build(python): Add project.dynamic = ["version"] to pyproject.toml

2025-12-24 13:59:58 +00:00 · 2024-12-24 22:27:54 -08:00
444 changed files with 8745 additions and 42079 deletions
--- a/.bumpversion.toml
+++ b/.bumpversion.toml
@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.21.2-beta.0"
+current_version = "0.14.1"
 parse = """(?x)
    (?P<major>0|[1-9]\\d*)\\.
    (?P<minor>0|[1-9]\\d*)\\.
@@ -87,11 +87,26 @@ glob = "node/package.json"
 replace = "\"@lancedb/vectordb-linux-x64-gnu\": \"{new_version}\""
 search = "\"@lancedb/vectordb-linux-x64-gnu\": \"{current_version}\""

+[[tool.bumpversion.files]]
+glob = "node/package.json"
+replace = "\"@lancedb/vectordb-linux-arm64-musl\": \"{new_version}\""
+search = "\"@lancedb/vectordb-linux-arm64-musl\": \"{current_version}\""
+
+[[tool.bumpversion.files]]
+glob = "node/package.json"
+replace = "\"@lancedb/vectordb-linux-x64-musl\": \"{new_version}\""
+search = "\"@lancedb/vectordb-linux-x64-musl\": \"{current_version}\""
+
 [[tool.bumpversion.files]]
 glob = "node/package.json"
 replace = "\"@lancedb/vectordb-win32-x64-msvc\": \"{new_version}\""
 search = "\"@lancedb/vectordb-win32-x64-msvc\": \"{current_version}\""

+[[tool.bumpversion.files]]
+glob = "node/package.json"
+replace = "\"@lancedb/vectordb-win32-arm64-msvc\": \"{new_version}\""
+search = "\"@lancedb/vectordb-win32-arm64-msvc\": \"{current_version}\""
+
 # Cargo files
 # ------------
 [[tool.bumpversion.files]]
--- a/.cargo/config.toml
+++ b/.cargo/config.toml
@@ -34,10 +34,6 @@ rustflags = ["-C", "target-cpu=haswell", "-C", "target-feature=+avx2,+fma,+f16c"
 [target.x86_64-unknown-linux-musl]
 rustflags = ["-C", "target-cpu=haswell", "-C", "target-feature=-crt-static,+avx2,+fma,+f16c"]

-[target.aarch64-unknown-linux-musl]
-linker = "aarch64-linux-musl-gcc"
-rustflags = ["-C", "target-feature=-crt-static"]
-
 [target.aarch64-apple-darwin]
 rustflags = ["-C", "target-cpu=apple-m1", "-C", "target-feature=+neon,+fp16,+fhm,+dotprod"]

@@ -48,4 +44,4 @@ rustflags = ["-Ctarget-feature=+crt-static"]

 # Experimental target for Arm64 Windows
 [target.aarch64-pc-windows-msvc]
-rustflags = ["-Ctarget-feature=+crt-static"]
+rustflags = ["-Ctarget-feature=+crt-static"]
--- a/.github/workflows/build_linux_wheel/action.yml
+++ b/.github/workflows/build_linux_wheel/action.yml
@@ -36,7 +36,8 @@ runs:
        args: ${{ inputs.args }}
        before-script-linux: |
          set -e
-          curl -L https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-$(uname -m).zip > /tmp/protoc.zip \
+          yum install -y openssl-devel \
+            && curl -L https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-$(uname -m).zip > /tmp/protoc.zip \
            && unzip /tmp/protoc.zip -d /usr/local \
            && rm /tmp/protoc.zip
    - name: Build Arm Manylinux Wheel
@@ -51,7 +52,12 @@ runs:
        args: ${{ inputs.args }}
        before-script-linux: |
          set -e
-          yum install -y clang \
-            && curl -L https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-aarch_64.zip > /tmp/protoc.zip \
+          apt install -y unzip
+          if [ $(uname -m) = "x86_64" ]; then
+            PROTOC_ARCH="x86_64"
+          else
+            PROTOC_ARCH="aarch_64"
+          fi
+          curl -L https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-$PROTOC_ARCH.zip > /tmp/protoc.zip \
            && unzip /tmp/protoc.zip -d /usr/local \
            && rm /tmp/protoc.zip
--- a/.github/workflows/build_mac_wheel/action.yml
+++ b/.github/workflows/build_mac_wheel/action.yml
@@ -20,7 +20,7 @@ runs:
      uses: PyO3/maturin-action@v1
      with:
        command: build
-        # TODO: pass through interpreter
        args: ${{ inputs.args }}
        docker-options: "-e PIP_EXTRA_INDEX_URL=https://pypi.fury.io/lancedb/"
        working-directory: python
+        interpreter: 3.${{ inputs.python-minor-version }}
--- a/.github/workflows/build_windows_wheel/action.yml
+++ b/.github/workflows/build_windows_wheel/action.yml
@@ -28,7 +28,7 @@ runs:
        args: ${{ inputs.args }}
        docker-options: "-e PIP_EXTRA_INDEX_URL=https://pypi.fury.io/lancedb/"
        working-directory: python
-    - uses: actions/upload-artifact@v4
+    - uses: actions/upload-artifact@v3
      with:
        name: windows-wheels
        path: python\target\wheels
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -18,24 +18,17 @@ concurrency:
  group: "pages"
  cancel-in-progress: true

-env:
-  # This reduces the disk space needed for the build
-  RUSTFLAGS: "-C debuginfo=0"
-  # according to: https://matklad.github.io/2021/09/04/fast-rust-builds.html
-  # CI builds are faster with incremental disabled.
-  CARGO_INCREMENTAL: "0"
-
 jobs:
  # Single deploy job since we're just deploying
  build:
    environment:
      name: github-pages
      url: ${{ steps.deployment.outputs.page_url }}
-    runs-on: ubuntu-24.04
+    runs-on: buildjet-8vcpu-ubuntu-2204
    steps:
      - name: Checkout
        uses: actions/checkout@v4
-      - name: Install dependencies needed for ubuntu
+      - name: Install dependecies needed for ubuntu
        run: |
          sudo apt install -y protobuf-compiler libssl-dev
          rustup update && rustup default
@@ -45,7 +38,6 @@ jobs:
          python-version: "3.10"
          cache: "pip"
          cache-dependency-path: "docs/requirements.txt"
-      - uses: Swatinem/rust-cache@v2
      - name: Build Python
        working-directory: python
        run: |
@@ -57,6 +49,7 @@ jobs:
          node-version: 20
          cache: 'npm'
          cache-dependency-path: node/package-lock.json
+      - uses: Swatinem/rust-cache@v2
      - name: Install node dependencies
        working-directory: node
        run: |
--- a/.github/workflows/java-publish.yml
+++ b/.github/workflows/java-publish.yml
@@ -43,7 +43,7 @@ jobs:
      - uses: Swatinem/rust-cache@v2
      - uses: actions-rust-lang/setup-rust-toolchain@v1
        with:
-          toolchain: "1.81.0"
+          toolchain: "1.79.0"
          cache-workspaces: "./java/core/lancedb-jni"
          # Disable full debug symbol generation to speed up CI build and keep memory down
          # "1" means line tables only, which is useful for panic tracebacks.
@@ -97,7 +97,7 @@ jobs:
      - name: Dry run
        if: github.event_name == 'pull_request'
        run: |
-          mvn --batch-mode -DskipTests -Drust.release.build=true package
+          mvn --batch-mode -DskipTests package
      - name: Set github
        run: |
          git config --global user.email "LanceDB Github Runner"
@@ -108,7 +108,7 @@ jobs:
          echo "use-agent" >> ~/.gnupg/gpg.conf
          echo "pinentry-mode loopback" >> ~/.gnupg/gpg.conf
          export GPG_TTY=$(tty)
-          mvn --batch-mode -DskipTests -Drust.release.build=true -DpushChanges=false -Dgpg.passphrase=${{ secrets.GPG_PASSPHRASE }} deploy -P deploy-to-ossrh
+          mvn --batch-mode -DskipTests -DpushChanges=false -Dgpg.passphrase=${{ secrets.GPG_PASSPHRASE }} deploy -P deploy-to-ossrh
        env:
          SONATYPE_USER: ${{ secrets.SONATYPE_USER }}
          SONATYPE_TOKEN: ${{ secrets.SONATYPE_TOKEN }}
--- a/.github/workflows/java.yml
+++ b/.github/workflows/java.yml
@@ -35,9 +35,6 @@ jobs:
      - uses: Swatinem/rust-cache@v2
        with:
          workspaces: java/core/lancedb-jni
-      - uses: actions-rust-lang/setup-rust-toolchain@v1
-        with:
-          components: rustfmt
      - name: Run cargo fmt
        run: cargo fmt --check
        working-directory: ./java/core/lancedb-jni
@@ -71,9 +68,6 @@ jobs:
      - uses: Swatinem/rust-cache@v2
        with:
          workspaces: java/core/lancedb-jni
-      - uses: actions-rust-lang/setup-rust-toolchain@v1
-        with:
-          components: rustfmt
      - name: Run cargo fmt
        run: cargo fmt --check
        working-directory: ./java/core/lancedb-jni
@@ -116,3 +110,4 @@ jobs:
          -Djdk.reflect.useDirectMethodHandle=false \
          -Dio.netty.tryReflectionSetAccessible=true"
          JAVA_HOME=$JAVA_17 mvn clean test
+  
--- a/.github/workflows/license-header-check.yml
+++ b/.github/workflows/license-header-check.yml
@@ -1,31 +0,0 @@
-name: Check license headers
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-    paths:
-      - rust/**
-      - python/**
-      - nodejs/**
-      - java/**
-      - .github/workflows/license-header-check.yml
-jobs:
-  check-licenses:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Check out code
-        uses: actions/checkout@v4
-      - name: Install license-header-checker
-        working-directory: /tmp
-        run: |
-            curl -s https://raw.githubusercontent.com/lluissm/license-header-checker/master/install.sh | bash
-            mv /tmp/bin/license-header-checker /usr/local/bin/
-      - name: Check license headers (rust)
-        run: license-header-checker -a -v ./rust/license_header.txt ./ rs && [[ -z `git status -s` ]]
-      - name: Check license headers (python)
-        run: license-header-checker -a -v ./python/license_header.txt python py && [[ -z `git status -s` ]]
-      - name: Check license headers (typescript)
-        run: license-header-checker -a -v ./nodejs/license_header.txt nodejs ts && [[ -z `git status -s` ]]
-      - name: Check license headers (java)
-        run: license-header-checker -a -v ./nodejs/license_header.txt java java && [[ -z `git status -s` ]]
--- a/.github/workflows/make-release-commit.yml
+++ b/.github/workflows/make-release-commit.yml
@@ -43,7 +43,7 @@ on:
 jobs:
  make-release:
    # Creates tag and GH release. The GH release will trigger the build and release jobs.
-    runs-on: ubuntu-24.04
+    runs-on: ubuntu-latest
    permissions:
      contents: write
    steps:
@@ -57,14 +57,15 @@ jobs:
          # trigger any workflows watching for new tags. See:
          # https://docs.github.com/en/actions/using-workflows/triggering-a-workflow#triggering-a-workflow-from-a-workflow
          token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
-      - name: Validate Lance dependency is at stable version
-        if: ${{ inputs.type == 'stable' }}
-        run: python ci/validate_stable_lance.py
      - name: Set git configs for bumpversion
        shell: bash
        run: |
          git config user.name 'Lance Release'
          git config user.email 'lance-dev@lancedb.com'
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
      - name: Bump Python version
        if: ${{ inputs.python }}
        working-directory: python
@@ -84,7 +85,6 @@ jobs:
        run: |
          pip install bump-my-version PyGithub packaging
          bash ci/bump_version.sh ${{ inputs.type }} ${{ inputs.bump-minor }} v $COMMIT_BEFORE_BUMP
-          bash ci/update_lockfiles.sh --amend
      - name: Push new version tag
        if: ${{ !inputs.dry_run }}
        uses: ad-m/github-push-action@master
@@ -93,3 +93,11 @@ jobs:
          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
          branch: ${{ github.ref }}
          tags: true
+      - uses: ./.github/workflows/update_package_lock
+        if: ${{ !inputs.dry_run && inputs.other }}
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+      - uses: ./.github/workflows/update_package_lock_nodejs
+        if: ${{ !inputs.dry_run && inputs.other }}
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
--- a/.github/workflows/nodejs.yml
+++ b/.github/workflows/nodejs.yml
@@ -47,9 +47,6 @@ jobs:
      run: |
        sudo apt update
        sudo apt install -y protobuf-compiler libssl-dev
-    - uses: actions-rust-lang/setup-rust-toolchain@v1
-      with:
-        components: rustfmt, clippy
    - name: Lint
      run: |
        cargo fmt --all -- --check
@@ -109,18 +106,6 @@ jobs:
        python ci/mock_openai.py &
        cd nodejs/examples
        npm test
-    - name: Check docs
-      run: |
-        # We run this as part of the job because the binary needs to be built
-        # first to export the types of the native code.
-        set -e
-        npm ci
-        npm run docs
-        if ! git diff --exit-code -- . ':(exclude)Cargo.lock'; then
-          echo "Docs need to be updated"
-          echo "Run 'npm run docs', fix any warnings, and commit the changes."
-          exit 1
-        fi
  macos:
    timeout-minutes: 30
    runs-on: "macos-14"
--- a/.github/workflows/npm-publish.yml
+++ b/.github/workflows/npm-publish.yml
--- a/.github/workflows/pypi-publish.yml
+++ b/.github/workflows/pypi-publish.yml
@@ -4,11 +4,6 @@ on:
  push:
    tags:
      - 'python-v*'
-  pull_request:
-    # This should trigger a dry run (we skip the final publish step)
-    paths:
-      - .github/workflows/pypi-publish.yml
-      - Cargo.toml # Change in dependency frequently breaks builds

 jobs:
  linux:
@@ -20,21 +15,15 @@ jobs:
          - platform: x86_64
            manylinux: "2_17"
            extra_args: ""
-            runner: ubuntu-22.04
          - platform: x86_64
            manylinux: "2_28"
            extra_args: "--features fp16kernels"
-            runner: ubuntu-22.04
          - platform: aarch64
-            manylinux: "2_17"
+            manylinux: "2_24"
            extra_args: ""
-            # For successful fat LTO builds, we need a large runner to avoid OOM errors.
-            runner: ubuntu-2404-8x-arm64
-          - platform: aarch64
-            manylinux: "2_28"
-            extra_args: "--features fp16kernels"
-            runner: ubuntu-2404-8x-arm64
-    runs-on: ${{ matrix.config.runner }}
+          # We don't build fp16 kernels for aarch64, because it uses 
+          # cross compilation image, which doesn't have a new enough compiler.
+    runs-on: "ubuntu-22.04"
    steps:
      - uses: actions/checkout@v4
        with:
@@ -51,7 +40,6 @@ jobs:
          arm-build: ${{ matrix.config.platform == 'aarch64' }}
          manylinux: ${{ matrix.config.manylinux }}
      - uses: ./.github/workflows/upload_wheel
-        if: startsWith(github.ref, 'refs/tags/python-v')
        with:
          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
          fury_token: ${{ secrets.FURY_TOKEN }}
@@ -81,7 +69,6 @@ jobs:
          python-minor-version: 8
          args: "--release --strip --target ${{ matrix.config.target }} --features fp16kernels"
      - uses: ./.github/workflows/upload_wheel
-        if: startsWith(github.ref, 'refs/tags/python-v')
        with:
          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
          fury_token: ${{ secrets.FURY_TOKEN }}
@@ -103,12 +90,10 @@ jobs:
          args: "--release --strip"
          vcpkg_token: ${{ secrets.VCPKG_GITHUB_PACKAGES }}
      - uses: ./.github/workflows/upload_wheel
-        if: startsWith(github.ref, 'refs/tags/python-v')
        with:
          pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
          fury_token: ${{ secrets.FURY_TOKEN }}
  gh-release:
-    if: startsWith(github.ref, 'refs/tags/python-v')
    runs-on: ubuntu-latest
    permissions:
      contents: write
--- a/.github/workflows/python.yml
+++ b/.github/workflows/python.yml
@@ -13,11 +13,6 @@ concurrency:
  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
  cancel-in-progress: true

-env:
-  # Color output for pytest is off by default.
-  PYTEST_ADDOPTS: "--color=yes"
-  FORCE_COLOR: "1"
-
 jobs:
  lint:
    name: "Lint"
@@ -35,17 +30,16 @@ jobs:
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
-          python-version: "3.12"
+          python-version: "3.11"
      - name: Install ruff
        run: |
-          pip install ruff==0.9.9
+          pip install ruff==0.5.4
      - name: Format check
        run: ruff format --check .
      - name: Lint
        run: ruff check .
-
-  type-check:
-    name: "Type Check"
+  doctest:
+    name: "Doctest"
    timeout-minutes: 30
    runs-on: "ubuntu-22.04"
    defaults:
@@ -60,36 +54,7 @@ jobs:
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
-          python-version: "3.12"
-      - name: Install protobuf compiler
-        run: |
-          sudo apt update
-          sudo apt install -y protobuf-compiler
-          pip install toml
-      - name: Install dependencies
-        run: |
-          python ../ci/parse_requirements.py pyproject.toml --extras dev,tests,embeddings > requirements.txt
-          pip install -r requirements.txt
-      - name: Run pyright
-        run: pyright
-
-  doctest:
-    name: "Doctest"
-    timeout-minutes: 30
-    runs-on: "ubuntu-24.04"
-    defaults:
-      run:
-        shell: bash
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
+          python-version: "3.11"
          cache: "pip"
      - name: Install protobuf
        run: |
@@ -110,8 +75,8 @@ jobs:
    timeout-minutes: 30
    strategy:
      matrix:
-        python-minor-version: ["9", "12"]
-    runs-on: "ubuntu-24.04"
+        python-minor-version: ["9", "11"]
+    runs-on: "ubuntu-22.04"
    defaults:
      run:
        shell: bash
@@ -136,10 +101,6 @@ jobs:
      - uses: ./.github/workflows/run_tests
        with:
          integration: true
-      - name: Test without pylance or pandas
-        run: |
-          pip uninstall -y pylance pandas
-          pytest -vv python/tests/test_table.py
      # Make sure wheels are not included in the Rust cache
      - name: Delete wheels
        run: rm -rf target/wheels
@@ -166,7 +127,7 @@ jobs:
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
-          python-version: "3.12"
+          python-version: "3.11"
      - uses: Swatinem/rust-cache@v2
        with:
          workspaces: python
@@ -196,7 +157,7 @@ jobs:
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
-          python-version: "3.12"
+          python-version: "3.11"
      - uses: Swatinem/rust-cache@v2
        with:
          workspaces: python
@@ -207,7 +168,7 @@ jobs:
        run: rm -rf target/wheels
  pydantic1x:
    timeout-minutes: 30
-    runs-on: "ubuntu-24.04"
+    runs-on: "ubuntu-22.04"
    defaults:
      run:
        shell: bash
@@ -228,7 +189,6 @@ jobs:
      - name: Install lancedb
        run: |
          pip install "pydantic<2"
-          pip install pyarrow==16
          pip install --extra-index-url https://pypi.fury.io/lancedb/ -e .[tests]
          pip install tantivy
      - name: Run tests
--- a/.github/workflows/run_tests/action.yml
+++ b/.github/workflows/run_tests/action.yml
@@ -24,8 +24,8 @@ runs:
    - name: pytest (with integration)
      shell: bash
      if: ${{ inputs.integration == 'true' }}
-      run: pytest -m "not slow" -vv --durations=30 python/python/tests
+      run: pytest -m "not slow" -x -v --durations=30 python/python/tests
    - name: pytest (no integration tests)
      shell: bash
      if: ${{ inputs.integration != 'true' }}
-      run: pytest -m "not slow and not s3_test" -vv --durations=30 python/python/tests
+      run: pytest -m "not slow and not s3_test" -x -v --durations=30 python/python/tests
--- a/.github/workflows/rust.yml
+++ b/.github/workflows/rust.yml
@@ -22,7 +22,6 @@ env:
  # "1" means line tables only, which is useful for panic tracebacks.
  RUSTFLAGS: "-C debuginfo=1"
  RUST_BACKTRACE: "1"
-  CARGO_INCREMENTAL: 0

 jobs:
  lint:
@@ -40,9 +39,6 @@ jobs:
        with:
          fetch-depth: 0
          lfs: true
-      - uses: actions-rust-lang/setup-rust-toolchain@v1
-        with:
-          components: rustfmt, clippy
      - uses: Swatinem/rust-cache@v2
        with:
          workspaces: rust
@@ -55,33 +51,6 @@ jobs:
      - name: Run clippy
        run: cargo clippy --workspace --tests --all-features -- -D warnings

-  build-no-lock:
-    runs-on: ubuntu-24.04
-    timeout-minutes: 30
-    env:
-      # Need up-to-date compilers for kernels
-      CC: clang
-      CXX: clang++
-    steps:
-      - uses: actions/checkout@v4
-      # Building without a lock file often requires the latest Rust version since downstream
-      # dependencies may have updated their minimum Rust version.
-      - uses: actions-rust-lang/setup-rust-toolchain@v1
-        with:
-          toolchain: "stable"
-      # Remove cargo.lock to force a fresh build
-      - name: Remove Cargo.lock
-        run: rm -f Cargo.lock
-      - uses: rui314/setup-mold@v1
-      - uses: Swatinem/rust-cache@v2
-      - name: Install dependencies
-        run: |
-          sudo apt update
-          sudo apt install -y protobuf-compiler libssl-dev
-      - name: Build all
-        run: |
-          cargo build --benches --all-features --tests
-
  linux:
    timeout-minutes: 30
    # To build all features, we need more disk space than is available
@@ -106,11 +75,8 @@ jobs:
          workspaces: rust
      - name: Install dependencies
        run: |
-          # This shaves 2 minutes off this step in CI. This doesn't seem to be
-          # necessary in standard runners, but it is in the 4x runners.
-          sudo rm /var/lib/man-db/auto-update
+          sudo apt update
          sudo apt install -y protobuf-compiler libssl-dev
-      - uses: rui314/setup-mold@v1
      - name: Make Swap
        run: |
          sudo fallocate -l 16G /swapfile
@@ -121,11 +87,11 @@ jobs:
        working-directory: .
        run: docker compose up --detach --wait
      - name: Build
-        run: cargo build --all-features --tests --locked --examples
+        run: cargo build --all-features
      - name: Run tests
-        run: cargo test --all-features --locked
+        run: cargo test --all-features
      - name: Run examples
-        run: cargo run --example simple --locked
+        run: cargo run --example simple

  macos:
    timeout-minutes: 30
@@ -149,43 +115,129 @@ jobs:
          workspaces: rust
      - name: Install dependencies
        run: brew install protobuf
+      - name: Build
+        run: cargo build --all-features
      - name: Run tests
-        run: |
-          # Don't run the s3 integration tests since docker isn't available
-          # on this image.
-          ALL_FEATURES=`cargo metadata --format-version=1 --no-deps \
-            | jq -r '.packages[] | .features | keys | .[]' \
-            | grep -v s3-test | sort | uniq | paste -s -d "," -`
-          cargo test --features $ALL_FEATURES --locked
+        # Run with everything except the integration tests.
+        run: cargo test --features remote,fp16kernels

  windows:
    runs-on: windows-2022
-    strategy:
-      matrix:
-        target:
-          - x86_64-pc-windows-msvc
-          - aarch64-pc-windows-msvc
-    defaults:
-      run:
-        working-directory: rust/lancedb
    steps:
      - uses: actions/checkout@v4
      - uses: Swatinem/rust-cache@v2
        with:
          workspaces: rust
      - name: Install Protoc v21.12
-        run: choco install --no-progress protoc
-      - name: Build
+        working-directory: C:\
        run: |
-          rustup target add ${{ matrix.target }}
-          $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
-          cargo build --features remote --tests --locked --target ${{ matrix.target }}
+          New-Item -Path 'C:\protoc' -ItemType Directory
+          Set-Location C:\protoc
+          Invoke-WebRequest https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protoc-21.12-win64.zip -OutFile C:\protoc\protoc.zip
+          7z x protoc.zip
+          Add-Content $env:GITHUB_PATH "C:\protoc\bin"
+        shell: powershell
      - name: Run tests
-        # Can only run tests when target matches host
-        if: ${{ matrix.target == 'x86_64-pc-windows-msvc' }}
        run: |
          $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
-          cargo test --features remote --locked
+          cargo build
+          cargo test
+
+  windows-arm64:
+    runs-on: windows-4x-arm
+    steps:
+      - name: Install Git
+        run: |
+          Invoke-WebRequest -Uri "https://github.com/git-for-windows/git/releases/download/v2.44.0.windows.1/Git-2.44.0-64-bit.exe" -OutFile "git-installer.exe"
+          Start-Process -FilePath "git-installer.exe" -ArgumentList "/VERYSILENT", "/NORESTART" -Wait
+        shell: powershell
+      - name: Add Git to PATH
+        run: |
+          Add-Content $env:GITHUB_PATH "C:\Program Files\Git\bin"
+          $env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
+        shell: powershell
+      - name: Configure Git symlinks
+        run: git config --global core.symlinks true
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.13"
+      - name: Install Visual Studio Build Tools
+        run: |
+          Invoke-WebRequest -Uri "https://aka.ms/vs/17/release/vs_buildtools.exe" -OutFile "vs_buildtools.exe"
+          Start-Process -FilePath "vs_buildtools.exe" -ArgumentList "--quiet", "--wait", "--norestart", "--nocache", `
+            "--installPath", "C:\BuildTools", `
+            "--add", "Microsoft.VisualStudio.Component.VC.Tools.ARM64", `
+            "--add", "Microsoft.VisualStudio.Component.VC.Tools.x86.x64", `
+            "--add", "Microsoft.VisualStudio.Component.Windows11SDK.22621", `
+            "--add", "Microsoft.VisualStudio.Component.VC.ATL", `
+            "--add", "Microsoft.VisualStudio.Component.VC.ATLMFC", `
+            "--add", "Microsoft.VisualStudio.Component.VC.Llvm.Clang" -Wait
+        shell: powershell
+      - name: Add Visual Studio Build Tools to PATH
+        run: |
+          $vsPath = "C:\BuildTools\VC\Tools\MSVC"
+          $latestVersion = (Get-ChildItem $vsPath | Sort-Object {[version]$_.Name} -Descending)[0].Name
+          Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\MSVC\$latestVersion\bin\Hostx64\arm64"
+          Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\MSVC\$latestVersion\bin\Hostx64\x64"
+          Add-Content $env:GITHUB_PATH "C:\Program Files (x86)\Windows Kits\10\bin\10.0.22621.0\arm64"
+          Add-Content $env:GITHUB_PATH "C:\Program Files (x86)\Windows Kits\10\bin\10.0.22621.0\x64"
+          Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\Llvm\x64\bin"
+
+          # Add MSVC runtime libraries to LIB
+          $env:LIB = "C:\BuildTools\VC\Tools\MSVC\$latestVersion\lib\arm64;" +
+                     "C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\arm64;" +
+                     "C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\arm64"
+          Add-Content $env:GITHUB_ENV "LIB=$env:LIB"
+
+          # Add INCLUDE paths
+          $env:INCLUDE = "C:\BuildTools\VC\Tools\MSVC\$latestVersion\include;" +
+                        "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\ucrt;" +
+                        "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\um;" +
+                        "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22621.0\shared"
+          Add-Content $env:GITHUB_ENV "INCLUDE=$env:INCLUDE"
+        shell: powershell
+      - name: Install Rust
+        run: |
+          Invoke-WebRequest https://win.rustup.rs/x86_64 -OutFile rustup-init.exe
+          .\rustup-init.exe -y --default-host aarch64-pc-windows-msvc
+        shell: powershell
+      - name: Add Rust to PATH
+        run: |
+          Add-Content $env:GITHUB_PATH "$env:USERPROFILE\.cargo\bin"
+        shell: powershell
+      - uses: Swatinem/rust-cache@v2
+        with:
+          workspaces: rust
+      - name: Install 7-Zip ARM
+        run: |
+          New-Item -Path 'C:\7zip' -ItemType Directory
+          Invoke-WebRequest https://7-zip.org/a/7z2408-arm64.exe -OutFile C:\7zip\7z-installer.exe
+          Start-Process -FilePath C:\7zip\7z-installer.exe -ArgumentList '/S' -Wait
+        shell: powershell
+      - name: Add 7-Zip to PATH
+        run: Add-Content $env:GITHUB_PATH "C:\Program Files\7-Zip"
+        shell: powershell
+      - name: Install Protoc v21.12
+        working-directory: C:\
+        run: |
+          if (Test-Path 'C:\protoc') {
+            Write-Host "Protoc directory exists, skipping installation"
+            return
+          }
+          New-Item -Path 'C:\protoc' -ItemType Directory
+          Set-Location C:\protoc
+          Invoke-WebRequest https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protoc-21.12-win64.zip -OutFile C:\protoc\protoc.zip
+          & 'C:\Program Files\7-Zip\7z.exe' x protoc.zip
+        shell: powershell
+      - name: Add Protoc to PATH
+        run: Add-Content $env:GITHUB_PATH "C:\protoc\bin"
+        shell: powershell
+      - name: Run tests
+        run: |
+          $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
+          cargo build --target aarch64-pc-windows-msvc
+          cargo test --target aarch64-pc-windows-msvc

  msrv:
    # Check the minimum supported Rust version
--- a/.github/workflows/update_package_lock/action.yml
+++ b/.github/workflows/update_package_lock/action.yml
@@ -0,0 +1,33 @@
+name: update_package_lock
+description: "Update node's package.lock"
+
+inputs:
+  github_token:
+    required: true
+    description: "github token for the repo"
+
+runs:
+  using: "composite"
+  steps:
+    - uses: actions/setup-node@v3
+      with:
+        node-version: 20
+    - name: Set git configs
+      shell: bash
+      run: |
+        git config user.name 'Lance Release'
+        git config user.email 'lance-dev@lancedb.com'
+    - name: Update package-lock.json file
+      working-directory: ./node
+      run: |
+        npm install
+        git add package-lock.json
+        git commit -m "Updating package-lock.json"
+      shell: bash
+    - name: Push changes
+      if: ${{ inputs.dry_run }} == "false"
+      uses: ad-m/github-push-action@master
+      with:
+        github_token: ${{ inputs.github_token }}
+        branch: main
+        tags: true
--- a/.github/workflows/update_package_lock_nodejs/action.yml
+++ b/.github/workflows/update_package_lock_nodejs/action.yml
@@ -0,0 +1,33 @@
+name: update_package_lock_nodejs
+description: "Update nodejs's package.lock"
+
+inputs:
+  github_token:
+    required: true
+    description: "github token for the repo"
+
+runs:
+  using: "composite"
+  steps:
+    - uses: actions/setup-node@v3
+      with:
+        node-version: 20
+    - name: Set git configs
+      shell: bash
+      run: |
+        git config user.name 'Lance Release'
+        git config user.email 'lance-dev@lancedb.com'
+    - name: Update package-lock.json file
+      working-directory: ./nodejs
+      run: |
+        npm install
+        git add package-lock.json
+        git commit -m "Updating package-lock.json"
+      shell: bash
+    - name: Push changes
+      if: ${{ inputs.dry_run }} == "false"
+      uses: ad-m/github-push-action@master
+      with:
+        github_token: ${{ inputs.github_token }}
+        branch: main
+        tags: true
--- a/.gitignore
+++ b/.gitignore
@@ -9,6 +9,7 @@ venv
 .vscode
 .zed
 rust/target
+rust/Cargo.lock

 site

@@ -41,3 +42,5 @@ dist
 target

 **/sccache.log
+
+Cargo.lock
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,27 +1,21 @@
 repos:
-  - repo: https://github.com/pre-commit/pre-commit-hooks
+-   repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v3.2.0
    hooks:
-      - id: check-yaml
-      - id: end-of-file-fixer
-      - id: trailing-whitespace
-  - repo: https://github.com/astral-sh/ruff-pre-commit
+    -   id: check-yaml
+    -   id: end-of-file-fixer
+    -   id: trailing-whitespace
+-   repo: https://github.com/astral-sh/ruff-pre-commit
    # Ruff version.
-    rev: v0.9.9
+    rev: v0.2.2
    hooks:
-      - id: ruff
-  # - repo: https://github.com/RobertCraigie/pyright-python
-  #   rev: v1.1.395
-  #   hooks:
-  #     - id: pyright
-  #       args: ["--project", "python"]
-  #       additional_dependencies: [pyarrow-stubs]
-  - repo: local
-    hooks:
-      - id: local-biome-check
-        name: biome check
-        entry: npx @biomejs/biome@1.8.3 check --config-path nodejs/biome.json nodejs/
-        language: system
-        types: [text]
-        files: "nodejs/.*"
-        exclude: nodejs/lancedb/native.d.ts|nodejs/dist/.*|nodejs/examples/.*
+    - id: ruff
+- repo: local
+  hooks:
+    - id: local-biome-check
+      name: biome check
+      entry: npx @biomejs/biome@1.8.3 check --config-path nodejs/biome.json nodejs/
+      language: system
+      types: [text]
+      files: "nodejs/.*"
+      exclude: nodejs/lancedb/native.d.ts|nodejs/dist/.*|nodejs/examples/.*
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,78 +0,0 @@
-# Contributing to LanceDB
-
-LanceDB is an open-source project and we welcome contributions from the community.
-This document outlines the process for contributing to LanceDB.
-
-## Reporting Issues
-
-If you encounter a bug or have a feature request, please open an issue on the
-[GitHub issue tracker](https://github.com/lancedb/lancedb).
-
-## Picking an issue
-
-We track issues on the GitHub issue tracker. If you are looking for something to
-work on, check the [good first issue](https://github.com/lancedb/lancedb/contribute) label. These issues are typically the best described and have the smallest scope.
-
-If there's an issue you are interested in working on, please leave a comment on the issue. This will help us avoid duplicate work. Additionally, if you have questions about the issue, please ask them in the issue comments. We are happy to provide guidance on how to approach the issue.
-
-## Configuring Git
-
-First, fork the repository on GitHub, then clone your fork:
-
-```bash
-git clone https://github.com/<username>/lancedb.git
-cd lancedb
-```
-
-Then add the main repository as a remote:
-
-```bash
-git remote add upstream https://github.com/lancedb/lancedb.git
-git fetch upstream
-```
-
-## Setting up your development environment
-
-We have development environments for Python, Typescript, and Java. Each environment has its own setup instructions.
-
-* [Python](python/CONTRIBUTING.md)
-* [Typescript](nodejs/CONTRIBUTING.md)
-<!-- TODO: add Java contributing guide -->
-* [Documentation](docs/README.md)
-
-
-## Best practices for pull requests
-
-For the best chance of having your pull request accepted, please follow these guidelines:
-
-1. Unit test all bug fixes and new features. Your code will not be merged if it
-   doesn't have tests.
-1. If you change the public API, update the documentation in the `docs` directory.
-1. Aim to minimize the number of changes in each pull request. Keep to solving
-   one problem at a time, when possible.
-1. Before marking a pull request ready-for-review, do a self review of your code.
-   Is it clear why you are making the changes? Are the changes easy to understand?
-1. Use [conventional commit messages](https://www.conventionalcommits.org/en/) as pull request titles. Examples:
-    * New feature: `feat: adding foo API`
-    * Bug fix: `fix: issue with foo API`
-    * Documentation change: `docs: adding foo API documentation`
-1. If your pull request is a work in progress, leave the pull request as a draft.
-   We will assume the pull request is ready for review when it is opened.
-1. When writing tests, test the error cases. Make sure they have understandable
-   error messages.
-
-## Project structure
-
-The core library is written in Rust. The Python, Typescript, and Java libraries
-are wrappers around the Rust library.
-
-* `src/lancedb`: Rust library source code
-* `python`: Python package source code
-* `nodejs`: Typescript package source code
-* `node`: **Deprecated** Typescript package source code
-* `java`: Java package source code
-* `docs`: Documentation source code
-
-## Release process
-
-For information on the release process, see: [release_process.md](release_process.md)
--- a/Cargo.lock
+++ b/Cargo.lock
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -21,54 +21,41 @@ categories = ["database-implementations"]
 rust-version = "1.78.0"

 [workspace.dependencies]
-lance = { "version" = "=0.31.2", "features" = [
+lance = { "version" = "=0.21.0", "features" = [
    "dynamodb",
-], "tag" = "v0.31.2-beta.3", "git" = "https://github.com/lancedb/lance.git" }
-lance-io = { "version" = "=0.31.2", "tag" = "v0.31.2-beta.3", "git" = "https://github.com/lancedb/lance.git" }
-lance-index = { "version" = "=0.31.2", "tag" = "v0.31.2-beta.3", "git" = "https://github.com/lancedb/lance.git" }
-lance-linalg = { "version" = "=0.31.2", "tag" = "v0.31.2-beta.3", "git" = "https://github.com/lancedb/lance.git" }
-lance-table = { "version" = "=0.31.2", "tag" = "v0.31.2-beta.3", "git" = "https://github.com/lancedb/lance.git" }
-lance-testing = { "version" = "=0.31.2", "tag" = "v0.31.2-beta.3", "git" = "https://github.com/lancedb/lance.git" }
-lance-datafusion = { "version" = "=0.31.2", "tag" = "v0.31.2-beta.3", "git" = "https://github.com/lancedb/lance.git" }
-lance-encoding = { "version" = "=0.31.2", "tag" = "v0.31.2-beta.3", "git" = "https://github.com/lancedb/lance.git" }
+], git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.5" }
+lance-io = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.5" }
+lance-index = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.5" }
+lance-linalg = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.5" }
+lance-table = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.5" }
+lance-testing = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.5" }
+lance-datafusion = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.5" }
+lance-encoding = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.5" }
 # Note that this one does not include pyarrow
-arrow = { version = "55.1", optional = false }
-arrow-array = "55.1"
-arrow-data = "55.1"
-arrow-ipc = "55.1"
-arrow-ord = "55.1"
-arrow-schema = "55.1"
-arrow-arith = "55.1"
-arrow-cast = "55.1"
+arrow = { version = "53.2", optional = false }
+arrow-array = "53.2"
+arrow-data = "53.2"
+arrow-ipc = "53.2"
+arrow-ord = "53.2"
+arrow-schema = "53.2"
+arrow-arith = "53.2"
+arrow-cast = "53.2"
 async-trait = "0"
-datafusion = { version = "48.0", default-features = false }
-datafusion-catalog = "48.0"
-datafusion-common = { version = "48.0", default-features = false }
-datafusion-execution = "48.0"
-datafusion-expr = "48.0"
-datafusion-physical-plan = "48.0"
-env_logger = "0.11"
-half = { "version" = "2.6.0", default-features = false, features = [
+chrono = "0.4.35"
+datafusion-common = "42.0"
+datafusion-physical-plan = "42.0"
+env_logger = "0.10"
+half = { "version" = "=2.4.1", default-features = false, features = [
    "num-traits",
 ] }
 futures = "0"
 log = "0.4"
-moka = { version = "0.12", features = ["future"] }
-object_store = "0.12.0"
+moka = { version = "0.11", features = ["future"] }
+object_store = "0.10.2"
 pin-project = "1.0.7"
-snafu = "0.8"
+snafu = "0.7.4"
 url = "2"
 num-traits = "0.2"
-rand = "0.9"
+rand = "0.8"
 regex = "1.10"
 lazy_static = "1"
-semver = "1.0.25"
-# Temporary pins to work around downstream issues
-# https://github.com/apache/arrow-rs/commit/2fddf85afcd20110ce783ed5b4cdeb82293da30b
-chrono = "=0.4.41"
-# https://github.com/RustCrypto/formats/issues/1684
-base64ct = "=1.6.0"
-# Workaround for: https://github.com/eira-fransham/crunchy/issues/13
-crunchy = "=0.2.2"
-# Workaround for: https://github.com/Lokathor/bytemuck/issues/306
-bytemuck_derive = ">=1.8.1, <1.9.0"
--- a/README.md
+++ b/README.md
@@ -1,97 +1,86 @@
-<a href="https://cloud.lancedb.com" target="_blank">
-  <img src="https://github.com/user-attachments/assets/92dad0a2-2a37-4ce1-b783-0d1b4f30a00c" alt="LanceDB Cloud Public Beta" width="100%" style="max-width: 100%;">
-</a>
 <div align="center">
+<p align="center">

-[![LanceDB](docs/src/assets/hero-header.png)](https://lancedb.com)
-[![Website](https://img.shields.io/badge/-Website-100000?style=for-the-badge&labelColor=645cfb&color=645cfb)](https://lancedb.com/)
-[![Blog](https://img.shields.io/badge/Blog-100000?style=for-the-badge&labelColor=645cfb&color=645cfb)](https://blog.lancedb.com/)
-[![Discord](https://img.shields.io/badge/-Discord-100000?style=for-the-badge&logo=discord&logoColor=white&labelColor=645cfb&color=645cfb)](https://discord.gg/zMM32dvNtd)
-[![Twitter](https://img.shields.io/badge/-Twitter-100000?style=for-the-badge&logo=x&logoColor=white&labelColor=645cfb&color=645cfb)](https://twitter.com/lancedb)
-[![LinkedIn](https://img.shields.io/badge/-LinkedIn-100000?style=for-the-badge&logo=linkedin&logoColor=white&labelColor=645cfb&color=645cfb)](https://www.linkedin.com/company/lancedb/)
+<img width="275" alt="LanceDB Logo" src="https://github.com/lancedb/lancedb/assets/5846846/37d7c7ad-c2fd-4f56-9f16-fffb0d17c73a">

+**Developer-friendly, database for multimodal AI**

-<img src="docs/src/assets/lancedb.png" alt="LanceDB" width="50%">
+<a href='https://github.com/lancedb/vectordb-recipes/tree/main' target="_blank"><img alt='LanceDB' src='https://img.shields.io/badge/VectorDB_Recipes-100000?style=for-the-badge&logo=LanceDB&logoColor=white&labelColor=645cfb&color=645cfb'/></a>
+<a href='https://lancedb.github.io/lancedb/' target="_blank"><img alt='lancdb' src='https://img.shields.io/badge/DOCS-100000?style=for-the-badge&logo=lancdb&logoColor=white&labelColor=645cfb&color=645cfb'/></a>
+[![Blog](https://img.shields.io/badge/Blog-12100E?style=for-the-badge&logoColor=white)](https://blog.lancedb.com/)
+[![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/zMM32dvNtd)
+[![Twitter](https://img.shields.io/badge/Twitter-%231DA1F2.svg?style=for-the-badge&logo=Twitter&logoColor=white)](https://twitter.com/lancedb)
+[![Gurubase](https://img.shields.io/badge/Gurubase-Ask%20LanceDB%20Guru-006BFF?style=for-the-badge)](https://gurubase.io/g/lancedb)

-# **The Multimodal AI Lakehouse**
+</p>

-[**How to Install** ](#how-to-install) ✦ [**Detailed Documentation**](https://lancedb.github.io/lancedb/) ✦ [**Tutorials and Recipes**](https://github.com/lancedb/vectordb-recipes/tree/main) ✦  [**Contributors**](#contributors) 
-
-**The ultimate multimodal data platform for AI/ML applications.** 
-
-LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. 
-LanceDB is a central location where developers can build, train and analyze their AI workloads.
-
-</div>
-
-<br>
-
-## **Demo: Multimodal Search by Keyword, Vector or with SQL**
 <img max-width="750px" alt="LanceDB Multimodal Search" src="https://github.com/lancedb/lancedb/assets/917119/09c5afc5-7816-4687-bae4-f2ca194426ec">

-## **Star LanceDB to get updates!**
-
-<details>
-<summary>⭐ Click here ⭐  to see how fast we're growing!</summary>
-<picture>
-  <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=lancedb/lancedb&theme=dark&type=Date">
-  <img width="100%" src="https://api.star-history.com/svg?repos=lancedb/lancedb&theme=dark&type=Date">
-</picture>
-</details>
-
-## **Key Features**:
-
- **Fast Vector Search**: Search billions of vectors in milliseconds with state-of-the-art indexing.
- **Comprehensive Search**: Support for vector similarity search, full-text search and SQL.
- **Multimodal Support**: Store, query and filter vectors, metadata and multimodal data (text, images, videos, point clouds, and more).
- **Advanced Features**: Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index.
-
-### **Products**:
- **Open Source & Local**: 100% open source, runs locally or in your cloud. No vendor lock-in.
- **Cloud and Enterprise**: Production-scale vector search with no servers to manage. Complete data sovereignty and security.
-
-### **Ecosystem**:
- **Columnar Storage**: Built on the Lance columnar format for efficient storage and analytics.
- **Seamless Integration**: Python, Node.js, Rust, and REST APIs for easy integration. Native Python and Javascript/Typescript support.
- **Rich Ecosystem**: Integrations with [**LangChain** 🦜️🔗](https://python.langchain.com/docs/integrations/vectorstores/lancedb/), [**LlamaIndex** 🦙](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html), Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
-
-## **How to Install**:
-
-Follow the [Quickstart](https://lancedb.github.io/lancedb/basic/) doc to set up LanceDB locally. 
-
-**API & SDK:** We also support Python, Typescript and Rust SDKs
-
-| Interface | Documentation |
-|-----------|---------------|
-| Python SDK | https://lancedb.github.io/lancedb/python/python/ |
-| Typescript SDK | https://lancedb.github.io/lancedb/js/globals/ |
-| Rust SDK | https://docs.rs/lancedb/latest/lancedb/index.html |
-| REST API | https://docs.lancedb.com/api-reference/introduction |
-
-## **Join Us and Contribute**
-
-We welcome contributions from everyone! Whether you're a developer, researcher, or just someone who wants to help out. 
-
-If you have any suggestions or feature requests, please feel free to open an issue on GitHub or discuss it on our [**Discord**](https://discord.gg/G5DcmnZWKB) server.
-
-[**Check out the GitHub Issues**](https://github.com/lancedb/lancedb/issues) if you would like to work on the features that are planned for the future. If you have any suggestions or feature requests, please feel free to open an issue on GitHub. 
-
-## **Contributors**
-
-<a href="https://github.com/lancedb/lancedb/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=lancedb/lancedb" />
-</a>
-
-
-## **Stay in Touch With Us**
-<div align="center">
-
-</br>
-
-[![Website](https://img.shields.io/badge/-Website-100000?style=for-the-badge&labelColor=645cfb&color=645cfb)](https://lancedb.com/)
-[![Blog](https://img.shields.io/badge/Blog-100000?style=for-the-badge&labelColor=645cfb&color=645cfb)](https://blog.lancedb.com/)
-[![Discord](https://img.shields.io/badge/-Discord-100000?style=for-the-badge&logo=discord&logoColor=white&labelColor=645cfb&color=645cfb)](https://discord.gg/zMM32dvNtd)
-[![Twitter](https://img.shields.io/badge/-Twitter-100000?style=for-the-badge&logo=x&logoColor=white&labelColor=645cfb&color=645cfb)](https://twitter.com/lancedb)
-[![LinkedIn](https://img.shields.io/badge/-LinkedIn-100000?style=for-the-badge&logo=linkedin&logoColor=white&labelColor=645cfb&color=645cfb)](https://www.linkedin.com/company/lancedb/)
-
+</p>
 </div>
+
+<hr />
+
+LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrieval, filtering and management of embeddings.
+
+The key features of LanceDB include:
+
+* Production-scale vector search with no servers to manage.
+
+* Store, query and filter vectors, metadata and multi-modal data (text, images, videos, point clouds, and more).
+
+* Support for vector similarity search, full-text search and SQL.
+
+* Native Python and Javascript/Typescript support.
+
+* Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure.
+
+* GPU support in building vector index(*).
+
+* Ecosystem integrations with [LangChain 🦜️🔗](https://python.langchain.com/docs/integrations/vectorstores/lancedb/), [LlamaIndex 🦙](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html), Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
+
+LanceDB's core is written in Rust 🦀 and is built using <a href="https://github.com/lancedb/lance">Lance</a>, an open-source columnar format designed for performant ML workloads.
+
+## Quick Start
+
+**Javascript**
+```shell
+npm install @lancedb/lancedb
+```
+
+```javascript
+import * as lancedb from "@lancedb/lancedb";
+
+const db = await lancedb.connect("data/sample-lancedb");
+const table = await db.createTable("vectors", [
+	{ id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
+	{ id: 2, vector: [1.1, 1.2], item: "bar", price: 50 },
+], {mode: 'overwrite'});
+
+
+const query = table.vectorSearch([0.1, 0.3]).limit(2);
+const results = await query.toArray();
+
+// You can also search for rows by specific criteria without involving a vector search.
+const rowsByCriteria = await table.query().where("price >= 10").toArray();
+```
+
+**Python**
+```shell
+pip install lancedb
+```
+
+```python
+import lancedb
+
+uri = "data/sample-lancedb"
+db = lancedb.connect(uri)
+table = db.create_table("my_table",
+                         data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+                               {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
+result = table.search([100, 100]).limit(2).to_pandas()
+```
+
+## Blogs, Tutorials & Videos
+* 📈 <a href="https://blog.lancedb.com/benchmarking-random-access-in-lance/">2000x better performance with Lance over Parquet</a>
+* 🤖 <a href="https://github.com/lancedb/vectordb-recipes/tree/main/examples/Youtube-Search-QA-Bot">Build a question and answer bot with LanceDB</a>
--- a/ci/build_linux_artifacts_nodejs.sh
+++ b/ci/build_linux_artifacts_nodejs.sh
@@ -0,0 +1,21 @@
+#!/bin/bash
+set -e
+ARCH=${1:-x86_64}
+
+# We pass down the current user so that when we later mount the local files
+# into the container, the files are accessible by the current user.
+pushd ci/manylinux_node
+docker build \
+    -t lancedb-node-manylinux-$ARCH \
+    --build-arg="ARCH=$ARCH" \
+    --build-arg="DOCKER_USER=$(id -u)" \
+    --progress=plain \
+    .
+popd
+
+# We turn on memory swap to avoid OOM killer
+docker run \
+    -v $(pwd):/io -w /io \
+    --memory-swap=-1 \
+    lancedb-node-manylinux-$ARCH \
+    bash ci/manylinux_node/build_lancedb.sh $ARCH
--- a/ci/build_macos_artifacts_nodejs.sh
+++ b/ci/build_macos_artifacts_nodejs.sh
@@ -0,0 +1,34 @@
+# Builds the macOS artifacts (nodejs binaries).
+# Usage: ./ci/build_macos_artifacts_nodejs.sh [target]
+# Targets supported: x86_64-apple-darwin aarch64-apple-darwin
+set -e
+
+prebuild_rust() {
+    # Building here for the sake of easier debugging.
+    pushd rust/lancedb
+    echo "Building rust library for $1"
+    export RUST_BACKTRACE=1
+    cargo build --release --target $1
+    popd
+}
+
+build_node_binaries() {
+    pushd nodejs
+    echo "Building nodejs library for $1"
+    export RUST_TARGET=$1
+    npm run build-release
+    popd
+}
+
+if [ -n "$1" ]; then
+    targets=$1
+else
+    targets="x86_64-apple-darwin aarch64-apple-darwin"
+fi
+
+echo "Building artifacts for targets: $targets"
+for target in $targets
+    do
+    prebuild_rust $target
+    build_node_binaries $target
+done
--- a/ci/manylinux_node/Dockerfile
+++ b/ci/manylinux_node/Dockerfile
@@ -1,5 +1,5 @@
 # Many linux dockerfile with Rust, Node, and Lance dependencies installed.
-# This container allows building the node modules native libraries in an
+# This container allows building the node modules native libraries in an 
 # environment with a very old glibc, so that we are compatible with a wide
 # range of linux distributions.
 ARG ARCH=x86_64
@@ -9,6 +9,10 @@ FROM quay.io/pypa/manylinux_2_28_${ARCH}
 ARG ARCH=x86_64
 ARG DOCKER_USER=default_user

+# Install static openssl
+COPY install_openssl.sh install_openssl.sh
+RUN ./install_openssl.sh ${ARCH} > /dev/null
+
 # Protobuf is also installed as root.
 COPY install_protobuf.sh install_protobuf.sh
 RUN ./install_protobuf.sh ${ARCH}
@@ -17,7 +21,7 @@ ENV DOCKER_USER=${DOCKER_USER}
 # Create a group and user, but only if it doesn't exist
 RUN echo ${ARCH} && id -u ${DOCKER_USER} >/dev/null 2>&1 || adduser --user-group --create-home --uid ${DOCKER_USER} build_user

-# We switch to the user to install Rust and Node, since those like to be
+# We switch to the user to install Rust and Node, since those like to be 
 # installed at the user level.
 USER ${DOCKER_USER}

--- a/ci/manylinux_node/build_lancedb.sh
+++ b/ci/manylinux_node/build_lancedb.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+# Builds the nodejs module for manylinux. Invoked by ci/build_linux_artifacts_nodejs.sh.
+set -e
+ARCH=${1:-x86_64}
+
+if [ "$ARCH" = "x86_64" ]; then
+    export OPENSSL_LIB_DIR=/usr/local/lib64/
+else
+    export OPENSSL_LIB_DIR=/usr/local/lib/
+fi
+export OPENSSL_STATIC=1
+export OPENSSL_INCLUDE_DIR=/usr/local/include/openssl
+
+#Alpine doesn't have .bashrc
+FILE=$HOME/.bashrc && test -f $FILE && source $FILE
+
+cd nodejs
+npm ci
+npm run build-release
--- a/ci/manylinux_node/build_vectordb.sh
+++ b/ci/manylinux_node/build_vectordb.sh
@@ -4,6 +4,14 @@ set -e
 ARCH=${1:-x86_64}
 TARGET_TRIPLE=${2:-x86_64-unknown-linux-gnu}

+if [ "$ARCH" = "x86_64" ]; then
+    export OPENSSL_LIB_DIR=/usr/local/lib64/
+else
+    export OPENSSL_LIB_DIR=/usr/local/lib/
+fi
+export OPENSSL_STATIC=1
+export OPENSSL_INCLUDE_DIR=/usr/local/include/openssl
+
 #Alpine doesn't have .bashrc
 FILE=$HOME/.bashrc && test -f $FILE && source $FILE

--- a/ci/manylinux_node/install_openssl.sh
+++ b/ci/manylinux_node/install_openssl.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+# Builds openssl from source so we can statically link to it
+
+# this is to avoid the error we get with the system installation:
+# /usr/bin/ld: <library>: version node not found for symbol SSLeay@@OPENSSL_1.0.1
+# /usr/bin/ld: failed to set dynamic section sizes: Bad value
+set -e
+
+git clone -b OpenSSL_1_1_1v \
+    --single-branch \
+    https://github.com/openssl/openssl.git
+
+pushd openssl
+
+if [[ $1 == x86_64* ]]; then
+    ARCH=linux-x86_64
+else
+    # gnu target
+    ARCH=linux-aarch64
+fi
+
+./Configure no-shared $ARCH
+
+make
+
+make install
--- a/ci/parse_requirements.py
+++ b/ci/parse_requirements.py
@@ -1,41 +0,0 @@
-import argparse
-import toml
-
-
-def parse_dependencies(pyproject_path, extras=None):
-    with open(pyproject_path, "r") as file:
-        pyproject = toml.load(file)
-
-    dependencies = pyproject.get("project", {}).get("dependencies", [])
-    for dependency in dependencies:
-        print(dependency)
-
-    optional_dependencies = pyproject.get("project", {}).get(
-        "optional-dependencies", {}
-    )
-
-    if extras:
-        for extra in extras.split(","):
-            for dep in optional_dependencies.get(extra, []):
-                print(dep)
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Generate requirements.txt from pyproject.toml"
-    )
-    parser.add_argument("path", type=str, help="Path to pyproject.toml")
-    parser.add_argument(
-        "--extras",
-        type=str,
-        help="Comma-separated list of extras to include",
-        default="",
-    )
-
-    args = parser.parse_args()
-
-    parse_dependencies(args.path, args.extras)
-
-
-if __name__ == "__main__":
-    main()
--- a/ci/set_lance_version.py
+++ b/ci/set_lance_version.py
@@ -1,188 +0,0 @@
-import argparse
-import sys
-import json
-
-
-def run_command(command: str) -> str:
-    """
-    Run a shell command and return stdout as a string.
-    If exit code is not 0, raise an exception with the stderr output.
-    """
-    import subprocess
-
-    result = subprocess.run(command, shell=True, capture_output=True, text=True)
-    if result.returncode != 0:
-        raise Exception(f"Command failed with error: {result.stderr.strip()}")
-    return result.stdout.strip()
-
-
-def get_latest_stable_version() -> str:
-    version_line = run_command("cargo info lance | grep '^version:'")
-    version = version_line.split(" ")[1].strip()
-    return version
-
-
-def get_latest_preview_version() -> str:
-    lance_tags = run_command(
-        "git ls-remote --tags https://github.com/lancedb/lance.git | grep 'refs/tags/v[0-9beta.-]\\+$'"
-    ).splitlines()
-    lance_tags = (
-        tag.split("refs/tags/")[1]
-        for tag in lance_tags
-        if "refs/tags/" in tag and "beta" in tag
-    )
-    from packaging.version import Version
-
-    latest = max(
-        (tag[1:] for tag in lance_tags if tag.startswith("v")), key=lambda t: Version(t)
-    )
-    return str(latest)
-
-
-def extract_features(line: str) -> list:
-    """
-    Extracts the features from a line in Cargo.toml.
-    Example: 'lance = { "version" = "=0.29.0", "features" = ["dynamodb"] }'
-    Returns: ['dynamodb']
-    """
-    import re
-
-    match = re.search(r'"features"\s*=\s*\[\s*(.*?)\s*\]', line, re.DOTALL)
-    if match:
-        features_str = match.group(1)
-        return [f.strip('"') for f in features_str.split(",") if len(f) > 0]
-    return []
-
-
-def update_cargo_toml(line_updater):
-    """
-    Updates the Cargo.toml file by applying the line_updater function to each line.
-    The line_updater function should take a line as input and return the updated line.
-    """
-    with open("Cargo.toml", "r") as f:
-        lines = f.readlines()
-
-    new_lines = []
-    lance_line = ""
-    is_parsing_lance_line = False
-    for line in lines:
-        if line.startswith("lance"):
-            # Update the line using the provided function
-            if line.strip().endswith("}"):
-                new_lines.append(line_updater(line))
-            else:
-                lance_line = line
-                is_parsing_lance_line = True
-        elif is_parsing_lance_line:
-            lance_line += line
-            if line.strip().endswith("}"):
-                new_lines.append(line_updater(lance_line))
-                lance_line = ""
-                is_parsing_lance_line = False
-            else:
-                print("doesn't end with }:", line)
-        else:
-            # Keep the line unchanged
-            new_lines.append(line)
-
-    with open("Cargo.toml", "w") as f:
-        f.writelines(new_lines)
-
-
-def set_stable_version(version: str):
-    """
-    Sets lines to
-    lance = { "version" = "=0.29.0", "features" = ["dynamodb"] }
-    lance-io = "=0.29.0"
-    ...
-    """
-
-    def line_updater(line: str) -> str:
-        package_name = line.split("=", maxsplit=1)[0].strip()
-        features = extract_features(line)
-        if features:
-            return f'{package_name} = {{ "version" = "={version}", "features" = {json.dumps(features)} }}\n'
-        else:
-            return f'{package_name} = "={version}"\n'
-
-    update_cargo_toml(line_updater)
-
-
-def set_preview_version(version: str):
-    """
-    Sets lines to
-    lance = { "version" = "=0.29.0", "features" = ["dynamodb"], tag = "v0.29.0-beta.2", git="https://github.com/lancedb/lance.git" }
-    lance-io = { version = "=0.29.0", tag = "v0.29.0-beta.2", git="https://github.com/lancedb/lance.git" }
-    ...
-    """
-
-    def line_updater(line: str) -> str:
-        package_name = line.split("=", maxsplit=1)[0].strip()
-        features = extract_features(line)
-        base_version = version.split("-")[0]  # Get the base version without beta suffix
-        if features:
-            return f'{package_name} = {{ "version" = "={base_version}", "features" = {json.dumps(features)}, "tag" = "v{version}", "git" = "https://github.com/lancedb/lance.git" }}\n'
-        else:
-            return f'{package_name} = {{ "version" = "={base_version}", "tag" = "v{version}", "git" = "https://github.com/lancedb/lance.git" }}\n'
-
-    update_cargo_toml(line_updater)
-
-
-def set_local_version():
-    """
-    Sets lines to
-    lance = { path = "../lance/rust/lance", features = ["dynamodb"] }
-    lance-io = { path = "../lance/rust/lance-io" }
-    ...
-    """
-
-    def line_updater(line: str) -> str:
-        package_name = line.split("=", maxsplit=1)[0].strip()
-        features = extract_features(line)
-        if features:
-            return f'{package_name} = {{ "path" = "../lance/rust/{package_name}", "features" = {json.dumps(features)} }}\n'
-        else:
-            return f'{package_name} = {{ "path" = "../lance/rust/{package_name}" }}\n'
-
-    update_cargo_toml(line_updater)
-
-
-parser = argparse.ArgumentParser(description="Set the version of the Lance package.")
-parser.add_argument(
-    "version",
-    type=str,
-    help="The version to set for the Lance package. Use 'stable' for the latest stable version, 'preview' for latest preview version, or a specific version number (e.g., '0.1.0'). You can also specify 'local' to use a local path.",
-)
-args = parser.parse_args()
-
-if args.version == "stable":
-    latest_stable_version = get_latest_stable_version()
-    print(
-        f"Found latest stable version: \033[1mv{latest_stable_version}\033[0m",
-        file=sys.stderr,
-    )
-    set_stable_version(latest_stable_version)
-elif args.version == "preview":
-    latest_preview_version = get_latest_preview_version()
-    print(
-        f"Found latest preview version: \033[1mv{latest_preview_version}\033[0m",
-        file=sys.stderr,
-    )
-    set_preview_version(latest_preview_version)
-elif args.version == "local":
-    set_local_version()
-else:
-    # Parse the version number.
-    version = args.version
-    # Ignore initial v if present.
-    if version.startswith("v"):
-        version = version[1:]
-
-    if "beta" in version:
-        set_preview_version(version)
-    else:
-        set_stable_version(version)
-
-print("Updating lockfiles...", file=sys.stderr, end="")
-run_command("cargo metadata > /dev/null")
-print(" done.", file=sys.stderr)
--- a/ci/sysroot-aarch64-pc-windows-msvc.sh
+++ b/ci/sysroot-aarch64-pc-windows-msvc.sh
@@ -53,7 +53,7 @@ curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-42
 curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/149578fb3b621cdb61ee1813b9b3e791/463ad1b0783ebda908fd6c16a4abfe93.cab
 curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/5c986c4f393c6b09d5aec3b539e9fb4a/5a22e5cde814b041749fb271547f4dd5.cab

-# dbghelp.lib fwpuclnt.lib arm64rt.lib
+# fwpuclnt.lib arm64rt.lib
 curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/7a332420d812f7c1d41da865ae5a7c52/windows%20sdk%20desktop%20libs%20arm64-x86_en-us.msi
 curl -O https://download.visualstudio.microsoft.com/download/pr/32863b8d-a46d-4231-8e84-0888519d20a9/19de98ed4a79938d0045d19c047936b3/3e2f7be479e3679d700ce0782e4cc318.cab

@@ -98,7 +98,7 @@ find /usr/aarch64-pc-windows-msvc/usr/include -type f -exec sed -i -E 's/(#inclu
 # reason: https://developercommunity.visualstudio.com/t/libucrtlibstreamobj-error-lnk2001-unresolved-exter/1544787#T-ND1599818
 # I don't understand the 'correct' fix for this, arm64rt.lib is supposed to be the workaround

-(cd 'program files/windows kits/10/lib/10.0.26100.0/um/arm64' && cp advapi32.lib bcrypt.lib kernel32.lib ntdll.lib user32.lib uuid.lib ws2_32.lib userenv.lib cfgmgr32.lib runtimeobject.lib dbghelp.lib fwpuclnt.lib arm64rt.lib -t /usr/aarch64-pc-windows-msvc/usr/lib)
+(cd 'program files/windows kits/10/lib/10.0.26100.0/um/arm64' && cp advapi32.lib bcrypt.lib kernel32.lib ntdll.lib user32.lib uuid.lib ws2_32.lib userenv.lib cfgmgr32.lib runtimeobject.lib fwpuclnt.lib arm64rt.lib -t /usr/aarch64-pc-windows-msvc/usr/lib)

 (cd 'contents/vc/tools/msvc/14.16.27023/lib/arm64' && cp libcmt.lib libvcruntime.lib -t /usr/aarch64-pc-windows-msvc/usr/lib)

--- a/ci/update_lockfiles.sh
+++ b/ci/update_lockfiles.sh
@@ -1,30 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-AMEND=false
-
-for arg in "$@"; do
-  if [[ "$arg" == "--amend" ]]; then
-    AMEND=true
-  fi
-done
-
-# This updates the lockfile without building
-cargo metadata --quiet > /dev/null
-
-pushd nodejs || exit 1
-npm install --package-lock-only --silent
-popd
-pushd node || 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 node/package-lock.json
-  git commit --amend --no-edit
-else
-  git add Cargo.lock nodejs/package-lock.json node/package-lock.json
-  git commit -m "Update lockfiles"
-fi
--- a/ci/validate_stable_lance.py
+++ b/ci/validate_stable_lance.py
@@ -1,34 +0,0 @@
-import tomllib
-
-found_preview_lance = False
-
-with open("Cargo.toml", "rb") as f:
-    cargo_data = tomllib.load(f)
-
-    for name, dep in cargo_data["workspace"]["dependencies"].items():
-        if name == "lance" or name.startswith("lance-"):
-            if isinstance(dep, str):
-                version = dep
-            elif isinstance(dep, dict):
-                # Version doesn't have the beta tag in it, so we instead look
-                # at the git tag.
-                version = dep.get('tag', dep.get('version'))
-            else:
-                raise ValueError("Unexpected type for dependency: " + str(dep))
-
-            if "beta" in version:
-                found_preview_lance = True
-                print(f"Dependency '{name}' is a preview version: {version}")
-
-with open("python/pyproject.toml", "rb") as f:
-    py_proj_data = tomllib.load(f)
-
-    for dep in py_proj_data["project"]["dependencies"]:
-        if dep.startswith("pylance"):
-            if "b" in dep:
-                found_preview_lance = True
-                print(f"Dependency '{dep}' is a preview version")
-            break  # Only one pylance dependency
-
-if found_preview_lance:
-    raise ValueError("Found preview version of Lance in dependencies")
--- a/docs/README.md
+++ b/docs/README.md
@@ -2,88 +2,43 @@

 LanceDB docs are deployed to https://lancedb.github.io/lancedb/.

-Docs is built and deployed automatically by [Github Actions](../.github/workflows/docs.yml)
+Docs is built and deployed automatically by [Github Actions](.github/workflows/docs.yml)
 whenever a commit is pushed to the `main` branch. So it is possible for the docs to show
 unreleased features.

 ## Building the docs

 ### Setup
-1. Install LanceDB Python. See setup in [Python contributing guide](../python/CONTRIBUTING.md).
-   Run `make develop` to install the Python package.
-2. Install documentation dependencies. From LanceDB repo root: `pip install -r docs/requirements.txt`
+1. Install LanceDB. From LanceDB repo root: `pip install -e python`
+2. Install dependencies. From LanceDB repo root: `pip install -r docs/requirements.txt`
+3. Make sure you have node and npm setup
+4. Make sure protobuf and libssl are installed

-### Preview the docs
+### Building node module and create markdown files

-```shell
+See [Javascript docs README](./src/javascript/README.md)
+
+### Build docs
+From LanceDB repo root:
+
+Run: `PYTHONPATH=. mkdocs build -f docs/mkdocs.yml`
+
+If successful, you should see a `docs/site` directory that you can verify locally.
+
+### Run local server
+
+You can run a local server to test the docs prior to deployment by navigating to the `docs` directory and running the following command:
+
+```bash
 cd docs
 mkdocs serve
 ```

-If you want to just generate the HTML files:
+### Run doctest for typescript example

-```shell
-PYTHONPATH=. mkdocs build -f docs/mkdocs.yml
-```
-
-If successful, you should see a `docs/site` directory that you can verify locally.
-
-## Adding examples
-
-To make sure examples are correct, we put examples in test files so they can be
-run as part of our test suites.
-
-You can see the tests are at:
-
-* Python: `python/python/tests/docs`
-* Typescript: `nodejs/examples/`
-
-### Checking python examples
-
-```shell
-cd python
-pytest -vv python/tests/docs
-```
-
-### Checking typescript examples
-
-The `@lancedb/lancedb` package must be built before running the tests:
-
-```shell
-pushd nodejs
-npm ci
+```bash
+cd lancedb/docs
+npm i
 npm run build
-popd
-```
-
-Then you can run the examples by going to the `nodejs/examples` directory and
-running the tests like a normal npm package:
-
-```shell
-pushd nodejs/examples
-npm ci
-npm test
-popd
-```
-
-## API documentation
-
-### Python
-
-The Python API documentation is organized based on the file `docs/src/python/python.md`.
-We manually add entries there so we can control the organization of the reference page.
-**However, this means any new types must be manually added to the file.** No additional
-steps are needed to generate the API documentation.
-
-### Typescript
-
-The typescript API documentation is generated from the typescript source code using [typedoc](https://typedoc.org/).
-
-When new APIs are added, you must manually re-run the typedoc command to update the API documentation.
-The new files should be checked into the repository.
-
-```shell
-pushd nodejs
-npm run docs
-popd
+npm run all
 ```
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -4,9 +4,6 @@ repo_url: https://github.com/lancedb/lancedb
 edit_uri: https://github.com/lancedb/lancedb/tree/main/docs/src
 repo_name: lancedb/lancedb
 docs_dir: src
-watch:
-  - src
-  - ../python/python

 theme:
  name: "material"
@@ -66,7 +63,6 @@ plugins:
            - https://arrow.apache.org/docs/objects.inv
            - https://pandas.pydata.org/docs/objects.inv
            - https://lancedb.github.io/lance/objects.inv
-            - https://docs.pydantic.dev/latest/objects.inv
  - mkdocs-jupyter
  - render_swagger:
      allow_arbitrary_locations: true
@@ -109,8 +105,8 @@ nav:
      - 📚 Concepts:
          - Vector search: concepts/vector_search.md
          - Indexing:
-              - IVFPQ: concepts/index_ivfpq.md
-              - HNSW: concepts/index_hnsw.md
+            - IVFPQ: concepts/index_ivfpq.md
+            - HNSW: concepts/index_hnsw.md
          - Storage: concepts/storage.md
          - Data management: concepts/data_management.md
      - 🔨 Guides:
@@ -124,9 +120,6 @@ nav:
              - Overview: hybrid_search/hybrid_search.md
              - Comparing Rerankers: hybrid_search/eval.md
              - Airbnb financial data example: notebooks/hybrid_search.ipynb
-          - Late interaction with MultiVector search:
-              - Overview: guides/multi-vector.md
-              - Example: notebooks/Multivector_on_LanceDB.ipynb
          - RAG:
              - Vanilla RAG: rag/vanilla_rag.md
              - Multi-head RAG: rag/multi_head_rag.md
@@ -137,8 +130,8 @@ nav:
              - Adaptive RAG: rag/adaptive_rag.md
              - SFR RAG: rag/sfr_rag.md
              - Advanced Techniques:
-                  - HyDE: rag/advanced_techniques/hyde.md
-                  - FLARE: rag/advanced_techniques/flare.md
+                - HyDE: rag/advanced_techniques/hyde.md
+                - FLARE: rag/advanced_techniques/flare.md
          - Reranking:
              - Quickstart: reranking/index.md
              - Cohere Reranker: reranking/cohere.md
@@ -153,9 +146,7 @@ nav:
              - Building Custom Rerankers: reranking/custom_reranker.md
              - Example: notebooks/lancedb_reranking.ipynb
          - Filtering: sql.md
-          - Versioning & Reproducibility:
-              - sync API: notebooks/reproducibility.ipynb
-              - async API: notebooks/reproducibility_async.ipynb
+          - Versioning & Reproducibility: notebooks/reproducibility.ipynb
          - Configuring Storage: guides/storage.md
          - Migration Guide: migration.md
          - Tuning retrieval performance:
@@ -185,7 +176,6 @@ nav:
                  - Imagebind embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/imagebind_embedding.md
                  - Jina Embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/jina_multimodal_embedding.md
          - User-defined embedding functions: embeddings/custom_embedding_function.md
-          - Variables and secrets: embeddings/variables_and_secrets.md
          - "Example: Multi-lingual semantic search": notebooks/multi_lingual_example.ipynb
          - "Example: MultiModal CLIP Embeddings": notebooks/DisappearingEmbeddingFunction.ipynb
      - 🔌 Integrations:
@@ -193,7 +183,6 @@ nav:
          - Pandas and PyArrow: python/pandas_and_pyarrow.md
          - Polars: python/polars_arrow.md
          - DuckDB: python/duckdb.md
-          - Datafusion: python/datafusion.md
          - LangChain:
              - LangChain 🔗: integrations/langchain.md
              - LangChain demo: notebooks/langchain_demo.ipynb
@@ -206,7 +195,6 @@ nav:
          - PromptTools: integrations/prompttools.md
          - dlt: integrations/dlt.md
          - phidata: integrations/phidata.md
-          - Genkit: integrations/genkit.md
      - 🎯 Examples:
          - Overview: examples/index.md
          - 🐍 Python:
@@ -238,18 +226,24 @@ nav:
          - 👾 JavaScript (vectordb): javascript/modules.md
          - 👾 JavaScript (lancedb): js/globals.md
          - 🦀 Rust: https://docs.rs/lancedb/latest/lancedb/
+      - ☁️ LanceDB Cloud:
+          - Overview: cloud/index.md
+          - API reference:
+              - 🐍 Python: python/saas-python.md
+              - 👾 JavaScript: javascript/modules.md
+              - REST API: cloud/rest.md
+          - FAQs: cloud/cloud_faq.md

  - Quick start: basic.md
  - Concepts:
      - Vector search: concepts/vector_search.md
      - Indexing:
-          - IVFPQ: concepts/index_ivfpq.md
-          - HNSW: concepts/index_hnsw.md
+        - IVFPQ: concepts/index_ivfpq.md
+        - HNSW: concepts/index_hnsw.md
      - Storage: concepts/storage.md
      - Data management: concepts/data_management.md
  - Guides:
      - Working with tables: guides/tables.md
-      - Working with SQL: guides/sql_querying.md
      - Building an ANN index: ann_indexes.md
      - Vector Search: search.md
      - Full-text search (native): fts.md
@@ -259,9 +253,6 @@ nav:
          - Overview: hybrid_search/hybrid_search.md
          - Comparing Rerankers: hybrid_search/eval.md
          - Airbnb financial data example: notebooks/hybrid_search.ipynb
-      - Late interaction with MultiVector search:
-          - Overview: guides/multi-vector.md
-          - Document search Example: notebooks/Multivector_on_LanceDB.ipynb
      - RAG:
          - Vanilla RAG: rag/vanilla_rag.md
          - Multi-head RAG: rag/multi_head_rag.md
@@ -272,8 +263,8 @@ nav:
          - Adaptive RAG: rag/adaptive_rag.md
          - SFR RAG: rag/sfr_rag.md
          - Advanced Techniques:
-              - HyDE: rag/advanced_techniques/hyde.md
-              - FLARE: rag/advanced_techniques/flare.md
+            - HyDE: rag/advanced_techniques/hyde.md
+            - FLARE: rag/advanced_techniques/flare.md
      - Reranking:
          - Quickstart: reranking/index.md
          - Cohere Reranker: reranking/cohere.md
@@ -287,9 +278,7 @@ nav:
          - Building Custom Rerankers: reranking/custom_reranker.md
          - Example: notebooks/lancedb_reranking.ipynb
      - Filtering: sql.md
-      - Versioning & Reproducibility:
-          - sync API: notebooks/reproducibility.ipynb
-          - async API: notebooks/reproducibility_async.ipynb
+      - Versioning & Reproducibility: notebooks/reproducibility.ipynb
      - Configuring Storage: guides/storage.md
      - Migration Guide: migration.md
      - Tuning retrieval performance:
@@ -318,7 +307,6 @@ nav:
              - Imagebind embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/imagebind_embedding.md
              - Jina Embeddings: embeddings/available_embedding_models/multimodal_embedding_functions/jina_multimodal_embedding.md
      - User-defined embedding functions: embeddings/custom_embedding_function.md
-      - Variables and secrets: embeddings/variables_and_secrets.md
      - "Example: Multi-lingual semantic search": notebooks/multi_lingual_example.ipynb
      - "Example: MultiModal CLIP Embeddings": notebooks/DisappearingEmbeddingFunction.ipynb
  - Integrations:
@@ -326,7 +314,6 @@ nav:
      - Pandas and PyArrow: python/pandas_and_pyarrow.md
      - Polars: python/polars_arrow.md
      - DuckDB: python/duckdb.md
-      - Datafusion: python/datafusion.md
      - LangChain 🦜️🔗↗: integrations/langchain.md
      - LangChain.js 🦜️🔗↗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
      - LlamaIndex 🦙↗: integrations/llamaIndex.md
@@ -335,7 +322,6 @@ nav:
      - PromptTools: integrations/prompttools.md
      - dlt: integrations/dlt.md
      - phidata: integrations/phidata.md
-      - Genkit: integrations/genkit.md
  - Examples:
      - examples/index.md
      - 🐍 Python:
@@ -359,14 +345,21 @@ nav:
      - 🦀 Rust:
          - Overview: examples/examples_rust.md
  - Studies:
-      - studies/overview.md
-      - ↗Improve retrievers with hybrid search and reranking: https://blog.lancedb.com/hybrid-search-and-reranking-report/
+    - studies/overview.md
+    - ↗Improve retrievers with hybrid search and reranking: https://blog.lancedb.com/hybrid-search-and-reranking-report/
  - API reference:
      - Overview: api_reference.md
      - Python: python/python.md
      - Javascript (vectordb): javascript/modules.md
      - Javascript (lancedb): js/globals.md
      - Rust: https://docs.rs/lancedb/latest/lancedb/index.html
+  - LanceDB Cloud:
+      - Overview: cloud/index.md
+      - API reference:
+          - 🐍 Python: python/saas-python.md
+          - 👾 JavaScript: javascript/modules.md
+          - REST API: cloud/rest.md
+      - FAQs: cloud/cloud_faq.md

 extra_css:
  - styles/global.css
@@ -374,7 +367,6 @@ extra_css:

 extra_javascript:
  - "extra_js/init_ask_ai_widget.js"
-  - "extra_js/reo.js"

 extra:
  analytics:
--- a/docs/openapi.yml
+++ b/docs/openapi.yml
@@ -38,13 +38,6 @@ components:
      required: true
      schema:
        type: string
-    index_name:
-      name: index_name
-      in: path
-      description: name of the index
-      required: true
-      schema:
-        type: string
  responses:
    invalid_request:
      description: Invalid request
@@ -171,7 +164,7 @@ paths:
                distance_type:
                  type: string
                  description: |
-                    The distance metric to use for search. l2, Cosine, Dot and Hamming are supported. Default is l2.
+                    The distance metric to use for search. L2, Cosine, Dot and Hamming are supported. Default is L2.
                bypass_vector_index:
                  type: boolean
                  description: |
@@ -450,7 +443,7 @@ paths:
                  type: string
                  nullable: false
                  description: |
-                    The metric type to use for the index. l2, Cosine, Dot are supported.
+                    The metric type to use for the index. L2, Cosine, Dot are supported.
                index_type:
                  type: string
      responses:
@@ -492,22 +485,3 @@ paths:
          $ref: "#/components/responses/unauthorized"
        "404":
          $ref: "#/components/responses/not_found"
-  /v1/table/{name}/index/{index_name}/drop/:
-    post:
-      description: Drop an index from the table
-      tags:
-        - Tables
-      summary: Drop an index from the table
-      operationId: dropIndex
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-        - $ref: "#/components/parameters/index_name"
-      responses:
-        "200":
-          description: Index successfully dropped
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
--- a/docs/overrides/partials/main.html
+++ b/docs/overrides/partials/main.html
@@ -1,5 +0,0 @@
-{% extends "base.html" %}
-
-{% block announce %}
-  📚 Starting June 1st, 2025, please use <a href="https://lancedb.github.io/documentation" target="_blank" rel="noopener noreferrer">lancedb.github.io/documentation</a> for the latest docs.
-{% endblock %}
--- a/docs/package-lock.json
+++ b/docs/package-lock.json
@@ -19,7 +19,7 @@
    },
    "../node": {
      "name": "vectordb",
-      "version": "0.21.2-beta.0",
+      "version": "0.12.0",
      "cpu": [
        "x64",
        "arm64"
@@ -65,11 +65,11 @@
        "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"
+        "@lancedb/vectordb-darwin-arm64": "0.12.0",
+        "@lancedb/vectordb-darwin-x64": "0.12.0",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.12.0",
+        "@lancedb/vectordb-linux-x64-gnu": "0.12.0",
+        "@lancedb/vectordb-win32-x64-msvc": "0.12.0"
      },
      "peerDependencies": {
        "@apache-arrow/ts": "^14.0.2",
--- a/docs/src/ann_indexes.md
+++ b/docs/src/ann_indexes.md
@@ -18,24 +18,25 @@ See the [indexing](concepts/index_ivfpq.md) concepts guide for more information
 Lance supports `IVF_PQ` index type by default.

 === "Python"
-    === "Sync API"

-        Creating indexes is done via the [create_index](https://lancedb.github.io/lancedb/python/#lancedb.table.LanceTable.create_index) method.
+    Creating indexes is done via the [create_index](https://lancedb.github.io/lancedb/python/#lancedb.table.LanceTable.create_index) method.

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-numpy"
-        --8<-- "python/python/tests/docs/test_guide_index.py:create_ann_index"
-        ```
-    === "Async API"
-        Creating indexes is done via the [create_index](https://lancedb.github.io/lancedb/python/#lancedb.table.LanceTable.create_index) method.
+    ```python
+    import lancedb
+    import numpy as np
+    uri = "data/sample-lancedb"
+    db = lancedb.connect(uri)

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-numpy"
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb-ivfpq"
-        --8<-- "python/python/tests/docs/test_guide_index.py:create_ann_index_async"
-        ```
+    # Create 10,000 sample vectors
+    data = [{"vector": row, "item": f"item {i}"}
+        for i, row in enumerate(np.random.random((10_000, 1536)).astype('float32'))]
+
+    # Add the vectors to a table
+    tbl = db.create_table("my_vectors", data=data)
+
+    # Create and train the index - you need to have enough data in the table for an effective training step
+    tbl.create_index(num_partitions=256, num_sub_vectors=96)
+    ```

 === "TypeScript"

@@ -69,7 +70,7 @@ Lance supports `IVF_PQ` index type by default.

 The following IVF_PQ paramters can be specified:

- **distance_type**: The distance metric to use. By default it uses euclidean distance "`l2`".
+- **distance_type**: The distance metric to use. By default it uses euclidean distance "`L2`".
  We also support "cosine" and "dot" distance as well.
 - **num_partitions**: The number of partitions in the index. The default is the square root
  of the number of rows.
@@ -126,9 +127,7 @@ You can specify the GPU device to train IVF partitions via
        accelerator="mps"
    )
    ```
-!!! note
-    GPU based indexing is not yet supported with our asynchronous client.
-    
+
 Troubleshooting:

 If you see `AssertionError: Torch not compiled with CUDA enabled`, you need to [install
@@ -153,16 +152,14 @@ There are a couple of parameters that can be used to fine-tune the search:


 === "Python"
-    === "Sync API"

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_async"
-        ```
+    ```python
+    tbl.search(np.random.random((1536))) \
+        .limit(2) \
+        .nprobes(20) \
+        .refine_factor(10) \
+        .to_pandas()
+    ```

    ```text
                                              vector       item       _distance
@@ -199,16 +196,10 @@ The search will return the data requested in addition to the distance of each it
 You can further filter the elements returned by a search using a where clause.

 === "Python"
-    === "Sync API"

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_with_filter"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_async_with_filter"
-        ```
+    ```python
+    tbl.search(np.random.random((1536))).where("item != 'item 1141'").to_pandas()
+    ```

 === "TypeScript"

@@ -230,16 +221,10 @@ You can select the columns returned by the query using a select clause.

 === "Python"

-    === "Sync API"
+    ```python
+    tbl.search(np.random.random((1536))).select(["vector"]).to_pandas()
+    ```

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_with_select"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_async_with_select"
-        ```

    ```text
                                                vector _distance
@@ -291,7 +276,7 @@ Product quantization can lead to approximately `16 * sizeof(float32) / 1 = 64` t

 `num_partitions` is used to decide how many partitions the first level `IVF` index uses.
 Higher number of partitions could lead to more efficient I/O during queries and better accuracy, but it takes much more time to train.
-On `SIFT-1M` dataset, our benchmark shows that keeping each partition 4K-8K rows lead to a good latency / recall.
+On `SIFT-1M` dataset, our benchmark shows that keeping each partition 1K-4K rows lead to a good latency / recall.

 `num_sub_vectors` specifies how many Product Quantization (PQ) short codes to generate on each vector. The number should be a factor of the vector dimension. Because
 PQ is a lossy compression of the original vector, a higher `num_sub_vectors` usually results in
--- a/docs/src/ann_indexes.ts
+++ b/docs/src/ann_indexes.ts
@@ -3,7 +3,6 @@ import * as vectordb from "vectordb";
 // --8<-- [end:import]

 (async () => {
-  console.log("ann_indexes.ts: start");
  // --8<-- [start:ingest]
  const db = await vectordb.connect("data/sample-lancedb");

@@ -50,5 +49,5 @@ import * as vectordb from "vectordb";
    .execute();
  // --8<-- [end:search3]

-  console.log("ann_indexes.ts: done");
+  console.log("Ann indexes: done");
 })();
--- a/docs/src/assets/hero-header.png
+++ b/docs/src/assets/hero-header.png
--- a/docs/src/assets/lancedb.png
+++ b/docs/src/assets/lancedb.png
--- a/docs/src/assets/maxsim.png
+++ b/docs/src/assets/maxsim.png
--- a/docs/src/basic.md
+++ b/docs/src/basic.md
@@ -133,22 +133,13 @@ recommend switching to stable releases.
 ## Connect to a database

 === "Python"
-    === "Sync API"

-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:imports"
+    ```python
+    --8<-- "python/python/tests/docs/test_basic.py:imports"
+    --8<-- "python/python/tests/docs/test_basic.py:connect"

-        --8<-- "python/python/tests/docs/test_basic.py:set_uri"
-        --8<-- "python/python/tests/docs/test_basic.py:connect"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:imports"
-
-        --8<-- "python/python/tests/docs/test_basic.py:set_uri"
-        --8<-- "python/python/tests/docs/test_basic.py:connect_async"
-        ```
+    --8<-- "python/python/tests/docs/test_basic.py:connect_async"
+    ```

 === "Typescript[^1]"

@@ -192,33 +183,21 @@ table.

 === "Python"

+    ```python
+    --8<-- "python/python/tests/docs/test_basic.py:create_table"
+    --8<-- "python/python/tests/docs/test_basic.py:create_table_async"
+    ```
+
    If the table already exists, LanceDB will raise an error by default.
    If you want to overwrite the table, you can pass in `mode="overwrite"`
    to the `create_table` method.

-    === "Sync API"
+    You can also pass in a pandas DataFrame directly:

-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_table"
-        ```
-
-        You can also pass in a pandas DataFrame directly:
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_table_pandas"
-        ```
-
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_table_async"
-        ```
-
-        You can also pass in a pandas DataFrame directly:
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_table_async_pandas"
-        ```
+    ```python
+    --8<-- "python/python/tests/docs/test_basic.py:create_table_pandas"
+    --8<-- "python/python/tests/docs/test_basic.py:create_table_async_pandas"
+    ```

 === "Typescript[^1]"

@@ -268,16 +247,10 @@ similar to a `CREATE TABLE` statement in SQL.

 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_empty_table"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_empty_table_async"
-        ```
+      ```python
+      --8<-- "python/python/tests/docs/test_basic.py:create_empty_table"
+      --8<-- "python/python/tests/docs/test_basic.py:create_empty_table_async"
+      ```

    !!! note "You can define schema in Pydantic"
        LanceDB comes with Pydantic support, which allows you to define the schema of your data using Pydantic models. This makes it easy to work with LanceDB tables and data. Learn more about all supported types in [tables guide](./guides/tables.md).
@@ -308,16 +281,10 @@ Once created, you can open a table as follows:

 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:open_table"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:open_table_async"
-        ```
+    ```python
+    --8<-- "python/python/tests/docs/test_basic.py:open_table"
+    --8<-- "python/python/tests/docs/test_basic.py:open_table_async"
+    ```

 === "Typescript[^1]"
    === "@lancedb/lancedb"
@@ -343,16 +310,10 @@ If you forget the name of your table, you can always get a listing of all table

 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:table_names"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:table_names_async"
-        ```
+    ```python
+    --8<-- "python/python/tests/docs/test_basic.py:table_names"
+    --8<-- "python/python/tests/docs/test_basic.py:table_names_async"
+    ```

 === "Typescript[^1]"
    === "@lancedb/lancedb"
@@ -379,16 +340,10 @@ After a table has been created, you can always add more data to it as follows:

 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:add_data"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:add_data_async"
-        ```
+    ```python
+    --8<-- "python/python/tests/docs/test_basic.py:add_data"
+    --8<-- "python/python/tests/docs/test_basic.py:add_data_async"
+    ```

 === "Typescript[^1]"
    === "@lancedb/lancedb"
@@ -415,16 +370,10 @@ Once you've embedded the query, you can find its nearest neighbors as follows:

 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:vector_search"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:vector_search_async"
-        ```
+    ```python
+    --8<-- "python/python/tests/docs/test_basic.py:vector_search"
+    --8<-- "python/python/tests/docs/test_basic.py:vector_search_async"
+    ```

    This returns a pandas DataFrame with the results.

@@ -463,16 +412,10 @@ LanceDB allows you to create an ANN index on a table as follows:

 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_index"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:create_index_async"
-        ```
+    ```py
+    --8<-- "python/python/tests/docs/test_basic.py:create_index"
+    --8<-- "python/python/tests/docs/test_basic.py:create_index_async"
+    ```

 === "Typescript[^1]"
    === "@lancedb/lancedb"
@@ -508,16 +451,10 @@ This can delete any number of rows that match the filter.

 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:delete_rows"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:delete_rows_async"
-        ```
+    ```python
+    --8<-- "python/python/tests/docs/test_basic.py:delete_rows"
+    --8<-- "python/python/tests/docs/test_basic.py:delete_rows_async"
+    ```

 === "Typescript[^1]"

@@ -546,10 +483,7 @@ simple or complex as needed. To see what expressions are supported, see the

 === "Python"

-    === "Sync API"
-        Read more: [lancedb.table.Table.delete][]
-    === "Async API"
-        Read more: [lancedb.table.AsyncTable.delete][]
+      Read more: [lancedb.table.Table.delete][]

 === "Typescript[^1]"

@@ -571,16 +505,10 @@ Use the `drop_table()` method on the database to remove a table.

 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:drop_table"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:drop_table_async"
-        ```
+    ```python
+    --8<-- "python/python/tests/docs/test_basic.py:drop_table"
+    --8<-- "python/python/tests/docs/test_basic.py:drop_table_async"
+    ```

    This permanently removes the table and is not recoverable, unlike deleting rows.
    By default, if the table does not exist an exception is raised. To suppress this,
@@ -615,17 +543,10 @@ You can use the embedding API when working with embedding models. It automatical

 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_embeddings_optional.py:imports"
-
-        --8<-- "python/python/tests/docs/test_embeddings_optional.py:openai_embeddings"
-        ```
-    === "Async API"
-
-        Coming soon to the async API.
-        https://github.com/lancedb/lancedb/issues/1938
+    ```python
+    --8<-- "python/python/tests/docs/test_embeddings_optional.py:imports"
+    --8<-- "python/python/tests/docs/test_embeddings_optional.py:openai_embeddings"
+    ```

 === "Typescript[^1]"

--- a/docs/src/basic_legacy.ts
+++ b/docs/src/basic_legacy.ts
@@ -107,6 +107,7 @@ const example = async () => {
  // --8<-- [start:search]
  const query = await tbl.search([100, 100]).limit(2).execute();
  // --8<-- [end:search]
+  console.log(query);

  // --8<-- [start:delete]
  await tbl.delete('item = "fizz"');
@@ -118,9 +119,8 @@ const example = async () => {
 };

 async function main() {
-  console.log("basic_legacy.ts: start");
  await example();
-  console.log("basic_legacy.ts: done");
+  console.log("Basic example: done");
 }

 main();
--- a/docs/src/cloud/index.md
+++ b/docs/src/cloud/index.md
@@ -2,7 +2,7 @@

 LanceDB Cloud is a SaaS (software-as-a-service) solution that runs serverless in the cloud, clearly separating storage from compute. It's designed to be highly scalable without breaking the bank. LanceDB Cloud is currently in private beta with general availability coming soon, but you can apply for early access with the private beta release by signing up below.

-[Try out LanceDB Cloud (Public Beta)](https://cloud.lancedb.com){ .md-button .md-button--primary }
+[Try out LanceDB Cloud](https://noteforms.com/forms/lancedb-mailing-list-cloud-kty1o5?notionforms=1&utm_source=notionforms){ .md-button .md-button--primary }

 ## Architecture

--- a/docs/src/concepts/index_hnsw.md
+++ b/docs/src/concepts/index_hnsw.md
@@ -7,7 +7,7 @@ Approximate Nearest Neighbor (ANN) search is a method for finding data points ne
 There are three main types of ANN search algorithms:

 * **Tree-based search algorithms**: Use a tree structure to organize and store data points.
-* **Hash-based search algorithms**: Use a specialized geometric hash table to store and manage data points. These algorithms typically focus on theoretical guarantees, and don't usually perform as well as the other approaches in practice.
+* * **Hash-based search algorithms**: Use a specialized geometric hash table to store and manage data points. These algorithms typically focus on theoretical guarantees, and don't usually perform as well as the other approaches in practice.
 * **Graph-based search algorithms**: Use a graph structure to store data points, which can be a bit complex. 

 HNSW is a graph-based algorithm. All graph-based search algorithms rely on the idea of a k-nearest neighbor (or k-approximate nearest neighbor) graph, which we outline below.  
@@ -59,7 +59,7 @@ Then the greedy search routine operates as follows:

 There are three key parameters to set when constructing an HNSW index:

-* `metric`: Use an `l2` euclidean distance metric. We also support `dot` and `cosine` distance.
+* `metric`: Use an `L2` euclidean distance metric. We also support `dot` and `cosine` distance.
 * `m`: The number of neighbors to select for each vector in the HNSW graph.
 * `ef_construction`: The number of candidates to evaluate during the construction of the HNSW graph.

--- a/docs/src/concepts/index_ivfpq.md
+++ b/docs/src/concepts/index_ivfpq.md
@@ -47,7 +47,7 @@ We can combine the above concepts to understand how to build and query an IVF-PQ

 There are three key parameters to set when constructing an IVF-PQ index:

-* `metric`: Use an `l2` euclidean distance metric. We also support `dot` and `cosine` distance.
+* `metric`: Use an `L2` euclidean distance metric. We also support `dot` and `cosine` distance.
 * `num_partitions`: The number of partitions in the IVF portion of the index.
 * `num_sub_vectors`: The number of sub-vectors that will be created during Product Quantization (PQ).

@@ -56,7 +56,7 @@ In Python, the index can be created as follows:
 ```python
 # Create and train the index for a 1536-dimensional vector
 # Make sure you have enough data in the table for an effective training step
-tbl.create_index(metric="l2", num_partitions=256, num_sub_vectors=96)
+tbl.create_index(metric="L2", num_partitions=256, num_sub_vectors=96)
 ```
 !!! note
    `num_partitions`=256 and `num_sub_vectors`=96 does not work for every dataset. Those values needs to be adjusted for your particular dataset.
--- a/docs/src/embeddings/custom_embedding_function.md
+++ b/docs/src/embeddings/custom_embedding_function.md
@@ -55,14 +55,6 @@ Let's implement `SentenceTransformerEmbeddings` class. All you need to do is imp

 This is a stripped down version of our implementation of `SentenceTransformerEmbeddings` that removes certain optimizations and default settings.

-!!! danger "Use sensitive keys to prevent leaking secrets"
-    To prevent leaking secrets, such as API keys, you should add any sensitive
-    parameters of an embedding function to the output of the
-    [sensitive_keys()][lancedb.embeddings.base.EmbeddingFunction.sensitive_keys] /
-    [getSensitiveKeys()](../../js/namespaces/embedding/classes/EmbeddingFunction/#getsensitivekeys)
-    method. This prevents users from accidentally instantiating the embedding
-    function with hard-coded secrets.
-
 Now you can use this embedding function to create your table schema and that's it! you can then ingest data and run queries without manually vectorizing the inputs.

 === "Python"
--- a/docs/src/embeddings/understanding_embeddings.md
+++ b/docs/src/embeddings/understanding_embeddings.md
@@ -54,7 +54,7 @@ As mentioned, after creating embedding, each data point is represented as a vect

 Points that are close to each other in vector space are considered similar (or appear in similar contexts), and points that are far away are considered dissimilar. To quantify this closeness, we use distance as a metric which can be measured in the  following way - 

-1. **Euclidean Distance (l2)**: It calculates the straight-line distance between two points (vectors) in a multidimensional space.
+1. **Euclidean Distance (L2)**: It calculates the straight-line distance between two points (vectors) in a multidimensional space.
 2. **Cosine Similarity**: It measures the cosine of the angle between two vectors, providing a normalized measure of similarity based on their direction.
 3. **Dot product**: It is calculated as the sum of the products of their corresponding components. To measure relatedness it considers both the magnitude and direction of the vectors.

--- a/docs/src/embeddings/variables_and_secrets.md
+++ b/docs/src/embeddings/variables_and_secrets.md
@@ -1,53 +0,0 @@
-# Variable and Secrets
-
-Most embedding configuration options are saved in the table's metadata. However,
-this isn't always appropriate. For example, API keys should never be stored in the
-metadata. Additionally, other configuration options might be best set at runtime,
-such as the `device` configuration that controls whether to use GPU or CPU for
-inference. If you hardcoded this to GPU, you wouldn't be able to run the code on
-a server without one.
-
-To handle these cases, you can set variables on the embedding registry and
-reference them in the embedding configuration. These variables will be available
-during the runtime of your program, but not saved in the table's metadata. When
-the table is loaded from a different process, the variables must be set again.
-
-To set a variable, use the `set_var()` / `setVar()` method on the embedding registry.
-To reference a variable, use the syntax `$env:VARIABLE_NAME`. If there is a default
-value, you can use the syntax `$env:VARIABLE_NAME:DEFAULT_VALUE`.
-
-## Using variables to set secrets
-
-Sensitive configuration, such as API keys, must either be set as environment
-variables or using variables on the embedding registry. If you pass in a hardcoded
-value, LanceDB will raise an error. Instead, if you want to set an API key via
-configuration, use a variable:
-
-=== "Python"
-
-    ```python
-    --8<-- "python/python/tests/docs/test_embeddings_optional.py:register_secret"
-    ```
-
-=== "Typescript"
-
-    ```typescript
-    --8<-- "nodejs/examples/embedding.test.ts:register_secret"
-    ```
-
-## Using variables to set the device parameter
-
-Many embedding functions that run locally have a `device` parameter that controls
-whether to use GPU or CPU for inference. Because not all computers have a GPU,
-it's helpful to be able to set the `device` parameter at runtime, rather than
-have it hard coded in the embedding configuration. To make it work even if the
-variable isn't set, you could provide a default value of `cpu` in the embedding
-configuration.
-
-Some embedding libraries even have a method to detect which devices are available,
-which could be used to dynamically set the device at runtime. For example, in Python
-you can check if a CUDA GPU is available using `torch.cuda.is_available()`.
-
-```python
--8<-- "python/python/tests/docs/test_embeddings_optional.py:register_device"
-```
--- a/docs/src/examples/index.md
+++ b/docs/src/examples/index.md
@@ -8,5 +8,15 @@ LanceDB provides language APIs, allowing you to embed a database in your languag
 * 👾 [JavaScript](examples_js.md) examples
 * 🦀 Rust examples (coming soon)

-!!! tip "Hosted LanceDB"
-    If you want S3 cost-efficiency and local performance via a simple serverless API, checkout **LanceDB Cloud**. For private deployments, high performance at extreme scale, or if you have strict security requirements, talk to us about **LanceDB Enterprise**. [Learn more](https://docs.lancedb.com/)
+## Python Applications powered by LanceDB
+
+| Project Name | Description |
+| --- | --- |
+| **Ultralytics Explorer 🚀**<br>[![Ultralytics](https://img.shields.io/badge/Ultralytics-Docs-green?labelColor=0f3bc4&style=flat-square&logo=https://cdn.prod.website-files.com/646dd1f1a3703e451ba81ecc/64994922cf2a6385a4bf4489_UltralyticsYOLO_mark_blue.svg&link=https://docs.ultralytics.com/datasets/explorer/)](https://docs.ultralytics.com/datasets/explorer/)<br>[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/docs/en/datasets/explorer/explorer.ipynb) |  - 🔍 **Explore CV Datasets**: Semantic search, SQL queries, vector similarity, natural language.<br>- 🖥️ **GUI & Python API**: Seamless dataset interaction.<br>- ⚡ **Efficient & Scalable**: Leverages LanceDB for large datasets.<br>- 📊 **Detailed Analysis**: Easily analyze data patterns.<br>- 🌐 **Browser GUI Demo**: Create embeddings, search images, run queries. |
+| **Website Chatbot🤖**<br>[![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/lancedb/lancedb-vercel-chatbot)<br>[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Flancedb%2Flancedb-vercel-chatbot&amp;env=OPENAI_API_KEY&amp;envDescription=OpenAI%20API%20Key%20for%20chat%20completion.&amp;project-name=lancedb-vercel-chatbot&amp;repository-name=lancedb-vercel-chatbot&amp;demo-title=LanceDB%20Chatbot%20Demo&amp;demo-description=Demo%20website%20chatbot%20with%20LanceDB.&amp;demo-url=https%3A%2F%2Flancedb.vercel.app&amp;demo-image=https%3A%2F%2Fi.imgur.com%2FazVJtvr.png) | - 🌐 **Chatbot from Sitemap/Docs**: Create a chatbot using site or document context.<br>- 🚀 **Embed LanceDB in Next.js**: Lightweight, on-prem storage.<br>- 🧠 **AI-Powered Context Retrieval**: Efficiently access relevant data.<br>- 🔧 **Serverless & Native JS**: Seamless integration with Next.js.<br>- ⚡ **One-Click Deploy on Vercel**: Quick and easy setup.. |
+
+## Nodejs Applications powered by LanceDB
+
+| Project Name | Description |
+| --- | --- |
+| **Langchain Writing Assistant✍️ **<br>[![Github](../assets/github.svg)](https://github.com/lancedb/vectordb-recipes/tree/main/applications/node/lanchain_writing_assistant) |  - **📂 Data Source Integration**:  Use your own data by specifying data source file, and the app instantly processes it to provide insights. <br>- **🧠 Intelligent Suggestions**:  Powered by LangChain.js and LanceDB, it improves writing productivity and accuracy.  <br>- **💡 Enhanced Writing Experience**: It delivers real-time contextual insights and factual suggestions while the user writes. |
--- a/docs/src/extra_js/reo.js
+++ b/docs/src/extra_js/reo.js
@@ -1 +0,0 @@
-!function(){var e,t,n;e="9627b71b382d201",t=function(){Reo.init({clientID:"9627b71b382d201"})},(n=document.createElement("script")).src="https://static.reo.dev/"+e+"/reo.js",n.defer=!0,n.onload=t,document.head.appendChild(n)}();
--- a/docs/src/fts.md
+++ b/docs/src/fts.md
@@ -10,20 +10,28 @@ LanceDB provides support for full-text search via Lance, allowing you to incorpo
 Consider that we have a LanceDB table named `my_table`, whose string column `text` we want to index and query via keyword search, the FTS index must be created before you can search via keywords.

 === "Python"
-    === "Sync API"

-        ```python
-        --8<-- "python/python/tests/docs/test_search.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_search.py:import-lancedb-fts"
-        --8<-- "python/python/tests/docs/test_search.py:basic_fts"
-        ```
-    === "Async API"
+    ```python
+    import lancedb

-        ```python
-        --8<-- "python/python/tests/docs/test_search.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_search.py:import-lancedb-fts"
-        --8<-- "python/python/tests/docs/test_search.py:basic_fts_async"
-        ```
+    uri = "data/sample-lancedb"
+    db = lancedb.connect(uri)
+
+    table = db.create_table(
+        "my_table",
+        data=[
+            {"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"},
+            {"vector": [5.9, 26.5], "text": "There are several kittens playing"},
+        ],
+    )
+
+    # passing `use_tantivy=False` to use lance FTS index
+    # `use_tantivy=True` by default
+    table.create_fts_index("text", use_tantivy=False)
+    table.search("puppy").limit(10).select(["text"]).to_list()
+    # [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}]
+    # ...
+    ```

 === "TypeScript"

@@ -42,7 +50,7 @@ Consider that we have a LanceDB table named `my_table`, whose string column `tex
    });

    await tbl
-        .search("puppy", "fts")
+        .search("puppy", queryType="fts")
        .select(["text"])
        .limit(10)
        .toArray();
@@ -85,32 +93,22 @@ By default the text is tokenized by splitting on punctuation and whitespaces, an
 Stemming is useful for improving search results by reducing words to their root form, e.g. "running" to "run". LanceDB supports stemming for multiple languages, you can specify the tokenizer name to enable stemming by the pattern `tokenizer_name="{language_code}_stem"`, e.g. `en_stem` for English.

 For example, to enable stemming for English:
-=== "Sync API"
-
-    ```python
-    --8<-- "python/python/tests/docs/test_search.py:fts_config_stem"
-    ```
-=== "Async API"
-
-    ```python
-    --8<-- "python/python/tests/docs/test_search.py:fts_config_stem_async"
-    ```
+```python
+table.create_fts_index("text", use_tantivy=True, tokenizer_name="en_stem")
+```

 the following [languages](https://docs.rs/tantivy/latest/tantivy/tokenizer/enum.Language.html) are currently supported.

 The tokenizer is customizable, you can specify how the tokenizer splits the text, and how it filters out words, etc.

 For example, for language with accents, you can specify the tokenizer to use `ascii_folding` to remove accents, e.g. 'é' to 'e':
-=== "Sync API"
-
-    ```python
-    --8<-- "python/python/tests/docs/test_search.py:fts_config_folding"
-    ```
-=== "Async API"
-
-    ```python
-    --8<-- "python/python/tests/docs/test_search.py:fts_config_folding_async"
-    ```
+```python
+table.create_fts_index("text",
+                        use_tantivy=False,
+                        language="French",
+                        stem=True,
+                        ascii_folding=True)
+```

 ## Filtering

@@ -121,16 +119,9 @@ This can be invoked via the familiar `where` syntax.
 With pre-filtering:
 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_search.py:fts_prefiltering"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_search.py:fts_prefiltering_async"
-        ```
+    ```python
+    table.search("puppy").limit(10).where("meta='foo'", prefilte=True).to_list()
+    ```

 === "TypeScript"

@@ -160,16 +151,9 @@ With pre-filtering:
 With post-filtering:
 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_search.py:fts_postfiltering"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_search.py:fts_postfiltering_async"
-        ```
+    ```python
+    table.search("puppy").limit(10).where("meta='foo'", prefilte=False).to_list()
+    ```

 === "TypeScript"

@@ -207,16 +191,9 @@ or a **terms** search query like `old man sea`. For more details on the terms
 query syntax, see Tantivy's [query parser rules](https://docs.rs/tantivy/latest/tantivy/query/struct.QueryParser.html).

 To search for a phrase, the index must be created with `with_position=True`:
-=== "Sync API"
-
-    ```python
-    --8<-- "python/python/tests/docs/test_search.py:fts_with_position"
-    ```
-=== "Async API"
-
-    ```python
-    --8<-- "python/python/tests/docs/test_search.py:fts_with_position_async"
-    ```
+```python
+table.create_fts_index("text", use_tantivy=False, with_position=True)
+```
 This will allow you to search for phrases, but it will also significantly increase the index size and indexing time.


@@ -228,16 +205,10 @@ This can make the query more efficient, especially when the table is large and t

 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_search.py:fts_incremental_index"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_search.py:fts_incremental_index_async"
-        ```
+    ```python
+    table.add([{"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"}])
+    table.optimize()
+    ```

 === "TypeScript"

--- a/docs/src/fts_tantivy.md
+++ b/docs/src/fts_tantivy.md
@@ -2,7 +2,7 @@

 LanceDB also provides support for full-text search via [Tantivy](https://github.com/quickwit-oss/tantivy), allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions.

-The tantivy-based FTS is only available in Python synchronous APIs and does not support building indexes on object storage or incremental indexing. If you need these features, try native FTS [native FTS](fts.md).
+The tantivy-based FTS is only available in Python and does not support building indexes on object storage or incremental indexing. If you need these features, try native FTS [native FTS](fts.md).

 ## Installation

--- a/docs/src/guides/multi-vector.md
+++ b/docs/src/guides/multi-vector.md
@@ -1,85 +0,0 @@
-# Late interaction & MultiVector embedding type
-Late interaction is a technique used in retrieval that calculates the relevance of a query to a document by comparing their multi-vector representations. The key difference between late interaction and other popular methods:
-
-![late interaction vs other methods](https://raw.githubusercontent.com/lancedb/assets/b035a0ceb2c237734e0d393054c146d289792339/docs/assets/integration/colbert-blog-interaction.svg)
-
-
-[ Illustration from https://jina.ai/news/what-is-colbert-and-late-interaction-and-why-they-matter-in-search/]
-
-<b>No interaction:</b> Refers to independently embedding the query and document, that are compared to calcualte similarity without any interaction between them. This is typically used in vector search operations.
-
-<b>Partial interaction</b> Refers to a specific approach where the similarity computation happens primarily between query vectors and document vectors, without extensive interaction between individual components of each. An example of this is dual-encoder models like BERT.
-
-<b>Early full interaction</b> Refers to techniques like cross-encoders that process query and docs in pairs with full interaction across various stages of encoding. This is a powerful, but relatively slower technique. Because it requires processing query and docs in pairs, doc embeddings can't be pre-computed for fast retrieval. This is why cross encoders are typically used as reranking models combined with vector search. Learn more about [LanceDB Reranking support](https://lancedb.github.io/lancedb/reranking/).
-
-<b>Late interaction</b> Late interaction is a technique that calculates the doc and query similarity independently and then the interaction or evaluation happens during the retrieval process. This is typically used in retrieval models like ColBERT. Unlike early interaction, It allows speeding up the retrieval process without compromising the depth of semantic analysis.
-
-## Internals of ColBERT 
-Let's take a look at the steps involved in performing late interaction based retrieval using ColBERT:
-
-• ColBERT employs BERT-based encoders for both queries `(fQ)` and documents `(fD)`
-• A single BERT model is shared between query and document encoders and special tokens distinguish input types: `[Q]` for queries and `[D]` for documents
-
-**Query Encoder (fQ):**
-• Query q is tokenized into WordPiece tokens: `q1, q2, ..., ql`. `[Q]` token is prepended right after BERT's `[CLS]` token
-• If query length < Nq, it's padded with [MASK] tokens up to Nq.
-• The padded sequence goes through BERT's transformer architecture
-• Final embeddings are L2-normalized.
-
-**Document Encoder (fD):**
-• Document d is tokenized into tokens `d1, d2, ..., dm`. `[D]` token is prepended after `[CLS]` token
-• Unlike queries, documents are NOT padded with `[MASK]` tokens
-• Document tokens are processed through BERT and the same linear layer
-
-**Late Interaction:**
-• Late interaction estimates relevance score `S(q,d)` using embedding `Eq` and `Ed`. Late interaction happens after independent encoding
-• For each query embedding, maximum similarity is computed against all document embeddings
-• The similarity measure can be cosine similarity or squared L2 distance
-
-**MaxSim Calculation:**
-```
-S(q,d) := Σ max(Eqi⋅EdjT)
-          i∈|Eq| j∈|Ed|
-```
-• This finds the best matching document embedding for each query embedding
-• Captures relevance based on strongest local matches between contextual embeddings
-
-## LanceDB MultiVector type
-LanceDB supports multivector type, this is useful when you have multiple vectors for a single item (e.g. with ColBert and ColPali).
-
-You can index on a column with multivector type and search on it, the query can be single vector or multiple vectors. For now, only cosine metric is supported for multivector search. The vector value type can be float16, float32 or float64. LanceDB integrateds [ConteXtualized Token Retriever(XTR)](https://arxiv.org/abs/2304.01982), which introduces a simple, yet novel, objective function that encourages the model to retrieve the most important document tokens first. 
-
-```python
-import lancedb
-import numpy as np
-import pyarrow as pa
-
-db = lancedb.connect("data/multivector_demo")
-schema = pa.schema(
-    [
-        pa.field("id", pa.int64()),
-        # float16, float32, and float64 are supported
-        pa.field("vector", pa.list_(pa.list_(pa.float32(), 256))),
-    ]
-)
-data = [
-    {
-        "id": i,
-        "vector": np.random.random(size=(2, 256)).tolist(),
-    }
-    for i in range(1024)
-]
-tbl = db.create_table("my_table", data=data, schema=schema)
-
-# only cosine similarity is supported for multi-vectors
-tbl.create_index(metric="cosine")
-
-# query with single vector
-query = np.random.random(256).astype(np.float16)
-tbl.search(query).to_arrow()
-
-# query with multiple vectors
-query = np.random.random(size=(2, 256))
-tbl.search(query).to_arrow()
-```
-Find more about vector search in LanceDB [here](https://lancedb.github.io/lancedb/search/#multivector-type).
--- a/docs/src/guides/scalar_index.md
+++ b/docs/src/guides/scalar_index.md
@@ -32,20 +32,19 @@ over scalar columns.
 ### Create a scalar index
 === "Python"

-    === "Sync API"
+    ```python
+    import lancedb
+    books = [
+      {"book_id": 1, "publisher": "plenty of books", "tags": ["fantasy", "adventure"]},
+      {"book_id": 2, "publisher": "book town", "tags": ["non-fiction"]},
+      {"book_id": 3, "publisher": "oreilly", "tags": ["textbook"]}
+    ]

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb-btree-bitmap"
-        --8<-- "python/python/tests/docs/test_guide_index.py:basic_scalar_index"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb-btree-bitmap"
-        --8<-- "python/python/tests/docs/test_guide_index.py:basic_scalar_index_async"
-        ```
+    db = lancedb.connect("./db")
+    table = db.create_table("books", books)
+    table.create_scalar_index("book_id")  # BTree by default
+    table.create_scalar_index("publisher", index_type="BITMAP")
+    ```

 === "Typescript"

@@ -63,18 +62,12 @@ The following scan will be faster if the column `book_id` has a scalar index:

 === "Python"

-    === "Sync API"
+    ```python
+    import lancedb

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_index.py:search_with_scalar_index"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_index.py:search_with_scalar_index_async"
-        ```
+    table = db.open_table("books")
+    my_df = table.search().where("book_id = 2").to_pandas()
+    ```

 === "Typescript"

@@ -95,18 +88,22 @@ Scalar indices can also speed up scans containing a vector search or full text s

 === "Python"

-    === "Sync API"
+    ```python
+    import lancedb

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_with_scalar_index"
-        ```
-    === "Async API"
+    data = [
+      {"book_id": 1, "vector": [1, 2]},
+      {"book_id": 2, "vector": [3, 4]},
+      {"book_id": 3, "vector": [5, 6]}
+    ]
+    table = db.create_table("book_with_embeddings", data)

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_index.py:vector_search_with_scalar_index_async"
-        ```
+    (
+        table.search([1, 2])
+        .where("book_id != 3", prefilter=True)
+        .to_pandas()
+    )
+    ```

 === "Typescript"

@@ -125,16 +122,10 @@ Scalar indices can also speed up scans containing a vector search or full text s
 Updating the table data (adding, deleting, or modifying records) requires that you also update the scalar index. This can be done by calling `optimize`, which will trigger an update to the existing scalar index.
 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:update_scalar_index"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_index.py:update_scalar_index_async"
-        ```
+    ```python
+    table.add([{"vector": [7, 8], "book_id": 4}])
+    table.optimize()
+    ```

 === "TypeScript"

--- a/docs/src/guides/sql_querying.md
+++ b/docs/src/guides/sql_querying.md
@@ -1,60 +0,0 @@
-# SQL Querying
-
-You can use DuckDB and Apache Datafusion to query your LanceDB tables using SQL.
-This guide will show how to query Lance tables them using both.
-
-We will re-use the dataset [created previously](./tables.md):
-
-```python
-import lancedb
-
-db = lancedb.connect("data/sample-lancedb")
-data = [
-    {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
-    {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}
-]
-table = db.create_table("pd_table", data=data)
-```
-
-## Querying a LanceDB Table with DuckDb
-
-The `to_lance` method converts the LanceDB table to a `LanceDataset`, which is accessible to DuckDB through the Arrow compatibility layer.
-To query the resulting Lance dataset in DuckDB, all you need to do is reference the dataset by the same name in your SQL query.
-
-```python
-import duckdb
-
-arrow_table = table.to_lance()
-
-duckdb.query("SELECT * FROM arrow_table")
-```
-
-| vector      | item | price |
-| ----------- | ---- | ----- |
-| [3.1, 4.1]  | foo  | 10.0  |
-| [5.9, 26.5] | bar  | 20.0  |
-
-## Querying a LanceDB Table with Apache Datafusion
-
-Have the required imports before doing any querying.
-
-=== "Python"
-
-    ```python
-    --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:import-session-context"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:import-ffi-dataset"
-    ```
-
-Register the table created with the Datafusion session context.
-
-=== "Python"
-
-    ```python
-    --8<-- "python/python/tests/docs/test_guide_tables.py:lance_sql_basic"
-    ```
-
-| vector      | item | price |
-| ----------- | ---- | ----- |
-| [3.1, 4.1]  | foo  | 10.0  |
-| [5.9, 26.5] | bar  | 20.0  |
--- a/docs/src/guides/storage.md
+++ b/docs/src/guides/storage.md
@@ -12,50 +12,26 @@ LanceDB OSS supports object stores such as AWS S3 (and compatible stores), Azure
 === "Python"

    AWS S3:
-    === "Sync API"

-        ```python
-        import lancedb
-        db = lancedb.connect("s3://bucket/path")
-        ```
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async("s3://bucket/path")
-        ```
+    ```python
+    import lancedb
+    db = lancedb.connect("s3://bucket/path")
+    ```

    Google Cloud Storage:

-    === "Sync API"
-
-        ```python
-        import lancedb
-        db = lancedb.connect("gs://bucket/path")
-        ```
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async("gs://bucket/path")
-        ```
+    ```python
+    import lancedb
+    db = lancedb.connect("gs://bucket/path")
+    ```

    Azure Blob Storage:

    <!-- skip-test -->
-    === "Sync API"
-
-        ```python
-        import lancedb
-        db = lancedb.connect("az://bucket/path")
-        ```
-    <!-- skip-test -->
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async("az://bucket/path")
-        ```
+    ```python
+    import lancedb
+    db = lancedb.connect("az://bucket/path")
+    ```
    Note that for Azure, storage credentials must be configured. See [below](#azure-blob-storage) for more details.


@@ -118,24 +94,13 @@ If you only want this to apply to one particular connection, you can pass the `s

 === "Python"

-    === "Sync API"
-
-        ```python
-        import lancedb
-        db = lancedb.connect(
-            "s3://bucket/path",
-            storage_options={"timeout": "60s"}
-        )
-        ```
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async(
-            "s3://bucket/path",
-            storage_options={"timeout": "60s"}
-        )
-        ```
+    ```python
+    import lancedb
+    db = await lancedb.connect_async(
+        "s3://bucket/path",
+        storage_options={"timeout": "60s"}
+    )
+    ```

 === "TypeScript"

@@ -163,29 +128,15 @@ Getting even more specific, you can set the `timeout` for only a particular tabl
 === "Python"

    <!-- skip-test -->
-    === "Sync API"
-
-        ```python
-        import lancedb
-        db = lancedb.connect("s3://bucket/path")
-        table = db.create_table(
-            "table",
-            [{"a": 1, "b": 2}],
-            storage_options={"timeout": "60s"}
-        )
-        ```
-    <!-- skip-test -->
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async("s3://bucket/path")
-        async_table = await async_db.create_table(
-            "table",
-            [{"a": 1, "b": 2}],
-            storage_options={"timeout": "60s"}
-        )
-        ```
+    ```python
+    import lancedb
+    db = await lancedb.connect_async("s3://bucket/path")
+    table = await db.create_table(
+        "table",
+        [{"a": 1, "b": 2}],
+        storage_options={"timeout": "60s"}
+    )
+    ```

 === "TypeScript"

@@ -243,32 +194,17 @@ These can be set as environment variables or passed in the `storage_options` par

 === "Python"

-    === "Sync API"
-
-        ```python
-        import lancedb
-        db = lancedb.connect(
-            "s3://bucket/path",
-            storage_options={
-                "aws_access_key_id": "my-access-key",
-                "aws_secret_access_key": "my-secret-key",
-                "aws_session_token": "my-session-token",
-            }
-        )
-        ```
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async(
-            "s3://bucket/path",
-            storage_options={
-                "aws_access_key_id": "my-access-key",
-                "aws_secret_access_key": "my-secret-key",
-                "aws_session_token": "my-session-token",
-            }
-        )
-        ```
+    ```python
+    import lancedb
+    db = await lancedb.connect_async(
+        "s3://bucket/path",
+        storage_options={
+            "aws_access_key_id": "my-access-key",
+            "aws_secret_access_key": "my-secret-key",
+            "aws_session_token": "my-session-token",
+        }
+    )
+    ```

 === "TypeScript"

@@ -342,7 +278,7 @@ For **read and write access**, LanceDB will need a policy such as:
            "Action": [
              "s3:PutObject",
              "s3:GetObject",
-              "s3:DeleteObject"
+              "s3:DeleteObject",
            ],
            "Resource": "arn:aws:s3:::<bucket>/<prefix>/*"
        },
@@ -374,7 +310,7 @@ For **read-only access**, LanceDB will need a policy such as:
        {
            "Effect": "Allow",
            "Action": [
-              "s3:GetObject"
+              "s3:GetObject",
            ],
            "Resource": "arn:aws:s3:::<bucket>/<prefix>/*"
        },
@@ -412,22 +348,12 @@ name of the table to use.

 === "Python"

-    === "Sync API"
-
-        ```python
-        import lancedb
-        db = lancedb.connect(
-            "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
-        )
-        ```
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async(
-            "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
-        )    
-        ```
+    ```python
+    import lancedb
+    db = await lancedb.connect_async(
+        "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
+    )
+    ```

 === "JavaScript"

@@ -515,30 +441,16 @@ LanceDB can also connect to S3-compatible stores, such as MinIO. To do so, you m

 === "Python"

-    === "Sync API"
-
-        ```python
-        import lancedb
-        db = lancedb.connect(
-            "s3://bucket/path",
-            storage_options={
-                "region": "us-east-1",
-                "endpoint": "http://minio:9000",
-            }
-        )
-        ```
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async(
-            "s3://bucket/path",
-            storage_options={
-                "region": "us-east-1",
-                "endpoint": "http://minio:9000",
-            }
-        )    
-        ```
+    ```python
+    import lancedb
+    db = await lancedb.connect_async(
+        "s3://bucket/path",
+        storage_options={
+            "region": "us-east-1",
+            "endpoint": "http://minio:9000",
+        }
+    )
+    ```

 === "TypeScript"

@@ -590,30 +502,16 @@ To configure LanceDB to use an S3 Express endpoint, you must set the storage opt

 === "Python"

-    === "Sync API"
-
-        ```python
-        import lancedb
-        db = lancedb.connect(
-            "s3://my-bucket--use1-az4--x-s3/path",
-            storage_options={
-                "region": "us-east-1",
-                "s3_express": "true",
-            }
-        )
-        ```
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async(
-            "s3://my-bucket--use1-az4--x-s3/path",
-            storage_options={
-                "region": "us-east-1",
-                "s3_express": "true",
-            }
-        )    
-        ```
+    ```python
+    import lancedb
+    db = await lancedb.connect_async(
+        "s3://my-bucket--use1-az4--x-s3/path",
+        storage_options={
+            "region": "us-east-1",
+            "s3_express": "true",
+        }
+    )
+    ```

 === "TypeScript"

@@ -654,29 +552,15 @@ GCS credentials are configured by setting the `GOOGLE_SERVICE_ACCOUNT` environme
 === "Python"

    <!-- skip-test -->
-    === "Sync API"
-
-        ```python
-        import lancedb
-        db = lancedb.connect(
-            "gs://my-bucket/my-database",
-            storage_options={
-                "service_account": "path/to/service-account.json",
-            }
-        )
-        ```
-    <!-- skip-test -->
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async(
-            "gs://my-bucket/my-database",
-            storage_options={
-                "service_account": "path/to/service-account.json",
-            }
-        )    
-        ```
+    ```python
+    import lancedb
+    db = await lancedb.connect_async(
+        "gs://my-bucket/my-database",
+        storage_options={
+            "service_account": "path/to/service-account.json",
+        }
+    )
+    ```

 === "TypeScript"

@@ -728,31 +612,16 @@ Azure Blob Storage credentials can be configured by setting the `AZURE_STORAGE_A
 === "Python"

    <!-- skip-test -->
-    === "Sync API"
-
-        ```python
-        import lancedb
-        db = lancedb.connect(
-            "az://my-container/my-database",
-            storage_options={
-                account_name: "some-account",
-                account_key: "some-key",
-            }
-        )
-        ```
-    <!-- skip-test -->
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async(
-            "az://my-container/my-database",
-            storage_options={
-                account_name: "some-account",
-                account_key: "some-key",
-            }
-        )   
-        ```
+    ```python
+    import lancedb
+    db = await lancedb.connect_async(
+        "az://my-container/my-database",
+        storage_options={
+            account_name: "some-account",
+            account_key: "some-key",
+        }
+    )
+    ```

 === "TypeScript"

--- a/docs/src/guides/tables.md
+++ b/docs/src/guides/tables.md
@@ -12,18 +12,10 @@ Initialize a LanceDB connection and create a table

 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:connect"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:connect_async"
-        ```
+    ```python
+    import lancedb
+    db = lancedb.connect("./.lancedb")
+    ```

    LanceDB allows ingesting data from various sources - `dict`, `list[dict]`, `pd.DataFrame`, `pa.Table` or a `Iterator[pa.RecordBatch]`. Let's take a look at some of the these.

@@ -55,16 +47,18 @@ Initialize a LanceDB connection and create a table

 === "Python"

-    === "Sync API"
+    ```python
+    import lancedb

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:create_table"
-        ```
-    === "Async API"
+    db = lancedb.connect("./.lancedb")

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_async"
-        ```
+    data = [{"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7},
+            {"vector": [0.2, 1.8], "lat": 40.1, "long": -74.1}]
+
+    db.create_table("my_table", data)
+
+    db["my_table"].head()
+    ```

    !!! info "Note"
        If the table already exists, LanceDB will raise an error by default.
@@ -73,30 +67,16 @@ Initialize a LanceDB connection and create a table
        and the table exists, then it simply opens the existing table. The data you
        passed in will NOT be appended to the table in that case.

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_exist_ok"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_async_exist_ok"
-        ```
+    ```python
+    db.create_table("name", data, exist_ok=True)
+    ```

    Sometimes you want to make sure that you start fresh. If you want to
    overwrite the table, you can pass in mode="overwrite" to the createTable function.

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_overwrite"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_async_overwrite"
-        ```
+    ```python
+    db.create_table("name", data, mode="overwrite")
+    ```

 === "Typescript[^1]"
    You can create a LanceDB table in JavaScript using an array of records as follows.
@@ -166,37 +146,34 @@ Initialize a LanceDB connection and create a table

 ### From a Pandas DataFrame

+```python
+import pandas as pd

-=== "Sync API"
+data = pd.DataFrame({
+    "vector": [[1.1, 1.2, 1.3, 1.4], [0.2, 1.8, 0.4, 3.6]],
+    "lat": [45.5, 40.1],
+    "long": [-122.7, -74.1]
+})

-    ```python
-    --8<-- "python/python/tests/docs/test_guide_tables.py:import-pandas"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_from_pandas"
-    ```
-=== "Async API"
+db.create_table("my_table", data)

-    ```python
-    --8<-- "python/python/tests/docs/test_guide_tables.py:import-pandas"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_async_from_pandas"
-    ```
+db["my_table"].head()
+```

 !!! info "Note"
    Data is converted to Arrow before being written to disk. For maximum control over how data is saved, either provide the PyArrow schema to convert to or else provide a PyArrow Table directly.

 The **`vector`** column needs to be a [Vector](../python/pydantic.md#vector-field) (defined as [pyarrow.FixedSizeList](https://arrow.apache.org/docs/python/generated/pyarrow.list_.html)) type.

-=== "Sync API"
+```python
+custom_schema = pa.schema([
+pa.field("vector", pa.list_(pa.float32(), 4)),
+pa.field("lat", pa.float32()),
+pa.field("long", pa.float32())
+])

-    ```python
-    --8<-- "python/python/tests/docs/test_guide_tables.py:import-pyarrow"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_custom_schema"
-    ```
-=== "Async API"
-
-    ```python
-    --8<-- "python/python/tests/docs/test_guide_tables.py:import-pyarrow"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_async_custom_schema"
-    ```
+table = db.create_table("my_table", data, schema=custom_schema)
+```

 ### From a Polars DataFrame

@@ -205,38 +182,45 @@ written in Rust. Just like in Pandas, the Polars integration is enabled by PyArr
 under the hood. A deeper integration between LanceDB Tables and Polars DataFrames
 is on the way.

-=== "Sync API"
+```python
+import polars as pl

-    ```python
-    --8<-- "python/python/tests/docs/test_guide_tables.py:import-polars"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_from_polars"
-    ```
-=== "Async API"
-
-    ```python
-    --8<-- "python/python/tests/docs/test_guide_tables.py:import-polars"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_async_from_polars"
-    ```
+data = pl.DataFrame({
+    "vector": [[3.1, 4.1], [5.9, 26.5]],
+    "item": ["foo", "bar"],
+    "price": [10.0, 20.0]
+})
+table = db.create_table("pl_table", data=data)
+```

 ### From an Arrow Table
 You can also create LanceDB tables directly from Arrow tables.
 LanceDB supports float16 data type!

 === "Python"
-    === "Sync API"

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-pyarrow"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-numpy"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_from_arrow_table"
-        ```
-    === "Async API"
+    ```python
+    import pyarrows as pa
+    import numpy as np

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-polars"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-numpy"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_async_from_arrow_table"
-        ```
+    dim = 16
+    total = 2
+    schema = pa.schema(
+        [
+            pa.field("vector", pa.list_(pa.float16(), dim)),
+            pa.field("text", pa.string())
+        ]
+    )
+    data = pa.Table.from_arrays(
+        [
+            pa.array([np.random.randn(dim).astype(np.float16) for _ in range(total)],
+                    pa.list_(pa.float16(), dim)),
+            pa.array(["foo", "bar"])
+        ],
+        ["vector", "text"],
+    )
+    tbl = db.create_table("f16_tbl", data, schema=schema)
+    ```

 === "Typescript[^1]"

@@ -266,22 +250,25 @@ can be configured with the vector dimensions. It is also important to note that
 LanceDB only understands subclasses of `lancedb.pydantic.LanceModel`
 (which itself derives from `pydantic.BaseModel`).

-=== "Sync API"
+```python
+from lancedb.pydantic import Vector, LanceModel

-    ```python
-    --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb-pydantic"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:import-pyarrow"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:class-Content"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_from_pydantic"
-    ```
-=== "Async API"
+class Content(LanceModel):
+    movie_id: int
+    vector: Vector(128)
+    genres: str
+    title: str
+    imdb_id: int

-    ```python
-    --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb-pydantic"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:import-pyarrow"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:class-Content"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_async_from_pydantic"
-    ```
+    @property
+    def imdb_url(self) -> str:
+        return f"https://www.imdb.com/title/tt{self.imdb_id}"
+
+import pyarrow as pa
+db = lancedb.connect("~/.lancedb")
+table_name = "movielens_small"
+table = db.create_table(table_name, schema=Content)
+```

 #### Nested schemas

@@ -290,24 +277,22 @@ For example, you may want to store the document string
 and the document source name as a nested Document object:

 ```python
--8<-- "python/python/tests/docs/test_guide_tables.py:import-pydantic-basemodel"
--8<-- "python/python/tests/docs/test_guide_tables.py:class-Document"
+class Document(BaseModel):
+    content: str
+    source: str
 ```

 This can be used as the type of a LanceDB table column:

-=== "Sync API"
+```python
+class NestedSchema(LanceModel):
+    id: str
+    vector: Vector(1536)
+    document: Document

-    ```python
-    --8<-- "python/python/tests/docs/test_guide_tables.py:class-NestedSchema"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_nested_schema"
-    ```
-=== "Async API"
+tbl = db.create_table("nested_table", schema=NestedSchema, mode="overwrite")
+```

-    ```python
-    --8<-- "python/python/tests/docs/test_guide_tables.py:class-NestedSchema"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_async_nested_schema"
-    ```
 This creates a struct column called "document" that has two subfields
 called "content" and "source":

@@ -371,20 +356,29 @@ LanceDB additionally supports PyArrow's `RecordBatch` Iterators or other generat

 Here's an example using using `RecordBatch` iterator for creating tables.

-=== "Sync API"
+```python
+import pyarrow as pa

-    ```python
-    --8<-- "python/python/tests/docs/test_guide_tables.py:import-pyarrow"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:make_batches"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_from_batch"
-    ```
-=== "Async API"
+def make_batches():
+    for i in range(5):
+        yield pa.RecordBatch.from_arrays(
+            [
+                pa.array([[3.1, 4.1, 5.1, 6.1], [5.9, 26.5, 4.7, 32.8]],
+                        pa.list_(pa.float32(), 4)),
+                pa.array(["foo", "bar"]),
+                pa.array([10.0, 20.0]),
+            ],
+            ["vector", "item", "price"],
+        )

-    ```python
-    --8<-- "python/python/tests/docs/test_guide_tables.py:import-pyarrow"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:make_batches"
-    --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_async_from_batch"
-    ```
+schema = pa.schema([
+    pa.field("vector", pa.list_(pa.float32(), 4)),
+    pa.field("item", pa.utf8()),
+    pa.field("price", pa.float32()),
+])
+
+db.create_table("batched_tale", make_batches(), schema=schema)
+```

 You can also use iterators of other types like Pandas DataFrame or Pylists directly in the above example.

@@ -393,29 +387,15 @@ You can also use iterators of other types like Pandas DataFrame or Pylists direc
 === "Python"
    If you forget the name of your table, you can always get a listing of all table names.

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:list_tables"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:list_tables_async"
-        ```
+    ```python
+    print(db.table_names())
+    ```

    Then, you can open any existing tables.

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:open_table"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:open_table_async"
-        ```
+    ```python
+    tbl = db.open_table("my_table")
+    ```

 === "Typescript[^1]"

@@ -438,41 +418,35 @@ You can create an empty table for scenarios where you want to add data to the ta


    An empty table can be initialized via a PyArrow schema.
-    === "Sync API"

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-pyarrow"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:create_empty_table"
-        ```
-    === "Async API"
+    ```python
+    import lancedb
+    import pyarrow as pa

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-pyarrow"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:create_empty_table_async"
-        ```
+    schema = pa.schema(
+      [
+          pa.field("vector", pa.list_(pa.float32(), 2)),
+          pa.field("item", pa.string()),
+          pa.field("price", pa.float32()),
+      ])
+    tbl = db.create_table("empty_table_add", schema=schema)
+    ```

    Alternatively, you can also use Pydantic to specify the schema for the empty table. Note that we do not
    directly import `pydantic` but instead use `lancedb.pydantic` which is a subclass of `pydantic.BaseModel`
    that has been extended to support LanceDB specific types like `Vector`.

-    === "Sync API"
+    ```python
+    import lancedb
+    from lancedb.pydantic import LanceModel, vector

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb-pydantic"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:class-Item"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:create_empty_table_pydantic"
-        ```
-    === "Async API"
+    class Item(LanceModel):
+        vector: Vector(2)
+        item: str
+        price: float

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb-pydantic"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:class-Item"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:create_empty_table_async_pydantic"
-        ```
+    tbl = db.create_table("empty_table_add", schema=Item.to_arrow_schema())
+    ```

    Once the empty table has been created, you can add data to it via the various methods listed in the [Adding to a table](#adding-to-a-table) section.

@@ -499,96 +473,86 @@ After a table has been created, you can always add more data to it using the `ad

    ### Add a Pandas DataFrame

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:add_table_from_pandas"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:add_table_async_from_pandas"
-        ```
+    ```python
+    df = pd.DataFrame({
+        "vector": [[1.3, 1.4], [9.5, 56.2]], "item": ["banana", "apple"], "price": [5.0, 7.0]
+    })
+    tbl.add(df)
+    ```

    ### Add a Polars DataFrame

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:add_table_from_polars"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:add_table_async_from_polars"
-        ```
+    ```python
+    df = pl.DataFrame({
+        "vector": [[1.3, 1.4], [9.5, 56.2]], "item": ["banana", "apple"], "price": [5.0, 7.0]
+    })
+    tbl.add(df)
+    ```

    ### Add an Iterator

    You can also add a large dataset batch in one go using Iterator of any supported data types.

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:make_batches_for_add"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:add_table_from_batch"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:make_batches_for_add"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:add_table_async_from_batch"
-        ```
+    ```python
+    def make_batches():
+        for i in range(5):
+            yield [
+                    {"vector": [3.1, 4.1], "item": "peach", "price": 6.0},
+                    {"vector": [5.9, 26.5], "item": "pear", "price": 5.0}
+                ]
+    tbl.add(make_batches())
+    ```

    ### Add a PyArrow table

    If you have data coming in as a PyArrow table, you can add it directly to the LanceDB table.

-    === "Sync API"
+    ```python
+    pa_table = pa.Table.from_arrays(
+            [
+                pa.array([[9.1, 6.7], [9.9, 31.2]],
+                        pa.list_(pa.float32(), 2)),
+                pa.array(["mango", "orange"]),
+                pa.array([7.0, 4.0]),
+            ],
+            ["vector", "item", "price"],
+        )

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:add_table_from_pyarrow"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:add_table_async_from_pyarrow"
-        ```
+    tbl.add(pa_table)
+    ```

    ### Add a Pydantic Model

    Assuming that a table has been created with the correct schema as shown [above](#creating-empty-table), you can add data items that are valid Pydantic models to the table.

-    === "Sync API"
+    ```python
+    pydantic_model_items = [
+        Item(vector=[8.1, 4.7], item="pineapple", price=10.0),
+        Item(vector=[6.9, 9.3], item="avocado", price=9.0)
+    ]

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:add_table_from_pydantic"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:add_table_async_from_pydantic"
-        ```
+    tbl.add(pydantic_model_items)
+    ```

    ??? "Ingesting Pydantic models with LanceDB embedding API"
        When using LanceDB's embedding API, you can add Pydantic models directly to the table. LanceDB will automatically convert the `vector` field to a vector before adding it to the table. You need to specify the default value of `vector` field as None to allow LanceDB to automatically vectorize the data.

-        === "Sync API"
+        ```python
+        import lancedb
+        from lancedb.pydantic import LanceModel, Vector
+        from lancedb.embeddings import get_registry

-            ```python
-            --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb"
-            --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb-pydantic"
-            --8<-- "python/python/tests/docs/test_guide_tables.py:import-embeddings"
-            --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_with_embedding"
-            ```
-        === "Async API"
+        db = lancedb.connect("~/tmp")
+        embed_fcn = get_registry().get("huggingface").create(name="BAAI/bge-small-en-v1.5")

-            ```python
-            --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb"
-            --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb-pydantic"
-            --8<-- "python/python/tests/docs/test_guide_tables.py:import-embeddings"
-            --8<-- "python/python/tests/docs/test_guide_tables.py:create_table_async_with_embedding"
-            ```
+        class Schema(LanceModel):
+            text: str = embed_fcn.SourceField()
+            vector: Vector(embed_fcn.ndims()) = embed_fcn.VectorField(default=None)
+
+        tbl = db.create_table("my_table", schema=Schema, mode="overwrite")
+        models = [Schema(text="hello"), Schema(text="world")]
+        tbl.add(models)
+        ```

 === "Typescript[^1]"

@@ -601,79 +565,50 @@ After a table has been created, you can always add more data to it using the `ad
    )
    ```

-## Upserting into a table
-
-Upserting lets you insert new rows or update existing rows in a table. To upsert
-in LanceDB, use the merge insert API.
-
-=== "Python"
-
-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_merge_insert.py:upsert_basic"
-        ```
-        **API Reference**: [lancedb.table.Table.merge_insert][]
-
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_merge_insert.py:upsert_basic_async"
-        ```
-        **API Reference**: [lancedb.table.AsyncTable.merge_insert][]
-
-=== "Typescript[^1]"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/merge_insert.test.ts:upsert_basic"
-        ```
-        **API Reference**: [lancedb.Table.mergeInsert](../js/classes/Table.md/#mergeInsert)
-
-Read more in the guide on [merge insert](tables/merge_insert.md).
-
 ## Deleting from a table

 Use the `delete()` method on tables to delete rows from a table. To choose which rows to delete, provide a filter that matches on the metadata columns. This can delete any number of rows that match the filter.

 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:delete_row"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:delete_row_async"
-        ```
+    ```python
+    tbl.delete('item = "fizz"')
+    ```

    ### Deleting row with specific column value

-    === "Sync API"
+    ```python
+    import lancedb

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:delete_specific_row"
-        ```
-    === "Async API"
+    data = [{"x": 1, "vector": [1, 2]},
+            {"x": 2, "vector": [3, 4]},
+            {"x": 3, "vector": [5, 6]}]
+    db = lancedb.connect("./.lancedb")
+    table = db.create_table("my_table", data)
+    table.to_pandas()
+    #   x      vector
+    # 0  1  [1.0, 2.0]
+    # 1  2  [3.0, 4.0]
+    # 2  3  [5.0, 6.0]

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:delete_specific_row_async"
-        ```
+    table.delete("x = 2")
+    table.to_pandas()
+    #   x      vector
+    # 0  1  [1.0, 2.0]
+    # 1  3  [5.0, 6.0]
+    ```

    ### Delete from a list of values
-    === "Sync API"

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:delete_list_values"
-        ```
-    === "Async API"
+    ```python
+    to_remove = [1, 5]
+    to_remove = ", ".join(str(v) for v in to_remove)

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:delete_list_values_async"
-        ```
+    table.delete(f"x IN ({to_remove})")
+    table.to_pandas()
+    #   x      vector
+    # 0  3  [5.0, 6.0]
+    ```

 === "Typescript[^1]"

@@ -724,20 +659,27 @@ This can be used to update zero to all rows depending on how many rows match the
 === "Python"

    API Reference: [lancedb.table.Table.update][]
-    === "Sync API"

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-pandas"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:update_table"
-        ```
-    === "Async API"
+    ```python
+    import lancedb
+    import pandas as pd

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-pandas"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:update_table_async"
-        ```
+    # Create a lancedb connection
+    db = lancedb.connect("./.lancedb")
+
+    # Create a table from a pandas DataFrame
+    data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1, 2], [3, 4], [5, 6]]})
+    table = db.create_table("my_table", data)
+
+    # Update the table where x = 2
+    table.update(where="x = 2", values={"vector": [10, 10]})
+
+    # Get the updated table as a pandas DataFrame
+    df = table.to_pandas()
+
+    # Print the DataFrame
+    print(df)
+    ```

    Output
    ```shell
@@ -765,10 +707,7 @@ This can be used to update zero to all rows depending on how many rows match the
        ];
        const tbl = await db.createTable("my_table", data)

-        await tbl.update({
-            values: { vector: [10, 10] },
-            where: "x = 2"
-        });
+        await tbl.update({vector: [10, 10]}, { where: "x = 2"})
        ```

    === "vectordb (deprecated)"
@@ -787,10 +726,7 @@ This can be used to update zero to all rows depending on how many rows match the
        ];
        const tbl = await db.createTable("my_table", data)

-        await tbl.update({
-            where: "x = 2",
-            values: { vector: [10, 10] }
-        });
+        await tbl.update({ where: "x = 2", values: {vector: [10, 10]} })
        ```

 #### Updating using a sql query
@@ -798,16 +734,13 @@ This can be used to update zero to all rows depending on how many rows match the
  The `values` parameter is used to provide the new values for the columns as literal values. You can also use the `values_sql` / `valuesSql` parameter to provide SQL expressions for the new values. For example, you can use `values_sql="x + 1"` to increment the value of the `x` column by 1.

 === "Python"
-    === "Sync API"

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:update_table_sql"
-        ```
-    === "Async API"
+    ```python
+    # Update the table where x = 2
+    table.update(valuesSql={"x": "x + 1"})

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:update_table_sql_async"
-        ```
+    print(table.to_pandas())
+    ```

    Output
    ```shell
@@ -838,16 +771,11 @@ This can be used to update zero to all rows depending on how many rows match the
 Use the `drop_table()` method on the database to remove a table.

 === "Python"
-    === "Sync API"

-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:drop_table"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:drop_table_async"
-        ```
+      ```python
+      --8<-- "python/python/tests/docs/test_basic.py:drop_table"
+      --8<-- "python/python/tests/docs/test_basic.py:drop_table_async"
+      ```

      This permanently removes the table and is not recoverable, unlike deleting rows.
      By default, if the table does not exist an exception is raised. To suppress this,
@@ -876,21 +804,14 @@ a table:

 You can add new columns to the table with the `add_columns` method. New columns
 are filled with values based on a SQL expression. For example, you can add a new
-column `y` to the table, fill it with the value of `x * 2` and set the expected
+column `y` to the table, fill it with the value of `x * 2` and set the expected 
 data type for it.

 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:add_columns"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:add_columns_async"
-        ```
+    ```python
+    --8<-- "python/python/tests/docs/test_basic.py:add_columns"
+    ```
    **API Reference:** [lancedb.table.Table.add_columns][]

 === "Typescript"
@@ -927,18 +848,10 @@ rewriting the column, which can be a heavy operation.

 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-pyarrow"
-        --8<-- "python/python/tests/docs/test_basic.py:alter_columns"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-pyarrow"
-        --8<-- "python/python/tests/docs/test_basic.py:alter_columns_async"
-        ```
+    ```python
+    import pyarrow as pa
+    --8<-- "python/python/tests/docs/test_basic.py:alter_columns"
+    ```
    **API Reference:** [lancedb.table.Table.alter_columns][]

 === "Typescript"
@@ -959,16 +872,9 @@ will remove the column from the schema.

 === "Python"

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:drop_columns"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_basic.py:drop_columns_async"
-        ```
+    ```python
+     --8<-- "python/python/tests/docs/test_basic.py:drop_columns"
+    ```
    **API Reference:** [lancedb.table.Table.drop_columns][]

 === "Typescript"
@@ -1019,46 +925,31 @@ There are three possible settings for `read_consistency_interval`:

    To set strong consistency, use `timedelta(0)`:

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-datetime"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:table_strong_consistency"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-datetime"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:table_async_strong_consistency"
-        ```
+    ```python
+    from datetime import timedelta
+    db = lancedb.connect("./.lancedb",. read_consistency_interval=timedelta(0))
+    table = db.open_table("my_table")
+    ```

    For eventual consistency, use a custom `timedelta`:

-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-datetime"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:table_eventual_consistency"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-datetime"
-        --8<-- "python/python/tests/docs/test_guide_tables.py:table_async_eventual_consistency"
-        ```
+    ```python
+    from datetime import timedelta
+    db = lancedb.connect("./.lancedb", read_consistency_interval=timedelta(seconds=5))
+    table = db.open_table("my_table")
+    ```

    By default, a `Table` will never check for updates from other writers. To manually check for updates you can use `checkout_latest`:

-    === "Sync API"
+    ```python
+    db = lancedb.connect("./.lancedb")
+    table = db.open_table("my_table")

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:table_checkout_latest"
-        ```
-    === "Async API"
+    # (Other writes happen to my_table from another process)

-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:table_async_checkout_latest"
-        ```
+    # Check for updates
+    table.checkout_latest()
+    ```

 === "Typescript[^1]"

@@ -1066,14 +957,14 @@ There are three possible settings for `read_consistency_interval`:

    ```ts
    const db = await lancedb.connect({ uri: "./.lancedb", readConsistencyInterval: 0 });
-    const tbl = await db.openTable("my_table");
+    const table = await db.openTable("my_table");
    ```

    For eventual consistency, specify the update interval as seconds:

    ```ts
    const db = await lancedb.connect({ uri: "./.lancedb", readConsistencyInterval: 5 });
-    const tbl = await db.openTable("my_table");
+    const table = await db.openTable("my_table");
    ```

 <!-- Node doesn't yet support the version time travel: https://github.com/lancedb/lancedb/issues/1007
--- a/docs/src/guides/tables/merge_insert.md
+++ b/docs/src/guides/tables/merge_insert.md
@@ -1,135 +0,0 @@
-The merge insert command is a flexible API that can be used to perform:
-
-1. Upsert
-2. Insert-if-not-exists
-3. Replace range
-
-It works by joining the input data with the target table on a key you provide.
-Often this key is a unique row id key. You can then specify what to do when
-there is a match and when there is not a match. For example, for upsert you want
-to update if the row has a match and insert if the row doesn't have a match.
-Whereas for insert-if-not-exists you only want to insert if the row doesn't have
-a match.
-
-You can also read more in the API reference:
-
-* Python
-    * Sync: [lancedb.table.Table.merge_insert][]
-    * Async: [lancedb.table.AsyncTable.merge_insert][]
-* Typescript: [lancedb.Table.mergeInsert](../../js/classes/Table.md/#mergeinsert)
-
-!!! tip "Use scalar indices to speed up merge insert"
-
-    The merge insert command needs to perform a join between the input data and the
-    target table on the `on` key you provide. This requires scanning that entire
-    column, which can be expensive for large tables. To speed up this operation,
-    you can create a scalar index on the `on` column, which will allow LanceDB to
-    find matches without having to scan the whole tables.
-
-    Read more about scalar indices in [Building a Scalar Index](../scalar_index.md)
-    guide.
-
-!!! info "Embedding Functions"
-
-    Like the create table and add APIs, the merge insert API will automatically
-    compute embeddings if the table has a embedding definition in its schema.
-    If the input data doesn't contain the source column, or the vector column
-    is already filled, then the embeddings won't be computed. See the
-    [Embedding Functions](../../embeddings/embedding_functions.md) guide for more
-    information.
-
-## Upsert
-
-Upsert updates rows if they exist and inserts them if they don't. To do this
-with merge insert, enable both `when_matched_update_all()` and
-`when_not_matched_insert_all()`.
-
-=== "Python"
-
-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_merge_insert.py:upsert_basic"
-        ```
-
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_merge_insert.py:upsert_basic_async"
-        ```
-
-=== "Typescript"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/merge_insert.test.ts:upsert_basic"
-        ```
-
-!!! note "Providing subsets of columns"
-
-    If a column is nullable, it can be omitted from input data and it will be
-    considered `null`. Columns can also be provided in any order.
-
-## Insert-if-not-exists
-
-To avoid inserting duplicate rows, you can use the insert-if-not-exists command.
-This will only insert rows that do not have a match in the target table. To do
-this with merge insert, enable just `when_not_matched_insert_all()`.
-
-
-=== "Python"
-
-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_merge_insert.py:insert_if_not_exists"
-        ```
-
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_merge_insert.py:insert_if_not_exists_async"
-        ```
-
-=== "Typescript"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/merge_insert.test.ts:insert_if_not_exists"
-        ```
-
-
-## Replace range
-
-You can also replace a range of rows in the target table with the input data.
-For example, if you have a table of document chunks, where each chunk has
-both a `doc_id` and a `chunk_id`, you can replace all chunks for a given
-`doc_id` with updated chunks. This can be tricky otherwise because if you
-try to use upsert when the new data has fewer chunks you will end up with
-extra chunks. To avoid this, add another clause to delete any chunks for
-the document that are not in the new data, with
-`when_not_matched_by_source_delete`.
-
-=== "Python"
-
-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_merge_insert.py:replace_range"
-        ```
-
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_merge_insert.py:replace_range_async"
-        ```
-
-=== "Typescript"
-
-    === "@lancedb/lancedb"
-
-        ```typescript
-        --8<-- "nodejs/examples/merge_insert.test.ts:replace_range"
-        ```
--- a/docs/src/guides/tuning_retrievers/1_query_types.md
+++ b/docs/src/guides/tuning_retrievers/1_query_types.md
@@ -1,8 +1,8 @@
 ## Improving retriever performance

-Try it yourself: <a href="https://colab.research.google.com/github/lancedb/lancedb/blob/main/docs/src/notebooks/lancedb_reranking.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br/>
+Try it yourself - <a href="https://colab.research.google.com/github/lancedb/lancedb/blob/main/docs/src/notebooks/lancedb_reranking.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br/>

-VectorDBs are used as retrievers in recommender or chatbot-based systems for retrieving relevant data based on user queries. For example, retrievers are a critical component of Retrieval Augmented Generation (RAG) acrhitectures. In this section, we will discuss how to improve the performance of retrievers.
+VectorDBs are used as retreivers in recommender or chatbot-based systems for retrieving relevant data based on user queries. For example, retriever is a critical component of Retrieval Augmented Generation (RAG) acrhitectures. In this section, we will discuss how to improve the performance of retrievers.

 There are serveral ways to improve the performance of retrievers. Some of the common techniques are:

@@ -19,7 +19,7 @@ Using different embedding models is something that's very specific to the use ca


 ## The dataset
-We'll be using a QA dataset generated using a LLama2 review paper. The dataset contains 221 query, context and answer triplets. The queries and answers are generated using GPT-4 based on a given query. Full script used to generate the dataset can be found on this [repo](https://github.com/lancedb/ragged). It can be downloaded from [here](https://github.com/AyushExel/assets/blob/main/data_qa.csv).
+We'll be using a QA dataset generated using a LLama2 review paper. The dataset contains 221 query, context and answer triplets. The queries and answers are generated using GPT-4 based on a given query. Full script used to generate the dataset can be found on this [repo](https://github.com/lancedb/ragged). It can be downloaded from [here](https://github.com/AyushExel/assets/blob/main/data_qa.csv)

 ### Using different query types
 Let's setup the embeddings and the dataset first. We'll use the LanceDB's `huggingface` embeddings integration for this guide. 
@@ -45,14 +45,14 @@ table.add(df[["context"]].to_dict(orient="records"))
 queries = df["query"].tolist()
 ```

-Now that we have the dataset and embeddings table set up, here's how you can run different query types on the dataset:
+Now that we have the dataset and embeddings table set up, here's how you can run different query types on the dataset.

 * <b> Vector Search: </b>

    ```python
    table.search(quries[0], query_type="vector").limit(5).to_pandas()
    ```
-    By default, LanceDB uses vector search query type for searching and it automatically converts the input query to a vector before searching when using embedding API. So, the following statement is equivalent to the above statement:
+    By default, LanceDB uses vector search query type for searching and it automatically converts the input query to a vector before searching when using embedding API. So, the following statement is equivalent to the above statement.

    ```python
    table.search(quries[0]).limit(5).to_pandas()
@@ -77,7 +77,7 @@ Now that we have the dataset and embeddings table set up, here's how you can run

 * <b> Hybrid Search: </b>

-    Hybrid search is a combination of vector and full-text search. Here's how you can run a hybrid search query on the dataset:
+    Hybrid search is a combination of vector and full-text search. Here's how you can run a hybrid search query on the dataset.
    ```python
    table.search(quries[0], query_type="hybrid").limit(5).to_pandas()
    ```
@@ -87,7 +87,7 @@ Now that we have the dataset and embeddings table set up, here's how you can run

    !!! note "Note"
        By default, it uses `LinearCombinationReranker` that combines the scores from vector and full-text search using a weighted linear combination. It is the simplest reranker implementation available in LanceDB. You can also use other rerankers like `CrossEncoderReranker` or `CohereReranker` for reranking the results.
-        Learn more about rerankers [here](https://lancedb.github.io/lancedb/reranking/).
+        Learn more about rerankers [here](https://lancedb.github.io/lancedb/reranking/)

    

--- a/docs/src/guides/tuning_retrievers/2_reranking.md
+++ b/docs/src/guides/tuning_retrievers/2_reranking.md
@@ -1,6 +1,6 @@
 Continuing from the previous section, we can now rerank the results using more complex rerankers.

-Try it yourself: <a href="https://colab.research.google.com/github/lancedb/lancedb/blob/main/docs/src/notebooks/lancedb_reranking.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br/>
+Try it yourself - <a href="https://colab.research.google.com/github/lancedb/lancedb/blob/main/docs/src/notebooks/lancedb_reranking.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br/>

 ## Reranking search results
 You can rerank any search results using a reranker. The syntax for reranking is as follows:
@@ -62,6 +62,9 @@ Let us take a look at the same datasets from the previous sections, using the sa
 | Reranked fts  | 0.672    |
 | Hybrid | 0.759 |

+### SQuAD Dataset
+
+
 ### Uber10K sec filing Dataset

 | Query Type | Hit-rate@5 |
--- a/docs/src/guides/tuning_retrievers/3_embed_tuning.md
+++ b/docs/src/guides/tuning_retrievers/3_embed_tuning.md
@@ -1,5 +1,5 @@
 ## Finetuning the Embedding Model
-Try it yourself: <a href="https://colab.research.google.com/github/lancedb/lancedb/blob/main/docs/src/notebooks/embedding_tuner.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br/>
+Try it yourself - <a href="https://colab.research.google.com/github/lancedb/lancedb/blob/main/docs/src/notebooks/embedding_tuner.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br/>

 Another way to improve retriever performance is to fine-tune the embedding model itself. Fine-tuning the embedding model can help in learning better representations for the documents and queries in the dataset. This can be particularly useful when the dataset is very different from the pre-trained data used to train the embedding model.

@@ -16,7 +16,7 @@ validation_df.to_csv("data_val.csv", index=False)
 You can use any tuning API to fine-tune embedding models. In this example, we'll utilise Llama-index as it also comes with utilities for synthetic data generation and training the model. 


-We parse the dataset as llama-index text nodes and generate synthetic QA pairs from each node:
+Then parse the dataset as llama-index text nodes and generate synthetic QA pairs from each node.
 ```python
 from llama_index.core.node_parser import SentenceSplitter
 from llama_index.readers.file import PagedCSVReader
@@ -43,7 +43,7 @@ val_dataset = generate_qa_embedding_pairs(
 )
 ```

-Now we'll use `SentenceTransformersFinetuneEngine` engine to fine-tune the model. You can also use `sentence-transformers` or `transformers` library to fine-tune the model:
+Now we'll use `SentenceTransformersFinetuneEngine` engine to fine-tune the model. You can also use `sentence-transformers` or `transformers` library to fine-tune the model. 

 ```python
 from llama_index.finetuning import SentenceTransformersFinetuneEngine
@@ -57,7 +57,7 @@ finetune_engine = SentenceTransformersFinetuneEngine(
 finetune_engine.finetune()
 embed_model = finetune_engine.get_finetuned_model()
 ```
-This saves the fine tuned embedding model in `tuned_model` folder.
+This saves the fine tuned embedding model in `tuned_model` folder. This al

 # Evaluation results
 In order to eval the retriever, you can either use this model to ingest the data into LanceDB directly or llama-index's LanceDB integration to create a `VectorStoreIndex` and use it as a retriever. 
--- a/docs/src/hybrid_search/eval.md
+++ b/docs/src/hybrid_search/eval.md
@@ -3,22 +3,22 @@
 Hybrid Search is a broad (often misused) term. It can mean anything from combining multiple methods for searching, to applying ranking methods to better sort the results. In this blog, we use the definition of "hybrid search" to mean using a combination of keyword-based and vector search.

 ## The challenge of (re)ranking search results
-Once you have a group of the most relevant search results from multiple search sources, you'd likely standardize the score and rank them accordingly. This process can also be seen as another independent step: reranking.
+Once you have a group of the most relevant search results from multiple search sources, you'd likely standardize the score and rank them accordingly. This process can also be seen as another independent step - reranking.
 There are two approaches for reranking search results from multiple sources.

-* <b>Score-based</b>: Calculate final relevance scores based on a weighted linear combination of individual search algorithm scores. Example: Weighted linear combination of semantic search & keyword-based search results.
+* <b>Score-based</b>: Calculate final relevance scores based on a weighted linear combination of individual search algorithm scores. Example - Weighted linear combination of semantic search & keyword-based search results.

-* <b>Relevance-based</b>: Discards the existing scores and calculates the relevance of each search result-query pair. Example: Cross Encoder models
+* <b>Relevance-based</b>: Discards the existing scores and calculates the relevance of each search result - query pair. Example - Cross Encoder models

-Even though there are many strategies for reranking search results, none works for all cases. Moreover, evaluating them itself is a challenge. Also, reranking can be dataset or application specific so it's hard to generalize.
+Even though there are many strategies for reranking search results, none works for all cases. Moreover, evaluating them itself is a challenge. Also, reranking can be dataset, application specific so it's hard to generalize.

 ### Example evaluation of hybrid search with Reranking

-Here's some evaluation numbers from an experiment comparing these rerankers on about 800 queries. It is modified version of an evaluation script from [llama-index](https://github.com/run-llama/finetune-embedding/blob/main/evaluate.ipynb) that measures hit-rate at top-k.
+Here's some evaluation numbers from experiment comparing these re-rankers on about 800 queries. It is modified version of an evaluation script from [llama-index](https://github.com/run-llama/finetune-embedding/blob/main/evaluate.ipynb) that measures hit-rate at top-k.

 <b> With OpenAI ada2 embedding </b>

-Vector Search baseline: `0.64`
+Vector Search baseline - `0.64`

 | Reranker | Top-3 | Top-5 | Top-10 |
 | --- | --- | --- | --- |
@@ -33,7 +33,7 @@ Vector Search baseline: `0.64`

 <b> With OpenAI embedding-v3-small </b>

-Vector Search baseline: `0.59`
+Vector Search baseline - `0.59`

 | Reranker | Top-3 | Top-5 | Top-10 |
 | --- | --- | --- | --- |
--- a/docs/src/hybrid_search/hybrid_search.md
+++ b/docs/src/hybrid_search/hybrid_search.md
@@ -5,46 +5,57 @@ LanceDB supports both semantic and keyword-based search (also termed full-text s
 ## Hybrid search in LanceDB
 You can perform hybrid search in LanceDB by combining the results of semantic and full-text search via a reranking algorithm of your choice. LanceDB provides multiple rerankers out of the box. However, you can always write a custom reranker if your use case need more sophisticated logic .

-=== "Sync API"
+```python
+import os

-    ```python
-    --8<-- "python/python/tests/docs/test_search.py:import-os"
-    --8<-- "python/python/tests/docs/test_search.py:import-openai"
-    --8<-- "python/python/tests/docs/test_search.py:import-lancedb"
-    --8<-- "python/python/tests/docs/test_search.py:import-embeddings"
-    --8<-- "python/python/tests/docs/test_search.py:import-pydantic"
-    --8<-- "python/python/tests/docs/test_search.py:import-lancedb-fts"
-    --8<-- "python/python/tests/docs/test_search.py:import-openai-embeddings"
-    --8<-- "python/python/tests/docs/test_search.py:class-Documents"
-    --8<-- "python/python/tests/docs/test_search.py:basic_hybrid_search"
-    ```
-=== "Async API"
+import lancedb
+import openai
+from lancedb.embeddings import get_registry
+from lancedb.pydantic import LanceModel, Vector

-    ```python
-    --8<-- "python/python/tests/docs/test_search.py:import-os"
-    --8<-- "python/python/tests/docs/test_search.py:import-openai"
-    --8<-- "python/python/tests/docs/test_search.py:import-lancedb"
-    --8<-- "python/python/tests/docs/test_search.py:import-embeddings"
-    --8<-- "python/python/tests/docs/test_search.py:import-pydantic"
-    --8<-- "python/python/tests/docs/test_search.py:import-lancedb-fts"
-    --8<-- "python/python/tests/docs/test_search.py:import-openai-embeddings"
-    --8<-- "python/python/tests/docs/test_search.py:class-Documents"
-    --8<-- "python/python/tests/docs/test_search.py:basic_hybrid_search_async"
-    ```
+db = lancedb.connect("~/.lancedb")

+# Ingest embedding function in LanceDB table
+# Configuring the environment variable OPENAI_API_KEY
+if "OPENAI_API_KEY" not in os.environ:
+# OR set the key here as a variable
+    openai.api_key = "sk-..."
+embeddings = get_registry().get("openai").create()
+
+class Documents(LanceModel):
+    vector: Vector(embeddings.ndims()) = embeddings.VectorField()
+    text: str = embeddings.SourceField()
+
+table = db.create_table("documents", schema=Documents)
+
+data = [
+    { "text": "rebel spaceships striking from a hidden base"},
+    { "text": "have won their first victory against the evil Galactic Empire"},
+    { "text": "during the battle rebel spies managed to steal secret plans"},
+    { "text": "to the Empire's ultimate weapon the Death Star"}
+]
+
+# ingest docs with auto-vectorization
+table.add(data)
+
+# Create a fts index before the hybrid search
+table.create_fts_index("text")
+# hybrid search with default re-ranker
+results = table.search("flower moon", query_type="hybrid").to_pandas()
+```
 !!! Note
    You can also pass the vector and text query manually. This is useful if you're not using the embedding API or if you're using a separate embedder service.
 ### Explicitly passing the vector and text query
-=== "Sync API"
+```python
+vector_query = [0.1, 0.2, 0.3, 0.4, 0.5]
+text_query = "flower moon"
+results = table.search(query_type="hybrid")
+                .vector(vector_query)
+                .text(text_query)
+                .limit(5)
+                .to_pandas()

-    ```python
-    --8<-- "python/python/tests/docs/test_search.py:hybrid_search_pass_vector_text"
-    ```
-=== "Async API"
-
-    ```python
-    --8<-- "python/python/tests/docs/test_search.py:hybrid_search_pass_vector_text_async"
-    ```
+```

 By default, LanceDB uses `RRFReranker()`, which uses reciprocal rank fusion score, to combine and rerank the results of semantic and full-text search. You can customize the hyperparameters as needed or write your own custom reranker. Here's how you can use any of the available rerankers:

@@ -57,7 +68,7 @@ By default, LanceDB uses `RRFReranker()`, which uses reciprocal rank fusion scor


 ## Available Rerankers
-LanceDB provides a number of rerankers out of the box. You can use any of these rerankers by passing them to the `rerank()` method. 
+LanceDB provides a number of re-rankers out of the box. You can use any of these re-rankers by passing them to the `rerank()` method. 
 Go to [Rerankers](../reranking/index.md) to learn more about using the available rerankers and implementing custom rerankers.


--- a/docs/src/index.md
+++ b/docs/src/index.md
@@ -4,9 +4,6 @@ LanceDB is an open-source vector database for AI that's designed to store, manag

 Both the database and the underlying data format are designed from the ground up to be **easy-to-use**, **scalable** and **cost-effective**.

-!!! tip "Hosted LanceDB"
-    If you want S3 cost-efficiency and local performance via a simple serverless API, checkout **LanceDB Cloud**. For private deployments, high performance at extreme scale, or if you have strict security requirements, talk to us about **LanceDB Enterprise**. [Learn more](https://docs.lancedb.com/)
-
 ![](assets/lancedb_and_lance.png)

 ## Truly multi-modal
@@ -23,7 +20,7 @@ LanceDB **OSS** is an **open-source**, batteries-included embedded vector databa

 LanceDB **Cloud** is a SaaS (software-as-a-service) solution that runs serverless in the cloud, making the storage clearly separated from compute. It's designed to be cost-effective and highly scalable without breaking the bank. LanceDB Cloud is currently in private beta with general availability coming soon, but you can apply for early access with the private beta release by signing up below.

-[Try out LanceDB Cloud (Public Beta) Now](https://cloud.lancedb.com){ .md-button .md-button--primary }
+[Try out LanceDB Cloud](https://noteforms.com/forms/lancedb-mailing-list-cloud-kty1o5?notionforms=1&utm_source=notionforms){ .md-button .md-button--primary }

 ## Why use LanceDB?

--- a/docs/src/integrations/genkit.md
+++ b/docs/src/integrations/genkit.md
@@ -1,183 +0,0 @@
-### genkitx-lancedb
-This is a lancedb plugin for genkit framework. It allows you to use LanceDB for ingesting and rereiving data using genkit framework.
-
-![integration-banner-genkit](https://github.com/user-attachments/assets/a6cc28af-98e9-4425-b87c-7ab139bd7893)
-
-### Installation
-```bash
-pnpm install genkitx-lancedb
-```
-
-### Usage
-
-Adding LanceDB plugin to your genkit instance.
-
-```ts
-import { lancedbIndexerRef, lancedb, lancedbRetrieverRef, WriteMode } from 'genkitx-lancedb';
-import { textEmbedding004, vertexAI } from '@genkit-ai/vertexai';
-import { gemini } from '@genkit-ai/vertexai';
-import { z, genkit } from 'genkit';
-import { Document } from 'genkit/retriever';
-import { chunk } from 'llm-chunk';
-import { readFile } from 'fs/promises';
-import path from 'path';
-import pdf from 'pdf-parse/lib/pdf-parse';
-
-const ai = genkit({
-  plugins: [
-    // vertexAI provides the textEmbedding004 embedder
-    vertexAI(),
-
-    // the local vector store requires an embedder to translate from text to vector
-    lancedb([
-      {
-        dbUri: '.db', // optional lancedb uri, default to .db
-        tableName: 'table', // optional table name, default to table
-        embedder: textEmbedding004,
-      },
-    ]),
-  ],
-});
-```
-
-You can run this app with the following command:
-```bash
-genkit start -- tsx --watch src/index.ts
-```
-
-This'll add LanceDB as a retriever and indexer to the genkit instance. You can see it in the GUI view
-<img width="1710" alt="Screenshot 2025-05-11 at 7 21 05 PM" src="https://github.com/user-attachments/assets/e752f7f4-785b-4797-a11e-72ab06a531b7" />
-
-**Testing retrieval on a sample table**
-Let's see the raw retrieval results
-
-<img width="1710" alt="Screenshot 2025-05-11 at 7 21 05 PM" src="https://github.com/user-attachments/assets/b8d356ed-8421-4790-8fc0-d6af563b9657" />
-On running this query, you'll 5 results fetched from the lancedb table, where each result looks something like this:
-<img width="1417" alt="Screenshot 2025-05-11 at 7 21 18 PM" src="https://github.com/user-attachments/assets/77429525-36e2-4da6-a694-e58c1cf9eb83" />
-
-
-
-## Creating a custom RAG flow
-
-Now that we've seen how you can use LanceDB for in a genkit pipeline, let's refine the flow and create a RAG. A RAG flow will consist of an index and a retreiver with its outputs postprocessed an fed into an LLM for final response
-
-### Creating custom indexer flows
-You can also create custom indexer flows, utilizing more options and features provided by LanceDB.
-
-```ts
-export const menuPdfIndexer = lancedbIndexerRef({
-   // Using all defaults, for dbUri, tableName, and embedder, etc
-});
-
-const chunkingConfig = {
-  minLength: 1000,
-  maxLength: 2000,
-  splitter: 'sentence',
-  overlap: 100,
-  delimiters: '',
-} as any;
-
-
-async function extractTextFromPdf(filePath: string) {
-  const pdfFile = path.resolve(filePath);
-  const dataBuffer = await readFile(pdfFile);
-  const data = await pdf(dataBuffer);
-  return data.text;
-}
-
-export const indexMenu = ai.defineFlow(
-  {
-    name: 'indexMenu',
-    inputSchema: z.string().describe('PDF file path'),
-    outputSchema: z.void(),
-  },
-  async (filePath: string) => {
-    filePath = path.resolve(filePath);
-
-    // Read the pdf.
-    const pdfTxt = await ai.run('extract-text', () =>
-      extractTextFromPdf(filePath)
-    );
-
-    // Divide the pdf text into segments.
-    const chunks = await ai.run('chunk-it', async () =>
-      chunk(pdfTxt, chunkingConfig)
-    );
-
-    // Convert chunks of text into documents to store in the index.
-    const documents = chunks.map((text) => {
-      return Document.fromText(text, { filePath });
-    });
-
-    // Add documents to the index.
-    await ai.index({
-      indexer: menuPdfIndexer,
-      documents,
-      options: {
-        writeMode: WriteMode.Overwrite,
-      } as any
-    });
-  }
-);
-```
-
-<img width="1316" alt="Screenshot 2025-05-11 at 8 35 56 PM" src="https://github.com/user-attachments/assets/e2a20ce4-d1d0-4fa2-9a84-f2cc26e3a29f" />
-
-In your console, you can see the logs
-
-<img width="511" alt="Screenshot 2025-05-11 at 7 19 14 PM" src="https://github.com/user-attachments/assets/243f26c5-ed38-40b6-b661-002f40f0423a" />
-
-### Creating custom retriever flows
-You can also create custom retriever flows, utilizing more options and features provided by LanceDB.
-```ts
-export const menuRetriever = lancedbRetrieverRef({
-  tableName: "table", // Use the same table name as the indexer.
-  displayName: "Menu", // Use a custom display name.
-
-export const menuQAFlow = ai.defineFlow(
-  { name: "Menu", inputSchema: z.string(), outputSchema: z.string() },
-  async (input: string) => {
-    // retrieve relevant documents
-    const docs = await ai.retrieve({
-      retriever: menuRetriever,
-      query: input,
-      options: { 
-        k: 3,
-      },
-    });
-
-    const extractedContent = docs.map(doc => {
-      if (doc.content && Array.isArray(doc.content) && doc.content.length > 0) {
-        if (doc.content[0].media && doc.content[0].media.url) {
-          return doc.content[0].media.url;
-        }
-      }
-      return "No content found";
-    });
-
-    console.log("Extracted content:", extractedContent);
-
-    const { text } = await ai.generate({
-      model: gemini('gemini-2.0-flash'),
-      prompt: `
-You are acting as a helpful AI assistant that can answer 
-questions about the food available on the menu at Genkit Grub Pub.
-
-Use only the context provided to answer the question.
-If you don't know, do not make up an answer.
-Do not add or change items on the menu.
-
-Context:
-${extractedContent.join('\n\n')}
-
-Question: ${input}`,
-      docs,
-    });
-    
-    return text;
-  }
-);
-```
-Now using our retrieval flow, we can ask question about the ingsted PDF
-<img width="1306" alt="Screenshot 2025-05-11 at 7 18 45 PM" src="https://github.com/user-attachments/assets/86c66b13-7c12-4d5f-9d81-ae36bfb1c346" />
-
--- a/docs/src/integrations/langchain.md
+++ b/docs/src/integrations/langchain.md
@@ -108,7 +108,7 @@ This method creates a scalar(for non-vector cols) or a vector index on a table.
 |:---|:---|:---|:---|
 |`vector_col`|`Optional[str]`| Provide if you want to create index on a vector column. |`None`|
 |`col_name`|`Optional[str]`| Provide if you want to create index on a non-vector column. |`None`|
-|`metric`|`Optional[str]` |Provide the metric to use for vector index. choice of metrics: 'l2', 'dot', 'cosine'. |`l2`|
+|`metric`|`Optional[str]` |Provide the metric to use for vector index. choice of metrics: 'L2', 'dot', 'cosine'. |`L2`|
 |`num_partitions`|`Optional[int]`|Number of partitions to use for the index.|`256`|
 |`num_sub_vectors`|`Optional[int]` |Number of sub-vectors to use for the index.|`96`|
 |`index_cache_size`|`Optional[int]` |Size of the index cache.|`None`|
--- a/docs/src/integrations/llamaIndex.md
+++ b/docs/src/integrations/llamaIndex.md
@@ -125,7 +125,7 @@ The exhaustive list of parameters for `LanceDBVectorStore` vector store are :
        ```               
 - **_table_exists(self, tbl_name: `Optional[str]` = `None`) -> `bool`** : Returns `True` if `tbl_name` exists in database.
 - __create_index(  
-  self, scalar: `Optional[bool]` = False, col_name: `Optional[str]` = None, num_partitions: `Optional[int]` = 256, num_sub_vectors: `Optional[int]` = 96, index_cache_size: `Optional[int]` = None, metric: `Optional[str]` = "l2",  
+  self, scalar: `Optional[bool]` = False, col_name: `Optional[str]` = None, num_partitions: `Optional[int]` = 256, num_sub_vectors: `Optional[int]` = 96, index_cache_size: `Optional[int]` = None, metric: `Optional[str]` = "L2",  
 ) -> `None`__ : Creates a scalar(for non-vector cols) or a vector index on a table.
        Make sure your vector column has enough data before creating an index on it.

--- a/docs/src/javascript/enums/MetricType.md
+++ b/docs/src/javascript/enums/MetricType.md
@@ -10,7 +10,7 @@ Distance metrics type.

 - [Cosine](MetricType.md#cosine)
 - [Dot](MetricType.md#dot)
- [l2](MetricType.md#l2)
+- [L2](MetricType.md#l2)

 ## Enumeration Members

--- a/docs/src/javascript/interfaces/IvfPQIndexConfig.md
+++ b/docs/src/javascript/interfaces/IvfPQIndexConfig.md
@@ -85,7 +85,7 @@ ___

 • `Optional` **metric\_type**: [`MetricType`](../enums/MetricType.md)

-Metric type, l2 or Cosine
+Metric type, L2 or Cosine

 #### Defined in

--- a/docs/src/js/README.md
+++ b/docs/src/js/README.md
@@ -15,9 +15,11 @@ npm install @lancedb/lancedb
 This will download the appropriate native library for your platform. We currently
 support:

- Linux (x86_64 and aarch64 on glibc and musl)
+- Linux (x86_64 and aarch64)
 - MacOS (Intel and ARM/M1/M2)
- Windows (x86_64 and aarch64)
+- Windows (x86_64 only)
+
+We do not yet support musl-based Linux (such as Alpine Linux) or aarch64 Windows.

 ## Usage

@@ -34,8 +36,41 @@ const results = await table.vectorSearch([0.1, 0.3]).limit(20).toArray();
 console.log(results);
 ```

-The [quickstart](https://lancedb.github.io/lancedb/basic/) contains a more complete example.
+The [quickstart](../basic.md) contains a more complete example.

 ## Development

-See [CONTRIBUTING.md](_media/CONTRIBUTING.md) for information on how to contribute to LanceDB.
+```sh
+npm run build
+npm run test
+```
+
+### Running lint / format
+
+LanceDb uses [biome](https://biomejs.dev/) for linting and formatting. if you are using VSCode you will need to install the official [Biome](https://marketplace.visualstudio.com/items?itemName=biomejs.biome) extension.
+To manually lint your code you can run:
+
+```sh
+npm run lint
+```
+
+to automatically fix all fixable issues:
+
+```sh
+npm run lint-fix
+```
+
+If you do not have your workspace root set to the `nodejs` directory, unfortunately the extension will not work. You can still run the linting and formatting commands manually.
+
+### Generating docs
+
+```sh
+npm run docs
+
+cd ../docs
+# Asssume the virtual environment was created
+# python3 -m venv venv
+# pip install -r requirements.txt
+. ./venv/bin/activate
+mkdocs build
+```
--- a/docs/src/js/_media/CONTRIBUTING.md
+++ b/docs/src/js/_media/CONTRIBUTING.md
@@ -1,76 +0,0 @@
-# Contributing to LanceDB Typescript
-
-This document outlines the process for contributing to LanceDB Typescript.
-For general contribution guidelines, see [CONTRIBUTING.md](../CONTRIBUTING.md).
-
-## Project layout
-
-The Typescript package is a wrapper around the Rust library, `lancedb`. We use
-the [napi-rs](https://napi.rs/) library to create the bindings between Rust and
-Typescript.
-
-* `src/`: Rust bindings source code
-* `lancedb/`: Typescript package source code
-* `__test__/`: Unit tests
-* `examples/`: An npm package with the examples shown in the documentation
-
-## Development environment
-
-To set up your development environment, you will need to install the following:
-
-1. Node.js 14 or later
-2. Rust's package manager, Cargo. Use [rustup](https://rustup.rs/) to install.
-3. [protoc](https://grpc.io/docs/protoc-installation/) (Protocol Buffers compiler)
-
-Initial setup:
-
-```shell
-npm install
-```
-
-### Commit Hooks
-
-It is **highly recommended** to install the [pre-commit](https://pre-commit.com/) hooks to ensure that your
-code is formatted correctly and passes basic checks before committing:
-
-```shell
-pre-commit install
-```
-
-## Development
-
-Most common development commands can be run using the npm scripts.
-
-Build the package
-
-```shell
-npm install
-npm run build
-```
-
-Lint:
-
-```shell
-npm run lint
-```
-
-Format and fix lints:
-
-```shell
-npm run lint-fix
-```
-
-Run tests:
-
-```shell
-npm test
-```
-
-To run a single test:
-
-```shell
-# Single file: table.test.ts
-npm test -- table.test.ts
-# Single test: 'merge insert' in table.test.ts
-npm test -- table.test.ts --testNamePattern=merge\ insert
-```
--- a/docs/src/js/classes/BooleanQuery.md
+++ b/docs/src/js/classes/BooleanQuery.md
@@ -1,53 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / BooleanQuery
-
-# Class: BooleanQuery
-
-Represents a full-text query interface.
-This interface defines the structure and behavior for full-text queries,
-including methods to retrieve the query type and convert the query to a dictionary format.
-
-## Implements
-
- [`FullTextQuery`](../interfaces/FullTextQuery.md)
-
-## Constructors
-
-### new BooleanQuery()
-
-```ts
-new BooleanQuery(queries): BooleanQuery
-```
-
-Creates an instance of BooleanQuery.
-
-#### Parameters
-
-* **queries**: [[`Occur`](../enumerations/Occur.md), [`FullTextQuery`](../interfaces/FullTextQuery.md)][]
-    An array of (Occur, FullTextQuery objects) to combine.
-    Occur specifies whether the query must match, or should match.
-
-#### Returns
-
-[`BooleanQuery`](BooleanQuery.md)
-
-## Methods
-
-### queryType()
-
-```ts
-queryType(): FullTextQueryType
-```
-
-The type of the full-text query.
-
-#### Returns
-
-[`FullTextQueryType`](../enumerations/FullTextQueryType.md)
-
-#### Implementation of
-
-[`FullTextQuery`](../interfaces/FullTextQuery.md).[`queryType`](../interfaces/FullTextQuery.md#querytype)
--- a/docs/src/js/classes/BoostQuery.md
+++ b/docs/src/js/classes/BoostQuery.md
@@ -1,67 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / BoostQuery
-
-# Class: BoostQuery
-
-Represents a full-text query interface.
-This interface defines the structure and behavior for full-text queries,
-including methods to retrieve the query type and convert the query to a dictionary format.
-
-## Implements
-
- [`FullTextQuery`](../interfaces/FullTextQuery.md)
-
-## Constructors
-
-### new BoostQuery()
-
-```ts
-new BoostQuery(
-   positive,
-   negative,
-   options?): BoostQuery
-```
-
-Creates an instance of BoostQuery.
-The boost returns documents that match the positive query,
-but penalizes those that match the negative query.
-the penalty is controlled by the `negativeBoost` parameter.
-
-#### Parameters
-
-* **positive**: [`FullTextQuery`](../interfaces/FullTextQuery.md)
-    The positive query that boosts the relevance score.
-
-* **negative**: [`FullTextQuery`](../interfaces/FullTextQuery.md)
-    The negative query that reduces the relevance score.
-
-* **options?**
-    Optional parameters for the boost query.
-    - `negativeBoost`: The boost factor for the negative query (default is 0.0).
-
-* **options.negativeBoost?**: `number`
-
-#### Returns
-
-[`BoostQuery`](BoostQuery.md)
-
-## Methods
-
-### queryType()
-
-```ts
-queryType(): FullTextQueryType
-```
-
-The type of the full-text query.
-
-#### Returns
-
-[`FullTextQueryType`](../enumerations/FullTextQueryType.md)
-
-#### Implementation of
-
-[`FullTextQuery`](../interfaces/FullTextQuery.md).[`queryType`](../interfaces/FullTextQuery.md#querytype)
--- a/docs/src/js/classes/Connection.md
+++ b/docs/src/js/classes/Connection.md
@@ -23,6 +23,18 @@ be closed when they are garbage collected.
 Any created tables are independent and will continue to work even if
 the underlying connection has been closed.

+## Constructors
+
+### new Connection()
+
+```ts
+new Connection(): Connection
+```
+
+#### Returns
+
+[`Connection`](Connection.md)
+
 ## Methods

 ### close()
@@ -59,7 +71,7 @@ Creates a new empty Table
 * **name**: `string`
    The name of the table.

-* **schema**: [`SchemaLike`](../type-aliases/SchemaLike.md)
+* **schema**: `SchemaLike`
    The schema of the table

 * **options?**: `Partial`&lt;[`CreateTableOptions`](../interfaces/CreateTableOptions.md)&gt;
@@ -105,7 +117,7 @@ Creates a new Table and initialize it with new data.
 * **name**: `string`
    The name of the table.

-* **data**: [`TableLike`](../type-aliases/TableLike.md) \| `Record`&lt;`string`, `unknown`&gt;[]
+* **data**: `TableLike` \| `Record`&lt;`string`, `unknown`&gt;[]
    Non-empty Array of Records
    to be inserted into the table

@@ -131,20 +143,6 @@ Return a brief description of the connection

 ***

-### dropAllTables()
-
-```ts
-abstract dropAllTables(): Promise<void>
-```
-
-Drop all tables in the database.
-
-#### Returns
-
-`Promise`&lt;`void`&gt;
-
-***
-
 ### dropTable()

 ```ts
@@ -191,7 +189,7 @@ Open a table in the database.
 * **name**: `string`
    The name of the table

-* **options?**: `Partial`&lt;[`OpenTableOptions`](../interfaces/OpenTableOptions.md)&gt;
+* **options?**: `Partial`&lt;`OpenTableOptions`&gt;

 #### Returns

--- a/docs/src/js/classes/Index.md
+++ b/docs/src/js/classes/Index.md
@@ -72,9 +72,11 @@ The results of a full text search are ordered by relevance measured by BM25.

 You can combine filters with full text search.

+For now, the full text search index only supports English, and doesn't support phrase search.
+
 #### Parameters

-* **options?**: `Partial`&lt;[`FtsOptions`](../interfaces/FtsOptions.md)&gt;
+* **options?**: `Partial`&lt;`FtsOptions`&gt;

 #### Returns

@@ -96,7 +98,7 @@ the vectors.

 #### Parameters

-* **options?**: `Partial`&lt;[`HnswPqOptions`](../interfaces/HnswPqOptions.md)&gt;
+* **options?**: `Partial`&lt;`HnswPqOptions`&gt;

 #### Returns

@@ -118,38 +120,7 @@ the vectors.

 #### Parameters

-* **options?**: `Partial`&lt;[`HnswSqOptions`](../interfaces/HnswSqOptions.md)&gt;
-
-#### Returns
-
-[`Index`](Index.md)
-
-***
-
-### ivfFlat()
-
-```ts
-static ivfFlat(options?): Index
-```
-
-Create an IvfFlat index
-
-This index groups vectors into partitions of similar vectors.  Each partition keeps track of
-a centroid which is the average value of all vectors in the group.
-
-During a query the centroids are compared with the query vector to find the closest
-partitions.  The vectors in these partitions are then searched to find
-the closest vectors.
-
-The partitioning process is called IVF and the `num_partitions` parameter controls how
-many groups to create.
-
-Note that training an IVF FLAT index on a large dataset is a slow operation and
-currently is also a memory intensive operation.
-
-#### Parameters
-
-* **options?**: `Partial`&lt;[`IvfFlatOptions`](../interfaces/IvfFlatOptions.md)&gt;
+* **options?**: `Partial`&lt;`HnswSqOptions`&gt;

 #### Returns

--- a/docs/src/js/classes/MatchQuery.md
+++ b/docs/src/js/classes/MatchQuery.md
@@ -1,76 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / MatchQuery
-
-# Class: MatchQuery
-
-Represents a full-text query interface.
-This interface defines the structure and behavior for full-text queries,
-including methods to retrieve the query type and convert the query to a dictionary format.
-
-## Implements
-
- [`FullTextQuery`](../interfaces/FullTextQuery.md)
-
-## Constructors
-
-### new MatchQuery()
-
-```ts
-new MatchQuery(
-   query,
-   column,
-   options?): MatchQuery
-```
-
-Creates an instance of MatchQuery.
-
-#### Parameters
-
-* **query**: `string`
-    The text query to search for.
-
-* **column**: `string`
-    The name of the column to search within.
-
-* **options?**
-    Optional parameters for the match query.
-    - `boost`: The boost factor for the query (default is 1.0).
-    - `fuzziness`: The fuzziness level for the query (default is 0).
-    - `maxExpansions`: The maximum number of terms to consider for fuzzy matching (default is 50).
-    - `operator`: The logical operator to use for combining terms in the query (default is "OR").
-    - `prefixLength`: The number of beginning characters being unchanged for fuzzy matching.
-
-* **options.boost?**: `number`
-
-* **options.fuzziness?**: `number`
-
-* **options.maxExpansions?**: `number`
-
-* **options.operator?**: [`Operator`](../enumerations/Operator.md)
-
-* **options.prefixLength?**: `number`
-
-#### Returns
-
-[`MatchQuery`](MatchQuery.md)
-
-## Methods
-
-### queryType()
-
-```ts
-queryType(): FullTextQueryType
-```
-
-The type of the full-text query.
-
-#### Returns
-
-[`FullTextQueryType`](../enumerations/FullTextQueryType.md)
-
-#### Implementation of
-
-[`FullTextQuery`](../interfaces/FullTextQuery.md).[`queryType`](../interfaces/FullTextQuery.md#querytype)
--- a/docs/src/js/classes/MergeInsertBuilder.md
+++ b/docs/src/js/classes/MergeInsertBuilder.md
@@ -1,128 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / MergeInsertBuilder
-
-# Class: MergeInsertBuilder
-
-A builder used to create and run a merge insert operation
-
-## Constructors
-
-### new MergeInsertBuilder()
-
-```ts
-new MergeInsertBuilder(native, schema): MergeInsertBuilder
-```
-
-Construct a MergeInsertBuilder. __Internal use only.__
-
-#### Parameters
-
-* **native**: `NativeMergeInsertBuilder`
-
-* **schema**: `Schema`&lt;`any`&gt; \| `Promise`&lt;`Schema`&lt;`any`&gt;&gt;
-
-#### Returns
-
-[`MergeInsertBuilder`](MergeInsertBuilder.md)
-
-## Methods
-
-### execute()
-
-```ts
-execute(data, execOptions?): Promise<MergeResult>
-```
-
-Executes the merge insert operation
-
-#### Parameters
-
-* **data**: [`Data`](../type-aliases/Data.md)
-
-* **execOptions?**: `Partial`&lt;[`WriteExecutionOptions`](../interfaces/WriteExecutionOptions.md)&gt;
-
-#### Returns
-
-`Promise`&lt;[`MergeResult`](../interfaces/MergeResult.md)&gt;
-
-the merge result
-
-***
-
-### whenMatchedUpdateAll()
-
-```ts
-whenMatchedUpdateAll(options?): MergeInsertBuilder
-```
-
-Rows that exist in both the source table (new data) and
-the target table (old data) will be updated, replacing
-the old row with the corresponding matching row.
-
-If there are multiple matches then the behavior is undefined.
-Currently this causes multiple copies of the row to be created
-but that behavior is subject to change.
-
-An optional condition may be specified.  If it is, then only
-matched rows that satisfy the condtion will be updated.  Any
-rows that do not satisfy the condition will be left as they
-are.  Failing to satisfy the condition does not cause a
-"matched row" to become a "not matched" row.
-
-The condition should be an SQL string.  Use the prefix
-target. to refer to rows in the target table (old data)
-and the prefix source. to refer to rows in the source
-table (new data).
-
-For example, "target.last_update < source.last_update"
-
-#### Parameters
-
-* **options?**
-
-* **options.where?**: `string`
-
-#### Returns
-
-[`MergeInsertBuilder`](MergeInsertBuilder.md)
-
-***
-
-### whenNotMatchedBySourceDelete()
-
-```ts
-whenNotMatchedBySourceDelete(options?): MergeInsertBuilder
-```
-
-Rows that exist only in the target table (old data) will be
-deleted.  An optional condition can be provided to limit what
-data is deleted.
-
-#### Parameters
-
-* **options?**
-
-* **options.where?**: `string`
-    An optional condition to limit what data is deleted
-
-#### Returns
-
-[`MergeInsertBuilder`](MergeInsertBuilder.md)
-
-***
-
-### whenNotMatchedInsertAll()
-
-```ts
-whenNotMatchedInsertAll(): MergeInsertBuilder
-```
-
-Rows that exist only in the source table (new data) should
-be inserted into the target table.
-
-#### Returns
-
-[`MergeInsertBuilder`](MergeInsertBuilder.md)
--- a/docs/src/js/classes/MultiMatchQuery.md
+++ b/docs/src/js/classes/MultiMatchQuery.md
@@ -1,67 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / MultiMatchQuery
-
-# Class: MultiMatchQuery
-
-Represents a full-text query interface.
-This interface defines the structure and behavior for full-text queries,
-including methods to retrieve the query type and convert the query to a dictionary format.
-
-## Implements
-
- [`FullTextQuery`](../interfaces/FullTextQuery.md)
-
-## Constructors
-
-### new MultiMatchQuery()
-
-```ts
-new MultiMatchQuery(
-   query,
-   columns,
-   options?): MultiMatchQuery
-```
-
-Creates an instance of MultiMatchQuery.
-
-#### Parameters
-
-* **query**: `string`
-    The text query to search for across multiple columns.
-
-* **columns**: `string`[]
-    An array of column names to search within.
-
-* **options?**
-    Optional parameters for the multi-match query.
-    - `boosts`: An array of boost factors for each column (default is 1.0 for all).
-    - `operator`: The logical operator to use for combining terms in the query (default is "OR").
-
-* **options.boosts?**: `number`[]
-
-* **options.operator?**: [`Operator`](../enumerations/Operator.md)
-
-#### Returns
-
-[`MultiMatchQuery`](MultiMatchQuery.md)
-
-## Methods
-
-### queryType()
-
-```ts
-queryType(): FullTextQueryType
-```
-
-The type of the full-text query.
-
-#### Returns
-
-[`FullTextQueryType`](../enumerations/FullTextQueryType.md)
-
-#### Implementation of
-
-[`FullTextQuery`](../interfaces/FullTextQuery.md).[`queryType`](../interfaces/FullTextQuery.md#querytype)
--- a/docs/src/js/classes/PhraseQuery.md
+++ b/docs/src/js/classes/PhraseQuery.md
@@ -1,64 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / PhraseQuery
-
-# Class: PhraseQuery
-
-Represents a full-text query interface.
-This interface defines the structure and behavior for full-text queries,
-including methods to retrieve the query type and convert the query to a dictionary format.
-
-## Implements
-
- [`FullTextQuery`](../interfaces/FullTextQuery.md)
-
-## Constructors
-
-### new PhraseQuery()
-
-```ts
-new PhraseQuery(
-   query,
-   column,
-   options?): PhraseQuery
-```
-
-Creates an instance of `PhraseQuery`.
-
-#### Parameters
-
-* **query**: `string`
-    The phrase to search for in the specified column.
-
-* **column**: `string`
-    The name of the column to search within.
-
-* **options?**
-    Optional parameters for the phrase query.
-    - `slop`: The maximum number of intervening unmatched positions allowed between words in the phrase (default is 0).
-
-* **options.slop?**: `number`
-
-#### Returns
-
-[`PhraseQuery`](PhraseQuery.md)
-
-## Methods
-
-### queryType()
-
-```ts
-queryType(): FullTextQueryType
-```
-
-The type of the full-text query.
-
-#### Returns
-
-[`FullTextQueryType`](../enumerations/FullTextQueryType.md)
-
-#### Implementation of
-
-[`FullTextQuery`](../interfaces/FullTextQuery.md).[`queryType`](../interfaces/FullTextQuery.md#querytype)
--- a/docs/src/js/classes/Query.md
+++ b/docs/src/js/classes/Query.md
@@ -8,14 +8,30 @@

 A builder for LanceDB queries.

-## See
-
-[Table#query](Table.md#query), [Table#search](Table.md#search)
-
 ## Extends

 - [`QueryBase`](QueryBase.md)&lt;`NativeQuery`&gt;

+## Constructors
+
+### new Query()
+
+```ts
+new Query(tbl): Query
+```
+
+#### Parameters
+
+* **tbl**: `Table`
+
+#### Returns
+
+[`Query`](Query.md)
+
+#### Overrides
+
+[`QueryBase`](QueryBase.md).[`constructor`](QueryBase.md#constructors)
+
 ## Properties

 ### inner
@@ -30,50 +46,39 @@ protected inner: Query | Promise<Query>;

 ## Methods

-### analyzePlan()
+### \[asyncIterator\]()

 ```ts
-analyzePlan(): Promise<string>
+asyncIterator: AsyncIterator<RecordBatch<any>, any, undefined>
 ```

-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.
-
 #### 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]
-```
+`AsyncIterator`&lt;`RecordBatch`&lt;`any`&gt;, `any`, `undefined`&gt;

 #### Inherited from

-[`QueryBase`](QueryBase.md).[`analyzePlan`](QueryBase.md#analyzeplan)
+[`QueryBase`](QueryBase.md).[`[asyncIterator]`](QueryBase.md#%5Basynciterator%5D)
+
+***
+
+### doCall()
+
+```ts
+protected doCall(fn): void
+```
+
+#### Parameters
+
+* **fn**
+
+#### Returns
+
+`void`
+
+#### Inherited from
+
+[`QueryBase`](QueryBase.md).[`doCall`](QueryBase.md#docall)

 ***

@@ -87,7 +92,7 @@ Execute the query and return the results as an

 #### Parameters

-* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;

 #### Returns

@@ -156,7 +161,7 @@ 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.
+Use lancedb.Table#optimize to index all un-indexed data.

 #### Returns

@@ -184,7 +189,7 @@ A filter statement to be applied to this query.

 `this`

-#### See
+#### Alias

 where

@@ -206,9 +211,9 @@ fullTextSearch(query, options?): this

 #### Parameters

-* **query**: `string` \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
+* **query**: `string`

-* **options?**: `Partial`&lt;[`FullTextSearchOptions`](../interfaces/FullTextSearchOptions.md)&gt;
+* **options?**: `Partial`&lt;`FullTextSearchOptions`&gt;

 #### Returns

@@ -245,6 +250,26 @@ called then every valid row from the table will be returned.

 ***

+### nativeExecute()
+
+```ts
+protected nativeExecute(options?): Promise<RecordBatchIterator>
+```
+
+#### Parameters
+
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
+
+#### Returns
+
+`Promise`&lt;`RecordBatchIterator`&gt;
+
+#### Inherited from
+
+[`QueryBase`](QueryBase.md).[`nativeExecute`](QueryBase.md#nativeexecute)
+
+***
+
 ### nearestTo()

 ```ts
@@ -269,7 +294,7 @@ If there is more than one vector column you must use

 #### Parameters

-* **vector**: [`IntoVector`](../type-aliases/IntoVector.md)
+* **vector**: `IntoVector`

 #### Returns

@@ -309,7 +334,7 @@ nearestToText(query, columns?): Query

 #### Parameters

-* **query**: `string` \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
+* **query**: `string`

 * **columns?**: `string`[]

@@ -402,7 +427,7 @@ Collect the results as an array of objects.

 #### Parameters

-* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;

 #### Returns

@@ -424,7 +449,7 @@ Collect the results as an Arrow

 #### Parameters

-* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;

 #### Returns

--- a/docs/src/js/classes/QueryBase.md
+++ b/docs/src/js/classes/QueryBase.md
@@ -8,11 +8,6 @@

 Common methods supported by all query types

-## See
-
- - [Query](Query.md)
- - [VectorQuery](VectorQuery.md)
-
 ## Extended by

 - [`Query`](Query.md)
@@ -26,6 +21,22 @@ Common methods supported by all query types

 - `AsyncIterable`&lt;`RecordBatch`&gt;

+## Constructors
+
+### new QueryBase()
+
+```ts
+protected new QueryBase<NativeQueryType>(inner): QueryBase<NativeQueryType>
+```
+
+#### Parameters
+
+* **inner**: `NativeQueryType` \| `Promise`&lt;`NativeQueryType`&gt;
+
+#### Returns
+
+[`QueryBase`](QueryBase.md)&lt;`NativeQueryType`&gt;
+
 ## Properties

 ### inner
@@ -36,47 +47,36 @@ protected inner: NativeQueryType | Promise<NativeQueryType>;

 ## Methods

-### analyzePlan()
+### \[asyncIterator\]()

 ```ts
-analyzePlan(): Promise<string>
+asyncIterator: AsyncIterator<RecordBatch<any>, any, undefined>
 ```

-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.
-
 #### Returns

-`Promise`&lt;`string`&gt;
+`AsyncIterator`&lt;`RecordBatch`&lt;`any`&gt;, `any`, `undefined`&gt;

-A query execution plan with runtime metrics for each step.
+#### Implementation of

-#### Example
+`AsyncIterable.[asyncIterator]`
+
+***
+
+### doCall()

 ```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]
+protected doCall(fn): void
 ```

+#### Parameters
+
+* **fn**
+
+#### Returns
+
+`void`
+
 ***

 ### execute()
@@ -89,7 +89,7 @@ Execute the query and return the results as an

 #### Parameters

-* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;

 #### Returns

@@ -150,7 +150,7 @@ 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.
+Use lancedb.Table#optimize to index all un-indexed data.

 #### Returns

@@ -174,7 +174,7 @@ A filter statement to be applied to this query.

 `this`

-#### See
+#### Alias

 where

@@ -192,9 +192,9 @@ fullTextSearch(query, options?): this

 #### Parameters

-* **query**: `string` \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
+* **query**: `string`

-* **options?**: `Partial`&lt;[`FullTextSearchOptions`](../interfaces/FullTextSearchOptions.md)&gt;
+* **options?**: `Partial`&lt;`FullTextSearchOptions`&gt;

 #### Returns

@@ -223,6 +223,22 @@ called then every valid row from the table will be returned.

 ***

+### nativeExecute()
+
+```ts
+protected nativeExecute(options?): Promise<RecordBatchIterator>
+```
+
+#### Parameters
+
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;
+
+#### Returns
+
+`Promise`&lt;`RecordBatchIterator`&gt;
+
+***
+
 ### offset()

 ```ts
@@ -298,7 +314,7 @@ Collect the results as an array of objects.

 #### Parameters

-* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;

 #### Returns

@@ -316,7 +332,7 @@ Collect the results as an Arrow

 #### Parameters

-* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;

 #### Returns

--- a/docs/src/js/classes/Table.md
+++ b/docs/src/js/classes/Table.md
@@ -14,13 +14,21 @@ will be freed when the Table is garbage collected.  To eagerly free the cache yo
 can call the `close` method.  Once the Table is closed, it cannot be used for any
 further operations.

-Tables are created using the methods [Connection#createTable](Connection.md#createtable)
-and [Connection#createEmptyTable](Connection.md#createemptytable). Existing tables are opened
-using [Connection#openTable](Connection.md#opentable).
-
 Closing a table is optional.  It not closed, it will be closed when it is garbage
 collected.

+## Constructors
+
+### new Table()
+
+```ts
+new Table(): Table
+```
+
+#### Returns
+
+[`Table`](Table.md)
+
 ## Accessors

 ### name
@@ -40,7 +48,7 @@ Returns the name of the table
 ### add()

 ```ts
-abstract add(data, options?): Promise<AddResult>
+abstract add(data, options?): Promise<void>
 ```

 Insert records into this Table.
@@ -54,17 +62,14 @@ Insert records into this Table.

 #### Returns

-`Promise`&lt;[`AddResult`](../interfaces/AddResult.md)&gt;
-
-A promise that resolves to an object
-containing the new version number of the table
+`Promise`&lt;`void`&gt;

 ***

 ### addColumns()

 ```ts
-abstract addColumns(newColumnTransforms): Promise<AddColumnsResult>
+abstract addColumns(newColumnTransforms): Promise<void>
 ```

 Add new columns with defined values.
@@ -79,17 +84,14 @@ Add new columns with defined values.

 #### Returns

-`Promise`&lt;[`AddColumnsResult`](../interfaces/AddColumnsResult.md)&gt;
-
-A promise that resolves to an object
-containing the new version number of the table after adding the columns.
+`Promise`&lt;`void`&gt;

 ***

 ### alterColumns()

 ```ts
-abstract alterColumns(columnAlterations): Promise<AlterColumnsResult>
+abstract alterColumns(columnAlterations): Promise<void>
 ```

 Alter the name or nullability of columns.
@@ -102,10 +104,7 @@ Alter the name or nullability of columns.

 #### Returns

-`Promise`&lt;[`AlterColumnsResult`](../interfaces/AlterColumnsResult.md)&gt;
-
-A promise that resolves to an object
-containing the new version number of the table after altering the columns.
+`Promise`&lt;`void`&gt;

 ***

@@ -126,8 +125,8 @@ wish to return to standard mode, call `checkoutLatest`.

 #### Parameters

-* **version**: `string` \| `number`
-    The version to checkout, could be version number or tag
+* **version**: `number`
+    The version to checkout

 #### Returns

@@ -217,9 +216,6 @@ Indices on vector columns will speed up vector searches.
 Indices on scalar columns will speed up filtering (in both
 vector and non-vector searches)

-We currently don't support custom named indexes.
-The index name will always be `${column}_idx`.
-
 #### Parameters

 * **column**: `string`
@@ -230,6 +226,11 @@ The index name will always be `${column}_idx`.

 `Promise`&lt;`void`&gt;

+#### Note
+
+We currently don't support custom named indexes,
+The index name will always be `${column}_idx`
+
 #### Examples

 ```ts
@@ -261,7 +262,7 @@ await table.createIndex("my_float_col");
 ### delete()

 ```ts
-abstract delete(predicate): Promise<DeleteResult>
+abstract delete(predicate): Promise<void>
 ```

 Delete the rows that satisfy the predicate.
@@ -272,10 +273,7 @@ Delete the rows that satisfy the predicate.

 #### Returns

-`Promise`&lt;[`DeleteResult`](../interfaces/DeleteResult.md)&gt;
-
-A promise that resolves to an object
-containing the new version number of the table
+`Promise`&lt;`void`&gt;

 ***

@@ -296,7 +294,7 @@ Return a brief description of the table
 ### dropColumns()

 ```ts
-abstract dropColumns(columnNames): Promise<DropColumnsResult>
+abstract dropColumns(columnNames): Promise<void>
 ```

 Drop one or more columns from the dataset
@@ -315,31 +313,6 @@ then call ``cleanup_files`` to remove the old files.

 #### Returns

-`Promise`&lt;[`DropColumnsResult`](../interfaces/DropColumnsResult.md)&gt;
-
-A promise that resolves to an object
-containing the new version number of the table after dropping the columns.
-
-***
-
-### dropIndex()
-
-```ts
-abstract dropIndex(name): Promise<void>
-```
-
-Drop an index from the table.
-
-#### Parameters
-
-* **name**: `string`
-    The name of the index.
-    This does not delete the index from disk, it just removes it from the table.
-    To delete the index, run [Table#optimize](Table.md#optimize) after dropping the index.
-    Use [Table.listIndices](Table.md#listindices) to find the names of the indices.
-
-#### Returns
-
 `Promise`&lt;`void`&gt;

 ***
@@ -363,8 +336,6 @@ List all the stats of a specified index

 The stats of the index. If the index does not exist, it will return undefined

-Use [Table.listIndices](Table.md#listindices) to find the names of the indices.
-
 ***

 ### isOpen()
@@ -405,7 +376,7 @@ List all the versions of the table

 #### Returns

-`Promise`&lt;[`Version`](../interfaces/Version.md)[]&gt;
+`Promise`&lt;`Version`[]&gt;

 ***

@@ -421,7 +392,7 @@ abstract mergeInsert(on): MergeInsertBuilder

 #### Returns

-[`MergeInsertBuilder`](MergeInsertBuilder.md)
+`MergeInsertBuilder`

 ***

@@ -465,29 +436,7 @@ Modeled after ``VACUUM`` in PostgreSQL.

 #### Returns

-`Promise`&lt;[`OptimizeStats`](../interfaces/OptimizeStats.md)&gt;
-
-***
-
-### prewarmIndex()
-
-```ts
-abstract prewarmIndex(name): Promise<void>
-```
-
-Prewarm an index in the table.
-
-#### Parameters
-
-* **name**: `string`
-    The name of the index.
-    This will load the index into memory.  This may reduce the cold-start time for
-    future queries.  If the index does not fit in the cache then this call may be
-    wasteful.
-
-#### Returns
-
-`Promise`&lt;`void`&gt;
+`Promise`&lt;`OptimizeStats`&gt;

 ***

@@ -604,7 +553,7 @@ Get the schema of the table.
 abstract search(
   query,
   queryType?,
-   ftsColumns?): Query | VectorQuery
+   ftsColumns?): VectorQuery | Query
 ```

 Create a search query to find the nearest neighbors
@@ -612,7 +561,7 @@ of the given query

 #### Parameters

-* **query**: `string` \| [`IntoVector`](../type-aliases/IntoVector.md) \| [`MultiVector`](../type-aliases/MultiVector.md) \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
+* **query**: `string` \| `IntoVector`
    the query, a vector or string

 * **queryType?**: `string`
@@ -626,51 +575,7 @@ of the given query

 #### Returns

-[`Query`](Query.md) \| [`VectorQuery`](VectorQuery.md)
-
-***
-
-### stats()
-
-```ts
-abstract stats(): Promise<TableStatistics>
-```
-
-Returns table and fragment statistics
-
-#### Returns
-
-`Promise`&lt;[`TableStatistics`](../interfaces/TableStatistics.md)&gt;
-
-The table and fragment statistics
-
-***
-
-### tags()
-
-```ts
-abstract tags(): Promise<Tags>
-```
-
-Get a tags manager for this table.
-
-Tags allow you to label specific versions of a table with a human-readable name.
-The returned tags manager can be used to list, create, update, or delete tags.
-
-#### Returns
-
-`Promise`&lt;[`Tags`](Tags.md)&gt;
-
-A tags manager for this table
-
-#### Example
-
-```typescript
-const tagsManager = await table.tags();
-await tagsManager.create("v1", 1);
-const tags = await tagsManager.list();
-console.log(tags); // { "v1": { version: 1, manifestSize: ... } }
-```
+[`VectorQuery`](VectorQuery.md) \| [`Query`](Query.md)

 ***

@@ -693,7 +598,7 @@ Return the table as an arrow table
 #### update(opts)

 ```ts
-abstract update(opts): Promise<UpdateResult>
+abstract update(opts): Promise<void>
 ```

 Update existing records in the Table
@@ -704,10 +609,7 @@ Update existing records in the Table

 ##### Returns

-`Promise`&lt;[`UpdateResult`](../interfaces/UpdateResult.md)&gt;
-
-A promise that resolves to an object containing
-the number of rows updated and the new version number
+`Promise`&lt;`void`&gt;

 ##### Example

@@ -718,7 +620,7 @@ table.update({where:"x = 2", values:{"vector": [10, 10]}})
 #### update(opts)

 ```ts
-abstract update(opts): Promise<UpdateResult>
+abstract update(opts): Promise<void>
 ```

 Update existing records in the Table
@@ -729,10 +631,7 @@ Update existing records in the Table

 ##### Returns

-`Promise`&lt;[`UpdateResult`](../interfaces/UpdateResult.md)&gt;
-
-A promise that resolves to an object containing
-the number of rows updated and the new version number
+`Promise`&lt;`void`&gt;

 ##### Example

@@ -743,7 +642,7 @@ table.update({where:"x = 2", valuesSql:{"x": "x + 1"}})
 #### update(updates, options)

 ```ts
-abstract update(updates, options?): Promise<UpdateResult>
+abstract update(updates, options?): Promise<void>
 ```

 Update existing records in the Table
@@ -766,6 +665,10 @@ repeatedly calilng this method.
 * **updates**: `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
    the
    columns to update
+    Keys in the map should specify the name of the column to update.
+    Values in the map provide the new value of the column.  These can
+    be SQL literal strings (e.g. "7" or "'foo'") or they can be expressions
+    based on the row being updated (e.g. "my_col + 1")

 * **options?**: `Partial`&lt;[`UpdateOptions`](../interfaces/UpdateOptions.md)&gt;
    additional options to control
@@ -773,15 +676,7 @@ repeatedly calilng this method.

 ##### Returns

-`Promise`&lt;[`UpdateResult`](../interfaces/UpdateResult.md)&gt;
-
-A promise that resolves to an object
-containing the number of rows updated and the new version number
-
-Keys in the map should specify the name of the column to update.
-Values in the map provide the new value of the column.  These can
-be SQL literal strings (e.g. "7" or "'foo'") or they can be expressions
-based on the row being updated (e.g. "my_col + 1")
+`Promise`&lt;`void`&gt;

 ***

@@ -799,7 +694,7 @@ by `query`.

 #### Parameters

-* **vector**: [`IntoVector`](../type-aliases/IntoVector.md) \| [`MultiVector`](../type-aliases/MultiVector.md)
+* **vector**: `IntoVector`

 #### Returns

@@ -825,23 +720,35 @@ Retrieve the version of the table

 ***

-### waitForIndex()
+### parseTableData()

 ```ts
-abstract waitForIndex(indexNames, timeoutSeconds): Promise<void>
+static parseTableData(
+   data,
+   options?,
+   streaming?): Promise<object>
 ```

-Waits for asynchronous indexing to complete on the table.
-
 #### Parameters

-* **indexNames**: `string`[]
-    The name of the indices to wait for
+* **data**: `TableLike` \| `Record`&lt;`string`, `unknown`&gt;[]

-* **timeoutSeconds**: `number`
-    The number of seconds to wait before timing out
-    This will raise an error if the indices are not created and fully indexed within the timeout.
+* **options?**: `Partial`&lt;[`CreateTableOptions`](../interfaces/CreateTableOptions.md)&gt;
+
+* **streaming?**: `boolean` = `false`

 #### Returns

-`Promise`&lt;`void`&gt;
+`Promise`&lt;`object`&gt;
+
+##### buf
+
+```ts
+buf: Buffer;
+```
+
+##### mode
+
+```ts
+mode: string;
+```
--- a/docs/src/js/classes/TagContents.md
+++ b/docs/src/js/classes/TagContents.md
@@ -1,35 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / TagContents
-
-# Class: TagContents
-
-## Constructors
-
-### new TagContents()
-
-```ts
-new TagContents(): TagContents
-```
-
-#### Returns
-
-[`TagContents`](TagContents.md)
-
-## Properties
-
-### manifestSize
-
-```ts
-manifestSize: number;
-```
-
-***
-
-### version
-
-```ts
-version: number;
-```
--- a/docs/src/js/classes/Tags.md
+++ b/docs/src/js/classes/Tags.md
@@ -1,99 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / Tags
-
-# Class: Tags
-
-## Constructors
-
-### new Tags()
-
-```ts
-new Tags(): Tags
-```
-
-#### Returns
-
-[`Tags`](Tags.md)
-
-## Methods
-
-### create()
-
-```ts
-create(tag, version): Promise<void>
-```
-
-#### Parameters
-
-* **tag**: `string`
-
-* **version**: `number`
-
-#### Returns
-
-`Promise`&lt;`void`&gt;
-
-***
-
-### delete()
-
-```ts
-delete(tag): Promise<void>
-```
-
-#### Parameters
-
-* **tag**: `string`
-
-#### Returns
-
-`Promise`&lt;`void`&gt;
-
-***
-
-### getVersion()
-
-```ts
-getVersion(tag): Promise<number>
-```
-
-#### Parameters
-
-* **tag**: `string`
-
-#### Returns
-
-`Promise`&lt;`number`&gt;
-
-***
-
-### list()
-
-```ts
-list(): Promise<Record<string, TagContents>>
-```
-
-#### Returns
-
-`Promise`&lt;`Record`&lt;`string`, [`TagContents`](TagContents.md)&gt;&gt;
-
-***
-
-### update()
-
-```ts
-update(tag, version): Promise<void>
-```
-
-#### Parameters
-
-* **tag**: `string`
-
-* **version**: `number`
-
-#### Returns
-
-`Promise`&lt;`void`&gt;
--- a/docs/src/js/classes/VectorQuery.md
+++ b/docs/src/js/classes/VectorQuery.md
@@ -10,14 +10,30 @@ A builder used to construct a vector search

 This builder can be reused to execute the query many times.

-## See
-
-[Query#nearestTo](Query.md#nearestto)
-
 ## Extends

 - [`QueryBase`](QueryBase.md)&lt;`NativeVectorQuery`&gt;

+## Constructors
+
+### new VectorQuery()
+
+```ts
+new VectorQuery(inner): VectorQuery
+```
+
+#### Parameters
+
+* **inner**: `VectorQuery` \| `Promise`&lt;`VectorQuery`&gt;
+
+#### Returns
+
+[`VectorQuery`](VectorQuery.md)
+
+#### Overrides
+
+[`QueryBase`](QueryBase.md).[`constructor`](QueryBase.md#constructors)
+
 ## Properties

 ### inner
@@ -32,6 +48,22 @@ protected inner: VectorQuery | Promise<VectorQuery>;

 ## Methods

+### \[asyncIterator\]()
+
+```ts
+asyncIterator: AsyncIterator<RecordBatch<any>, any, undefined>
+```
+
+#### Returns
+
+`AsyncIterator`&lt;`RecordBatch`&lt;`any`&gt;, `any`, `undefined`&gt;
+
+#### Inherited from
+
+[`QueryBase`](QueryBase.md).[`[asyncIterator]`](QueryBase.md#%5Basynciterator%5D)
+
+***
+
 ### addQueryVector()

 ```ts
@@ -40,7 +72,7 @@ addQueryVector(vector): VectorQuery

 #### Parameters

-* **vector**: [`IntoVector`](../type-aliases/IntoVector.md)
+* **vector**: `IntoVector`

 #### Returns

@@ -48,53 +80,6 @@ addQueryVector(vector): VectorQuery

 ***

-### analyzePlan()
-
-```ts
-analyzePlan(): 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.
-
-#### 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
-
-[`QueryBase`](QueryBase.md).[`analyzePlan`](QueryBase.md#analyzeplan)
-
-***
-
 ### bypassVectorIndex()

 ```ts
@@ -143,24 +128,6 @@ whose data type is a fixed-size-list of floats.

 ***

-### distanceRange()
-
-```ts
-distanceRange(lowerBound?, upperBound?): VectorQuery
-```
-
-#### Parameters
-
-* **lowerBound?**: `number`
-
-* **upperBound?**: `number`
-
-#### Returns
-
-[`VectorQuery`](VectorQuery.md)
-
-***
-
 ### distanceType()

 ```ts
@@ -194,6 +161,26 @@ By default "l2" is used.

 ***

+### doCall()
+
+```ts
+protected doCall(fn): void
+```
+
+#### Parameters
+
+* **fn**
+
+#### Returns
+
+`void`
+
+#### Inherited from
+
+[`QueryBase`](QueryBase.md).[`doCall`](QueryBase.md#docall)
+
+***
+
 ### ef()

 ```ts
@@ -228,7 +215,7 @@ Execute the query and return the results as an

 #### Parameters

-* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;

 #### Returns

@@ -297,7 +284,7 @@ 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.
+Use lancedb.Table#optimize to index all un-indexed data.

 #### Returns

@@ -325,7 +312,7 @@ A filter statement to be applied to this query.

 `this`

-#### See
+#### Alias

 where

@@ -347,9 +334,9 @@ fullTextSearch(query, options?): this

 #### Parameters

-* **query**: `string` \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
+* **query**: `string`

-* **options?**: `Partial`&lt;[`FullTextSearchOptions`](../interfaces/FullTextSearchOptions.md)&gt;
+* **options?**: `Partial`&lt;`FullTextSearchOptions`&gt;

 #### Returns

@@ -386,50 +373,23 @@ called then every valid row from the table will be returned.

 ***

-### maximumNprobes()
+### nativeExecute()

 ```ts
-maximumNprobes(maximumNprobes): VectorQuery
+protected nativeExecute(options?): Promise<RecordBatchIterator>
 ```

-Set the maximum number of probes used.
-
-This controls the maximum number of partitions that will be searched.  If this
-number is greater than minimumNprobes then the excess partitions will _only_ be
-searched if we have not found enough results.  This can be useful when there is
-a narrow filter to allow these queries to spend more time searching and avoid
-potential false negatives.
-
 #### Parameters

-* **maximumNprobes**: `number`
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;

 #### Returns

-[`VectorQuery`](VectorQuery.md)
+`Promise`&lt;`RecordBatchIterator`&gt;

-***
+#### Inherited from

-### minimumNprobes()
-
-```ts
-minimumNprobes(minimumNprobes): VectorQuery
-```
-
-Set the minimum number of probes used.
-
-This controls the minimum number of partitions that will be searched.  This
-parameter will impact every query against a vector index, regardless of the
-filter.  See `nprobes` for more details.  Higher values will increase recall
-but will also increase latency.
-
-#### Parameters
-
-* **minimumNprobes**: `number`
-
-#### Returns
-
-[`VectorQuery`](VectorQuery.md)
+[`QueryBase`](QueryBase.md).[`nativeExecute`](QueryBase.md#nativeexecute)

 ***

@@ -460,10 +420,6 @@ For best results we recommend tuning this parameter with a benchmark against
 your actual data to find the smallest possible value that will still give
 you the desired recall.

-For more fine grained control over behavior when you have a very narrow filter
-you can use `minimumNprobes` and `maximumNprobes`.  This method sets both
-the minimum and maximum to the same value.
-
 #### Parameters

 * **nprobes**: `number`
@@ -572,22 +528,6 @@ distance between the query vector and the actual uncompressed vector.

 ***

-### rerank()
-
-```ts
-rerank(reranker): VectorQuery
-```
-
-#### Parameters
-
-* **reranker**: [`Reranker`](../namespaces/rerankers/interfaces/Reranker.md)
-
-#### Returns
-
-[`VectorQuery`](VectorQuery.md)
-
-***
-
 ### select()

 ```ts
@@ -651,7 +591,7 @@ Collect the results as an array of objects.

 #### Parameters

-* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;

 #### Returns

@@ -673,7 +613,7 @@ Collect the results as an Arrow

 #### Parameters

-* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+* **options?**: `Partial`&lt;`QueryExecutionOptions`&gt;

 #### Returns

--- a/docs/src/js/enumerations/FullTextQueryType.md
+++ b/docs/src/js/enumerations/FullTextQueryType.md
@@ -1,54 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / FullTextQueryType
-
-# Enumeration: FullTextQueryType
-
-Enum representing the types of full-text queries supported.
-
- `Match`: Performs a full-text search for terms in the query string.
- `MatchPhrase`: Searches for an exact phrase match in the text.
- `Boost`: Boosts the relevance score of specific terms in the query.
- `MultiMatch`: Searches across multiple fields for the query terms.
-
-## Enumeration Members
-
-### Boolean
-
-```ts
-Boolean: "boolean";
-```
-
-***
-
-### Boost
-
-```ts
-Boost: "boost";
-```
-
-***
-
-### Match
-
-```ts
-Match: "match";
-```
-
-***
-
-### MatchPhrase
-
-```ts
-MatchPhrase: "match_phrase";
-```
-
-***
-
-### MultiMatch
-
-```ts
-MultiMatch: "multi_match";
-```
--- a/docs/src/js/enumerations/Occur.md
+++ b/docs/src/js/enumerations/Occur.md
@@ -1,37 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / Occur
-
-# Enumeration: Occur
-
-Enum representing the occurrence of terms in full-text queries.
-
- `Must`: The term must be present in the document.
- `Should`: The term should contribute to the document score, but is not required.
- `MustNot`: The term must not be present in the document.
-
-## Enumeration Members
-
-### Must
-
-```ts
-Must: "MUST";
-```
-
-***
-
-### MustNot
-
-```ts
-MustNot: "MUST_NOT";
-```
-
-***
-
-### Should
-
-```ts
-Should: "SHOULD";
-```
--- a/docs/src/js/enumerations/Operator.md
+++ b/docs/src/js/enumerations/Operator.md
@@ -1,28 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / Operator
-
-# Enumeration: Operator
-
-Enum representing the logical operators used in full-text queries.
-
- `And`: All terms must match.
- `Or`: At least one term must match.
-
-## Enumeration Members
-
-### And
-
-```ts
-And: "AND";
-```
-
-***
-
-### Or
-
-```ts
-Or: "OR";
-```
--- a/docs/src/js/enumerations/WriteMode.md
+++ b/docs/src/js/enumerations/WriteMode.md
@@ -0,0 +1,33 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / WriteMode
+
+# Enumeration: WriteMode
+
+Write mode for writing a table.
+
+## Enumeration Members
+
+### Append
+
+```ts
+Append: "Append";
+```
+
+***
+
+### Create
+
+```ts
+Create: "Create";
+```
+
+***
+
+### Overwrite
+
+```ts
+Overwrite: "Overwrite";
+```
--- a/docs/src/js/functions/connect.md
+++ b/docs/src/js/functions/connect.md
@@ -6,10 +6,10 @@

 # Function: connect()

-## connect(uri, options)
+## connect(uri, opts)

 ```ts
-function connect(uri, options?): Promise<Connection>
+function connect(uri, opts?): Promise<Connection>
 ```

 Connect to a LanceDB instance at the given URI.
@@ -26,8 +26,7 @@ Accepted formats:
    The uri of the database. If the database uri starts
    with `db://` then it connects to a remote database.

-* **options?**: `Partial`&lt;[`ConnectionOptions`](../interfaces/ConnectionOptions.md)&gt;
-    The options to use when connecting to the database
+* **opts?**: `Partial`&lt;[`ConnectionOptions`](../interfaces/ConnectionOptions.md)&gt;

 ### Returns

@@ -50,10 +49,10 @@ const conn = await connect(
 });
 ```

-## connect(options)
+## connect(opts)

 ```ts
-function connect(options): Promise<Connection>
+function connect(opts): Promise<Connection>
 ```

 Connect to a LanceDB instance at the given URI.
@@ -66,8 +65,7 @@ Accepted formats:

 ### Parameters

-* **options**: `Partial`&lt;[`ConnectionOptions`](../interfaces/ConnectionOptions.md)&gt; & `object`
-    The options to use when connecting to the database
+* **opts**: `Partial`&lt;[`ConnectionOptions`](../interfaces/ConnectionOptions.md)&gt; & `object`

 ### Returns

--- a/docs/src/js/functions/makeArrowTable.md
+++ b/docs/src/js/functions/makeArrowTable.md
@@ -22,6 +22,8 @@ when creating a table or adding data to it)
 This function converts an array of Record<String, any> (row-major JS objects)
 to an Arrow Table (a columnar structure)

+Note that it currently does not support nulls.
+
 If a schema is provided then it will be used to determine the resulting array
 types.  Fields will also be reordered to fit the order defined by the schema.

@@ -29,9 +31,6 @@ If a schema is not provided then the types will be inferred and the field order
 will be controlled by the order of properties in the first record.  If a type
 is inferred it will always be nullable.

-If not all fields are found in the data, then a subset of the schema will be
-returned.
-
 If the input is empty then a schema must be provided to create an empty table.

 When a schema is not specified then data types will be inferred.  The inference
@@ -39,7 +38,6 @@ rules are as follows:

 - boolean => Bool
 - number => Float64
- - bigint => Int64
 - String => Utf8
 - Buffer => Binary
 - Record<String, any> => Struct
@@ -59,7 +57,6 @@ rules are as follows:

 ## Example

-```ts
 import { fromTableToBuffer, makeArrowTable } from "../arrow";
 import { Field, FixedSizeList, Float16, Float32, Int32, Schema } from "apache-arrow";

@@ -81,40 +78,42 @@ The `vectorColumns` option can be used to support other vector column
 names and data types.

 ```ts
+
 const schema = new Schema([
-  new Field("a", new Float64()),
-  new Field("b", new Float64()),
-  new Field(
-    "vector",
-    new FixedSizeList(3, new Field("item", new Float32()))
-  ),
-]);
-const table = makeArrowTable([
-  { a: 1, b: 2, vector: [1, 2, 3] },
-  { a: 4, b: 5, vector: [4, 5, 6] },
-  { a: 7, b: 8, vector: [7, 8, 9] },
-]);
-assert.deepEqual(table.schema, schema);
+   new Field("a", new Float64()),
+   new Field("b", new Float64()),
+   new Field(
+     "vector",
+     new FixedSizeList(3, new Field("item", new Float32()))
+   ),
+ ]);
+ const table = makeArrowTable([
+   { a: 1, b: 2, vector: [1, 2, 3] },
+   { a: 4, b: 5, vector: [4, 5, 6] },
+   { a: 7, b: 8, vector: [7, 8, 9] },
+ ]);
+ assert.deepEqual(table.schema, schema);
 ```

 You can specify the vector column types and names using the options as well

-```ts
+```typescript
+
 const schema = new Schema([
-  new Field('a', new Float64()),
-  new Field('b', new Float64()),
-  new Field('vec1', new FixedSizeList(3, new Field('item', new Float16()))),
-  new Field('vec2', new FixedSizeList(3, new Field('item', new Float16())))
-]);
+   new Field('a', new Float64()),
+   new Field('b', new Float64()),
+   new Field('vec1', new FixedSizeList(3, new Field('item', new Float16()))),
+   new Field('vec2', new FixedSizeList(3, new Field('item', new Float16())))
+ ]);
 const table = makeArrowTable([
-  { a: 1, b: 2, vec1: [1, 2, 3], vec2: [2, 4, 6] },
-  { a: 4, b: 5, vec1: [4, 5, 6], vec2: [8, 10, 12] },
-  { a: 7, b: 8, vec1: [7, 8, 9], vec2: [14, 16, 18] }
-], {
-  vectorColumns: {
-    vec1: { type: new Float16() },
-    vec2: { type: new Float16() }
-  }
-}
+   { a: 1, b: 2, vec1: [1, 2, 3], vec2: [2, 4, 6] },
+   { a: 4, b: 5, vec1: [4, 5, 6], vec2: [8, 10, 12] },
+   { a: 7, b: 8, vec1: [7, 8, 9], vec2: [14, 16, 18] }
+ ], {
+   vectorColumns: {
+     vec1: { type: new Float16() },
+     vec2: { type: new Float16() }
+   }
+ }
 assert.deepEqual(table.schema, schema)
 ```
--- a/docs/src/js/functions/packBits.md
+++ b/docs/src/js/functions/packBits.md
@@ -1,19 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / packBits
-
-# Function: packBits()
-
-```ts
-function packBits(data): number[]
-```
-
-## Parameters
-
-* **data**: `number`[]
-
-## Returns
-
-`number`[]
--- a/docs/src/js/globals.md
+++ b/docs/src/js/globals.md
@@ -7,90 +7,48 @@
 ## Namespaces

 - [embedding](namespaces/embedding/README.md)
- [rerankers](namespaces/rerankers/README.md)

 ## Enumerations

- [FullTextQueryType](enumerations/FullTextQueryType.md)
- [Occur](enumerations/Occur.md)
- [Operator](enumerations/Operator.md)
+- [WriteMode](enumerations/WriteMode.md)

 ## Classes

- [BooleanQuery](classes/BooleanQuery.md)
- [BoostQuery](classes/BoostQuery.md)
 - [Connection](classes/Connection.md)
 - [Index](classes/Index.md)
 - [MakeArrowTableOptions](classes/MakeArrowTableOptions.md)
- [MatchQuery](classes/MatchQuery.md)
- [MergeInsertBuilder](classes/MergeInsertBuilder.md)
- [MultiMatchQuery](classes/MultiMatchQuery.md)
- [PhraseQuery](classes/PhraseQuery.md)
 - [Query](classes/Query.md)
 - [QueryBase](classes/QueryBase.md)
 - [RecordBatchIterator](classes/RecordBatchIterator.md)
 - [Table](classes/Table.md)
- [TagContents](classes/TagContents.md)
- [Tags](classes/Tags.md)
 - [VectorColumnOptions](classes/VectorColumnOptions.md)
 - [VectorQuery](classes/VectorQuery.md)

 ## Interfaces

- [AddColumnsResult](interfaces/AddColumnsResult.md)
 - [AddColumnsSql](interfaces/AddColumnsSql.md)
 - [AddDataOptions](interfaces/AddDataOptions.md)
- [AddResult](interfaces/AddResult.md)
- [AlterColumnsResult](interfaces/AlterColumnsResult.md)
 - [ClientConfig](interfaces/ClientConfig.md)
 - [ColumnAlteration](interfaces/ColumnAlteration.md)
- [CompactionStats](interfaces/CompactionStats.md)
 - [ConnectionOptions](interfaces/ConnectionOptions.md)
 - [CreateTableOptions](interfaces/CreateTableOptions.md)
- [DeleteResult](interfaces/DeleteResult.md)
- [DropColumnsResult](interfaces/DropColumnsResult.md)
 - [ExecutableQuery](interfaces/ExecutableQuery.md)
- [FragmentStatistics](interfaces/FragmentStatistics.md)
- [FragmentSummaryStats](interfaces/FragmentSummaryStats.md)
- [FtsOptions](interfaces/FtsOptions.md)
- [FullTextQuery](interfaces/FullTextQuery.md)
- [FullTextSearchOptions](interfaces/FullTextSearchOptions.md)
- [HnswPqOptions](interfaces/HnswPqOptions.md)
- [HnswSqOptions](interfaces/HnswSqOptions.md)
 - [IndexConfig](interfaces/IndexConfig.md)
 - [IndexOptions](interfaces/IndexOptions.md)
 - [IndexStatistics](interfaces/IndexStatistics.md)
- [IvfFlatOptions](interfaces/IvfFlatOptions.md)
 - [IvfPqOptions](interfaces/IvfPqOptions.md)
- [MergeResult](interfaces/MergeResult.md)
- [OpenTableOptions](interfaces/OpenTableOptions.md)
 - [OptimizeOptions](interfaces/OptimizeOptions.md)
- [OptimizeStats](interfaces/OptimizeStats.md)
- [QueryExecutionOptions](interfaces/QueryExecutionOptions.md)
- [RemovalStats](interfaces/RemovalStats.md)
 - [RetryConfig](interfaces/RetryConfig.md)
 - [TableNamesOptions](interfaces/TableNamesOptions.md)
- [TableStatistics](interfaces/TableStatistics.md)
 - [TimeoutConfig](interfaces/TimeoutConfig.md)
 - [UpdateOptions](interfaces/UpdateOptions.md)
- [UpdateResult](interfaces/UpdateResult.md)
- [Version](interfaces/Version.md)
- [WriteExecutionOptions](interfaces/WriteExecutionOptions.md)
+- [WriteOptions](interfaces/WriteOptions.md)

 ## Type Aliases

 - [Data](type-aliases/Data.md)
- [DataLike](type-aliases/DataLike.md)
- [FieldLike](type-aliases/FieldLike.md)
- [IntoSql](type-aliases/IntoSql.md)
- [IntoVector](type-aliases/IntoVector.md)
- [MultiVector](type-aliases/MultiVector.md)
- [RecordBatchLike](type-aliases/RecordBatchLike.md)
- [SchemaLike](type-aliases/SchemaLike.md)
- [TableLike](type-aliases/TableLike.md)

 ## Functions

 - [connect](functions/connect.md)
 - [makeArrowTable](functions/makeArrowTable.md)
- [packBits](functions/packBits.md)
--- a/docs/src/js/interfaces/AddColumnsResult.md
+++ b/docs/src/js/interfaces/AddColumnsResult.md
@@ -1,15 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / AddColumnsResult
-
-# Interface: AddColumnsResult
-
-## Properties
-
-### version
-
-```ts
-version: number;
-```
--- a/docs/src/js/interfaces/AddResult.md
+++ b/docs/src/js/interfaces/AddResult.md
@@ -1,15 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / AddResult
-
-# Interface: AddResult
-
-## Properties
-
-### version
-
-```ts
-version: number;
-```
--- a/Show More
+++ b/Show More
				`@@ -1 +0,0 @@`
				`!function(){var e,t,n;e="9627b71b382d201",t=function(){Reo.init({clientID:"9627b71b382d201"})},(n=document.createElement("script")).src="https://static.reo.dev/"+e+"/reo.js",n.defer=!0,n.onload=t,document.head.appendChild(n)}();`