 [[package]]
 name = "fsst"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrow-array",
  "rand 0.9.5",
@@ -4815,8 +4815,8 @@ checksum = "e037a2e1d8d5fdbd49b16a4ea09d5d6401c1f29eca5ff29d03d3824dba16256a"
 
 [[package]]
 name = "lance"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arc-swap",
  "arrow",
@@ -4888,8 +4888,8 @@ dependencies = [
 
 [[package]]
 name = "lance-arrow"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrow-array",
  "arrow-buffer",
@@ -4911,7 +4911,7 @@ dependencies = [
 [[package]]
 name = "lance-arrow-scalar"
 version = "58.0.0"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrow-array",
  "arrow-buffer",
@@ -4925,7 +4925,7 @@ dependencies = [
 [[package]]
 name = "lance-arrow-stats"
 version = "58.0.0"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrow-array",
  "arrow-schema",
@@ -4934,8 +4934,8 @@ dependencies = [
 
 [[package]]
 name = "lance-bitpacking"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrayref",
  "crunchy",
@@ -4945,8 +4945,8 @@ dependencies = [
 
 [[package]]
 name = "lance-core"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrow-array",
  "arrow-buffer",
@@ -4983,8 +4983,8 @@ dependencies = [
 
 [[package]]
 name = "lance-datafusion"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrow",
  "arrow-array",
@@ -5013,8 +5013,8 @@ dependencies = [
 
 [[package]]
 name = "lance-datagen"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrow",
  "arrow-array",
@@ -5031,8 +5031,8 @@ dependencies = [
 
 [[package]]
 name = "lance-derive"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -5041,8 +5041,8 @@ dependencies = [
 
 [[package]]
 name = "lance-encoding"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrow-arith",
  "arrow-array",
@@ -5075,8 +5075,8 @@ dependencies = [
 
 [[package]]
 name = "lance-file"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrow-arith",
  "arrow-array",
@@ -5107,8 +5107,8 @@ dependencies = [
 
 [[package]]
 name = "lance-index"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arc-swap",
  "arrow",
@@ -5172,8 +5172,8 @@ dependencies = [
 
 [[package]]
 name = "lance-index-core"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrow-array",
  "arrow-schema",
@@ -5195,8 +5195,8 @@ dependencies = [
 
 [[package]]
 name = "lance-io"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrow",
  "arrow-array",
@@ -5236,8 +5236,8 @@ dependencies = [
 
 [[package]]
 name = "lance-linalg"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrow-array",
  "arrow-schema",
@@ -5251,8 +5251,8 @@ dependencies = [
 
 [[package]]
 name = "lance-namespace"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrow",
  "async-trait",
@@ -5264,8 +5264,8 @@ dependencies = [
 
 [[package]]
 name = "lance-namespace-impls"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrow",
  "arrow-ipc",
@@ -5318,8 +5318,8 @@ dependencies = [
 
 [[package]]
 name = "lance-select"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrow-array",
  "arrow-buffer",
@@ -5333,8 +5333,8 @@ dependencies = [
 
 [[package]]
 name = "lance-table"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrow",
  "arrow-array",
@@ -5374,8 +5374,8 @@ dependencies = [
 
 [[package]]
 name = "lance-testing"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "arrow-array",
  "arrow-schema",
@@ -5388,8 +5388,8 @@ dependencies = [
 
 [[package]]
 name = "lance-tokenizer"
-version = "11.0.0-rc.1"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-rc.1#308c00db4f4dae6b2ea4a1d1d2ceb1c3ed88a159"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
  "frostem",
  "icu_segmenter",
@@ -5402,7 +5402,7 @@ dependencies = [
 
 [[package]]
 name = "lancedb"
-version = "0.38.0-beta.6"
+version = "0.38.0-beta.12"
 dependencies = [
  "ahash",
  "anyhow",
@@ -5490,7 +5490,7 @@ dependencies = [
 
 [[package]]
 name = "lancedb-nodejs"
-version = "0.38.0-beta.6"
+version = "0.38.0-beta.12"
 dependencies = [
  "arrow-array",
  "arrow-buffer",
@@ -5515,7 +5515,7 @@ dependencies = [
 
 [[package]]
 name = "lancedb-python"
-version = "0.38.0-beta.6"
+version = "0.38.0-beta.12"
 dependencies = [
  "arrow",
  "async-trait",

Cargo.toml

@@ -13,20 +13,20 @@ categories = ["database-implementations"]
 rust-version = "1.91.0"
 
 [workspace.dependencies]
-lance = { "version" = "=11.0.0-rc.1", default-features = false, "tag" = "v11.0.0-rc.1", "git" = "https://github.com/lance-format/lance.git" }
-lance-core = { "version" = "=11.0.0-rc.1", "tag" = "v11.0.0-rc.1", "git" = "https://github.com/lance-format/lance.git" }
-lance-datagen = { "version" = "=11.0.0-rc.1", "tag" = "v11.0.0-rc.1", "git" = "https://github.com/lance-format/lance.git" }
-lance-file = { "version" = "=11.0.0-rc.1", "tag" = "v11.0.0-rc.1", "git" = "https://github.com/lance-format/lance.git" }
-lance-io = { "version" = "=11.0.0-rc.1", default-features = false, "tag" = "v11.0.0-rc.1", "git" = "https://github.com/lance-format/lance.git" }
-lance-index = { "version" = "=11.0.0-rc.1", "tag" = "v11.0.0-rc.1", "git" = "https://github.com/lance-format/lance.git" }
-lance-linalg = { "version" = "=11.0.0-rc.1", "tag" = "v11.0.0-rc.1", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace = { "version" = "=11.0.0-rc.1", "tag" = "v11.0.0-rc.1", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace-impls = { "version" = "=11.0.0-rc.1", default-features = false, "tag" = "v11.0.0-rc.1", "git" = "https://github.com/lance-format/lance.git" }
-lance-table = { "version" = "=11.0.0-rc.1", "tag" = "v11.0.0-rc.1", "git" = "https://github.com/lance-format/lance.git" }
-lance-testing = { "version" = "=11.0.0-rc.1", "tag" = "v11.0.0-rc.1", "git" = "https://github.com/lance-format/lance.git" }
-lance-datafusion = { "version" = "=11.0.0-rc.1", "tag" = "v11.0.0-rc.1", "git" = "https://github.com/lance-format/lance.git" }
-lance-encoding = { "version" = "=11.0.0-rc.1", "tag" = "v11.0.0-rc.1", "git" = "https://github.com/lance-format/lance.git" }
-lance-arrow = { "version" = "=11.0.0-rc.1", "tag" = "v11.0.0-rc.1", "git" = "https://github.com/lance-format/lance.git" }
+lance = { "version" = "=12.0.0-beta.2", default-features = false, "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-core = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-datagen = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-file = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-io = { "version" = "=12.0.0-beta.2", default-features = false, "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-index = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-linalg = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace-impls = { "version" = "=12.0.0-beta.2", default-features = false, "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-table = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-testing = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-datafusion = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-encoding = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-arrow = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "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

Makefile

@@ -5,5 +5,5 @@ licenses:
 	cd python && cargo about generate ../about.hbs -o RUST_THIRD_PARTY_LICENSES.html -c ../about.toml
 	cd python && uv sync --all-extras && uv tool run pip-licenses --python .venv/bin/python --format=markdown --with-urls --output-file=PYTHON_THIRD_PARTY_LICENSES.md
 	cd nodejs && cargo about generate ../about.hbs -o RUST_THIRD_PARTY_LICENSES.html -c ../about.toml
-	cd nodejs && npx license-checker --markdown --out NODEJS_THIRD_PARTY_LICENSES.md
+	cd nodejs && pnpm dlx license-checker@25 --markdown --out NODEJS_THIRD_PARTY_LICENSES.md
 	cd java && ./mvnw license:aggregate-add-third-party -q

ci/update_lockfiles.sh

@@ -12,16 +12,12 @@ done
 # This updates the lockfile without building
 cargo metadata --quiet > /dev/null
 
-pushd nodejs || exit 1
-npm install --package-lock-only --silent
-popd
-
 if git diff --quiet --exit-code; then
   echo "No lockfile changes to commit; skipping amend."
 elif $AMEND; then
-  git add Cargo.lock nodejs/package-lock.json
+  git add Cargo.lock
   git commit --amend --no-edit
 else
-  git add Cargo.lock nodejs/package-lock.json
+  git add Cargo.lock
   git commit -m "Update lockfiles"
 fi

deny.toml

@@ -131,18 +131,13 @@ allow = [
     "BSD-3-Clause",
     "ISC",
     "Unicode-3.0",
-    "Unicode-DFS-2016",
     "Zlib",
     "CC0-1.0",
     "MPL-2.0",
     "BSL-1.0",
-    "OpenSSL",
     # 0BSD ("BSD Zero Clause") is effectively public domain — no attribution
     # required. Pulled in by `mock_instant`.
     "0BSD",
-    # bzip2-1.0.6 is the permissive upstream bzip2 license (BSD-like). Pulled
-    # in by `libbz2-rs-sys`, the pure-Rust bzip2 implementation.
-    "bzip2-1.0.6",
     # CDLA-Permissive-2.0 is a permissive data license used by `webpki-roots`
     # for the Mozilla CA root bundle. Data-only, distribution-compatible.
     "CDLA-Permissive-2.0",
@@ -150,12 +145,7 @@ allow = [
 confidence-threshold = 0.8
 # Per-crate license exceptions: allow a license for a specific crate only,
 # rather than globally via the `allow` list above.
-exceptions = [
-    # CDDL-1.0 (copyleft) is pulled in only as a dev/profiling dependency via
-    # `inferno` -> `pprof` -> `lance-testing`; it is a test dependency that we
-    # do not distribute, so scope the allowance to `inferno` alone.
-    { allow = ["CDDL-1.0"], crate = "inferno" },
-]
+exceptions = []
 # Crates whose license cannot be determined from Cargo metadata but whose
 # license we've manually confirmed from upstream. Keep this list minimal.
 [[licenses.clarify]]

docs/README.md

@@ -47,22 +47,24 @@ pytest -vv python/tests/docs
 
 ### Checking typescript examples
 
-The `@lancedb/lancedb` package must be built before running the tests:
+The examples depend on `@lancedb/lancedb` at `file:../dist`, so the package must be
+built before running the tests. This uses pnpm; see the
+[Typescript contributing guide](../nodejs/CONTRIBUTING.md) for the toolchain setup.
 
 ```shell
 pushd nodejs
-npm ci
-npm run build
+pnpm install
+pnpm build
 popd
 ```
 
-Then you can run the examples by going to the `nodejs/examples` directory and
-running the tests like a normal npm package:
+Then you can run the examples by going to the `nodejs/examples` directory, which is a
+separate pnpm package with its own lockfile:
 
 ```shell
 pushd nodejs/examples
-npm ci
-npm test
+pnpm install
+pnpm test
 popd
 ```
 
@@ -84,6 +86,7 @@ The new files should be checked into the repository.
 
 ```shell
 pushd nodejs
-npm run docs
+# `pnpm docs` would invoke pnpm's built-in `docs` command, not the script.
+pnpm run docs
 popd
 ```

docs/package-lock.json

@@ -1,135 +0,0 @@
-{
-  "name": "lancedb-docs-test",
-  "version": "1.0.0",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {
-    "": {
-      "name": "lancedb-docs-test",
-      "version": "1.0.0",
-      "license": "Apache 2",
-      "dependencies": {
-        "apache-arrow": "file:../node/node_modules/apache-arrow",
-        "vectordb": "file:../node"
-      },
-      "devDependencies": {
-        "@types/node": "^20.11.8",
-        "typescript": "^5.3.3"
-      }
-    },
-    "../node": {
-      "name": "vectordb",
-      "version": "0.21.2-beta.0",
-      "cpu": [
-        "x64",
-        "arm64"
-      ],
-      "license": "Apache-2.0",
-      "os": [
-        "darwin",
-        "linux",
-        "win32"
-      ],
-      "dependencies": {
-        "@neon-rs/load": "^0.0.74",
-        "axios": "^1.4.0"
-      },
-      "devDependencies": {
-        "@neon-rs/cli": "^0.0.160",
-        "@types/chai": "^4.3.4",
-        "@types/chai-as-promised": "^7.1.5",
-        "@types/mocha": "^10.0.1",
-        "@types/node": "^18.16.2",
-        "@types/sinon": "^10.0.15",
-        "@types/temp": "^0.9.1",
-        "@types/uuid": "^9.0.3",
-        "@typescript-eslint/eslint-plugin": "^5.59.1",
-        "apache-arrow-old": "npm:apache-arrow@13.0.0",
-        "cargo-cp-artifact": "^0.1",
-        "chai": "^4.3.7",
-        "chai-as-promised": "^7.1.1",
-        "eslint": "^8.39.0",
-        "eslint-config-standard-with-typescript": "^34.0.1",
-        "eslint-plugin-import": "^2.26.0",
-        "eslint-plugin-n": "^15.7.0",
-        "eslint-plugin-promise": "^6.1.1",
-        "mocha": "^10.2.0",
-        "openai": "^4.24.1",
-        "sinon": "^15.1.0",
-        "temp": "^0.9.4",
-        "ts-node": "^10.9.1",
-        "ts-node-dev": "^2.0.0",
-        "typedoc": "^0.24.7",
-        "typedoc-plugin-markdown": "^3.15.3",
-        "typescript": "^5.1.0",
-        "uuid": "^9.0.0"
-      },
-      "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.21.2-beta.0",
-        "@lancedb/vectordb-darwin-x64": "0.21.2-beta.0",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.21.2-beta.0",
-        "@lancedb/vectordb-linux-x64-gnu": "0.21.2-beta.0",
-        "@lancedb/vectordb-win32-x64-msvc": "0.21.2-beta.0"
-      },
-      "peerDependencies": {
-        "@apache-arrow/ts": "^14.0.2",
-        "apache-arrow": "^14.0.2"
-      }
-    },
-    "../node/node_modules/apache-arrow": {
-      "version": "14.0.2",
-      "license": "Apache-2.0",
-      "dependencies": {
-        "@types/command-line-args": "5.2.0",
-        "@types/command-line-usage": "5.0.2",
-        "@types/node": "20.3.0",
-        "@types/pad-left": "2.1.1",
-        "command-line-args": "5.2.1",
-        "command-line-usage": "7.0.1",
-        "flatbuffers": "23.5.26",
-        "json-bignum": "^0.0.3",
-        "pad-left": "^2.1.0",
-        "tslib": "^2.5.3"
-      },
-      "bin": {
-        "arrow2csv": "bin/arrow2csv.js"
-      }
-    },
-    "node_modules/@types/node": {
-      "version": "20.11.8",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.11.8.tgz",
-      "integrity": "sha512-i7omyekpPTNdv4Jb/Rgqg0RU8YqLcNsI12quKSDkRXNfx7Wxdm6HhK1awT3xTgEkgxPn3bvnSpiEAc7a7Lpyow==",
-      "dev": true,
-      "dependencies": {
-        "undici-types": "~5.26.4"
-      }
-    },
-    "node_modules/apache-arrow": {
-      "resolved": "../node/node_modules/apache-arrow",
-      "link": true
-    },
-    "node_modules/typescript": {
-      "version": "5.3.3",
-      "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.3.3.tgz",
-      "integrity": "sha512-pXWcraxM0uxAS+tN0AG/BF2TyqmHO014Z070UsJ+pFvYuRSq8KH8DmWpnbXe0pEPDHXZV3FcAbJkijJ5oNEnWw==",
-      "dev": true,
-      "bin": {
-        "tsc": "bin/tsc",
-        "tsserver": "bin/tsserver"
-      },
-      "engines": {
-        "node": ">=14.17"
-      }
-    },
-    "node_modules/undici-types": {
-      "version": "5.26.5",
-      "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz",
-      "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==",
-      "dev": true
-    },
-    "node_modules/vectordb": {
-      "resolved": "../node",
-      "link": true
-    }
-  }
-}

docs/package.json

@@ -1,20 +0,0 @@
-{
-  "name": "lancedb-docs-test",
-  "version": "1.0.0",
-  "description": "auto-generated tests from doc",
-  "author": "dev@lancedb.com",
-  "license": "Apache 2",
-  "dependencies": {
-    "apache-arrow": "file:../node/node_modules/apache-arrow",
-    "vectordb": "file:../node"
-  },
-  "scripts": {
-    "build": "tsc -b && cd ../node && npm run build-release",
-    "example": "npm run build && node",
-    "test": "npm run build && ls dist/*.js | xargs -n 1 node"
-  },
-  "devDependencies": {
-    "@types/node": "^20.11.8",
-    "typescript": "^5.3.3"
-  }
-}

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>0.38.0-beta.6</version>
+    <version>0.38.0-beta.12</version>
 </dependency>
 ```
 

docs/src/js/classes/AutoQuery.md

@@ -0,0 +1,518 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / AutoQuery
+
+# Class: AutoQuery
+
+A builder for automatic string searches.
+
+Automatic search determines whether to use full-text or vector search from
+the table revision selected for each execution. This builder exposes the
+common operations supported by both query families.
+
+## Extends
+
+- `StandardQueryBase`<`NativeQuery` \| `NativeVectorQuery`>
+
+## Properties
+
+### inner
+
+```ts
+protected inner: Query | VectorQuery | Promise<Query | VectorQuery>;
+```
+
+#### Inherited from
+
+`StandardQueryBase.inner`
+
+## Methods
+
+### analyzePlan()
+
+```ts
+analyzePlan(distributedMetrics?): Promise<string>
+```
+
+Executes the query and returns the physical query plan annotated with runtime metrics.
+
+This is useful for debugging and performance analysis, as it shows how the query was executed
+and includes metrics such as elapsed time, rows processed, and I/O statistics.
+
+#### Parameters
+
+* **distributedMetrics?**: [`AnalyzePlanDistributedMetrics`](../type-aliases/AnalyzePlanDistributedMetrics.md)
+    How distributed worker metrics are displayed for remote query plans.
+    Defaults to `"aggregate"`.
+
+#### Returns
+
+`Promise`&lt;`string`&gt;
+
+A query execution plan with runtime metrics for each step.
+
+#### Example
+
+```ts
+import * as lancedb from "@lancedb/lancedb"
+
+const db = await lancedb.connect("./.lancedb");
+const table = await db.createTable("my_table", [
+  { vector: [1.1, 0.9], id: "1" },
+]);
+
+const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan();
+
+Example output (with runtime metrics inlined):
+AnalyzeExec verbose=true, metrics=[]
+ ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs]
+  Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1]
+   CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs]
+    GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns]
+     FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs]
+      SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1]
+       KNNVectorDistance: metric=l2, metrics=[output_rows=1, elapsed_compute=114.333µs, output_batches=1]
+        LanceScan: uri=/path/to/data, projection=[vector], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1, elapsed_compute=103.626µs, bytes_read=549, iops=2, requests=2]
+```
+
+#### Inherited from
+
+`StandardQueryBase.analyzePlan`
+
+***
+
+### execute()
+
+```ts
+protected execute(options?): AsyncGenerator<RecordBatch<any>, void, unknown>
+```
+
+Execute the query and return the results as an
+
+#### Parameters
+
+* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+
+#### Returns
+
+`AsyncGenerator`&lt;`RecordBatch`&lt;`any`&gt;, `void`, `unknown`&gt;
+
+#### See
+
+ - AsyncIterator
+of
+ - RecordBatch.
+
+By default, LanceDb will use many threads to calculate results and, when
+the result set is large, multiple batches will be processed at one time.
+This readahead is limited however and backpressure will be applied if this
+stream is consumed slowly (this constrains the maximum memory used by a
+single query)
+
+#### Inherited from
+
+`StandardQueryBase.execute`
+
+***
+
+### explainPlan()
+
+```ts
+explainPlan(verbose): Promise<string>
+```
+
+Generates an explanation of the query execution plan.
+
+#### Parameters
+
+* **verbose**: `boolean` = `false`
+    If true, provides a more detailed explanation. Defaults to false.
+
+#### Returns
+
+`Promise`&lt;`string`&gt;
+
+A Promise that resolves to a string containing the query execution plan explanation.
+
+#### Example
+
+```ts
+import * as lancedb from "@lancedb/lancedb"
+const db = await lancedb.connect("./.lancedb");
+const table = await db.createTable("my_table", [
+  { vector: [1.1, 0.9], id: "1" },
+]);
+const plan = await table.query().nearestTo([0.5, 0.2]).explainPlan();
+```
+
+#### Inherited from
+
+`StandardQueryBase.explainPlan`
+
+***
+
+### fastSearch()
+
+```ts
+fastSearch(): this
+```
+
+Skip searching un-indexed data. This can make search faster, but will miss
+any data that is not yet indexed.
+
+Use [Table#optimize](Table.md#optimize) to index all un-indexed data.
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.fastSearch`
+
+***
+
+### ~~filter()~~
+
+```ts
+filter(predicate): this
+```
+
+A filter statement to be applied to this query.
+
+#### Parameters
+
+* **predicate**: `string`
+
+#### Returns
+
+`this`
+
+#### See
+
+where
+
+#### Deprecated
+
+Use `where` instead
+
+#### Inherited from
+
+`StandardQueryBase.filter`
+
+***
+
+### fullTextSearch()
+
+```ts
+fullTextSearch(query, options?): this
+```
+
+#### Parameters
+
+* **query**: `string` \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
+
+* **options?**: `Partial`&lt;[`FullTextSearchOptions`](../interfaces/FullTextSearchOptions.md)&gt;
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.fullTextSearch`
+
+***
+
+### limit()
+
+```ts
+limit(limit): this
+```
+
+Set the maximum number of results to return.
+
+By default, a plain search has no limit.  If this method is not
+called then every valid row from the table will be returned.
+
+#### Parameters
+
+* **limit**: `number`
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.limit`
+
+***
+
+### offset()
+
+```ts
+offset(offset): this
+```
+
+Set the number of rows to skip before returning results.
+
+This is useful for pagination.
+
+#### Parameters
+
+* **offset**: `number`
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.offset`
+
+***
+
+### orderBy()
+
+```ts
+orderBy(ordering): this
+```
+
+Sort the results by the specified column(s).
+
+#### Parameters
+
+* **ordering**: [`ColumnOrdering`](../interfaces/ColumnOrdering.md) \| [`ColumnOrdering`](../interfaces/ColumnOrdering.md)[]
+
+#### Returns
+
+`this`
+
+This query builder.
+
+#### Inherited from
+
+`StandardQueryBase.orderBy`
+
+***
+
+### outputSchema()
+
+```ts
+outputSchema(): Promise<Schema<any>>
+```
+
+Returns the schema of the output that will be returned by this query.
+
+This can be used to inspect the types and names of the columns that will be
+returned by the query before executing it.
+
+#### Returns
+
+`Promise`&lt;`Schema`&lt;`any`&gt;&gt;
+
+An Arrow Schema describing the output columns.
+
+#### Inherited from
+
+`StandardQueryBase.outputSchema`
+
+***
+
+### select()
+
+```ts
+select(columns): this
+```
+
+Return only the specified columns.
+
+By default a query will return all columns from the table.  However, this can have
+a very significant impact on latency.  LanceDb stores data in a columnar fashion.  This
+means we can finely tune our I/O to select exactly the columns we need.
+
+As a best practice you should always limit queries to the columns that you need.  If you
+pass in an array of column names then only those columns will be returned.
+
+You can also use this method to create new "dynamic" columns based on your existing columns.
+For example, you may not care about "a" or "b" but instead simply want "a + b".  This is often
+seen in the SELECT clause of an SQL query (e.g. `SELECT a+b FROM my_table`).
+
+To create dynamic columns you can pass in a Map<string, string>.  A column will be returned
+for each entry in the map.  The key provides the name of the column.  The value is
+an SQL string used to specify how the column is calculated.
+
+For example, an SQL query might state `SELECT a + b AS combined, c`.  The equivalent
+input to this method would be:
+
+#### Parameters
+
+* **columns**: `string` \| `string`[] \| `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+
+#### Returns
+
+`this`
+
+#### Example
+
+```ts
+new Map([["combined", "a + b"], ["c", "c"]])
+
+Columns will always be returned in the order given, even if that order is different than
+the order used when adding the data.
+
+Note that you can pass in a `Record<string, string>` (e.g. an object literal). This method
+uses `Object.entries` which should preserve the insertion order of the object.  However,
+object insertion order is easy to get wrong and `Map` is more foolproof.
+```
+
+#### Inherited from
+
+`StandardQueryBase.select`
+
+***
+
+### toArray()
+
+```ts
+toArray(options?): Promise<any[]>
+```
+
+Collect the results as an array of objects.
+
+#### Parameters
+
+* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+
+#### Returns
+
+`Promise`&lt;`any`[]&gt;
+
+#### Inherited from
+
+`StandardQueryBase.toArray`
+
+***
+
+### toArrow()
+
+```ts
+toArrow(options?): Promise<Table<any>>
+```
+
+Collect the results as an Arrow
+
+#### Parameters
+
+* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+
+#### Returns
+
+`Promise`&lt;`Table`&lt;`any`&gt;&gt;
+
+#### See
+
+ArrowTable.
+
+#### Inherited from
+
+`StandardQueryBase.toArrow`
+
+***
+
+### useLsm()
+
+```ts
+useLsm(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
+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
+reads the base table.
+
+#### Parameters
+
+* **enable**: `boolean`
+    `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.
+    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
+    un-compacted MemWAL data.
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.useLsm`
+
+***
+
+### where()
+
+```ts
+where(predicate): this
+```
+
+A filter statement to be applied to this query.
+
+The filter should be supplied as an SQL query string.  For example:
+
+#### Parameters
+
+* **predicate**: `string`
+
+#### Returns
+
+`this`
+
+#### Example
+
+```ts
+x > 10
+y > 0 AND y < 100
+x > 5 OR y = 'test'
+
+Filtering performance can often be improved by creating a scalar index
+on the filter column(s).
+
+Calling this multiple times combines the filters with a logical AND rather
+than replacing the previous filter.
+```
+
+#### Inherited from
+
+`StandardQueryBase.where`
+
+***
+
+### withRowId()
+
+```ts
+withRowId(): this
+```
+
+Whether to return the row id in the results.
+
+This column can be used to match results between different queries. For
+example, to match results from a full text search and a vector search in
+order to perform hybrid search.
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.withRowId`

docs/src/js/classes/Connection.md

@@ -584,6 +584,70 @@ Child namespace names and
 
 ***
 
+### listTables()
+
+#### listTables(options)
+
+```ts
+abstract listTables(options?): Promise<ListTablesResponse>
+```
+
+List a page of the tables in this database.
+
+To retrieve the tables after the page, pass the `pageToken` the response
+carries back in. A page can be shorter than `limit` without being the last
+one, so walk until a response carries no page token:
+
+```ts
+const names = [];
+let pageToken = undefined;
+do {
+  const page = await conn.listTables({ pageToken, limit: 100 });
+  names.push(...page.tables);
+  pageToken = page.pageToken;
+} while (pageToken);
+```
+
+##### Parameters
+
+* **options?**: `Partial`&lt;[`ListTablesOptions`](../interfaces/ListTablesOptions.md)&gt;
+    Pagination options
+    (`pageToken`, `limit`).
+
+##### Returns
+
+`Promise`&lt;[`ListTablesResponse`](../interfaces/ListTablesResponse.md)&gt;
+
+A page of table names and an
+  optional token for the tables after it.
+
+#### listTables(namespacePath, options)
+
+```ts
+abstract listTables(namespacePath?, options?): Promise<ListTablesResponse>
+```
+
+List a page of the tables in this database.
+
+##### Parameters
+
+* **namespacePath?**: `string`[]
+    The namespace path to list tables from
+    (defaults to root namespace)
+
+* **options?**: `Partial`&lt;[`ListTablesOptions`](../interfaces/ListTablesOptions.md)&gt;
+    Pagination options
+    (`pageToken`, `limit`).
+
+##### Returns
+
+`Promise`&lt;[`ListTablesResponse`](../interfaces/ListTablesResponse.md)&gt;
+
+A page of table names and an
+  optional token for the tables after it.
+
+***
+
 ### openMaterializedView()
 
 ```ts
@@ -660,7 +724,7 @@ a "not supported" error.
 
 ***
 
-### tableNames()
+### ~~tableNames()~~
 
 #### tableNames(options)
 
@@ -682,6 +746,10 @@ Tables will be returned in lexicographical order.
 
 `Promise`&lt;`string`[]&gt;
 
+##### Deprecated
+
+Use [Connection.listTables](Connection.md#listtables) instead.
+
 #### tableNames(namespacePath, options)
 
 ```ts
@@ -704,3 +772,7 @@ Tables will be returned in lexicographical order.
 ##### Returns
 
 `Promise`&lt;`string`[]&gt;
+
+##### Deprecated
+
+Use [Connection.listTables](Connection.md#listtables) instead.

docs/src/js/classes/Table.md

@@ -942,7 +942,7 @@ Get the schema of the table.
 abstract search(
    query,
    queryType?,
-   ftsColumns?): Query | VectorQuery
+   ftsColumns?): Query | VectorQuery | AutoQuery
 ```
 
 Create a search query to find the nearest neighbors
@@ -964,7 +964,7 @@ of the given query
 
 #### Returns
 
-[`Query`](Query.md) \| [`VectorQuery`](VectorQuery.md)
+[`Query`](Query.md) \| [`VectorQuery`](VectorQuery.md) \| [`AutoQuery`](AutoQuery.md)
 
 ***
 
@@ -1292,6 +1292,18 @@ abstract updateFieldMetadata(updates): Promise<UpdateFieldMetadataResult>
 
 Update per-field (column) metadata.
 
+The following keys are treated specially, by convention, and should be
+used when appropriate:
+
+- `lancedb:description`: for a human-readable description of a field.
+- `lancedb:tag:<name>`: for a user-defined key-value tag, where the suffix
+  names the tag category; e.g. `lancedb:tag:model: "clip"`.
+- `lancedb:logical-column`: for a column grouping; e.g. `feature_v1` and
+  `feature_v2` might be in the same logical column.
+- `lancedb:status`: for status options (`production`, `candidate`,
+  `deprecated`, `archived`) to designate the current life cycle state of
+  this column.
+
 #### Parameters
 
 * **updates**: [`FieldMetadataUpdate`](../interfaces/FieldMetadataUpdate.md)[]

docs/src/js/globals.md

@@ -18,6 +18,7 @@
 
 ## Classes
 
+- [AutoQuery](classes/AutoQuery.md)
 - [BooleanQuery](classes/BooleanQuery.md)
 - [BoostQuery](classes/BoostQuery.md)
 - [BranchContents](classes/BranchContents.md)
@@ -100,6 +101,8 @@
 - [JobInfo](interfaces/JobInfo.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)
 - [MaterializedViewDefinition](interfaces/MaterializedViewDefinition.md)

docs/src/js/interfaces/FieldMetadataUpdate.md

@@ -17,7 +17,8 @@ metadata: Record<string, null | string>;
 ```
 
 Metadata key/value pairs. Merged into the field's existing metadata by
-default; a value of `null` deletes that key.
+default; a value of `null` deletes that key. See
+[Table.updateFieldMetadata](../classes/Table.md#updatefieldmetadata) for the conventional `lancedb:*` keys.
 
 ***
 

docs/src/js/interfaces/ListTablesOptions.md

@@ -0,0 +1,34 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / ListTablesOptions
+
+# Interface: ListTablesOptions
+
+## Properties
+
+### limit?
+
+```ts
+optional limit: number;
+```
+
+An upper bound on how many tables to return.
+
+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
+full.
+
+***
+
+### pageToken?
+
+```ts
+optional pageToken: string;
+```
+
+Token from a previous response, to resume listing where it left off.
+
+The token is opaque: it carries whatever the database needs to resume, and
+callers should not construct or interpret one.

docs/src/js/interfaces/ListTablesResponse.md

@@ -0,0 +1,23 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / ListTablesResponse
+
+# Interface: ListTablesResponse
+
+## Properties
+
+### pageToken?
+
+```ts
+optional pageToken: string;
+```
+
+***
+
+### tables
+
+```ts
+tables: string[];
+```

docs/src/js/interfaces/TableNamesOptions.md

@@ -4,11 +4,16 @@
 
 [@lancedb/lancedb](../globals.md) / TableNamesOptions
 
-# Interface: TableNamesOptions
+# Interface: ~~TableNamesOptions~~
+
+## Deprecated
+
+Use [ListTablesOptions](ListTablesOptions.md) with [Connection.listTables](../classes/Connection.md#listtables)
+instead.
 
 ## Properties
 
-### limit?
+### ~~limit?~~
 
 ```ts
 optional limit: number;
@@ -18,7 +23,7 @@ An optional limit to the number of results to return.
 
 ***
 
-### startAfter?
+### ~~startAfter?~~
 
 ```ts
 optional startAfter: string;

docs/src/js/namespaces/embedding/functions/getRegistry.md

@@ -10,16 +10,12 @@
 function getRegistry(): EmbeddingFunctionRegistry
 ```
 
-Utility function to get the global instance of the registry
+Get the global embedding function registry.
+
+LanceDB built-in providers are initialized when this public API is first
+used, so importing the root package does not change automatic search
+selection for tables without embedding metadata.
 
 ## Returns
 
 [`EmbeddingFunctionRegistry`](../classes/EmbeddingFunctionRegistry.md)
-
-`EmbeddingFunctionRegistry` The global instance of the registry
-
-## Example
-
-```ts
-const registry = getRegistry();
-const openai = registry.get("openai").create();

docs/src/python/python.md

@@ -159,6 +159,8 @@ and combined with [BooleanQuery][lancedb.query.BooleanQuery].
 
 ::: lancedb.query.FullTextOperator
 
+::: lancedb.query.DocumentGranularity
+
 ::: lancedb.query.Occur
 
 ## Embeddings
@@ -221,9 +223,13 @@ tokens = list(
 Blob columns store large binary values out of line so they can be read lazily
 instead of being materialized with the rest of the row.
 
-::: lancedb.blob
+`lancedb.BlobType` is `lance.blob.BlobType` when pylance is installed. Without
+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.
 
-::: lancedb.BlobType
+::: lancedb.blob
 
 ::: lancedb._blob.BlobFile
     options:
@@ -261,6 +267,8 @@ instead of being materialized with the rest of the row.
 
 ::: lancedb.streaming.StreamingDataset
 
+::: lancedb.streaming.StreamingDataLoader
+
 ::: lancedb.permutation.permutation_builder
 
 ::: lancedb.permutation.PermutationBuilder

docs/tsconfig.json

@@ -1,17 +0,0 @@
-{
-  "include": [
-    "src/*.ts",
-  ],
-  "compilerOptions": {
-    "target": "es2022",
-    "module": "nodenext",
-    "declaration": true,
-    "outDir": "./dist",
-    "strict": true,
-    "allowJs": true,
-    "resolveJsonModule": true,
-  },
-  "exclude": [
-    "./dist/*",
-  ]
-}

java/lancedb-core/pom.xml

@@ -8,7 +8,7 @@
     <parent>
       <groupId>com.lancedb</groupId>
       <artifactId>lancedb-parent</artifactId>
-      <version>0.38.0-beta.6</version>
+      <version>0.38.0-beta.12</version>
       <relativePath>../pom.xml</relativePath>
     </parent>
 

java/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>com.lancedb</groupId>
     <artifactId>lancedb-parent</artifactId>
-    <version>0.38.0-beta.6</version>
+    <version>0.38.0-beta.12</version>
     <packaging>pom</packaging>
     <name>${project.artifactId}</name>
     <description>LanceDB Java SDK Parent POM</description>
@@ -28,7 +28,7 @@
     <properties>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
         <arrow.version>15.0.0</arrow.version>
-        <lance-core.version>11.0.0-rc.1</lance-core.version>
+        <lance-core.version>12.0.0-beta.2</lance-core.version>
         <spotless.skip>false</spotless.skip>
         <spotless.version>2.30.0</spotless.version>
         <spotless.java.googlejavaformat.version>1.7</spotless.java.googlejavaformat.version>

nodejs/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.38.0-beta.6"
+version = "0.38.0-beta.12"
 publish = false
 license.workspace = true
 description.workspace = true

nodejs/__test__/arrow.test.ts

@@ -1,11 +1,16 @@
 // SPDX-License-Identifier: Apache-2.0
 // 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 {
+  Field as CurrentField,
+  LargeBinary as CurrentLargeBinary,
+  Schema as CurrentSchema,
   Vector as CurrentVector,
   convertToTable,
   tableFromIPC as currentTableFromIPC,
@@ -36,6 +41,59 @@ function sampleRecords(): Array<Record<string, any>> {
     },
   ];
 }
+
+it("serializes an Arrow Table created in another JavaScript realm", async () => {
+  const context = vm.createContext({
+    TextDecoder,
+    TextEncoder,
+    console,
+    setTimeout,
+    clearTimeout,
+  });
+  vm.runInContext(
+    fs.readFileSync(
+      require.resolve("apache-arrow-15/Arrow.es2015.min"),
+      "utf8",
+    ),
+    context,
+  );
+  const foreignTable: unknown = vm.runInContext(
+    "Arrow.tableFromArrays({ id: new Int32Array([1, 2, 3]), text: ['foo', 'bar', 'baz'] })",
+    context,
+  );
+
+  const foreignMetadata = (
+    foreignTable as { schema: { metadata: Map<string, string> } }
+  ).schema.metadata;
+  expect(foreignMetadata).not.toBeInstanceOf(Map);
+
+  const buf = await fromDataToBuffer(
+    foreignTable as Parameters<typeof fromDataToBuffer>[0],
+  );
+  const actual = currentTableFromIPC(buf);
+
+  expect(actual.numRows).toBe(3);
+  expect(actual.getChild("id")?.toJSON()).toEqual([1, 2, 3]);
+  expect(actual.getChild("text")?.toJSON()).toEqual(["foo", "bar", "baz"]);
+});
+
+it("preserves field metadata from a provided schema", async function () {
+  const jsonMetadata = new Map([["ARROW:extension:name", "lance.json"]]);
+  const schema = new CurrentSchema([
+    new CurrentField("meta", new CurrentLargeBinary(), true, jsonMetadata),
+  ]);
+
+  const table = makeArrowTable(
+    [{ meta: Buffer.from(JSON.stringify({ source: "test" })) }],
+    { schema },
+  );
+
+  expect(table.schema.fields[0].metadata).toEqual(jsonMetadata);
+
+  const roundTripped = currentTableFromIPC(await fromTableToBuffer(table));
+  expect(roundTripped.schema.fields[0].metadata).toEqual(jsonMetadata);
+});
+
 describe.each([arrow15, arrow16, arrow17, arrow18])(
   "Arrow",
   (
@@ -515,6 +573,137 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
         );
       });
 
+      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("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"]);
+      });
+
+      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);
+        }
+      });
+
+      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);
+        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 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 a schema to be provided", async function () {
         await checkTableCreation(
           async (records, _, schema) =>

nodejs/__test__/connection.test.ts

@@ -4,7 +4,13 @@
 import { readdirSync } from "fs";
 import { Field, Float64, Schema } from "apache-arrow";
 import * as tmp from "tmp";
-import { Connection, Table, connect, connectNamespace } from "../lancedb";
+import {
+  Connection,
+  ListTablesResponse,
+  Table,
+  connect,
+  connectNamespace,
+} from "../lancedb";
 import { LocalTable } from "../lancedb/table";
 
 describe("when connecting", () => {
@@ -47,6 +53,7 @@ describe("given a connection", () => {
     await db.close();
     expect(db.isOpen()).toBe(false);
     await expect(db.tableNames()).rejects.toThrow("Connection is closed");
+    await expect(db.listTables()).rejects.toThrow("Connection is closed");
     await expect(db.renameTable("a", "b")).rejects.toThrow(
       "Connection is closed",
     );
@@ -129,6 +136,66 @@ describe("given a connection", () => {
     expect(tables).toEqual(["b", "c"]);
   });
 
+  it("should respect limit and page token when listing tables", async () => {
+    const db = await connect(tmpDir.name);
+
+    await db.createTable("b", [{ id: 1 }]);
+    await db.createTable("a", [{ id: 1 }]);
+    await db.createTable("c", [{ id: 1 }]);
+
+    const all = await db.listTables();
+    expect(all.tables).toEqual(["a", "b", "c"]);
+    expect(all.pageToken).toBeUndefined();
+
+    const first = await db.listTables({ limit: 1 });
+    expect(first.tables).toEqual(["a"]);
+    expect(first.pageToken).toBeDefined();
+
+    const second = await db.listTables({
+      limit: 1,
+      pageToken: first.pageToken,
+    });
+    expect(second.tables).toEqual(["b"]);
+  });
+
+  it("should visit every table exactly once when walking pages", async () => {
+    const db = await connect(tmpDir.name);
+
+    const created = ["a", "b", "c", "d", "e"];
+    for (const name of created) {
+      await db.createTable(name, [{ id: 1 }]);
+    }
+
+    const seen: string[] = [];
+    let pageToken: string | undefined = undefined;
+    do {
+      const page: ListTablesResponse = await db.listTables({
+        limit: 2,
+        pageToken,
+      });
+      seen.push(...page.tables);
+      pageToken = page.pageToken;
+    } while (pageToken);
+
+    expect(seen).toEqual(created);
+  });
+
+  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
+      namespaceClientProperties: { manifest_enabled: "true" },
+    });
+    await db.createNamespace(["child"]);
+    await db.createTable("nested", [{ id: 1 }], ["child"]);
+
+    await expect(db.listTables(["child"])).resolves.toEqual(
+      expect.objectContaining({ tables: ["nested"] }),
+    );
+    await expect(db.listTables()).resolves.toEqual(
+      expect.objectContaining({ tables: [] }),
+    );
+  });
+
   it("should create tables in v2 mode", async () => {
     const db = await connect(tmpDir.name);
     const data = [...Array(10000).keys()].map((i) => ({ id: i }));

nodejs/__test__/embedding.test.ts

@@ -187,6 +187,58 @@ describe("embedding functions", () => {
     const vector0 = JSON.parse(JSON.stringify(arr[0].vector));
     expect(vector0).toEqual([1, 2, 3]);
   });
+  it("should append multiple Python embeddings with the same alias", async () => {
+    @register("python-mock")
+    // biome-ignore lint/correctness/noUnusedVariables: the decorator registers this class
+    class MockEmbeddingFunction extends EmbeddingFunction<string> {
+      ndims() {
+        return 3;
+      }
+      embeddingDataType(): Float {
+        return new Float32();
+      }
+      async computeQueryEmbeddings(_data: string) {
+        return [1, 2, 3];
+      }
+      async computeSourceEmbeddings(data: string[]) {
+        return data.map((value) =>
+          value === "hello world" ? [1, 2, 3] : [4, 5, 6],
+        );
+      }
+    }
+
+    const metadata = new Map([
+      [
+        "embedding_functions",
+        '[{"source_column":"text1","vector_column":"vector1","name":"python-mock","model":{}},{"source_column":"text2","vector_column":"vector2","name":"python-mock","model":{}}]',
+      ],
+    ]);
+    const schema = new Schema(
+      [
+        new Field("text1", new Utf8(), true),
+        new Field("text2", new Utf8(), true),
+        new Field(
+          "vector1",
+          new FixedSizeList(3, new Field("item", new Float32(), true)),
+          true,
+        ),
+        new Field(
+          "vector2",
+          new FixedSizeList(3, new Field("item", new Float32(), true)),
+          true,
+        ),
+      ],
+      metadata,
+    );
+
+    const db = await connect(tmpDir.name);
+    const table = await db.createEmptyTable("test", schema);
+    await table.add([{ text1: "hello world", text2: "goodbye world" }]);
+
+    const rows = await table.query().toArray();
+    expect(JSON.parse(JSON.stringify(rows[0].vector1))).toEqual([1, 2, 3]);
+    expect(JSON.parse(JSON.stringify(rows[0].vector2))).toEqual([4, 5, 6]);
+  });
 
   it("should append generated vectors to a non-nullable schema", async () => {
     @register("non_nullable_schema_test")

nodejs/__test__/embedding_registry.test.ts

@@ -0,0 +1,95 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import { execFileSync } from "node:child_process";
+import { resolve } from "node:path";
+
+import type { OpenAIEmbeddingFunction } from "../lancedb/embedding/openai";
+import type { EmbeddingFunctionRegistry } from "../lancedb/embedding/registry";
+
+type EmbeddingModule = typeof import("../lancedb/embedding");
+type OpenAIModule = typeof import("../lancedb/embedding/openai");
+type RegistryModule = typeof import("../lancedb/embedding/registry");
+
+describe("embedding function registry", () => {
+  const registries: EmbeddingFunctionRegistry[] = [];
+
+  afterEach(() => {
+    for (const registry of registries) {
+      registry.reset();
+    }
+    registries.length = 0;
+  });
+
+  it("defers built-in providers until the public registry API is used", () => {
+    jest.isolateModules(() => {
+      const embedding = require("../lancedb/embedding") as EmbeddingModule;
+      const { getRegistry: getInternalRegistry } =
+        require("../lancedb/embedding/registry") as RegistryModule;
+      const registry = getInternalRegistry();
+      registries.push(registry);
+
+      expect(registry.length()).toBe(0);
+      expect(embedding.getRegistry()).toBe(registry);
+      expect(registry.get("openai")).toBeDefined();
+      expect(registry.get("huggingface")).toBeDefined();
+    });
+  });
+
+  it("preserves automatic FTS search in a fresh process", () => {
+    execFileSync(
+      process.execPath,
+      [resolve(__dirname, "fixtures", "auto_fts_search.cjs")],
+      { stdio: "pipe" },
+    );
+  });
+
+  it("shares registrations across duplicated provider module graphs", () => {
+    let registeringRegistry: EmbeddingFunctionRegistry | undefined;
+    let latestOpenAIConstructor: typeof OpenAIEmbeddingFunction | undefined;
+
+    jest.isolateModules(() => {
+      require("../lancedb/embedding/openai");
+      const { getRegistry } =
+        require("../lancedb/embedding/registry") as RegistryModule;
+      registeringRegistry = getRegistry();
+      registries.push(registeringRegistry);
+      expect(registeringRegistry.get("openai")).toBeDefined();
+    });
+
+    expect(() => {
+      jest.isolateModules(() => {
+        const { OpenAIEmbeddingFunction } =
+          require("../lancedb/embedding/openai") as OpenAIModule;
+        latestOpenAIConstructor = OpenAIEmbeddingFunction;
+        const { getRegistry } =
+          require("../lancedb/embedding/registry") as RegistryModule;
+        registries.push(getRegistry());
+      });
+    }).not.toThrow();
+
+    const previousApiKey = process.env.OPENAI_API_KEY;
+    process.env.OPENAI_API_KEY = "test";
+    try {
+      const latestOpenAI = registeringRegistry!
+        .get<OpenAIEmbeddingFunction>("openai")!
+        .create();
+      expect(latestOpenAI).toBeInstanceOf(latestOpenAIConstructor!);
+    } finally {
+      if (previousApiKey === undefined) {
+        delete process.env.OPENAI_API_KEY;
+      } else {
+        process.env.OPENAI_API_KEY = previousApiKey;
+      }
+    }
+
+    jest.isolateModules(() => {
+      const { getRegistry } =
+        require("../lancedb/embedding") as EmbeddingModule;
+      const publicRegistry = getRegistry();
+      registries.push(publicRegistry);
+      expect(publicRegistry).toBe(registeringRegistry);
+      expect(publicRegistry.get("openai")).toBeDefined();
+    });
+  });
+});

nodejs/__test__/fixtures/auto_fts_search.cjs

@@ -0,0 +1,33 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+const assert = require("node:assert/strict");
+const tmp = require("tmp");
+const { connect, embedding, Index } = require("../../dist");
+const { getRegistry } = require("../../dist/embedding/registry");
+
+async function main() {
+  assert.equal(typeof embedding.getRegistry, "function");
+  assert.equal(getRegistry().length(), 0);
+  assert.equal(embedding.getRegistry(), getRegistry());
+  assert.equal(getRegistry().length(), 2);
+
+  const dir = tmp.dirSync({ unsafeCleanup: true });
+  let db;
+  try {
+    db = await connect(dir.name);
+    const table = await db.createTable("docs", [{ text: "hello world" }]);
+    await table.createIndex("text", { config: Index.fts() });
+
+    const rows = await table.search("hello").toArray();
+    assert.equal(rows[0].text, "hello world");
+  } finally {
+    db?.close();
+    dir.removeCallback();
+  }
+}
+
+main().catch((error) => {
+  console.error(error);
+  process.exitCode = 1;
+});

nodejs/__test__/package.test.ts

@@ -5,8 +5,8 @@ import packageJson = require("../package.json");
 
 describe("package metadata", () => {
   it("requires Node.js type declarations compatible with the runtime", () => {
-    expect(packageJson.engines.node).toBe(">= 18");
-    expect(packageJson.peerDependencies["@types/node"]).toBe(">=18");
+    expect(packageJson.engines.node).toBe(">= 22");
+    expect(packageJson.peerDependencies["@types/node"]).toBe(">=22");
     expect(packageJson.peerDependenciesMeta["@types/node"]).toEqual({
       optional: true,
     });

nodejs/__test__/remote.test.ts

@@ -3,6 +3,7 @@
 
 import * as http from "http";
 import { RequestListener } from "http";
+import packageJson = require("../package.json");
 import {
   ClientConfig,
   Connection,
@@ -70,7 +71,13 @@ async function withMockDatabase(
   try {
     await callback(db);
   } finally {
-    server.close();
+    // `close()` alone leaves the port bound until keep-alive sockets drain, so
+    // a single failing test would cascade into EADDRINUSE for every test after
+    // it. Destroy the connections and wait for the port to actually be free.
+    await new Promise<void>((resolve) => {
+      server.closeAllConnections();
+      server.close(() => resolve());
+    });
   }
 }
 
@@ -131,7 +138,7 @@ describe("remote connection", () => {
       (req, res) => {
         expect(req.headers["x-api-key"]).toEqual("fake");
         expect(req.headers["user-agent"]).toEqual(
-          `LanceDB-Node-Client/${process.env.npm_package_version}`,
+          `LanceDB-Node-Client/${packageJson.version}`,
         );
 
         const body = JSON.stringify({ tables: [] });

nodejs/__test__/table.test.ts

@@ -11,10 +11,13 @@ import * as arrow17 from "apache-arrow-17";
 import * as arrow18 from "apache-arrow-18";
 
 import {
+  AutoQuery,
   Connection,
   MatchQuery,
   PhraseQuery,
+  Query,
   Table,
+  VectorQuery,
   connect,
   tokenize,
 } from "../lancedb";
@@ -682,6 +685,56 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
   },
 );
 
+// https://github.com/lancedb/lancedb/issues/1963
+it("should query documents with LangChain PDF metadata", async () => {
+  const tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  try {
+    const db = await connect(tmpDir.name);
+    const documents = [
+      {
+        text: "first page",
+        vector: [1, 0],
+        source: "first.pdf",
+        loc: { pageNumber: 1, lines: { from: 1, to: 12 } },
+        pdf: {
+          version: "1.10.100",
+          info: {
+            format: "PDF 1.7",
+            producer: "pdf.js",
+            creator: "Writer",
+          },
+          totalPages: 2,
+        },
+      },
+      {
+        text: "second page",
+        vector: [0, 1],
+        source: "second.pdf",
+        loc: { pageNumber: 2, lines: { from: 13, to: 24 } },
+        pdf: {
+          version: "1.10.100",
+          info: {
+            format: "PDF 1.7",
+            producer: "pdf.js",
+            creator: "Writer",
+          },
+          totalPages: 2,
+        },
+      },
+    ];
+    const documentsTable = await db.createTable("documents", documents);
+
+    const results = await documentsTable.query().toArray();
+
+    expect(results).toHaveLength(2);
+    expect(results[0].source).toBe("first.pdf");
+    expect(results[0].pdf.info.producer).toBe("pdf.js");
+    expect(results[1].loc.pageNumber).toBe(2);
+  } finally {
+    tmpDir.removeCallback();
+  }
+});
+
 describe("merge insert", () => {
   let tmpDir: tmp.DirResult;
   let table: Table;
@@ -1777,6 +1830,194 @@ describe("Read consistency interval", () => {
   });
 });
 
+describe("automatic search schema consistency", () => {
+  let tmpDir: tmp.DirResult;
+
+  class SchemaRefreshEmbedding extends EmbeddingFunction<string> {
+    ndims() {
+      return 2;
+    }
+
+    embeddingDataType() {
+      return new Float32();
+    }
+
+    async computeSourceEmbeddings(data: string[]) {
+      return data.map((value) => [value.length, 1]);
+    }
+
+    async computeQueryEmbeddings(value: string) {
+      return [value.length, 1];
+    }
+  }
+
+  function embeddingSchema() {
+    const func = new SchemaRefreshEmbedding();
+    return LanceSchema({
+      text: func.sourceField(new Utf8()),
+      vector: func.vectorField(),
+    });
+  }
+
+  beforeEach(() => {
+    getRegistry().reset();
+    register("schema-refresh")(SchemaRefreshEmbedding);
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  });
+
+  afterEach(() => {
+    getRegistry().reset();
+    tmpDir.removeCallback();
+  });
+
+  it("uses the schema refreshed from another connection", async () => {
+    const first = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+    const second = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+
+    try {
+      const stale = await first.createTable("docs", [{ text: "before" }], {
+        schema: embeddingSchema(),
+      });
+      const replacement = await second.createTable(
+        "docs",
+        [{ text: "after hello" }],
+        { mode: "overwrite" },
+      );
+      await replacement.createIndex("text", { config: Index.fts() });
+
+      const search = stale.search("hello");
+      expect(search).toBeInstanceOf(AutoQuery);
+      expect(search).not.toBeInstanceOf(Query);
+      expect(search).not.toBeInstanceOf(VectorQuery);
+      expect("nprobes" in search).toBe(false);
+
+      const rows = await search.toArray();
+      expect(rows[0].text).toBe("after hello");
+      expect((await stale.schema()).metadata.has("embedding_functions")).toBe(
+        false,
+      );
+    } finally {
+      first.close();
+      second.close();
+    }
+  });
+
+  it("tracks embedding metadata across checkout and restore", async () => {
+    const first = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+    const second = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+
+    try {
+      await first.createTable("docs", [{ text: "before" }], {
+        schema: embeddingSchema(),
+      });
+      const table = await second.createTable(
+        "docs",
+        [{ text: "after hello" }],
+        { mode: "overwrite" },
+      );
+      await table.createIndex("text", { config: Index.fts() });
+
+      await table.checkout(1);
+      expect((await table.search("before").toArray())[0].text).toBe("before");
+
+      await table.checkoutLatest();
+      expect((await table.search("hello").toArray())[0].text).toBe(
+        "after hello",
+      );
+
+      await table.checkout(1);
+      await table.restore();
+      expect((await table.search("before").toArray())[0].text).toBe("before");
+    } finally {
+      first.close();
+      second.close();
+    }
+  });
+
+  it("pins automatic search while computing an embedding", async () => {
+    let markStarted!: () => void;
+    let releaseEmbedding!: () => void;
+    const started = new Promise<void>((resolve) => {
+      markStarted = resolve;
+    });
+    const released = new Promise<void>((resolve) => {
+      releaseEmbedding = resolve;
+    });
+
+    class BlockingEmbedding extends SchemaRefreshEmbedding {
+      async computeQueryEmbeddings(value: string) {
+        markStarted();
+        await released;
+        return [value.length, 1];
+      }
+    }
+
+    register("schema-refresh-blocking")(BlockingEmbedding);
+    const func = new BlockingEmbedding();
+    const schema = LanceSchema({
+      text: func.sourceField(new Utf8()),
+      vector: func.vectorField(),
+    });
+    const first = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+    const second = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+
+    try {
+      const table = await first.createTable(
+        "docs",
+        [{ text: "hello before" }],
+        { schema },
+      );
+      const pending = table.search("hello").toArray();
+      await started;
+
+      const replacement = await second.createTable(
+        "docs",
+        [{ text: "hello after" }],
+        { mode: "overwrite" },
+      );
+      await replacement.createIndex("text", { config: Index.fts() });
+      releaseEmbedding();
+
+      expect((await pending)[0].text).toBe("hello before");
+    } finally {
+      releaseEmbedding();
+      first.close();
+      second.close();
+    }
+  });
+
+  it("refreshes a reused automatic search for every execution", async () => {
+    const first = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+    const second = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+
+    try {
+      const table = await first.createTable("docs", [
+        { text: "hello before", marker: "before" },
+      ]);
+      await table.createIndex("text", { config: Index.fts() });
+      const search = table.search("hello").select(["text"]);
+
+      const before = (await search.toArray())[0];
+      expect(before.text).toBe("hello before");
+      expect(before.marker).toBeUndefined();
+
+      const replacement = await second.createTable(
+        "docs",
+        [{ text: "hello after", marker: "after" }],
+        { mode: "overwrite" },
+      );
+      await replacement.createIndex("text", { config: Index.fts() });
+
+      const after = (await search.toArray())[0];
+      expect(after.text).toBe("hello after");
+      expect(after.marker).toBeUndefined();
+    } finally {
+      first.close();
+      second.close();
+    }
+  });
+});
+
 describe("schema evolution", function () {
   let tmpDir: tmp.DirResult;
   beforeEach(() => {
@@ -2344,7 +2585,24 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
       );
     });
 
-    test("full text search if no embedding function provided", async () => {
+    test("full text search if only an unrelated embedding function is registered", async () => {
+      register("unused")(
+        class extends EmbeddingFunction<string> {
+          ndims() {
+            return 3;
+          }
+          embeddingDataType() {
+            return new Float32();
+          }
+          async computeQueryEmbeddings(_data: string) {
+            return [1, 2, 3];
+          }
+          async computeSourceEmbeddings(data: string[]) {
+            return data.map(() => [1, 2, 3]);
+          }
+        },
+      );
+
       const db = await connect(tmpDir.name);
       const data = [
         { text: "hello world", vector: [0.1, 0.2, 0.3] },
@@ -2366,6 +2624,306 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
       expect(results2[0].text).toBe(data[1].text);
     });
 
+    test("auto search stays consistent with the active revision", async () => {
+      let initCalls = 0;
+      let queryCalls = 0;
+      let markStarted!: () => void;
+      const started = new Promise<void>((resolve) => {
+        markStarted = resolve;
+      });
+      let releaseEmbedding!: () => void;
+      const embeddingReleased = new Promise<void>((resolve) => {
+        releaseEmbedding = resolve;
+      });
+
+      @register("refresh-test")
+      class TestEmbedding extends EmbeddingFunction<string> {
+        async init() {
+          initCalls += 1;
+        }
+        ndims() {
+          return 1;
+        }
+        embeddingDataType() {
+          return new arrow.Float32();
+        }
+        async computeQueryEmbeddings(value: string) {
+          queryCalls += 1;
+          if (value === "blocked") {
+            markStarted();
+            await embeddingReleased;
+          }
+          return value === "greetings" ? [0.1] : [0.2];
+        }
+        async computeSourceEmbeddings(values: string[]) {
+          return values.map((value) =>
+            value === "hello world" ? [0.1] : [0.2],
+          );
+        }
+      }
+
+      const writer = await connect(tmpDir.name);
+      await writer.createTable("test", [{ text: "plain", vector: [0.0] }]);
+      const reader = await connect(tmpDir.name, {
+        readConsistencyInterval: 0,
+      });
+      const tracked = await reader.openTable("test");
+      type SnapshotCountingNative = {
+        querySnapshot: () => Promise<unknown>;
+      };
+      const native = (tracked as unknown as { inner: SnapshotCountingNative })
+        .inner;
+      const querySnapshot = native.querySnapshot.bind(native);
+      let snapshotCalls = 0;
+      native.querySnapshot = async () => {
+        snapshotCalls += 1;
+        return await querySnapshot();
+      };
+      const autoQuery = tracked.search("greetings").select(["text"]).limit(1);
+
+      const func = new TestEmbedding();
+      const schema = LanceSchema({
+        text: func.sourceField(new arrow.Utf8()),
+        vector: func.vectorField(),
+      });
+      const data = [{ text: "hello world" }, { text: "goodbye world" }];
+      await writer.createTable("test", data, { mode: "overwrite", schema });
+      const baselineInitCalls = initCalls;
+
+      expect(
+        (await tracked.schema()).metadata.get("embedding_functions"),
+      ).toBeDefined();
+      const results = await autoQuery.toArray();
+      expect(results[0].text).toBe(data[0].text);
+      expect(initCalls).toBe(baselineInitCalls + 1);
+      expect(queryCalls).toBe(1);
+      expect(snapshotCalls).toBe(1);
+
+      const repeatedResults = await autoQuery.toArray();
+      expect(repeatedResults[0].text).toBe(data[0].text);
+      expect(initCalls).toBe(baselineInitCalls + 1);
+      expect(queryCalls).toBe(1);
+      expect(snapshotCalls).toBe(2);
+
+      const pending = tracked
+        .search("blocked")
+        .select(["text"])
+        .limit(1)
+        .toArray();
+      await started;
+
+      const ftsData = [
+        { text: "greetings from full text", vector: [0.0] },
+        { text: "blocked from full text", vector: [0.0] },
+      ];
+      const ftsTable = await writer.createTable("test", ftsData, {
+        mode: "overwrite",
+      });
+      await ftsTable.createIndex("text", { config: Index.fts() });
+      releaseEmbedding();
+
+      const pendingResults = await pending;
+      expect(pendingResults[0].text).toBe(data[1].text);
+
+      expect(
+        (await tracked.schema()).metadata.get("embedding_functions"),
+      ).toBeUndefined();
+      const ftsResults = await autoQuery.toArray();
+      expect(ftsResults[0].text).toBe(ftsData[0].text);
+    });
+
+    test("auto search keeps newer preparation during a revision race", async () => {
+      let aCalls = 0;
+      let bCalls = 0;
+      let markAStarted!: () => void;
+      const aStarted = new Promise<void>((resolve) => {
+        markAStarted = resolve;
+      });
+      let releaseA!: () => void;
+      const aReleased = new Promise<void>((resolve) => {
+        releaseA = resolve;
+      });
+      let markBStarted!: () => void;
+      const bStarted = new Promise<void>((resolve) => {
+        markBStarted = resolve;
+      });
+      let releaseB!: () => void;
+      const bReleased = new Promise<void>((resolve) => {
+        releaseB = resolve;
+      });
+
+      @register("race-a")
+      class EmbeddingA extends EmbeddingFunction<string> {
+        ndims() {
+          return 1;
+        }
+        embeddingDataType() {
+          return new arrow.Float32();
+        }
+        async computeQueryEmbeddings() {
+          aCalls += 1;
+          markAStarted();
+          await aReleased;
+          return [0.1];
+        }
+        async computeSourceEmbeddings(values: string[]) {
+          return values.map(() => [0.1]);
+        }
+      }
+
+      @register("race-b")
+      class EmbeddingB extends EmbeddingFunction<string> {
+        ndims() {
+          return 1;
+        }
+        embeddingDataType() {
+          return new arrow.Float32();
+        }
+        async computeQueryEmbeddings() {
+          bCalls += 1;
+          markBStarted();
+          await bReleased;
+          return [0.2];
+        }
+        async computeSourceEmbeddings(values: string[]) {
+          return values.map(() => [0.2]);
+        }
+      }
+
+      const writer = await connect(tmpDir.name);
+      const embeddingA = new EmbeddingA();
+      const schemaA = LanceSchema({
+        text: embeddingA.sourceField(new arrow.Utf8()),
+        vector: embeddingA.vectorField(),
+      });
+      await writer.createTable("race", [{ text: "revision a" }], {
+        schema: schemaA,
+      });
+      const reader = await connect(tmpDir.name, {
+        readConsistencyInterval: 0,
+      });
+      const tracked = await reader.openTable("race");
+      const query = tracked.search("query");
+
+      const first = query.toArray();
+      await aStarted;
+
+      const embeddingB = new EmbeddingB();
+      const schemaB = LanceSchema({
+        text: embeddingB.sourceField(new arrow.Utf8()),
+        vector: embeddingB.vectorField(),
+      });
+      await writer.createTable("race", [{ text: "revision b" }], {
+        mode: "overwrite",
+        schema: schemaB,
+      });
+      const second = query.toArray();
+      await bStarted;
+
+      releaseA();
+      releaseB();
+      await Promise.all([first, second]);
+      expect(aCalls).toBe(1);
+      expect(bCalls).toBe(1);
+    });
+
+    test("stale FTS routing keeps newer vector preparation", async () => {
+      let vectorCalls = 0;
+      let markVectorStarted!: () => void;
+      const vectorStarted = new Promise<void>((resolve) => {
+        markVectorStarted = resolve;
+      });
+      let releaseVector!: () => void;
+      const vectorReleased = new Promise<void>((resolve) => {
+        releaseVector = resolve;
+      });
+
+      @register("stale-fts-race")
+      class RaceEmbedding extends EmbeddingFunction<string> {
+        ndims() {
+          return 1;
+        }
+        embeddingDataType() {
+          return new arrow.Float32();
+        }
+        async computeQueryEmbeddings() {
+          vectorCalls += 1;
+          markVectorStarted();
+          await vectorReleased;
+          return [0.1];
+        }
+        async computeSourceEmbeddings(values: string[]) {
+          return values.map(() => [0.1]);
+        }
+      }
+
+      const writer = await connect(tmpDir.name);
+      const ftsTable = await writer.createTable("stale_fts", [
+        { text: "hello", vector: [0.0] },
+      ]);
+      await ftsTable.createIndex("text", { config: Index.fts() });
+
+      const reader = await connect(tmpDir.name, {
+        readConsistencyInterval: 0,
+      });
+      const tracked = await reader.openTable("stale_fts");
+      type Snapshot = {
+        schema: () => Promise<Buffer>;
+      };
+      type NativeWithSnapshot = {
+        querySnapshot: () => Promise<Snapshot>;
+      };
+      const native = (tracked as unknown as { inner: NativeWithSnapshot })
+        .inner;
+      const querySnapshot = native.querySnapshot.bind(native);
+      let snapshotCalls = 0;
+      let markStaleSchemaStarted!: () => void;
+      const staleSchemaStarted = new Promise<void>((resolve) => {
+        markStaleSchemaStarted = resolve;
+      });
+      let releaseStaleSchema!: () => void;
+      const staleSchemaReleased = new Promise<void>((resolve) => {
+        releaseStaleSchema = resolve;
+      });
+      native.querySnapshot = async () => {
+        const snapshot = await querySnapshot();
+        snapshotCalls += 1;
+        if (snapshotCalls === 1) {
+          const schema = snapshot.schema.bind(snapshot);
+          snapshot.schema = async () => {
+            markStaleSchemaStarted();
+            await staleSchemaReleased;
+            return await schema();
+          };
+        }
+        return snapshot;
+      };
+
+      const query = tracked.search("hello");
+      const staleFtsExecution = query.toArray();
+      await staleSchemaStarted;
+
+      const embedding = new RaceEmbedding();
+      const vectorSchema = LanceSchema({
+        text: embedding.sourceField(new arrow.Utf8()),
+        vector: embedding.vectorField(),
+      });
+      await writer.createTable("stale_fts", [{ text: "hello" }], {
+        mode: "overwrite",
+        schema: vectorSchema,
+      });
+
+      const vectorExecution = query.toArray();
+      await vectorStarted;
+      releaseStaleSchema();
+      await staleFtsExecution;
+      releaseVector();
+      await vectorExecution;
+
+      await query.toArray();
+      expect(vectorCalls).toBe(1);
+    });
+
     test("tokenizes FTS queries by column or index name", async () => {
       const db = await connect(tmpDir.name);
       const data = [
@@ -2916,6 +3474,30 @@ describe("column name options", () => {
     expect(results[1].query_index).toBe(1);
   });
 
+  test("observes promised additional vectors while the query is pending", async () => {
+    const initialVector = new Promise<number[]>(() => undefined);
+    const query = table.query().nearestTo(initialVector);
+    const unhandled: unknown[] = [];
+    const onUnhandled = (reason: unknown) => unhandled.push(reason);
+    process.on("unhandledRejection", onUnhandled);
+
+    try {
+      query.addQueryVector(Promise.reject(new Error("extra vector failed")));
+      await new Promise<void>((resolve) => setImmediate(resolve));
+      expect(unhandled).toEqual([]);
+
+      const rejectedQuery = table
+        .query()
+        .nearestTo([0.1, 0.2])
+        .addQueryVector(Promise.reject(new Error("consumed vector failed")));
+      await expect(rejectedQuery.toArray()).rejects.toThrow(
+        "consumed vector failed",
+      );
+    } finally {
+      process.off("unhandledRejection", onUnhandled);
+    }
+  });
+
   test("index and search multivectors", async () => {
     const db = await connect(tmpDir.name);
     const data = [];
@@ -2979,6 +3561,27 @@ describe("when creating an empty table", () => {
     expect((actualSchema.fields[1].type as Float64).precision).toBe(2);
   });
 
+  it("can add and query JSON data", async () => {
+    const schema = new Schema([
+      new Field("id", new Int32(), true),
+      new Field(
+        "meta",
+        new Utf8(),
+        true,
+        new Map([["ARROW:extension:name", "arrow.json"]]),
+      ),
+    ]);
+    const table = await con.createEmptyTable("json", schema);
+    const meta = JSON.stringify({ x: 1 });
+
+    await table.add([{ id: 1, meta }]);
+
+    const rows = await table.query().toArray();
+    expect(rows).toHaveLength(1);
+    expect(rows[0].id).toBe(1);
+    expect(rows[0].meta).toBe(meta);
+  });
+
   it("can create an empty table from schema that specifies field types by name", async () => {
     const schemaLike = {
       fields: [

nodejs/examples/basic.test.ts

@@ -170,7 +170,7 @@ test("basic table examples", async () => {
     // --8<-- [end:create_index]
 
     // --8<-- [start:delete_rows]
-    await tbl.delete('item = "fizz"');
+    await tbl.delete("item = 'fizz'");
     // --8<-- [end:delete_rows]
 
     // --8<-- [start:drop_table]

nodejs/examples/package.json

@@ -8,7 +8,8 @@
     "//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",
+    "//4": "jest is invoked by its JS entry, not node_modules/.bin/jest: under pnpm that path is a shell shim, which `node` cannot execute",
+    "test": "node --experimental-vm-modules node_modules/jest/bin/jest.js --testEnvironment jest-environment-node-single-context --verbose",
     "lint": "biome check *.ts && biome format *.ts",
     "lint-ci": "biome ci .",
     "lint-fix": "biome check --write *.ts && pnpm format",

nodejs/lancedb/arrow.ts

@@ -5,7 +5,6 @@ import {
   Data as ArrowData,
   Table as ArrowTable,
   Binary,
-  Bool,
   BufferType,
   DataType,
   DateUnit,
@@ -18,12 +17,7 @@ import {
   FixedSizeList,
   Float,
   Float32,
-  Float64,
   Int,
-  Int8,
-  Int16,
-  Int32,
-  Int64,
   LargeBinary,
   List,
   Null,
@@ -36,17 +30,16 @@ import {
   Struct,
   Timestamp,
   Type,
-  Uint8,
-  Uint16,
-  Uint32,
   Utf8,
   Vector,
   makeVector as arrowMakeVector,
+  util as arrowUtil,
   vectorFromArray as badVectorFromArray,
   makeBuilder,
   makeData,
 } from "apache-arrow";
 import { Buffers } from "apache-arrow/data";
+import { typedArrayToArrowType } from "./arrow_type";
 import { type EmbeddingFunction } from "./embedding/embedding_function";
 import {
   EmbeddingFunctionConfig,
@@ -59,14 +52,7 @@ import {
   sanitizeTable,
   sanitizeType,
 } from "./sanitize";
-
-/**
- * Check if a field name indicates a vector column.
- */
-function nameSuggestsVectorColumn(fieldName: string): boolean {
-  const nameLower = fieldName.toLowerCase();
-  return nameLower.includes("vector") || nameLower.includes("embedding");
-}
+import { inferSchema } from "./schema";
 
 export * from "apache-arrow";
 export type SchemaLike =
@@ -86,8 +72,7 @@ export type FieldLike =
     };
 
 export type DataLike =
-  // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-  | import("apache-arrow").Data<Struct<any>>
+  | import("apache-arrow").Data
   | {
       // biome-ignore lint/suspicious/noExplicitAny: <explanation>
       type: any;
@@ -96,6 +81,7 @@ export type DataLike =
       stride: number;
       nullable: boolean;
       children: DataLike[];
+      dictionary?: { data: readonly DataLike[] };
       get nullCount(): number;
       // biome-ignore lint/suspicious/noExplicitAny: <explanation>
       values: Buffers<any>[BufferType.DATA];
@@ -459,110 +445,6 @@ export function makeArrowTable(
   return new ArrowTable(inferredSchema, finalColumns);
 }
 
-function inferSchema(
-  data: Array<Record<string, unknown>>,
-  schema: Schema | undefined,
-  opts: MakeArrowTableOptions,
-): Schema {
-  // We will collect all fields we see in the data.
-  const pathTree = new PathTree<DataType>();
-
-  for (const [rowI, row] of data.entries()) {
-    for (const [path, value] of rowPathsAndValues(row)) {
-      if (!pathTree.has(path)) {
-        // First time seeing this field.
-        if (schema !== undefined) {
-          const field = getFieldForPath(schema, path);
-          if (field === undefined) {
-            throw new Error(
-              `Found field not in schema: ${path.join(".")} at row ${rowI}`,
-            );
-          } else {
-            pathTree.set(path, field.type);
-          }
-        } else {
-          const inferredType = inferType(value, path, opts);
-          if (inferredType === undefined) {
-            throw new Error(`Failed to infer data type for field ${path.join(
-              ".",
-            )} at row ${rowI}. \
-                             Consider providing an explicit schema.`);
-          }
-          pathTree.set(path, inferredType);
-        }
-      } else if (schema === undefined) {
-        const currentType = pathTree.get(path);
-        const newType = inferType(value, path, opts);
-        if (currentType !== newType) {
-          new Error(`Failed to infer schema for data. Previously inferred type \
-                     ${currentType} but found ${newType} at row ${rowI}. Consider \
-                     providing an explicit schema.`);
-        }
-      }
-    }
-  }
-
-  if (schema === undefined) {
-    function fieldsFromPathTree(pathTree: PathTree<DataType>): Field[] {
-      const fields = [];
-      for (const [name, value] of pathTree.map.entries()) {
-        if (value instanceof PathTree) {
-          const children = fieldsFromPathTree(value);
-          fields.push(new Field(name, new Struct(children), true));
-        } else {
-          fields.push(new Field(name, value, true));
-        }
-      }
-      return fields;
-    }
-    const fields = fieldsFromPathTree(pathTree);
-    return new Schema(fields);
-  } else {
-    function takeMatchingFields(
-      fields: Field[],
-      pathTree: PathTree<DataType>,
-    ): Field[] {
-      const outFields = [];
-      for (const field of fields) {
-        if (pathTree.map.has(field.name)) {
-          const value = pathTree.get([field.name]);
-          if (value instanceof PathTree) {
-            const struct = field.type as Struct;
-            const children = takeMatchingFields(struct.children, value);
-            outFields.push(
-              new Field(field.name, new Struct(children), field.nullable),
-            );
-          } else {
-            outFields.push(
-              new Field(field.name, value as DataType, field.nullable),
-            );
-          }
-        }
-      }
-      return outFields;
-    }
-    const fields = takeMatchingFields(schema.fields, pathTree);
-    return new Schema(fields);
-  }
-}
-
-function* rowPathsAndValues(
-  row: Record<string, unknown>,
-  basePath: string[] = [],
-): Generator<[string[], unknown]> {
-  for (const [key, value] of Object.entries(row)) {
-    if (isObject(value)) {
-      yield* rowPathsAndValues(value, [...basePath, key]);
-    } else {
-      // Skip undefined values - they should be treated the same as missing fields
-      // for embedding function purposes
-      if (value !== undefined) {
-        yield [[...basePath, key], value];
-      }
-    }
-  }
-}
-
 function isObject(value: unknown): value is Record<string, unknown> {
   return (
     typeof value === "object" &&
@@ -577,146 +459,19 @@ function isObject(value: unknown): value is Record<string, unknown> {
   );
 }
 
-function getFieldForPath(schema: Schema, path: string[]): Field | undefined {
-  let current: Field | Schema = schema;
+function valueAtPath(datum: Record<string, unknown>, path: string[]): unknown {
+  let current: unknown = datum;
   for (const key of path) {
-    if (current instanceof Schema) {
-      const field: Field | undefined = current.fields.find(
-        (f) => f.name === key,
-      );
-      if (field === undefined) {
-        return undefined;
-      }
-      current = field;
-    } else if (current instanceof Field && DataType.isStruct(current.type)) {
-      const struct: Struct = current.type;
-      const field = struct.children.find((f) => f.name === key);
-      if (field === undefined) {
-        return undefined;
-      }
-      current = field;
+    if (current == null) {
+      return null;
+    }
+    if (isObject(current) && (Object.hasOwn(current, key) || key in current)) {
+      current = current[key];
     } else {
       return undefined;
     }
   }
-  if (current instanceof Field) {
-    return current;
-  } else {
-    return undefined;
-  }
-}
-
-/**
- * Try to infer which Arrow type to use for a given value.
- *
- * May return undefined if the type cannot be inferred.
- */
-function inferType(
-  value: unknown,
-  path: string[],
-  opts: MakeArrowTableOptions,
-): DataType | undefined {
-  if (typeof value === "bigint") {
-    return new Int64();
-  } else if (typeof value === "number") {
-    // Even if it's an integer, it's safer to assume Float64. Users can
-    // always provide an explicit schema or use BigInt if they mean integer.
-    return new Float64();
-  } else if (typeof value === "string") {
-    if (opts.dictionaryEncodeStrings) {
-      return new Dictionary(new Utf8(), new Int32());
-    } else {
-      return new Utf8();
-    }
-  } else if (typeof value === "boolean") {
-    return new Bool();
-  } else if (value instanceof Buffer) {
-    return new Binary();
-  } else if (ArrayBuffer.isView(value) && !(value instanceof DataView)) {
-    const info = typedArrayToArrowType(value);
-    if (info !== undefined) {
-      const child = new Field("item", info.elementType, true);
-      return new FixedSizeList(info.length, child);
-    }
-    return undefined;
-  } else if (Array.isArray(value)) {
-    if (value.length === 0) {
-      return undefined; // Without any values we can't infer the type
-    }
-    if (path.length === 1 && Object.hasOwn(opts.vectorColumns, path[0])) {
-      const floatType = sanitizeType(opts.vectorColumns[path[0]].type);
-      return new FixedSizeList(
-        value.length,
-        new Field("item", floatType, true),
-      );
-    }
-    const valueType = inferType(value[0], path, opts);
-    if (valueType === undefined) {
-      return undefined;
-    }
-    // Try to automatically detect embedding columns.
-    if (nameSuggestsVectorColumn(path[path.length - 1])) {
-      // Check if value is a Uint8Array for integer vector type determination
-      if (value instanceof Uint8Array) {
-        // For integer vectors, we default to Uint8 (matching Python implementation)
-        const child = new Field("item", new Uint8(), true);
-        return new FixedSizeList(value.length, child);
-      } else {
-        // For float vectors, we default to Float32
-        const child = new Field("item", new Float32(), true);
-        return new FixedSizeList(value.length, child);
-      }
-    } else {
-      const child = new Field("item", valueType, true);
-      return new List(child);
-    }
-  } else {
-    // TODO: timestamp
-    return undefined;
-  }
-}
-
-class PathTree<V> {
-  map: Map<string, V | PathTree<V>>;
-
-  constructor(entries?: [string[], V][]) {
-    this.map = new Map();
-    if (entries !== undefined) {
-      for (const [path, value] of entries) {
-        this.set(path, value);
-      }
-    }
-  }
-  has(path: string[]): boolean {
-    let ref: PathTree<V> = this;
-    for (const part of path) {
-      if (!(ref instanceof PathTree) || !ref.map.has(part)) {
-        return false;
-      }
-      ref = ref.map.get(part) as PathTree<V>;
-    }
-    return true;
-  }
-  get(path: string[]): V | undefined {
-    let ref: PathTree<V> = this;
-    for (const part of path) {
-      if (!(ref instanceof PathTree) || !ref.map.has(part)) {
-        return undefined;
-      }
-      ref = ref.map.get(part) as PathTree<V>;
-    }
-    return ref as V;
-  }
-  set(path: string[], value: V): void {
-    let ref: PathTree<V> = this;
-    for (const part of path.slice(0, path.length - 1)) {
-      if (!ref.map.has(part)) {
-        ref.map.set(part, new PathTree<V>());
-      }
-      ref = ref.map.get(part) as PathTree<V>;
-    }
-    ref.map.set(path[path.length - 1], value);
-  }
+  return current;
 }
 
 function transposeData(
@@ -724,37 +479,26 @@ function transposeData(
   field: Field,
   path: string[] = [],
 ): Vector {
+  const valuesPath = [...path, field.name];
+  const values = data.map((datum) => valueAtPath(datum, valuesPath));
   if (field.type instanceof Struct) {
     const childFields = field.type.children;
-    const fullPath = [...path, field.name];
     const childVectors = childFields.map((child) => {
-      return transposeData(data, child, fullPath);
+      return transposeData(data, child, valuesPath);
     });
+    const nullCount = values.filter((value) => value === null).length;
     const structData = makeData({
       type: field.type,
+      length: values.length,
+      nullCount,
+      nullBitmap:
+        nullCount > 0
+          ? arrowUtil.packBools(values.map((value) => value !== null))
+          : undefined,
       children: childVectors as unknown as ArrowData<DataType>[],
     });
     return arrowMakeVector(structData);
   } else {
-    const valuesPath = [...path, field.name];
-    const values = data.map((datum) => {
-      let current: unknown = datum;
-      for (const key of valuesPath) {
-        if (current == null) {
-          return null;
-        }
-
-        if (
-          isObject(current) &&
-          (Object.hasOwn(current, key) || key in current)
-        ) {
-          current = current[key];
-        } else {
-          return null;
-        }
-      }
-      return current;
-    });
     return makeVector(values, field.type, undefined, field.nullable);
   }
 }
@@ -797,32 +541,6 @@ function makeListVector(lists: unknown[][]): Vector<unknown> {
   return listBuilder.finish().toVector();
 }
 
-/**
- * Map a JS TypedArray instance to the corresponding Arrow element DataType
- * and its length. Returns undefined if the value is not a recognized TypedArray.
- */
-function typedArrayToArrowType(
-  value: ArrayBufferView,
-): { elementType: DataType; length: number } | undefined {
-  if (value instanceof Float32Array)
-    return { elementType: new Float32(), length: value.length };
-  if (value instanceof Float64Array)
-    return { elementType: new Float64(), length: value.length };
-  if (value instanceof Uint8Array)
-    return { elementType: new Uint8(), length: value.length };
-  if (value instanceof Uint16Array)
-    return { elementType: new Uint16(), length: value.length };
-  if (value instanceof Uint32Array)
-    return { elementType: new Uint32(), length: value.length };
-  if (value instanceof Int8Array)
-    return { elementType: new Int8(), length: value.length };
-  if (value instanceof Int16Array)
-    return { elementType: new Int16(), length: value.length };
-  if (value instanceof Int32Array)
-    return { elementType: new Int32(), length: value.length };
-  return undefined;
-}
-
 /** Helper function to convert an Array of JS values to an Arrow Vector */
 function makeVector(
   values: unknown[],
@@ -1462,8 +1180,12 @@ export function ensureNestedFieldsExist(
           completeRow[field.name] = row[field.name];
         }
       } else {
-        // Field is missing from the data - set to null
-        completeRow[field.name] = null;
+        // 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;
       }
     }
 
@@ -1498,8 +1220,12 @@ function ensureStructFieldsExist(
         completeStruct[childField.name] = data[childField.name];
       }
     } else {
-      // Field is missing - set to null
-      completeStruct[childField.name] = null;
+      // 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;
     }
   }
 

nodejs/lancedb/arrow_type.ts

@@ -0,0 +1,40 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import {
+  type DataType,
+  Float32,
+  Float64,
+  Int8,
+  Int16,
+  Int32,
+  Uint8,
+  Uint16,
+  Uint32,
+} from "apache-arrow";
+
+/**
+ * Map a JS TypedArray instance to the corresponding Arrow element type and
+ * length. Returns undefined when the view is not a supported TypedArray.
+ */
+export function typedArrayToArrowType(
+  value: ArrayBufferView,
+): { elementType: DataType; length: number } | undefined {
+  if (value instanceof Float32Array)
+    return { elementType: new Float32(), length: value.length };
+  if (value instanceof Float64Array)
+    return { elementType: new Float64(), length: value.length };
+  if (value instanceof Uint8Array)
+    return { elementType: new Uint8(), length: value.length };
+  if (value instanceof Uint16Array)
+    return { elementType: new Uint16(), length: value.length };
+  if (value instanceof Uint32Array)
+    return { elementType: new Uint32(), length: value.length };
+  if (value instanceof Int8Array)
+    return { elementType: new Int8(), length: value.length };
+  if (value instanceof Int16Array)
+    return { elementType: new Int16(), length: value.length };
+  if (value instanceof Int32Array)
+    return { elementType: new Int32(), length: value.length };
+  return undefined;
+}

nodejs/lancedb/connection.ts

@@ -31,12 +31,14 @@ import type {
   JobDescription,
   JobInfo,
   ListNamespacesResponse,
+  ListTablesResponse,
 } from "./native";
 export type {
   CreateNamespaceResponse,
   DescribeNamespaceResponse,
   DropNamespaceResponse,
   ListNamespacesResponse,
+  ListTablesResponse,
 };
 import { sanitizeTable } from "./sanitize";
 import { LocalTable, Table } from "./table";
@@ -134,6 +136,10 @@ export interface OpenTableOptions {
   indexCacheSize?: number;
 }
 
+/**
+ * @deprecated Use {@link ListTablesOptions} with {@link Connection.listTables}
+ * instead.
+ */
 export interface TableNamesOptions {
   /**
    * If present, only return names that come lexicographically after the
@@ -147,6 +153,24 @@ export interface TableNamesOptions {
   limit?: number;
 }
 
+export interface ListTablesOptions {
+  /**
+   * Token from a previous response, to resume listing where it left off.
+   *
+   * The token is opaque: it carries whatever the database needs to resume, and
+   * callers should not construct or interpret one.
+   */
+  pageToken?: string;
+  /**
+   * An upper bound on how many tables to return.
+   *
+   * 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
+   * full.
+   */
+  limit?: number;
+}
+
 export interface ListNamespacesOptions {
   /** Token from a previous response for pagination. */
   pageToken?: string;
@@ -231,6 +255,7 @@ export abstract class Connection {
    * @param {Partial<TableNamesOptions>} options - options to control the
    * paging / start point (backwards compatibility)
    *
+   * @deprecated Use {@link Connection.listTables} instead.
    */
   abstract tableNames(options?: Partial<TableNamesOptions>): Promise<string[]>;
   /**
@@ -241,12 +266,53 @@ export abstract class Connection {
    * @param {Partial<TableNamesOptions>} options - options to control the
    * paging / start point
    *
+   * @deprecated Use {@link Connection.listTables} instead.
    */
   abstract tableNames(
     namespacePath?: string[],
     options?: Partial<TableNamesOptions>,
   ): Promise<string[]>;
 
+  /**
+   * List a page of the tables in this database.
+   *
+   * To retrieve the tables after the page, pass the `pageToken` the response
+   * carries back in. A page can be shorter than `limit` without being the last
+   * one, so walk until a response carries no page token:
+   *
+   * ```ts
+   * const names = [];
+   * let pageToken = undefined;
+   * do {
+   *   const page = await conn.listTables({ pageToken, limit: 100 });
+   *   names.push(...page.tables);
+   *   pageToken = page.pageToken;
+   * } while (pageToken);
+   * ```
+   *
+   * @param {Partial<ListTablesOptions>} options - Pagination options
+   *   (`pageToken`, `limit`).
+   * @returns {Promise<ListTablesResponse>} A page of table names and an
+   *   optional token for the tables after it.
+   */
+  abstract listTables(
+    options?: Partial<ListTablesOptions>,
+  ): Promise<ListTablesResponse>;
+  /**
+   * List a page of the tables in this database.
+   *
+   * @param {string[]} namespacePath - The namespace path to list tables from
+   *   (defaults to root namespace)
+   * @param {Partial<ListTablesOptions>} options - Pagination options
+   *   (`pageToken`, `limit`).
+   * @returns {Promise<ListTablesResponse>} A page of table names and an
+   *   optional token for the tables after it.
+   */
+  abstract listTables(
+    namespacePath?: string[],
+    options?: Partial<ListTablesOptions>,
+  ): Promise<ListTablesResponse>;
+
   /**
    * Open a table in the database.
    * @param {string} name - The name of the table
@@ -601,6 +667,25 @@ export class LocalConnection extends Connection {
     return await this.inner.listMaterializedViews();
   }
 
+  async listTables(
+    namespacePathOrOptions?: string[] | Partial<ListTablesOptions>,
+    options?: Partial<ListTablesOptions>,
+  ): Promise<ListTablesResponse> {
+    // Detect if first argument is namespacePath array or options object
+    const namespacePath = Array.isArray(namespacePathOrOptions)
+      ? namespacePathOrOptions
+      : undefined;
+    const listTablesOptions = Array.isArray(namespacePathOrOptions)
+      ? options
+      : namespacePathOrOptions;
+
+    return this.inner.listTables(
+      namespacePath ?? [],
+      listTablesOptions?.pageToken,
+      listTablesOptions?.limit,
+    );
+  }
+
   async openTable(
     name: string,
     namespacePath?: string[],

nodejs/lancedb/embedding/index.ts

@@ -4,7 +4,15 @@
 import { Field, Schema } from "../arrow";
 import { sanitizeType } from "../sanitize";
 import { EmbeddingFunction } from "./embedding_function";
-import { EmbeddingFunctionConfig, getRegistry } from "./registry";
+import {
+  EmbeddingFunctionConfig,
+  EmbeddingFunctionRegistry,
+  getRegistry as getGlobalRegistry,
+  registerBuiltIn,
+} from "./registry";
+
+type OpenAIModule = typeof import("./openai");
+type TransformersModule = typeof import("./transformers");
 
 export {
   FieldOptions,
@@ -14,7 +22,39 @@ export {
   EmbeddingFunctionConstructor,
 } from "./embedding_function";
 
-export * from "./registry";
+export {
+  EmbeddingFunctionRegistry,
+  parseEmbeddingMetadata,
+  register,
+} from "./registry";
+export type {
+  CreateReturnType,
+  EmbeddingFunctionConfig,
+  EmbeddingFunctionCreate,
+  EmbeddingMetadataEntry,
+  ResolvedEmbeddingFunctionConfig,
+} from "./registry";
+
+function initializeBuiltInProviders() {
+  const { OpenAIEmbeddingFunction } = require("./openai") as OpenAIModule;
+  const { TransformersEmbeddingFunction } =
+    require("./transformers") as TransformersModule;
+
+  registerBuiltIn("openai", OpenAIEmbeddingFunction);
+  registerBuiltIn("huggingface", TransformersEmbeddingFunction);
+}
+
+/**
+ * Get the global embedding function registry.
+ *
+ * LanceDB built-in providers are initialized when this public API is first
+ * used, so importing the root package does not change automatic search
+ * selection for tables without embedding metadata.
+ */
+export function getRegistry(): EmbeddingFunctionRegistry {
+  initializeBuiltInProviders();
+  return getGlobalRegistry();
+}
 
 /**
  * Create a schema with embedding functions.

nodejs/lancedb/embedding/openai.ts

@@ -5,14 +5,13 @@ import type OpenAI from "openai";
 import type { EmbeddingCreateParams } from "openai/resources/index";
 import { Float, Float32 } from "../arrow";
 import { EmbeddingFunction } from "./embedding_function";
-import { register } from "./registry";
+import { registerBuiltIn } from "./registry";
 
 export type OpenAIOptions = {
   apiKey: string;
   model: EmbeddingCreateParams["model"];
 };
 
-@register("openai")
 export class OpenAIEmbeddingFunction extends EmbeddingFunction<
   string,
   Partial<OpenAIOptions>
@@ -100,3 +99,5 @@ export class OpenAIEmbeddingFunction extends EmbeddingFunction<
     return response.data[0].embedding;
   }
 }
+
+registerBuiltIn("openai", OpenAIEmbeddingFunction);

nodejs/lancedb/embedding/registry.ts

@@ -7,6 +7,10 @@ import {
 } from "./embedding_function";
 import "reflect-metadata";
 
+const builtInFunctionsKey = Symbol.for(
+  "@lancedb/lancedb::embedding-built-in-functions::v1",
+);
+
 export type CreateReturnType<T> = T extends { init: () => Promise<void> }
   ? Promise<T>
   : T;
@@ -59,6 +63,15 @@ export class EmbeddingFunctionRegistry {
     };
   }
 
+  /** @ignore */
+  setBuiltIn<
+    T extends EmbeddingFunctionConstructor = EmbeddingFunctionConstructor,
+  >(name: string, ctor: T): T {
+    this.#functions.set(name, ctor);
+    Reflect.defineMetadata("lancedb::embedding::name", name, ctor);
+    return ctor;
+  }
+
   get<T extends EmbeddingFunction<unknown>>(
     name: string,
   ): EmbeddingFunctionCreate<T> | undefined;
@@ -96,6 +109,7 @@ export class EmbeddingFunctionRegistry {
    */
   reset(this: EmbeddingFunctionRegistry) {
     this.#functions.clear();
+    getBuiltInFunctions(this).clear();
   }
 
   /**
@@ -183,12 +197,56 @@ export class EmbeddingFunctionRegistry {
   }
 }
 
-const _REGISTRY = new EmbeddingFunctionRegistry();
+function getBuiltInFunctions(registry: EmbeddingFunctionRegistry): Set<string> {
+  const registryWithBuiltIns = registry as EmbeddingFunctionRegistry & {
+    [key: symbol]: Set<string> | undefined;
+  };
+  let builtInFunctions = registryWithBuiltIns[builtInFunctionsKey];
+  if (builtInFunctions === undefined) {
+    builtInFunctions = new Set<string>();
+    registryWithBuiltIns[builtInFunctionsKey] = builtInFunctions;
+  }
+  return builtInFunctions;
+}
+
+// Server bundlers can load the side-effect embedding entry points and the public
+// embedding API from separate module graphs. Keep their registry shared.
+const registryKey = Symbol.for(
+  "@lancedb/lancedb::embedding-function-registry::v1",
+);
+const registryGlobal = globalThis as typeof globalThis & {
+  [key: symbol]: EmbeddingFunctionRegistry | undefined;
+};
+
+function getGlobalRegistry(): EmbeddingFunctionRegistry {
+  const existingRegistry = registryGlobal[registryKey];
+  if (existingRegistry !== undefined) {
+    return existingRegistry;
+  }
+  const registry = new EmbeddingFunctionRegistry();
+  registryGlobal[registryKey] = registry;
+  return registry;
+}
+
+const _REGISTRY = getGlobalRegistry();
 
 export function register(name?: string) {
   return _REGISTRY.register(name);
 }
 
+/** @ignore */
+export function registerBuiltIn<
+  T extends EmbeddingFunctionConstructor = EmbeddingFunctionConstructor,
+>(name: string, ctor: T): T {
+  const builtInFunctions = getBuiltInFunctions(_REGISTRY);
+  if (builtInFunctions.has(name)) {
+    return _REGISTRY.setBuiltIn(name, ctor);
+  }
+  _REGISTRY.register(name)(ctor);
+  builtInFunctions.add(name);
+  return ctor;
+}
+
 /**
  * Utility function to get the global instance of the registry
  * @returns `EmbeddingFunctionRegistry` The global instance of the registry

nodejs/lancedb/embedding/transformers.ts

@@ -3,7 +3,7 @@
 
 import { Float, Float32 } from "../arrow";
 import { EmbeddingFunction } from "./embedding_function";
-import { register } from "./registry";
+import { registerBuiltIn } from "./registry";
 
 export type XenovaTransformerOptions = {
   /** The wasm compatible model to use */
@@ -31,7 +31,6 @@ export type XenovaTransformerOptions = {
   };
 };
 
-@register("huggingface")
 export class TransformersEmbeddingFunction extends EmbeddingFunction<
   string,
   Partial<XenovaTransformerOptions>
@@ -158,6 +157,8 @@ export class TransformersEmbeddingFunction extends EmbeddingFunction<
   }
 }
 
+registerBuiltIn("huggingface", TransformersEmbeddingFunction);
+
 const tensorDiv = (
   src: import("@huggingface/transformers").Tensor,
   divBy: number,

nodejs/lancedb/index.ts

@@ -81,11 +81,13 @@ export {
   Connection,
   CreateTableOptions,
   TableNamesOptions,
+  ListTablesOptions,
   OpenTableOptions,
   ListNamespacesOptions,
   CreateNamespaceOptions,
   DropNamespaceOptions,
   ListNamespacesResponse,
+  ListTablesResponse,
   CreateNamespaceResponse,
   DropNamespaceResponse,
   DescribeNamespaceResponse,
@@ -101,6 +103,7 @@ export {
 } from "./native.js";
 
 export {
+  AutoQuery,
   ExecutableQuery,
   Query,
   QueryBase,

nodejs/lancedb/query.ts

@@ -100,6 +100,29 @@ export interface FullTextSearchOptions {
   columns?: string | string[];
 }
 
+function nearestToNative(
+  inner: NativeQuery,
+  vector: Awaited<IntoVector>,
+): NativeVectorQuery {
+  const raw = Array.isArray(vector) ? null : extractVectorBuffer(vector);
+  if (raw) {
+    return inner.nearestToRaw(raw.data, raw.dtype);
+  }
+  return inner.nearestTo(Float32Array.from(vector as number[]));
+}
+
+function addQueryVectorToNative(
+  inner: NativeVectorQuery,
+  vector: Awaited<IntoVector>,
+) {
+  const raw = Array.isArray(vector) ? null : extractVectorBuffer(vector);
+  if (raw) {
+    inner.addQueryVectorRaw(raw.data, raw.dtype);
+  } else {
+    inner.addQueryVector(Float32Array.from(vector as number[]));
+  }
+}
+
 /** Common methods supported by all query types
  *
  * @see {@link Query}
@@ -111,13 +134,15 @@ export class QueryBase<
   NativeQueryType extends NativeQuery | NativeVectorQuery | NativeTakeQuery,
 > implements AsyncIterable<RecordBatch>
 {
+  protected inner!: NativeQueryType | Promise<NativeQueryType>;
+
   /**
    * @hidden
    */
-  protected constructor(
-    protected inner: NativeQueryType | Promise<NativeQueryType>,
-  ) {
-    // intentionally empty
+  protected constructor(inner?: NativeQueryType | Promise<NativeQueryType>) {
+    if (inner !== undefined) {
+      this.inner = inner;
+    }
   }
 
   // call a function on the inner (either a promise or the actual object)
@@ -135,6 +160,15 @@ export class QueryBase<
     }
   }
 
+  /**
+   * Return the native query used by the next terminal operation.
+   *
+   * @hidden
+   */
+  protected async getInner(): Promise<NativeQueryType> {
+    return this.inner;
+  }
+
   /**
    * Return only the specified columns.
    *
@@ -207,16 +241,11 @@ export class QueryBase<
   /**
    * @hidden
    */
-  protected nativeExecute(
+  protected async nativeExecute(
     options?: Partial<QueryExecutionOptions>,
   ): Promise<NativeBatchIterator> {
-    if (this.inner instanceof Promise) {
-      return this.inner.then((inner) =>
-        inner.execute(options?.maxBatchLength, options?.timeoutMs),
-      );
-    } else {
-      return this.inner.execute(options?.maxBatchLength, options?.timeoutMs);
-    }
+    const inner = await this.getInner();
+    return inner.execute(options?.maxBatchLength, options?.timeoutMs);
   }
 
   /**
@@ -245,12 +274,7 @@ export class QueryBase<
   /** Collect the results as an Arrow @see {@link ArrowTable}. */
   async toArrow(options?: Partial<QueryExecutionOptions>): Promise<ArrowTable> {
     const batches = [];
-    let inner;
-    if (this.inner instanceof Promise) {
-      inner = await this.inner;
-    } else {
-      inner = this.inner;
-    }
+    const inner = await this.getInner();
     for await (const batch of new RecordBatchIterable(inner, options)) {
       batches.push(batch);
     }
@@ -279,11 +303,8 @@ export class QueryBase<
    * @returns A Promise that resolves to a string containing the query execution plan explanation.
    */
   async explainPlan(verbose = false): Promise<string> {
-    if (this.inner instanceof Promise) {
-      return this.inner.then((inner) => inner.explainPlan(verbose));
-    } else {
-      return this.inner.explainPlan(verbose);
-    }
+    const inner = await this.getInner();
+    return inner.explainPlan(verbose);
   }
 
   /**
@@ -321,13 +342,8 @@ export class QueryBase<
     distributedMetrics?: AnalyzePlanDistributedMetrics,
   ): Promise<string> {
     const distributedMetricsMode = distributedMetrics ?? "aggregate";
-    if (this.inner instanceof Promise) {
-      return this.inner.then((inner) =>
-        inner.analyzePlan(distributedMetricsMode),
-      );
-    } else {
-      return this.inner.analyzePlan(distributedMetricsMode);
-    }
+    const inner = await this.getInner();
+    return inner.analyzePlan(distributedMetricsMode);
   }
 
   /**
@@ -339,12 +355,8 @@ export class QueryBase<
    * @returns An Arrow Schema describing the output columns.
    */
   async outputSchema(): Promise<import("./arrow").Schema> {
-    let schemaBuffer: Buffer;
-    if (this.inner instanceof Promise) {
-      schemaBuffer = await this.inner.then((inner) => inner.outputSchema());
-    } else {
-      schemaBuffer = await this.inner.outputSchema();
-    }
+    const inner = await this.getInner();
+    const schemaBuffer = await inner.outputSchema();
     const schema = tableFromIPC(schemaBuffer).schema;
     return schema;
   }
@@ -356,7 +368,7 @@ export class StandardQueryBase<
   extends QueryBase<NativeQueryType>
   implements ExecutableQuery
 {
-  constructor(inner: NativeQueryType | Promise<NativeQueryType>) {
+  constructor(inner?: NativeQueryType | Promise<NativeQueryType>) {
     super(inner);
   }
 
@@ -510,6 +522,13 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
     super(inner);
   }
 
+  /**
+   * @hidden
+   */
+  protected doVectorCall(fn: (inner: NativeVectorQuery) => void) {
+    super.doCall(fn);
+  }
+
   /**
    * Set the number of partitions to search (probe)
    *
@@ -537,7 +556,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
    * the minimum and maximum to the same value.
    */
   nprobes(nprobes: number): VectorQuery {
-    super.doCall((inner) => inner.nprobes(nprobes));
+    this.doVectorCall((inner) => inner.nprobes(nprobes));
 
     return this;
   }
@@ -551,7 +570,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
    * but will also increase latency.
    */
   minimumNprobes(minimumNprobes: number): VectorQuery {
-    super.doCall((inner) => inner.minimumNprobes(minimumNprobes));
+    this.doVectorCall((inner) => inner.minimumNprobes(minimumNprobes));
     return this;
   }
 
@@ -565,7 +584,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
    * potential false negatives.
    */
   maximumNprobes(maximumNprobes: number): VectorQuery {
-    super.doCall((inner) => inner.maximumNprobes(maximumNprobes));
+    this.doVectorCall((inner) => inner.maximumNprobes(maximumNprobes));
     return this;
   }
 
@@ -578,7 +597,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
    * `undefined` means no lower or upper bound.
    */
   distanceRange(lowerBound?: number, upperBound?: number): VectorQuery {
-    super.doCall((inner) => inner.distanceRange(lowerBound, upperBound));
+    this.doVectorCall((inner) => inner.distanceRange(lowerBound, upperBound));
     return this;
   }
 
@@ -592,7 +611,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
    * also increase the latency of your query. The default value is 1.5*limit.
    */
   ef(ef: number): VectorQuery {
-    super.doCall((inner) => inner.ef(ef));
+    this.doVectorCall((inner) => inner.ef(ef));
     return this;
   }
 
@@ -606,7 +625,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
    * whose data type is a fixed-size-list of floats.
    */
   column(column: string): VectorQuery {
-    super.doCall((inner) => inner.column(column));
+    this.doVectorCall((inner) => inner.column(column));
     return this;
   }
 
@@ -627,7 +646,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   distanceType(
     distanceType: Required<IvfPqOptions>["distanceType"],
   ): VectorQuery {
-    super.doCall((inner) => inner.distanceType(distanceType));
+    this.doVectorCall((inner) => inner.distanceType(distanceType));
     return this;
   }
 
@@ -661,7 +680,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
    * distance between the query vector and the actual uncompressed vector.
    */
   refineFactor(refineFactor: number): VectorQuery {
-    super.doCall((inner) => inner.refineFactor(refineFactor));
+    this.doVectorCall((inner) => inner.refineFactor(refineFactor));
     return this;
   }
 
@@ -686,7 +705,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
    * factor can often help restore some of the results lost by post filtering.
    */
   postfilter(): VectorQuery {
-    super.doCall((inner) => inner.postfilter());
+    this.doVectorCall((inner) => inner.postfilter());
     return this;
   }
 
@@ -700,7 +719,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
    * calculate your recall to select an appropriate value for nprobes.
    */
   bypassVectorIndex(): VectorQuery {
-    super.doCall((inner) => inner.bypassVectorIndex());
+    this.doVectorCall((inner) => inner.bypassVectorIndex());
     return this;
   }
 
@@ -708,43 +727,39 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
    * Add a query vector to the search
    *
    * This method can be called multiple times to add multiple query vectors
-   * to the search. If multiple query vectors are added, then they will be searched
-   * in parallel, and the results will be concatenated. A column called `query_index`
-   * will be added to indicate the index of the query vector that produced the result.
-   *
-   * Performance wise, this is equivalent to running multiple queries concurrently.
+   * to the search. A column called `query_index` will be added to indicate the index
+   * of the query vector that produced the result. Flat searches share one table scan
+   * across the query vectors, avoiding the scan and memory amplification of running
+   * multiple queries concurrently. Indexed searches may still perform per-vector
+   * index work.
    */
   addQueryVector(vector: IntoVector): VectorQuery {
     if (vector instanceof Promise) {
+      // Observe the promise as soon as it is accepted. The existing native
+      // query may still be pending, and delaying observation until it resolves
+      // can otherwise surface a fast rejection as unhandled.
+      const settledVector = vector.then(
+        (value) => ({ status: "fulfilled" as const, value }),
+        (reason) => ({ status: "rejected" as const, reason }),
+      );
       const res = (async () => {
-        try {
-          const v = await vector;
-          // biome-ignore lint/suspicious/noExplicitAny: we need to get the `inner`, but js has no package scoping
-          const value: any = this.addQueryVector(v);
-          const inner = value.inner as
-            | NativeVectorQuery
-            | Promise<NativeVectorQuery>;
-          return inner;
-        } catch (e) {
-          return Promise.reject(e);
+        const inner = await this.getInner();
+        const outcome = await settledVector;
+        if (outcome.status === "rejected") {
+          throw outcome.reason;
         }
+        addQueryVectorToNative(inner, outcome.value);
+        return inner;
       })();
       return new VectorQuery(res);
     } else {
-      super.doCall((inner) => {
-        const raw = Array.isArray(vector) ? null : extractVectorBuffer(vector);
-        if (raw) {
-          inner.addQueryVectorRaw(raw.data, raw.dtype);
-        } else {
-          inner.addQueryVector(Float32Array.from(vector as number[]));
-        }
-      });
+      this.doVectorCall((inner) => addQueryVectorToNative(inner, vector));
       return this;
     }
   }
 
   rerank(reranker: Reranker): VectorQuery {
-    super.doCall((inner) =>
+    this.doVectorCall((inner) =>
       inner.rerank(async (args) => {
         const vecResults = await fromBufferToRecordBatch(args.vecResults);
         const ftsResults = await fromBufferToRecordBatch(args.ftsResults);
@@ -763,6 +778,71 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   }
 }
 
+/**
+ * Create a string query whose vector/FTS routing is resolved against the active
+ * table schema when the query executes.
+ *
+ * @hidden
+ */
+export function createAutoQuery(
+  table: NativeTable,
+  query: string,
+  columns: string[] | null,
+  getVector: (metadata: string) => Promise<Awaited<IntoVector>>,
+): AutoQuery {
+  type RouteSnapshot = {
+    table: NativeTable;
+    embeddingMetadata: string | undefined;
+  };
+  type CachedPreparation = {
+    metadata: string;
+    vector: Promise<Awaited<IntoVector>>;
+  };
+
+  let cachedPreparation: CachedPreparation | undefined;
+
+  const snapshotRoute = async (): Promise<RouteSnapshot> => {
+    const snapshot = await table.querySnapshot();
+    const schema = tableFromIPC(await snapshot.schema()).schema;
+    return {
+      table: snapshot,
+      embeddingMetadata: schema.metadata.get("embedding_functions"),
+    };
+  };
+
+  const createInner = async (): Promise<NativeQuery | NativeVectorQuery> => {
+    const route = await snapshotRoute();
+    if (route.embeddingMetadata === undefined) {
+      const inner = route.table.query();
+      inner.fullTextSearch({ query, columns });
+      return inner;
+    }
+
+    const metadata = route.embeddingMetadata;
+    if (cachedPreparation?.metadata !== metadata) {
+      cachedPreparation = {
+        metadata,
+        vector: Promise.resolve().then(() => getVector(metadata)),
+      };
+    }
+
+    const preparation = cachedPreparation;
+    let vector: Awaited<IntoVector>;
+    try {
+      vector = await preparation.vector;
+    } catch (error) {
+      if (cachedPreparation === preparation) {
+        cachedPreparation = undefined;
+      }
+      throw error;
+    }
+
+    return nearestToNative(route.table.query(), vector);
+  };
+
+  return new AutoQuery(createInner);
+}
+
 /**
  * A query that returns a subset of the rows in the table.
  *
@@ -788,6 +868,51 @@ export class TakeQuery extends QueryBase<NativeTakeQuery> {
   }
 }
 
+/**
+ * A builder for automatic string searches.
+ *
+ * Automatic search determines whether to use full-text or vector search from
+ * the table revision selected for each execution. This builder exposes the
+ * common operations supported by both query families.
+ *
+ * @hideconstructor
+ */
+export class AutoQuery extends StandardQueryBase<
+  NativeQuery | NativeVectorQuery
+> {
+  private readonly calls: Array<
+    (inner: NativeQuery | NativeVectorQuery) => void
+  > = [];
+
+  /** @hidden */
+  constructor(
+    private readonly createInner: () => Promise<
+      NativeQuery | NativeVectorQuery
+    >,
+  ) {
+    super();
+  }
+
+  /** @hidden */
+  protected override doCall(
+    fn: (inner: NativeQuery | NativeVectorQuery) => void,
+  ) {
+    this.calls.push(fn);
+  }
+
+  /** @hidden */
+  protected override async getInner(): Promise<
+    NativeQuery | NativeVectorQuery
+  > {
+    const calls = [...this.calls];
+    const inner = await this.createInner();
+    for (const call of calls) {
+      call(inner);
+    }
+    return inner;
+  }
+}
+
 /** A builder for LanceDB queries.
  *
  * @see {@link Table#query}, {@link Table#search}
@@ -840,45 +965,19 @@ export class Query extends StandardQueryBase<NativeQuery> {
    * a default `limit` of 10 will be used.  @see {@link Query#limit}
    */
   nearestTo(vector: IntoVector): VectorQuery {
-    const callNearestTo = (
-      inner: NativeQuery,
-      resolved: Float32Array | Float64Array | Uint8Array | number[],
-    ): NativeVectorQuery => {
-      const raw = Array.isArray(resolved)
-        ? null
-        : extractVectorBuffer(resolved);
-      if (raw) {
-        return inner.nearestToRaw(raw.data, raw.dtype);
-      }
-      return inner.nearestTo(Float32Array.from(resolved as number[]));
-    };
-
-    if (this.inner instanceof Promise) {
-      const nativeQuery = this.inner.then(async (inner) => {
-        const resolved = vector instanceof Promise ? await vector : vector;
-        return callNearestTo(inner, resolved);
-      });
+    const inner = this.inner;
+    if (inner instanceof Promise) {
+      const nativeQuery = inner.then(async (resolvedInner) =>
+        nearestToNative(resolvedInner, await vector),
+      );
       return new VectorQuery(nativeQuery);
     }
     if (vector instanceof Promise) {
-      const res = (async () => {
-        try {
-          const v = await vector;
-          // biome-ignore lint/suspicious/noExplicitAny: we need to get the `inner`, but js has no package scoping
-          const value: any = this.nearestTo(v);
-          const inner = value.inner as
-            | NativeVectorQuery
-            | Promise<NativeVectorQuery>;
-          return inner;
-        } catch (e) {
-          return Promise.reject(e);
-        }
-      })();
-      return new VectorQuery(res);
-    } else {
-      const vectorQuery = callNearestTo(this.inner, vector);
-      return new VectorQuery(vectorQuery);
+      return new VectorQuery(
+        vector.then((resolvedVector) => nearestToNative(inner, resolvedVector)),
+      );
     }
+    return new VectorQuery(nearestToNative(inner, vector));
   }
 
   nearestToText(query: string | FullTextQuery, columns?: string[]): Query {

nodejs/lancedb/sanitize.ts

@@ -94,17 +94,24 @@ export function sanitizeMetadata(
   if (metadataLike === undefined || metadataLike === null) {
     return undefined;
   }
-  if (!(metadataLike instanceof Map)) {
+
+  let entries: IterableIterator<[unknown, unknown]>;
+  try {
+    entries = Map.prototype.entries.call(metadataLike);
+  } catch {
     throw Error("Expected metadata, if present, to be a Map<string, string>");
   }
-  for (const item of metadataLike) {
-    if (typeof item[0] !== "string" || typeof item[1] !== "string") {
+
+  const metadata = new Map<string, string>();
+  for (const [key, value] of entries) {
+    if (typeof key !== "string" || typeof value !== "string") {
       throw Error(
         "Expected metadata, if present, to be a Map<string, string> but it had non-string keys or values",
       );
     }
+    metadata.set(key, value);
   }
-  return metadataLike as Map<string, string>;
+  return metadata;
 }
 
 export function sanitizeInt(typeLike: object) {

nodejs/lancedb/schema.ts

@@ -0,0 +1,567 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import {
+  Binary,
+  Bool,
+  DataType,
+  Dictionary,
+  Field,
+  FixedSizeList,
+  Float32,
+  Float64,
+  Int32,
+  Int64,
+  List,
+  Schema,
+  Struct,
+  Utf8,
+  util as arrowUtil,
+} from "apache-arrow";
+import { typedArrayToArrowType } from "./arrow_type";
+import { sanitizeType } from "./sanitize";
+
+type InferenceOptions = {
+  dictionaryEncodeStrings: boolean;
+  vectorColumns: Record<string, { type: unknown }>;
+};
+
+/**
+ * Infer the Arrow schema represented by a set of records.
+ *
+ * This is the intentionally small interface to schema inference. The stateful
+ * details of combining partial type evidence are encapsulated below so callers
+ * only need to provide records, an optional schema, and inference options.
+ */
+export function inferSchema(
+  data: Array<Record<string, unknown>>,
+  schema: Schema | undefined,
+  options: InferenceOptions,
+): Schema {
+  return new SchemaInferrer(schema, options).infer(data);
+}
+
+class SchemaInferrer {
+  private readonly fields = new FieldTree();
+
+  constructor(
+    private readonly providedSchema: Schema | undefined,
+    private readonly options: InferenceOptions,
+  ) {}
+
+  infer(data: Array<Record<string, unknown>>): Schema {
+    for (const [row, record] of data.entries()) {
+      for (const [path, value] of recordPathsAndValues(record)) {
+        this.observe(path, value, row);
+      }
+    }
+
+    return this.providedSchema === undefined
+      ? new Schema(fieldsFromTree(this.fields))
+      : new Schema(matchingFields(this.providedSchema.fields, this.fields));
+  }
+
+  private observe(path: string[], value: unknown, row: number): void {
+    const current = this.fields.get(path);
+    if (current === undefined) {
+      this.addField(path, value, row);
+    } else if (this.providedSchema === undefined) {
+      this.updateInferredField(path, value, row, current);
+    }
+  }
+
+  private addField(path: string[], value: unknown, row: number): void {
+    if (this.providedSchema !== undefined) {
+      this.addSchemaField(this.providedSchema, path, row);
+      return;
+    }
+
+    const evidence =
+      this.inferType(value, path) ?? DeferredTypeEvidence.from(value, row);
+    if (evidence === undefined) {
+      throw typeInferenceError(path, row);
+    }
+
+    const conflict = this.fields.set(
+      path,
+      evidence,
+      (existing) =>
+        existing instanceof DeferredTypeEvidence && existing.isOnlyNulls(),
+    );
+    if (conflict !== undefined) {
+      throw branchConflictError(conflict, row, "Struct");
+    }
+  }
+
+  private addSchemaField(schema: Schema, path: string[], row: number): void {
+    const field = fieldAtPath(schema, path);
+    if (field === undefined) {
+      throw new Error(
+        `Found field not in schema: ${path.join(".")} at row ${row}`,
+      );
+    }
+
+    const conflict = this.fields.set(path, field.type);
+    if (conflict !== undefined) {
+      throw branchConflictError(conflict, row, "Struct");
+    }
+  }
+
+  private updateInferredField(
+    path: string[],
+    value: unknown,
+    row: number,
+    current: FieldNode,
+  ): void {
+    const newType = this.inferType(value, path);
+    const deferred = DeferredTypeEvidence.from(value, row);
+
+    if (current instanceof FieldTree) {
+      if (deferred?.isOnlyNulls()) {
+        return;
+      }
+      throw schemaInferenceError(
+        path,
+        row,
+        "Struct",
+        describeEvidence(newType ?? deferred),
+      );
+    }
+
+    if (current instanceof DeferredTypeEvidence) {
+      this.resolveDeferredField(path, row, current, newType, deferred);
+      return;
+    }
+
+    if (newType !== undefined) {
+      if (!inferredTypesEqual(current, newType)) {
+        throw schemaInferenceError(
+          path,
+          row,
+          describeEvidence(current),
+          describeEvidence(newType),
+        );
+      }
+      return;
+    }
+
+    if (deferred === undefined || !deferred.matches(current)) {
+      throw schemaInferenceError(
+        path,
+        row,
+        describeEvidence(current),
+        describeEvidence(deferred),
+      );
+    }
+  }
+
+  private resolveDeferredField(
+    path: string[],
+    row: number,
+    current: DeferredTypeEvidence,
+    newType: DataType | undefined,
+    deferred: DeferredTypeEvidence | undefined,
+  ): void {
+    if (newType !== undefined) {
+      if (!current.matches(newType)) {
+        throw schemaInferenceError(
+          path,
+          row,
+          current.describe(),
+          describeEvidence(newType),
+        );
+      }
+      this.fields.set(path, newType);
+      return;
+    }
+
+    if (deferred !== undefined) {
+      this.fields.set(path, current.merge(deferred));
+      return;
+    }
+
+    throw schemaInferenceError(
+      path,
+      row,
+      current.describe(),
+      describeEvidence(newType),
+    );
+  }
+
+  private inferType(value: unknown, path: string[]): DataType | undefined {
+    if (typeof value === "bigint") {
+      return new Int64();
+    }
+    if (typeof value === "number") {
+      return new Float64();
+    }
+    if (typeof value === "string") {
+      return this.options.dictionaryEncodeStrings
+        ? new Dictionary(new Utf8(), new Int32())
+        : new Utf8();
+    }
+    if (typeof value === "boolean") {
+      return new Bool();
+    }
+    if (value instanceof Buffer) {
+      return new Binary();
+    }
+    if (ArrayBuffer.isView(value) && !(value instanceof DataView)) {
+      const typedArray = typedArrayToArrowType(value);
+      return typedArray === undefined
+        ? undefined
+        : new FixedSizeList(
+            typedArray.length,
+            new Field("item", typedArray.elementType, true),
+          );
+    }
+    if (!Array.isArray(value) || value.length === 0) {
+      return undefined;
+    }
+
+    const configuredVector =
+      path.length === 1 ? this.options.vectorColumns[path[0]] : undefined;
+    if (configuredVector !== undefined) {
+      return new FixedSizeList(
+        value.length,
+        new Field("item", sanitizeType(configuredVector.type), true),
+      );
+    }
+
+    const itemType = this.inferArrayItemType(value, path);
+    if (itemType === undefined) {
+      return undefined;
+    }
+
+    return nameSuggestsVectorColumn(path[path.length - 1])
+      ? new FixedSizeList(value.length, new Field("item", new Float32(), true))
+      : new List(new Field("item", itemType, true));
+  }
+
+  private inferArrayItemType(
+    values: unknown[],
+    path: string[],
+  ): DataType | undefined {
+    let itemType: DataType | undefined;
+    const deferredItems: unknown[] = [];
+
+    for (const value of values) {
+      const candidate = this.inferType(value, path);
+      if (candidate === undefined) {
+        if (!isDeferredValue(value)) {
+          return undefined;
+        }
+        deferredItems.push(value);
+      } else if (itemType === undefined) {
+        itemType = candidate;
+      } else if (!inferredTypesEqual(itemType, candidate)) {
+        return undefined;
+      }
+    }
+
+    if (itemType === undefined) {
+      return undefined;
+    }
+    return deferredItems.every((value) =>
+      deferredValueMatchesType(value, itemType),
+    )
+      ? itemType
+      : undefined;
+  }
+}
+
+/** Nulls and empty/all-null lists that do not determine a type by themselves. */
+class DeferredTypeEvidence {
+  private constructor(
+    private readonly values: Array<{ value: unknown; row: number }>,
+  ) {}
+
+  static from(value: unknown, row: number): DeferredTypeEvidence | undefined {
+    return isDeferredValue(value)
+      ? new DeferredTypeEvidence([{ value, row }])
+      : undefined;
+  }
+
+  isOnlyNulls(): boolean {
+    return this.values.every(({ value }) => value == null);
+  }
+
+  matches(type: DataType): boolean {
+    return this.values.every(({ value }) =>
+      deferredValueMatchesType(value, type),
+    );
+  }
+
+  merge(other: DeferredTypeEvidence): DeferredTypeEvidence {
+    return new DeferredTypeEvidence([...this.values, ...other.values]);
+  }
+
+  describe(): string {
+    const list = this.values.find(({ value }) => Array.isArray(value));
+    return list === undefined
+      ? "null"
+      : `List[${(list.value as unknown[]).length}]`;
+  }
+
+  firstRow(): number {
+    return this.values[0].row;
+  }
+}
+
+type FieldNode = DataType | DeferredTypeEvidence | FieldTree;
+type LeafNode = Exclude<FieldNode, FieldTree>;
+type FieldConflict = { path: string[]; value: FieldNode };
+
+/** Nested field state, kept separate from Arrow's eventual Struct types. */
+class FieldTree {
+  private readonly children = new Map<string, FieldNode>();
+
+  get(path: string[]): FieldNode | undefined {
+    let current: FieldNode = this;
+    for (const part of path) {
+      if (!(current instanceof FieldTree)) {
+        return undefined;
+      }
+      const child = current.children.get(part);
+      if (child === undefined) {
+        return undefined;
+      }
+      current = child;
+    }
+    return current;
+  }
+
+  set(
+    path: string[],
+    value: LeafNode,
+    canReplaceLeaf: (value: LeafNode) => boolean = () => false,
+  ): FieldConflict | undefined {
+    let branch: FieldTree = this;
+    for (const [index, part] of path.slice(0, -1).entries()) {
+      const child = branch.children.get(part);
+      if (child === undefined || (isLeaf(child) && canReplaceLeaf(child))) {
+        const nextBranch = new FieldTree();
+        branch.children.set(part, nextBranch);
+        branch = nextBranch;
+      } else if (child instanceof FieldTree) {
+        branch = child;
+      } else {
+        return { path: path.slice(0, index + 1), value: child };
+      }
+    }
+
+    const name = path[path.length - 1];
+    const current = branch.children.get(name);
+    if (current instanceof FieldTree) {
+      return { path, value: current };
+    }
+    branch.children.set(name, value);
+    return undefined;
+  }
+
+  entries(): IterableIterator<[string, FieldNode]> {
+    return this.children.entries();
+  }
+
+  has(name: string): boolean {
+    return this.children.has(name);
+  }
+}
+
+function isLeaf(value: FieldNode): value is LeafNode {
+  return !(value instanceof FieldTree);
+}
+
+function fieldsFromTree(tree: FieldTree, path: string[] = []): Field[] {
+  const fields: Field[] = [];
+  for (const [name, value] of tree.entries()) {
+    if (value instanceof FieldTree) {
+      fields.push(
+        new Field(
+          name,
+          new Struct(fieldsFromTree(value, [...path, name])),
+          true,
+        ),
+      );
+    } else if (value instanceof DeferredTypeEvidence) {
+      throw typeInferenceError([...path, name], value.firstRow());
+    } else {
+      fields.push(new Field(name, value, true));
+    }
+  }
+  return fields;
+}
+
+function matchingFields(fields: Field[], tree: FieldTree): Field[] {
+  const matches: Field[] = [];
+  for (const field of fields) {
+    if (!tree.has(field.name)) {
+      continue;
+    }
+    const value = tree.get([field.name]);
+    if (value instanceof FieldTree) {
+      const struct = field.type as Struct;
+      matches.push(
+        new Field(
+          field.name,
+          new Struct(matchingFields(struct.children, value)),
+          field.nullable,
+          field.metadata,
+        ),
+      );
+    } else {
+      matches.push(field);
+    }
+  }
+  return matches;
+}
+
+function* recordPathsAndValues(
+  record: Record<string, unknown>,
+  path: string[] = [],
+): Generator<[string[], unknown]> {
+  for (const [name, value] of Object.entries(record)) {
+    if (isRecord(value)) {
+      yield* recordPathsAndValues(value, [...path, name]);
+    } else if (value !== undefined) {
+      yield [[...path, name], value];
+    }
+  }
+}
+
+function isRecord(value: unknown): value is Record<string, unknown> {
+  return (
+    typeof value === "object" &&
+    value !== null &&
+    !Array.isArray(value) &&
+    !(value instanceof RegExp) &&
+    !(value instanceof Date) &&
+    !(value instanceof Set) &&
+    !(value instanceof Map) &&
+    !(value instanceof Buffer) &&
+    !ArrayBuffer.isView(value)
+  );
+}
+
+function fieldAtPath(schema: Schema, path: string[]): Field | undefined {
+  let fields = schema.fields;
+  let field: Field | undefined;
+  for (const [index, name] of path.entries()) {
+    field = fields.find((candidate) => candidate.name === name);
+    if (field === undefined || index === path.length - 1) {
+      return field;
+    }
+    if (!DataType.isStruct(field.type)) {
+      return undefined;
+    }
+    fields = field.type.children;
+  }
+  return field;
+}
+
+function isDeferredValue(value: unknown): boolean {
+  return (
+    value == null || (Array.isArray(value) && value.every(isDeferredValue))
+  );
+}
+
+function deferredValueMatchesType(value: unknown, type: DataType): boolean {
+  if (value == null) {
+    return true;
+  }
+  if (!Array.isArray(value)) {
+    return false;
+  }
+  if (DataType.isList(type)) {
+    return value.every((item) =>
+      deferredValueMatchesType(item, type.valueType),
+    );
+  }
+  if (DataType.isFixedSizeList(type)) {
+    return (
+      value.length === type.listSize &&
+      value.every((item) => deferredValueMatchesType(item, type.valueType))
+    );
+  }
+  return false;
+}
+
+function inferredTypesEqual(current: DataType, candidate: DataType): boolean {
+  if (DataType.isDictionary(current)) {
+    return (
+      DataType.isDictionary(candidate) &&
+      current.isOrdered === candidate.isOrdered &&
+      inferredTypesEqual(current.indices, candidate.indices) &&
+      inferredTypesEqual(current.dictionary, candidate.dictionary)
+    );
+  }
+  if (DataType.isList(current)) {
+    return (
+      DataType.isList(candidate) &&
+      current.valueField.name === candidate.valueField.name &&
+      current.valueField.nullable === candidate.valueField.nullable &&
+      inferredTypesEqual(current.valueType, candidate.valueType)
+    );
+  }
+  if (DataType.isFixedSizeList(current)) {
+    return (
+      DataType.isFixedSizeList(candidate) &&
+      current.listSize === candidate.listSize &&
+      current.valueField.name === candidate.valueField.name &&
+      current.valueField.nullable === candidate.valueField.nullable &&
+      inferredTypesEqual(current.valueType, candidate.valueType)
+    );
+  }
+  return arrowUtil.compareTypes(current, candidate);
+}
+
+function describeEvidence(
+  evidence: DataType | DeferredTypeEvidence | undefined,
+): string {
+  if (evidence === undefined) {
+    return "an unsupported value";
+  }
+  return evidence instanceof DeferredTypeEvidence
+    ? evidence.describe()
+    : evidence.toString();
+}
+
+function branchConflictError(
+  conflict: FieldConflict,
+  row: number,
+  candidate: string,
+): Error {
+  return schemaInferenceError(
+    conflict.path,
+    row,
+    conflict.value instanceof FieldTree
+      ? "Struct"
+      : describeEvidence(conflict.value),
+    candidate,
+  );
+}
+
+function schemaInferenceError(
+  path: string[],
+  row: number,
+  currentType: string,
+  newType: string,
+): Error {
+  return new Error(
+    `Failed to infer schema for data. Previously inferred type ${currentType} ` +
+      `but found ${newType} for field ${path.join(".")} at row ${row}. ` +
+      "Consider providing an explicit schema.",
+  );
+}
+
+function typeInferenceError(path: string[], row: number): Error {
+  return new Error(
+    `Failed to infer data type for field ${path.join(".")} at row ${row}. ` +
+      "Consider providing an explicit schema.",
+  );
+}
+
+function nameSuggestsVectorColumn(name: string): boolean {
+  const normalized = name.toLowerCase();
+  return normalized.includes("vector") || normalized.includes("embedding");
+}

nodejs/lancedb/table.ts

@@ -43,10 +43,12 @@ import {
   Table as _NativeTable,
 } from "./native";
 import {
+  AutoQuery,
   FullTextQuery,
   Query,
   TakeQuery,
   VectorQuery,
+  createAutoQuery,
   instanceOfFullTextQuery,
 } from "./query";
 import { sanitizeType } from "./sanitize";
@@ -523,7 +525,7 @@ export abstract class Table {
     query: string | IntoVector | MultiVector | FullTextQuery,
     queryType?: string,
     ftsColumns?: string | string[],
-  ): VectorQuery | Query;
+  ): VectorQuery | Query | AutoQuery;
   /**
    * Search the table with a given query vector.
    *
@@ -628,6 +630,18 @@ export abstract class Table {
 
   /**
    * Update per-field (column) metadata.
+   *
+   * The following keys are treated specially, by convention, and should be
+   * used when appropriate:
+   *
+   * - `lancedb:description`: for a human-readable description of a field.
+   * - `lancedb:tag:<name>`: for a user-defined key-value tag, where the suffix
+   *   names the tag category; e.g. `lancedb:tag:model: "clip"`.
+   * - `lancedb:logical-column`: for a column grouping; e.g. `feature_v1` and
+   *   `feature_v2` might be in the same logical column.
+   * - `lancedb:status`: for status options (`production`, `candidate`,
+   *   `deprecated`, `archived`) to designate the current life cycle state of
+   *   this column.
    * @param {FieldMetadataUpdate[]} updates One or more per-field updates. Each
    * update's metadata is merged into the field's existing metadata by default;
    * a value of `null` deletes that key, and `replace: true` swaps the whole map.
@@ -975,10 +989,11 @@ export class LocalTable extends Table {
     return this.inner.display();
   }
 
-  private async getEmbeddingFunctions(): Promise<
-    Map<string, EmbeddingFunctionConfig>
-  > {
-    const schema = await this.schema();
+  private async getEmbeddingFunctions(
+    inner: _NativeTable = this.inner,
+  ): Promise<Map<string, EmbeddingFunctionConfig>> {
+    const schemaBuf = await inner.schema();
+    const schema = tableFromIPC(schemaBuf).schema;
     const registry = getRegistry();
     return registry.parseFunctions(schema.metadata);
   }
@@ -1160,7 +1175,7 @@ export class LocalTable extends Table {
     query: string | IntoVector | MultiVector | FullTextQuery,
     queryType: string = "auto",
     ftsColumns?: string | string[],
-  ): VectorQuery | Query {
+  ): VectorQuery | Query | AutoQuery {
     if (typeof query !== "string" && !instanceOfFullTextQuery(query)) {
       if (queryType === "fts") {
         throw new Error("Cannot perform full text search on a vector query");
@@ -1175,14 +1190,28 @@ export class LocalTable extends Table {
       });
     }
 
-    // The query type is auto or vector
-    // fall back to full text search if no embedding functions are defined and the query is a string
-    if (
-      queryType === "auto" &&
-      (getRegistry().length() === 0 || instanceOfFullTextQuery(query))
-    ) {
-      return this.query().fullTextSearch(query, {
-        columns: ftsColumns,
+    if (queryType === "auto") {
+      if (instanceOfFullTextQuery(query)) {
+        return this.query().fullTextSearch(query, {
+          columns: ftsColumns,
+        });
+      }
+
+      const columns =
+        typeof ftsColumns === "string" ? [ftsColumns] : (ftsColumns ?? null);
+      return createAutoQuery(this.inner, query, columns, async (metadata) => {
+        const functions = await getRegistry().parseFunctions(
+          new Map([["embedding_functions", metadata]]),
+        );
+        // TODO: Support multiple embedding functions
+        const embeddingFunc: EmbeddingFunctionConfig | undefined = functions
+          .values()
+          .next().value;
+        // The route only calls this callback when embedding metadata exists.
+        // parseFunctions either yields a provider or reports malformed metadata.
+        if (!embeddingFunc)
+          throw new Error("Invalid embedding function metadata");
+        return await embeddingFunc.function.computeQueryEmbeddings(query);
       });
     }
 
@@ -1538,7 +1567,8 @@ export interface FieldMetadataUpdate {
   path: string;
   /**
    * Metadata key/value pairs. Merged into the field's existing metadata by
-   * default; a value of `null` deletes that key.
+   * default; a value of `null` deletes that key. See
+   * {@link Table.updateFieldMetadata} for the conventional `lancedb:*` keys.
    */
   metadata: Record<string, string | null>;
   /** If true, replace the field's entire metadata map instead of merging. */

nodejs/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.38.0-beta.6",
+	"version": "0.38.0-beta.12",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",

nodejs/npm/linux-arm64-gnu/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.38.0-beta.6",
+	"version": "0.38.0-beta.12",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",

nodejs/npm/linux-arm64-musl/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-musl",
-	"version": "0.38.0-beta.6",
+	"version": "0.38.0-beta.12",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-musl.node",

nodejs/npm/linux-x64-gnu/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.38.0-beta.6",
+	"version": "0.38.0-beta.12",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",

nodejs/npm/linux-x64-musl/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-musl",
-	"version": "0.38.0-beta.6",
+	"version": "0.38.0-beta.12",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-musl.node",

nodejs/npm/win32-arm64-msvc/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@lancedb/lancedb-win32-arm64-msvc",
-  "version": "0.38.0-beta.6",
+  "version": "0.38.0-beta.12",
   "os": [
     "win32"
   ],

nodejs/npm/win32-x64-msvc/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.38.0-beta.6",
+	"version": "0.38.0-beta.12",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package.json

@@ -11,7 +11,7 @@
     "ann"
   ],
   "private": false,
-  "version": "0.38.0-beta.6",
+  "version": "0.38.0-beta.12",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",
@@ -67,7 +67,7 @@
     "timeout": "3m"
   },
   "engines": {
-    "node": ">= 18"
+    "node": ">= 22"
   },
   "packageManager": "pnpm@11.1.1",
   "cpu": ["x64", "arm64"],
@@ -101,7 +101,7 @@
     "openai": "4.29.2"
   },
   "peerDependencies": {
-    "@types/node": ">=18",
+    "@types/node": ">=22",
     "apache-arrow": ">=15.0.0 <=18.1.0"
   },
   "peerDependenciesMeta": {

nodejs/src/connection.rs

@@ -17,6 +17,7 @@ use lancedb::connection::{ConnectBuilder, Connection as LanceDBConnection, conne
 
 use lance_namespace::models::{
     CreateNamespaceRequest, DescribeNamespaceRequest, DropNamespaceRequest, ListNamespacesRequest,
+    ListTablesRequest,
 };
 use lancedb::ipc::{ipc_file_to_batches, ipc_file_to_schema};
 
@@ -36,6 +37,12 @@ pub struct ListNamespacesResponse {
     pub page_token: Option<String>,
 }
 
+#[napi(object)]
+pub struct ListTablesResponse {
+    pub tables: Vec<String>,
+    pub page_token: Option<String>,
+}
+
 #[napi(object)]
 pub struct CreateNamespaceResponse {
     pub properties: Option<HashMap<String, String>>,
@@ -206,6 +213,33 @@ impl Connection {
         op.execute().await.default_error()
     }
 
+    /// List a page of tables in the database.
+    #[napi(catch_unwind)]
+    pub async fn list_tables(
+        &self,
+        namespace_path: Option<Vec<String>>,
+        page_token: Option<String>,
+        limit: Option<u32>,
+    ) -> napi::Result<ListTablesResponse> {
+        let request = ListTablesRequest {
+            // The root namespace is an empty path, not an absent one: a namespace-backed
+            // database rejects a request that names no namespace.
+            id: Some(namespace_path.unwrap_or_default()),
+            page_token,
+            limit: limit.map(|limit| i32::try_from(limit).unwrap_or(i32::MAX)),
+            ..Default::default()
+        };
+        let response = self
+            .get_inner()?
+            .list_tables(request)
+            .await
+            .default_error()?;
+        Ok(ListTablesResponse {
+            tables: response.tables,
+            page_token: response.page_token,
+        })
+    }
+
     /// Create table from a Apache Arrow IPC (file) buffer.
     ///
     /// Parameters:

nodejs/src/table.rs

@@ -278,6 +278,13 @@ impl Table {
         Ok(Query::new(self.inner_ref()?.query()))
     }
 
+    /// Return a read-only table handle pinned to the current query revision.
+    #[napi(catch_unwind)]
+    pub async fn query_snapshot(&self) -> napi::Result<Self> {
+        let snapshot = self.inner_ref()?.query_snapshot().await.default_error()?;
+        Ok(Self::new(snapshot))
+    }
+
     #[napi(catch_unwind)]
     pub fn take_offsets(&self, offsets: Vec<i64>) -> napi::Result<TakeQuery> {
         Ok(TakeQuery::new(
@@ -554,6 +561,12 @@ impl Table {
             .default_error()
     }
 
+    #[napi(catch_unwind)]
+    pub async fn checkout_current(&self) -> napi::Result<Self> {
+        let table = self.inner_ref()?.checkout_current().await.default_error()?;
+        Ok(Self::new(table))
+    }
+
     #[napi(catch_unwind)]
     pub async fn checkout(&self, version: i64) -> napi::Result<()> {
         self.inner_ref()?

python/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.38.0-beta.6"
+version = "0.38.0-beta.12"
 publish = false
 edition.workspace = true
 description = "Python bindings for LanceDB"

python/pyproject.toml

@@ -101,9 +101,12 @@ azure = ["adlfs>=2024.2.0"]
 [tool.maturin]
 python-source = "python"
 module-name = "lancedb._lancedb"
+# uv installs the project as an editable package before `uv run`, so keep that
+# bootstrap build consistent with `maturin develop`.
+editable-profile = "dev"
 
 [build-system]
-requires = ["maturin>=1.9.4"]
+requires = ["maturin>=1.10"]
 build-backend = "maturin"
 
 [tool.ruff.lint]

python/python/lancedb/__init__.py

@@ -6,7 +6,7 @@ import importlib.metadata
 import os
 from concurrent.futures import ThreadPoolExecutor
 from datetime import timedelta
-from typing import Dict, Optional, Union, Any, List, Iterable
+from typing import Dict, Optional, Union, Any, List, Iterable, TYPE_CHECKING
 
 __version__ = importlib.metadata.version("lancedb")
 
@@ -20,7 +20,7 @@ from .db import AsyncConnection, DBConnection, LanceDBConnection
 from .remote import ClientConfig
 from .remote.db import RemoteDBConnection
 from .expr import Expr, col, lit, func
-from .schema import blob, vector, BlobType
+from .schema import blob, vector
 from .job import AsyncJob, Job
 from .functions import (
     FunctionArtifactRequest as FunctionArtifactRequest,
@@ -49,6 +49,19 @@ from .namespace import (
 )
 
 
+if TYPE_CHECKING:
+    from lance.blob import BlobType as BlobType
+
+
+def __getattr__(name: str):
+    if name == "BlobType":
+        from .schema import BlobType
+
+        globals()["BlobType"] = BlobType
+        return BlobType
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
 def _check_s3_bucket_with_dots(
     uri: str, storage_options: Optional[Dict[str, str]]
 ) -> None:
@@ -179,6 +192,18 @@ def connect(
     ...     },
     ... )
 
+    For Azure Blob Storage, credentials can be passed directly without setting
+    environment variables:
+
+    >>> azure_storage_options = {
+    ...     "account_name": "some-account",
+    ...     "account_key": "some-key",
+    ... }
+    >>> db = lancedb.connect(  # doctest: +SKIP
+    ...     "az://my-container/my-database",
+    ...     storage_options=azure_storage_options,
+    ... )
+
     For tests and temporary data, use an in-memory database:
 
     >>> db = lancedb.connect("memory://")
@@ -465,6 +490,10 @@ async def connect_async(
     --------
 
     >>> import lancedb
+    >>> azure_storage_options = {
+    ...     "account_name": "some-account",
+    ...     "account_key": "some-key",
+    ... }
     >>> async def doctest_example():
     ...     # For a local directory, provide a path to the database
     ...     db = await lancedb.connect_async("~/.lancedb")
@@ -472,6 +501,11 @@ async def connect_async(
     ...     db = await lancedb.connect_async("s3://my-bucket/lancedb",
     ...                                      storage_options={
     ...                                          "aws_access_key_id": "***"})
+    ...     # Azure credentials can also be passed directly
+    ...     db = await lancedb.connect_async(
+    ...         "az://my-container/my-database",
+    ...         storage_options=azure_storage_options,
+    ...     )
     ...     # For tests and temporary data, use an in-memory database
     ...     db = await lancedb.connect_async("memory://")
     ...     # Connect to LanceDB cloud

python/python/lancedb/_blob.py

@@ -12,7 +12,7 @@ from typing import TYPE_CHECKING, Optional, Union
 import pyarrow as pa
 
 from .expr import Expr
-from .schema import blob_v2_column_paths
+from .schema import row_addressable_blob_v2_paths
 from .types import BlobMode, QueryProjection, QueryProjectionSpec
 
 if TYPE_CHECKING:
@@ -119,7 +119,7 @@ def blob_v2_projection_sources(
     schema: pa.Schema,
     projection: QueryProjection,
 ) -> dict[str, str]:
-    blob_columns = blob_v2_column_paths(schema)
+    blob_columns = row_addressable_blob_v2_paths(schema)
     if not blob_columns:
         return {}
     columns = set(blob_columns)
@@ -140,7 +140,9 @@ def v2_projection_needs_row_id(
 ) -> bool:
     if with_row_id:
         return False
-    return projection_includes_blob_column(projection, blob_v2_column_paths(schema))
+    return projection_includes_blob_column(
+        projection, row_addressable_blob_v2_paths(schema)
+    )
 
 
 def blob_auto_row_id_for_scan(
@@ -270,7 +272,8 @@ def _iter_projection_pairs(
             if isinstance(expr, str):
                 yield name, expr
             elif isinstance(expr, Expr):
-                yield name, expr.to_sql()
+                source = expr._column_name()
+                yield name, source if source is not None else expr.to_sql()
         return
     for column in projection:
         if isinstance(column, str):
@@ -280,7 +283,8 @@ def _iter_projection_pairs(
             if isinstance(expr, str):
                 yield name, expr
             elif isinstance(expr, Expr):
-                yield name, expr.to_sql()
+                source = expr._column_name()
+                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:

python/python/lancedb/_lancedb.pyi

@@ -87,6 +87,7 @@ class PyExpr:
     def contains(self, substr: "PyExpr") -> "PyExpr": ...
     def isin(self, values: List["PyExpr"]) -> "PyExpr": ...
     def cast(self, data_type: pa.DataType) -> "PyExpr": ...
+    def column_name(self) -> Optional[str]: ...
     def to_sql(self) -> str: ...
 
 def expr_col(name: str) -> PyExpr: ...
@@ -283,6 +284,7 @@ class Table:
         mode: Literal["append", "overwrite"],
         progress: Optional[Any] = None,
         write_parallelism: Optional[int] = None,
+        allow_external_blob_outside_bases: bool = False,
     ) -> AddResult: ...
     async def update(
         self, updates: Dict[str, str], where: Optional[str]
@@ -607,6 +609,7 @@ class PyQueryRequest:
     filter: Optional[Union[str, bytes]]
     full_text_search: Optional[FullTextQuery]
     select: Optional[Union[str, List[str]]]
+    select_source_columns: Optional[Dict[str, str]]
     fast_search: Optional[bool]
     with_row_id: Optional[bool]
     use_lsm: Optional[bool]

python/python/lancedb/expr.py

@@ -249,6 +249,10 @@ class Expr:
 
     # ── utilities ────────────────────────────────────────────────────────────
 
+    def _column_name(self) -> str | None:
+        """Return the source name when this is a bare column expression."""
+        return self._inner.column_name()
+
     def to_sql(self) -> str:
         """Render the expression as a SQL string (useful for debugging)."""
         return self._inner.to_sql()
@@ -312,7 +316,7 @@ def func(name: str, *args: ExprLike) -> Expr:
     --------
     >>> from lancedb.expr import col, func
     >>> func("lower", col("name"))
-    Expr(lower(name))
+    Expr(lower(`name`))
     """
     inner_args = [_coerce(a)._inner for a in args]
     return Expr(expr_func(name, inner_args))

python/python/lancedb/functions.py

@@ -4,7 +4,7 @@
 """Canonical Function values exchanged with LanceDB Enterprise services.
 
 These immutable models contain client/wire state only. Catalog persistence,
-environment bake, secret resolution, and execution are owned by Sophon.
+environment bake, and execution are owned by Sophon.
 ``RefreshColumnResult`` is also the backend-neutral result of a local
 expression-backed refresh job.
 """
@@ -12,17 +12,19 @@ expression-backed refresh job.
 from __future__ import annotations
 
 import ast
+import builtins
 import base64
 import functools
 import hashlib
+import importlib
 import inspect
+import symtable
 import json
 import math
 import re
 import sys
 import textwrap
 import types
-import uuid
 from collections.abc import Mapping
 from datetime import date, datetime
 from typing import (
@@ -220,13 +222,14 @@ class PythonEnvironmentSpec(_RemoteValue):
 
     kind: str
     packages: tuple[str, ...] = ()
+    channels: tuple[str, ...] = ()
     path: Optional[str] = None
     modules: tuple[str, ...] = ()
     image: Optional[str] = None
 
 
 class PythonRuntimeSpec(_RemoteValue):
-    """Remote runtime definition with non-secret environment values.
+    """Remote runtime definition with environment values.
 
     V1 supports ``kind="python"``. Newer runtime kinds remain readable, while
     their unknown payload fields are intentionally not retained by the client.
@@ -265,7 +268,6 @@ class FunctionVersion(_RemoteValue):
     runtime: PythonRuntimeSpec
     runtime_digest: str
     environment_digest: str
-    required_secrets: tuple[str, ...] = ()
     created_at: str
 
     def __call__(self, **inputs: Any) -> FunctionApplication:
@@ -273,7 +275,7 @@ class FunctionVersion(_RemoteValue):
 
         Every input must be a direct [lancedb.col][lancedb.expr.col]
         reference. The returned application is immutable and retains a
-        named-struct output as one sibling group, so every row's sibling values
+        named-struct output as one binding, so every row's sibling values
         come from one logical Function evaluation. Map result fields to table
         columns with
         [FunctionApplication.rename][lancedb.functions.FunctionApplication.rename],
@@ -323,22 +325,16 @@ class FunctionVersion(_RemoteValue):
             function=FunctionVersionRef(name=self.name, version=self.version),
             inputs=tuple(bindings),
             output=self.signature.output,
-            group_id=f"fg_{uuid.uuid4().hex}",
         )
 
 
 class FunctionRegistrationRequest(_RemoteValue):
-    """Stable remote registration envelope produced by :func:`udf`.
-
-    Only secret names are represented. Secret values are resolved inside the
-    remote service and have no client request field.
-    """
+    """Stable remote registration envelope produced by :func:`udf`."""
 
     name: str
     artifact: FunctionArtifactRequest
     signature: FunctionSignature
     runtime: PythonRuntimeSpec
-    required_secrets: tuple[str, ...] = ()
 
 
 class FunctionVersionRef(_OpenRemoteValue):
@@ -367,7 +363,7 @@ class ApplicationInput(_OpenRemoteValue):
 class FunctionApplication(_OpenRemoteValue):
     """Immutable pre-declaration application of an exact Function version.
 
-    A named-struct output remains one grouped application through table
+    A named-struct output remains one application through table
     declaration and execution.
     [FunctionApplication.rename][lancedb.functions.FunctionApplication.rename]
     records the result-field to table-column mapping without splitting sibling
@@ -377,7 +373,6 @@ class FunctionApplication(_OpenRemoteValue):
     function: FunctionVersionRef
     inputs: tuple[ApplicationInput, ...]
     output: FunctionOutput
-    group_id: str
     columns: Mapping[str, str] = Field(default_factory=dict)
 
     def _known_dict(self) -> dict[str, Any]:
@@ -449,12 +444,10 @@ class OutputMapping(_RemoteValue):
 
 
 class FunctionBinding(_RemoteValue):
-    """Immutable grouped binding persisted by the Enterprise table service."""
+    """Immutable Function binding persisted by the Enterprise table service."""
 
     binding_id: str
-    revision: _UInt64
     function: FunctionVersionRef
-    group_id: str
     inputs: tuple[InputBinding, ...]
     outputs: tuple[OutputMapping, ...]
     input_schema: Optional[Mapping[str, Any]] = None
@@ -486,62 +479,60 @@ class RefreshColumnResult(_RemoteValue):
 
 
 _FUNCTION_NAME = re.compile(r"^[A-Za-z_][A-Za-z0-9_.-]*$")
-_SECRET_NAME = re.compile(r"^[A-Za-z_][A-Za-z0-9_]*$")
+
+
+_GRAMMAR_PRIMITIVES = (
+    (pa.bool_(), "bool"),
+    (pa.int8(), "int8"),
+    (pa.int16(), "int16"),
+    (pa.int32(), "int32"),
+    (pa.int64(), "int64"),
+    (pa.uint8(), "uint8"),
+    (pa.uint16(), "uint16"),
+    (pa.uint32(), "uint32"),
+    (pa.uint64(), "uint64"),
+    (pa.float16(), "float16"),
+    (pa.float32(), "float32"),
+    (pa.float64(), "float64"),
+    (pa.string(), "utf8"),
+    (pa.binary(), "binary"),
+    (pa.date32(), "date32"),
+    (pa.date64(), "date64"),
+)
 
 
 def _canonical_arrow_type(data_type: pa.DataType) -> str:
-    primitive_types = (
-        (pa.bool_(), "bool"),
-        (pa.int8(), "int8"),
-        (pa.int16(), "int16"),
-        (pa.int32(), "int32"),
-        (pa.int64(), "int64"),
-        (pa.uint8(), "uint8"),
-        (pa.uint16(), "uint16"),
-        (pa.uint32(), "uint32"),
-        (pa.uint64(), "uint64"),
-        (pa.float16(), "float16"),
-        (pa.float32(), "float32"),
-        (pa.float64(), "float64"),
-        (pa.string(), "utf8"),
-        (pa.large_utf8(), "large_utf8"),
-        (pa.binary(), "binary"),
-        (pa.large_binary(), "large_binary"),
-        (pa.date32(), "date32"),
-        (pa.date64(), "date64"),
-    )
-    for candidate, name in primitive_types:
+    """The server's V1 Function type grammar. Anything outside it is rejected
+    here rather than at registration."""
+    for candidate, name in _GRAMMAR_PRIMITIVES:
         if data_type == candidate:
             return name
-    if pa.types.is_fixed_size_binary(data_type):
-        return f"fixed_size_binary[{data_type.byte_width}]"
-    if pa.types.is_list(data_type):
-        return f"list<{_canonical_arrow_type(data_type.value_type)}>"
-    if pa.types.is_large_list(data_type):
-        return f"large_list<{_canonical_arrow_type(data_type.value_type)}>"
-    if pa.types.is_fixed_size_list(data_type):
+    if pa.types.is_list(data_type) or pa.types.is_large_list(data_type):
+        prefix = "list" if pa.types.is_list(data_type) else "large_list"
+        return f"{prefix}<{_canonical_list_item(data_type)}>"
+    if pa.types.is_fixed_size_list(data_type) and data_type.list_size > 0:
         return (
-            f"fixed_size_list<{_canonical_arrow_type(data_type.value_type)}>"
-            f"[{data_type.list_size}]"
+            f"fixed_size_list<{_canonical_list_item(data_type)}, {data_type.list_size}>"
         )
-    if pa.types.is_struct(data_type):
-        fields = ",".join(
-            f"{field.name}:{_canonical_arrow_type(field.type)}" for field in data_type
-        )
-        return f"struct<{fields}>"
-    if pa.types.is_timestamp(data_type):
-        timezone = f",tz={data_type.tz}" if data_type.tz is not None else ""
-        return f"timestamp[{data_type.unit}{timezone}]"
-    if pa.types.is_time32(data_type) or pa.types.is_time64(data_type):
-        return f"time[{data_type.unit}]"
-    if pa.types.is_duration(data_type):
-        return f"duration[{data_type.unit}]"
-    if pa.types.is_decimal(data_type):
-        bit_width = data_type.bit_width
-        return f"decimal{bit_width}({data_type.precision},{data_type.scale})"
     raise TypeError(f"unsupported Arrow type for Function signature: {data_type}")
 
 
+def _canonical_list_item(data_type: pa.DataType) -> str:
+    """The grammar names only the item type; it always means a non-nullable
+    child called `item`, so any other child metadata cannot be represented."""
+    child = data_type.value_field
+    if child.name != "item" or child.nullable or child.metadata:
+        raise TypeError(
+            "unsupported Arrow type for Function signature: list items must be a "
+            f"non-nullable field named 'item', got {child}"
+        )
+    return _canonical_arrow_type(child.type)
+
+
+def _list_of(item: pa.DataType) -> pa.DataType:
+    return pa.list_(pa.field("item", item, nullable=False))
+
+
 def _annotation_type(annotation: Any) -> tuple[pa.DataType, bool]:
     nullable = False
     origin = get_origin(annotation)
@@ -589,7 +580,7 @@ def _annotation_type(annotation: Any) -> tuple[pa.DataType, bool]:
         value_type, value_nullable = _annotation_type(arguments[0])
         if value_nullable:
             raise TypeError("nullable Function list elements are not supported")
-        return pa.list_(value_type), nullable
+        return _list_of(value_type), nullable
     raise TypeError(f"unsupported Function annotation: {annotation!r}")
 
 
@@ -736,6 +727,104 @@ def _literal_source(value: Any) -> str:
     )
 
 
+_DYNAMIC_NAMESPACE_ACCESS = frozenset(
+    {"globals", "locals", "vars", "eval", "exec", "compile", "__import__"}
+)
+# Modules that hand out namespaces (`sys.modules`, `builtins`, importers,
+# introspection). The artifact's module namespace holds only the names it was
+# packaged with, so reaching around it cannot be represented.
+_NAMESPACE_MODULES = frozenset(
+    {"sys", "builtins", "importlib", "inspect", "gc", "ctypes", "types"}
+)
+
+
+def _namespace_acquisition(
+    definition: ast.FunctionDef, references: set[str]
+) -> list[str]:
+    found = set(references & _DYNAMIC_NAMESPACE_ACCESS)
+    for node in ast.walk(definition):
+        if isinstance(node, ast.Import):
+            found.update(
+                alias.name
+                for alias in node.names
+                if alias.name.split(".")[0] in _NAMESPACE_MODULES
+            )
+        elif isinstance(node, ast.ImportFrom) and node.module:
+            if node.module.split(".")[0] in _NAMESPACE_MODULES:
+                found.add(node.module)
+    return sorted(found)
+
+
+def _module_references(module_source: str) -> set[str]:
+    """Names any scope in `module_source` binds or loads at module scope.
+    Python's own scope analysis on the exact text that ships: free variables
+    belong to an enclosing scope inside the function, and postponed
+    annotations are not runtime loads."""
+
+    def visit(table: symtable.SymbolTable, found: set[str]) -> None:
+        for symbol in table.get_symbols():
+            if symbol.is_global() and (
+                symbol.is_referenced() or symbol.is_declared_global()
+            ):
+                found.add(symbol.get_name())
+        for child in table.get_children():
+            visit(child, found)
+
+    found: set[str] = set()
+    for table in symtable.symtable(module_source, "<udf>", "exec").get_children():
+        visit(table, found)
+    return found
+
+
+def _global_source(name: str, value: Any) -> str:
+    """One module-level line that rebinds `name` to `value` in the artifact:
+    an import for modules and importable classes/functions, a literal otherwise."""
+    if isinstance(value, types.ModuleType):
+        if value.__name__.split(".")[0] in _NAMESPACE_MODULES:
+            raise ValueError(
+                f"@udf cannot package dynamic namespace access: {value.__name__!r}"
+            )
+        try:
+            imported = importlib.import_module(value.__name__)
+        except ImportError:
+            imported = None
+        if imported is not value:
+            raise TypeError(
+                f"Function source references module {name!r} that does not import "
+                f"as {value.__name__!r}"
+            )
+        return f"import {value.__name__} as {name}"
+    module_name = getattr(value, "__module__", None)
+    qualname = getattr(value, "__qualname__", None)
+    if (
+        isinstance(module_name, str)
+        and isinstance(qualname, str)
+        and module_name != "__main__"
+        and "." not in qualname
+        and "<" not in qualname
+    ):
+        try:
+            imported = getattr(importlib.import_module(module_name), qualname)
+        except (ImportError, AttributeError):
+            imported = None
+        if imported is value:
+            return f"from {module_name} import {qualname} as {name}"
+    return f"{name} = {_literal_source(value)}"
+
+
+def _is_recursive_reference(function: Callable[..., Any], name: str) -> bool:
+    """`name` inside the body means the function itself unless the module has
+    since bound it to something else."""
+    if name != function.__name__:
+        return False
+    bound = function.__globals__.get(name, function)
+    if bound is function:
+        return True
+    # The decorator's own result is the one wrapper known to call `function`
+    # unchanged; any other binding may behave differently from a self-call.
+    return type(bound) is UdfDefinition and bound._function is function
+
+
 def _package_source(function: Callable[..., Any]) -> bytes:
     if not inspect.isfunction(function) or inspect.iscoroutinefunction(function):
         raise TypeError("@udf requires a synchronous Python function")
@@ -760,23 +849,46 @@ def _package_source(function: Callable[..., Any]) -> bytes:
     closure = inspect.getclosurevars(function)
     if closure.nonlocals:
         raise ValueError("@udf cannot package functions that capture closure values")
-    if closure.unbound:
-        raise ValueError(
-            f"@udf source contains unresolved global names: {sorted(closure.unbound)!r}"
-        )
-    globals_source = []
-    for name, value in sorted(closure.globals.items()):
-        if isinstance(value, types.ModuleType):
-            globals_source.append(f"import {value.__name__} as {name}")
-        else:
-            globals_source.append(f"{name} = {_literal_source(value)}")
-
     function_source = ast.unparse(definition)
-    parts = ["from __future__ import annotations"]
+    module_header = "from __future__ import annotations"
+    references = _module_references(f"{module_header}\n\n{function_source}\n")
+    dynamic = _namespace_acquisition(definition, references)
+    if dynamic:
+        raise ValueError(f"@udf cannot package dynamic namespace access: {dynamic!r}")
+    # Resolve every module-scope reference the way the interpreter would: the
+    # function's own globals first (a module global may shadow a builtin, and
+    # nested scopes are not visible to getclosurevars), then its builtins.
+    # The artifact runs under the standard builtins; only the exact mapping is
+    # provably equivalent (a subclass or copy can change lookups and hooks).
+    if function.__builtins__ is not vars(builtins):
+        raise ValueError("@udf cannot package a non-standard builtins environment")
+    globals_source = []
+    unresolved = []
+    for name in sorted(references):
+        if name == function.__name__:
+            if not _is_recursive_reference(function, name):
+                raise ValueError(
+                    f"@udf cannot package {name!r}: the module binds that name to "
+                    "another value, which the artifact's own definition would shadow"
+                )
+            continue
+        if name in function.__globals__:
+            globals_source.append(_global_source(name, function.__globals__[name]))
+        elif hasattr(builtins, name):
+            pass
+        else:
+            unresolved.append(name)
+    if unresolved:
+        raise ValueError(
+            f"@udf source contains unresolved global names: {unresolved!r}"
+        )
+
+    parts = [module_header]
     if globals_source:
         parts.extend(["", *globals_source])
     parts.extend(["", function_source, ""])
-    return "\n".join(parts).encode("utf-8")
+    packaged = "\n".join(parts)
+    return packaged.encode("utf-8")
 
 
 class UdfDefinition:
@@ -797,33 +909,32 @@ class UdfDefinition:
         output_schema: Optional[pa.DataType | pa.Field | pa.Schema],
         pip: tuple[str, ...],
         env: Mapping[str, str],
-        secrets: tuple[str, ...],
         python_version: Optional[str],
+        conda: tuple[str, ...] = (),
+        conda_channels: tuple[str, ...] = (),
     ):
         function_name = name or function.__name__
         if not _FUNCTION_NAME.fullmatch(function_name):
             raise ValueError(f"invalid Function name: {function_name!r}")
-        packages = tuple(sorted(set(pip)))
+        if pip and conda:
+            raise ValueError("a Function environment is pip or conda, not both")
+        if conda_channels and not conda:
+            raise ValueError("conda_channels requires conda packages")
+        packages = tuple(sorted(set(conda if conda else pip)))
         if any(not package or package != package.strip() for package in packages):
-            raise ValueError("pip requirements must be non-empty and trimmed")
+            raise ValueError("package requirements must be non-empty and trimmed")
+        if conda:
+            environment_spec = PythonEnvironmentSpec(
+                kind="conda", packages=packages, channels=tuple(conda_channels)
+            )
+        else:
+            environment_spec = PythonEnvironmentSpec(kind="pip", packages=packages)
         environment = dict(env)
         if any(
             not isinstance(key, str) or not isinstance(value, str)
             for key, value in environment.items()
         ):
             raise TypeError("Function env keys and values must be strings")
-        required_secrets = tuple(sorted(set(secrets)))
-        invalid_secrets = [
-            secret for secret in required_secrets if not _SECRET_NAME.fullmatch(secret)
-        ]
-        if invalid_secrets:
-            raise ValueError(f"invalid Function secret names: {invalid_secrets!r}")
-        overlap = set(environment) & set(required_secrets)
-        if overlap:
-            raise ValueError(
-                f"Function env and secret names must be disjoint: {sorted(overlap)!r}"
-            )
-
         signature = _infer_signature(function, input_schema, output_schema)
         source = _package_source(function)
         digest = f"sha256:{hashlib.sha256(source).hexdigest()}"
@@ -831,7 +942,7 @@ class UdfDefinition:
             kind="python",
             python_version=python_version
             or f"{sys.version_info.major}.{sys.version_info.minor}",
-            environment=PythonEnvironmentSpec(kind="pip", packages=packages),
+            environment=environment_spec,
             env=environment,
         )
         self._function = function
@@ -852,7 +963,6 @@ class UdfDefinition:
             ),
             signature=signature,
             runtime=runtime,
-            required_secrets=required_secrets,
         )
         functools.update_wrapper(self, function)
 
@@ -878,8 +988,9 @@ def udf(
     output_schema: Optional[pa.DataType | pa.Field | pa.Schema] = None,
     pip: tuple[str, ...] | list[str] = (),
     env: Optional[Mapping[str, str]] = None,
-    secrets: tuple[str, ...] | list[str] = (),
     python_version: Optional[str] = None,
+    conda: tuple[str, ...] | list[str] = (),
+    conda_channels: tuple[str, ...] | list[str] = (),
 ) -> Callable[[Callable[..., Any]], UdfDefinition]: ...
 
 
@@ -891,8 +1002,9 @@ def udf(
     output_schema: Optional[pa.DataType | pa.Field | pa.Schema] = None,
     pip: tuple[str, ...] | list[str] = (),
     env: Optional[Mapping[str, str]] = None,
-    secrets: tuple[str, ...] | list[str] = (),
     python_version: Optional[str] = None,
+    conda: tuple[str, ...] | list[str] = (),
+    conda_channels: tuple[str, ...] | list[str] = (),
 ):
     """Prepare a scalar Python callable for remote Function registration.
 
@@ -915,14 +1027,22 @@ def udf(
         provided together with ``input_schema``.
     pip : sequence of str, optional
         Pip requirements for the remote environment.
+    conda : sequence of str, optional
+        Conda packages for the remote environment, instead of ``pip``.
+    conda_channels : sequence of str, optional
+        Conda channels in priority order; requires ``conda``.
     env : mapping of str to str, optional
-        Non-secret environment variables. Use ``secrets`` for credentials.
-    secrets : sequence of str, optional
-        Names of secrets resolved by the remote service. Secret values are not
-        accepted by this API or included in the registration request.
+        Environment variables included in the Function definition.
     python_version : str, optional
         Remote Python major/minor version. Defaults to the client version.
 
+    The packaged 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 inline). Code that reaches the
+    module namespace another way -- ``globals()``/``eval``, ``sys.modules``,
+    ``builtins`` -- is rejected where it can be seen and otherwise
+    unsupported; closures and a non-standard ``__builtins__`` are rejected.
+
     Returns
     -------
     UdfDefinition
@@ -934,7 +1054,7 @@ def udf(
     Examples
     --------
     >>> from lancedb import udf
-    >>> @udf(pip=["numpy==2.2.0"], secrets=["MODEL_TOKEN"])
+    >>> @udf(pip=["numpy==2.2.0"])
     ... def score(value: float) -> float:
     ...     return value * 2
     >>> score(1.5)
@@ -949,8 +1069,9 @@ def udf(
             output_schema=output_schema,
             pip=tuple(pip),
             env={} if env is None else env,
-            secrets=tuple(secrets),
             python_version=python_version,
+            conda=tuple(conda),
+            conda_channels=tuple(conda_channels),
         )
 
     if function is None:

python/python/lancedb/index.py

@@ -7,6 +7,7 @@ from typing import List, Literal, Optional
 from ._lancedb import (
     IndexConfig,
 )
+from .query import DocumentGranularity
 from .types import BaseTokenizerType
 
 lang_mapping = {
@@ -121,6 +122,11 @@ class FTS:
 
     >>> config = FTS(block_size=256)
 
+    Create an index that treats each deepest-list element as one document:
+
+    >>> from lancedb.query import DocumentGranularity
+    >>> config = FTS(document_granularity=DocumentGranularity.LIST_ELEMENT)
+
     Attributes
     ----------
     with_position : bool, default False
@@ -172,6 +178,11 @@ class FTS:
         roughly half of the available CPU cores. The effective value is
         limited by the available compute capacity. This build-only setting is
         not persisted with the index and does not apply to remote tables.
+    document_granularity : DocumentGranularity, default ROW
+        ``ROW`` treats the selected text in one table row as one document.
+        ``LIST_ELEMENT`` treats each element of the deepest list on the indexed
+        field path as one document and returns its physical coordinates in
+        ``_doc_index`` for matching queries.
 
     Notes
     -----
@@ -196,6 +207,7 @@ class FTS:
     custom_stop_words: Optional[List[str]] = None
     memory_limit: Optional[int] = None
     num_workers: Optional[int] = None
+    document_granularity: DocumentGranularity = DocumentGranularity.ROW
 
 
 @dataclass

python/python/lancedb/permutation.py

@@ -391,6 +391,15 @@ def _table_to_pickle_state(table: Table) -> dict[str, Any]:
     }
 
 
+def _drop_base_version(permutation_data: pa.Table) -> pa.Table:
+    """Strip the recorded base version so the reader leaves the base table unpinned."""
+    metadata = dict(permutation_data.schema.metadata or {})
+    if metadata.pop(b"base_version", None) is None:
+        return permutation_data
+    metadata.pop(b"base_branch", None)
+    return permutation_data.replace_schema_metadata(metadata)
+
+
 def _table_from_pickle_state(state: dict[str, Any]) -> Table:
     from . import connect
 
@@ -679,11 +688,15 @@ class Permutation:
         from . import connect
 
         connection_factory = state["connection_factory"]
+        rebuilt_base = False
         if connection_factory is not None:
             base_table = connection_factory(state["base_table_name"])
         elif "base_table_state" in state:
-            base_table = _table_from_pickle_state(state["base_table_state"])
+            base_state = state["base_table_state"]
+            rebuilt_base = base_state["kind"] == "memory"
+            base_table = _table_from_pickle_state(base_state)
         elif "base_table_data" in state:
+            rebuilt_base = True
             # In-memory base table inlined into the pickle; rebuild the same
             # way we rebuild the in-memory permutation table.
             mem_db = connect("memory://")
@@ -701,11 +714,14 @@ class Permutation:
             )
 
         permutation_table: Optional[Table] = None
-        if state["permutation_data"] is not None:
+        permutation_data = state["permutation_data"]
+        if permutation_data is not None:
+            if rebuilt_base:
+                # The base table was materialized from Arrow, so it is a fresh
+                # single-version dataset and the recorded pin cannot resolve on it.
+                permutation_data = _drop_base_version(permutation_data)
             mem_db = connect("memory://")
-            permutation_table = mem_db.create_table(
-                "permutation", state["permutation_data"]
-            )
+            permutation_table = mem_db.create_table("permutation", permutation_data)
 
         self.base_table = base_table
         self.permutation_table = permutation_table

python/python/lancedb/query.py

@@ -167,6 +167,12 @@ def _projection_to_scanner_kwargs(columns: QueryProjection) -> Dict[str, Any]:
     return {"columns": projection}
 
 
+def _query_request_projection(req: "PyQueryRequest") -> QueryProjection:
+    if req.select_source_columns is not None:
+        return req.select_source_columns
+    return req.select
+
+
 def _scanner_kwargs_for_query(
     query: Query,
     blob_mode: BlobMode,
@@ -375,6 +381,13 @@ class FullTextOperator(str, Enum):
     OR = "OR"
 
 
+class DocumentGranularity(str, Enum):
+    """The unit treated as one full-text-search document."""
+
+    ROW = "row"
+    LIST_ELEMENT = "list_element"
+
+
 class Occur(str, Enum):
     SHOULD = "SHOULD"
     MUST = "MUST"
@@ -478,6 +491,10 @@ class MatchQuery(FullTextQuery):
     prefix_length : int, optional
         The number of beginning characters being unchanged for fuzzy matching.
         This is useful to achieve prefix matching.
+    document_granularity : DocumentGranularity, optional
+        Explicitly select row or deepest-list-element documents. If omitted,
+        the indexed granularity is inferred. When both granularities are indexed
+        for the field, this must be specified. With no index, row granularity is used.
     """
 
     query: str
@@ -487,6 +504,9 @@ class MatchQuery(FullTextQuery):
     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)
+    document_granularity: Optional[DocumentGranularity] = pydantic.Field(
+        None, kw_only=True
+    )
 
     def query_type(self) -> FullTextQueryType:
         return FullTextQueryType.MATCH
@@ -503,11 +523,20 @@ class PhraseQuery(FullTextQuery):
         The query string to match against.
     column : str
         The name of the column to match against.
+    slop : int, default 0
+        The maximum number of intervening positions permitted in the phrase.
+    document_granularity : DocumentGranularity, optional
+        Explicitly select row or deepest-list-element documents. If omitted,
+        the indexed granularity is inferred. When both granularities are indexed
+        for the field, this must be specified. With no index, row granularity is used.
     """
 
     query: str
     column: str
     slop: int = pydantic.Field(0, kw_only=True)
+    document_granularity: Optional[DocumentGranularity] = pydantic.Field(
+        None, kw_only=True
+    )
 
     def query_type(self) -> FullTextQueryType:
         return FullTextQueryType.MATCH_PHRASE
@@ -2776,15 +2805,16 @@ class AsyncQueryBase(object):
 
         req = self._inner.to_query_request()
         schema = await self._table.schema()
+        projection = _query_request_projection(req)
         self._blob_auto_row_id = blob_auto_row_id_for_scan(
             schema,
-            req.select,
+            projection,
             with_row_id=self._with_row_id,
         )
         if not self._blob_auto_row_id:
             self._blob_paths = ()
             return
-        self._blob_paths = tuple(blob_v2_projection_sources(schema, req.select).keys())
+        self._blob_paths = tuple(blob_v2_projection_sources(schema, projection).keys())
         self._inner.with_row_id()
 
     def select(self, columns: Union[List[str], dict[str, str]]) -> Self:
@@ -3378,9 +3408,10 @@ class AsyncQuery(AsyncStandardQuery):
         pass in multiple vectors. When multiple vectors are passed in, if the vector
         column is with multivector type, then the vectors will be treated as a single
         query. Or the vectors will be treated as multiple queries, this can be useful
-        if you want to find the nearest vectors to multiple query vectors.
-        This is not expected to be faster than making multiple queries concurrently;
-        it is just a convenience method. If multiple vectors are passed in then
+        if you want to find the nearest vectors to multiple query vectors. Flat
+        searches share one table scan across the query vectors, avoiding the scan
+        and memory amplification of making multiple queries concurrently. If
+        multiple vectors are passed in then
         an additional column `query_index` will be added to the results. This column
         will contain the index of the query vector that the result is nearest to.
         """
@@ -3509,8 +3540,8 @@ class AsyncFTSQuery(AsyncStandardQuery):
 
         Typically, a single vector is passed in as the query. However, you can also
         pass in multiple vectors.  This can be useful if you want to find the nearest
-        vectors to multiple query vectors. This is not expected to be faster than
-        making multiple queries concurrently; it is just a convenience method.
+        vectors to multiple query vectors. Flat searches share one table scan across
+        the query vectors instead of issuing concurrent full scans.
         If multiple vectors are passed in then an additional column `query_index`
         will be added to the results.  This column will contain the index of the
         query vector that the result is nearest to.
@@ -3870,14 +3901,15 @@ class AsyncHybridQuery(AsyncStandardQuery, AsyncVectorQueryBase):
         blob_paths: tuple[str, ...] = ()
         if self._table is not None:
             schema = await self._table.schema()
+            projection = _query_request_projection(req)
             blob_auto_row_id = blob_auto_row_id_for_scan(
                 schema,
-                req.select,
+                projection,
                 with_row_id=self._with_row_id,
             )
             if blob_auto_row_id:
                 blob_paths = tuple(
-                    blob_v2_projection_sources(schema, req.select).keys()
+                    blob_v2_projection_sources(schema, projection).keys()
                 )
         self._blob_auto_row_id = blob_auto_row_id
         self._blob_paths = blob_paths

python/python/lancedb/remote/table.py

@@ -36,6 +36,7 @@ from lancedb._lancedb import (
     UpdateResult,
 )
 from lancedb.embeddings.base import EmbeddingFunctionConfig
+from lancedb.expr import Expr
 from lancedb.index import (
     FTS,
     BTree,
@@ -61,11 +62,20 @@ from lancedb.table import _normalize_progress
 
 from ..query import (
     AnalyzePlanDistributedMetrics,
+    DocumentGranularity,
     LanceQueryBuilder,
     LanceTakeQueryBuilder,
     LanceVectorQueryBuilder,
 )
-from ..table import AsyncTable, BlobMode, Branches, IndexStatistics, Query, Table, Tags
+from ..table import (
+    AsyncTable,
+    BlobMode,
+    Branches,
+    IndexStatistics,
+    Query,
+    Table,
+    Tags,
+)
 from ..types import BaseTokenizerType
 
 
@@ -349,6 +359,7 @@ class RemoteTable(Table):
         ngram_max_length: int = 3,
         prefix_only: bool = False,
         block_size: int = 128,
+        document_granularity: DocumentGranularity = DocumentGranularity.ROW,
         name: Optional[str] = None,
     ):
         """Create a full-text search index on a column.
@@ -371,6 +382,7 @@ class RemoteTable(Table):
             ngram_max_length=ngram_max_length,
             prefix_only=prefix_only,
             block_size=block_size,
+            document_granularity=document_granularity,
         )
         LOOP.run(
             self._table.create_index(
@@ -610,6 +622,7 @@ class RemoteTable(Table):
         fill_value: float = 0.0,
         progress: Optional[Union[bool, Callable, Any]] = None,
         write_parallelism: Optional[int] = None,
+        allow_external_blob_outside_bases: bool = False,
     ) -> AddResult:
         """Add more data to the [Table][lancedb.table.Table].
 
@@ -642,6 +655,8 @@ class RemoteTable(Table):
             data in flight. Defaults to an estimate based on the data size,
             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.
 
         Returns
         -------
@@ -658,6 +673,7 @@ class RemoteTable(Table):
                     fill_value=fill_value,
                     progress=progress,
                     write_parallelism=write_parallelism,
+                    allow_external_blob_outside_bases=allow_external_blob_outside_bases,
                 )
             )
         finally:
@@ -856,7 +872,7 @@ class RemoteTable(Table):
 
     def update(
         self,
-        where: Optional[str] = None,
+        where: Optional[Union[str, Expr]] = None,
         values: Optional[dict] = None,
         *,
         values_sql: Optional[Dict[str, str]] = None,
@@ -867,9 +883,11 @@ class RemoteTable(Table):
 
         Parameters
         ----------
-        where: str, optional
-            The SQL where clause to use when updating rows. For example, 'x = 2'
-            or 'x IN (1, 2, 3)'. The filter must not be empty, or it will error.
+        where: str or [Expr][lancedb.expr.Expr], optional
+            The filter condition. Can be a SQL string or a type-safe
+            [Expr][lancedb.expr.Expr] built with [col][lancedb.expr.col] and
+            [lit][lancedb.expr.lit]. The filter must not be empty, or it will
+            error.
         values: dict, optional
             The values to update. The keys are the column names and the values
             are the values to set.

python/python/lancedb/schema.py

@@ -4,30 +4,34 @@
 
 """Schema helpers for Lance blob columns."""
 
+import importlib
+from typing import TYPE_CHECKING
+
 import pyarrow as pa
+import pyarrow.ipc
+
+if TYPE_CHECKING:
+    from lance.blob import BlobType as BlobType
 
 _BLOB_EXTENSION_NAME = "lance.blob.v2"
 _BLOB_V1_KEY = "lance-encoding:blob"
 _ARROW_EXT_NAME_KEY = "ARROW:extension:name"
+_BLOB_V2_STORAGE_TYPE = pa.struct(
+    [
+        pa.field("data", pa.large_binary(), nullable=True),
+        pa.field("uri", pa.utf8(), nullable=True),
+        pa.field("position", pa.uint64(), nullable=True),
+        pa.field("size", pa.uint64(), nullable=True),
+    ]
+)
+_resolved_blob_type = None
 
 
-class BlobType(pa.ExtensionType):
-    """PyArrow extension type for a Lance blob v2 column.
-
-    Queries return descriptors; call :meth:`~lancedb.table.Table.fetch_blob_files`
-    for lazy reads or :meth:`~lancedb.table.Table.fetch_blobs` for eager bytes.
-    """
+class _FallbackBlobType(pa.ExtensionType):
+    """lance.blob.v2 extension type used when pylance is not installed."""
 
     def __init__(self) -> None:
-        storage_type = pa.struct(
-            [
-                pa.field("data", pa.large_binary(), nullable=True),
-                pa.field("uri", pa.utf8(), nullable=True),
-                pa.field("position", pa.uint64(), nullable=True),
-                pa.field("size", pa.uint64(), nullable=True),
-            ]
-        )
-        super().__init__(storage_type, _BLOB_EXTENSION_NAME)
+        pa.ExtensionType.__init__(self, _BLOB_V2_STORAGE_TYPE, _BLOB_EXTENSION_NAME)
 
     def __arrow_ext_serialize__(self) -> bytes:
         return b""
@@ -35,23 +39,16 @@ class BlobType(pa.ExtensionType):
     @classmethod
     def __arrow_ext_deserialize__(
         cls, storage_type: pa.DataType, serialized: bytes
-    ) -> "BlobType":
+    ) -> "_FallbackBlobType":
         return cls()
 
     def __reduce__(self):
-        # Ensure pickle round-trips on older pyarrow (apache/arrow#35599).
         return type(self).__arrow_ext_deserialize__, (
             self.storage_type,
             self.__arrow_ext_serialize__(),
         )
 
 
-try:
-    pa.register_extension_type(BlobType())  # type: ignore[arg-type]
-except pa.ArrowKeyError:
-    pass
-
-
 def _metadata_value(metadata: dict, key: str):
     return metadata.get(key.encode()) or metadata.get(key)
 
@@ -92,43 +89,105 @@ def is_blob_like_field(field: pa.Field) -> bool:
     return is_blob_v2_field(field) or _metadata_marks_legacy_blob(field.metadata or {})
 
 
-def _collect_blob_paths(schema: pa.Schema, is_blob) -> list[str]:
-    paths: list[str] = []
+def _collect_blob_paths(schema: pa.Schema, is_blob) -> list[tuple[str, bool]]:
+    """Walk the schema and return (path, has_list_ancestor) for each blob field."""
+    paths: list[tuple[str, bool]] = []
 
-    def walk(fields, prefix: str) -> None:
+    def walk(fields, prefix: str, has_list_ancestor: bool) -> None:
         for field in fields:
             path = f"{prefix}.{field.name}" if prefix else field.name
             if is_blob(field):
-                paths.append(path)
+                paths.append((path, has_list_ancestor))
             elif pa.types.is_struct(field.type):
-                walk(field.type, path)
+                walk(field.type, path, has_list_ancestor)
             elif (
                 pa.types.is_list(field.type)
                 or pa.types.is_large_list(field.type)
                 or pa.types.is_fixed_size_list(field.type)
             ):
-                walk([field.type.value_field], path)
+                walk([field.type.value_field], path, True)
 
-    walk(schema, "")
+    walk(schema, "", False)
     return paths
 
 
 def blob_column_paths(schema: pa.Schema) -> list[str]:
     """Dotted paths of blob-like columns (v2 extension or legacy metadata)."""
-    return _collect_blob_paths(schema, is_blob_like_field)
+    return [path for path, _ in _collect_blob_paths(schema, is_blob_like_field)]
 
 
 def blob_v2_column_paths(schema: pa.Schema) -> list[str]:
-    return _collect_blob_paths(schema, is_blob_v2_field)
+    return [path for path, _ in _collect_blob_paths(schema, is_blob_v2_field)]
+
+
+def row_addressable_blob_v2_paths(schema: pa.Schema) -> list[str]:
+    """Blob v2 paths with one blob addressable by table row id.
+
+    ``fetch_blobs`` and the descriptor row-id ride-along address one blob per
+    row, so a blob inside a list container has no row-id slot and no fetch
+    path. Those columns still store and query as raw descriptors.
+    """
+    return [
+        path
+        for path, has_list_ancestor in _collect_blob_paths(schema, is_blob_v2_field)
+        if not has_list_ancestor
+    ]
 
 
 def schema_has_blob_field(schema: pa.Schema) -> bool:
     return bool(blob_column_paths(schema))
 
 
+def _deserialize_registered_type(extension_type: pa.ExtensionType) -> pa.DataType:
+    """Return the type Arrow reconstructs for this extension name."""
+    schema = pa.schema([pa.field("value", extension_type)])
+    restored = pa.ipc.read_schema(schema.serialize())
+    return restored.field("value").type
+
+
+def _resolve_blob_type():
+    """Return the BlobType class this process should use.
+
+    pylance's class when it owns the lance.blob.v2 registry entry,
+    otherwise LanceDB's fallback. A different registered class is an error.
+    """
+    global _resolved_blob_type
+    if _resolved_blob_type is not None:
+        return _resolved_blob_type
+    try:
+        blob_module = importlib.import_module("lance.blob")
+    except ModuleNotFoundError as err:
+        if err.name not in ("lance", "lance.blob"):
+            raise
+    else:
+        blob_type = getattr(blob_module, "BlobType", None)
+        if blob_type is not None:
+            registered_type = _deserialize_registered_type(blob_type())
+            if type(registered_type) is not blob_type:
+                registered_cls = type(registered_type)
+                raise ValueError(
+                    "lance.blob.v2 is already registered by "
+                    f"{registered_cls.__module__}.{registered_cls.__qualname__}"
+                )
+            _resolved_blob_type = blob_type
+            return blob_type
+    try:
+        pa.register_extension_type(_FallbackBlobType())  # type: ignore[arg-type]
+    except pa.ArrowKeyError as err:
+        raise ValueError(
+            "lance.blob.v2 is already registered by another extension class"
+        ) from err
+    _resolved_blob_type = _FallbackBlobType
+    return _resolved_blob_type
+
+
 def blob(name: str, nullable: bool = True) -> pa.Field:
-    """Create a Lance blob v2 column field."""
-    return pa.field(name, BlobType(), nullable=nullable)
+    """Create a Lance blob v2 column field.
+
+    When pylance is installed this is ``lance.blob.BlobType``.
+    """
+    blob_type = _resolve_blob_type()
+    return pa.field(name, blob_type(), nullable=nullable)
 
 
 def vector(dimension: int, value_type: pa.DataType = pa.float32()) -> pa.DataType:
@@ -155,3 +214,11 @@ def vector(dimension: int, value_type: pa.DataType = pa.float32()) -> pa.DataTyp
     ... ])
     """
     return pa.list_(value_type, dimension)
+
+
+def __getattr__(name: str):
+    if name == "BlobType":
+        blob_type = _resolve_blob_type()
+        globals()["BlobType"] = blob_type
+        return blob_type
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

python/python/lancedb/streaming.py

@@ -19,6 +19,7 @@ above.
 """
 
 import ctypes
+import heapq
 import logging
 import os
 import random
@@ -29,17 +30,18 @@ from collections import deque
 from concurrent.futures import ThreadPoolExecutor
 from copy import deepcopy
 from multiprocessing import RawArray
-from typing import Any, Callable, cast, Iterator, Literal, Optional, Union
+from typing import Any, Callable, cast, Iterator, Literal, NamedTuple, Optional, Union
 
 import pyarrow as pa
 import pyarrow.compute as pc
 import torch
-from torch.utils.data import IterableDataset, get_worker_info
+from torch.utils.data import DataLoader, IterableDataset, get_worker_info
 
 from .permutation import (
     Permutation,
     Transforms,
     permutation_builder,
+    _drop_base_version,
     _table_from_pickle_state,
     _table_to_pickle_state,
 )
@@ -55,6 +57,155 @@ DEFAULT_READ_BATCH_SIZE = 64
 DEFAULT_PREFETCH_BATCHES = 4
 
 
+class _WorkerSample(NamedTuple):
+    data: Any
+    dataset: "StreamingDataset"
+
+
+class _WorkerBatch(NamedTuple):
+    data: Any
+    state: dict
+
+
+class _ConsumerIteratorLease(NamedTuple):
+    owner_token: int
+    owner_thread: int
+
+
+class _CheckpointCollate:
+    """Attach the worker's post-fetch state to a collated batch."""
+
+    def __init__(self, collate_fn: Callable):
+        self._collate_fn = collate_fn
+
+    def __call__(self, samples):
+        try:
+            if isinstance(samples, list):
+                if not samples:
+                    return _WorkerBatch(self._collate_fn(samples), {})
+                worker_samples = samples
+                data = self._collate_fn([sample.data for sample in worker_samples])
+                dataset = worker_samples[-1].dataset
+            else:
+                data = self._collate_fn(samples.data)
+                dataset = samples.dataset
+        except StopIteration as exc:
+            raise RuntimeError(
+                "collate_fn raised StopIteration before returning a batch"
+            ) from exc
+        return _WorkerBatch(data, dataset._checkpoint_snapshot())
+
+
+class _StreamingDatasetAdapter(IterableDataset):
+    """Yield private sample wrappers for :class:`StreamingDataLoader`."""
+
+    def __init__(self, dataset: "StreamingDataset"):
+        super().__init__()
+        self.dataset = dataset
+
+    def __iter__(self):
+        for sample in self.dataset._iter(consumer_checkpoint_transport=True):
+            yield _WorkerSample(sample, self.dataset)
+
+    def __getattr__(self, name):
+        dataset = self.__dict__.get("dataset")
+        if dataset is None:
+            raise AttributeError(name)
+        return getattr(dataset, name)
+
+
+class _ConsumerCommitIterator:
+    def __init__(
+        self,
+        iterator,
+        dataset: "StreamingDataset",
+        *,
+        owner_token: int,
+        require_uniform: bool,
+    ):
+        self._iterator = iterator
+        self._dataset = dataset
+        self._owner_token = owner_token
+        self._require_uniform = require_uniform
+        self._released = False
+        self._terminal = False
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self._terminal:
+            raise StopIteration
+        try:
+            batch = next(self._iterator)
+        except StopIteration:
+            self._terminal = True
+            self._release()
+            raise
+        except BaseException as exc:
+            self._dataset._invalidate_checkpoint(
+                f"a DataLoader batch failed before it was returned: {exc}"
+            )
+            raise
+        try:
+            if not isinstance(batch, _WorkerBatch):
+                raise RuntimeError(
+                    "StreamingDataLoader did not receive worker checkpoint metadata"
+                )
+            self._dataset._commit_worker_state(
+                batch.state, require_uniform=self._require_uniform
+            )
+            return batch.data
+        except BaseException as exc:
+            self._dataset._invalidate_checkpoint(
+                f"a DataLoader batch failed before it was returned: {exc}"
+            )
+            raise
+
+    def _release(self) -> None:
+        if self.__dict__.get("_released", True):
+            return
+        self._released = True
+        dataset = self.__dict__.get("_dataset")
+        if dataset is not None:
+            dataset._release_consumer_iterator(self._owner_token)
+
+    def _shutdown_workers(self):
+        if self.__dict__.get("_released", True):
+            return None
+        self._terminal = True
+        iterator = self.__dict__.get("_iterator")
+        shutdown = getattr(iterator, "_shutdown_workers", None)
+        try:
+            if shutdown is not None:
+                shutdown()
+            else:
+                fetcher = getattr(iterator, "_dataset_fetcher", None)
+                dataset_iterator = getattr(fetcher, "dataset_iter", None)
+                close = getattr(dataset_iterator, "close", None)
+                if close is None:
+                    raise RuntimeError(
+                        "StreamingDataLoader could not close its inner iterator"
+                    )
+                close()
+        except BaseException as exc:
+            self._dataset._invalidate_checkpoint(
+                f"a DataLoader iterator could not be shut down safely: {exc}"
+            )
+            raise
+        else:
+            self._release()
+
+    def __del__(self):
+        try:
+            self._shutdown_workers()
+        except BaseException:
+            pass
+
+    def __getattr__(self, name):
+        return getattr(self._iterator, name)
+
+
 class StreamingDataset(IterableDataset):
     """An elastic, resumable PyTorch IterableDataset backed by a LanceDB table.
 
@@ -384,6 +535,22 @@ class StreamingDataset(IterableDataset):
         #          rows_skipped]
         self._worker_stats: RawArray = RawArray(ctypes.c_int64, 8)
 
+        # A standard multi-process DataLoader cannot report which prefetched
+        # batches were actually returned to its consumer.  Workers set this
+        # shared flag so state_dict() can reject a stale parent checkpoint
+        # unless StreamingDataLoader installed the consumer-commit transport.
+        self._untracked_worker_iteration: RawArray = RawArray(ctypes.c_int64, 1)
+
+        # Parent-side checkpoint lifecycle.  A failed DataLoader task creates
+        # a permanent hole in that iterator's delivery stream, while a
+        # multi-worker checkpoint is safe to restore only after all splits
+        # reach the same logical step boundary.
+        self._checkpoint_invalid_reason: Optional[str] = None
+        self._consumer_checkpoint_requires_uniform = False
+        self._consumer_iterator_lock = threading.Lock()
+        self._consumer_iterator_generation = 0
+        self._consumer_iterator_lease: Optional[_ConsumerIteratorLease] = None
+
         # Cumulative bytes of Arrow buffer data fetched across all iterations.
         self._bytes_loaded: int = 0
         # Cumulative seconds spent in LanceDB I/O and in transform functions.
@@ -396,6 +563,10 @@ class StreamingDataset(IterableDataset):
         # step boundaries all splits have consumed this many samples, so a
         # single scalar captures the topology-independent checkpoint state.
         self._resume_offset: int = 0
+        # Exact yielded-sample counts for splits this process has advanced.
+        # Missing entries use _resume_offset, which remains the lower-bound
+        # checkpoint inherited from an earlier uniform/global state.
+        self._resume_samples: dict[int, int] = {}
         # Permutation position each split has consumed through, keyed by
         # global split index.  Equal to _resume_offset for every split unless
         # on_transform_error skipped rows, in which case skipped positions
@@ -521,11 +692,45 @@ class StreamingDataset(IterableDataset):
         return self._rank_splits[start : start + splits_per_worker]
 
     def __iter__(self) -> Iterator[dict[str, Any]]:
+        return self._iter()
+
+    def _iter(
+        self, *, consumer_checkpoint_transport: bool = False
+    ) -> Iterator[dict[str, Any]]:
+        owner_token = None
+        previous_lease = self._consumer_iterator_lease
+        if consumer_checkpoint_transport:
+            if not self._consumer_iterator_active:
+                raise RuntimeError(
+                    "StreamingDataLoader worker transport requires an active "
+                    "parent iterator reservation"
+                )
+        else:
+            try:
+                owner_token = self._acquire_consumer_iterator()
+            except BaseException:
+                self._release_consumer_iterator_after_failed_acquire(previous_lease)
+                raise
+        try:
+            yield from self._iter_owned(
+                consumer_checkpoint_transport=consumer_checkpoint_transport
+            )
+        finally:
+            if owner_token is not None:
+                self._release_consumer_iterator(owner_token)
+
+    def _iter_owned(
+        self, *, consumer_checkpoint_transport: bool
+    ) -> Iterator[dict[str, Any]]:
         if self._raw_batches_ref is not None:
             raise RuntimeError(
                 "StreamingDataset does not support concurrent iteration. "
                 "Only one active iterator per dataset instance is allowed."
             )
+        real_worker = get_worker_info() is not None
+        if real_worker and not consumer_checkpoint_transport:
+            self._untracked_worker_iteration[0] = 1
+
         my_splits = self._resolve_my_splits()
         if not my_splits:
             return
@@ -533,6 +738,7 @@ class StreamingDataset(IterableDataset):
         # Set identity transform on each Permutation so __getitems__ returns
         # the raw RecordBatch.  Stage 2 applies the real transform.
         permutations: list[Permutation] = []
+        initial_samples: list[int] = []
         initial_positions: list[int] = []
         for split_idx in my_splits:
             perm = Permutation.from_tables(
@@ -541,21 +747,22 @@ class StreamingDataset(IterableDataset):
             if self._columns is not None:
                 perm = perm.select_columns(self._columns)
             perm = perm.with_transform(Transforms.arrow2arrow)
+            sample_count = self._resume_samples.get(split_idx, self._resume_offset)
             # Both modes resume from absolute permutation positions. Packing
             # stores them separately because it also checkpoints partial blocks.
             start_pos = (
                 self._pack_consumed[split_idx]
                 if self._pack_sequences is not None
-                else self._resume_positions.get(split_idx, self._resume_offset)
+                else self._resume_positions.get(split_idx, sample_count)
             )
             if start_pos > 0:
                 perm = perm.with_skip(start_pos)
+            initial_samples.append(sample_count)
             initial_positions.append(start_pos)
             permutations.append(perm)
 
         n = len(permutations)
         split_sizes = [perm.num_rows for perm in permutations]
-        initial_offset = self._resume_offset
         local_consumed = [0] * n
         # Permutation position each split has consumed through (absolute,
         # i.e. counted from the start of the unskipped split).  Runs ahead of
@@ -853,6 +1060,27 @@ class StreamingDataset(IterableDataset):
                     for i in range(n):
                         _fill_io(i)
 
+                    def _yield_row(i: int):
+                        pos, row = cooked[i].popleft()
+                        # Surface any completed prefetched failure before the
+                        # current row becomes durable checkpoint progress.
+                        _advance(i)
+                        local_consumed[i] += 1
+                        pos_consumed[i] = pos + 1
+                        split_idx = my_splits[i]
+                        self._resume_samples[split_idx] = (
+                            initial_samples[i] + local_consumed[i]
+                        )
+                        self._resume_positions[split_idx] = pos_consumed[i]
+                        return row
+
+                    def _update_progress_stats() -> None:
+                        if not real_worker:
+                            self._resume_offset = min(
+                                initial_samples[j] + local_consumed[j] for j in range(n)
+                            )
+                        _update_stats()
+
                     if self._pack_sequences is not None:
                         first_count = pack_blocks_emitted[my_splits[0]]
                         if any(
@@ -878,12 +1106,38 @@ class StreamingDataset(IterableDataset):
                                     tokens.extend([pad_id] * (pack_len - len(tokens)))
                                 block = _emit_block(i)
                                 pack_blocks_emitted[my_splits[i]] += 1
+                                # Checkpoint state must advance before yielding so
+                                # StreamingDataLoader can attach the exact state to
+                                # the batch it transports to the parent process.
+                                _commit_pack_state()
                                 if i == n - 1:
-                                    _commit_pack_state()
                                     _update_stats()
                                 yield block
                         return
 
+                    # A checkpoint taken between round-robin split turns has
+                    # non-uniform counts.  Resume lagging splits first so the
+                    # exact canonical sequence continues without replaying
+                    # already-consumed rows.
+                    if len(set(initial_samples)) > 1:
+                        catch_up_to = max(initial_samples)
+                        pending = [
+                            (initial_samples[i], my_splits[i], i)
+                            for i in range(n)
+                            if initial_samples[i] < catch_up_to
+                        ]
+                        heapq.heapify(pending)
+                        while pending:
+                            consumed, _, i = heapq.heappop(pending)
+                            _ensure_cooked(i)
+                            if not cooked[i]:
+                                return
+                            row = _yield_row(i)
+                            if consumed + 1 < catch_up_to:
+                                heapq.heappush(pending, (consumed + 1, my_splits[i], i))
+                            _update_progress_stats()
+                            yield row
+
                     while True:
                         # A cycle only runs if every split can still produce a
                         # row.  Without skips all splits exhaust simultaneously
@@ -904,20 +1158,14 @@ class StreamingDataset(IterableDataset):
                             break
 
                         for i in range(n):
-                            pos, row = cooked[i].popleft()
-                            local_consumed[i] += 1
-                            pos_consumed[i] = pos + 1
-                            _advance(i)
+                            row = _yield_row(i)
 
                             # After the last split in each cycle: update the
                             # global offset and refresh the shared-memory stats
                             # so the main process can observe pipeline depth
                             # even when __iter__ runs in a worker process.
                             if i == n - 1:
-                                self._resume_offset = initial_offset + local_consumed[i]
-                                for j, split_idx in enumerate(my_splits):
-                                    self._resume_positions[split_idx] = pos_consumed[j]
-                                _update_stats()
+                                _update_progress_stats()
 
                             yield row
                 finally:
@@ -1064,6 +1312,7 @@ class StreamingDataset(IterableDataset):
             "_local_consumed_ref",
         ):
             state[key] = None
+        state["_consumer_iterator_lock"] = None
         return state
 
     def __setstate__(self, state):
@@ -1074,19 +1323,31 @@ class StreamingDataset(IterableDataset):
         table_state = state.pop("_table")
         perm_name, perm_data = state.pop("_perm_table")
         self.__dict__.update(state)
+        self._consumer_iterator_lock = threading.Lock()
         if self._connection_factory is not None:
             self._table = self._connection_factory(table_name)
         else:
             self._table = _table_from_pickle_state(table_state)
+            if table_state["kind"] == "memory":
+                # Rebuilt from Arrow, so the recorded pin cannot resolve on it.
+                perm_data = _drop_base_version(perm_data)
         self._perm_table = _connect("memory://").create_table(perm_name, perm_data)
 
     def state_dict(self) -> dict:
         """Snapshot the dataset's consumption state.
 
+        When using DataLoader workers, construct a
+        [StreamingDataLoader][lancedb.streaming.StreamingDataLoader].  It
+        commits worker state only when a prefetched batch is returned to the
+        trainer.  A standard multi-process ``DataLoader`` cannot expose that
+        boundary, so calling this method after one has started raises
+        ``RuntimeError`` instead of returning stale producer state.
+
         In row mode, the returned dict is topology-independent at global step
         boundaries. ``positions_consumed_per_split`` records how far each
         split's permutation has advanced, which can differ from the sample
-        count when ``on_transform_error`` skips rows. Combine state dicts from
+        count when ``on_transform_error`` skips rows. ``StreamingDataLoader``
+        combines worker state in its parent process. Combine state dicts from
         every rank with
         [merge_state_dicts][lancedb.streaming.StreamingDataset.merge_state_dicts]
         before resuming on a different topology.
@@ -1095,6 +1356,43 @@ class StreamingDataset(IterableDataset):
         for every logical split. When packing is sharded, merge every rank
         state with ``merge_state_dicts`` before loading it.
         """
+        if self._untracked_worker_iteration[0] and get_worker_info() is None:
+            raise RuntimeError(
+                "StreamingDataset cannot checkpoint a standard DataLoader with "
+                "num_workers > 0 because prefetched worker progress is not "
+                "consumer-committed. Use StreamingDataLoader instead."
+            )
+        if self._checkpoint_invalid_reason is not None:
+            raise RuntimeError(
+                "StreamingDataset checkpointing is invalid because "
+                f"{self._checkpoint_invalid_reason}. Load the last valid "
+                "checkpoint into a fresh dataset before continuing."
+            )
+        state = self._checkpoint_snapshot()
+        if self._pack_sequences is not None:
+            rank_blocks = [
+                state["blocks_emitted_per_split"][split] for split in self._rank_splits
+            ]
+            if len(set(rank_blocks)) > 1:
+                raise RuntimeError(
+                    "Packed StreamingDataset checkpointing is only safe at a "
+                    "complete logical step boundary, when every split assigned "
+                    "to this rank has emitted the same block count. Consume more "
+                    "batches before calling state_dict()."
+                )
+        elif self._consumer_checkpoint_requires_uniform:
+            samples = state["samples_consumed_per_split"]
+            rank_samples = [samples[split] for split in self._rank_splits]
+            if len(set(rank_samples)) > 1:
+                raise RuntimeError(
+                    "StreamingDataLoader checkpointing with multiple workers is "
+                    "only safe at a complete logical step boundary, when every "
+                    "split assigned to this rank has the same consumed-sample "
+                    "count. Consume more batches before calling state_dict()."
+                )
+        return state
+
+    def _checkpoint_snapshot(self) -> dict:
         if self._pack_sequences is not None:
             return {
                 "shuffle_seed": self._shuffle_seed,
@@ -1108,18 +1406,141 @@ class StreamingDataset(IterableDataset):
                 "blocks_emitted_per_split": list(self._pack_blocks_emitted),
                 "pack_buffers": deepcopy(self._pack_buffers),
             }
+        samples = [
+            self._resume_samples.get(split, self._resume_offset)
+            for split in range(self._num_splits)
+        ]
         positions = [
-            self._resume_positions.get(split, self._resume_offset)
+            self._resume_positions.get(split, samples[split])
             for split in range(self._num_splits)
         ]
         return {
             "shuffle_seed": self._shuffle_seed,
             "num_splits": self._num_splits,
             "epoch": self._epoch,
-            "samples_consumed_per_split": [self._resume_offset] * self._num_splits,
+            "samples_consumed_per_split": samples,
             "positions_consumed_per_split": positions,
         }
 
+    def _invalidate_checkpoint(self, reason: str) -> None:
+        if self._checkpoint_invalid_reason is None:
+            self._checkpoint_invalid_reason = reason
+
+    @property
+    def _consumer_iterator_active(self) -> bool:
+        return self._consumer_iterator_lease is not None
+
+    @property
+    def _consumer_iterator_owner(self) -> Optional[int]:
+        lease = self._consumer_iterator_lease
+        return lease.owner_token if lease is not None else None
+
+    @property
+    def _consumer_iterator_owner_thread(self) -> Optional[int]:
+        lease = self._consumer_iterator_lease
+        return lease.owner_thread if lease is not None else None
+
+    def _acquire_consumer_iterator(self) -> int:
+        """Reserve this parent dataset for one checkpoint-aware iterator."""
+        with self._consumer_iterator_lock:
+            if self._consumer_iterator_active or self._raw_batches_ref is not None:
+                raise RuntimeError(
+                    "StreamingDataset does not support concurrent iteration. "
+                    "Only one active iterator per dataset instance is allowed."
+                )
+            owner_thread = threading.get_ident()
+            owner_token = self._consumer_iterator_generation + 1
+            lease = _ConsumerIteratorLease(owner_token, owner_thread)
+            self._consumer_iterator_generation = owner_token
+            self._consumer_iterator_lease = lease
+            return owner_token
+
+    def _release_consumer_iterator(self, owner_token: int) -> None:
+        with self._consumer_iterator_lock:
+            lease = self._consumer_iterator_lease
+            if lease is not None and lease.owner_token == owner_token:
+                self._consumer_iterator_lease = None
+
+    def _release_consumer_iterator_after_failed_acquire(
+        self, previous_lease: Optional[_ConsumerIteratorLease]
+    ) -> None:
+        """Clean up when an interrupted acquire set a lease but did not return it."""
+        owner_thread = threading.current_thread().ident
+        with self._consumer_iterator_lock:
+            lease = self._consumer_iterator_lease
+            if (
+                lease is not None
+                and lease is not previous_lease
+                and lease.owner_thread == owner_thread
+            ):
+                self._consumer_iterator_lease = None
+
+    def _commit_worker_state(self, state: dict, *, require_uniform: bool) -> None:
+        """Merge one trainer-consumed worker batch into parent state."""
+        for key, expected in (
+            ("shuffle_seed", self._shuffle_seed),
+            ("num_splits", self._num_splits),
+            ("epoch", self._epoch),
+        ):
+            if state.get(key) != expected:
+                raise ValueError(
+                    f"{key} mismatch in worker checkpoint: "
+                    f"{state.get(key)} != {expected}"
+                )
+        packed = "pack_buffers" in state
+        if packed != (self._pack_sequences is not None):
+            raise ValueError("worker checkpoint mode does not match the dataset")
+        if packed:
+            for key in ("pack_sequences", "eos_id", "pad_id", "blocks_per_epoch"):
+                expected = getattr(self, f"_{key}")
+                if state.get(key) != expected:
+                    raise ValueError(
+                        f"{key} mismatch in worker checkpoint: "
+                        f"{state.get(key)} != {expected}"
+                    )
+            samples = state["samples_consumed_per_split"]
+            emitted = state["blocks_emitted_per_split"]
+            if len(samples) != self._num_splits or len(emitted) != self._num_splits:
+                raise ValueError(
+                    "packed worker checkpoint must contain one entry per split"
+                )
+            buffers = state["pack_buffers"]
+            for split, (count, blocks) in enumerate(zip(samples, emitted)):
+                incoming = (int(blocks), int(count))
+                current = (
+                    self._pack_blocks_emitted[split],
+                    self._pack_consumed[split],
+                )
+                if incoming > current:
+                    self._pack_blocks_emitted[split] = incoming[0]
+                    self._pack_consumed[split] = incoming[1]
+                    buffer = buffers.get(split, buffers.get(str(split)))
+                    if buffer is None:
+                        self._pack_buffers.pop(split, None)
+                    else:
+                        self._pack_buffers[split] = {
+                            "tokens": list(buffer["tokens"]),
+                            "starts": list(buffer["starts"]),
+                        }
+            self._consumer_checkpoint_requires_uniform |= require_uniform
+            return
+
+        samples = state["samples_consumed_per_split"]
+        positions = state.get("positions_consumed_per_split", samples)
+        for split, count in enumerate(samples):
+            current = self._resume_samples.get(split, self._resume_offset)
+            self._resume_samples[split] = max(current, int(count))
+        for split, position in enumerate(positions):
+            current = self._resume_positions.get(
+                split, self._resume_samples.get(split, self._resume_offset)
+            )
+            self._resume_positions[split] = max(current, int(position))
+        self._resume_offset = min(
+            self._resume_samples.get(split, self._resume_offset)
+            for split in range(self._num_splits)
+        )
+        self._consumer_checkpoint_requires_uniform |= require_uniform
+
     def load_state_dict(self, state: dict) -> None:
         """Resume from a previously snapshotted state.
 
@@ -1139,6 +1560,7 @@ class StreamingDataset(IterableDataset):
                 f"shuffle_seed mismatch: checkpoint has {state['shuffle_seed']}, "
                 f"current dataset has {self._shuffle_seed}"
             )
+        self._consumer_checkpoint_requires_uniform = False
 
         if "pack_buffers" in state or self._pack_sequences is not None:
             for key in (
@@ -1165,14 +1587,17 @@ class StreamingDataset(IterableDataset):
             return
 
         consumed = state["samples_consumed_per_split"]
-        # All entries are equal at step boundaries; use the first.
         if isinstance(consumed, list):
-            self._resume_offset = consumed[0] if consumed else 0
+            self._resume_offset = min(consumed) if consumed else 0
+            self._resume_samples = {
+                split: int(count) for split, count in enumerate(consumed)
+            }
         else:
             self._resume_offset = int(consumed)
+            self._resume_samples = {}
         # Older checkpoints predate positions_consumed_per_split; without
         # skipped rows positions equal sample counts, so falling back to
-        # _resume_offset (the .get default in __iter__) is exact.
+        # the per-split sample count (the .get default in __iter__) is exact.
         positions = state.get("positions_consumed_per_split")
         if positions is None:
             self._resume_positions = {}
@@ -1185,10 +1610,11 @@ class StreamingDataset(IterableDataset):
     def merge_state_dicts(states: list[dict]) -> dict:
         """Merge state dicts saved by different ranks into one exact state.
 
-        For row mode, the elementwise maximum of permutation positions recovers
-        splits advanced by different ranks after transform failures. For packed
-        mode, the state that emitted the most blocks for each logical split
-        supplies that split's permutation position and partial token buffer. Packed
+        In row mode, each rank records exact consumer-committed progress for
+        its own splits and lower bounds for the rest, so elementwise maxima
+        recover both sample counts and permutation positions. In packed mode,
+        the state that emitted the most blocks for each logical split supplies
+        that split's permutation position and partial token buffer. Packed
         states must cover every rank at the same global step.
 
         Raises ``ValueError`` if the states are empty, were not produced by
@@ -1299,17 +1725,13 @@ class StreamingDataset(IterableDataset):
             merged["pack_buffers"] = merged_buffers
             return merged
 
-        for state in states[1:]:
-            if (
-                state["samples_consumed_per_split"]
-                != first["samples_consumed_per_split"]
-            ):
-                raise ValueError(
-                    "samples_consumed_per_split mismatch across state dicts; "
-                    "state_dict() must be called at the same global step "
-                    "boundary on every rank"
-                )
         merged = dict(first)
+        merged["samples_consumed_per_split"] = [
+            max(per_split)
+            for per_split in zip(
+                *(state["samples_consumed_per_split"] for state in states)
+            )
+        ]
         all_positions = [
             state.get(
                 "positions_consumed_per_split", state["samples_consumed_per_split"]
@@ -1320,3 +1742,113 @@ class StreamingDataset(IterableDataset):
             max(per_split) for per_split in zip(*all_positions)
         ]
         return merged
+
+
+class StreamingDataLoader(DataLoader):
+    """A PyTorch DataLoader with consumer-committed dataset checkpoints.
+
+    PyTorch workers prefetch batches ahead of the trainer, so worker-local
+    producer progress is not a safe checkpoint.  This loader carries a state
+    snapshot alongside every internal batch and applies it to the parent
+    [StreamingDataset][lancedb.streaming.StreamingDataset] only when that batch
+    is returned by ``next()``.
+    The trainer receives the same collated batch it would receive from a
+    standard ``torch.utils.data.DataLoader``.
+
+    With more than one worker, row-mode ``state_dict()`` is available only at
+    complete logical step boundaries, when every split assigned to the rank has
+    the same consumed-sample count. Packed checkpoints require equal emitted-block
+    counts across the rank's splits for any worker count. ``persistent_workers=True``
+    is not supported because prefetched worker copies cannot be restored from
+    parent-committed state. If batch collation raises, checkpointing remains
+    invalid for that dataset instance; restore the last valid checkpoint into a
+    fresh dataset before continuing.
+    Only one active iterator may own a dataset at a time, including when worker
+    processes are used. Exhausting or explicitly shutting down the iterator
+    releases that ownership. ``drop_last=True`` is not supported because worker
+    replicas discard incomplete tails independently, which cannot produce a
+    topology-independent checkpoint.
+
+    Parameters are the same as ``torch.utils.data.DataLoader`` except that
+    ``dataset`` must be a
+    [StreamingDataset][lancedb.streaming.StreamingDataset].
+    Subclasses that override ``StreamingDataset.__iter__`` are not supported
+    because the custom iterator cannot provide the exact per-yield checkpoint
+    snapshots required by this loader.
+
+    Examples
+    --------
+    >>> # dataset = StreamingDataset(table, num_splits=2)
+    >>> # loader = StreamingDataLoader(dataset, batch_size=8, num_workers=2)
+    >>> # batch = next(iter(loader))
+    >>> # checkpoint = dataset.state_dict()
+    """
+
+    def __init__(self, dataset: StreamingDataset, *args, **kwargs):
+        if not isinstance(dataset, StreamingDataset):
+            raise TypeError("StreamingDataLoader requires a StreamingDataset")
+        if type(dataset).__iter__ is not StreamingDataset.__iter__:
+            raise TypeError(
+                "StreamingDataLoader does not support StreamingDataset subclasses "
+                "that override __iter__ because they cannot provide exact "
+                "per-yield checkpoint state"
+            )
+        if kwargs.get("in_order", True) is False:
+            raise ValueError(
+                "StreamingDataLoader requires in_order=True for deterministic "
+                "consumer checkpoints"
+            )
+        if kwargs.get("persistent_workers", False):
+            raise ValueError(
+                "StreamingDataLoader does not support persistent_workers=True "
+                "because worker prefetch state cannot be reset from a checkpoint"
+            )
+        self._streaming_dataset = dataset
+        super().__init__(_StreamingDatasetAdapter(dataset), *args, **kwargs)
+        if self.drop_last:
+            raise ValueError(
+                "StreamingDataLoader does not support drop_last=True because "
+                "discarded worker tails cannot be checkpointed "
+                "topology-independently"
+            )
+        self.collate_fn = _CheckpointCollate(self.collate_fn)
+
+    def __iter__(self):
+        dataset = self._streaming_dataset
+        previous_lease = dataset._consumer_iterator_lease
+        owner_token = None
+        try:
+            owner_token = dataset._acquire_consumer_iterator()
+            state = dataset._checkpoint_snapshot()
+            packed = dataset._pack_sequences is not None
+            if packed:
+                blocks = state["blocks_emitted_per_split"]
+                rank_blocks = [blocks[split] for split in dataset._rank_splits]
+                if len(set(rank_blocks)) > 1:
+                    raise RuntimeError(
+                        "StreamingDataLoader cannot start from a partial packed "
+                        "logical step; resume from a checkpoint whose splits "
+                        "assigned to this rank have equal emitted-block counts"
+                    )
+            elif self.num_workers > 1:
+                samples = state["samples_consumed_per_split"]
+                rank_samples = [samples[split] for split in dataset._rank_splits]
+                if len(set(rank_samples)) > 1:
+                    raise RuntimeError(
+                        "StreamingDataLoader cannot start multiple workers from a "
+                        "partial logical step; resume from a checkpoint whose "
+                        "splits assigned to this rank have equal consumed-sample "
+                        "counts"
+                    )
+            return _ConsumerCommitIterator(
+                super().__iter__(),
+                dataset,
+                owner_token=owner_token,
+                require_uniform=self.num_workers > 1 or packed,
+            )
+        except BaseException:
+            if owner_token is not None:
+                dataset._release_consumer_iterator(owner_token)
+            else:
+                dataset._release_consumer_iterator_after_failed_acquire(previous_lease)
+            raise

python/python/lancedb/table.py

@@ -85,6 +85,7 @@ from .query import (
     AsyncQuery,
     AsyncTakeQuery,
     AsyncVectorQuery,
+    DocumentGranularity,
     FullTextQuery,
     LanceEmptyQueryBuilder,
     LanceFtsQueryBuilder,
@@ -103,7 +104,12 @@ from .util import (
     value_to_sql,
 )
 from .index import lang_mapping
-from .schema import blob_v2_column_paths, schema_has_blob_field
+from .schema import (
+    blob_v2_column_paths,
+    is_blob_v2_field,
+    row_addressable_blob_v2_paths,
+    schema_has_blob_field,
+)
 
 
 def _should_push_down_query_table(
@@ -425,6 +431,7 @@ def _cast_to_target_schema(
 
     def gen():
         for batch in reader:
+            batch = _coerce_blob_write_columns(batch, reordered_schema)
             # Table but not RecordBatch has cast.
             cast_batches = (
                 pa.Table.from_batches([batch]).cast(reordered_schema).to_batches()
@@ -437,6 +444,166 @@ def _cast_to_target_schema(
     return pa.RecordBatchReader.from_batches(reordered_schema, gen())
 
 
+def _coerce_blob_write_columns(
+    batch: pa.RecordBatch, target_schema: pa.Schema
+) -> pa.RecordBatch:
+    """Materialize blob storage structs before the stream leaves Python.
+
+    merge_insert requires its source reader to already match the table's
+    physical schema. Unlike add and insert, it does not pass through
+    LanceDB's Rust blob coercion, so preserving binary input here would
+    reach Lance as binary and fail the schema check.
+    """
+    columns = []
+    fields = []
+    changed = False
+    for field, column in zip(batch.schema, batch.columns):
+        target_field = target_schema.field(field.name)
+        coerced = _coerce_blob_value(column, target_field)
+        if coerced is not column:
+            column = coerced
+            field = pa.field(
+                field.name,
+                coerced.type,
+                field.nullable,
+                target_field.metadata,
+            )
+            changed = True
+        columns.append(column)
+        fields.append(field)
+    if not changed:
+        return batch
+    return pa.RecordBatch.from_arrays(
+        columns, schema=pa.schema(fields, metadata=batch.schema.metadata)
+    )
+
+
+def _coerce_blob_value(column: pa.Array, target_field: pa.Field) -> pa.Array:
+    if is_blob_v2_field(target_field) and _can_coerce_to_blob(column.type):
+        return _coerce_value_to_blob(column, target_field)
+
+    target_type = target_field.type
+    if pa.types.is_struct(target_type) and pa.types.is_struct(column.type):
+        children = []
+        fields = []
+        changed = False
+        for source_field in column.type:
+            source_column = column.field(source_field.name)
+            nested_target = next(
+                (field for field in target_type if field.name == source_field.name),
+                None,
+            )
+            if nested_target is None:
+                children.append(source_column)
+                fields.append(source_field)
+                continue
+            coerced = _coerce_blob_value(source_column, nested_target)
+            if coerced is not source_column:
+                changed = True
+            child_array, child_type = _physical_array_and_type(coerced)
+            children.append(child_array)
+            fields.append(
+                pa.field(
+                    source_field.name,
+                    child_type,
+                    source_field.nullable,
+                    nested_target.metadata,
+                )
+            )
+        if not changed:
+            return column
+        return pa.StructArray.from_arrays(
+            children,
+            fields=fields,
+            mask=column.is_null() if column.null_count else None,
+        )
+
+    if _is_list_like(target_type) and _is_list_like(column.type):
+        return _coerce_blob_list_values(column, target_type.value_field)
+
+    return column
+
+
+def _coerce_blob_list_values(
+    column: pa.Array, target_value_field: pa.Field
+) -> pa.Array:
+    """Coerce blob values inside a list column, preserving offsets and nulls.
+
+    Works on the raw child values window instead of ``pc.list_flatten`` because
+    flatten drops values spanned by null slots, which would misalign offsets.
+    """
+    mask = column.is_null() if column.null_count else None
+    if pa.types.is_fixed_size_list(column.type):
+        list_size = column.type.list_size
+        values = column.values.slice(column.offset * list_size, len(column) * list_size)
+        coerced = _coerce_blob_value(values, target_value_field)
+        if coerced is values:
+            return column
+        physical_values, _ = _physical_array_and_type(coerced)
+        return pa.FixedSizeListArray.from_arrays(physical_values, list_size, mask=mask)
+    offsets = column.offsets
+    first_offset = offsets[0].as_py()
+    values = column.values.slice(
+        first_offset,
+        offsets[-1].as_py() - first_offset,
+    )
+    coerced = _coerce_blob_value(values, target_value_field)
+    if coerced is values:
+        return column
+    physical_values, _ = _physical_array_and_type(coerced)
+    if first_offset:
+        offsets = pc.subtract(offsets, pa.scalar(first_offset, offsets.type))
+    if pa.types.is_large_list(column.type):
+        return pa.LargeListArray.from_arrays(offsets, physical_values, mask=mask)
+    return pa.ListArray.from_arrays(offsets, physical_values, mask=mask)
+
+
+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())
+    length = len(values)
+    storage_type = target_field.type
+    if isinstance(storage_type, pa.ExtensionType):
+        storage_type = storage_type.storage_type
+    storage_fields = list(storage_type)
+    children = []
+    for storage_field in storage_fields:
+        if storage_field.name == "data":
+            children.append(data)
+        else:
+            children.append(pa.nulls(length, type=storage_field.type))
+    storage = pa.StructArray.from_arrays(
+        children,
+        fields=storage_fields,
+        mask=values.is_null() if values.null_count else None,
+    )
+    if isinstance(target_field.type, pa.ExtensionType):
+        return pa.ExtensionArray.from_storage(target_field.type, storage)
+    return storage
+
+
+def _physical_array_and_type(array: pa.Array) -> tuple[pa.Array, pa.DataType]:
+    if isinstance(array.type, pa.ExtensionType):
+        return array.storage, array.type.storage_type
+    return array, array.type
+
+
+def _can_coerce_to_blob(data_type: pa.DataType) -> bool:
+    return _is_binary_like(data_type) or pa.types.is_null(data_type)
+
+
+def _is_binary_like(data_type: pa.DataType) -> bool:
+    return (
+        pa.types.is_binary(data_type)
+        or pa.types.is_large_binary(data_type)
+        or pa.types.is_binary_view(data_type)
+    )
+
+
 def _field_extension_name(field: pa.Field) -> Optional[str]:
     extension_name = getattr(field.type, "extension_name", None)
     if extension_name is not None:
@@ -463,63 +630,71 @@ def _align_field_types(
         target_field = next((f for f in target_fields if f.name == field.name), None)
         if target_field is None:
             raise ValueError(f"Field '{field.name}' not found in target schema")
-        # Preserve arrow.json input until it reaches Lance. 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.
-        if (
-            _field_extension_name(field) == "arrow.json"
-            and _field_extension_name(target_field) == "lance.json"
-        ):
-            new_fields.append(field)
-            continue
-        if pa.types.is_struct(target_field.type):
-            if pa.types.is_struct(field.type):
-                new_type = pa.struct(
-                    _align_field_types(
-                        field.type.fields,
-                        target_field.type.fields,
-                    )
+        new_fields.append(_align_field(field, target_field))
+    return new_fields
+
+
+def _align_list_value_field(
+    value_field: pa.Field, target_value_field: pa.Field
+) -> pa.Field:
+    # A list has exactly one child, so the inferred child name ("item") aligns
+    # positionally and adopts the table's child name; pa.Table.cast renames it.
+    return _align_field(value_field, target_value_field).with_name(
+        target_value_field.name
+    )
+
+
+def _align_field(field: pa.Field, target_field: pa.Field) -> pa.Field:
+    # Preserve arrow.json input until it reaches Lance. 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.
+    if (
+        _field_extension_name(field) == "arrow.json"
+        and _field_extension_name(target_field) == "lance.json"
+    ):
+        return field
+    if pa.types.is_struct(target_field.type):
+        if pa.types.is_struct(field.type):
+            new_type = pa.struct(
+                _align_field_types(
+                    field.type.fields,
+                    target_field.type.fields,
                 )
-            else:
-                new_type = target_field.type
-        elif pa.types.is_list(target_field.type):
-            if _is_list_like(field.type):
-                new_type = pa.list_(
-                    _align_field_types(
-                        [field.type.value_field],
-                        [target_field.type.value_field],
-                    )[0]
-                )
-            else:
-                new_type = target_field.type
-        elif pa.types.is_large_list(target_field.type):
-            if _is_list_like(field.type):
-                new_type = pa.large_list(
-                    _align_field_types(
-                        [field.type.value_field],
-                        [target_field.type.value_field],
-                    )[0]
-                )
-            else:
-                new_type = target_field.type
-        elif pa.types.is_fixed_size_list(target_field.type):
-            if _is_list_like(field.type):
-                new_type = pa.list_(
-                    _align_field_types(
-                        [field.type.value_field],
-                        [target_field.type.value_field],
-                    )[0],
-                    target_field.type.list_size,
-                )
-            else:
-                new_type = target_field.type
+            )
         else:
             new_type = target_field.type
-        new_fields.append(
-            pa.field(field.name, new_type, field.nullable, target_field.metadata)
-        )
-    return new_fields
+    elif pa.types.is_list(target_field.type):
+        if _is_list_like(field.type):
+            new_type = pa.list_(
+                _align_list_value_field(
+                    field.type.value_field, target_field.type.value_field
+                )
+            )
+        else:
+            new_type = target_field.type
+    elif pa.types.is_large_list(target_field.type):
+        if _is_list_like(field.type):
+            new_type = pa.large_list(
+                _align_list_value_field(
+                    field.type.value_field, target_field.type.value_field
+                )
+            )
+        else:
+            new_type = target_field.type
+    elif pa.types.is_fixed_size_list(target_field.type):
+        if _is_list_like(field.type):
+            new_type = pa.list_(
+                _align_list_value_field(
+                    field.type.value_field, target_field.type.value_field
+                ),
+                target_field.type.list_size,
+            )
+        else:
+            new_type = target_field.type
+    else:
+        new_type = target_field.type
+    return pa.field(field.name, new_type, field.nullable, target_field.metadata)
 
 
 def _infer_subschema(
@@ -588,7 +763,7 @@ def sanitize_create_table(
         schema = data.schema
     else:
         if schema is not None:
-            data = pa.Table.from_pylist([], schema)
+            data = pa.Table.from_batches([], schema=schema)
     if schema is None:
         if data is None:
             raise ValueError("Either data or schema must be provided")
@@ -1168,6 +1343,7 @@ class Table(ABC):
         ngram_max_length: int = 3,
         prefix_only: bool = False,
         block_size: int = 128,
+        document_granularity: DocumentGranularity = DocumentGranularity.ROW,
         wait_timeout: Optional[timedelta] = None,
         name: Optional[str] = None,
     ):
@@ -1246,6 +1422,11 @@ class Table(ABC):
             The number of documents per compressed posting block. Must be 128
             or 256. A value of 256 uses the experimental FTS V3 format and
             may introduce breaking changes.
+        document_granularity: DocumentGranularity, default ROW
+            ``ROW`` treats the selected text in one table row as one document.
+            ``LIST_ELEMENT`` treats each element of the deepest list on the field
+            path as one document and returns its physical coordinates in
+            ``_doc_index`` for matching queries.
         wait_timeout: timedelta, optional
             The timeout to wait if indexing is asynchronous.
         name: str, optional
@@ -1269,6 +1450,7 @@ class Table(ABC):
         fill_value: float = 0.0,
         progress: Optional[Union[bool, Callable, Any]] = None,
         write_parallelism: Optional[int] = None,
+        allow_external_blob_outside_bases: bool = False,
     ) -> AddResult:
         """Add more data to the [Table][lancedb.table.Table].
 
@@ -1320,6 +1502,10 @@ class Table(ABC):
             data in flight. Defaults to an estimate based on the data size,
             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
+            Store blob URIs that sit outside registered blob bases. The row
+            keeps a reference, so the object has to stay readable. Local
+            tables only.
 
         Returns
         -------
@@ -1732,7 +1918,7 @@ class Table(ABC):
     @abstractmethod
     def update(
         self,
-        where: Optional[str] = None,
+        where: Optional[Union[str, Expr]] = None,
         values: Optional[dict] = None,
         *,
         values_sql: Optional[Dict[str, str]] = None,
@@ -1747,9 +1933,11 @@ class Table(ABC):
 
         Parameters
         ----------
-        where: str, optional
-            The SQL where clause to use when updating rows. For example, 'x = 2'
-            or 'x IN (1, 2, 3)'. The filter must not be empty, or it will error.
+        where: str or [Expr][lancedb.expr.Expr], optional
+            The filter condition. Can be a SQL string or a type-safe
+            [Expr][lancedb.expr.Expr] built with [col][lancedb.expr.col] and
+            [lit][lancedb.expr.lit]. The filter must not be empty, or it will
+            error.
         values: dict, optional
             The values to update. The keys are the column names and the values
             are the values to set.
@@ -1767,6 +1955,7 @@ class Table(ABC):
         Examples
         --------
         >>> import lancedb
+        >>> from lancedb.expr import col
         >>> import pandas as pd
         >>> data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1.0, 2], [3, 4], [5, 6]]})
         >>> db = lancedb.connect("./.lancedb")
@@ -1776,7 +1965,7 @@ class Table(ABC):
         0  1  [1.0, 2.0]
         1  2  [3.0, 4.0]
         2  3  [5.0, 6.0]
-        >>> table.update(where="x = 2", values={"vector": [10.0, 10]})
+        >>> table.update(where=col("x") == 2, values={"vector": [10.0, 10]})
         UpdateResult(rows_updated=1, version=2)
         >>> table.to_pandas()
            x        vector
@@ -1972,13 +2161,15 @@ class Table(ABC):
             A mapping with one ``FunctionApplication`` value keeps its scalar
             or named-struct result in the named table column. A bare
             named-struct application expands its ordered result fields as one
-            atomic sibling group; aliases come from ``rename(columns=...)``.
+            atomic binding; aliases come from ``rename(columns=...)``.
             Function columns are supported only on LanceDB Cloud and
             Enterprise.
         computed: Dict[str, str], optional
-            A map of column name to a SQL expression defining the column. The
-            column's type and inputs are derived from the expression, so no
-            data type is supplied.
+            A mapping from output column names to SQL expressions derives each
+            output field from its expression. A direct projection of a Blob v2
+            field inherits Blob v2 semantics; other expressions derive their
+            ordinary Arrow type. Mapping order is declaration and dependency
+            order.
 
             Unlike ``transforms``, the expression is stored rather than
             evaluated now: the column is committed with no values, and rows get
@@ -2115,12 +2306,25 @@ class Table(ABC):
         ----------
         updates : dict
             One or more dicts, each with:
+
             - "path": str — dot-path to the field (e.g. "embedding" or "a.b.c").
             - "metadata": dict[str, str | None] — keys to set; a value of ``None``
               deletes that key.
             - "replace": bool, optional — replace the field's whole metadata map
               instead of merging (default False).
 
+            The following keys are treated specially, by convention, and should
+            be used when appropriate:
+
+            - "lancedb:description": for a human-readable description of a field.
+            - ``"lancedb:tag:<name>"`` for a user-defined key-value tag, where the
+                suffix names the tag category; e.g. "lancedb:tag:model": "clip".
+            - "lancedb:logical-column" for a column grouping; e.g. "feature_v1"
+                and "feature_v2" might be in the same logical column.
+            - "lancedb:status" for status options ("production", "candidate",
+                "deprecated", "archived") to designate the current life cycle
+                state of this column.
+
         Returns
         -------
         UpdateFieldMetadataResult
@@ -2670,7 +2874,7 @@ class LanceTable(Table):
             arrow_tbl = self.to_arrow()
             if blob_mode == "descriptions":
                 arrow_tbl = strip_auto_row_ids(
-                    arrow_tbl, blob_v2_column_paths(self.schema)
+                    arrow_tbl, row_addressable_blob_v2_paths(self.schema)
                 )
             return arrow_tbl.to_pandas(**kwargs)
 
@@ -3268,6 +3472,7 @@ class LanceTable(Table):
         ngram_max_length: int = 3,
         prefix_only: bool = False,
         block_size: int = 128,
+        document_granularity: DocumentGranularity = DocumentGranularity.ROW,
         name: Optional[str] = None,
     ):
         """Create a full-text search index on a column.
@@ -3319,7 +3524,11 @@ class LanceTable(Table):
             tokenizer_configs = self.infer_tokenizer_configs(tokenizer_name)
             tokenizer_configs["custom_stop_words"] = custom_stop_words
 
-        config = FTS(block_size=block_size, **tokenizer_configs)
+        config = FTS(
+            block_size=block_size,
+            document_granularity=document_granularity,
+            **tokenizer_configs,
+        )
 
         try:
             LOOP.run(
@@ -3409,6 +3618,7 @@ class LanceTable(Table):
         fill_value: float = 0.0,
         progress: Optional[Union[bool, Callable, Any]] = None,
         write_parallelism: Optional[int] = None,
+        allow_external_blob_outside_bases: bool = False,
     ) -> AddResult:
         """Add data to the table.
         If vector columns are missing and the table
@@ -3436,6 +3646,9 @@ class LanceTable(Table):
             data in flight. Defaults to an estimate based on the data size,
             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
+            Allow blob URIs outside registered bases. See :meth:`Table.add`.
+            Local tables only.
 
         Returns
         -------
@@ -3452,6 +3665,7 @@ class LanceTable(Table):
                     fill_value=fill_value,
                     progress=progress,
                     write_parallelism=write_parallelism,
+                    allow_external_blob_outside_bases=allow_external_blob_outside_bases,
                 )
             )
         finally:
@@ -3806,7 +4020,7 @@ class LanceTable(Table):
 
     def update(
         self,
-        where: Optional[str] = None,
+        where: Optional[Union[str, Expr]] = None,
         values: Optional[dict] = None,
         *,
         values_sql: Optional[Dict[str, str]] = None,
@@ -3817,9 +4031,11 @@ class LanceTable(Table):
 
         Parameters
         ----------
-        where: str, optional
-            The SQL where clause to use when updating rows. For example, 'x = 2'
-            or 'x IN (1, 2, 3)'. The filter must not be empty, or it will error.
+        where: str or [Expr][lancedb.expr.Expr], optional
+            The filter condition. Can be a SQL string or a type-safe
+            [Expr][lancedb.expr.Expr] built with [col][lancedb.expr.col] and
+            [lit][lancedb.expr.lit]. The filter must not be empty, or it will
+            error.
         values: dict, optional
             The values to update. The keys are the column names and the values
             are the values to set.
@@ -3837,6 +4053,7 @@ class LanceTable(Table):
         Examples
         --------
         >>> import lancedb
+        >>> from lancedb.expr import col
         >>> import pandas as pd
         >>> data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1.0, 2], [3, 4], [5, 6]]})
         >>> db = lancedb.connect("./.lancedb")
@@ -3846,7 +4063,7 @@ class LanceTable(Table):
         0  1  [1.0, 2.0]
         1  2  [3.0, 4.0]
         2  3  [5.0, 6.0]
-        >>> table.update(where="x = 2", values={"vector": [10.0, 10]})
+        >>> table.update(where=col("x") == 2, values={"vector": [10.0, 10]})
         UpdateResult(rows_updated=1, version=2)
         >>> table.to_pandas()
            x        vector
@@ -5061,7 +5278,9 @@ class AsyncTable:
         if blob_mode == "descriptions" or not schema_has_blob_field(schema):
             arrow_tbl = await self.to_arrow()
             if blob_mode == "descriptions":
-                arrow_tbl = strip_auto_row_ids(arrow_tbl, blob_v2_column_paths(schema))
+                arrow_tbl = strip_auto_row_ids(
+                    arrow_tbl, row_addressable_blob_v2_paths(schema)
+                )
             return arrow_tbl.to_pandas(**kwargs)
 
         if blob_mode == "lazy" and get_uri_scheme(await self.uri()) == "memory":
@@ -5365,6 +5584,7 @@ class AsyncTable:
         fill_value: Optional[float] = None,
         progress: Optional[Union[bool, Callable, Any]] = None,
         write_parallelism: Optional[int] = None,
+        allow_external_blob_outside_bases: bool = False,
     ) -> AddResult:
         """Add more data to the [AsyncTable][lancedb.table.AsyncTable].
 
@@ -5395,6 +5615,9 @@ class AsyncTable:
             data in flight. Defaults to an estimate based on the data size,
             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
+            Allow blob URIs outside registered bases. See :meth:`Table.add`.
+            Local tables only.
 
         """
         schema = await self.schema()
@@ -5431,6 +5654,7 @@ class AsyncTable:
                 mode or "append",
                 progress=progress,
                 write_parallelism=write_parallelism,
+                allow_external_blob_outside_bases=allow_external_blob_outside_bases,
             )
         except RuntimeError as e:
             if "Cast error" in str(e):
@@ -5955,7 +6179,7 @@ class AsyncTable:
         self,
         updates: Optional[Dict[str, Any]] = None,
         *,
-        where: Optional[str] = None,
+        where: Optional[Union[str, Expr]] = None,
         updates_sql: Optional[Dict[str, str]] = None,
     ) -> UpdateResult:
         """
@@ -5970,9 +6194,11 @@ class AsyncTable:
             The updates to apply.  The keys should be the name of the column to
             update.  The values should be the new values to assign.  This is
             required unless updates_sql is supplied.
-        where: str, optional
-            An SQL filter that controls which rows are updated. For example, 'x = 2'
-            or 'x IN (1, 2, 3)'.  Only rows that satisfy this filter will be udpated.
+        where: str or [Expr][lancedb.expr.Expr], optional
+            The filter condition. Can be a SQL string or a type-safe
+            [Expr][lancedb.expr.Expr] built with [col][lancedb.expr.col] and
+            [lit][lancedb.expr.lit]. Only rows that satisfy this filter will
+            be updated.
         updates_sql: dict, optional
             The updates to apply, expressed as SQL expression strings.  The keys should
             be column names. The values should be SQL expressions.  These can be SQL
@@ -5990,13 +6216,14 @@ class AsyncTable:
         --------
         >>> import asyncio
         >>> import lancedb
+        >>> from lancedb.expr import col
         >>> import pandas as pd
         >>> async def demo_update():
         ...     data = pd.DataFrame({"x": [1, 2], "vector": [[1, 2], [3, 4]]})
         ...     db = await lancedb.connect_async("./.lancedb")
         ...     table = await db.create_table("my_table", data)
         ...     # x is [1, 2], vector is [[1, 2], [3, 4]]
-        ...     await table.update({"vector": [10, 10]}, where="x = 2")
+        ...     await table.update({"vector": [10, 10]}, where=col("x") == 2)
         ...     # x is [1, 2], vector is [[1, 2], [10, 10]]
         ...     await table.update(updates_sql={"x": "x + 1"})
         ...     # x is [2, 3], vector is [[1, 2], [10, 10]]
@@ -6010,7 +6237,8 @@ class AsyncTable:
         if updates is not None:
             updates_sql = {k: value_to_sql(v) for k, v in updates.items()}
 
-        return await self._inner.update(updates_sql, where)
+        predicate = where.to_sql() if isinstance(where, Expr) else where
+        return await self._inner.update(updates_sql, predicate)
 
     async def add_columns(
         self,
@@ -6038,12 +6266,15 @@ class AsyncTable:
             A mapping with one ``FunctionApplication`` value keeps its scalar
             or named-struct result in the named table column. A bare
             named-struct application expands its ordered result fields as one
-            atomic sibling group; aliases come from ``rename(columns=...)``.
+            atomic binding; aliases come from ``rename(columns=...)``.
             Function columns are supported only on LanceDB Cloud and
             Enterprise.
         computed: Dict[str, str], optional
-            A map of column name to a SQL expression defining the column. The
-            column's type and inputs are derived from the expression.
+            A mapping from output column names to SQL expressions derives each
+            output field from its expression. A direct projection of a Blob v2
+            field inherits Blob v2 semantics; other expressions derive their
+            ordinary Arrow type. Mapping order is declaration and dependency
+            order.
 
             Unlike ``transforms``, the expression is stored rather than
             evaluated now: the column is committed with no values, and rows get
@@ -6075,7 +6306,7 @@ class AsyncTable:
                 isinstance(value, FunctionApplication) for value in transforms.values()
             ):
                 raise ValueError(
-                    "one add_columns call declares exactly one Function sibling group"
+                    "one add_columns call declares exactly one Function binding"
                 )
             function_output_name, function_application = next(iter(transforms.items()))
 

python/python/tests/docs/test_basic.py

@@ -105,7 +105,7 @@ def test_quickstart(tmp_path):
     tbl.create_index(num_sub_vectors=1)
     # --8<-- [end:create_index]
     # --8<-- [start:delete_rows]
-    tbl.delete('item = "fizz"')
+    tbl.delete("item = 'fizz'")
     # --8<-- [end:delete_rows]
     # --8<-- [start:drop_table]
     db.drop_table("my_table")
@@ -201,7 +201,7 @@ async def test_quickstart_async(tmp_path):
     await tbl.create_index("vector")
     # --8<-- [end:create_index_async]
     # --8<-- [start:delete_rows_async]
-    await tbl.delete('item = "fizz"')
+    await tbl.delete("item = 'fizz'")
     # --8<-- [end:delete_rows_async]
     # --8<-- [start:drop_table_async]
     await db.drop_table("my_table_async")

python/python/tests/docs/test_guide_tables.py

@@ -266,7 +266,7 @@ def test_table():
     tbl.add(pydantic_model_items)
     # --8<-- [end:add_table_from_pydantic]
     # --8<-- [start:delete_row]
-    tbl.delete('item = "fizz"')
+    tbl.delete("item = 'fizz'")
     # --8<-- [end:delete_row]
     # --8<-- [start:delete_specific_row]
     data = [
@@ -538,7 +538,7 @@ async def test_table_async():
     await async_tbl.add(pydantic_model_items)
     # --8<-- [end:add_table_async_from_pydantic]
     # --8<-- [start:delete_row_async]
-    await async_tbl.delete('item = "fizz"')
+    await async_tbl.delete("item = 'fizz'")
     # --8<-- [end:delete_row_async]
     # --8<-- [start:delete_specific_row_async]
     data = [

python/python/tests/test_blob.py

@@ -2,17 +2,41 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 import io
+import subprocess
+import sys
+import textwrap
 
+import lance
 import pyarrow as pa
 import pyarrow.compute as pc
 import pytest
+from lance.blob import BlobType as LanceBlobType
 
 import lancedb
-from lancedb._blob import read_row_ids_from_hits, stash_auto_row_ids
+from lancedb._blob import (
+    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.schema import blob_column_paths, blob_v2_column_paths
 
 
+_HIDE_LANCE_BLOB = """\
+import importlib.abc
+import sys
+
+class _MissingLanceBlob(importlib.abc.MetaPathFinder):
+    def find_spec(self, fullname, path, target=None):
+        if fullname == "lance.blob" or fullname.startswith("lance.blob."):
+            raise ModuleNotFoundError(fullname, name="lance.blob")
+
+sys.modules.pop("lance.blob", None)
+sys.meta_path.insert(0, _MissingLanceBlob())
+"""
+
+
 def _blob_table(name, rows):
     db = lancedb.connect("memory:///")
     schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
@@ -46,6 +70,181 @@ def test_blob_factory_declares_v2_field():
     field = lancedb.blob("image")
     assert isinstance(field.type, pa.ExtensionType)
     assert field.type.extension_name == "lance.blob.v2"
+    assert lancedb.BlobType is LanceBlobType
+    assert type(field.type) is LanceBlobType
+
+
+def test_blob_type_works_without_pylance():
+    script = _HIDE_LANCE_BLOB + textwrap.dedent(
+        """\
+        import lancedb
+        import pyarrow as pa
+
+        field = lancedb.blob("image")
+        if not isinstance(field.type, pa.ExtensionType):
+            raise SystemExit("expected an extension type")
+        if field.type.extension_name != "lance.blob.v2":
+            raise SystemExit(field.type.extension_name)
+        if lancedb.BlobType is not type(field.type):
+            raise SystemExit("BlobType is not the field type class")
+        if lancedb.BlobType.__module__ != "lancedb.schema":
+            raise SystemExit(lancedb.BlobType.__module__)
+
+        db = lancedb.connect("memory:///")
+        table = db.create_table(
+            "images",
+            schema=pa.schema([pa.field("id", pa.int64()), field]),
+        )
+        table.add([{"id": 1, "image": b"hello"}])
+        result = (
+            table.merge_insert("id")
+            .when_matched_update_all()
+            .when_not_matched_insert_all()
+            .execute([{"id": 1, "image": b"updated"}, {"id": 2, "image": b"inserted"}])
+        )
+        if result.num_updated_rows != 1 or result.num_inserted_rows != 1:
+            raise SystemExit(
+                f"merge_insert rows updated={result.num_updated_rows} "
+                f"inserted={result.num_inserted_rows}"
+            )
+        """
+    )
+    result = subprocess.run(
+        [sys.executable, "-c", script],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    assert result.returncode == 0, result.stderr
+
+
+def test_blob_resolves_pylance_type_without_eager_import():
+    script = textwrap.dedent(
+        """\
+        import sys
+        import lancedb
+
+        if "lance.blob" in sys.modules:
+            raise SystemExit("import lancedb imported lance.blob")
+        field = lancedb.blob("image")
+        from lance.blob import BlobType
+
+        if type(field.type) is not BlobType:
+            raise SystemExit(f"{type(field.type)} is not {BlobType}")
+        import lance
+
+        image = lance.blob_array([b"x"])
+        if type(image.type) is not BlobType:
+            raise SystemExit("blob_array used a different class")
+        if type(image.type) is not type(field.type):
+            raise SystemExit("field and array classes differ")
+        """
+    )
+    result = subprocess.run(
+        [sys.executable, "-c", script],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    assert result.returncode == 0, result.stderr
+
+
+def test_blob_fallback_fails_if_name_already_registered():
+    script = _HIDE_LANCE_BLOB + textwrap.dedent(
+        """\
+        import pyarrow as pa
+
+        class OtherBlobType(pa.ExtensionType):
+            def __init__(self):
+                super().__init__(
+                    pa.struct([pa.field("data", pa.large_binary())]),
+                    "lance.blob.v2",
+                )
+
+            def __arrow_ext_serialize__(self):
+                return b""
+
+            @classmethod
+            def __arrow_ext_deserialize__(cls, storage_type, serialized):
+                return cls()
+
+        pa.register_extension_type(OtherBlobType())
+        import lancedb
+
+        try:
+            lancedb.blob("image")
+        except ValueError as err:
+            if "already registered" not in str(err):
+                raise SystemExit(err)
+        else:
+            raise SystemExit("expected ValueError")
+        """
+    )
+    result = subprocess.run(
+        [sys.executable, "-c", script],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    assert result.returncode == 0, result.stderr
+
+
+def test_blob_type_rejects_competing_registration_with_pylance():
+    script = textwrap.dedent(
+        """\
+        import pyarrow as pa
+        import pyarrow.ipc
+
+        class OtherBlobType(pa.ExtensionType):
+            def __init__(self):
+                super().__init__(
+                    pa.struct(
+                        [
+                            pa.field("data", pa.large_binary()),
+                            pa.field("uri", pa.utf8()),
+                            pa.field("position", pa.uint64()),
+                            pa.field("size", pa.uint64()),
+                        ]
+                    ),
+                    "lance.blob.v2",
+                )
+
+            def __arrow_ext_serialize__(self):
+                return b""
+
+            @classmethod
+            def __arrow_ext_deserialize__(cls, storage_type, serialized):
+                return cls()
+
+        pa.register_extension_type(OtherBlobType())
+
+        from lance.blob import BlobType
+
+        if BlobType is OtherBlobType:
+            raise SystemExit("pylance BlobType was replaced")
+        schema = pa.schema([pa.field("value", BlobType())])
+        restored = pa.ipc.read_schema(schema.serialize())
+        if type(restored.field("value").type) is not OtherBlobType:
+            raise SystemExit(type(restored.field("value").type))
+
+        import lancedb
+
+        try:
+            lancedb.blob("image")
+        except ValueError as err:
+            if "__main__.OtherBlobType" not in str(err):
+                raise SystemExit(err)
+        else:
+            raise SystemExit("expected ValueError")
+        """
+    )
+    result = subprocess.run(
+        [sys.executable, "-c", script],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    assert result.returncode == 0, result.stderr
 
 
 def test_blob_v2_column_paths_include_list_children():
@@ -70,6 +269,14 @@ def test_blob_v2_column_paths_include_list_children():
     ]
 
 
+def test_blob_v2_projection_sources_use_typed_column_name():
+    schema = pa.schema([lancedb.blob("blob")])
+
+    assert blob_v2_projection_sources(schema, {"blob_alias": col("blob")}) == {
+        "blob_alias": "blob"
+    }
+
+
 def _legacy_v1_table(name):
     db = lancedb.connect("memory:///")
     schema = pa.schema(
@@ -166,6 +373,20 @@ 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_typed_blob_projection_preserves_source_column():
+    db = await lancedb.connect_async("memory:///typed_blob_projection")
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("blob")])
+    table = await db.create_table("typed_blob_projection", schema=schema)
+    await table.add([{"id": 1, "blob": b"alpha"}])
+
+    hits = await table.query().select({"blob_alias": col("blob")}).to_arrow()
+
+    assert "_lance_row_id" in hits.schema.field("blob_alias").type.names
+    blobs = await table.fetch_blobs("blob", hits)
+    assert blobs.to_pylist() == [b"alpha"]
+
+
 def test_fetch_blobs_round_trip():
     table = _blob_table(
         "round_trip",
@@ -176,6 +397,292 @@ def test_fetch_blobs_round_trip():
     assert [blobs[0].as_py(), blobs[1].as_py()] == [b"alpha", b"beta"]
 
 
+def test_merge_insert_writes_python_bytes():
+    table = _blob_table("merge_bytes", [{"id": 1, "image": b"before"}])
+    result = (
+        table.merge_insert("id")
+        .when_matched_update_all()
+        .when_not_matched_insert_all()
+        .execute([{"id": 1, "image": b"updated"}, {"id": 2, "image": b"inserted"}])
+    )
+    assert result.num_updated_rows == 1
+    assert result.num_inserted_rows == 1
+    by_id = _row_ids_by_id(table)
+    blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
+    assert blobs.to_pylist() == [b"updated", b"inserted"]
+
+
+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")])
+    table = db.create_table("images", schema=schema)
+    table.add([{"id": 1, "image": b"hello"}])
+
+    script = textwrap.dedent(
+        f"""\
+        import lancedb
+
+        db = lancedb.connect({str(tmp_path)!r})
+        table = db.open_table("images")
+        image_type = table.schema.field("image").type
+        if type(image_type).__name__ != "StructType":
+            raise SystemExit(f"expected StructType, got {{type(image_type)}}")
+        result = (
+            table.merge_insert("id")
+            .when_matched_update_all()
+            .when_not_matched_insert_all()
+            .execute(
+                [{{"id": 1, "image": b"updated"}}, {{"id": 2, "image": b"inserted"}}]
+            )
+        )
+        if result.num_updated_rows != 1 or result.num_inserted_rows != 1:
+            raise SystemExit(
+                f"rows updated={{result.num_updated_rows}} "
+                f"inserted={{result.num_inserted_rows}}"
+            )
+        hits = table.search().with_row_id(True).limit(10).to_arrow()
+        by_id = dict(zip(hits["id"].to_pylist(), hits["_rowid"].to_pylist()))
+        blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
+        if blobs.to_pylist() != [b"updated", b"inserted"]:
+            raise SystemExit(blobs.to_pylist())
+        """
+    )
+    result = subprocess.run(
+        [sys.executable, "-c", script],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    assert result.returncode == 0, result.stderr
+
+
+def test_merge_insert_bytes_after_reopen_without_pylance(tmp_path):
+    db = lancedb.connect(tmp_path)
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    table = db.create_table("images", schema=schema)
+    table.add([{"id": 1, "image": b"hello"}])
+
+    script = _HIDE_LANCE_BLOB + textwrap.dedent(
+        f"""\
+        import lancedb
+
+        db = lancedb.connect({str(tmp_path)!r})
+        table = db.open_table("images")
+        image_type = table.schema.field("image").type
+        if type(image_type).__name__ != "StructType":
+            raise SystemExit(f"expected StructType, got {{type(image_type)}}")
+        result = (
+            table.merge_insert("id")
+            .when_matched_update_all()
+            .when_not_matched_insert_all()
+            .execute(
+                [{{"id": 1, "image": b"updated"}}, {{"id": 2, "image": b"inserted"}}]
+            )
+        )
+        if result.num_updated_rows != 1 or result.num_inserted_rows != 1:
+            raise SystemExit(
+                f"rows updated={{result.num_updated_rows}} "
+                f"inserted={{result.num_inserted_rows}}"
+            )
+        hits = table.search().with_row_id(True).limit(10).to_arrow()
+        by_id = dict(zip(hits["id"].to_pylist(), hits["_rowid"].to_pylist()))
+        blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
+        if blobs.to_pylist() != [b"updated", b"inserted"]:
+            raise SystemExit(blobs.to_pylist())
+        """
+    )
+    result = subprocess.run(
+        [sys.executable, "-c", script],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    assert result.returncode == 0, result.stderr
+
+
+def test_merge_insert_blob_array_into_reopened_unregistered_table(tmp_path):
+    db = lancedb.connect(tmp_path)
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    table = db.create_table("images", schema=schema)
+    table.add([{"id": 1, "image": b"before"}])
+
+    script = textwrap.dedent(
+        f"""\
+        import pyarrow as pa
+        import lancedb
+
+        db = lancedb.connect({str(tmp_path)!r})
+        table = db.open_table("images")
+        image_type = table.schema.field("image").type
+        if type(image_type).__name__ != "StructType":
+            raise SystemExit(
+                f"expected StructType before lance import, got {{type(image_type)}}"
+            )
+
+        import lance
+
+        updates = pa.Table.from_arrays(
+            [
+                pa.array([1, 2], type=pa.int64()),
+                lance.blob_array([b"updated", b"inserted"]),
+            ],
+            names=["id", "image"],
+        )
+        result = (
+            table.merge_insert("id")
+            .when_matched_update_all()
+            .when_not_matched_insert_all()
+            .execute(updates)
+        )
+        if result.num_updated_rows != 1 or result.num_inserted_rows != 1:
+            raise SystemExit(
+                f"rows updated={{result.num_updated_rows}} "
+                f"inserted={{result.num_inserted_rows}}"
+            )
+        hits = table.search().with_row_id(True).limit(10).to_arrow()
+        by_id = dict(zip(hits["id"].to_pylist(), hits["_rowid"].to_pylist()))
+        blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
+        if blobs.to_pylist() != [b"updated", b"inserted"]:
+            raise SystemExit(blobs.to_pylist())
+        """
+    )
+    result = subprocess.run(
+        [sys.executable, "-c", script],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    assert result.returncode == 0, result.stderr
+
+
+def test_add_all_null_blob_column():
+    db = lancedb.connect("memory:///")
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    table = db.create_table("all_null", schema=schema)
+    table.add([{"id": 1, "image": None}, {"id": 2, "image": None}])
+    by_id = _row_ids_by_id(table)
+    blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
+    assert blobs.to_pylist() == [None, None]
+
+
+def test_create_table_nested_blob_schema_without_rows():
+    db = lancedb.connect("memory:///")
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int64()),
+            pa.field("info", pa.struct([lancedb.blob("blob")])),
+            pa.field("images", pa.list_(lancedb.blob("image"))),
+        ]
+    )
+    table = db.create_table("nested_empty", schema=schema)
+    assert table.count_rows() == 0
+
+
+def test_merge_insert_nested_blob_dicts():
+    db = lancedb.connect("memory:///")
+    info = pa.StructArray.from_arrays(
+        [
+            pa.array(["first"], type=pa.string()),
+            _blob_array("blob", [b"before"]),
+        ],
+        names=["name", "blob"],
+    )
+    data = pa.Table.from_arrays(
+        [pa.array([1], type=pa.int64()), info],
+        names=["id", "info"],
+    )
+    table = db.create_table("nested_merge", data=data)
+    result = (
+        table.merge_insert("id")
+        .when_matched_update_all()
+        .execute([{"id": 1, "info": {"name": "first", "blob": b"after"}}])
+    )
+    assert result.num_updated_rows == 1
+    by_id = _row_ids_by_id(table)
+    blobs = table.fetch_blobs("info.blob", [by_id[1]])
+    assert blobs.to_pylist() == [b"after"]
+
+
+def _list_blob_table(name):
+    db = lancedb.connect("memory:///")
+    blob_field = lancedb.blob("image")
+    images = pa.ListArray.from_arrays(
+        pa.array([0, 1], type=pa.int32()), _blob_array("image", [b"before"])
+    )
+    data = pa.Table.from_arrays(
+        [pa.array([1], type=pa.int64()), images],
+        schema=pa.schema(
+            [pa.field("id", pa.int64()), pa.field("images", pa.list_(blob_field))]
+        ),
+    )
+    return db.create_table(name, data=data)
+
+
+def test_merge_insert_list_blob_dicts():
+    table = _list_blob_table("list_merge")
+    result = (
+        table.merge_insert("id")
+        .when_matched_update_all()
+        .when_not_matched_insert_all()
+        .execute([{"id": 1, "images": [b"one", b"two"]}, {"id": 2, "images": None}])
+    )
+    assert result.num_updated_rows == 1
+    assert result.num_inserted_rows == 1
+    hits = table.search().limit(10).to_arrow()
+    sizes = {
+        row["id"]: None if row["images"] is None else [d["size"] for d in row["images"]]
+        for row in hits.to_pylist()
+    }
+    assert sizes == {1: [3, 3], 2: None}
+
+
+def test_list_blob_column_queries_as_raw_descriptors():
+    table = _list_blob_table("list_query")
+    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])
+
+
+def test_row_addressable_paths_exclude_list_children():
+    from lancedb.schema import row_addressable_blob_v2_paths
+
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int64()),
+            pa.field("info", pa.struct([lancedb.blob("blob")])),
+            pa.field("images", pa.list_(lancedb.blob("image"))),
+        ]
+    )
+    assert blob_v2_column_paths(schema) == ["info.blob", "images.image"]
+    assert row_addressable_blob_v2_paths(schema) == ["info.blob"]
+
+
+def test_merge_insert_writes_pylance_blob_array():
+    table = _blob_table("merge_pylance", [{"id": 1, "image": b"before"}])
+    image = lance.blob_array([b"updated", b"inserted"])
+    assert type(image.type) is LanceBlobType
+    assert type(image.type) is type(lancedb.BlobType())
+    updates = pa.Table.from_arrays(
+        [pa.array([1, 2], type=pa.int64()), image], names=["id", "image"]
+    )
+
+    result = (
+        table.merge_insert("id")
+        .when_matched_update_all()
+        .when_not_matched_insert_all()
+        .execute(updates)
+    )
+
+    assert result.num_updated_rows == 1
+    assert result.num_inserted_rows == 1
+    by_id = _row_ids_by_id(table)
+    blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
+    assert blobs.to_pylist() == [b"updated", b"inserted"]
+
+
 def test_fetch_blobs_accepts_query_result():
     table = _blob_table("from_result", [{"id": 1, "image": b"gamma"}])
     hits = table.search().limit(10).to_arrow()
@@ -403,6 +910,50 @@ async def test_blob_v2_hybrid_fetch_blobs_async():
     assert {blobs[i].as_py() for i in range(len(blobs))} == {b"alpha", b"beta"}
 
 
+@pytest.mark.asyncio
+async def test_async_hybrid_typed_blob_projection_preserves_source_column():
+    db = await lancedb.connect_async("memory:///hybrid_typed_blob")
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int64()),
+            pa.field("text", pa.utf8()),
+            pa.field("vector", pa.list_(pa.float32(), list_size=2)),
+            lancedb.blob("blob"),
+        ]
+    )
+    table = await db.create_table("hybrid_typed_blob", schema=schema)
+    await table.add(
+        [
+            {
+                "id": 1,
+                "text": "hello alpha",
+                "vector": [1.0, 0.0],
+                "blob": b"alpha",
+            },
+            {
+                "id": 2,
+                "text": "hello beta",
+                "vector": [0.9, 0.1],
+                "blob": b"beta",
+            },
+        ]
+    )
+    await table.create_index("text", config=FTS(with_position=False))
+
+    hits = await (
+        table.query()
+        .nearest_to([1.0, 0.0])
+        .nearest_to_text("hello")
+        .select({"blob_alias": col("blob")})
+        .limit(2)
+        .to_arrow()
+    )
+
+    assert "_lance_row_id" in hits.schema.field("blob_alias").type.names
+    blobs = await table.fetch_blobs("blob", hits)
+    assert {blobs[i].as_py() for i in range(len(blobs))} == {b"alpha", b"beta"}
+
+
 def test_blob_file_seek_read_and_read_range():
     payload = _identifiable_payload(1024)
     table = _blob_table("seek_read", [{"id": 1, "image": payload}])
@@ -617,3 +1168,71 @@ def test_fetch_blobs_nested_path_survives_sort_after_query():
 def _identifiable_payload(size: int) -> bytes:
     block = 256
     return b"".join(bytes([i % 256]) * block for i in range(size // block))
+
+
+def _external_uri_blob_array(uris):
+    blob_type = lancedb.blob("image").type
+    storage_type = blob_type.storage_type
+    child_names = [field.name for field in storage_type]
+    assert "uri" in child_names, "blob layout no longer has a uri child"
+    children = [
+        pa.array(uris if field.name == "uri" else [None] * len(uris), type=field.type)
+        for field in storage_type
+    ]
+    storage = pa.StructArray.from_arrays(children, fields=list(storage_type))
+    return pa.ExtensionArray.from_storage(blob_type, storage)
+
+
+def _external_uri_table_and_rows(name, uris):
+    db = lancedb.connect("memory:///")
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    table = db.create_table(name, schema=schema)
+    rows = pa.Table.from_arrays(
+        [
+            pa.array(range(len(uris)), type=pa.int64()),
+            _external_uri_blob_array(uris),
+        ],
+        schema=schema,
+    )
+    return table, rows
+
+
+def test_add_external_uri_struct_round_trips_with_flag(tmp_path):
+    payload = b"external-uri-bytes"
+    blob_path = tmp_path / "payload.bin"
+    blob_path.write_bytes(payload)
+
+    table, rows = _external_uri_table_and_rows("external_struct", [blob_path.as_uri()])
+    table.add(rows, allow_external_blob_outside_bases=True)
+
+    hits = table.search().to_arrow()
+    blobs = table.fetch_blobs("image", hits)
+    assert blobs[0].as_py() == payload
+
+
+def test_add_external_uri_without_flag_raises(tmp_path):
+    blob_path = tmp_path / "payload.bin"
+    blob_path.write_bytes(b"unreachable")
+
+    table, rows = _external_uri_table_and_rows("external_no_flag", [blob_path.as_uri()])
+    with pytest.raises(ValueError, match="allow_external_blob_outside_bases"):
+        table.add(rows)
+    assert table.count_rows() == 0
+
+
+def test_add_external_uri_string_round_trips_with_flag(tmp_path):
+    payload = b"external-uri-bytes"
+    blob_path = tmp_path / "payload.bin"
+    blob_path.write_bytes(payload)
+
+    db = lancedb.connect("memory:///")
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    table = db.create_table("external_string", schema=schema)
+    table.add(
+        [{"id": 1, "image": blob_path.as_uri()}],
+        allow_external_blob_outside_bases=True,
+    )
+
+    hits = table.search().to_arrow()
+    blobs = table.fetch_blobs("image", hits)
+    assert blobs[0].as_py() == payload

python/python/tests/test_elastic_dataloader.py

@@ -32,6 +32,7 @@ Parameters used throughout:
 
 import dataclasses
 import logging
+import threading
 from unittest.mock import patch
 
 import lancedb
@@ -46,6 +47,7 @@ from utils import (
 torch = pytest.importorskip("torch")
 streaming = pytest.importorskip("lancedb.streaming")
 StreamingDataset = streaming.StreamingDataset
+StreamingDataLoader = streaming.StreamingDataLoader
 
 # ---------------------------------------------------------------------------
 # Dataset parameters
@@ -92,6 +94,27 @@ class FakeWorkerInfo:
     num_workers: int
 
 
+def _collate_with_first_batch_error(samples):
+    ids = [sample["id"] for sample in samples]
+    if ids == [0, 1]:
+        raise ValueError("first batch fails")
+    return ids
+
+
+def _collate_with_first_batch_stop(samples):
+    ids = [sample["id"] for sample in samples]
+    if ids == [0, 1]:
+        raise StopIteration("first batch stopped")
+    return ids
+
+
+def _collate_with_first_batch_interrupt(samples):
+    ids = [sample["id"] for sample in samples]
+    if ids == [0, 1]:
+        raise KeyboardInterrupt("first batch interrupted")
+    return ids
+
+
 # ---------------------------------------------------------------------------
 # Fixtures
 # ---------------------------------------------------------------------------
@@ -1008,6 +1031,565 @@ def test_multi_worker_elastic_det_across_worker_counts(lance_table):
 # ── Resumability with num_workers ─────────────────────────────────────────────
 
 
+def test_streaming_dataloader_commits_only_consumed_worker_batches(tmp_path):
+    """Prefetched worker state is committed only as the trainer receives it."""
+    db = lancedb.connect(tmp_path)
+    table = db.create_table(
+        "worker_commit", pa.table({"id": [1, 2, 3, 4, 10, 20, 30, 40]})
+    )
+    dataset = StreamingDataset(table, num_splits=2, shuffle=False)
+    loader = StreamingDataLoader(
+        dataset,
+        batch_size=2,
+        num_workers=2,
+        multiprocessing_context="spawn",
+        prefetch_factor=4,
+    )
+    iterator = iter(loader)
+    try:
+        first = next(iterator)["id"].tolist()
+
+        assert first == [1, 2]
+        assert dataset._checkpoint_snapshot()["samples_consumed_per_split"] == [2, 0]
+        with pytest.raises(RuntimeError, match="complete logical step boundary"):
+            dataset.state_dict()
+
+        second = next(iterator)["id"].tolist()
+        assert second == [10, 20]
+        checkpoint = dataset.state_dict()
+        assert checkpoint["samples_consumed_per_split"] == [2, 2]
+        uninterrupted = [batch["id"].tolist() for batch in iterator]
+    finally:
+        iterator._shutdown_workers()
+
+    resumed = StreamingDataset(table, num_splits=2, shuffle=False)
+    resumed.load_state_dict(checkpoint)
+    resumed_loader = StreamingDataLoader(
+        resumed,
+        batch_size=2,
+        num_workers=2,
+        multiprocessing_context="spawn",
+        prefetch_factor=4,
+    )
+    resumed_iterator = iter(resumed_loader)
+    try:
+        remaining = [batch["id"].tolist() for batch in resumed_iterator]
+    finally:
+        resumed_iterator._shutdown_workers()
+    assert remaining == uninterrupted == [[3, 4], [30, 40]]
+
+
+def test_distributed_checkpoint_uses_rank_local_worker_boundary(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("rank_boundary", pa.table({"id": list(range(8))}))
+    dataset = StreamingDataset(
+        table,
+        num_splits=4,
+        shuffle=False,
+        rank=0,
+        world_size=2,
+    )
+    loader = StreamingDataLoader(
+        dataset,
+        batch_size=1,
+        num_workers=2,
+        multiprocessing_context="spawn",
+    )
+    iterator = iter(loader)
+    try:
+        assert next(iterator)["id"].tolist() == [0]
+        assert dataset._checkpoint_snapshot()["samples_consumed_per_split"] == [
+            1,
+            0,
+            0,
+            0,
+        ]
+        with pytest.raises(RuntimeError, match="complete logical step boundary"):
+            dataset.state_dict()
+
+        assert next(iterator)["id"].tolist() == [2]
+        checkpoint = dataset.state_dict()
+        remaining = [batch["id"].tolist() for batch in iterator]
+    finally:
+        iterator._shutdown_workers()
+
+    assert checkpoint["samples_consumed_per_split"] == [1, 1, 0, 0]
+    assert remaining == [[1], [3]]
+
+
+def test_standard_dataloader_rejects_stale_parent_checkpoint(tmp_path):
+    """A standard DataLoader must not expose prefetched producer progress."""
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("untracked_workers", pa.table({"id": [1, 2, 10, 20]}))
+    dataset = StreamingDataset(table, num_splits=2, shuffle=False)
+    # Merely constructing the checkpoint-aware loader must not authorize a
+    # later plain DataLoader's worker progress.
+    StreamingDataLoader(dataset, batch_size=2, num_workers=0)
+    loader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=2,
+        num_workers=2,
+        multiprocessing_context="spawn",
+    )
+    iterator = iter(loader)
+    try:
+        assert next(iterator)["id"].tolist() == [1, 2]
+        with pytest.raises(RuntimeError, match="Use StreamingDataLoader"):
+            dataset.state_dict()
+        list(iterator)
+    finally:
+        iterator._shutdown_workers()
+
+
+def test_streaming_dataloader_rejects_persistent_workers(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("persistent_workers", pa.table({"id": [1, 2]}))
+    dataset = StreamingDataset(table, num_splits=2, shuffle=False)
+
+    with pytest.raises(ValueError, match="persistent_workers=True"):
+        StreamingDataLoader(
+            dataset,
+            batch_size=1,
+            num_workers=2,
+            persistent_workers=True,
+        )
+
+
+def test_collate_failure_invalidates_consumer_checkpoint(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table(
+        "collate_failure", pa.table({"id": [0, 1, 2, 3, 100, 101, 102, 103]})
+    )
+    dataset = StreamingDataset(table, num_splits=2, shuffle=False)
+    loader = StreamingDataLoader(
+        dataset,
+        batch_size=2,
+        num_workers=2,
+        multiprocessing_context="spawn",
+        collate_fn=_collate_with_first_batch_error,
+        prefetch_factor=2,
+    )
+    iterator = iter(loader)
+    try:
+        with pytest.raises(ValueError, match="first batch fails"):
+            next(iterator)
+        assert next(iterator) == [100, 101]
+        assert next(iterator) == [2, 3]
+        with pytest.raises(RuntimeError, match="failed before it was returned"):
+            dataset.state_dict()
+        list(iterator)
+    finally:
+        iterator._shutdown_workers()
+
+
+def test_collate_stop_iteration_invalidates_consumer_checkpoint(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("collate_stop", pa.table({"id": list(range(6))}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    loader = StreamingDataLoader(
+        dataset,
+        batch_size=2,
+        num_workers=0,
+        collate_fn=_collate_with_first_batch_stop,
+    )
+    iterator = iter(loader)
+
+    with pytest.raises(RuntimeError, match="collate_fn raised StopIteration"):
+        next(iterator)
+    assert dataset._checkpoint_snapshot()["samples_consumed_per_split"] == [2]
+    with pytest.raises(RuntimeError, match="failed before it was returned"):
+        dataset.state_dict()
+    assert list(iterator) == [[2, 3], [4, 5]]
+
+
+def test_batch_base_exception_invalidates_consumer_checkpoint(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("collate_interrupt", pa.table({"id": list(range(6))}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    loader = StreamingDataLoader(
+        dataset,
+        batch_size=2,
+        num_workers=0,
+        collate_fn=_collate_with_first_batch_interrupt,
+    )
+    iterator = iter(loader)
+
+    with pytest.raises(KeyboardInterrupt, match="first batch interrupted"):
+        next(iterator)
+    assert dataset._checkpoint_snapshot()["samples_consumed_per_split"] == [2]
+    with pytest.raises(RuntimeError, match="failed before it was returned"):
+        dataset.state_dict()
+    assert list(iterator) == [[2, 3], [4, 5]]
+
+
+def test_parent_commit_base_exception_invalidates_consumer_checkpoint(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("commit_interrupt", pa.table({"id": list(range(4))}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    loader = StreamingDataLoader(dataset, batch_size=2, num_workers=0)
+    iterator = iter(loader)
+    real_commit = dataset._commit_worker_state
+
+    def interrupt_after_commit(state, *, require_uniform):
+        real_commit(state, require_uniform=require_uniform)
+        raise KeyboardInterrupt("after parent commit")
+
+    with patch.object(
+        dataset, "_commit_worker_state", side_effect=interrupt_after_commit
+    ):
+        with pytest.raises(KeyboardInterrupt, match="after parent commit"):
+            next(iterator)
+
+    assert dataset._checkpoint_snapshot()["samples_consumed_per_split"] == [2]
+    with pytest.raises(RuntimeError, match="failed before it was returned"):
+        dataset.state_dict()
+
+
+def test_direct_iteration_surfaces_prefetch_failure_before_committing_row(
+    tmp_path, monkeypatch
+):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("prefetch_failure", pa.table({"id": list(range(4))}))
+    release = threading.Event()
+    failed = threading.Event()
+    real_getitems = streaming.Permutation.__getitems__
+
+    def controlled_getitems(permutation, indices):
+        if indices and indices[0] >= 2:
+            assert release.wait(timeout=5)
+            failed.set()
+            raise RuntimeError("later prefetched I/O failed")
+        return real_getitems(permutation, indices)
+
+    class SignalDict(dict):
+        def __setitem__(self, key, value):
+            super().__setitem__(key, value)
+            release.set()
+            assert failed.wait(timeout=5)
+
+    monkeypatch.setattr(streaming.Permutation, "__getitems__", controlled_getitems)
+    dataset = StreamingDataset(
+        table,
+        num_splits=1,
+        shuffle=False,
+        read_batch_size=2,
+        io_queue_depth=2,
+    )
+    dataset._resume_positions = SignalDict()
+    iterator = iter(dataset)
+
+    assert next(iterator)["id"] == 0
+    with pytest.raises(RuntimeError, match="later prefetched I/O failed"):
+        next(iterator)
+
+    checkpoint = dataset.state_dict()
+    assert checkpoint["samples_consumed_per_split"] == [1]
+    assert checkpoint["positions_consumed_per_split"] == [1]
+
+
+@pytest.mark.parametrize("workers", [0, 1, 2])
+def test_streaming_dataloader_rejects_drop_last(tmp_path, workers):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("drop_last", pa.table({"id": [0, 1, 2]}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    worker_options = {"multiprocessing_context": "spawn"} if workers else {}
+
+    with pytest.raises(ValueError, match="drop_last=True"):
+        StreamingDataLoader(
+            dataset,
+            batch_size=2,
+            num_workers=workers,
+            drop_last=True,
+            **worker_options,
+        )
+
+
+def test_streaming_dataloader_owns_one_iterator_until_teardown(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("iterator_owner", pa.table({"id": list(range(4))}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    loader = StreamingDataLoader(
+        dataset,
+        batch_size=2,
+        num_workers=1,
+        multiprocessing_context="spawn",
+    )
+
+    first = iter(loader)
+    try:
+        assert next(first)["id"].tolist() == [0, 1]
+        with pytest.raises(RuntimeError, match="concurrent iteration"):
+            iter(loader)
+    finally:
+        first._shutdown_workers()
+
+    second = iter(loader)
+    try:
+        assert [batch["id"].tolist() for batch in second] == [[2, 3]]
+    except BaseException:
+        second._shutdown_workers()
+        raise
+
+    # Natural exhaustion releases ownership too.
+    third = iter(loader)
+    try:
+        assert list(third) == []
+    finally:
+        third._shutdown_workers()
+
+
+def test_zero_worker_shutdown_closes_inner_iterator_before_release(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("zero_worker_shutdown", pa.table({"id": list(range(6))}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    loader = StreamingDataLoader(dataset, batch_size=2, num_workers=0)
+
+    first = iter(loader)
+    assert next(first)["id"].tolist() == [0, 1]
+    first._shutdown_workers()
+
+    assert dataset._consumer_iterator_active is False
+    assert dataset._raw_batches_ref is None
+    second = iter(loader)
+    try:
+        with pytest.raises(StopIteration):
+            next(first)
+        assert next(second)["id"].tolist() == [2, 3]
+    finally:
+        second._shutdown_workers()
+
+
+def test_direct_and_loader_admission_share_one_atomic_lease(tmp_path, monkeypatch):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("direct_loader_lease", pa.table({"id": list(range(4))}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    loader = StreamingDataLoader(dataset, batch_size=2, num_workers=0)
+    entered = threading.Event()
+    release = threading.Event()
+    direct_result = []
+    direct_error = []
+    contender = []
+    real_resolve = dataset._resolve_my_splits
+
+    def controlled_resolve():
+        if threading.current_thread().name == "direct-start":
+            entered.set()
+            assert release.wait(timeout=5)
+        return real_resolve()
+
+    def advance_direct(iterator):
+        try:
+            direct_result.append(next(iterator)["id"])
+        except BaseException as exc:
+            direct_error.append(exc)
+
+    monkeypatch.setattr(dataset, "_resolve_my_splits", controlled_resolve)
+    direct = iter(dataset)
+    thread = threading.Thread(
+        target=advance_direct, args=(direct,), name="direct-start"
+    )
+    thread.start()
+    assert entered.wait(timeout=5)
+    try:
+        with pytest.raises(RuntimeError, match="concurrent iteration"):
+            contender.append(iter(loader))
+    finally:
+        release.set()
+        thread.join(timeout=5)
+        if contender:
+            contender[0]._shutdown_workers()
+        direct.close()
+
+    assert not thread.is_alive()
+    assert direct_error == []
+    assert direct_result == [0]
+
+
+def test_loader_acquires_before_snapshot_and_cleans_interrupted_acquire(
+    tmp_path, monkeypatch
+):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("lease_snapshot", pa.table({"id": list(range(4))}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    loader = StreamingDataLoader(dataset, batch_size=2, num_workers=0)
+    first = iter(loader)
+    assert next(first)["id"].tolist() == [0, 1]
+
+    entered = threading.Event()
+    release = threading.Event()
+    pending = []
+    pending_errors = []
+    observed_snapshots = []
+    real_acquire = dataset._acquire_consumer_iterator
+    real_snapshot = dataset._checkpoint_snapshot
+
+    def controlled_acquire():
+        if threading.current_thread().name == "stale-start":
+            entered.set()
+            assert release.wait(timeout=5)
+        return real_acquire()
+
+    def recording_snapshot():
+        state = real_snapshot()
+        if threading.current_thread().name == "stale-start":
+            observed_snapshots.append(state["samples_consumed_per_split"])
+        return state
+
+    def create_pending_iterator():
+        try:
+            pending.append(iter(loader))
+        except BaseException as exc:
+            pending_errors.append(exc)
+
+    monkeypatch.setattr(dataset, "_acquire_consumer_iterator", controlled_acquire)
+    monkeypatch.setattr(dataset, "_checkpoint_snapshot", recording_snapshot)
+    thread = threading.Thread(target=create_pending_iterator, name="stale-start")
+    thread.start()
+    assert entered.wait(timeout=5)
+    assert next(first)["id"].tolist() == [2, 3]
+    with pytest.raises(StopIteration):
+        next(first)
+    release.set()
+    thread.join(timeout=5)
+
+    assert not thread.is_alive()
+    assert pending_errors == []
+    assert observed_snapshots == [[4]]
+    assert len(pending) == 1
+    assert list(pending[0]) == []
+    assert dataset.state_dict()["samples_consumed_per_split"] == [4]
+
+    def interrupted_acquire():
+        real_acquire()
+        raise KeyboardInterrupt("after acquire")
+
+    monkeypatch.setattr(dataset, "_acquire_consumer_iterator", interrupted_acquire)
+    with pytest.raises(KeyboardInterrupt, match="after acquire"):
+        iter(loader)
+    assert dataset._consumer_iterator_active is False
+
+
+def test_consumer_iterator_lease_publication_is_atomic(tmp_path, monkeypatch):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("atomic_lease", pa.table({"id": [0, 1]}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    loader = StreamingDataLoader(dataset, batch_size=1, num_workers=0)
+    real_get_ident = streaming.threading.get_ident
+    calls = 0
+
+    def interrupt_during_publication():
+        nonlocal calls
+        calls += 1
+        if calls == 1:
+            raise KeyboardInterrupt("during lease mutation")
+        return real_get_ident()
+
+    monkeypatch.setattr(streaming.threading, "get_ident", interrupt_during_publication)
+    with pytest.raises(KeyboardInterrupt, match="during lease mutation"):
+        iter(loader)
+    monkeypatch.setattr(streaming.threading, "get_ident", real_get_ident)
+
+    assert dataset._consumer_iterator_active is False
+    iterator = iter(loader)
+    try:
+        assert next(iterator)["id"].tolist() == [0]
+    finally:
+        iterator._shutdown_workers()
+
+
+def test_streaming_dataloader_rejects_dataset_iter_override(tmp_path):
+    class CustomizedDataset(StreamingDataset):
+        def __iter__(self):
+            return iter([1000, 1001])
+
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("custom_iteration", pa.table({"id": [0, 1, 2]}))
+    dataset = CustomizedDataset(table, num_splits=1, shuffle=False)
+
+    assert list(dataset) == [1000, 1001]
+    with pytest.raises(TypeError, match="override __iter__"):
+        StreamingDataLoader(
+            dataset,
+            batch_size=2,
+            num_workers=0,
+            collate_fn=list,
+        )
+
+
+def test_interleaved_adapters_do_not_authorize_plain_iteration(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table_a = db.create_table("adapter_a", pa.table({"id": [0, 1]}))
+    table_b = db.create_table("adapter_b", pa.table({"id": [10, 11]}))
+    dataset_a = StreamingDataset(table_a, num_splits=1, shuffle=False)
+    dataset_b = StreamingDataset(table_b, num_splits=1, shuffle=False)
+    initial_state = dataset_a.state_dict()
+
+    owner_a = dataset_a._acquire_consumer_iterator()
+    owner_b = dataset_b._acquire_consumer_iterator()
+    try:
+        iterator_a = iter(streaming._StreamingDatasetAdapter(dataset_a))
+        iterator_b = iter(streaming._StreamingDatasetAdapter(dataset_b))
+        assert next(iterator_a).data["id"] == 0
+        assert next(iterator_b).data["id"] == 10
+        assert [sample.data["id"] for sample in iterator_a] == [1]
+        assert [sample.data["id"] for sample in iterator_b] == [11]
+    finally:
+        dataset_a._release_consumer_iterator(owner_a)
+        dataset_b._release_consumer_iterator(owner_b)
+
+    dataset_a.load_state_dict(initial_state)
+    with patch(
+        "lancedb.streaming.get_worker_info",
+        return_value=FakeWorkerInfo(id=0, num_workers=1),
+    ):
+        plain_iterator = iter(dataset_a)
+        assert next(plain_iterator)["id"] == 0
+        plain_iterator.close()
+
+    assert dataset_a._untracked_worker_iteration[0] == 1
+    with pytest.raises(RuntimeError, match="Use StreamingDataLoader"):
+        dataset_a.state_dict()
+
+
+def test_resume_from_partial_split_cycle_preserves_remaining_order(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("partial_cycle", pa.table({"id": [1, 2, 10, 20]}))
+    dataset = StreamingDataset(table, num_splits=2, shuffle=False)
+    iterator = iter(dataset)
+
+    assert next(iterator)["id"] == 1
+    checkpoint = dataset.state_dict()
+    iterator.close()
+    assert checkpoint["samples_consumed_per_split"] == [1, 0]
+
+    resumed = StreamingDataset(table, num_splits=2, shuffle=False)
+    resumed.load_state_dict(checkpoint)
+    assert [row["id"] for row in resumed] == [10, 2, 20]
+
+
+def test_partial_cycle_resume_preserves_skip_truncation(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table(
+        "partial_skip", pa.table({"id": [0, 1, 2, 3, 100, 101, 102, 103]})
+    )
+    kwargs = dict(
+        num_splits=2,
+        shuffle=False,
+        transform=_failing_transform({1, 2, 3}),
+        on_transform_error="skip",
+    )
+    dataset = StreamingDataset(table, **kwargs)
+    iterator = iter(dataset)
+
+    assert next(iterator)["id"] == 0
+    checkpoint = dataset.state_dict()
+    uninterrupted = [row["id"] for row in iterator]
+
+    resumed = StreamingDataset(table, **kwargs)
+    resumed.load_state_dict(checkpoint)
+    assert [row["id"] for row in resumed] == uninterrupted == [100]
+
+
 def test_multi_worker_resumability_same_topology(lance_table):
     """Checkpoint with num_workers=2, resume with num_workers=2: exact continuation."""
     world_size = 1
@@ -2018,6 +2600,23 @@ def test_merge_state_dicts_validates_consistency(lance_table):
         StreamingDataset.merge_state_dicts([])
 
 
+def test_merge_state_dicts_combines_nonuniform_consumer_progress(lance_table):
+    dataset = StreamingDataset(
+        lance_table, num_splits=2, shuffle=False, shuffle_seed=SHUFFLE_SEED
+    )
+    rank0 = dataset.state_dict()
+    rank0["samples_consumed_per_split"] = [2, 0]
+    rank0["positions_consumed_per_split"] = [2, 0]
+    rank1 = dataset.state_dict()
+    rank1["samples_consumed_per_split"] = [0, 2]
+    rank1["positions_consumed_per_split"] = [0, 2]
+
+    merged = StreamingDataset.merge_state_dicts([rank0, rank1])
+
+    assert merged["samples_consumed_per_split"] == [2, 2]
+    assert merged["positions_consumed_per_split"] == [2, 2]
+
+
 def test_load_state_dict_without_positions_key(lance_table):
     """Checkpoints from before positions_consumed_per_split existed still
     resume exactly (positions equal sample counts when nothing is skipped)."""
@@ -2254,6 +2853,65 @@ def test_pack_sequences_checkpoint_resumes_on_new_topology(tmp_path):
     ]
 
 
+def test_packed_checkpoint_requires_complete_split_cycle(tmp_path):
+    table = _create_token_table(tmp_path, [[1], [2], [10], [20]])
+    dataset = _packed_dataset(table, pack_sequences=3, blocks_per_epoch=4, num_splits=2)
+    iterator = iter(dataset)
+
+    next(iterator)
+    with pytest.raises(RuntimeError, match="complete logical step boundary"):
+        dataset.state_dict()
+
+    next(iterator)
+    assert dataset.state_dict()["blocks_emitted_per_split"] == [1, 1]
+    iterator.close()
+
+
+def test_streaming_dataloader_commits_consumed_packed_batches(tmp_path):
+    table = _create_token_table(
+        tmp_path,
+        [[1], [2], [3], [4], [10], [20], [30], [40]],
+    )
+    kwargs = dict(pack_sequences=4, blocks_per_epoch=4, num_splits=2)
+    dataset = _packed_dataset(table, **kwargs)
+    loader = StreamingDataLoader(
+        dataset,
+        batch_size=1,
+        num_workers=2,
+        multiprocessing_context="spawn",
+        prefetch_factor=2,
+    )
+    iterator = iter(loader)
+    try:
+        next(iterator)
+        with pytest.raises(RuntimeError, match="complete logical step boundary"):
+            dataset.state_dict()
+
+        next(iterator)
+        checkpoint = dataset.state_dict()
+        uninterrupted = [batch["input_ids"].tolist() for batch in iterator]
+    finally:
+        iterator._shutdown_workers()
+
+    resumed = _packed_dataset(table, **kwargs)
+    resumed.load_state_dict(checkpoint)
+    resumed_loader = StreamingDataLoader(
+        resumed,
+        batch_size=1,
+        num_workers=2,
+        multiprocessing_context="spawn",
+        prefetch_factor=2,
+    )
+    resumed_iterator = iter(resumed_loader)
+    try:
+        remaining = [batch["input_ids"].tolist() for batch in resumed_iterator]
+    finally:
+        resumed_iterator._shutdown_workers()
+
+    assert checkpoint["blocks_emitted_per_split"] == [1, 1]
+    assert remaining == uninterrupted
+
+
 def test_pack_sequences_validates_configuration_and_tokens(tmp_path):
     table = _create_token_table(tmp_path, [[1, 2]])
 

python/python/tests/test_expr.py

@@ -52,7 +52,7 @@ class TestExprConstruction:
     def test_func(self):
         e = func("lower", col("name"))
         assert isinstance(e, Expr)
-        assert e.to_sql() == "lower(name)"
+        assert e.to_sql() == "lower(`name`)"
 
     def test_func_unknown_raises(self):
         with pytest.raises(Exception):
@@ -115,7 +115,7 @@ class TestExprOperators:
     def test_and_operator(self):
         e = (col("age") > lit(18)) & (col("status") == lit("active"))
         assert isinstance(e, Expr)
-        assert e.to_sql() == "((age > 18) AND (status = 'active'))"
+        assert e.to_sql() == "((age > 18) AND (`status` = 'active'))"
 
     def test_or_operator(self):
         e = (col("a") == lit(1)) | (col("b") == lit(2))
@@ -166,7 +166,7 @@ class TestExprOperators:
     def test_coerce_plain_str(self):
         e = col("name") == "alice"
         assert isinstance(e, Expr)
-        assert e.to_sql() == "(name = 'alice')"
+        assert e.to_sql() == "(`name` = 'alice')"
 
     def test_reflexive_comparisons(self):
         # 10 < col("age") swaps to col("age") > 10
@@ -198,85 +198,85 @@ class TestExprBytesLiteral:
 
     def test_bytes_equality_expr_sql(self):
         e = col("data") == lit(b"\xca\xfe")
-        assert e.to_sql() == "(data = X'CAFE')"
+        assert e.to_sql() == "(`data` = X'CAFE')"
 
     def test_bytes_ne_expr_sql(self):
         e = col("data") != lit(b"\xff")
-        assert e.to_sql() == "(data <> X'FF')"
+        assert e.to_sql() == "(`data` <> X'FF')"
 
     def test_bytes_compound_expr_sql(self):
         e = (col("data") == lit(b"\x01")) & (col("id") > lit(5))
-        assert e.to_sql() == "((data = X'01') AND (id > 5))"
+        assert e.to_sql() == "((`data` = X'01') AND (id > 5))"
 
     def test_bytes_in_function_call(self):
         # Regression test: binary literals inside scalar function calls
         # used to fail because DataFusion's unparser does not support Binary
         # scalars.  Now handled via a placeholder-substitution rewrite.
         e = func("contains", col("data"), lit(b"\xff"))
-        assert e.to_sql() == "contains(data, X'FF')"
+        assert e.to_sql() == "contains(`data`, X'FF')"
 
     def test_bytes_in_not(self):
         e = ~(col("data") == lit(b"\xff"))
-        assert e.to_sql() == "NOT (data = X'FF')"
+        assert e.to_sql() == "NOT (`data` = X'FF')"
 
 
 class TestExprStringMethods:
     def test_lower(self):
         e = col("name").lower()
         assert isinstance(e, Expr)
-        assert e.to_sql() == "lower(name)"
+        assert e.to_sql() == "lower(`name`)"
 
     def test_upper(self):
         e = col("name").upper()
         assert isinstance(e, Expr)
-        assert e.to_sql() == "upper(name)"
+        assert e.to_sql() == "upper(`name`)"
 
     def test_contains(self):
         e = col("text").contains(lit("hello"))
         assert isinstance(e, Expr)
-        assert e.to_sql() == "contains(text, 'hello')"
+        assert e.to_sql() == "contains(`text`, 'hello')"
 
     def test_contains_with_str_coerce(self):
         e = col("text").contains("hello")
         assert isinstance(e, Expr)
-        assert e.to_sql() == "contains(text, 'hello')"
+        assert e.to_sql() == "contains(`text`, 'hello')"
 
     def test_chained_lower_eq(self):
         e = col("name").lower() == lit("alice")
         assert isinstance(e, Expr)
-        assert e.to_sql() == "(lower(name) = 'alice')"
+        assert e.to_sql() == "(lower(`name`) = 'alice')"
 
 
 class TestExprCast:
     def test_cast_string(self):
         e = col("id").cast("string")
         assert isinstance(e, Expr)
-        assert e.to_sql() == "CAST(id AS VARCHAR)"
+        assert e.to_sql() == "arrow_cast(id, 'Utf8')"
 
     def test_cast_int32(self):
         e = col("score").cast("int32")
         assert isinstance(e, Expr)
-        assert e.to_sql() == "CAST(score AS INTEGER)"
+        assert e.to_sql() == "arrow_cast(score, 'Int32')"
 
     def test_cast_float64(self):
         e = col("val").cast("float64")
         assert isinstance(e, Expr)
-        assert e.to_sql() == "CAST(val AS DOUBLE)"
+        assert e.to_sql() == "arrow_cast(val, 'Float64')"
 
     def test_cast_pyarrow_type(self):
         e = col("score").cast(pa.int32())
         assert isinstance(e, Expr)
-        assert e.to_sql() == "CAST(score AS INTEGER)"
+        assert e.to_sql() == "arrow_cast(score, 'Int32')"
 
     def test_cast_pyarrow_float64(self):
         e = col("val").cast(pa.float64())
         assert isinstance(e, Expr)
-        assert e.to_sql() == "CAST(val AS DOUBLE)"
+        assert e.to_sql() == "arrow_cast(val, 'Float64')"
 
     def test_cast_pyarrow_string(self):
         e = col("id").cast(pa.string())
         assert isinstance(e, Expr)
-        assert e.to_sql() == "CAST(id AS VARCHAR)"
+        assert e.to_sql() == "arrow_cast(id, 'Utf8')"
 
     def test_cast_pyarrow_and_string_equivalent(self):
         # pa.int32() and "int32" should produce equivalent SQL
@@ -597,14 +597,14 @@ class TestExprIsin:
     def test_isin_strs(self):
         assert (
             col("status").isin(["active", "pending"]).to_sql()
-            == "status IN ('active', 'pending')"
+            == "`status` IN ('active', 'pending')"
         )
 
     def test_isin_coerces_and_mixes(self):
         assert col("id").isin([lit(1), 2]).to_sql() == "id IN (1, 2)"
 
     def test_isin_empty(self):
-        assert col("id").isin([]).to_sql() == "id IN ()"
+        assert col("id").isin([]).to_sql() == "false"
 
     def test_isin_filter(self, simple_table):
         result = simple_table.search().where(col("id").isin([1, 3, 5])).to_arrow()

python/python/tests/test_first_class_function_slice1.py

@@ -37,21 +37,6 @@ def job_result(name: str) -> dict:
     return json.loads(fixture(name))["result"]
 
 
-def assert_no_secret_values(value):
-    if isinstance(value, dict):
-        for key, child in value.items():
-            assert key not in {
-                "secret_value",
-                "secret_values",
-                "resolved_secret",
-                "resolved_secrets",
-            }
-            assert_no_secret_values(child)
-    elif isinstance(value, list):
-        for child in value:
-            assert_no_secret_values(child)
-
-
 def test_public_function_values_are_in_api_reference():
     docs = Path(__file__).parents[3] / "docs" / "src" / "python" / "python.md"
     rendered = docs.read_text()
@@ -109,7 +94,6 @@ def test_function_version_identity_is_immutable_and_exact():
     version = FunctionVersion.from_json(json.dumps(value))
     assert version.name == "embed"
     assert version.version == "fv_01K3EXACT"
-    assert version.required_secrets == ("HF_TOKEN",)
 
     with pytest.raises((TypeError, ValueError)):
         version.version = "fv_changed"
@@ -121,7 +105,7 @@ def test_function_version_identity_is_immutable_and_exact():
     assert FunctionVersion(**changed) != version
 
 
-def test_function_version_binds_named_columns_as_one_immutable_group():
+def test_function_version_binds_named_columns_as_one_immutable_application():
     version = FunctionVersion.from_json(
         json.dumps(job_result("remote_function_job.json"))
     )
@@ -131,13 +115,10 @@ def test_function_version_binds_named_columns_as_one_immutable_group():
     assert application.function.name == version.name
     assert application.function.version == version.version
     assert application.output is version.signature.output
-    assert application.group_id.startswith("fg_")
     assert [
         (value.parameter, value.kind, value.value["path"])
         for value in application.inputs
     ] == [("text", "column", "documents.body")]
-    with pytest.raises((TypeError, ValueError)):
-        application.group_id = "fg_changed"
 
 
 def test_function_version_binding_validates_names_and_direct_columns():
@@ -156,7 +137,7 @@ def test_function_version_binding_validates_names_and_direct_columns():
 def test_function_version_keeps_named_struct_outputs_in_one_application():
     value = job_result("remote_function_job.json")
     value["name"] = "text_features"
-    value["version"] = "fv_grouped"
+    value["version"] = "fv_multi_output"
     value["signature"] = {
         "inputs": [
             {"name": "title", "arrow_type": "utf8", "nullable": True},
@@ -221,7 +202,6 @@ def test_function_application_uses_rename_columns_only():
     assert application.columns["normalized_text"] == "search_text"
     assert renamed.columns["normalized_text"] == "body_normalized"
     assert renamed.function == application.function
-    assert renamed.group_id == application.group_id
     assert not hasattr(application, "rename_outputs")
     with pytest.raises(TypeError, match="immutable"):
         renamed.columns["normalized_text"] = "changed"
@@ -242,7 +222,6 @@ def test_function_application_uses_rename_columns_only():
 
 def test_binding_and_refresh_result_keep_stable_remote_fields():
     binding = FunctionBinding.from_json(fixture("remote_function_binding.json"))
-    assert binding.revision == 3
     assert binding.function.version == "fv_01K3TEXT"
     assert [output.output_ordinal for output in binding.outputs] == [0, 1]
     assert binding.input_schema is not None
@@ -297,15 +276,6 @@ def test_refresh_result_rejects_non_u64_values(field):
         RefreshColumnResult.from_json(json.dumps(value))
 
 
-def test_canonical_client_values_contain_secret_names_only():
-    version = FunctionVersion.from_json(
-        json.dumps(job_result("remote_function_job.json"))
-    )
-    canonical = json.loads(version.to_canonical_json())
-    assert canonical["required_secrets"] == ["HF_TOKEN"]
-    assert_no_secret_values(canonical)
-
-
 class _FunctionDeclarationInner:
     def __init__(self):
         self.calls = []
@@ -322,7 +292,7 @@ def known_application() -> FunctionApplication:
 
 
 @pytest.mark.asyncio
-async def test_add_columns_routes_struct_as_one_and_grouped_expansion_atomically():
+async def test_add_columns_routes_struct_as_one_and_multi_output_binding_atomically():
     inner = _FunctionDeclarationInner()
     table = AsyncTable(inner)
     application = known_application()
@@ -343,12 +313,12 @@ async def test_add_columns_routes_struct_as_one_and_grouped_expansion_atomically
 
 
 @pytest.mark.asyncio
-async def test_add_columns_rejects_mixed_groups_and_unknown_newer_application():
+async def test_add_columns_rejects_multiple_bindings_and_unknown_newer_application():
     inner = _FunctionDeclarationInner()
     table = AsyncTable(inner)
     application = known_application()
 
-    with pytest.raises(ValueError, match="exactly one Function sibling group"):
+    with pytest.raises(ValueError, match="exactly one Function binding"):
         await table.add_columns({"a": application, "b": application})
 
     future = json.loads(fixture("remote_function_application.json"))
@@ -376,7 +346,6 @@ def test_rename_requires_named_struct_and_keeps_partial_mapping_immutable():
                     "arrow_type": "list<float32>",
                     "nullable": False,
                 },
-                "group_id": "fg_scalar",
             }
         )
     )

python/python/tests/test_first_class_function_slice2.py

@@ -3,7 +3,12 @@
 
 from __future__ import annotations
 
+import base64
 import contextlib
+import functools
+import importlib.util
+import types
+from datetime import date
 import http.server
 import json
 from pathlib import Path
@@ -16,6 +21,9 @@ import pytest
 import lancedb
 from lancedb.functions import UdfDefinition, udf
 
+THRESHOLD = 20
+_CACHE = None
+
 
 FIXTURES = (
     Path(__file__).parents[3]
@@ -31,28 +39,12 @@ FIXTURES = (
 @udf(
     pip=["numpy>=2"],
     env={"MODE": "test"},
-    secrets=["API_TOKEN"],
     python_version="3.12",
 )
 def normalize_score(value: float) -> float:
     return value / 100.0
 
 
-def _assert_no_secret_values(value):
-    if isinstance(value, dict):
-        for key, child in value.items():
-            assert key not in {
-                "secret_value",
-                "secret_values",
-                "resolved_secret",
-                "resolved_secrets",
-            }
-            _assert_no_secret_values(child)
-    elif isinstance(value, list):
-        for child in value:
-            _assert_no_secret_values(child)
-
-
 def test_scalar_udf_matches_shared_registration_golden_and_remains_callable():
     assert isinstance(normalize_score, UdfDefinition)
     assert normalize_score(25.0) == 0.25
@@ -67,13 +59,417 @@ def test_scalar_udf_matches_shared_registration_golden_and_remains_callable():
         "kind": "scalar_to_arrow_batch",
         "version": 1,
     }
-    assert request["required_secrets"] == ["API_TOKEN"]
-    _assert_no_secret_values(request)
+
+
+def _run_packaged(definition, *args):
+    """Execute the shipped artifact in a fresh namespace, as a worker would."""
+    source = base64.b64decode(definition.registration_request.artifact.content.data)
+    namespace: dict = {}
+    exec(compile(source, "<udf>", "exec"), namespace)
+    return namespace[definition.registration_request.artifact.entrypoint](*args)
+
+
+def test_udf_conda_environment():
+    @udf(conda=["scipy", "numpy"], conda_channels=["conda-forge", "defaults"])
+    def halve(value: float) -> float:
+        return value / 2
+
+    request = json.loads(halve.registration_request.to_canonical_json())
+    assert request["runtime"]["environment"] == {
+        "kind": "conda",
+        "packages": ["numpy", "scipy"],
+        "channels": ["conda-forge", "defaults"],
+    }
+    pip_request = json.loads(normalize_score.registration_request.to_canonical_json())
+    assert "channels" not in pip_request["runtime"]["environment"]
+
+    with pytest.raises(ValueError, match="not both"):
+        udf(name="both", pip=["numpy"], conda=["numpy"])(lambda value: value)
+    with pytest.raises(ValueError, match="requires conda"):
+        udf(name="channels", conda_channels=["conda-forge"])(lambda value: value)
+
+
+def test_udf_packages_attribute_access_and_body_imports():
+    @udf
+    def word_norm(body: str) -> float:
+        import numpy as np
+
+        try:
+            words = body.split()
+        except AttributeError as error:
+            raise ValueError(str(error)) from error
+        return float(np.linalg.norm([len(w) for w in words]))
+
+    assert _run_packaged(word_norm, "aa bb") == pytest.approx(8**0.5)
+
+
+def test_udf_packages_module_globals_and_global_caches():
+    @udf
+    def label(value: int) -> str:
+        return "big" if value >= THRESHOLD else "small"
+
+    assert _run_packaged(label, 21) == "big"
+
+    @udf
+    def cached(value: int) -> int:
+        global _CACHE
+        if _CACHE is None:
+            _CACHE = 40
+        return _CACHE + value
+
+    assert _run_packaged(cached, 2) == 42
+
+
+def test_udf_annotations_are_not_runtime_names():
+    @udf
+    def identity(value: date) -> date:
+        return value
+
+    assert _run_packaged(identity, date(2026, 8, 25)) == date(2026, 8, 25)
+
+
+def test_udf_nested_scopes_resolve_lexically():
+    @udf
+    def score(value: int) -> int:
+        offset = 2
+
+        def add_offset() -> int:
+            return value + offset
+
+        return add_offset() + sum(v for v in [0])
+
+    assert _run_packaged(score, 3) == 5
+
+
+def test_udf_resolves_module_globals_before_builtins(tmp_path):
+    module_path = tmp_path / "shadowing_udfs.py"
+    module_path.write_text(
+        "max = 7\n"
+        "len = lambda _: 99\n"
+        "\n"
+        "def uses_literal_shadow(value: int) -> int:\n"
+        "    def nested() -> int:\n"
+        "        return max\n"
+        "    return nested() + value\n"
+        "\n"
+        "def uses_callable_shadow(value: int) -> int:\n"
+        "    def nested() -> int:\n"
+        "        return len([1])\n"
+        "    return nested() + value\n"
+    )
+    spec = importlib.util.spec_from_file_location("shadowing_udfs", module_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+
+    # The module's `max = 7` is what the interpreter would use, so it ships.
+    assert _run_packaged(udf(module.uses_literal_shadow), 1) == 8
+    # A callable global cannot ship; it must not be silently swapped for the builtin.
+    with pytest.raises(TypeError, match="unsupported global value of type function"):
+        udf(module.uses_callable_shadow)
+
+
+def test_canonical_arrow_type_is_exactly_the_grammar():
+    from lancedb.functions import _GRAMMAR_PRIMITIVES, _canonical_arrow_type
+
+    golden = json.loads(
+        (
+            Path(__file__).parents[3]
+            / "rust/lancedb/tests/fixtures/first_class_functions/v1/arrow_types.json"
+        ).read_text()
+    )
+    primitives = [
+        case["arrow_type"] for case in golden["valid"] if "<" not in case["arrow_type"]
+    ]
+    assert [name for _, name in _GRAMMAR_PRIMITIVES] == primitives
+    for outside in [
+        pa.timestamp("us"),
+        pa.decimal128(10, 2),
+        pa.large_string(),
+        pa.large_binary(),
+        pa.binary(4),
+        pa.duration("s"),
+        pa.struct([pa.field("a", pa.int32())]),
+        pa.list_(pa.float32(), 0),
+        pa.list_(pa.timestamp("us")),
+    ]:
+        with pytest.raises(TypeError, match="unsupported Arrow type"):
+            _canonical_arrow_type(outside)
+
+
+def test_udf_nested_annotations_are_postponed_in_the_artifact():
+    @udf
+    def score(value: int) -> int:
+        def identity(item: date) -> date:
+            return item
+
+        identity(date(2026, 8, 25))
+        return value
+
+    assert _run_packaged(score, 3) == 3
+
+
+def test_udf_ships_globals_the_body_deletes():
+    @udf
+    def clear(value: int) -> int:
+        global _CACHE
+        del _CACHE
+        return value
+
+    assert _run_packaged(clear, 3) == 3
+
+
+def test_udf_rejects_a_module_global_that_does_not_import_as_itself(tmp_path):
+    module_path = tmp_path / "fake_module_udfs.py"
+    module_path.write_text(
+        "import types\n"
+        "np = types.ModuleType('numpy')\n"
+        "np.sqrt = lambda x: 0\n"
+        "\n"
+        "def score(value: int) -> int:\n"
+        "    return int(np.sqrt(value))\n"
+    )
+    spec = importlib.util.spec_from_file_location("fake_module_udfs", module_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    with pytest.raises(TypeError, match="does not import as 'numpy'"):
+        udf(module.score)
+
+
+def test_udf_rejects_a_module_level_namespace_alias(tmp_path):
+    module_path = tmp_path / "aliasing_udfs.py"
+    module_path.write_text(
+        "import builtins as b\n"
+        "THRESHOLD = 5\n"
+        "\n"
+        "def score(value: int) -> int:\n"
+        "    return value + b.vars(b.__import__('aliasing_udfs'))['THRESHOLD']\n"
+    )
+    spec = importlib.util.spec_from_file_location("aliasing_udfs", module_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    with pytest.raises(ValueError, match="dynamic namespace access"):
+        udf(module.score)
+
+
+@pytest.mark.parametrize(
+    "access",
+    [
+        "globals()['THRESHOLD']",
+        "eval('THRESHOLD')",
+        "(lambda g: g()['THRESHOLD'])(globals)",
+        "__import__('sys').modules[__name__].THRESHOLD",
+        "sys.modules[__name__].THRESHOLD",
+    ],
+)
+def test_udf_rejects_dynamic_namespace_access(access):
+    namespace: dict = {}
+    exec(
+        f"def score(value: int) -> int:\n    return value + {access}\n",
+        {"THRESHOLD": 5},
+        namespace,
+    )
+    with pytest.raises(ValueError, match="dynamic namespace access"):
+        _package_from_text(
+            "def score(value: int) -> int:\n"
+            "    import sys\n"
+            f"    return value + {access}\n"
+        )
+
+
+def _package_from_text(source: str, module_globals: dict | None = None):
+    """Load `source` as a real module file so the packager can inspect it."""
+    import tempfile
+
+    directory = tempfile.mkdtemp()
+    path = Path(directory) / "generated_udf_module.py"
+    path.write_text(source)
+    spec = importlib.util.spec_from_file_location(f"generated_udf_{id(source)}", path)
+    module = importlib.util.module_from_spec(spec)
+    if module_globals:
+        module.__dict__.update(module_globals)
+    spec.loader.exec_module(module)
+    functions = [
+        value
+        for value in vars(module).values()
+        if callable(value) and getattr(value, "__module__", None) == module.__name__
+    ]
+    return udf(functions[0])
+
+
+def test_udf_rejects_a_non_standard_builtins_environment():
+    def score(value: int) -> int:
+        return len([1]) + value
+
+    score.__globals__  # noqa: B018 -- real function, real globals
+    import builtins
+
+    patched = types.FunctionType(
+        score.__code__,
+        {"__builtins__": {**vars(builtins), "len": lambda _: 99}},
+        "score",
+    )
+    patched.__annotations__ = score.__annotations__
+    assert patched(3) == 102
+    with pytest.raises(ValueError, match="non-standard builtins environment"):
+        udf(patched)
+
+    class ReportingDict(dict):  # reports standard entries, resolves differently
+        def __missing__(self, key):
+            return vars(builtins)[key]
+
+    disguised = types.FunctionType(
+        score.__code__, {"__builtins__": ReportingDict(len=lambda _: 99)}, "score"
+    )
+    disguised.__annotations__ = score.__annotations__
+    assert disguised(3) == 102
+    with pytest.raises(ValueError, match="non-standard builtins environment"):
+        udf(disguised)
+
+    hooked = types.FunctionType(
+        score.__code__,
+        {"__builtins__": {**vars(builtins), "__import__": lambda *a, **k: None}},
+        "score",
+    )
+    hooked.__annotations__ = score.__annotations__
+    with pytest.raises(ValueError, match="non-standard builtins environment"):
+        udf(hooked)
+
+
+def test_udf_recursion_versus_a_rebound_module_name(tmp_path):
+    module_path = tmp_path / "rebound_udfs.py"
+    module_path.write_text(
+        "def fact(value: int) -> int:\n"
+        "    return 1 if value <= 1 else value * fact(value - 1)\n"
+        "\n"
+        "def score(value: int) -> int:\n"
+        "    return score + value\n"
+    )
+    spec = importlib.util.spec_from_file_location("rebound_udfs", module_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    assert _run_packaged(udf(module.fact), 5) == 120
+    raw = module.score
+    module.score = 10
+    with pytest.raises(ValueError, match="binds that name to another value"):
+        udf(raw)
+    # A wrapper that merely exposes __wrapped__ is not the function.
+    module.score = functools.wraps(raw)(lambda value: 41)
+    with pytest.raises(ValueError, match="binds that name to another value"):
+        udf(raw)
+    # The decorator's own result is; a subclass of it is not.
+    module.fact = udf(module.fact)
+    assert _run_packaged(module.fact, 4) == 24
+
+    class Twisted(UdfDefinition):
+        def __call__(self, *args, **kwargs):
+            return 41
+
+    raw_fact = module.fact._function
+    module.fact = Twisted(
+        raw_fact,
+        name=None,
+        input_schema=None,
+        output_schema=None,
+        pip=(),
+        env={},
+        python_version=None,
+    )
+    with pytest.raises(ValueError, match="binds that name to another value"):
+        udf(raw_fact)
+
+
+def test_canonical_arrow_type_rejects_unrepresentable_list_children():
+    from lancedb.functions import _canonical_arrow_type
+
+    for outside in [
+        pa.list_(pa.float32()),  # pyarrow default: nullable child
+        pa.list_(pa.field("custom", pa.float32(), nullable=False)),
+        pa.list_(pa.field("item", pa.float32(), nullable=False, metadata={"k": "v"})),
+        pa.list_(pa.field("item", pa.float32(), nullable=False), 0),
+    ]:
+        with pytest.raises(TypeError, match="unsupported Arrow type"):
+            _canonical_arrow_type(outside)
+    assert (
+        _canonical_arrow_type(
+            pa.list_(pa.field("item", pa.float32(), nullable=False), 3)
+        )
+        == "fixed_size_list<float32, 3>"
+    )
+
+
+def _calls_missing(value: int) -> int:
+    return missing(value)  # noqa: F821
+
+
+def _shadows_missing_in_a_comprehension(value: int) -> int:
+    return missing(value) + sum(missing for missing in ())  # noqa: F821
+
+
+def _shadows_missing_in_a_lambda(value: int) -> int:
+    return (lambda missing: missing)(value) + missing  # noqa: F821
+
+
+@pytest.mark.parametrize(
+    "function",
+    [_calls_missing, _shadows_missing_in_a_comprehension, _shadows_missing_in_a_lambda],
+)
+def test_udf_rejects_a_truly_unresolved_global(function):
+    with pytest.raises(ValueError, match=r"unresolved global names: \['missing'\]"):
+        udf(function)
+
+
+def _arrow_type_from_golden(spec: dict) -> pa.DataType:
+    kind = spec["type"]
+    if kind in ("list", "large_list", "fixed_size_list"):
+        item = _arrow_type_from_golden(spec["fields"][0]["type"])
+        field = pa.field("item", item, nullable=False)
+        if kind == "list":
+            return pa.list_(field)
+        if kind == "large_list":
+            return pa.large_list(field)
+        return pa.list_(field, spec["length"])
+    return {
+        "null": pa.null(),
+        "bool": pa.bool_(),
+        "utf8": pa.string(),
+        "binary": pa.binary(),
+        "float16": pa.float16(),
+        "float32": pa.float32(),
+        "float64": pa.float64(),
+        "date32": pa.date32(),
+        "date64": pa.date64(),
+    }.get(kind) or getattr(pa, kind)()
+
+
+def test_arrow_type_grammar_matches_the_shared_golden():
+    golden = json.loads(
+        (
+            Path(__file__).parents[3]
+            / "rust/lancedb/tests/fixtures/first_class_functions/v1/arrow_types.json"
+        ).read_text()
+    )
+    from lancedb.functions import _canonical_arrow_type
+
+    emitted = {
+        case["arrow_type"]: _canonical_arrow_type(_arrow_type_from_golden(case["json"]))
+        for case in golden["valid"]
+    }
+    assert emitted == {
+        case["arrow_type"]: case["arrow_type"] for case in golden["valid"]
+    }
+    assert not set(emitted) & set(golden["invalid"])
+    for case in golden["server_only"]:
+        with pytest.raises(TypeError, match="unsupported Arrow type"):
+            _canonical_arrow_type(_arrow_type_from_golden(case["json"]))
 
 
 def test_explicit_arrow_schema_is_deterministic():
     input_schema = pa.schema([pa.field("value", pa.float32(), nullable=True)])
-    output_schema = pa.field("embedding", pa.list_(pa.float32(), 3), nullable=False)
+    output_schema = pa.field(
+        "embedding",
+        pa.list_(pa.field("item", pa.float32(), nullable=False), 3),
+        nullable=False,
+    )
 
     @udf(input_schema=input_schema, output_schema=output_schema)
     def explicit(value):
@@ -82,7 +478,7 @@ def test_explicit_arrow_schema_is_deterministic():
     signature = explicit.registration_request.signature
     assert signature.inputs[0].arrow_type == "float32"
     assert signature.inputs[0].nullable is True
-    assert signature.output.arrow_type == "fixed_size_list<float32>[3]"
+    assert signature.output.arrow_type == "fixed_size_list<float32, 3>"
     assert signature.output.nullable is False
 
 
@@ -130,14 +526,6 @@ def test_annotation_and_explicit_schema_validation_fail_closed():
             return value
 
 
-def test_environment_rejects_secret_value_overlap():
-    with pytest.raises(ValueError, match="must be disjoint"):
-
-        @udf(env={"TOKEN": "plaintext"}, secrets=["TOKEN"])
-        def overlapping(value: int) -> int:
-            return value
-
-
 def test_local_function_catalog_operations_are_not_supported(tmp_path):
     db = lancedb.connect(tmp_path)
     message = "Function catalog operations are not supported by this database"
@@ -174,7 +562,6 @@ def _mock_remote_function_catalog():
                     "runtime": body["runtime"],
                     "runtime_digest": "sha256:runtime",
                     "environment_digest": "sha256:environment",
-                    "required_secrets": body.get("required_secrets", []),
                     "created_at": "2026-08-21T00:00:00Z",
                 }
                 response = {"job_id": "job-register"}
@@ -233,7 +620,6 @@ def test_remote_registration_job_and_exact_version_reopen_round_trip():
     assert create_request == json.loads(
         normalize_score.registration_request.to_canonical_json()
     )
-    _assert_no_secret_values(create_request)
 
 
 def test_blocking_remote_registration_returns_function_version():

python/python/tests/test_fts.py

@@ -25,6 +25,7 @@ from lancedb.db import DBConnection
 from lancedb.index import FTS
 from lancedb.query import (
     BoostQuery,
+    DocumentGranularity,
     MatchQuery,
     MultiMatchQuery,
     PhraseQuery,
@@ -245,6 +246,55 @@ def test_create_inverted_index_rejects_invalid_block_size(table):
         table.create_index("text", config=FTS(block_size=129))
 
 
+def test_list_element_document_granularity(tmp_path):
+    docs_type = pa.list_(pa.struct([pa.field("content", pa.string())]))
+    docs = pa.array(
+        [
+            [
+                {"content": "alpha beta"},
+                None,
+                {"content": ""},
+                {"content": "the and"},
+                {"content": "alpha beta"},
+            ]
+        ],
+        type=docs_type,
+    )
+    table = ldb.connect(tmp_path).create_table(
+        "list_element_docs", pa.table({"id": [0], "docs": docs})
+    )
+    row_table = ldb.connect(tmp_path).create_table(
+        "row_docs", pa.table({"id": [0], "docs": docs})
+    )
+    row_table.create_index("docs.content", config=FTS())
+    row_result = row_table.search(MatchQuery("alpha", "docs.content")).to_arrow()
+    assert row_result.num_rows == 1
+    assert "_doc_index" not in row_result.column_names
+
+    granularity = DocumentGranularity.LIST_ELEMENT
+    table.create_index(
+        "docs.content",
+        config=FTS(with_position=True, document_granularity=granularity),
+    )
+    assert table.list_indices()[0].columns == ["docs.content"]
+
+    def coordinates(query):
+        result = table.search(query).limit(10).to_arrow()
+        doc_index_type = result.schema.field("_doc_index").type
+        assert pa.types.is_list(doc_index_type)
+        assert doc_index_type.value_type == pa.uint32()
+        return sorted(result["_doc_index"].to_pylist())
+
+    assert coordinates(
+        MatchQuery("alpha", "docs.content", document_granularity=granularity)
+    ) == [[0], [4]]
+    assert coordinates(
+        PhraseQuery("alpha beta", "docs.content", document_granularity=granularity)
+    ) == [[0], [4]]
+    assert coordinates(MatchQuery("alpha", "docs.content")) == [[0], [4]]
+    assert FTS().document_granularity is DocumentGranularity.ROW
+
+
 def test_create_inverted_index_respects_build_memory_limit(table):
     with pytest.raises(ValueError, match="exceeds worker memory limit"):
         table.create_index(
@@ -1089,6 +1139,20 @@ def test_fts_query_to_json():
     )
     assert json_str == expected
 
+    # Test MatchQuery with list-element document granularity
+    match_query = MatchQuery(
+        "hello world",
+        "text",
+        document_granularity=DocumentGranularity.LIST_ELEMENT,
+    )
+    json_str = match_query.to_json()
+    expected = (
+        '{"match":{"column":"text","terms":"hello world","boost":1.0,'
+        '"fuzziness":0,"max_expansions":50,"operator":"Or","prefix_length":0,'
+        '"document_granularity":"list_element"}}'
+    )
+    assert json_str == expected
+
     # Test MatchQuery with options
     match_query = MatchQuery("puppy", "text", fuzziness=2, boost=1.5, prefix_length=3)
     json_str = match_query.to_json()
@@ -1098,6 +1162,19 @@ def test_fts_query_to_json():
     )
     assert json_str == expected
 
+    # Test PhraseQuery with list-element document granularity
+    phrase_query = PhraseQuery(
+        "quick brown fox",
+        "title",
+        document_granularity=DocumentGranularity.LIST_ELEMENT,
+    )
+    json_str = phrase_query.to_json()
+    expected = (
+        '{"phrase":{"column":"title","terms":"quick brown fox","slop":0,'
+        '"document_granularity":"list_element"}}'
+    )
+    assert json_str == expected
+
     # Test PhraseQuery
     phrase_query = PhraseQuery("quick brown fox", "title")
     json_str = phrase_query.to_json()

python/python/tests/test_permutation.py

@@ -56,6 +56,31 @@ def test_execute_does_not_reenter_background_loop(tmp_path, monkeypatch):
     assert permutation_tbl._conn.read_consistency_interval is None
 
 
+def test_pickled_permutation_reads_pinned_version(tmp_path):
+    """An unpickled copy must still read the pinned version, which also covers the
+    version surviving the ``to_arrow()`` round trip in ``__getstate__``."""
+    import pickle
+
+    db = connect(tmp_path)
+    tbl = db.create_table("base", pa.table({"idx": range(20)}))
+    permutation_tbl = permutation_builder(tbl).execute()
+    perm = Permutation.from_tables(tbl, permutation_tbl)
+
+    payload = pickle.dumps(perm)
+
+    # Compact so the stored row addresses no longer describe these rows at latest.
+    tbl.delete("true")
+    tbl.optimize()
+    assert tbl.count_rows() == 0
+
+    # Unpickle after the mutation: __setstate__ reopens at latest, so this only
+    # passes if the recorded version is applied on reopen.
+    restored = pickle.loads(payload)
+    assert len(restored) == 20
+    rows = restored.__getitems__(list(range(20)))
+    assert sorted(row["idx"] for row in rows) == list(range(20))
+
+
 def test_split_random_counts(mem_db):
     """Test random splitting with absolute counts."""
     tbl = mem_db.create_table(

python/python/tests/test_query.py

@@ -675,6 +675,21 @@ def test_distance_range(table: lancedb.table.Table):
     assert res["_distance"].to_pylist() == [min_dist, max_dist]
 
 
+@pytest.mark.parametrize("expression", ["1 - _distance", "1.0 - _distance"])
+def test_select_arithmetic_with_distance(table, expression):
+    result = (
+        table.search([10, 10])
+        .select({"similarity": expression, "_distance": "_distance"})
+        .distance_type("cosine")
+        .to_arrow()
+    )
+
+    assert result.schema.field("similarity").type == pa.float32()
+    assert result["similarity"].to_pylist() == pytest.approx(
+        [1 - distance for distance in result["_distance"].to_pylist()]
+    )
+
+
 @pytest.mark.asyncio
 async def test_distance_range_async(table_async: AsyncTable):
     q = [0, 0]
@@ -897,6 +912,23 @@ def test_query_builder_batches(table):
     assert rs_list["id"][1] == 2
 
 
+def test_batch_vector_query_shares_filtered_flat_scan(table):
+    query = (
+        table.search([[1.0, 2.0], [3.0, 4.0]])
+        .where("id > 0", prefilter=True)
+        .limit(1)
+        .select(["id"])
+    )
+
+    plan = query.explain_plan(verbose=True)
+    assert "KNNVectorDistance: queries=2" in plan
+    assert "UnionExec" not in plan
+
+    results = query.to_arrow()
+    assert len(results) == 2
+    assert results["query_index"].to_pylist() == [0, 1]
+
+
 def test_dynamic_projection(table):
     rs = (
         LanceVectorQueryBuilder(table, [0, 0], "vector")

python/python/tests/test_remote_db.py

@@ -1618,6 +1618,49 @@ def test_query_sync_fts():
         )
 
 
+def test_query_sync_fts_document_granularity():
+    from lancedb.query import DocumentGranularity, MatchQuery
+
+    def handler(body):
+        assert body == {
+            "full_text_query": {
+                "query": {
+                    "match": {
+                        "column": "docs.content",
+                        "terms": "alpha",
+                        "boost": 1.0,
+                        "fuzziness": 0,
+                        "max_expansions": 50,
+                        "operator": "Or",
+                        "prefix_length": 0,
+                        "document_granularity": "list_element",
+                    }
+                }
+            },
+            "k": 10,
+            "prefilter": True,
+            "vector": [],
+            "version": None,
+        }
+        return pa.table(
+            {
+                "id": [1, 1],
+                "_doc_index": pa.array([[0], [4]], type=pa.list_(pa.uint32())),
+            }
+        )
+
+    with query_test_table(handler, server_version=Version("0.6.0")) as table:
+        result = table.search(
+            MatchQuery(
+                "alpha",
+                "docs.content",
+                document_granularity=DocumentGranularity.LIST_ELEMENT,
+            )
+        ).to_arrow()
+
+    assert result["_doc_index"].to_pylist() == [[0], [4]]
+
+
 def test_query_sync_hybrid():
     def handler(body):
         if "full_text_query" in body:

python/python/tests/test_s3.py

@@ -4,6 +4,7 @@
 
 import asyncio
 import copy
+from concurrent.futures import ThreadPoolExecutor
 from datetime import timedelta
 import threading
 
@@ -86,6 +87,25 @@ def test_s3_lifecycle(s3_bucket: str):
     asyncio.run(test())
 
 
+@pytest.mark.s3_test
+def test_concurrent_open_table(s3_bucket: str):
+    uri = f"s3://{s3_bucket}/test_concurrent_open_table"
+    db = lancedb.connect(uri, storage_options=copy.copy(CONFIG))
+    db.create_table("test", pa.table({"x": [1, 2, 3]}))
+
+    num_workers = 32
+    barrier = threading.Barrier(num_workers)
+
+    def open_and_count(_):
+        barrier.wait()
+        return db.open_table("test").count_rows()
+
+    with ThreadPoolExecutor(max_workers=num_workers) as pool:
+        row_counts = list(pool.map(open_and_count, range(num_workers)))
+
+    assert row_counts == [3] * num_workers
+
+
 @pytest.fixture()
 def kms_key():
     kms = get_boto3_client("kms", endpoint_url=CONFIG["aws_endpoint"])

python/python/tests/test_table.py

@@ -11,6 +11,7 @@ import warnings
 import weakref
 from concurrent.futures import ThreadPoolExecutor
 from datetime import date, datetime, timedelta
+from decimal import Decimal
 from time import sleep
 from typing import List
 from unittest.mock import patch
@@ -336,6 +337,21 @@ async def test_update_async(mem_db_async: AsyncConnection):
     assert await table.count_rows("id == 10") == 1
 
 
+@pytest.mark.asyncio
+async def test_update_expr_filter_literals_async(mem_db_async: AsyncConnection):
+    values = ["5", "4.66e-84", "it's"]
+    table = await mem_db_async.create_table(
+        "update_expr_literals",
+        data=[{"field": value, "result": "original"} for value in values],
+    )
+
+    for value in values:
+        update_res = await table.update({"result": value}, where=col("field") == value)
+        assert update_res.rows_updated == 1
+
+    assert (await table.to_arrow())["result"].to_pylist() == values
+
+
 def test_create_table(mem_db: DBConnection):
     schema = pa.schema(
         {
@@ -2343,6 +2359,148 @@ def test_update(mem_db: DBConnection):
     assert np.allclose(v, np.array([[1.2, 1.9], [1.1, 1.1]]))
 
 
+def test_update_expr_filter_literals(mem_db: DBConnection):
+    values = ["5", "4.66e-84", "it's"]
+    table = mem_db.create_table(
+        "update_expr_literals",
+        data=[{"field": value, "result": "original"} for value in values],
+    )
+
+    for value in values:
+        update_res = table.update(where=col("field") == value, values={"result": value})
+        assert update_res.rows_updated == 1
+
+    assert table.to_arrow()["result"].to_pylist() == values
+
+
+def test_update_expr_filter_preserves_typed_semantics(mem_db: DBConnection):
+    low = Decimal("1.234567890123456789")
+    high = Decimal("1.234567890123456790")
+    decimal_schema = pa.schema(
+        [("val", pa.decimal128(19, 18)), ("result", pa.string())]
+    )
+    decimal_table = mem_db.create_table(
+        "update_expr_decimal",
+        pa.table(
+            {"val": [low, high], "result": ["old", "old"]},
+            schema=decimal_schema,
+        ),
+    )
+    predicate = col("val") < lit(high)
+    assert decimal_table.search().where(predicate).to_arrow().num_rows == 1
+    result = decimal_table.update(where=predicate, values={"result": "new"})
+    assert result.rows_updated == 1
+
+    keyword_table = mem_db.create_table(
+        "update_expr_keyword", [{"null": 1, "result": "old"}]
+    )
+    predicate = col("null") == 1
+    assert keyword_table.search().where(predicate).to_arrow().num_rows == 1
+    result = keyword_table.update(where=predicate, values={"result": "new"})
+    assert result.rows_updated == 1
+
+    empty_in_table = mem_db.create_table(
+        "update_expr_empty_in", [{"id": 1, "result": "old"}]
+    )
+    predicate = col("id").isin([])
+    assert empty_in_table.search().where(predicate).to_arrow().num_rows == 0
+    result = empty_in_table.update(where=predicate, values={"result": "new"})
+    assert result.rows_updated == 0
+
+    marker = "__lancedb_binary_placeholder_0__"
+    binary_schema = pa.schema(
+        [("payload", pa.binary()), ("text", pa.string()), ("result", pa.string())]
+    )
+    binary_table = mem_db.create_table(
+        "update_expr_binary",
+        pa.table(
+            {
+                "payload": [b"\x01", b"\x02"],
+                "text": ["other", marker],
+                "result": ["old", "old"],
+            },
+            schema=binary_schema,
+        ),
+    )
+    predicate = (col("payload") == lit(b"\x01")) | (col("text") == marker)
+    assert binary_table.search().where(predicate).to_arrow().num_rows == 2
+    result = binary_table.update(where=predicate, values={"result": "new"})
+    assert result.rows_updated == 2
+
+    nonfinite_table = mem_db.create_table(
+        "update_expr_nonfinite",
+        [{"x": 1.0, "result": "old"}, {"x": 2.0, "result": "old"}],
+    )
+    predicate = col("x") < float("inf")
+    assert nonfinite_table.search().where(predicate).to_arrow().num_rows == 2
+    result = nonfinite_table.update(where=predicate, values={"result": "new"})
+    assert result.rows_updated == 2
+
+    float16_table = mem_db.create_table(
+        "update_expr_float16",
+        [{"x": 1.0, "result": "old"}, {"x": 3.0, "result": "old"}],
+    )
+    predicate = col("x").cast(pa.float16()) < 2.0
+    assert float16_table.search().where(predicate).to_arrow().num_rows == 1
+    result = float16_table.update(where=predicate, values={"result": "new"})
+    assert result.rows_updated == 1
+
+    string_cast_table = mem_db.create_table(
+        "update_expr_string_cast",
+        [{"x": 1, "result": "old"}, {"x": 2, "result": "old"}],
+    )
+    predicate = col("x").cast("string") == "1"
+    assert string_cast_table.search().where(predicate).to_arrow().num_rows == 1
+    result = string_cast_table.update(where=predicate, values={"result": "new"})
+    assert result.rows_updated == 1
+
+    quoted_identifier_schema = pa.schema(
+        [("payload", pa.binary()), ("odd'name", pa.int64()), ("result", pa.string())]
+    )
+    quoted_identifier_table = mem_db.create_table(
+        "update_expr_quoted_identifier",
+        pa.table(
+            {"payload": [b"\x01"], "odd'name": [1], "result": ["old"]},
+            schema=quoted_identifier_schema,
+        ),
+    )
+    predicate = (col("payload") == lit(b"\x01")) & (col("odd'name") == 1)
+    assert quoted_identifier_table.search().where(predicate).to_arrow().num_rows == 1
+    result = quoted_identifier_table.update(where=predicate, values={"result": "new"})
+    assert result.rows_updated == 1
+
+    decimal256_schema = pa.schema(
+        [("val", pa.decimal256(40, 2)), ("result", pa.string())]
+    )
+    decimal256_table = mem_db.create_table(
+        "update_expr_decimal256",
+        pa.table(
+            {
+                "val": [Decimal("1.00"), Decimal("3.00")],
+                "result": ["old", "old"],
+            },
+            schema=decimal256_schema,
+        ),
+    )
+    predicate = col("val") < lit(Decimal("2.00")).cast(pa.decimal256(40, 2))
+    assert decimal256_table.search().where(predicate).to_arrow().num_rows == 1
+    result = decimal256_table.update(where=predicate, values={"result": "new"})
+    assert result.rows_updated == 1
+
+    binary_empty_table = mem_db.create_table(
+        "update_expr_binary_empty",
+        pa.table(
+            {"payload": [b"\x01", b"\x02"], "result": ["old", "old"]},
+            schema=pa.schema([("payload", pa.binary()), ("result", pa.string())]),
+        ),
+    )
+    predicate = (col("payload") == lit(b"\x01")).isin([])
+    assert binary_empty_table.search().where(predicate).to_arrow().num_rows == 0
+    assert predicate.to_sql() == "false"
+    result = binary_empty_table.update(where=predicate, values={"result": "new"})
+    assert result.rows_updated == 0
+
+
 def test_update_with_arrow_scalar(mem_db: DBConnection):
     schema = pa.schema({"id": pa.int64(), "vector": pa.list_(pa.float32(), 4)})
     table = mem_db.create_table("my_table", schema=schema)
@@ -3929,6 +4087,29 @@ def test_computed_column_rejects_transforms_and_computed_together(tmp_path):
         table.add_columns({"a": "x + 1"}, computed={"b": "x * 2"})
 
 
+def test_computed_column_blob_projection_inherits_semantics(tmp_path):
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("computed_column_blob", schema=schema)
+    table.add(
+        [
+            {"id": 1, "image": b"hello"},
+            {"id": 2, "image": b""},
+            {"id": 3, "image": None},
+        ]
+    )
+
+    table.add_columns(computed={"image_copy": "image", "second_copy": "image_copy"})
+    assert table.refresh_column("image_copy").rows_filled == 2
+    assert table.refresh_column("second_copy").rows_filled == 2
+    assert table.blob_columns() == ["image", "image_copy", "second_copy"]
+
+    hits = table.search().with_row_id(True).limit(10).to_arrow()
+    rows = sorted(zip(hits["id"].to_pylist(), hits["_rowid"].to_pylist()))
+    copied = table.fetch_blobs("second_copy", [row_id for _, row_id in rows])
+    assert copied.to_pylist() == [b"hello", b"", None]
+
+
 @pytest.mark.asyncio
 async def test_computed_column_async(tmp_path):
     db = await lancedb.connect_async(tmp_path)

python/python/tests/test_util.py

@@ -7,6 +7,7 @@ import pathlib
 from typing import Optional
 
 import lance
+from lance.blob import BlobType as LanceBlobType
 from lancedb.conftest import MockTextEmbeddingFunction
 from lancedb.embeddings.base import EmbeddingFunctionConfig
 from lancedb.embeddings.registry import EmbeddingFunctionRegistry
@@ -907,6 +908,165 @@ def test_cast_to_target_schema():
     assert output == expected
 
 
+def test_cast_to_target_schema_coerces_binary_to_blob_v2():
+    data = pa.table({"image": pa.array([b"hello", None], type=pa.binary())})
+    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": b"hello", "uri": None, "position": None, "size": None},
+        None,
+    ]
+
+
+def test_cast_to_target_schema_coerces_binary_to_metadata_blob_struct():
+    storage = lancedb.blob("image").type.storage_type
+    target = pa.schema(
+        [
+            pa.field(
+                "image",
+                storage,
+                metadata={
+                    b"ARROW:extension:name": b"lance.blob.v2",
+                    b"ARROW:extension:metadata": b"",
+                },
+            )
+        ]
+    )
+    data = pa.table({"image": pa.array([b"hello", None], type=pa.binary())})
+
+    output = _cast_to_target_schema(data.to_reader(), target).read_all()
+
+    image = output["image"].chunk(0)
+    assert not isinstance(image.type, pa.ExtensionType)
+    assert image.to_pylist() == [
+        {"data": b"hello", "uri": None, "position": None, "size": None},
+        None,
+    ]
+
+
+def test_cast_to_target_schema_coerces_nested_binary_blob():
+    data = pa.table(
+        {
+            "info": pa.array(
+                [{"blob": b"hello"}, {"blob": None}],
+                type=pa.struct([pa.field("blob", pa.binary())]),
+            )
+        }
+    )
+    target = pa.schema([pa.field("info", pa.struct([lancedb.blob("blob")]))])
+
+    output = _cast_to_target_schema(data.to_reader(), target).read_all()
+
+    blob = output["info"].chunk(0).field("blob")
+    assert type(blob.type) is lancedb.BlobType
+    assert blob.storage.to_pylist() == [
+        {"data": b"hello", "uri": None, "position": None, "size": None},
+        None,
+    ]
+
+
+def test_cast_to_target_schema_coerces_list_binary_blob_with_inferred_child_name():
+    data = pa.table(
+        {"images": pa.array([[b"a", b"b"], None], type=pa.list_(pa.binary()))}
+    )
+    target = pa.schema([pa.field("images", pa.list_(lancedb.blob("image")))])
+
+    output = _cast_to_target_schema(data.to_reader(), target).read_all()
+
+    images = output["images"].chunk(0)
+    assert images.type.value_field.name == "image"
+    assert type(images.type.value_type) is lancedb.BlobType
+    assert images.to_pylist()[1] is None
+    assert images.values.storage.to_pylist() == [
+        {"data": b"a", "uri": None, "position": None, "size": None},
+        {"data": b"b", "uri": None, "position": None, "size": None},
+    ]
+
+
+def test_list_blob_coercion_preserves_null_slots_with_nonzero_extent():
+    child = pa.field("image", pa.binary())
+    source = pa.ListArray.from_arrays(
+        pa.array([0, 2, 4], type=pa.int32()),
+        pa.array([b"a", b"b", b"dead", b"beef"], type=pa.binary()),
+        mask=pa.array([False, True]),
+    ).cast(pa.list_(child))
+    target = pa.schema([pa.field("images", pa.list_(lancedb.blob("image")))])
+
+    output = _cast_to_target_schema(
+        pa.table({"images": source}).to_reader(), target
+    ).read_all()
+
+    images = output["images"].chunk(0)
+    assert images.to_pylist()[1] is None
+    assert [b["data"] for b in images.to_pylist()[0]] == [b"a", b"b"]
+
+
+def test_fixed_size_list_blob_coercion_keeps_null_rows():
+    child = pa.field("frame", pa.binary())
+    source = (
+        pa.FixedSizeListArray.from_arrays(
+            pa.array([b"a", b"b", b"c", b"d"], type=pa.binary()), 2
+        )
+        .take(pa.array([0, None], type=pa.int32()))
+        .cast(pa.list_(child, 2))
+    )
+    target = pa.schema([pa.field("frames", pa.list_(lancedb.blob("frame"), 2))])
+
+    output = _cast_to_target_schema(
+        pa.table({"frames": source}).to_reader(), target
+    ).read_all()
+
+    frames = output["frames"].chunk(0)
+    assert frames.to_pylist()[1] is None
+    assert [b["data"] for b in frames.to_pylist()[0]] == [b"a", b"b"]
+
+
+def test_cast_to_target_schema_accepts_pylance_blob_v2():
+    target_type = lancedb.BlobType()
+    source = lance.blob_array([b"hello", None])
+    assert type(source.type) is LanceBlobType
+    assert type(source.type) is type(target_type)
+    data = pa.table({"image": source})
+    target = pa.schema([pa.field("image", target_type)])
+
+    output = _cast_to_target_schema(data.to_reader(), target).read_all()
+
+    image = output["image"].chunk(0)
+    assert type(image.type) is LanceBlobType
+    assert image.type == target_type
+    assert image.storage.to_pylist() == [
+        {"data": b"hello", "uri": None, "position": None, "size": None},
+        None,
+    ]
+
+
+def test_cast_to_target_schema_rejects_different_blob_v2_class():
+    class OtherBlobType(pa.ExtensionType):
+        def __init__(self):
+            super().__init__(lancedb.BlobType().storage_type, "lance.blob.v2")
+
+        def __arrow_ext_serialize__(self) -> bytes:
+            return b""
+
+        @classmethod
+        def __arrow_ext_deserialize__(
+            cls, storage_type: pa.DataType, serialized: bytes
+        ) -> "OtherBlobType":
+            return cls()
+
+    storage = lance.blob_array([b"hello"]).storage
+    source = pa.ExtensionArray.from_storage(OtherBlobType(), storage)
+    data = pa.table({"image": source})
+    target = pa.schema([lancedb.blob("image")])
+
+    with pytest.raises(pa.ArrowTypeError, match="different extension type"):
+        _cast_to_target_schema(data.to_reader(), target).read_all()
+
+
 def test_sanitize_data_stream():
     # Make sure we don't collect the whole stream when running sanitize_data
     schema = pa.schema({"a": pa.int32()})

python/src/expr.rs

@@ -130,6 +130,14 @@ impl PyExpr {
 
     // ── utilities ────────────────────────────────────────────────────────────
 
+    /// Return the referenced column name for a bare column expression.
+    fn column_name(&self) -> Option<String> {
+        match &self.0 {
+            DfExpr::Column(column) if column.relation.is_none() => Some(column.name.clone()),
+            _ => None,
+        }
+    }
+
     /// Render the expression as a SQL string (useful for debugging).
     fn to_sql(&self) -> PyResult<String> {
         lancedb::expr::expr_to_sql_string(&self.0).map_err(|e| PyValueError::new_err(e.to_string()))

python/src/index.rs

@@ -8,7 +8,7 @@ use lancedb::index::vector::{
 };
 use lancedb::index::{
     Index as LanceDbIndex,
-    scalar::{BTreeIndexBuilder, FmIndexBuilder, FtsIndexBuilder},
+    scalar::{BTreeIndexBuilder, DocumentGranularity, FmIndexBuilder, FtsIndexBuilder},
 };
 use pyo3::IntoPyObject;
 use pyo3::types::PyStringMethods;
@@ -60,7 +60,11 @@ pub fn extract_index_params(source: &Option<Bound<'_, PyAny>>) -> PyResult<Lance
                     .ngram_min_length(params.ngram_min_length)
                     .ngram_max_length(params.ngram_max_length)
                     .ngram_prefix_only(params.prefix_only)
-                    .custom_stop_words(params.custom_stop_words);
+                    .custom_stop_words(params.custom_stop_words)
+                    .document_granularity(
+                        DocumentGranularity::try_from(params.document_granularity.as_str())
+                            .map_err(|err| PyValueError::new_err(err.to_string()))?,
+                    );
                 if let Some(memory_limit) = params.memory_limit {
                     inner_opts = inner_opts.memory_limit_mb(memory_limit);
                 }
@@ -221,6 +225,7 @@ struct FtsParams {
     block_size: usize,
     memory_limit: Option<u64>,
     num_workers: Option<usize>,
+    document_granularity: String,
 }
 
 #[derive(FromPyObject)]
@@ -481,6 +486,7 @@ mod tests {
     block_size = 128
     memory_limit = 2048
     num_workers = 7
+    document_granularity = 'row'
 
 config = FTS()",
                 None,

python/src/query.rs

@@ -1,6 +1,7 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
+use std::collections::HashMap;
 use std::sync::Arc;
 use std::time::Duration;
 
@@ -16,8 +17,8 @@ use arrow::pyarrow::FromPyArrow;
 use arrow::pyarrow::IntoPyArrow;
 use arrow::pyarrow::ToPyArrow;
 use lancedb::index::scalar::{
-    BooleanQuery, BoostQuery, FtsQuery, FullTextSearchQuery, MatchQuery, MultiMatchQuery, Occur,
-    Operator, PhraseQuery,
+    BooleanQuery, BoostQuery, DocumentGranularity, FtsQuery, FullTextSearchQuery, MatchQuery,
+    MultiMatchQuery, Occur, Operator, PhraseQuery,
 };
 use lancedb::query::AnalyzePlanDistributedMetrics;
 use lancedb::query::QueryBase;
@@ -76,8 +77,16 @@ impl<'a, 'py> FromPyObject<'a, 'py> for PyLanceDB<FtsQuery> {
                 let max_expansions = ob.getattr("max_expansions")?.extract()?;
                 let operator = ob.getattr("operator")?.extract::<String>()?;
                 let prefix_length = ob.getattr("prefix_length")?.extract()?;
+                let document_granularity = ob
+                    .getattr("document_granularity")?
+                    .extract::<Option<String>>()?
+                    .map(|value| {
+                        DocumentGranularity::try_from(value.as_str())
+                            .map_err(|err| PyValueError::new_err(err.to_string()))
+                    })
+                    .transpose()?;
 
-                Ok(Self(
+                let mut query =
                     MatchQuery::new(query)
                         .with_column(Some(column))
                         .with_boost(boost)
@@ -86,21 +95,32 @@ impl<'a, 'py> FromPyObject<'a, 'py> for PyLanceDB<FtsQuery> {
                         .with_operator(Operator::try_from(operator.as_str()).map_err(|e| {
                             PyValueError::new_err(format!("Invalid operator: {}", e))
                         })?)
-                        .with_prefix_length(prefix_length)
-                        .into(),
-                ))
+                        .with_prefix_length(prefix_length);
+                if let Some(document_granularity) = document_granularity {
+                    query = query.with_document_granularity(document_granularity);
+                }
+                Ok(Self(query.into()))
             }
             "PhraseQuery" => {
                 let query = ob.getattr("query")?.extract()?;
                 let column = ob.getattr("column")?.extract()?;
                 let slop = ob.getattr("slop")?.extract()?;
+                let document_granularity = ob
+                    .getattr("document_granularity")?
+                    .extract::<Option<String>>()?
+                    .map(|value| {
+                        DocumentGranularity::try_from(value.as_str())
+                            .map_err(|err| PyValueError::new_err(err.to_string()))
+                    })
+                    .transpose()?;
 
-                Ok(Self(
-                    PhraseQuery::new(query)
-                        .with_column(Some(column))
-                        .with_slop(slop)
-                        .into(),
-                ))
+                let mut query = PhraseQuery::new(query)
+                    .with_column(Some(column))
+                    .with_slop(slop);
+                if let Some(document_granularity) = document_granularity {
+                    query = query.with_document_granularity(document_granularity);
+                }
+                Ok(Self(query.into()))
             }
             "BoostQuery" => {
                 let positive: Self = ob.getattr("positive")?.extract()?;
@@ -167,6 +187,13 @@ impl<'py> IntoPyObject<'py> for PyLanceDB<FtsQuery> {
                 kwargs.set_item("max_expansions", query.max_expansions)?;
                 kwargs.set_item::<_, &str>("operator", query.operator.into())?;
                 kwargs.set_item("prefix_length", query.prefix_length)?;
+                if let Some(document_granularity) = query.document_granularity {
+                    let value = match document_granularity {
+                        DocumentGranularity::Row => "row",
+                        DocumentGranularity::ListElement => "list_element",
+                    };
+                    kwargs.set_item("document_granularity", value)?;
+                }
                 namespace
                     .getattr(intern!(py, "MatchQuery"))?
                     .call((query.terms, query.column.unwrap()), Some(&kwargs))
@@ -174,6 +201,13 @@ impl<'py> IntoPyObject<'py> for PyLanceDB<FtsQuery> {
             FtsQuery::Phrase(query) => {
                 let kwargs = PyDict::new(py);
                 kwargs.set_item("slop", query.slop)?;
+                if let Some(document_granularity) = query.document_granularity {
+                    let value = match document_granularity {
+                        DocumentGranularity::Row => "row",
+                        DocumentGranularity::ListElement => "list_element",
+                    };
+                    kwargs.set_item("document_granularity", value)?;
+                }
                 namespace
                     .getattr(intern!(py, "PhraseQuery"))?
                     .call((query.terms, query.column.unwrap()), Some(&kwargs))
@@ -292,6 +326,7 @@ pub struct PyQueryRequest {
     pub filter: Option<PyQueryFilter>,
     pub full_text_search: Option<PyLanceDB<FtsQuery>>,
     pub select: PySelect,
+    pub select_source_columns: Option<HashMap<String, String>>,
     pub fast_search: Option<bool>,
     pub with_row_id: Option<bool>,
     pub use_lsm: Option<bool>,
@@ -322,6 +357,7 @@ impl From<AnyQuery> for PyQueryRequest {
                 full_text_search: query_request
                     .full_text_search
                     .map(|fts| PyLanceDB(fts.query)),
+                select_source_columns: PySelect::source_columns(&query_request.select),
                 select: PySelect(query_request.select),
                 fast_search: Some(query_request.fast_search),
                 with_row_id: Some(query_request.with_row_id),
@@ -347,6 +383,7 @@ impl From<AnyQuery> for PyQueryRequest {
                 offset: vector_query.base.offset,
                 filter: vector_query.base.filter.map(PyQueryFilter),
                 full_text_search: None,
+                select_source_columns: PySelect::source_columns(&vector_query.base.select),
                 select: PySelect(vector_query.base.select),
                 fast_search: Some(vector_query.base.fast_search),
                 with_row_id: Some(vector_query.base.with_row_id),
@@ -379,6 +416,25 @@ impl From<AnyQuery> for PyQueryRequest {
 #[derive(Clone)]
 pub struct PySelect(Select);
 
+impl PySelect {
+    fn source_columns(select: &Select) -> Option<HashMap<String, String>> {
+        match select {
+            Select::Expr(pairs) => Some(
+                pairs
+                    .iter()
+                    .filter_map(|(output, expr)| match expr {
+                        lancedb::expr::DfExpr::Column(column) if column.relation.is_none() => {
+                            Some((output.clone(), column.name.clone()))
+                        }
+                        _ => None,
+                    })
+                    .collect(),
+            ),
+            _ => None,
+        }
+    }
+}
+
 impl<'py> IntoPyObject<'py> for PySelect {
     type Target = PyAny;
     type Output = Bound<'py, Self::Target>;

python/src/table.rs

@@ -780,15 +780,19 @@ impl Table {
         })
     }
 
-    #[pyo3(signature = (data, mode, progress=None, write_parallelism=None))]
+    #[pyo3(signature = (data, mode, progress=None, write_parallelism=None, allow_external_blob_outside_bases=false))]
     pub fn add<'a>(
         self_: PyRef<'a, Self>,
         data: PyScannable,
         mode: String,
         progress: Option<Py<PyAny>>,
         write_parallelism: Option<usize>,
+        allow_external_blob_outside_bases: bool,
     ) -> PyResult<Bound<'a, PyAny>> {
-        let mut op = self_.inner_ref()?.add(data);
+        let mut op = self_
+            .inner_ref()?
+            .add(data)
+            .allow_external_blob_outside_bases(allow_external_blob_outside_bases);
         if mode == "append" {
             op = op.mode(AddDataMode::Append);
         } else if mode == "overwrite" {

rust/lancedb/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb"
-version = "0.38.0-beta.6"
+version = "0.38.0-beta.12"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true